https://ranges-support.anatrack.com/w/index.php?title=Special:NewPages&feed=atom&limit=50&offset=&namespace=0&username=&tagfilter=Anatrack Ranges User Guide - New pages [en-gb]2024-03-29T09:50:25ZFrom Anatrack Ranges User GuideMediaWiki 1.23.2https://ranges-support.anatrack.com/wiki/Large_Data_And_Java_MemoryLarge Data And Java Memory2014-11-10T13:55:03Z<p>Admin: </p>
<hr />
<div>Ranges can now handle huge files, the largest being raster files used to define habitats and to use as background maps.<br />
<br />
64 bit operating systems can use essentially limitless amounts of computer memory (RAM). If you are running 64 bit Windows or Mac OS, you can run Ranges so that it also has access to the huge amounts of memory that your state-of-the-art computer has.<br />
<br />
== The Ranges batch file ==<br />
<br />
The Ranges large memory batch file (''ranges-largememory.bat'') in the Ranges installation folder can be tweaked to run Ranges with a large amount of memory depending the memory installed in your computer and your operating sysmte. Open this file in a plain text editor such as notepad to see the following:<br />
<br />
<pre><br />
::ranges-largememory.bat : starts up java GUI with DOS command window<br />
::adjust the -Xmx setting to run Ranges with more RAM if your computer has it<br />
::<br />
::e.g. to run Ranges with 4GB of memory use<br />
::<br />
::java -Xmx4096m -jar ranges.jar <br />
::<br />
::note that the maximum available to 32 bit Windows operating systems is about 1.4GB<br />
<br />
java -Xmx1024m -jar ranges.jar <br />
<br />
exit<br />
</pre><br />
<br />
Following the instructions in the file, you can edit the java command line and increase the memory. Once you have edited and saved the file, run it to run Ranges with the increased memory.</div>Adminhttps://ranges-support.anatrack.com/wiki/Privacy_PolicyPrivacy Policy2014-11-10T12:18:28Z<p>Admin: Created page with "This privacy policy sets out how Anatrack uses and protects any information that you give Anatrack when you use this website. Anatrack is committed to ensuring that your priv..."</p>
<hr />
<div>This privacy policy sets out how Anatrack uses and protects any information that you give Anatrack when you use this website.<br />
<br />
Anatrack is committed to ensuring that your privacy is protected. Should we ask you to provide certain information by which you can be identified when using this website, then you can be assured that it will only be used in accordance with this privacy statement.<br />
<br />
Anatrack may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. This policy is effective from 27 September 2010.<br />
<br />
== What we collect ==<br />
<br />
We may collect the following information:<br />
<br />
* name and job title<br />
* contact information including email address<br />
* demographic information such as postcode, preferences and interests<br />
*other information relevant to customer surveys and/or offers<br />
<br />
== What we do with the information we gather == <br />
<br />
We require this information to understand your needs and provide you with a better service, and in particular for the following reasons:<br />
<br />
* Internal record keeping.<br />
* We may use the information to improve our products and services.<br />
* We may periodically send promotional emails about new products, special offers or other information which we think you may find interesting using the email address which you have provided.<br />
* From time to time, we may also use your information to contact you for market research purposes. We may contact you by email, phone, fax or mail. We may use the information to customise the website according to your interests.<br />
<br />
== Security ==<br />
<br />
We are committed to ensuring that your information is secure. In order to prevent unauthorised access or disclosure, we have put in place suitable physical, electronic and managerial procedures to safeguard and secure the information we collect online.<br />
<br />
== How we use cookies ==<br />
<br />
A cookie is a small file which asks permission to be placed on your computer's hard drive. Once you agree, the file is added and the cookie helps analyse web traffic or lets you know when you visit a particular site. Cookies allow web applications to respond to you as an individual. The web application can tailor its operations to your needs, likes and dislikes by gathering and remembering information about your preferences.<br />
<br />
We use traffic log cookies to identify which pages are being used. This helps us analyse data about webpage traffic and improve our website in order to tailor it to customer needs. We only use this information for statistical analysis purposes and then the data is removed from the system.<br />
<br />
Overall, cookies help us provide you with a better website by enabling us to monitor which pages you find useful and which you do not. A cookie in no way gives us access to your computer or any information about you, other than the data you choose to share with us.<br />
<br />
You can choose to accept or decline cookies. Most web browsers automatically accept cookies, but you can usually modify your browser setting to decline cookies if you prefer. This may prevent you from taking full advantage of the website.<br />
<br />
== Links to other websites ==<br />
<br />
Our website may contain links to other websites of interest. However, once you have used these links to leave our site, you should note that we do not have any control over that other website. Therefore, we cannot be responsible for the protection and privacy of any information which you provide whilst visiting such sites and such sites are not governed by this privacy statement. You should exercise caution and look at the privacy statement applicable to the website in question.<br />
<br />
== Controlling your personal information ==<br />
<br />
You may choose to restrict the collection or use of your personal information in the following ways:<br />
<br />
* whenever you are asked to fill in a form on the website, look for the box that you can click to indicate that you do not want the information to be used by anybody for direct marketing purposes<br />
* if you have previously agreed to us using your personal information for direct marketing purposes, you may change your mind at any time by writing to or emailing us at info@anatrack.com or using our contact form<br />
<br />
We will not sell, distribute or lease your personal information to third parties unless we have your permission or are required by law to do so. We may use your personal information to send you promotional information about third parties which we think you may find interesting if you tell us that you wish this to happen.<br />
<br />
You may request details of personal information which we hold about you under the Data Protection Act 1998. A small fee will be payable. If you would like a copy of the information held on you please write to Anatrack Ltd, 52 Furzebrook Road, Wareham, BH20 5AX, Dorset,United Kingdom.<br />
<br />
If you believe that any information we are holding on you is incorrect or incomplete, please write to or email us as soon as possible at the above address. We will promptly correct any information found to be incorrect.</div>Adminhttps://ranges-support.anatrack.com/wiki/Modelling_AnalysisModelling Analysis2014-11-10T11:58:18Z<p>RobertKenward: /* Kaplan-Meier Survival */</p>
<hr />
<div>When Ranges 4 was launched in 1990, individual-based modelling of animal populations was in its infancy. However, it was becoming clear that not only was such modelling powerful for predicting population beyond the envelope of conditions in which individuals were measured, but also that radio-tracking could provide the linkages of habitats and sociality with persistence or dispersal, and survival and productivity, that would be needed for modelling. So the provision of a toolkit for modelling was a long-term aspiration for this type of software [[Bibliography|(Kenward 1992)]]. <br />
<br />
== Introduction ==<br />
<br />
The initial contribution to modelling is a new approach to analysing resources, such as habitats, which can estimate minimal requirements of individual animals and hence enable individual-based modelling. There is also a method for estimating survival or dispersal rates that is convenient for data from radio-tagging. There are illustrated explanations of both methods in ([[Bibliography|Kenward 2001]]). Further components of a toolkit for modelling will be added to this tab in due course, with the ultimate aspiration of linking these in order to automate population modelling from location data and maps.<br />
<br />
== Resource Area Dependence Analysis ==<br />
<br />
The principle that underlies this analysis is that if an animal requires a particular amount of a resource, such as a particular tree or area of habitat, then it will extend its home-range to an extent necessary to contain than amount of resource. If the resource is rarer, range outlines will be larger. In this case, there will be a negative relationship between range area and resource content. For strong resource dependence, the relationship tends to become negative exponential ([[Bibliography|Kenward 1982]]), but is linear with negative correlation if the logarithm of resource content is plotted against the logarithm of range area. Moreover, the range area at a point where the resource proportion is 1 is an estimate of the minimum area of resource required.<br />
<br />
Another important consideration is that a single patch of habitat enclosed within range outlines of varying size will show a negative relationship of proportion with area by chance. To avoid misinterpretation of random events, the significance of observed correlations should be compared with random range placement in the same areas. In the case of a single resource, its occurrence significantly more frequently in observed ranges than in random placements may be the best indication of its importance.<br />
<br />
This analysis requires an edge file and a habitat file. Suitable example files are in the folder <i>squirrel</i>, as described for [[Habitat#sources|habitat analysis]].<br />
<br />
=== analysis options ===<br />
<br />
For a rapid examination of whether the prevalence of any of the habitats in a set correlate negatively with range size, analysis of <i>observed values only</i> is appropriate. The statistics available from such are run are the observed value of <i>r</i>, the slope <i>b</i> for the regression of (log) habitat prevalence on (log) range area, the standard error of <i>b</i>, the (log) area intercept <i>c</i> for 100% habitat, the percentage of ranges with no habitat at all in the core, and the percentage with none of the habitat in a particular row. On the graph, the green regression line is for the observed values.<br />
<br />
To investigate significance, randomisations are available with 99, 199 and 999 iterations. During randomisation, outlines of all the observed ranges are randomly rotated and displaced within an envelope. By default, that envelope is the minimum convex polygon round all observed outlines for the largest core size among a set of core sizes. <i>N</i> outlines are chosen at random with replacement from the <i>N</i> observed outlines, and an <i>r</i>, <i>b</i> and <i>c</i> calculated in each case. <br />
<br />
Statistics from randomisations include the mean and median values for <i>r</i> by randomisation, <i>z</i> for the difference of this <i>r</i> from the observed<i>r</i>, with associated 95% confidence limits, on the assumption that <i>r</i> is distributed normally. The next value is a more robust test statistic, which is the number of random <i>r</i> values more extremely negative than the observed value. In a two-tailed test, with 999 iterations, a value less than 25 indicates <i>P</i><0.05, with 5 or less for <i>P</i><=0.01 and 0 for <i>P</i><=0.02. There are then mean values for <i>b</i>, its SE and <i>c</i> by randomisation, which are used to plot the yellow line on the graph, and finally the proportion of random placements of the range outlines that lack the relevant habitat. Percentages below the observed percentage of ranges without the habitat indicate non-random placement of observed range outlines with respect to that habitat.<br />
<br />
=== zero handling ===<br />
<br />
Animals may differ in their use of resources. Some may specialise in a quite different resource to the majority, either through choice or exclusion, so that it does not occur in their range. Excluded animals may have above average range size, in which case addition of a value below other values (which is done automatically for the <i>replace zeros</i> option), will tend to maintain negative correlations. However, if resource strategy is divergent, inclusion of missing (or very low) proportions of the resource may conceal a major effect. At present, a choice of excluding missing values is possible, with two options; <i>resample zeros</i> to obtain resource within all outlines is appropriate if it is suspected that large ranges are more likely to include habitat by chance; otherwise the <i>ignore zeros</i> option will give very similar results but will be faster and will estimate the proportion of randomly-placed outlines that lack the resource. When there are very low values of resource in some observed ranges, it may in future be possible to exclude these objectively as statistical outliers and then examine these ranges for different resource area dependence relationships.<br />
<br />
=== exclude habitats ===<br />
<br />
As in analyses of habitat preference, disproportionate use of one relatively abundant resource can conceal a dependence also on one or more uncommon resources. This effect can be avoided by removing the area of the first resource from the range and then re-analysing for the second, in a step-wise approach. Resource exclusion of this type is supported in Ranges 9. The Ctrl key can be held to select multiple habitats to exclude, and is also required to remove previous selections.<br />
<br />
=== envelope ===<br />
<br />
The default envelope, <i> mcp around max edge file core</i> may allow very little rotation and displacement of large ranges in a small area, which can greatly slow analyses. If resources have a wide distribution, a larger <i>user defined</i> envelope may be used to speed the randomisation, at least for a first quick test, by loading the envelope separately. This is also useful if analysis is focussed in small cores (say, 50% cluster cores), but a polygon around all the locations is being used to standardise the envelope.<br />
<br />
== Kaplan-Meier Survival ==<br />
<br />
The Kaplan-Meier approach ([[Bibliography|Kaplan & Meier 1958]]), as described for radio-tracking by [[Bibliography|Pollock et al. (1989)]], is provided as a first survival estimation technique. Its interval-based estimation procedure adapts well to the asynchronous (staggered) entries and departures for unknown reasons that are typical for groups of radio-tagged animals. <br />
<br />
Example data are in the folder <i>goshawk</i> from 205 first year goshawks that were tagged in or near their natal nests (<i>Juv_Male.srv</i> and <i>Juv_Female.srv</i>. <br />
<br />
=== analysis options ===<br />
<br />
Choice of <i>one set</i> will run an analysis on one survival file, with a plot that includes error bars for 95% confidence limits based on [[Bibliography|Cox-Oakes (1984)]] variance estimation. The statistics include, for each time interval in the analysis, the number of animals with active tags at the beginning and end of the interval, the number that <i>died</i>, had <i>lost</i> signals for unexplained reasons, were known to have <i>lived</i> through expiry of tag (e.g. due to battery exhaustion) or were added through tag attachment. There is then an estimate of the survival with two types of 95% confidence limits, and the survival decrease since the last period. The numbers in each category are summed at the bottom of the table, with a count of the total number of active tag-days. If the statistics file is saved, it can be opening in Excel or other spreadsheet for <i>.csv</i> files. The <i>.kms</i> graphics file can be opened at a later date in the graphics window.<br />
<br />
Choice of <i>two sets for comparison</i> will run two plots as above, but also estimate statistics for the comparison between the survival rates. These are log-rank chi-square statistics with one degree of freedom, estimated in progressively more conservative ways, on the penultimate row of the table, and a comparison (see [[Bibliography|Pollock et al. (1989)]] and [[Bibliography|Kenward (2001)]] for further details. The two-sets option enables re-entry of the same file as for the first set, using a second '''[[Selections|Make Selections]]''' button and box to choose a different category of animal (e.g. adult rather than juvenile) within the file.<br />
<br />
=== time interval ===<br />
<br />
The length of time intervals for analysis should be great enough to provide opportunity for a number of deaths, but not too long to detect seasonal differences in timing of mortality. A choice of <i>days</i> rather than <i>one month</i> will bring up a box in which the number of days for each interval can be entered. Typically, monthly intervals are selected unless the period to be analysed is less than about 3 months.<br />
<br />
=== set 1 start date ===<br />
<br />
Although the default is the <i>first animal start date</i>, this often starts the analysis with too few animals in the first time interval; there should ideally be at least 20, because otherwise the confidence limits will be very large, with a tendency for differences between categories to lack significance. Even when many animals are marked within a short time, there may be a need to delay the start of analysis to exclude animals with possible adversely affects of capture or considered more vulnerable while adjusting to tags. Selecting <i>specified date</i> will bring up a calendar to assist the choice of date.<br />
<br />
=== set 1 end date ===<br />
<br />
The default of <i>last animal end date</i> will often result in very few individuals in the last sample interval, and hence undesirably large confidence limits. It is therefore possible either to set a <i>specified date</i> with a calendar, or to give a <i>duration in days</i> for the analysis.<br />
=== set 2 start date and end date ===<br />
<br />
For a comparison run, two further option boxes appear. For cases where the time period for comparison is the same in both files, or categories within the same file, it is convenient to be able to choose <i>set 1 start date</i> and <i>set 1 end date</i>, as well as having other options similar to those for the first set of data.<br />
<br />
=== treat lost as dead ===<br />
<br />
When carcases are found, the category of <i>died</i> is not hard to assign in survival files. Likewise, when tracking is stopped at a particular date, or tag cell is due to expire the fate category <i>lived</i> can be assigned. However, a problem arises when tracking animals for which deaths are frequently associated with destruction of the radio (e.g. through trauma) or severe loss of signal range or transport of the carcase away from a monitored area. In this case, survival is overestimated by the default of treating the <i>lost</i> signals as tag failure. For conservative estimates of survival during population modelling, it may be most appropriate to treat signals lost before the likely end of tag cell life as if they represent deaths, by ticking this box. The difference in survival estimated by merely censoring the radios will not be large if radios are highly reliable. Correction for“lost” animals that are subsequently retrapped or resighted after the study period can involve reclassifying their fate as <i>lived</i>; more sophisticated correction from such data ([[Bibliography|Kenward 2001]]) will be added in due course.</div>Adminhttps://ranges-support.anatrack.com/wiki/TutorialTutorial2014-11-09T12:09:16Z<p>RobertKenward: /* Kaplan Meier Survival Analysis */</p>
<hr />
<div>For a tutorial on the free demonstration version of Ranges, see the [[Demo Tutorial|Demo Tutorial]].<br />
<br />
== Introduction ==<br />
<br />
This is a brief tutorial to work you through the basics of using Ranges. It concentrates on the practical steps you need to take to perform different analyses. It contains little explanation of what the routines do (see overview and links from there for that).<br />
<br />
Lines starting with a bullet point (&bull;) describe actions to perform. Click on the images to the right to enlarge them.<br />
<br />
== Getting your data in and viewing ==<br />
<br />
==== Location data, the simplest option - just coordinates for a single range ====<br />
<br />
[[File:Import locations1.png|thumb|right|upright=1.6|Press import and select ''Location file from column text file''.]]<br />
[[File:Import locations2.png|thumb|right|upright=1.6|Set Scale Of Coordinate Units and Tracking resolution to 10.]]<br />
* Open Ranges to the [[Input & Graphics| main window]]<br /><br />
* Press '''import''' and select ''Location column file''<br /><br />
* Select the file <i><RangesFolder>\samples\blackbird\blackbird_indiv1_coords_only.txt</i><br />
<br />
If you look at this file in a spreadsheet you’ll see it contains 2 columns of data with the headers E & N.<br />
<br />
The import routine detects the file contents, and sets up the defaults accordingly.<br />
<br />
* Set '''Scale Of Coordinate Units''' and '''Tracking resolution''' to 10. Press '''OK''' to accept other defaults <br />
<br />
This will have read the two columns in as a single range, the points will be displayed in the map display and you can alter range attributes in the ''Ranges'' table, or the coordinates themselves in the ''Location'' table.<br />
<br />
* Press '''save''' to save this as a Ranges location file that can be used as an input in other Ranges routines.<br />
<br />
(Note that an alternative way of doing this is to press '''new''', then open the text file in Excel, copy the coordinates and paste them into the empty '''locations''' table).<br />
<br />
<br />
==== Location data - coordinates for multiple ranges ==== <br />
<br />
* Open Ranges to the [[Input & Graphics| main window]]<br /><br />
* Press '''import''' and select ''Location file from column text file''<br /><br />
* Select the file <i><RangesFolder>\samples\blackbird\blackbird_ids_and_coords.txt</i><br />
<br />
If you look at this file in a spreadsheet you’ll see it contains 3 columns of data with the headers ID, E & N.<br />
<br />
* Set '''Scale Of Coordinate Units''' and '''Tracking Resolution''' to 10. Press '''OK''' to accept the defaults. <br />
<br />
This will have read the coordinates into four ranges according to the ID specified in the first column.<br />
<br />
* Press '''save''' to save this as a Ranges location file.<br />
<br />
<br />
==== Location data - coordinates, range attributes and location qualifying variables ====<br />
<br />
[[File:Import locations3.png|thumb|right|upright=1.6|Range and location data]]<br />
* Open Ranges to the [[Input & Graphics| main window]]<br /><br />
* Press '''import''' and select ''Location file from column textfile''.<br /><br />
* Select the file <i><RangesFolder>\samples\blackbird\blackbird.txt</i><br />
<br />
If you look at this file in a spreadsheet you’ll see it contains 17 columns of data.<br />
<br />
* Set '''Scale Of Coordinate Units''' and '''Tracking Resolution''' to 10. Press '''OK''' to accept the defaults.<br />
<br />
This will have read the coordinates into four ranges as above, but it will also have read in range attribute data that will be displayed in the ''Ranges'' table and location qualifying variables that will be displayed in the ''Locations'' table.<br />
<br />
* Press '''save''' to save this as a Ranges location file, but note that an identical file named <i>blackbird.loc</i> is already provided in the blackbird directory.<br />
<br />
<br />
==== Viewing a large multi-range file ====<br />
<br />
[[File:Import locations4.png|thumb|right|upright=1.6|Display all ranges, selected range in blue, selected location in red.]]<br />
* Open Ranges to the [[Input & Graphics| main window]]<br /><br />
* Press '''open''' and select <i><RangesFolder>\samples\buzzard\buzzards101.loc</i><br /><br />
* Above the map display, change from ''display selected range(s)'' to ''display all ranges''<br /><br />
* Make sure the '''range colours''' next to the display option box is set to ''selection''<br /><br />
* Click the left mouse button in the left column of the ''Ranges'' table.<br /><br />
* Use the arrow keys to move down the table (see how the selected range is displayed in blue, and the data for the selected range is displayed in the ''Locations'' table.<br /><br />
* Select the left column in the ''Locations'' table.<br /><br />
* Use the arrow keys to move between locations in the table.<br />
<br />
See how the selected location is circled in red.<br />
<br />
* Left click on a blue location in the map display while holding down the SHIFT key. <br />
<br />
See how the corresponding row in the ''Locations'' table becomes selected, and the location is coloured red to show that it is selected.<br />
<br />
* Try editing the E or N values for a location in the ''Locations'' table and see how its position in the map display is changed.<br /><br />
* Change the '''range colour''' option box to ‘sex’. <br />
<br />
You will see males displayed in blue and females in red.<br />
<br />
<br />
==== Viewing GPS collected movement paths with associated time information ====<br />
<br />
[[File:Import locations5.png|thumb|right|upright=1.6|GPS-collected lion data animated by time]]<br />
* Open Ranges to the [[Input & Graphics| main window]]<br /><br />
* Press '''open''' and select ''<RangesFolder>\samples\lion\lions.loc''<br /><br />
* Above the map display change from ''display selected range(s)'' to ''display all ranges''<br /><br />
* Change the range colour to ''sex''. <br />
<br />
You should see the male locations overlapping with those of the two females.<br />
<br />
* Tick '''Background map''' and browse to select ''lions.loc'' as the background file too.<br /><br />
* Above the map display, change from ''display all ranges'' to ''animate locations by time''.<br /><br />
* Change the option from ''Play fast'' to ''Play med'' or slower<br />
<br />
You should be able to follow the movements of the 3 individuals over time, the first female enters the area covered by the male on the 20th of October and leaves on the 21st, the male doesn’t enter the area covered by the first female until the 27th.<br />
<br />
<br />
== Location analyses ==<br />
<br />
==== Inter-location distances ====<br />
<br />
[[File:Location analysis1.png|thumb|right|upright=1.6|Setting up the inter-location analysis.]]<br />
[[File:Location analysis2.png|thumb|right|upright=1.6|Inter-location analysis data, map and plot.]]<br />
* Click on the '''Location''' button to open the Location Analysis setup window.<br /><br />
* Choose ''inter-location measures''.<br /><br />
* Under '''Input Files''' press '''browse''' and find the file ''<RangesFolder>\samples\blackbird\blackbird.loc''.<br /><br />
* Under '''Analysis Options''', select ''distances'', ''location interval'' and leave the '''Per number of locations''' set at 1.<br /><br />
* Press the ''Run Analysis'' button. <br />
<br />
The progress window will flash up (this is a fast-running analysis. Then the Statistics window and a Plot window will be displayed over the main window.<br />
<br />
* Select different ranges within the ranges table on the upper left.<br />
<br />
The results for them will be displayed in the Plot window.<br />
<br />
* Select different locations in the locations table on the lower left and the corresponding location will be displayed in red in the plot.<br /><br />
<br />
Note that a new column ''Distances(m)'' has been added to the file in the Locations table.<br />
<br />
<br />
==== 100% Minimum convex polygons ====<br />
<br />
[[File:Location analysis3.png|thumb|right|upright=1.6|100% convex polygon results with selected in background option.]]<br />
<br />
* Click on the '''Location''' button to open the Location Analysis setup window.<br /><br />
<br />
* Choose ''convex polygons''<br />
<br />
* If a file is not loaded, under '''Input Files''' press '''browse''' and find the file ''<RangesFolder>\samples\blackbird\blackbird.loc''.<br />
<br />
* Under '''Analysis Options''' select ''100% cores''.<br />
<br />
* In '''Output Files''', select ''Output edge file'' and accept the default filename (''blackbird_x.edg'').<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
The edge file created will be displayed in main window.<br />
<br />
* Select different ranges in the Edge shapes table at the upper left.<br />
<br />
The range edge and the locations used to create it will be displayed ( the latter occurs because the tickbox for ''selected'' is the current option for the '''Background'''. <br />
<br />
If you wish to export the polygon edge file to ArcView or another GIS package do the following :<br />
<br />
* From the main window press '''export''', and select ''ArcView Shapefile Polyline''.<br />
<br />
<br />
==== Convex polygons at 5% intervals ====<br />
<br />
[[File:Location analysis4-1.png|thumb|right|upright=1.6|5% intervals convex polygon results with plot, map and statistics. See the core size and area columns in the statistics window.]]<br />
<br />
* Click on the '''Location''' button to open the Location Analysis setup window.<br /><br />
<br />
* Choose ''convex polygons''.<br />
<br />
* If a file is not loaded, under '''Input Files''' press '''browse''' and find the file ''<RangesFolder>\samples\blackbird\blackbird.loc''.<br />
<br />
* Under '''Analysis Options'' select ''cores at 5% intervals''<br />
<br />
* Under '''Peel centre''' select ''focal site'' (this means that locations will be excluded based upon their distance from the focal site).<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
The cores, and a utilisation plot will be displayed in the main window for the first range.<br />
<br />
* Select the first row in the upper left table and use the arrow key to move down through the range cores and on to the next ranges.<br />
<br />
<br />
==== Incremental area plots ====<br />
<br />
[[File:Location analysis5.png|thumb|right|upright=1.6|Incremental area analysis plot.]]<br />
<br />
Incremental area plots display how the area of an estimated home range core changes as successive locations are added.<br />
<br />
* repeat the steps in [[#Convex polygons at 5% intervals|Convex polygons at 5% intervals]], but before pressing '''Run Analysis''' change the following options :<br />
<br />
* Under '''Analysis Options''' select ''incremental area analysis''<br />
<br />
* Keep the core % at the default value of ''100''.<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
The incremental area plot will be displayed for each range in the main window.<br />
<br />
<br />
==== Cluster analysis with objective cores ====<br />
<br />
[[File:Location analysis7.png|thumb|right|upright=1.6|Cluster analysis with objective cores.]]<br />
<br />
* Click on the '''Location''' button to open the Location Analysis setup window.<br /><br />
<br />
* Choose "neighbour-linkage''.<br />
<br />
* If a file is not loaded, under '''Input Files''' press '''browse''' and find the file ''<RangesFolder>\samples\squirrel\furzey.loc''.<br />
<br />
* Under '''Analysis Options'' select ''objective cores''<br />
<br />
* Under '''Linkage method''' select ''cluster convex polygons''<br />
<br />
* Leave '''Inclusive or cluster''' at the default of ''separate cluster polygons'' <br />
<br />
* Leave '''Polygon display method''' at the default of ''corner & cell polygons''; these options give the classic cluster analysis from [[Bibliography|Kenward (1987), Kenward et al. (2001)]]).<br />
<br />
* Under '''Outlier exclusion''' select ''iterative alpha 0.1%'' (this choice gives the most conservative exclusive of outlying locations – i.e. the [[Glossary|outlier exclusion distances]] are large) and hence only the most extremely excursive locations are excluded.<br />
<br />
* Under '''Output files''' click the box to select ''output edge''; this should create a file ''furzey_cxoi01s.edg''<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
The edges will be displayed in the main window for the first range, with cluster analysis statistics [[Glossary|see nuclei, partial area, diversity of areas and diversity of locations in the Glossary]] in a separate window.<br />
<br />
* In the '''Outlier exclusion''' box at the top of the graphics screen, select ''display all''.<br />
<br />
* In the '''Background''' box above the graphics screen, press the (third) ''open'' button and select the file ''furhab.ves''; in the same box select ''all'' instead of ''clipped'' in the second box.<br />
<br />
The map of Furzey Island will plot in brown under the range outlines, with the (Scots pine) woodland used by the red squirrels shown in black. The island is surrounded by sea, and has two areas large of concrete used for oil-drilling within the woodland. Note that one squirrel location was on the beach to the north of the island, and (if you open the file ''furzey.loc'' instead of the edges in the '''Data''' box and select the (first) button to display the map ''faint'' in the '''Background''' box) that squirrels rarely used the oil-extraction sites.<br />
<br />
==== Dispersal detection via inter-location distances ====<br />
<br />
[[File:Location analysis6.png|thumb|right|upright=1.6|Dispersal detection map and plot. Note the new columns in the Locations table.]]<br />
<br />
* Click on the '''Location''' button to open the Location Analysis setup window.<br /><br />
<br />
* Choose ''inter-location measures''<br />
<br />
* Under '''Input files''' press ''browse'' and find the file ''<RangesFolder>\samples\buzzard\buzzard_dispersal.loc''.<br />
<br />
* Under '''Analysis Options''', select ''distances'', ''site to location'' <br />
<br />
* Tick the '''dispersal detection''' box<br />
<br />
* Set '''Minimum dispersal distance’ to '''1000''' and '''Alpha for dispersal detection''' to ''none''. <br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
The distances from the focal site to each location over time for the first range will be displayed in a separate window, with a red vertical line marking where dispersal was classed to occur by the entered criteria.<br />
<br />
* Select different ranges within the ranges table on the upper left and the results for them will be displayed in the plot window.<br />
<br />
Note that new columns ''Distances*'' and ''Dispersal*'' have been added to the file in the Locations table. The latter contains 0 prior to dispersal and 1 after it.<br />
<br />
== Overlap analysis ==<br />
<br />
==== Creating an overlap matrix ====<br />
<br />
[[File:Overlap analysis 1.png|thumb|right|upright=1.6|Dispersal detection map and plot. Note the new columns in the Locations table.]]<br />
<br />
* If you have not already done so, run through [[#100% Minimum convex polygons|100% Minimum convex polygons]] to create the file ''blackbird_x.edg]].<br />
<br />
* Click on the '''Overlap''' button.<br />
<br />
* Select ''range overlap''.<br />
<br />
* Press '''browse''' and select the edge file ''<RangesFolder>\samples\blackbird\blackbird_x.edg''<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
In the statistics window and you will be able to see the percentage overlap of the ranges in rows by the ranges in columns followed by the area itself. A new map has been built of the union of each range's overlap with all the others.<br />
<br />
<br />
== Interaction analysis ==<br />
<br />
==== Autocorrelations ====<br />
<br />
[[File:Interaction analysis1.png|thumb|right|upright=1.6|Autocorrelation analysis results.]]<br />
<br />
* Click on the '''Interaction''' button.<br />
<br />
* Select ''autocorrelations''.<br />
<br />
* Press '''browse''' and select ''<RangesFolder>\samples\buzzard\buzzards.loc''.<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
* When the analysis has finished, click the Log button to see the ''time to independence (Schoeners 1) '' for each range. These are displayed in the plot.<br />
<br />
<br />
==== Dynamic interactions ====<br />
<br />
[[File:Interaction analysis2.png|thumb|right|upright=1.6|Dynamic interation statistics showing strong positive association between the movements of the second female and the male lions.]]<br />
<br />
* Click on the '''Interaction''' button.<br />
<br />
* Select '''dynamic interactions'''.<br />
<br />
* Press '''browse''' and select ''<RangesFolder>\samples\lion\lions.loc''.<br />
<br />
* Select ''all'’ for ''Individual selection''<br />
<br />
* Select ''time attributes of locations'' for '''Same time observations defined by'''.<br />
<br />
* Enter ''30'' for '''Input threshold between same-time observations (minutes)'''.<br />
<br />
Locations were collected approximately hourly; this will allow for the slight variation while not considering consecutive locations as being taken at the same time.<br />
<br />
* Leave the default '''Maximum randomisation sample''' (5000).<br />
<br />
* In '''Output files''' select 'means for each range''.<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
On completion, you should see in the statistics file that the Jacobs indices in the final three columns are less than 0.1 for all combinations except for the last one (which is for the second female and the male). This generates Jacobs indices of > 0.75 using the different means, indicating strong positive association between the movements.<br />
<br />
<br />
== Habitat analysis ==<br />
[[File:Habitat analysis1-1.png|thumb|right|upright=1.6|Habitat content of ranges analysis results map and statistics.]]<br />
<br />
==== Habitat content of ranges ====<br />
<br />
* Click on the '''Habitat''' button.<br />
<br />
* Select ''habitat content of ranges''.<br />
<br />
* For the '''map''' file, press '''browse''' and select ''<RangesFolder>\samples\buzzard\buzzmap.rst''.<br />
<br />
* for the '''edge''' file, press '''browse''' and select ''<RangesFolder>\samples\buzzard\buzzards.edg''.<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
When the analysis completes, the statistics window will contain the area calculations for each habitat in each range.<br />
<br />
The map in the main window contains the edge file in the foreground with the habitat raster in the background ''clipped'' to the edges and ''faint'' to make the edge lines clearer.<br />
<br />
* To use a vector habitat file rather than a raster file use the files ''<RangesFolder>\samples\blackbird\blackbird_map.ves'' and ''<RangesFolder>\samples\blackbird\blackbird_x.edg'' (the latter created in [[#100% Minimum convex polygons|100% Minimum convex polygons]]).<br />
<br />
<br />
==== Habitat in buffers around locations ====<br />
<br />
[[File:Habitat analysis2.png|thumb|right|upright=1.6|Habitat at locations analysis map results.]]<br />
<br />
* Click on the '''Habitat''' button.<br />
<br />
* Select ''habitat at locations''.<br />
<br />
* The map file should be already set but if not, press '''browse''' and select ''<RangesFolder>\samples\buzzard\buzzmap.rst''.<br />
<br />
* For the '''location''' file, press '''browse''' and select ''<RangesFolder>\samples\buzzard\buzzards.loc''.<br />
<br />
* In '''Analysis Options''', select '''buffers around locations'''<br />
<br />
* Change '''Input circle radius''' to ''100''.<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
* When the analysis has finished, circles around each habitat will be displayed on the map.<br />
<br />
<br />
== Fish analyses, midline and clipping ==<br />
<br />
==== Midline inter-location distances ====<br />
<br />
[[File:Midline analysis1.png|thumb|right|upright=1.6|Results of midline inter=location distances showing a section between two locations. The blue line first goes from the location to the midline at right angles, then along the midline until it is parallel with the next location.]]<br />
<br />
* Click on the '''Location''' button to open the Location Analysis setup window.<br /><br />
<br />
* Choose ''midline inter-location''.<br />
<br />
* Under '''Input Files''' for location file, press '''browse''' and find the file ''<RangesFolder>\samples\fish\fish.loc''.<br />
<br />
* For the midline file, press ''browse'' and find the file ''<RangesFolder>\samples\fish\midline.vel''.<br />
<br />
* Under '''Analysis Options''', select ''distances'', ''location interval'' and leave the '''Per number of locations''' set at ''1''.<br />
<br />
* Click the '''link midline to locations''' checkbox.<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
The statistics file will contain the distances file. The main window map will display the paths between all locations on the map. To view each path individually:<br />
<br />
* change from ‘display all’ to ‘display selected edge(s)’, and use the mouse or cursor keys to select different paths within the upper left table.<br />
<br />
<br />
==== Midline linear ranges ====<br />
<br />
[[File:Midline analysis2.png|thumb|right|upright=1.6|Midline linear ranges results map showing the linear range in blue with the locations as a background map. Only the area covered by the fish is shown.]]<br />
<br />
Using the same input files as above<br />
<br />
* Select ''midline linear ranges''<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
The length of each linear range will be displayed in the statistics file. The main window map will display each linear range, with the locations in the background. <br />
<br />
* Try changing the background map to ''<RangesFolder>\samples\fish\midline.vel'' to see how far the river extends beyond the fishes' range.<br />
<br />
<br />
==== Clipping home ranges by a river outline ====<br />
<br />
[[File:Midline analysis3.png|thumb|right|upright=1.6|Clipping home ranges: map output from 100% cores convex polygons analysis of the fish data.]]<br />
[[File:Midline analysis4.png|thumb|right|upright=1.6|Clipping home ranges: edge file overlayed on the map of the river.]]<br />
<br />
* Click on the '''Location''' button to open the Location Analysis setup window.<br /><br />
<br />
* Choose ''convex polygons'', ''100% cores''<br />
<br />
* Use the same location file as above (''<RangesFolder>\samples\fish\fish.loc''.)<br />
<br />
* Check '''Output edge file''' in '''Output Files''' to create file ''<RangesFolder>\samples\fish\fish_x.edg''.<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
When the analysis is complete the map will show the convex polygon, with the locations in the background.<br />
<br />
* Click on the '''Habitat''' button to open the Habitat Analysis setup window.<br /><br />
<br />
* Select ‘habitat content of ranges’<br />
<br />
* For the '''map file''', press '''browse''' and select ''<RangesFolder>\samples\fish\river.ves''.<br />
<br />
* For the '''edge file''', press '''browse''' & select ''<RangesFolder>\samples\fish\fish_x.edg (created at the start of this exercise)<br />
<br />
* Check '''Output clip file''' in '''Output Files''' to create file ''<RangesFolder>\samples\fish\fish_x_Hab_river_clip.edg''.<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
The clip file be loaded in the main window. You can load the original file ''fish_x.edg'' as a background to see the difference between them. The statistics will show the total area of the MCP, then the percentage of that area that is in the main river (dark blue) and tributary (light blue).<br />
<br />
<p>&nbsp;</p><br />
<p>&nbsp;</p><br />
<p>&nbsp;</p><br />
<p>&nbsp;</p><br />
<p>&nbsp;</p><br />
<p>&nbsp;</p><br />
<br />
== Modelling ==<br />
<br />
These routines estimate resource-requirement and survival parameters which can be used in modelling. Future work will focus on refining these parameters and providing new ones with further automated analyses of the type being conducted in Resource Area Dependence Analysis.<br />
<br />
=== Resource Area Dependence Analysis ===<br />
<br />
==== Plotting observed data only ====<br />
<br />
[[File:Modelling_analysis1.png|thumb|right|upright=1.6|Plotting observed data only.]]<br />
<br />
* Click on the '''Modelling''' button to open the setup window.<br /><br />
<br />
* Select ''resource area dependence''.<br />
<br />
* Under '''Map file''' press '''browse''' and find the file ''<RangesFolder>\samples\squirrel\furhab.ves''.<br />
<br />
* Similarly, under '''Edge file''' press '''browse''' and find the file ''<RangesFolder>\samples\squirrel\furzey_cxoi01s.edg''. <br />
If that file is not in the folder, you need to create it in the Tutorial sequence [[Tutorial#Cluster analysis with objective cores| Cluster analysis with objective cores]]. <br />
<br />
* Under '''Analysis Options''' select ''observed only''<br />
<br />
* Press the '''Run Analysis''' button. <br />
<br />
The computer does 15 habitat analyses and displays the results in a graph of the log proportion of resource on the log area of the range. As the first resource is the area of island ''inclusive of woodland'' (and thus unlike the exclusive habitat areas in most habitat analysis), it fills the range of most of the 15 squirrels, but six of them also included appreciable proportions of oil-extraction area which is not included as usable island; as these squirrels also had the larger range outlines there is a negative correlation (''r'' = -0.579), which repeats in the statistics window. If you click ''WOODL'' at the top left of the main window, the graph shows a stronger relationship (''r'' = -0.863) with steeper green regression line. The prediction of minimum woodland area required, from the intercept ''c'', is 10^-1 and therefore about 0.1 ha.<br />
<br />
==== Comparing observed and random data ====<br />
<br />
[[File:Modelling_analysis2.png|thumb|right|upright=1.6|Comparing observed and random data.]]<br />
<br />
* Click on the '''Modelling''' button to open the setup window.<br /><br />
<br />
All the previous options and files should still be selected, but make them again if not<br />
<br />
* Under '''Analysis Options''' select ''observed and 199 random iterations''<br />
<br />
* Press the '''Run Analysis''' button.<br />
<br />
* Have a cup of tea or coffee while your computer does more than 3,000 habitat analyses.<br />
<br />
This time the graph includes a yellow line based on the mean slope ''b'' and intercept ''c'' for the regressions from 199 random samplings with replacement among the 15 observed range outlines, each of which was randomly rotated and displaced within the envelope round all the observed outlines in a separate window; these mean values are given in the statistics window, also with 95% confidence limits for ''r'' assuming a normal distribution, and a z value for the difference between the observed ''r'' and that mean value. However, the most robust statistic for tests is in the next column: the number of negative random values beyond the observed ''r''. The value will vary with each run, but will average about 3. In a two-tailed test with 200 samples (including the observed), ''P'' < 0.05 for a value less than 5. For ''WOODL''and, there are no random values beyond the observed (i.e. ''P'' < 0.01), nor in repeated runs with 999 iterations (i.e. ''P'' < 0.002). <br />
<br />
* Now under '''envelope''' select ''user defined'', after making a file with an expansive outline (e.g. 99% ellipses)<br />
<br />
You will find with this particular file that, due to the greater availability of sea round the island for random outlines, nearly 10% of the random outlines are in the sea if using either the default ''replace zeros'' or the option ''ignore zeros''. In these circumstances the random regressions have a tendency to be positive, because small range outlines have a greater chance of including no island or woodland. The option ''resample zeros'' is then most conservative, because it forces random samples to contain habitat and tends to produce regression with ''b''=0.<br />
<br />
=== Kaplan Meier Survival Analysis ===<br />
<br />
==== Plotting survival for a single data set ====<br />
<br />
[[File:Modelling_analysis3.png|thumb|right|upright=1.6|Plotting survival for a single data set.]]<br />
<br />
* Click on the '''Modelling''' button to open the setup window.<br /><br />
<br />
* Select ''kaplan meier survival''.<br />
<br />
* Under '''Analysis Options''' ensure ''one set'' is selected<br />
<br />
* Under '''Input files''' press '''browse''' and find, for the ''set 1 survival file'' the file ''<RangesFolder>\samples\goshawk\Juv_Male.srv''.<br />
<br />
* Press the '''Run Analysis''' button. <br />
<br />
Looking at the statistics window, the sample size was not adequate until the start of the second month, and became less than 30 animals due to moulting of tail-mounted radio tags after 30 April. Run the analysis again, selecting as '''set 1 start date''' the ''specified date'' and use the calendar to select 1 July 1980 (around the fledging date), then in '''set 1 end date''' again select ''specified date'' and set 31 March 1981. This estimates juvenile male survival to the start of the next egg-laying period, as used in [[Bibliography|Kenward et al. (1999)]].<br />
<br />
==== Comparing two sets of survival data ====<br />
<br />
[[File:Modelling_analysis4.png|thumb|right|upright=1.6|Comparing two sets of survival data .]]<br />
<br />
* Under '''Analysis Options''' select ''two sets for comparison''<br />
<br />
* Under '''Input files''' check that the ''set 1 survival file'' is still ''<RangesFolder>\samples\goshawk\Juv_Male.srv''.<br />
<br />
* Check that '''set 1 start date''' has ''specified date'' selected, of 1 July 1980. <br />
<br />
* Check that '''set 1 end date''' has ''specified date'' selected, of 31 March 1981.<br />
<br />
* Press '''browse''' for the ''set 2 survival file'' and find ''<RangesFolder>\samples\goshawk\Juv_Female.srv''.<br />
<br />
* For the '''set 2 start date''', use the ''set 1 start date'' option.<br />
<br />
* For the '''set 2 end date''', use the ''set 2 end date'' option.<br />
<br />
* Press the '''Run Analysis''' button. <br />
<br />
The survival is poorer for juvenile males than for juvenile females due mainly to differences from October (month 4) onwards. The ''z'' values of 2.07 to 2.13 are higher than the value of 1.96 for alpha = 0.05, so ''P'' < 0.05.<br />
<br />
== Data Sources ==<br />
<br />
The squirrel data were collected in the 1980s, before the introduction of location qualifying variables: the data therefore consist of simple x,y coordinates. One set, which was collected at Elton Estate in work conducted from Monks Wood Experimental Station, was used to illustrate the introduction of compositional analysis ([[Bibliography|Aebischer et al. 1993]]). A second set, collected on Furzey Island in Poole Harbour in work conducted from Furzebrook Research Station, was used to illustrate the effectiveness of [[Glossary|Cluster analysis]] in [[Bibliography|Kenward (2001), Kenward et al. (2001)]].<br />
<br />
The buzzard data were collected for a study of relationships between demography and habitat conducted from Furzebrook Research Station ([[Bibliography|Kenward et al. 2000, 2001, South & Kenward 2001, Walls & Kenward 1995, 1998, 2001]]). Note that some of the buzzard data have been altered slightly to avoid use used without the authors' permission. Goshawk survival data were collected on the Swedish island of Gotland ([[Bibliography|Kenward et al. 1993, 1999]]); the natal year of all fledged cohorts has been set to 1980 in the example files. <br />
<br />
The blackbird data represent material on an abundant European passerine species collected by Ben Kenward during a pilot study for work on garden birds. There are two adult males, an adult female that was incubating eggs and one that was not breeding at the time. All but the breeding female foraged in a garden during the day and roosted at night in a Rhododendron thicket some 200m away across an open field. The birds were tracked on foot, and locations for triangulation taken from less than 100m away, giving a tracking resolution of about 10m. For this reason, coordinates were entered in 10m units. <br />
<br />
The lion data come from 3 lions (2 females and 1 male), GPS collared in Botswana by Graham Hemson of the Wildlife Conservation Research Unit, Oxford (see [[Bibliography|Hemson et al. 2005]]). The collars were scheduled to take 15 locations in every 24 hour period and the sample given here is for October 2001. The data coordinates are expressed in the UTM projection. In the first half of the month the movements of the first female are restricted to an area approximately 10 x 10 km in the west. Then over the following 10 days she makes a return trip of over 70 km coinciding with the start of the zebra and wildebeest migrations. The movements of the 2nd female and the male are very similar at the start of the month. Later in the month the male makes an excursion into an area crossed by the first female about a week beforehand. <br />
<br />
The squirrel, buzzard and goshawk data came from work funded by the UK Natural Environment Research Council in its Centre for Ecology and Hydrology (formerly Institute of Terrestrial Ecology), in the last case in cooperation with Uppsala University and the Swedish Hunters' Association. The blackbird and lion data were collected during student and post-graduate projects from the University of Oxford, the first supervised externally within NERC-CEH. The files within the folder fish are a simulated dataset created by Sean Walls.</div>Adminhttps://ranges-support.anatrack.com/wiki/Raster_File_AttributesRaster File Attributes2014-11-09T11:46:34Z<p>Admin: </p>
<hr />
<div>This page describes the ''raster file setup screen'' which allows [[File Types#Raster Files|raster files]] to be created and edited and is reached from the[[Input & Graphics|main form]] when the '''new''', '''import''' or '''modify'' buttons are pressed and the raster option selected. There will be differences in the appearance of the frame, according to which button was pressed and the contents of the existing file for '''import''' and '''modify'''. <br />
<br />
There are two main sections: [[#Map scaling text boxes|Map scaling text boxes]] and [[#Habitats and habitat groups tables|Habitats and habitat groups tables]].<br />
<br />
== Map scaling text boxes ==<br />
<br />
==== Width (no. cells), Height (no. cells) ====<br />
<br />
These determine the size of the raster map in terms of the number of cells it will contain. These boxes are greyed out for '''import''' and '''modify''' because the size of the map is defined by the number of rows and columns in the existing files. In '''new''' mode, they may be edited, in which case the values of '''E''' and '''N''' will change (to E = W + (width * coord units) & N = S + (width * coord units) ).<br />
<br />
==== Units Per Cell ====<br />
<br />
This determines the size of each cell in terms of coordinate units. Note that if you change this the values of ‘E’ & ‘N’ will change as indicated above.<br />
<br />
==== W, S ====<br />
<br />
'''W''' & '''S''' determine the coordinates of the left, lower corner of the map. Changes to these will also cause changes in '''E''' & '''N'''.<br />
<br />
==== E, N ====<br />
<br />
'''E''' & '''N''' are the coordinates for the right, upper corner of the map. These text boxes are not editable (as indicated by their grey colour), but the values can be changed by altering any of the other text boxes. <br />
<br />
=== Scale Of Coordinate Units ===<br />
<br />
This allows the coordinate scale of the map to be altered. Note that this is different from coordinate '''Units Per Cell''' which determines the dimensions of each raster cell in the number of coordinate units. <br />
<br />
== Habitats and habitat groups tables ==<br />
<br />
These determine how the cell values stored within the raster map are converted into habitats for display and analysis. The system will take a little bit of learning, but once that is done is very powerful, allowing habitat classifications to be changed easily for different analyses.<br />
<br />
The system consists of '''Habitats''' and '''Habitat Groups'''. Each habitat is represented by the same value wherever it occurs in the map and has its own label. Each habitat may belong to one '''Habitat group'''. Each habitat group has a colour (which is used in the the [[Input & Graphics| main window]] map, and in [[Habitat Analysis|Habitat Analysis]] options) and a label which is made up from the labels of its constituent habitats. The habitat group is the classification used in [[Habitat Analysis|Habitat Analysis]] - so for example the contents of a range will be analysed in terms of habitat groups (however if you have just one habitat per habitat group this is equivalent to using habitats themselves). This allows for great flexibility in altering habitat classifications for different analyses, without having to alter the raw data in the map itself.<br />
<br />
==== Habitats ====<br />
<br />
Click in the habitat column to alter the label for individual habitats.<br />
<br />
Click in the group column to alter the habitat group that a habitat belongs to.<br />
<br />
Use the '''add habitat''' button to add new habitats.<br />
<br />
==== Habitat groups ====<br />
<br />
Click in the colour column to alter the colour assigned to a habitat group.<br />
<br />
Use the '''add group''' button to add new habitat groups.<br />
<br />
The label given to each habitat group is set from its constituent habitats. If there is just one habitat in the habitat group the labels will be the same. For any additional habitats, the habitat group label will be constructed as follows :<br />
<br />
: Label1+MapValue2+MapValue3…+MapValueN<br />
<br />
This provides a unique label whilst minimising the number of characters. Labels with greater than 11 characters may obstruct the map display in habitat analysis options.<br />
<br />
==== Quick Tip ====<br />
<br />
When creating a new file, first add all of the desired habitat groups (the right hand table), then add single habitats (the left hand table). For each habitat, the habitat group will be set automatically, to the same number as the habitat or to the highest habitat group if that is lower. <br />
<br />
==== Constraints ====<br />
<br />
A number of constraints are placed upon the choice of habitat groups and colours:<br />
<br />
* The colour for cell value 0 is always black, and cannot be edited.<br />
* There are currently a maximum of 49 habitat groups (+ 0 for no data)<br />
* The colour of the 49th habitat group is set to white and cannot be edited.<br />
* No two habitat groups may have the same colour.<br />
* No other habitat group than 0 may have the colour black, and no other habitat group than 49 may have the colour white.<br />
* Map values must be sequential integers starting at 1.</div>Adminhttps://ranges-support.anatrack.com/wiki/Output_FilesOutput Files2014-11-08T11:33:27Z<p>RobertKenward: /* Statistics files */</p>
<hr />
<div>== Introduction ==<br />
<br />
Output files options are offered in the fourth column of the user interface for [[Location Analysis|Location Analysis]], [[Overlap Analysis|Overlap]], [[Interaction Analysis|Interaction]], [[Habitat Analysis|Habitat]] and [[Modelling Analysis|Modelling]]. The options that are offered are dependent upon the analysis being conducted.<br />
<br />
If an output file tick box is visible, selecting this tick box will cause the creation of a permanent file with a default filename created by taking the name (and folder) of the input file and adding a code identifying the analysis used to create it. If the tickbox is not selected the output file is given a temporary name and will be overwritten by subsequent analyses. To choose a different filename from the default, select the tickbox and type in the text box or press browse to search your files. If you choose an existing file you will be asked whether you want to overwrite that file, if you choose '''Yes''' the existing file will be renamed to overwrite.bak in the Ranges directory, and the new file will be created after the '''Run Analysis''' button is pressed.<br />
<br />
All data files in Ranges contain text, and can therefore be viewed in text editors, except for raster maps (''.rst'') and image files (''.ima'') which are byte arrays. Coverage of the following output files can be seen in File Types.<br />
<br />
==== Statistics files ====<br />
<br />
These are files with column headers saved in comma-separated variable (''.csv'') format that can be opened in the Statistics window, double-clicked to open in Microsoft Excel or imported to an alternative spreadsheet.<br />
<br />
==== Ranges files ====<br />
<br />
Other files are mainly to be used as inputs for other Ranges analyses and for display as maps and as plots. They can also be viewed in a spreadsheet but the data are arranged to save space rather than for ease of interpretation. <br />
<br />
== Output filename codes for location analyses ==<br />
<br />
The following codes are added to default output filenames, enabling the analysis that created them to be identified. Codes are highlighted in bold.<br />
<br />
<u>Conve<b>X</b> polygons</u><br />
<br />
Peel centre: <br />
<br />
: <b>f</b>ocal site <br /><br />
: <b>h</b>armonic mean <br /><br />
: <b>k</b>ernel centre <br /><br />
: <b>a</b>rithmetic mean centre <br /> <br />
: <b>r</b>ecalculated arithmetic mean <br /> <br />
<br />
<u>Conca<b>V</b>e polygons</u><br />
<br />
Edge restriction:<br />
<br />
: <b>01</b> to <b>09</b> for 0.1 to 0.9, or in metres : 10m <br />
<br />
<u><b>E</b>llipses</u><br />
<br />
<u><b>C</b>lusters</u><br />
<br />
conca<b>V</b>e or conve<b>X</b><br /><br />
Joining priority:<br />
: <b>n</b>earest neighbour, centroid<br /><br />
<b>o</b>bjective cores :<br /><br />
: outlier exclusion: <br />
: <b>t</b>runcation of distance distribution, iterative at 0.1,0.5,1% : <b>i01</b>, <b>i05</b>, <b>i10</b><br /><br />
for convex: <br /><br />
: <b>s</b>eparate polygons<br /><br />
: <b>i</b>nclusive polygon<br /><br />
for concave:<br /> <br />
: edge restrictions : <b>001</b> to <b>10</b> for 0.01 to 1.0<br /><br />
<br />
<u><b>K</b>ernels or <b>H</b>armonic mean contours</u><br />
<br />
Contours: <br />
: <b>d</b>ensity, <b>l</b>ocation inclusive (fitted to locations)<br /><br />
Harmonic mean options:<br /> <br />
: Locations <b>c</b>entred, <b>u</b>nmodified<br /><br />
Kernel type:<br /><br />
: <b>f</b>ixed, <b>t</b>ail weighted, <b>c</b>ore weighted<br /><br />
Kernel smoothing:<br /> <br />
: multiplier <b>01</b> to <b>20</b>, lscv <b>I</b>nflection, lscv <b>L</b>ocal min., lscv <b>G</b>lobal min<br /><br />
Matrix:<br /><br />
: number of cells : e.g. <b>n40</b>, size of cells e.g. <b>s10</b><br />
<br />
<u>Cores</u><br />
<br />
e.g.:<br/><br />
: selected cores : <b>_50%75%95%</b><br/><br />
: 5% intervals : <b>_25to100%</b></div>Adminhttps://ranges-support.anatrack.com/wiki/Range_Use_PlotsRange Use Plots2014-11-08T11:15:07Z<p>RobertKenward: </p>
<hr />
<div>== Introduction ==<br />
<br />
In Ranges you are able to load [[Input & Graphics#Utilisation plots|utilisation plots]] and [[Input & Graphics#Incremental area plots|incremental area analysis plots]] created in [[Location Analysis|location analyses]]. In both cases, plots can be for single ranges, or to show means and limiting values for combinations of ranges. Ranges will automatically load these plots in the [[Input & Graphics|main window]] after a run has been conducted, and if on conducting the run, you specify a permanent filename you will be able to load the files into Ranges later.<br />
<br />
== Utilisation plots == <br />
<br />
Examination of utilisation plots ([[Bibliography|Ford & Krumme 1979]]) provides a method of deciding on the percentage of locations that define a core range. In Ranges utilisation plots display the area of estimated home range cores at 5% intervals from 20-100%. A more objective technique for excluding outlying locations to estimate a range core is available in [[Clusters|cluster analysis]], but is appropriate only for multinuclear analyses. The inspection technique is more appropriate for peeled polygons such as [[Convex Polygons|MCPs]], which are mononuclear, and not at all appropriate for ellipses because of their intrinsically smooth distribution. <br />
<br />
If you have individually selected ranges displayed in the map display then just the values for those ranges will be displayed in the plots. If you choose the map display option ''display all'' then the mean values will also be displayed.<br />
<br />
==== Individual ranges ==== <br />
<br />
If there are a few locations far from the range centre, the slope of the plot is initially steep, but becomes shallower when only the core locations remain. This slope discontinuity, if present, is a useful indicator of how many locations constitute the core. Discontinuity tends to be most marked in cluster analysis, which is particularly effective for eliminating outliers. Discontinuities can also be seen in contours plotted to contain a proportion of the locations (especially for [[Harmonic Mean Contours|Harmonic Mean contours]]), but not in the smooth distributions from ellipses or contours based solely on location density. <br />
<br />
The most appropriate use of inspection is to estimate a single proportion of core locations to be used across all the ranges, for example as the largest percentage of locations that include the core for 95% of the ranges. This will produce areas slightly less than the true core for some ranges, but variation in area is much less than if a small number of outermost core locations are excluded than if they are included. When selecting a single core value, ranges with no discontinuity (and typically a relatively small area that includes all the locations) are treated as 100% core (i.e. lacking excursive locations). In cluster analysis, 85% cores seem to exclude excursive locations effectively for a number of species. Moreover, the distribution of cores estimated subjectively in this way gave very similar results to those obtained by [[Clusters#Outlier exclusion|objective outlier exclusion]] ([[Bibliography|Hodder et al. 1998]]).<br />
<br />
==== All ranges ==== <br />
<br />
This displays the means and data values for utilisation distributions across all ranges. <br />
<br />
== Incremental area analysis plots == <br />
<br />
With this option, you can examine how the range area changes as successive locations are added, starting with the first 3 locations. In a pilot study, this helps to decide how many locations are needed to define each range. Areas are shown as the percentage of the area that uses all the locations. <br />
<br />
Note that all cores, and outer edges based on density estimates (ellipses and contours) may decrease in size after an initial increase. This happens when an increase in density of core locations reduces the influence of outliers. In this case, the number of locations required is that at which range areas tend to become stable. <br />
<br />
If you have individually selected ranges displayed in the map display then just the values for those ranges will be displayed in the plots. If you choose the map display option ''display all ranges'' then the mean values will also be displayed.<br />
<br />
==== Individual ranges ==== <br />
<br />
This gives a good impression of when most achieve stability only if all have similar numbers of locations, so it can be best to exclude ranges with relatively few locations (the reason for there being few locations may also indicate an atypical range). <br />
<br />
==== All ranges ==== <br />
<br />
In this case, a line connects the mean area for all the ranges, and all of the data values are also shown.</div>Adminhttps://ranges-support.anatrack.com/wiki/OverlapOverlap2014-11-07T16:52:17Z<p>RobertKenward: /* Range Overlap (2D) */</p>
<hr />
<div>== Introduction ==<br />
<br />
This routine uses range edge files prepared in location analysis options. It will analyse the overlap of each range outline in the file, or of each range outline on the locations in another file<br />
<br />
== Range Overlap (2D) ==<br />
<br />
This calculates the overlap of each range on each other. The resulting overlap matrix (''.csv''), contains both the percentages and the absolute areas of overlap for each range with each other. Each row in the file contains the rangeID followed by overlap on that range of the range in each column ( i.e. the overlap of the range in the row by the range in the column). The percentages file is automatically loaded to Stats Viewer, and can also be opened in Excel. The [[Selections|'''Make Selections''']] button can be used to select a subset of ranges for the analysis.<br />
<br />
It is also possible to create new edge files (''.edg'') from the overlaps; these will be displayed in the map panel when the analysis completes. There are two options:<br />
<br />
==== New range per overlapped pair ====<br />
<br />
This builds a new range for each pair of ranges containing the overlap between them.<br />
<br />
==== Union of overlaps ====<br />
<br />
This creates one range for each input range containing the union of overlaps with all the other ranges.<br />
<br />
== Overlap of Ranges on Locations ==<br />
<br />
This option estimates the proportion of locations from file A that fall within the edges of file B. The resulting overlap matrix is saved to a file (''.csv'' )containing the proportion of locations from ranges in each column that occur within the outline of the range in each row. The file is automatically loaded to the Statistics window, and can also be opened in Excel. The [[Selections|'''Make Selections''']] button can be used to select a subset of ranges and/or locations for the analysis.</div>Adminhttps://ranges-support.anatrack.com/wiki/Output_File_HeadersOutput File Headers2014-11-07T10:32:19Z<p>RobertKenward: /* Kaplan Meier survival */</p>
<hr />
<div>This page describes the column headers for statistics files. These are output from Ranges analyses and can be loaded in the Statistics window. <br />
<br />
== Inter-location headings (all values) == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|-<br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female)<br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|-<br />
|LocE || Location easting <br />
|-<br />
|LocN || Location northing <br />
|-<br />
|Headings(degrees) || Heading angle from track to previous location<br />
|}<br />
<br />
== Inter-location headings (means for each range) == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|-<br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|-<br />
|-<br />
|N || Number of locations <br />
|-<br />
|Average || Average heading of locations in degrees<br />
|-<br />
|SE+- || Standard Error of heading<br />
|-<br />
|GeoMean || Geometric mean heading <br />
|-<br />
|SE*/ || Standard Error of geometric heading (when using logs you must multiply and divide rather than add and subtract the SE) <br />
|} <br />
<br />
== Inter-location distances (all values) == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|-<br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started<br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|-<br />
|LocE || Location easting <br />
|-<br />
|LocN || Location northing <br />
|-<br />
|Distances(m) || Distance of location in metres<br />
|-<br />
|Dispersal(distx) || 1 if animal has dispersed by this location, 1 otherwise (only when dispersal detector selected). <br />
|-<br />
|LQV1 || as specified in the location file <br />
|-<br />
|... LQVn || as specified in the location file <br />
|}<br />
<br />
NB. It is possible to reimport this csv file and then use distances as an LQV. This means that they can be used for analyses of, say, "only locations at least 100m from den". <br />
<br />
== Inter-location distances (means for each range) ==<br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female)<br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|-<br />
|N || Number of locations <br />
|-<br />
|Average || Average distance of locations (m) <br />
|-<br />
|SE+- || Standard Error of distance <br />
|-<br />
|GeoMean || Geometric mean distance <br />
|-<br />
|SE*/ || Standard Error of geometric distance (when using logs you must multiply and divide rather than add and subtract the SE) <br />
|} <br />
<br />
== Inter-location times (all values) == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|-<br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female)<br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|-<br />
|LocE || Location easting <br />
|-<br />
|LocN || Location northing <br />
|-<br />
|Times(minutes) || Time between locations<br />
|}<br />
<br />
== Inter-location times (means for each range) == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Month tracking started<br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|-<br />
|N || Number of locations <br />
|-<br />
|Average || Average time between locations in minutes <br />
|-<br />
|SE+- || Standard Error of time between locations <br />
|-<br />
|GeoMean || Geometric mean time between locations <br />
|-<br />
|SE*/ || Standard Error of geometric time between locations (when using logs you must multiply and divide rather than add and subtract the SE) <br />
|} <br />
<br />
== Inter-location speeds (all values) == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|-<br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female)<br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|-<br />
|LocE || Location easting <br />
|-<br />
|LocN || Location northing <br />
|-<br />
|Speeds(km/h) || Speeds in kilometres per hour<br />
|}<br />
<br />
== Inter-location speeds (means for each range) == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|-<br />
|N || Number of locations <br />
|-<br />
|Average || Average speed <br />
|-<br />
|SE+- || Standard Error of speed<br />
|-<br />
|GeoMean || Geometric mean speed<br />
|-<br />
|SE*/ || Standard Error of geometric speed (when using logs you must multiply and divide rather than add and subtract the SE) <br />
|} <br />
<br />
== Convex polygons == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female)<br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|CentE || Easting for the range centre <br />
|-<br />
|CentN || Northing for the range centre <br />
|-<br />
|Span || Maximum distance between locations <br />
|-<br />
|Mean || Mean distance of locations from range centre <br />
|-<br />
|Median || Median distance of locations from range centre <br />
|-<br />
|Max || Maximum distance of locations from range centre <br />
|-<br />
|dummy || Unused variable <br />
|-<br />
|Core1 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|Core2 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|Core3 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|etc || <br />
|} <br />
<br />
== Concave polygons == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|Span || Maximum distance between locations <br />
|- <br />
|dummy || Unused variable <br />
|- <br />
|dummy || Unused variable <br />
|- <br />
|dummy || Unused variable <br />
|- <br />
|dummy || Unused variable <br />
|- <br />
|dummy || Unused variable <br />
|- <br />
|dummy || Unused variable <br />
|- <br />
|Core% || Core percentage, always 100 for concave polygons <br />
|- <br />
|Area || Area of that core (ha) <br />
|} <br />
<br />
== Neighbour-linkage == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female)<br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|-<br />
|Core1 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|Nuclei || Number of nuclei <br />
|-<br />
|Partial || Partial area <br />
|-<br />
|DivLocs || Simpson's index for location diversity <br />
|-<br />
|DivArea || Simpson's index for area diversity <br />
|-<br />
|Core2 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|Nuclei || Number of nuclei <br />
|-<br />
|Partial || Partial area <br />
|-<br />
|DivLocs || Simpson's index for location diversity <br />
|-<br />
|DivArea || Simpson's index for area diversity <br />
|-<br />
|Core3 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|Nuclei || Number of nuclei <br />
|-<br />
|Partial || Partial area <br />
|-<br />
|DivLocs || Simpson's index for location diversity <br />
|-<br />
|DivArea || Simpson's index for area diversity <br />
|-<br />
|Core1 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|etc ||<br />
|}<br />
<br />
== Ellipses == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|CentE || Easting for the range centre <br />
|-<br />
|CentN || Northing for the range centre <br />
|-<br />
|MARAD || Radius of the major axis for the maximum core size selected <br />
|-<br />
|MIRAD || Radius of the minor axis for the maximum core size selected <br />
|-<br />
|THETA || Angle major axis is inclined from horizontal <br />
|-<br />
|X-VAR || Variance in eastings <br />
|-<br />
|Y-VAR || Variance in northings <br />
|-<br />
|COVAR || Covariance of eastings x northings <br />
|-<br />
|r-STAT || correlation coefficient <br />
|-<br />
|JTAsym || Standard deviation of the major axis divided by that of the minor axis. <br />
|-<br />
|Core1 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|Core2 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|etc || <br />
|} <br />
<br />
== Harmonic mean contours == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female)<br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|CentE || Arithmetic mean of eastings (range centre if with CentN) <br />
|-<br />
|CentN || Arithmetic mean of northings (range centre if with CentE) <br />
|-<br />
|Value || Density score at the central location <br />
|-<br />
|Spread || Range spread at central location <br />
|-<br />
|Dispersion || An index of dispersion of the distribution is given by the peak density value (at the range centre <br />
location) divided by the standard deviation of the density value across all the locations <br />
|-<br />
|Skew || An index of skew in the distribution is given by the Euclidean distance between the arithmetic mean centre and the location with the peak density value, divided by the standard deviation of the density value across all the locations <br />
|-<br />
|Kurtosis || Kurtosis of the location distribution <br />
|-<br />
|hRef || Not relevant to Harmonic means <br />
|-<br />
|hUsed || Not relevant to Harmonic means <br />
|-<br />
|Core1 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|Core2 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|Core3 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|etc || <br />
|} <br />
<br />
== Kernel contours ==<br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female)<br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|CentE || Arithmetic mean of eastings (range centre if with CentN) <br />
|-<br />
|CentN || Arithmetic mean of northings (range centre if with CentE) <br />
|-<br />
|Value || Density score at the central location <br />
|-<br />
|Spread || Range spread at central location <br />
|-<br />
|Dispersion || An index of dispersion of the distribution is given by the peak density value (at the range centre <br />
location) divided by the standard deviation of the density value across all the locations <br />
|-<br />
|Skew || An index of skew in the distribution is given by the Euclidean distance between the arithmetic mean centre and the location with the peak density value, divided by the standard deviation of the density value across all the locations <br />
|-<br />
|Kurtosis || Kurtosis of the location distribution <br />
|-<br />
|hRef || Not relevant to Harmonic means <br />
|-<br />
|hUsed || Not relevant to Harmonic means <br />
|-<br />
|xhRef ||Multiple of hRef used (user defined or calculated by LSCV) <br />
|-<br />
|interval || Size (m) of lattice cells used in kernel estimation <br />
|-<br />
|Core1 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|Core2 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|Core3 || Core percentage <br />
|-<br />
|Area || Area of that core (ha) <br />
|-<br />
|etc || <br />
|} <br />
<br />
== Range overlap ==<br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | overlap_ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female)<br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|CentE || Arithmetic mean of eastings (range centre if with CentN) <br />
|-<br />
|CORE(%) !! Core percentage<br />
|-<br />
|ID1(%) || Percentage of overlapID range overlaps with range ID1 for this core<br />
|-<br />
|ID2(%) || Percentage of overlapID range overlaps with range ID2 for this core<br />
|-<br />
|ID3(%) || Percentage of overlapID range overlaps with range ID3 for this core<br />
|-<br />
|ID4(%) || Percentage of overlapID range overlaps with range ID4 for this core<br />
|-<br />
|etc || <br />
|-<br />
|ID1(m2) || Area of overlap for overlapID range and range ID1 for this core<br />
|-<br />
|ID2(m2) || Area of overlap for overlapID range and range ID2 for this core<br />
|-<br />
|ID3(m2) || Area of overlap for overlapID range and range ID3 for this core<br />
|-<br />
|ID4(m2) || Area of overlap for overlapID range and range ID4 for this core <br />
|-<br />
|etc ||<br />
|}<br />
<br />
== Overlap of range on locations ==<br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | overlap_ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female)<br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|CentE || Arithmetic mean of eastings (range centre if with CentN) <br />
|-<br />
|CORE(%) !! Core percentage<br />
|-<br />
|ID1(%) || Percentage of overlapID range overlaps with locations in range ID1 for this core<br />
|-<br />
|ID2(%) || Percentage of overlapID range overlaps with locations in range ID2 for this core<br />
|-<br />
|ID3(%) || Percentage of overlapID range overlaps with locations in range ID3 for this core<br />
|-<br />
|ID4(%) || Percentage of overlapID range overlaps with locations in range ID4 for this core<br />
|}<br />
<br />
== Autocorrelations == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female)<br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|-<br />
|SIpast1(h) || **TODO **<br />
|-<br />
|SIpast2(h) || **TODO **<br />
|-<br />
|Mins || **TODO **<br />
|-<br />
|1xMins || **TODO **<br />
|-<br />
|2xMins || **TODO **<br />
|-<br />
|3xMins || **TODO **<br />
|-<br />
|4xMins || **TODO **<br />
|-<br />
|5xMins || **TODO **<br />
|-<br />
|etc ||<br />
|}<br />
<br />
== Dynamic interactions == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | range1ID || First individual's identification number<br />
|- <br />
|AGE || First individual's age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || First individual's sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || First individual's month tracking started <br />
|-<br />
|YEAR || First individual's year tracking started <br />
|-<br />
|FOC-E || First individual's focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || First individual's focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|range2ID || Second individual's identification number<br />
|- <br />
|AGE || Second individual's age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Second individual's sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Second individual's month tracking started <br />
|-<br />
|YEAR || Second individual's year tracking started <br />
|-<br />
|FOC-E || Second individual's focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Second individual's focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|Nobs || Number of observed distances <br />
|-<br />
|AMEANObs || Arithmetic mean of observed distances <br />
|-<br />
|SUMObs || Sum of observed distances <br />
|-<br />
|SoSobs || Sum of Squares of observed distances <br />
|-<br />
|GMEANobs || Geometric mean of the observed distances <br />
|-<br />
|LSUMobs || Sum of logged observed distances <br />
|-<br />
|LSoSobs || Sum of squares of logged observed distances <br />
|-<br />
|MEDobs || Median Observed distance <br />
|-<br />
|Nran || Number of random locations <br />
|-<br />
|AMEANran || Arithmetic mean of random distances <br />
|-<br />
|SUMran || Sum of random distances <br />
|-<br />
|SoSran || Sum of Squares of random distances <br />
|-<br />
|GMEANran || Geometric mean of the random distances <br />
|-<br />
|LSUMran || Sum of logged random distances <br />
|-<br />
|LSoSran || Sum of squares of logged random distances <br />
|-<br />
|MEDran || Median random distance <br />
|-<br />
|JacobsAMEAN || Jacob's index of arithmetic mean distances <br />
|-<br />
|JacobsGMEAN || Jacob's index of geometric mean distances <br />
|-<br />
|JacobsMED || Jacob's index of median distances <br />
|} <br />
<br />
== Location-point distances == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|Nobs || Number of observed distances <br />
|-<br />
|AMEANObs || Arithmetic mean of observed distances <br />
|-<br />
|SUMObs || Sum of observed distances <br />
|-<br />
|SoSobs || Sum of Squares of observed distances <br />
|-<br />
|GMEANobs || Geometric mean of the observed distances <br />
|-<br />
|LSUMobs || Sum of logged observed distances <br />
|-<br />
|LSoSobs || Sum of squares of logged observed distances <br />
|-<br />
|MEDobs || Median Observed distance <br />
|-<br />
|Nran || Number of random locations <br />
|-<br />
|AMEANran || Arithmetic mean of random distances <br />
|-<br />
|SUMran || Sum of random distances <br />
|-<br />
|SoSran || Sum of Squares of random distances <br />
|-<br />
|GMEANran || Geometric mean of the random distances <br />
|-<br />
|LSUMran || Sum of logged random distances <br />
|-<br />
|LSoSran || Sum of squares of logged random distances <br />
|-<br />
|MEDran || Median random distance <br />
|-<br />
|JacobsAMEAN || Jacob's index of arithmetic mean distances <br />
|-<br />
|JacobsGMEAN || Jacob's index of geometric mean distances <br />
|-<br />
|JacobsMED || Jacob's index of median distances <br />
|} <br />
<br />
<br />
== Range centre spacing == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | Nobs || Number of observed distances <br />
|- <br />
|MEANobs || Arithmetic mean of observed distances <br />
|-<br />
|SUMobs || Sum of observed distances <br />
|-<br />
|SoSobs || Sum of Squares of observed distances <br />
|-<br />
|SEobs || Standard Error of observed distances <br />
|-<br />
|AREA || Area of range <br />
|-<br />
|MEANest || Estimated mean distance <br />
|-<br />
|SEest || Standard error of estimated distances <br />
|-<br />
|t-obs/est || Student's t-test for regular spacing of estimated observations <br />
|-<br />
|Nran || Number of random locations <br />
|-<br />
|MEANran || Arithmetic mean of random distances <br />
|-<br />
|SUMran || Sum of random distances <br />
|-<br />
|SoSran || Sum of Squares of random distances <br />
|-<br />
|SEran || Standard error of random distances <br />
|-<br />
|t-obs/ran || Student's t-test for regular spacing of random estimates <br />
|} <br />
<br />
== Habitat in a map rectangle == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | W-edge || Western edge <br />
|-<br />
|E-edge || Eastern edge <br />
|-<br />
|S-edge || Southern Edge <br />
|-<br />
|N-edge || Northern edge <br />
|-<br />
|Area || Area of assessed rectangle <br />
|-<br />
|HabitatGroup1 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup2 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup3 || % of the area made up of this habitat <br />
|-<br />
|etc ||<br />
|}<br />
<br />
== Habitat in a map circle == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | cent-E || Western edge <br />
|-<br />
|cent-N || Circle centre northing <br />
|-<br />
|radius || radius of circle in metres <br />
|-<br />
|Area || Area of circle <br />
|-<br />
|HabitatGroup1 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup2 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup3 || % of the area made up of this habitat <br />
|-<br />
|etc ||<br />
|}<br />
<br />
== Habitat content of ranges == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|Core1 || Core percent <br />
|-<br />
|Area || Area of core <br />
|-<br />
|HabitatGroup1 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup2 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup3 || % of the area made up of this habitat <br />
|-<br />
|etc ||<br />
|-<br />
|Core2 || Core size <br />
|-<br />
|Area || Area of core 2 <br />
|-<br />
|HabitatGroup1 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup2 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup3 || % of the area made up of this habitat <br />
|-<br />
|etc ||<br />
|}<br />
<br />
== Habitat points within ranges == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|Core1 || Core percent <br />
|-<br />
|Area || Area of core 1 <br />
|-<br />
|sites || Number of sites within core 1 <br />
|-<br />
|Core2 || Core percent <br />
|-<br />
|Area || Area of core 2 <br />
|-<br />
|sites || Number of sites within core 2 <br />
|-<br />
|etc ||<br />
|}<br />
<br />
== Habitat at locations == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|N-locs || Number of locations <br />
|-<br />
|HabitatGroup1 || % of the locations (or ellipse area if error ellipses) of this habitat <br />
|-<br />
|HabitatGroup2 || % of the locations (or ellipse area if error ellipses) of this habitat <br />
|-<br />
|HabitatGroup3 || % of the locations (or ellipse area if error ellipses) of this habitat <br />
|-<br />
|etc ||<br />
|}<br />
<br />
== Habitat preference in ranges == <br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | ID || Individual identification number<br />
|- <br />
|AGE || Age (e.g. 1 = Juvenile, 2 = Yearling, 3 = Adult) <br />
|-<br />
|SEX || Sex (e.g. 1 = Male, 2 = Female) <br />
|-<br />
|MONTH || Month tracking started <br />
|-<br />
|YEAR || Year tracking started <br />
|-<br />
|FOC-E || Focal site Easting (e.g. nest, den, trap site etc) <br />
|-<br />
|FOC-N || Focal site Northing (e.g. nest, den, trap site etc) <br />
|- <br />
|Core1 || Core percent <br />
|-<br />
|Area || Area of core <br />
|-<br />
|HabitatGroup1 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup2 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup3 || % of the area made up of this habitat <br />
|-<br />
|etc || <br />
|-<br />
|Core2 || Core size <br />
|-<br />
|Area || Area of core 2 <br />
|-<br />
|HabitatGroup1 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup2 || % of the area made up of this habitat <br />
|-<br />
|HabitatGroup3 || % of the area made up of this habitat <br />
|-<br />
|etc ||<br />
|-<br />
|N-locs || Number of locations <br />
|-<br />
|HabitatGroup1 || % of the locations (or ellipse area if error ellipses) of this habitat <br />
|-<br />
|HabitatGroup2 || % of the locations (or ellipse area if error ellipses) of this habitat <br />
|-<br />
|HabitatGroup3 || % of the locations (or ellipse area if error ellipses) of this habitat <br />
|-<br />
|etc ||<br />
|}<br />
<br />
== Resource area dependence ==<br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" |Habitat || The habitat group name<br />
|-<br />
|Core || The core %<br />
|-<br />
|ExclusionStatus || Habitat Excluded or not<br />
|-<br />
|rObserved || Pearson's r for observations<br />
|-<br />
|bObserved || Slope of regression for observations<br />
|-<br />
|SEbObserved || Standard error of slope for observations<br />
|-<br />
|cObserved || Area at y-axis intercept for observations <br />
|-<br />
|0-Core%Observed || Number of observed habitat cores with zero core area<br />
|-<br />
|0-Habitat%Observed || Number of observed habitat cores without this habitat in the core<br />
|-<br />
|rMeanRandom || Mean Pearson's r for randomly placed range cores<br />
|-<br />
|rMedianRandom || Median Pearson's r for randomly placed range cores<br />
|-<br />
|z || z for for randomly placed range cores<br />
|-<br />
|&#43;r95%CL || lower confidence limit for random r if normally distributed <br />
|-<br />
|r95%CL || upper confidence limit for random r if normally distributed <br />
|-<br />
|Beyondr || number of random r values more extreme than the observed r<br />
|-<br />
|bMeanRandom || Mean slope for randomly placed range cores<br />
|-<br />
|SEbMeanRandom || Mean standard error of slope for randomly placed range cores<br />
|-<br />
|cMeanRandom || Mean area at y-axis intercept for random rs<br />
|-<br />
|0-habitat%Random || number of random habitat cores without this habitat in the core<br />
|}<br />
<br />
== Kaplan Meier survival ==<br />
<br />
{| class="wikitable"<br />
| style="width: 200px;" | File || The name of the analysis survival file <br />
|-<br />
|Dates || The dates set for the analysis<br />
|-<br />
|Interval || the interval number<br />
|-<br />
|Ending || the end date of the interval<br />
|-<br />
|Present At Start || Individuals present at start of interval<br />
|-<br />
|Present At End || Individuals present at end of interval<br />
|-<br />
|Censor Dead || Individuals died during interval<br />
|-<br />
|Censor Lost || Individuals lost during interval<br />
|-<br />
|Alive at End || Individuals alive at end of interval<br />
|-<br />
|Added During || Individuals added during interval<br />
|-<br />
|Survival Total || Individuals survived for interval<br />
|-<br />
|Survival Decrease|| Decrease in individuals for interval <br />
|-<br />
|Variance Greenwood || Greenwood variance for interval<br />
|-<br />
|Variance Cox/Oakes || Cox/Oakes variance for interval<br />
|-<br />
|Tag days || Total number of days for all individuals<br />
|}</div>Adminhttps://ranges-support.anatrack.com/wiki/New_FeaturesNew Features2014-11-07T09:49:39Z<p>Admin: </p>
<hr />
<div>== Ranges 9 == <br />
<br />
Release date: November 2014<br />
<br />
* Resource Area Dependence analysis<br />
* Kaplan-Meier Survival analysis<br />
* An up-to-date look with a more user-friendly interface but without loosing the familiar Ranges feel<br />
* Better data and map split postioning on screen with an adjustable divider to optimise space for each<br />
* Map rendering improvements: both faster and with less artifacts<br />
* Improved map furniture: coordinates, scale bar, zoom and pan controls etc<br />
* Zoom to selection with right mouse button<br />
* Zoom in and out to cursor with mouse scroll button<br />
* Ability to create locations and vector points with CTRL + left mouse click, remove from the end with CTRL + right mouse click<br />
* Improved data charts for utilization and inc files, autocorrelation and interlocation analyses with scaling axes and better labels and headings<br />
* Ability to make map backgrounds paler to make foreground show up better. <br />
* Faster file loading <br />
* Huge file handling including large location files from GPS devices and large raster maps<br />
* Importing locations in latitidue-longitude format<br />
* Exporting to KML using lat-lng<br />
* Location file merging; edge file sampling and merging<br />
* Display locations and analysis maps on Google Maps<br />
* Save maps and plots to image file<br />
* Range overlap analysis map output<br />
<br />
== Ranges 8 ==<br />
<br />
Release date: May 2013<br />
<br />
* Ranges for Apple Macintosh<br />
<br />
Release date: September 2009<br />
<br />
* Curve and hole edge polygons<br />
* Shape selection by clicking on the maps<br />
* Random Subsampling in selections panel<br />
<br />
Release date: December 2008<br />
<br />
* Faster: all routines now programmed in C++ or Java, 10x faster (no more DOS screens).<br />
* New neighbourhood-Linkage home range method - OREPs<br />
* Features for fish, Midline analyses for distances and ranges along river networks, clipping to exclude bank areas from home ranges<br />
* Copy and paste graphics into other applications<br />
* Animation of location data added to Input & Graphics<br />
* New display options in Input & Graphics, much greater flexibility<br />
* Images can be used as backgrounds in ‘Input & Graphics’<br />
* Map Maker: On-screen digitising to trace images and create maps that can be used in analyses <br />
* Graphics Window: Utilisation, Incremental and inter-location plots now appear in windows that can be resized and printed.<br />
* Limit on number of map colours increased from 15 to 50 <br />
* Stats Viewer for viewing results tables generated by Ranges<br />
* Log that keeps a record of all analyses run in a session<br />
* Filter: Ability to save selected locations based on attributes using '''modify''' in Input & Graphics<br />
* Random Selector: to select a random number of locations from each individual<br />
<br />
== Ranges 6 ==<br />
<br />
Release date: 2003<br />
<br />
* Raster and Vector Shape Background maps<br />
* Import of Idrisi raster files<br />
* ArcView Shapefile import and export. Ranges6 will now import ESRI ArcView shapefiles (either points, lines or shapes) for use in habitat analysis or as background maps. Shapefiles containing points can also be imported as Ranges6 Location files. Ranges6 location files, edge (home range) and vector files can be exported as ESRI shapefiles for display in ArcView or for import to other GIS packages.<br />
* Printing. Ranges6 location, edge, vector and raster files can now be printed directly from the viewing window in Data Entry. This allows you to choose the area for printing by zooming in and out, and to choose different background maps. A title is printed with the filename and for edge files a description of the analysis used to create them.<br />
* Export of files for publication quality figures in Excel. Ranges6 location, edge and vector files can now be exported to text files that can be readily pasted into an Excel workbook which is set up to display any data pasted in. You can then use the flexibility of Excel to modify your figures as required. These figures can then easily be cut and pasted into other applications such as Word.<br />
* Changes and additions to Kernel LSCV calculations. The implementation of Least Squares Cross Validation (LSCV) has been changed slightly based upon our continuing research. This may cause differences in the results produced by this and the previous version. The new version makes it less likely that the method will default to the reference smoothing multiplier of 1. The steps have been changed so that the reference smoothing multiplier will only be used if the method has failed to find an inflection or minimum (and a multiplier of 1.01 or 0.99 indicates that an inflection or minimum has been found very close to 1). For completeness we have added implementations that search for a local or global minimum, although we still recommend use of the method that searches for a local inflection.<br />
* Changed default contour settings for harmonic mean and kernel analyses. The default contouring method is now based on the location density alone, rather than being fitted to locations, because this is thought to be the more robust approach. Similarly for harmonic means the default for location centring has been set to unmodified rather than centred as this is thought to be more robust. The default matrix size for this option has been changed from 40 to 150 to help avoid matrix size dependency.<br />
* Option to set size of matrix cells in Kernel analysis. The option to set the size of matrix cells rather than the number of cells in the matrix has been added.<br />
* Added display options in Data Entry. New options have been added to the map display window in Data Entry. These include displaying vector files in their assigned category colours and displaying location and edge files in range specific colours. Now a greater range of background maps (displayed in grey) can be used, e.g. a location file can be used as a background map for an edge file, to see how a home range estimate relates to the points used to generate it, or an edge file can be used as the background for another edge file to see how different range estimates compare.<br />
* Display of utilisation plots in Data Entry. Utilisation plots for individual ranges can now be displayed and printed from Data Entry.<br />
* GPS sample data To illustrate how Ranges6 can deal with GPS data we have added a file (''lion\lions.loc'') containing a months worth of data for 3 lions ( ~ 500 locations each) collected in Botswana by Graham Hemson. The helpfiles also contain more information on using location data collected from GPS, and an example of performing dynamic interaction analysis has been added to the tutorial.<br />
* Unlimited numbers of ranges or edge shapes. Now no limits are placed on the numbers of ranges or edge shapes in a single file.<br />
* Identifying the analysis used to create an edge file. We have added two features that make it easier to identify which location analysis was used to create a particular range estimate stored in an edge file. Firstly, when an edge file is opened in Data Entry (as either the primary file or a background map), holding the mouse pointer over the filename will display a description of the analysis used to create the file. Secondly, in Location analyses, default output filenames are created containing codes that enable the precise analysis used to be identified.<br />
* A utility to convert non-integer codes to integers prior to import This enables you to convert columns of non-integer data into columns of integers. This is useful if you have potential range identifiers or location qualifying variables stored as non-integers and allows you to convert them prior to import into Ranges. It also generates a key, so that you can easily see the conversion from your original data to its integer equivalent.<br />
* Raster and Vector shape Background maps: Raster and vector shape files can now be displayed as colour background maps for your location or edge files in Data Entry.</div>Adminhttps://ranges-support.anatrack.com/wiki/Midline_AnalysisMidline Analysis2014-11-07T09:24:32Z<p>Admin: </p>
<hr />
<div>Midline analyses use the distance along a line instead of the straight line distances between locations.<br />
<br />
== Creating a midline file ==<br />
<br />
The midline file which is used to defined the potential routes between locations needs to be a Ranges [[File Types#Vector Files|vector line file]]. At its simplest it could be a single line. If it represents a river the coordinates should be arranged going upstream. For a network, such as a river system, secondary (or higher order) branches should be represented as other, later lines in the file, and their first point should be one of the points from the primary line (or one of the other lines that is of a lower order). This is to ensure that the network joins up. For a simple example see the file ''/fish/midline.vel''. One way to create such a file from scratch is to use the Ranges [[Input & Graphics#On-screen digitising|on-screen digitising]] tool to draw up the middle of a scanned river image from a map or aerial photo.<br />
<br />
== How does Ranges calculate midline distances? ==<br />
<br />
For each of a pair of locations Ranges finds the closest line within the midline file, and calculates the shortest (perpendicular) distance to the line. The distance along the midline between these two points is then added to the two perpendicular distances. Where there is more than one potential route between two points in the network Ranges will attempt to find the shortest route (making the assumption that the individual is most likely to have taken the shortest route). For a simple river network with no islands or braiding there will just be one potential route. Ranges can cope with islands and braiding, however it may fail if confronted with a more complex network such as one that might be generated from a large system of hedgerows.<br />
<br />
== Midline inter-location measures == <br />
<br />
This option can be used to calculate the distances and speeds between locations along the midline. The ''headings'' option is not available for midline analyses, and the ''times'' option will give the same results as conventional [[Inter-location Measures|inter-location measures]]. The distance calculation produces a file called ''temp_paths.edg'' in the ranges folder, which contains the path used to calculate the distance between each pair of locations. The statistics (csv) output file for distances contains the total distance between each pair of locations, and then the number of linear sections, the length of each with positive values indicating upstream and negative downstream (assuming that the lines were input with the coordinates going upstream). There is an option ('''link midline to locations''') to include the perpendicular sections from the location on to the midline.<br />
<br />
== Midline linear ranges ==<br />
<br />
The midline linear range is a line or group of lines which extends to the furthest locations for that range, it will be a subsection of the midline file. It produces an edge file (''.edg'') containing these linear ranges, and a statistics file (''.csv'') which stores their lengths. There is an option ('''link midline to locations''') to include the perpendicular sections from the location on to the midline.<br />
<br />
== Midline cluster analysis ==<br />
<br />
A problem with trying to calculate the home range areas of fish within a river, is that if the river bends, the home range estimate may end up including areas of the bank which the fish has not used. Cluster analysis creates groups of locations that are close to each other and then puts a polygon around these. The rational behind using midline distances, was that locations that were close together as the crow flies but not as the fish swims (e.g. locations either side of the neck of an oxbow) were less likely to end up in the same group. Initial investigations have shown that conventional (Euclidean) [[Clusters|cluster analysis]] can produce good estimates of fish home ranges with relatively little bank included, and that midline cluster analysis produces little improvement. However it is possible that midline cluster analysis may prove useful in other systems, so we have included it her in case you wish to investigate.</div>Adminhttps://ranges-support.anatrack.com/wiki/Review_Of_Home_Range_AnalysesReview Of Home Range Analyses2014-11-07T09:17:28Z<p>RobertKenward: </p>
<hr />
<div>Ranges are analysed for many reasons, including estimation of range sizes and habitat use for conservation projects, estimation of range structure and overlap for behavioural studies, and combinations of all these parameters for demographic modelling. Just as there is no one best method for all statistical tests, there is no single best method of range analysis. Methods differ in smoothness of fit to locations and are constrained by sample size.<br />
<br />
The different analysis techniques divide loosely into families, whose relationships and properties are outlined in the next few paragraphs. The special value of each technique is mentioned again at the start of the section dealing with its implementation in Ranges. For a more comprehensive recent review, see "A Manual for Wildlife Radio Tagging" ([[Bibliography|Kenward 2001]]).<br />
<br />
The two main families of range analysis techniques are either primarily parametric, based on estimating location density distributions, or non-parametric, based on linkage distances between individual locations and usually involving a ranking process. The density-based techniques are fundamentally most smoothing. An early circular approach ([[Bibliography|Calhoun & Casby 1958]], [[Bibliography|Harrison 1958]]) was extended to estimate bivariate normal ellipses ([[Bibliography|Jennrich & Turner 1969]]). These approaches assume that locations are distributed normally on one or two axes about the arithmetic mean x and y coordinates for all the locations. The implicit assumptions of normality and that ranges are mono-nuclear are seldom met ([[Bibliography|White & Garrott 1990]]), but ellipses are still useful for extremely smoothed estimates of range size when few locations are available to give appreciable detail.<br />
<br />
More sophisticated parametric techniques use kernel estimators to allow for multinuclear distributions ([[Bibliography|Dixon & Chapman 1980]], [[Bibliography|Donn & Rennolls 1983]], [[Bibliography|Worton 1989]]). Location density is estimated over a matrix of intersections of a grid, which is placed arbitrarily (i.e. without reference to the coordinate system used for the locations). Contours are then interpolated between the intersections. Density-contouring confers shape that is lacking in ellipse models, but still includes assumptions about the density distribution that substantially affect the results. Contouring on Gaussian kernels is mathematically more robust than harmonic mean contouring, and use of techniques such as least squares cross validation ([[Bibliography|Worton 1989]]) to estimate the smoothness of the contouring have some value for providing optimal smoothing ([[Bibliography|Seaman & Powell 1996]]), at least for moderate numbers of locations ([[Bibliography|Hemson et al. 2005]]). However, applying any continuous density distribution smooths contours into unused areas that border high-use areas, especially when outlying locations extend the density distribution, and the more smoothing the less precise the fit to the pattern of locations.<br />
<br />
The linkage techniques are based on creating polygons with lines that link adjacent locations. The earliest approach linked peripheral locations to give a minimum sum of linkage distances (imagine stretching string round locations marked by pegs) as a convex polygon ([[Bibliography|Dalke & Sime 1938]], [[Bibliography|Mohr 1947]]), which provides comparability between studies due to widespread use ([[Bibliography|Harris et al. 1990]]). However, outlying locations cause a convex polygon round the peripheral locations to include large unvisited areas. This problem is avoided by excluding the largest linkage distances, either by excluding locations furthest from a range centre to give mononuclear peeled polygons that estimate single-outline territories ([[Bibliography|Michener 1979]]), or by restricting the linkage distance along edges to create concave polygons ([[Bibliography|Stickel 1954]], [[Bibliography|Harvey & Barbour 1965]]) that may fragment into separate areas, or by creating multi-nuclear clusters of locations with a minimal sum of nearest-neighbour distances ([[Bibliography|Kenward 1987]], [[Bibliography|Kenward et al. 2001]]). In effect, the peripheral convex polygon uses largest linkages and is the most smoothed of these techniques, while restricting the edge distance reduces the smoothing (as does minimising the sum of nearest-neighbour distances). The totally unsmoothed (effectively unlinked) option is achieved when each location is surrounded by a grid cell that has the width of the minimal linkage between locations (i.e. the tracking resolution). Finding the maximum range areas without any smoothing to link locations requires very large numbers of observations to record animals in each grid cell they visit, but with smaller samples of locations the neighbour linkage methods can give detailed outlines enclosing adjacent locations to approximate multinuclear cores. <br />
<br />
The original concave polygons had the peripheral linkage distances restricted to half the span of maximum distance between any locations. However, the span is strongly influenced by outlying locations and using a maximum linkage of half the span is an arbitrary decision and is therefore best replaced by one of two more rigorous uses of neighbour-linkage distances. <br />
<br />
These two methods are implemented as incremental hierarchical cluster analysis ([[Bibliography|Kenward 1987]]) and local convex hulls ([[Bibliography|Getz & Wilmers 2004]]). Both use nearest neighbour distances to define polygons that are inclusive of the densest locations, and then fuse polygons that have one or more locations in common. Both can produce utilisation plots as distances are increased to reduce density. However they differ in terms of how they reduce location density within polygons and in the fusing rules. Cluster analysis starts with 3 locations with the smallest sum of nearest neighbour distances and adds locations in each cluster, or starts new clusters of 3, to minimise this sum. LoCoH forms a convex hull of N locations round each location, then ranks the hulls according to size and fuses outlines of those that touch. Although an arbitrary choice of N is problematic, the hull-outline fusing can enable voids (holes) within polygon outlines and solves a problem in cluster analysis of outlines around separate clusters occasionally overlapping if they are convex polygons. However, using concave outlines can now solve this problem in cluster analysis, in which the hierarchical incremental use of nearest-neighbour distances avoids arbitrary choices. <br />
<br />
Another recent refinement to avoid arbitrary choices is the objective definition of a distance that excludes as outliers those locations with nearest neighbour distances beyond the normal distribution ([[Bibliography|Kenward et al. 2001]]), which produces an excursion-exclusive home range, of usual movements in the sense of Burt ([[Bibliography|1943]]). The use of an objective exclusion distance was originally implemented in cluster analysis, but can also be applied to derive Objective Restricted-Edge Polygons (OREPs) which, like LoCoH, can include voids within polygon outlines.<br />
<br />
All linkage-based (polygon-generating) range analyses are best used with a boundary strip. The strip is half the tracking resolution, which makes allowance for the real position of the location being up to this distance on either side of the registered coordinates. Use of a boundary strip provides a consistent relationship between the different linkage techniques. Thus, outlying locations in cluster or restricted edge analyses become isolated grid cells; moreover, if the tracking resolution is used to set the linkage-restriction of concave polygons, the range is estimated as grid cells<br />
<br />
A number of studies have examined how the performance of different analysis methods, for estimating area, shape and internal structure of home ranges, is affected by numbers and distribution of locations. Estimating minimum numbers of locations required is important, because there is now a consensus that individual ranges, not locations, should be the sample units for statistical tests, to avoid assumptions about independence of locations within each range ([[Bibliography|Kenward 1992]], [[Bibliography|Aebischer et al. 1993]], [[Bibliography|Otis & White 1999]]).<br />
<br />
In general, the more detail on shape and structure that is required, the more locations that are needed to define a range. The most stable ranges are ellipses, which can require just 10 fixes to give a stable index of area, assuming that consecutive fixes are independent in time. However, ellipses estimate an animal’s true trajectory least precisely ([[Bibliography|Robertson et al. 1998]]) and are highly sensitive to outlying locations unless there is centre-weighting ([[Bibliography|Samuel & Garton 1985]]). Density contouring improves on precision, and can give stable area estimates with only 15 - 20 fixes, although at least 30 locations are often necessary for smoothing of kernel contour estimates by Least Squares Cross Validation ([[Bibliography|Seaman et al. 1999]]). Harmonic mean estimation gives greater precision than kernels with nominal (fixed) smoothing ([[Bibliography|Robertson et al. 1998]]) and lower sensitivity to outliers, but can require unusually extensive calculations to minimise matrix-dependence effects. Compared with contouring, polygon peeling gives similar or better precision, but also typically requires at least 30 locations for stability. Expansive range outlines, including ellipses and contours containing 95% of the location density or MCPs round all the locations, seem suitable for estimating habitat available to an animal, while peeled polygons or contours containing 40-90% of the locations are useful for examining range overlaps. Cores from cluster polygons contain the highest density of trajectory. Cluster polygons seem especially appropriate for examining habitat use and internal structure of ranges when habitats are coarse-grained relative to range size ([[Bibliography|Kenward et al. 2001]]). However, the number of locations required for stability with both these techniques is large (albeit less than with unconnected grid cells).<br />
<br />
Ranges implements all of the analyses that have been used in more than one refereed publication and are robust to a wide variety of location distributions and sample sizes. The latter condition excludes Fourier analysis ([[Bibliography|Anderson 1982]]). Polygon peeling techniques differ from the location-density polygon of Hartigan ([[Bibliography|1987]]) and mononuclear peeled convex hulls ([[Bibliography|Glendinning 1991]], [[Bibliography|Worton 1995a]]); however, peeling with iterative recalculation of the arithmetic mean should give very comparable results. Cluster analysis should give results similar to those from Dirichlet tessellations ([[Bibliography|Wray et al. 1992]]), without the problems of setting limits to outer tiles. The favouring of a minimal sum of nearest neighbour distances for optimising Local Convex Hulls ([[Bibliography|Getz et al. 2007]]) also converges that technique on cluster analysis.<br />
<br />
Which method to use depends on the number of locations and the biological questions. With 10-12 locations, ellipses can give robust results for questions of range size. With more locations, density contours can give some shape to ranges for questions about sociality and use of resources. If you have at least 30 locations with low spatio-temporal correlation, linkage techniques become appropriate for more precise definition of areas visited, with clustering or edge restriction based on outlier exclusion for defining range core polygons that conform to abrupt habitat boundaries. To avoid a priori choices, you may choose several techniques and adjust alpha, say to 0.01 for P<0.05 with 5 techniques; a broad selection could be 99% ellipses as an expansive size index of which overlaps define neighbouring animals with 95% kernel contours for a size index with shape for more subtle social and habitat assessments ([[Bibliography|Walls & Kenward 2001]]), outlier-excluded cluster cores for a tight definition of home range in grainy habitats, 50% kernels as a more probabilistic index at similar size, and 50% clusters to see if there is a tight focus on any particular resources. The peripheral (100%) convex polygons can provide comparability with older studies but tend not to outperform the other 5 in statistical tests.</div>Adminhttps://ranges-support.anatrack.com/wiki/Kernel_ContoursKernel Contours2014-11-07T09:07:21Z<p>RobertKenward: </p>
<hr />
<div>The pros & cons of different analysis techniques are discussed in detail in the [[Review Of Home Range Analyses|Review Of Home Range Analyses]] and for a more comprehensive recent review, see "A Manual for Wildlife Radio Tagging" ([[Bibliography|Kenward 2001]]) and Kenward et al. ([[Bibliography|2001]]). <br />
<br />
== Introduction ==<br />
<br />
Kernels and [[Harmonic Mean Contours|Harmonic means]] are both contour analyses which estimate density indices for locations at intersections of a matrix set across an animals range, and then interpolate contours between the values of that matrix. <br />
<br />
([[Bibliography|Worton (1989)]] noted that the Harmonic Mean estimator was one of a family of Kernel estimators and that the bivariate normal estimator (among others) could handle d=0 without special treatment. The bivariate normal kernel used by ([[Bibliography|Worton (1989)]] uses a negative exponential function of location distance from estimation points for estimating location density indices. As exp(0)=1, this function has no problem handling locations that coincide with estimation points. However, the function is also inherently more smoothing than Harmonic Mean contours. This makes it more appropriate than Harmonic Mean approaches for obtaining stable estimates of range size with small samples of locations, but less precise for estimating cores to be used in analyses of habitat content and sociality, especially when there are distant outliers.<br />
<br />
Smoothing of kernel analyses involves estimation of a smoothing parameter (h), sometimes called the reference bandwidth or window width (([[Bibliography|Seaman & Powell 1996]]). ([[Bibliography|Worton (1989)]] pointed out that his reference h might overestimate range areas by over-smoothing the distribution when ranges are strongly multimodal. He suggested the use of a fractional multiplier of h, and proposed estimating this smoothing factor by Least Squares Cross Validation (LSCV) of the mean integrated square error. See below for an explanation of how you can alter the [[#Smoothing|smoothing parameter]].<br />
<br />
As for Harmonic Mean estimation, the contours can be based solely on the mean and variance of the density index distribution across locations, which is most appropriate for range size estimates, or fitted to selected proportions of the locations, which may better define cores for subsequent analyses of sociality and habitat. The matrix size can be varied from its default of 40x40, or frozen to prevent re-scaling. [[#Matrix size|Matrix size]] does not influence the kernel estimates greatly. However, the expansive nature of outermost kernel contours can result after re-scaling in ranges covering only 50% of the matrix, so selection of matrices larger than the default 40x40 may help to obtain smooth contours across multimodal distributions. <br />
<br />
The majority of options for kernel contours are exactly the same as those for [[Harmonic Mean Contours|harmonic mean contours]] except [[#Kernel type|Kernel type]] and [[#Smoothing|Smoothing]] which replaces whether to centre locations between matrix intersections.<br />
<br />
<br />
== Locations Kc and statistics ==<br />
<br />
The density index can be estimated at each location. This enables estimation of statistics for the density distribution, and also provides values for interpolating contours either to the locations or based on location density alone. The Locations, Kc & statistics option runs more quickly than other options in contour analyses because the routine does not have to calculate a matrix of location density values. <br />
<br />
The statistics produced can give a useful insight into the structure of the range based on the distribution of the locations. A range statistics output file can be specified in the [[Output Files|'''Output Files''']]. The statistics include range spread and the density score at the central fix, with the dispersion, skewness and kurtosis of the density distribution ([[Bibliography|Spencer & Barrett 1984]]).<br />
<br />
== Selected cores ==<br />
<br />
This option allows you to examine range structure and to define core areas. By excluding low density areas the edges enclose areas most used by the animal. See the introduction to [[Location Analysis|Location Analyses]] for more details. <br />
<br />
You can choose one or more values for the percentage of locations or of location density to be included. Type them in ascending order, separated by either spaces or commas.<br />
<br />
The display shows coordinates of the Kc (Kernel centre) and its distance from the focal site.<br />
<br />
In the [[Output Files|'''Output Files''']] column you can specify a range areas and statistics output files. The estimates are in column format, suitable for spreadsheets. Each row has the 7 range variables, followed by X,Y coordinates for the range centre, followed by 5 range statistics followed by as many areas as there were core percentages. The statistics include the Kc coordinates, and the spread, dispersion, skew and kurtosis of the location distribution ([[Bibliography|Spencer & Barrett 1984]]). <br />
<br />
== Cores at 5% intervals ==<br />
<br />
This option provides plots which help to decide which locations are part of a core, and which are outliers. <br />
<br />
You can choose to save both edge (polygon) and utilisation files the latter can be plotted in [[Input & Graphics|the main window]]. Density analyses such as this save edges from 20-99% (because 100% of the distribution cannot be estimated). The cores are saved at 5% intervals, a total of 17 sets. <br />
<br />
The analysis produces a results screen which shows a map of the range edges, a table showing the area and % of total area of each % edge, and a graph of utilisation distribution. <br />
<br />
Core ranges<br />
<br />
Graphs of utilisation distribution can be used to assess (by eye) whether a sharp discontinuity in area, after the elimination of outlying locations, indicates a core range. Better estimation may be possible if the utilisation file is saved and plotted in [[Input & Graphics|the main window]].<br />
<br />
== Incremental area analysis ==<br />
<br />
Incremental area analysis is used to answer the question "how many locations do I need to estimate a home range?" Starting with the first three locations (the minimum needed to estimate a polygon area without a boundary strip), the new area is estimated as each location is added. This permits the consecutive areas, which tend to increase initially as the animal is observed using different parts of its range, to be plotted against number of locations until there is evidence of stability, which indicates that adding further locations will not improve the home range estimate. The default is to plot the edge round all the locations that have been added, but it is also possible to choose a single, smaller core. The results are saved to a ''.inc'' file which can be examined using opened in [[Input & Graphics|the main window]].<br />
<br />
== Kernel Type ==<br />
<br />
When [[Bibliography|Worton (1989)]] proposed the use of the bivariate normal kernel estimator, he noted that the smoothing parameter (h) can be adjusted in several ways. One approach is to vary h locally across the matrix, by weighting initial values to produce an "adaptive kernel".<br />
<br />
==== Fixed kernels ====<br />
<br />
These use the reference smoothing parameter (bandwidth), which is the standard deviation of x and y coordinates divided by the sixth root of the sample size.<br />
<br />
==== Tail weighting, ‘adaptive’ ====<br />
<br />
[[Bibliography|Worton (1989)]] suggested weighting by the inverse of the initial density index, to emphasise the tail of the distribution. Subsequent reviews show this to overestimate range sizes ([[Bibliography|Worton 1995b]], [[Bibliography|Seaman & Powell 1996]]).<br />
<br />
==== Core weighting ====<br />
<br />
An alternative is to core-weight the estimates, which tends to de-emphasise the tail of the density distribution. However, it is probably best to base estimates on the default, fixed value.<br />
<br />
== Contours ==<br />
<br />
==== Contours based on location density alone (default) ====<br />
<br />
In this option, contour plots are based solely on the mean and variance for the distribution of density indices across the locations. The contours estimate the probability of including a particular proportion of locations and may include more or less than the proportion of locations actually recorded. As in ellipse analyses, the outermost contour is estimated to include 99% of the location distribution, because the location density at 100% would be infinitely small. This classic approach is most appropriate for estimating stable range sizes, typically to include 95% of the density distribution, for which reason it is the default in Ranges 6. <br />
<br />
==== Contours fitted to locations ==== <br />
<br />
When contours are fitted to locations, the density index values at locations are ranked. Contours are then plotted to just include a given percentage of the locations. This approach is most analogous to the polygon approach. For example, it puts the 100% contour through the outermost location. There tend also to be irregular gaps between contours fitted to locations, which simplify detection of a core by inspection of utilisation plots and may define cores best for analyses of sociality and habitat content.<br />
<br />
== Smoothing multiplier, hRef and LSCV ==<br />
<br />
==== hRef ====<br />
<br />
The reference smoothing parameter (hRef) is the standard deviation of rescaled x and y coordinates divided by the sixth root of the number of locations. <br />
<br />
==== Fixed multiplier ====<br />
<br />
As the reference smoothing parameter tends to overestimate range areas, it can be multiplied by a fractional value ([[Bibliography|Worton 1989]]), in which case the contour areas from bivariate normal kernels tend to approach those of Harmonic Mean contours. Ranges allows multipliers between 0.1 (which often produces tight contours round locations) and 2.0 (which smooths contours highly).<br />
<br />
==== LSCV (Least Squares Cross Validation) ====<br />
<br />
LSCV provides an objective way to find a multiple of hRef. There are a number of potential ways of implementing LSCV. In Ranges, the routine starts with a multiplier of 1.51 and works downwards in steps of 0.02 to 0.09. The default implementation [LSCV Inflection] stops if it reaches an inflection, at which a decreasing downward slope either becomes an upward slope (indicating a local minimum) or increases again in a downward direction (indicating that a local minimum would have been likely with a much smaller step size than 0.02). [Ranges also supports LSCV Local minimum (in which case inflections other than true minima are ignored) and LSCV Global minimum (in which case the minimum that gives tightest smoothing is used). These options are denoted by LSCVI, LSCVL and LSCVG respectively.] <br />
<br />
If no minimum or inflexion is found, the reference value (*1) is used. Thus, a multiplier of 1 is an indication that the routine has failed ( the start and interval value used for the local minimum search means that it can find an optimal multiplier of 0.99 or 1.01 but not 1 itself ). <br />
<br />
If you have chosen to use least squares cross validation the message "OPTIMISING THE SMOOTHING CONSTANT" appears while the programme is running and the value of the multiplier is displayed.<br />
<br />
LSCV can be applied on a range-by-range basis, but that makes the size of individual ranges dependent not only on the area covered by the locations but also on how they are distributed within that area ([[Bibliography|Kenward 2001]]). An alternative approach is to use all values that are not 1 (the reference value) to estimate a median fraction for the multiplier, and then apply that multiple of hRef to all the ranges. The value of this multiple should be stated when describing the analysis. To do this first run an LSCV analysis with a single selected core, and ‘Output stats and areas file’ selected, the multipliers will be in the ''xhRef'' column, then repeat the analysis selecting [[#Fixed multiplier|Fixed Multiplier] and entering the median value you have calculated. <br />
<br />
LSCV tends not to work well with fewer than 30 locations or for data with large resolution relative to range size ([[Bibliography|Seaman et al. 1999]]). If a minimum is found only for very few ranges in a set, it may be better to use the reference value (hRef * 1), or to adopt a value that another study has found to be appropriate for the species. <br />
<br />
== Matrix size ==<br />
<br />
==== Matrix, set no. of cells ====<br />
<br />
Contouring is most detailed when there are small distances between intersections of the estimation matrix. The default in Ranges is a 40x40 matrix, because this gives rapid runs, little change in definition for larger matrices and comparability with RangesV. However, Ranges lacks the memory constraints of previous versions and matrix size can be increased up to 200.<br />
<br />
==== Matrix, set size of cells ====<br />
<br />
This new option allows the size of matrix cells, rather than the number, to be set by the user. This allows you to retain the same plotting resolution for home ranges of very different size (perhaps predator and prey), but may result in somewhat coarse plots for the smaller home range. If you set the size of cells such that the resulting matrix would be greater than the maximum of 200 cells, cell size will be set to create a 200 cell matrix and the warning ‘Matrix interval enlarged’ will be displayed at the end of the run. The size of the matrix cells is displayed in the ‘interval’ column in the statistics output file.<br />
<br />
== Matrix rescaling ==<br />
<br />
Contours tend to extend beyond the outermost locations, especially when based on location density alone. To plot such contours, the matrix is set to extend beyond the locations. In Ranges, the default is to set the locations to span the central 70% of the matrix. If an initial estimation of density at grid edges indicates that the outermost contour will still extend beyond the matrix, the proportion of the matrix spanned is decreased automatically in steps of 5% until a fit is likely. Re-scaling of the matrix is prevented by selecting the Freeze matrix option. This option is not available if ‘Matrix, set size of cells’ is selected.<br />
<br />
Differences that have been noted in contour estimates between different software packages ([[Bibliography|Larkin & Halkin 1994]]) are likely to depend partly on aspects of the matrix, such as whether the quoted size includes or excludes a "contour-completion" boundary. For comparability between packages, estimation conditions must be set carefully. For example, if you want a 25x25 grid across the locations in Ranges, where the proportion of matrix spanning the locations is by default 70%, you should select a 36x36 grid and freeze it.</div>Adminhttps://ranges-support.anatrack.com/wiki/Harmonic_Mean_ContoursHarmonic Mean Contours2014-11-07T08:07:48Z<p>RobertKenward: </p>
<hr />
<div><br />
The pros & cons of different analysis techniques are discussed in detail in the [[Review Of Home Range Analyses|Review Of Home Range Analyses]] and for a more comprehensive recent review, see "A Manual for Wildlife Radio Tagging" ([[Bibliography|Kenward 2001]]) and Kenward et al. ([[Bibliography|2001]]). <br />
<br />
== Introduction ==<br />
<br />
Harmonic means and [[Kernel Contours|kernels]] are both contour analyses which estimate density indices for locations at intersections of a matrix set across an animals range, and then interpolate contours between the values of that matrix. <br />
<br />
In Harmonic means, density indices are estimated as reciprocal functions of distance (1/r) to all the locations at matrix intersections. The very small value of 1/r for distant locations gives them a very small contribution to the Harmonic mean function (S1/r)/n, so the function emphasises local density. However, a location that occurs at the point of estimation makes an infinite contribution. To avoid this problem, the original implementation ([[Bibliography|Chapman & Dixon 1980]]) set r=1 (and hence 1/r=1) for r<1.<br />
<br />
This solution creates a dependence of the contours, and hence the area estimates, on the scale used for the location coordinates relative to the [[#Matrix size|matrix size]] used for contouring. In Ranges the calculations are performed in units of [[File Types#Tracking resolution|tracking resolution]]. As long as the size of each matrix cell is not greater than half the tracking resolution, locations will never be more than 1 matrix interval from an intersection and many 1/r values are 1. This smooths the distribution across the matrix and produces acceptable contouring. However, if matrix cells are much larger than half the tracking resolution (due to a small matrix, or small tracking resolution), harmonic mean values tend to be very small at intersections unless there is a nearby location. This results in an inadequately smoothed distribution and tends to produce contours as rings round locations.<br />
<br />
This problem of scale dependence can be solved in two ways. One is to enlarge the number of matrix cells so that intervals are no more than half of the tracking resolution. The large matrices required are practical in the memories of modern computers, but estimation is slow. It is critical for tracking resolution to be registered correctly if this option is chosen. If the matrix is too small relative to the tracking resolution you will be presented with the warning ‘Matrix interval enlarged’ at the end of the run (more details in [[#Location centring|Location centring]]). The second approach is to treat all locations as if are at the centre of a square formed between the 4 nearest matrix intersections. The pros and cons of this location centring are discussed below in more detail. However, the large matrices (and hence slow runs) required for robust analyses should not deter use of Harmonic Mean estimates. The use of reciprocal distances gives less dependence on distant locations than with bivariate normal [[Kernel Contours|kernels]], and thus a better fit of contours to range cores as well as less sensitivity to outlying locations. <br />
<br />
== Locations Hc and statistics ==<br />
<br />
The density index can be estimated at each location. This enables estimation of statistics for the density distribution, and also provides values for interpolating contours either to the locations or based on location density alone. The Locations, Hc & statistics option runs more quickly than other options in contour analyses because the routine does not have to calculate a matrix of location density values. <br />
<br />
The statistics produced can give a useful insight into the structure of the range based on the distribution of the locations. A range statistics output file can be specified in [[Output Files|'''Output Files''']]. The statistics include range spread and the density score at the central fix, with the dispersion, skewness and kurtosis of the density distribution ([[Bibliography|Spencer & Barrett 1984]]).<br />
<br />
== Selected cores ==<br />
<br />
This option allows you to examine range structure and to define core areas. By excluding low density areas, the edges enclose areas most used by the animal. See the introduction to [[Location Analysis|Location Analyses]] for more details. <br />
<br />
You can choose one or more values for the percentage of locations or of location density to be included. Type them in ascending order, separated by either spaces or commas.<br />
<br />
The display shows coordinates of the Hc (Harmonic mean location) and its distance from the focal site.<br />
<br />
In the [[Output Files|'''Output Files''']] column you can specify a range areas and statistics output files. The estimates are in column format, suitable for spreadsheets. Each row has the 7 range variables, followed by X,Y coordinates for the range centre, followed by 5 range statistics followed by as many areas as there were core percentages. The statistics include the Hc coordinates, and the spread, dispersion, skew and kurtosis of the location distribution ([[Bibliography|Spencer & Barrett 1984]]). <br />
<br />
== Cores at 5% intervals ==<br />
<br />
This option provides plots which help to decide which locations are part of a core, and which are outliers. <br />
<br />
You can choose to save both edge (polygon) and utilisation files, the latter can be plotted in [[Input & Graphics| the main window]] . Density analyses such as this save edges from 20-99% (because 100% of the distribution cannot be estimated). The cores are saved at 5% intervals, a total of 17 sets. <br />
<br />
==== Core ranges ==== <br />
<br />
Graphs of utilisation distribution can be used to assess (by eye) whether a sharp discontinuity in area, after the elimination of outlying locations, indicates a core range. Better estimation may be possible if the utilisation file is saved and plotted in [[Input & Graphics| the main window]] .<br />
<br />
== Incremental area analysis ==<br />
<br />
Incremental area analysis is used to answer the question "how many locations do I need to estimate a home range?" Starting with the first three locations (the minimum needed to estimate a polygon area without a boundary strip), the new area is estimated as each location is added. This permits the consecutive areas, which tend to increase initially as the animal is observed using different parts of its range, to be plotted against number of locations until there is evidence of stability, which indicates that adding further locations will not improve the home range estimate. The default is to plot the edge round all the locations that have been added, but it is also possible to choose a single, smaller core. The results are saved to a ''.inc'' file which can be examined using opened in [[Input & Graphics| the main window]] .<br />
<br />
== Contours ==<br />
<br />
==== Contours based on location density alone (default) ==== <br />
<br />
In this option, contour plots are based solely on the mean and variance for the distribution of density indices across the locations. The contours estimate the probability of including a particular proportion of locations and may include more or less than the proportion of locations actually recorded. As in ellipse analyses, the outermost contour is estimated to include 99% of the location distribution, because the location density at 100% would be infinitely small. This classic approach is most appropriate for estimating stable range sizes, typically to include 95% of the density distribution, for which reason it is the default in Ranges. <br />
<br />
==== Contours fitted to locations ==== <br />
<br />
When contours are fitted to locations, the density index values at locations are ranked. Contours are then plotted to just include a given percentage of the locations. This approach is most analogous to the polygon approach. For example, it puts the 100% contour through the outermost location. There tend also to be irregular gaps between contours fitted to locations, which simplify detection of a core by inspection of utilisation plots and may define cores best for analyses of sociality and habitat content<br />
<br />
== Location centring ==<br />
<br />
==== Unmodified locations ==== <br />
<br />
Relatively invariable estimations can be obtained in Harmonic Mean contouring if the intervals between intersection of the density matrix are no more than half of the minimum distance between locations. Analyses in Ranges are based on units of tracking resolution, which effectively relates scale to range size. In this way, a species with ranges that span up to 10km and [[File Types#Tracking resolution|tracking resolution]] of 100m has the same number of resolution units across the largest ranges (100) as a species tracked with 1m resolution over ranges that span 100m.<br />
<br />
As the analysis with unmodified locations is the more robust treatment of locations ([[Bibliography|Kenward 2001]]), this is now the default for Ranges. The default matrix for the option is set to 150x150. If this results in any range having less than 2 matrix cells per unit of tracking resolution the warning : '''Matrix interval enlarged''' will be displayed ( and the resulting contours are likely to be under-smoothed, fitting very tightly to the locations). If this happens it is advisable (a) to check that the resolution value is appropriate (was the technique for a large animal really accurate to 1m?) and, if so, (b) to run that range with an appropriately large matrix. <br />
<br />
The maximum recommended range spans (max. distance in either the N-S or E-W directions) for a grid size of 150 are 210m, 2.1 km and 21 km for tracking resolutions of 1m, 10m and 100m respectively. ( Maximum recommended range span = 0.7 * matrix size * tracking resolution * 2, the 0.7 allows for the fact that contours may extend beyond the span of the locations ).<br />
<br />
(Note that the same warning message is also given if the size of matrix cells is set such that the resulting matrix would be greater than the maximum of 200 cells).<br />
<br />
==== Centred in matrix squares ==== <br />
<br />
The approach of [[Bibliography|Spencer & Barrett (1984)]] was to treat all locations as if they were centred between the 4 nearest matrix intersections, such that 1/r values became functions of the intersection intervals. This removes the scaling problem, but instead makes smoothing decrease with increase in matrix size, such that the area estimated for the same range on a 100x100 matrix can be less than half that on a 40x40 matrix.<br />
<br />
== Matrix size ==<br />
<br />
==== Matrix, set no. of cells ==== <br />
<br />
Contouring is most detailed when there are small distances between intersections of the estimation matrix. The default in Ranges is a 40x40 matrix. However, Ranges lacks the memory constraints of previous versions and matrix size can be increased up to a maximum of 200. Larger matrices than 40x40 are advisable, and are set by default, for some Harmonic Mean analyses. See [[#Location centring|Location centring]] for dependency on matrix size of some harmonic mean analyses.<br />
<br />
==== Matrix, set size of cells ====<br />
<br />
This new option allows the size of matrix cells, rather than the number, to be set by the user. This allows you to retain the same plotting resolution for home ranges of very different size (perhaps predator and prey), but may result in somewhat coarse plots for the smaller home range. If you set the size of cells such that the resulting matrix would be greater than the maximum of 200 cells, cell size will be set to create a 200 cell matrix and the warning ‘Matrix interval enlarged’ will be displayed at the end of the run (Note that the same warning is displayed if the chosen matrix has less than 2 cells per unit of tracking resolution - see [[#Location centring|Location centring]] for details). The size of the matrix cells is displayed in the ‘interval’ column in the statistics output file.<br />
<br />
== Matrix rescaling ==<br />
<br />
Contours tend to extend beyond the outermost locations, especially when based on location density alone. To plot such contours, the matrix is set to extend beyond the locations. In Ranges, the default is to set the locations to span the central 70% of the matrix. If an initial estimation of density at grid edges indicates that the outermost contour will still extend beyond the matrix, the proportion of the matrix spanned is decreased automatically in steps of 5% until a fit is likely. Re-scaling of the matrix is prevented by selecting the Freeze matrix option. <br />
<br />
Differences that have been noted in contour estimates between different software packages ([[Bibliography|Larkin & Halkin 1994]]) are likely to depend partly on aspects of the matrix, such as whether the quoted size includes or excludes a "contour-completion" boundary. For comparability between packages, estimation conditions must be set carefully. For example, if you want a 25x25 grid across the locations in Ranges, where the proportion of matrix spanning the locations is by default 70%, you should select a 36x36 grid and freeze it.</div>Adminhttps://ranges-support.anatrack.com/wiki/EllipsesEllipses2014-11-06T20:19:57Z<p>RobertKenward: </p>
<hr />
<div>The pros & cons of different analysis techniques are discussed in detail in the [[Review Of Home Range Analyses|Review Of Home Range Analyses]] and for a more comprehensive recent review, see "A Manual for Wildlife Radio Tagging" ([[Bibliography|Kenward 2001]]) and Kenward et al. ([[Bibliography|2001]]). <br />
<br />
== Introduction ==<br />
<br />
Ellipses are plotted using the original Jennrich & Turner ([[Bibliography|1969]]) approach. Assuming a bivariate normal distribution of the locations, their variance and covariance are used to estimate their density about major and minor axes centred on the arithmetic mean coordinates. The major axis is inclined at ? degrees to the horizontal. A measure of distribution asymmetry (JTasym) is the standard deviation of the major axis divided by that of the minor axis. <br />
<br />
The Ellipses options menu is the same as for [[Convex Polygons|convex polygons]], except that it is unnecessary to choose a range centre. <br />
<br />
== 99% cores ==<br />
<br />
Whereas convex polygon cores are based on shapes that include the required percentage of locations, ellipses enclose a proportion of the bivariate normal density distribution. They may therefore include more or less than the appropriate percentage of locations. The outermost ellipse is estimated to include 99% of the density distribution because the 100% ellipse would be at infinity.<br />
<br />
If you choose to file data, you will be offered the option of filing edges and of creating another file containing the distances from a selected range centre to each location. The distances are in column format, suitable for input to a spreadsheet. Each row contains the distance preceded by the 7 range variables and followed by the ellipse statistics. This is a .csv file with column headers that can be double-clicked to open in Microsoft Excel or imported to an alternative spreadsheet.<br />
<br />
== Selected cores ==<br />
<br />
This option allows you to examine range structure and to define core areas. By excluding low density areas the edges enclose areas most used by the animal. N.B. if the range is multinuclear (i.e. has more than 1 core area) the home range is best described by [[Clusters|cluster analyses]] or by contours. See the introduction to [[Location Analysis|Location Analyses]] for more details. <br />
<br />
You can choose one or more values for the percentage of locations or of location density to be included. Type them in ascending order, separated by either spaces or commas.<br />
<br />
In the [[Output Files|'''Output Files''']] column you can specify a range areas and statistics output file. The estimates are in column format, suitable for spreadsheets. Each row has the 7 range variables, followed by X,Y coordinates for the range centre, followed by 5 range statistics followed by as many areas as there were core percentages. Structure statistics include the ellipse centre coordinates, the radius of the major and minor axis for the maximum core size, the inclination, the x-variance, y-variance and covariance, the correlation coefficient and the asymmetry ratio. This is a .csv file with column headers that can be opened in Stats Viewer, double-clicked to open in Microsoft Excel or imported to an alternative spreadsheet. <br />
<br />
== Cores at 5% intervals ==<br />
<br />
This option provides plots which help to decide which locations are part of a core, and which are outliers. You can choose to save both edge (polygon) and utilisation files. The cores are saved at 5% intervals, from 20-99% (because 100% of the distribution cannot be estimated), a total of 17 sets. <br />
<br />
Utilisation files can be opened on [[Input & Graphics| the main window]] where the plot will be displayed. Note that ellipses have a smooth utilisation distribution plots (which are of little use) because they are based purely on a normal distribution. <br />
<br />
== Incremental area analysis ==<br />
<br />
Incremental area analysis is used to answer the question "how many locations do I need to estimate a home range?" Starting with the first three locations (the minimum needed to estimate a polygon area without a boundary strip), the new area is estimated as each location is added. This permits the consecutive areas, which tend to increase initially as the animal is observed using different parts of its range, to be plotted against number of locations until there is evidence of stability, which indicates that adding further locations will not improve the home range estimate. The default is to plot the edge round all the locations that have been added, but it is also possible to choose a single, smaller core. The consecutive area estimates have to be saved to an output file, so that the result can be examined in the [[Input & Graphics|main window]]. <br />
<br />
The smoothing entailed in ellipse estimation leads to rapid achievement of an asymptote in incremental analyses, often with as few as 10 locations. However, if several successive locations add to a core area following a number of outliers, the estimated ellipse areas may fall after the initial increase.</div>Adminhttps://ranges-support.anatrack.com/wiki/ClustersClusters2014-11-06T18:16:46Z<p>RobertKenward: </p>
<hr />
<div><br />
The pros & cons of different analysis techniques are discussed in detail in the [[Review Of Home Range Analyses|Review Of Home Range Analyses]] and for a more comprehensive review, see "A Manual for Wildlife Radio Tagging" ([[Bibliography|Kenward 2001]]) and Kenward et al. ([[Bibliography|2001]]). <br />
<br />
<br />
== Neighbour-linkage methods ==<br />
<br />
Peripheral convex polygons have been used for range analysis since the start of radio tagging. Their size is strongly influenced by outlying locations and can include large areas not visited by animals. One way of addressing this was to estimate probabilities of encounter within outlines based on density of locations, first as ellipses and then as contours. However, these too are outlier-sensitive and their parametric smoothing is prone to expand into unvisited areas. Another approach was to attribute a grid cell, with dimensions based on tracking accuracy, to locations at each point. The problem with this approach is a tendency to underestimate the areas visited unless there are very large numbers of locations available, or "joining rules" are used to link grid cells. However, joining rules that link neighbouring locations can also be used to estimate polygons that define visited areas with great accuracy, estimating range cores as polygons to which isolated grid cells at outlying locations contribute little area. <br />
<br />
An early neighbour-linkage method involved restricting polygon edges plotted between locations to a fraction (initially half) of the span of maximum distance between any locations, which gave "concave polygons". However, the span remains strongly influenced by outliers and the choice of fraction is an arbitrary decision. A subsequent method defined clusters of locations, by minimising sums of nearest neighbour distances as locations with longer distances are added. The analysis starts the first cluster by identifying the two locations that are closest together and have the nearest 3rd location (i.e. the minimal sum of linkage distances). It then finds the location nearest to one in this initial cluster. If this is less than the distance to the 3rd location in any other potential cluster, the 4th location joins the original cluster. If not, a new cluster forms. If two clusters have nearest neighbours at equal distances, the location that joins is the one that minimises the distance to all locations in the cluster (i.e. a centroid rule resolves ties). If the nearest neighbour is already assigned to another cluster, the two clusters join. When the required percentage of locations has been assigned, a polygon (which was initially convex but can also be concave) is drawn around each cluster and their areas summed ([[Bibliography|Kenward 1987]]), as implemented in Ranges 4 in 1990. <br />
<br />
Four additions to the original Cluster implementation were added to Ranges 6 in 2003: Objective cores, concave polygons, alternative joining rules and the ability to construct a single inclusive polygon. All these are based on the hierarchical incremental nearest-neighbour technique as modified by Kenward et al. ([[Bibliography|2001]]). A fifth technique of Objective Restricted-Edge Polygons was added in Ranges 8, to enable a faster analysis in large data sets, encompass voids (holes) in hull-based outlines as pioneered by Getz and Wilmers ([[Bibliography|2004]]) and to avoid a cluster-overlap issue. This method addresses distances between neighbouring locations that are potential edges to peripheral outlines or to holes, and adds to a family of Neighbour-linkage polygons. The OREP implementation introduced “Curve and Hole” plotting as an alternative to the original “Corner and Cell” approach.<br />
<br />
On the results screens, statistics include the number of range nuclei for each % polygon. In cluster analysis, outliers tend to have a stronger effect on areas than in other analyses, which makes utilisation plots very suitable for identifying range cores by inspection. Inspection and objective coring typically indicate that up to 15% of locations are used for excursive activity, so that 85% polygons often provide convenient cluster core boundaries. Core clusters often remain separate at this point, whereas clusters separated by less than the outlier distances will fuse when all the locations are included. <br />
<br />
Statistics are output with column headers as ''.csv'' files with column headers that can be double-clicked to open in Microsoft Excel or imported to an alternative spreadsheet.<br />
<br />
== Selected cores ==<br />
<br />
This option allows you to examine range structure and to define core areas. By excluding outlying locations the edges enclose areas most used by the animal. See the introduction to Location Analyses for more details. <br />
<br />
You can choose one or more values for the percentage of locations or of location density to be included. Type them in ascending order, separated by either spaces or commas.<br />
<br />
In the '''Output Files''' column you can specify a range areas and statistics output file. The estimates are in column format, suitable for spreadsheets. Each row has the 7 range variables, followed by X,Y coordinates for the range centre, followed by 5 range statistics followed by as many areas as there were core percentages. Structure statistics include, after the area estimate, the number of nuclei in a core, its partial area (the sum of areas of separate polygons / the area of a single polygon round all the clusters). Simpson’s Index for diversity of number of locations across clusters and Simpson’s Index for diversity of area across clusters. This is a .csv file with column headers that can be double-clicked to open in Microsoft Excel or imported to an alternative spreadsheet. <br />
<br />
== Cores at 5% intervals ==<br />
<br />
This option provides plots which help to decide which locations are part of a core, and which are outliers. You can choose to save both edge (polygon) and utilisation files. The cores are saved at 5% intervals, from 20-100, a total of 17 sets. <br />
<br />
Utilisation files can be plotted in Input & Graphics. <br />
<br />
== Objective cores ==<br />
<br />
Rather than choosing a particular core size, it is more scientifically rigorous to have an objective core calculated from the distribution of the locations. The distribution of nearest-neighbour distances can be used for detecting and excluding outlying locations ([[Bibliography|Kenward et al. 2001]]) resulting in an objective core. The ways in which outliers are excluded are discussed below. Objective coring sometimes estimates core areas larger than those from an equivalent number of locations in the standard analysis. This is because the standard approach estimates polygons as soon as a required percentage of polygons are included, whereas objective coring continues to merge clusters that are separated by less than the exclusion distance. In these cases, the "single inclusive polygon" option gives the same result for both methods.<br />
<br />
== Incremental area analysis ==<br />
<br />
Incremental area analysis is used to answer the question "how many locations do I need to estimate a home range?" Starting with the first three locations (the minimum needed to estimate a polygon area without a boundary strip), the area is re-estimated as each location is added. This permits the consecutive areas, which tend to increase initially as the animal is observed using different parts of its range, to be plotted against number of locations until there is evidence of stability, which indicates that adding further locations will not improve the home range estimate. The default is to plot the edge round all the locations that have been added, but it is also possible to choose a single, smaller core. The consecutive area estimates have to be saved to an output file, so that the result can be examined using Input & Graphics.<br />
<br />
== Convex or concave cluster polygons ==<br />
<br />
Concave polygons are offered as well as convex polygons. The edge restriction of concave polygons is based on a fraction of the span of each cluster. The concave polygon option prevents the (very rare) overlap of a small cluster within the limits of a larger, curved cluster.<br />
<br />
== Outlier exclusion ==<br />
<br />
If objective cores are selected, exclusion can be of locations in the largest 5% of the nearest-neighbour distance distribution (by analogy with plotting contours or ellipses to 95% of the density distribution), which is the Ranges default. Alternatively, an iterative process excludes the location with the most extreme linkage distance if it is beyond 1%, 0.5% or 0.1% of the distribution estimated by the remainder, and repeats this process until all distances are within the chosen alpha-level on a normal distribution. The 0.1% alpha level excludes only the most extreme outliers. The display shows the Outlier Exclusion Distance (OED) beyond which locations are excluded. In cluster analyses, polygons then plot round clusters with no nearest-neighbour locations beyond this distance. For objective-restricted edges, the exclusion distance has a strip added equivalent to the resolution distances between locations.<br />
<br />
== Joining priority ==<br />
<br />
The third addition offers the centroid rule, of joining locations to clusters when all linkage distances are minimal, as a priority over the nearest-neighbour rule, which is then used only as a tie-breaker. Centroid priority suppresses chaining along linear habitats and is thus less appropriate than the nearest-neighbour priority when species are expected to be minimising their travel distances (which is likely the usual circumstance and is therefore the default).<br />
<br />
== Separate cluster polygons or Single inclusive polygon ==<br />
<br />
The fourth addition is the option of plotting a single polygon round all the clusters. This excludes locations that are outliers to the main core but includes those between the clusters and which probably represent times when animals were detected on transition between clusters rather than making true excursions. This single polygon, called a "usual area" by Johnstone ([[Bibliography|1994]]), may provide a better estimate of a core territory.<br />
<br />
== Curve and hole polygons ==<br />
<br />
Although incremental cluster analysis conveniently defines groups of core locations separated from outliers, plotting outlines round clusters can be problematic. Convex polygons around separate clusters occasionally overlap (e.g. if a small cluster occurs within the horns of a crescent-shaped cluster) and simple concave solutions to that overlap problem use arbitrary or subjective edge distances. This problem can be avoided by selecting the curve and hole option, which fuses overlapping polygons.<br />
<br />
== Objective-restricted-edge polygons ==<br />
<br />
Objective-Restricted-Edge Polygons depend on curve and hole outlines by default, and are otherwise equivalent to cluster polygons in which a core of locations is defined by an outlier exclusion distance (OED). However, instead of first identifying clusters of locations with the minimal sum of nearest-neighbour distances (which is slow to compute for many locations), OREPs are plotted immediately as concave polygons with an edge restriction based on the OED. OREPs have three advantages over clustering, namely (i) simplicity (hence speed), (ii) polygons cannot overlap and (iii) habitat at all locations is included (because a single grid cell is attributed to outliers beyond the OED). <br />
<br />
OREP edge distances can be based either on the distribution of Nearest-Neighbour Exclusion Distances (NNED), as used for objective coring in cluster analysis, or on the distribution of mean distances from each location to all others as estimated in kernel analyses. The Kernel Exclusion Distance (KED) is our default, because it (a) gives more normal distributions than nearest neighbour distances, (b) gives smoother outlines than NNED especially for small samples and (c) is analogous to exclusion of outlier locations to prevent their excessive influence on contours. <br />
<br />
Range cores defined by OREPs are equivalent both to cluster analysis with objective coring ([[Bibliography|Kenward et al. 2001]]), but without risk of polygon overlap, and also to the fusing of convex hulls based on the sum of distances to nearest neighbour locations which is now the preferred a-LoCoH implementation of Getz & Wilmers ([[Bibliography|2004]], [[Bibliography|Getz et al. 2007]]), but with the advantage of an objective choice of the edge-restriction distance. With kernel-based outlier exclusion distances, OREPs unify range analyses based on grid-cell, polygon and location density techniques. However, they are as yet an experimental technique which needs testing to establish whether possible advantages over nearest-neighbour-based techniques with different polygon fusing approaches (as in Ranges clustering and LoCoH implementations) occur for real field data.</div>Adminhttps://ranges-support.anatrack.com/wiki/Convex_PolygonsConvex Polygons2014-11-06T16:53:54Z<p>RobertKenward: </p>
<hr />
<div>The pros & cons of different analysis techniques are discussed in detail in the [[Review Of Home Range Analyses|Review Of Home Range Analyses]] and for a more comprehensive recent review, see "A Manual for Wildlife Radio Tagging" ([[Bibliography|Kenward 2001]]) and Kenward et al. ([[Bibliography|2001]]). <br />
<br />
== Introduction ==<br />
<br />
Ranges estimates convex polygons by finding the most southwest location as the first corner, then seeking the least clockwise location (i.e. the next location on the outside of the range moving in a clockwise direction) as a second corner, and then working through each location that is least clockwise to the vector from the previous corner until reaching the most southwest location again. The 100% convex polygon contains all the recorded locations ([[Bibliography|Mohr 1947]]), but a core territory can be defined by plotting a convex polygon around a lesser proportion of the locations. A mononuclear peeled polygon, estimated by excluding a proportion of locations furthest from a [[#Choose Peel Centre|peel centre]] is a simple index of the area most used by an animal ([[Bibliography|Kenward 1987]]).<br />
<br />
After you provide an input filename, the analysis options menu will be displayed. This menu is similar in all other range analyses. It allows you to select particular ranges from a set, to select particular types of location (defined by LQVs) within a file, to select different types of display (including use of a background map) and to file range edges, areas and structure statistics for subsequent analyses.<br />
<br />
== 100% cores ==<br />
<br />
The 100% cores option gives a rapid estimation of a convex polygon around all the locations in a range. If you choose to file data, you will be offered the option of filing edges and of creating another file containing the distances from a selected range centre to each location. This is a .csv file with column headers that can be double-clicked to open in Microsoft Excel or imported to an alternative spreadsheet. Each row contains the distance preceded by the 7 range variables and followed by the LQVs. <br />
<br />
== Selected cores ==<br />
<br />
This option allows you to examine range structure and to define core areas. By excluding outlying locations (in linkage analyses) or low density areas (in Ellipse or Contour analyses), the edges enclose areas most used by the animal. N.B. if the range is multinuclear (i.e. has more than 1 core area) the home range is best described by [[Clusters|cluster analyses]] or by Contours. See the introduction to [[Location Analysis|Location Analyses]] for more details. <br />
<br />
You can choose one or more values for the percentage of locations or of location density to be included. Type them in ascending order, separated by either spaces or commas.<br />
<br />
In the [[Output Files|'''Output Files''']] column you can specify a range areas and statistics output files. The estimates are in column format, suitable for spreadsheets. Each row has the 7 range variables, followed by X,Y coordinates for the range centre, followed by 5 range statistics followed by as many areas as there were core percentages. Structure statistics include mean, median and maximum distances from locations to the range centre, and the span of maximum distance between any two locations. This is a .csv file with column headers that can be double-clicked to open in Microsoft Excel or imported to an alternative spreadsheet.<br />
<br />
== Cores at 5% intervals ==<br />
<br />
This option provides plots which help to decide which locations are part of a core, and which are outliers. <br />
<br />
You can choose to save both edge (polygon) and utilisation files. Linkage analyses save edges from 20-100%, density analyses generally from 20-99% (because 100% of the distribution cannot be estimated). The cores are saved at 5% intervals, a total of 17 sets. <br />
<br />
Utilisation files can be opened on [[Input & Graphics| the main window]] where the plot will be displayed.<br />
<br />
==== Core ranges ====<br />
<br />
Graphs of utilisation distribution can be used to assess (by eye) whether a sharp discontinuity in area, after the elimination of outlying locations, indicates a core range. Better estimation may be possible if the utilisation file is saved and plotted in [[Input & Graphics| the main window]].<br />
<br />
== Incremental area analysis ==<br />
<br />
Incremental area analysis is used to answer the question "how many locations do I need to estimate a home range?" Starting with the first three locations (the minimum needed to estimate a polygon area without a boundary strip), the new area is estimated as each location is added. This permits the consecutive areas, which tend to increase initially as the animal is observed using different parts of its range, to be plotted against number of locations until there is evidence of stability, which indicates that adding further locations will not improve the home range estimate. The default is to plot the edge round all the locations that have been added, but it is also possible to choose a single, smaller core. The consecutive area estimates have to be saved to an output file, so that the result can be examined using [[Range Use Plots|Range Use Plots]].<br />
<br />
Note that for polygons, adding locations will result in the same size range (if the new locations are within the area where the animal has already been recorded), or a bigger range (if the new location is outside those already recorded). However, contours may decrease in size as locations are added, because there is more certainty about where the distribution lies. <br />
<br />
== Choose Peel Centre ==<br />
<br />
To estimate cores that exclude some locations, it is necessary to choose a peel centre. The furthest locations from the peel centre are excluded first.<br />
<br />
==== Focal site ==== <br />
<br />
The focal site is defined in the range label of the location file. If this option is chosen and no focal site is specified within the location file, a short warning of ‘no site’ will be displayed for that range. The routine will then continue analysis on other ranges within the file.<br />
<br />
==== Harmonic mean centre (Hc) ==== <br />
<br />
The harmonic mean centre is the location where the inverse reciprocal mean distance to all the other fixes is a minimum ([[Bibliography|Spencer and Barrett 1984]]). This provides a more robust estimator than the simple arithmetic mean, which can be estimated in an area devoid of locations.<br />
<br />
==== Kernel centre (Kc) ==== <br />
<br />
The kernel centre is the location at which the Gaussian kernel estimator indicates highest density ([[Bibliography|Worton 1989]]). This is frequently the same location as the harmonic mean centre.<br />
<br />
==== Arithmetic mean centre (Ac) ==== <br />
<br />
The arithmetic mean of all the x and y coordinates defines the "centre of activity" sensu Hayne ([[Bibliography|1949]]). However, its position is strongly influenced by outlying locations, and it can lie in an area devoid of locations, especially in multinuclear ranges.<br />
<br />
==== Recalculated Ac (RAc) ==== <br />
<br />
This tends to focus on the area of densest locations by recalculating the arithmetic mean position after excluding each furthest location. When running this analysis you will see the centre drift as more locations are excluded.</div>Adminhttps://ranges-support.anatrack.com/wiki/Concave_PolygonsConcave Polygons2014-11-06T16:46:13Z<p>RobertKenward: </p>
<hr />
<div>The pros & cons of different analysis techniques are discussed in detail in the [[Review Of Home Range Analyses|Review Of Home Range Analyses]] and for a more comprehensive recent review, see "A Manual for Wildlife Radio Tagging" ([[Bibliography|Kenward 2001]]) and Kenward et al. ([[Bibliography|2001]]). <br />
<br />
== Introduction ==<br />
<br />
Ranges creates concave polygons by seeking least clockwise locations (i.e. the next location on the outside of the range moving in a clockwise direction) from the most southwest location, as for [[Convex Polygons|convex polygons]]. If the distance to the next convex corner is less than the selected edge restriction distance, it seeks the next least clockwise location within that restriction distance, followed by the most clockwise location from the vector back towards the previous location until reaching the next convex corner. It starts a new polygon if the separation between locations is greater than the restriction distance. Outlying locations become grid cells if a boundary strip is in use. If the restriction distance is set less than the [[File Types#Tracking resultion|tracking resolution]], the range is estimated entirely as grid cells, with side lengths defined by the resolution of the tracking technique. <br />
<br />
Cores at 5% intervals, which provide a utilisation distribution in other range analyses, are not available for concave polygons. This is because all the locations are included in the analyses. However, a process analogous to utilisation plotting can be obtained by progressively reducing the restriction distance.<br />
<br />
== Selected edge restriction ==<br />
<br />
The edges of concave polygons are selected either by specifying a proportion of the maximum range width (which by default is 0.5 and must be less than 1) or by giving a value greater than 1 (which defines a convex polygon) to set a restriction length in metres. The default follows Harvey & Barbour ([[Bibliography|1968]]). The edge length is standardised for all animals in the file if you use metres. <br />
<br />
==== Output of grid cells ====<br />
<br />
If you select a proportion (or length) that is smaller than the minimum distance between locations (your tracking resolution), the output is of single grid cells. If you set the length equal to your resolution, adjacent occupied grid cells are joined using the "Rook's" rule in Chess (i.e. adjacent horizontal and vertical cells join, but not adjacent diagonal cells).<br />
<br />
== Incremental area analysis ==<br />
<br />
Incremental area analysis is used to answer the question "how many locations do I need to estimate a home range?" Starting with the first three locations (the minimum needed to estimate a polygon area without a boundary strip), the new area is estimated as each location is added. This permits the consecutive areas, which tend to increase initially as the animal is observed using different parts of its range, to be plotted against number of locations until there is evidence of stability, which indicates that adding further locations will not improve the home range estimate. The default is to plot the edge round all the locations that have been added, but it is also possible to choose a single, smaller core. The consecutive area estimates have to be saved to an output file, so that the result can be examined using [[Input & Graphics| in the main window]].<br />
<br />
Note that incremental plots of concave polygons tend to require more locations to reach stability than those from convex polygons. This is because ranges can fragment.</div>Adminhttps://ranges-support.anatrack.com/wiki/Interaction_AnalysisInteraction Analysis2014-11-06T14:07:20Z<p>Admin: </p>
<hr />
<div>== Introduction ==<br />
<br />
The general purpose of these routines is to analyse spatial interactions between animals or between patterns of locations (spatial and temporal) for individual animals. [[#Autocorrelations|Autocorrelation analyses]] spatio-temporal relationships between locations, which can help to identify optimal sampling intervals for recording home ranges from data collected in a pilot study. Analysis of [[#Dynamic interactions|dynamic interactions]] is a complement to the static interactions that are described by the [[Overlap Analysis|overlap analysis]] of home range outlines, because it examines whether animals tended to cohere or avoid each other within home ranges that overlapped strongly (for example because the animals were sibs or clan-mates). Investigation of whether [[#Location-point distance|location-point distances]] differ from random spacing can reveal whether animals favoured or avoided particular points, for example the dens of other individuals. Examination of [[#Range centre spacing|range centre spacing]] can reveal whether animals tended to distribute themselves at random, or in regular patterns that suggest avoidance, or in clumps that suggest cohesion.<br />
<br />
== Autocorrelations ==<br />
<br />
Autocorrelation analysis examines the way that distances between locations change with sampling intervals. It is, therefore, useful for estimating the optimal time interval to use between recording consecutive locations. If locations are recorded with short time intervals, individuals will not have had time to travel far. As a result there will be high spatio-temporal dependence between locations, and more locations will be required to define a home range ([[Bibliography|Harris et al. 1990]]). The originators of the technique ([[Bibliography|Swihart & Slade 1985]]) suggested that locations could be considered spatio-temporally independent if they met a randomness criterion by having 3 consecutive values of Schoener’s index scoring greater than 2. This estimated a "time to independence" for recording intervals. However, the estimation is based on a circular normal distribution of locations. This is often unrealistic, because animals create multimodal (multinuclear) ranges by favouring particular areas for food, nests and travel - in other words their locations are never truly independent and should not be treated as such in analyses ([[Bibliography|Kenward 1992, Otis & White 1999]]).<br />
<br />
[[Bibliography|Swihart & Slade (1987)]] showed that their "time to independence" had value for investigating home range activity, because it estimates a measure of time to cross a home range. However, animals often move in ways that fail to meet the independence criteria for several days, if at all, such that taking locations at intervals greater than the "time to independence" under-samples their home range. A general value of Schoener’s index for a sampling interval at which the least number of locations define a home range has yet to be determined, but may lie closer to 1 than to 2 ([[Bibliography|Kenward 2001]]). <br />
<br />
As time intervals are integral to the analysis, suitable location files must include correctly labelled time [[File Types#Location qualifying variables (LQVs)|location-qualifying variables]]. Following the format used by the originators of the technique ([[Bibliography|Swihart & Slade 1985]]), the programme plots increasing sampling intervals in minutes on the x-axis and Schoener's index on the y-axis.<br />
<br />
Schoener's index indicates the degree of independence between distance and time. If the value of the index is low, this indicates a high correlation between distance and time, e.g., because during short sampling intervals the animal did not have time to move far before it was next located.<br />
<br />
The programme works through the sequence of locations from start to finish for each set of sampling intervals. It then tests for correlations between distance and time for this set. If using a 5-minute minimum interval the routine first estimates distances for all locations from 2.5 to 7.5 minutes apart and estimates the [[Bibliography|Schoener (1981)]] index V (= mean squared distances between locations / mean squared distance from each location to the arithmetic activity centre). It then processes those between 7.5 to 12.5 minutes apart and so on up to the default maximum of 48 hours apart.<br />
<br />
==== Minimum time intervals ====<br />
<br />
This allows you to alter the length of the sample interval. This is by default based on the minimum time interval within your data file. Choosing an appropriate interval is important. If, for example, locations have been taken approximately one hour apart, there may be too few locations to estimate in Schoener's index using 5 minute sampling intervals (i.e., 57.5 to 62.5, 62,5 to 67.5 etc.). However, good results may be obtained with 30-minute sampling intervals (i.e., 45 to 75 etc.). However, note that there may also tend to be few records of very long time intervals, so that plots often start smoothly and then become "ragged" as variance of the index increases with reduction in sample size.<br />
<br />
==== Plot time ====<br />
<br />
This allows you to set the total length of time of the recording period to be used in the analysis. The default is to plot the estimate for 48 hours. Be careful to pick an appropriate time scale for the data that you have. It is very easy not to.<br />
<br />
==== Results plots ====<br />
<br />
These are displayed in the chart window for each range. Selecting different Ranges within the ranges table will display the corresponding autocorrelation plot and locations. A green line shows where a Schoeners index of 1 is exceeded by 3 consecutive locations, and an orange line where a Schoeners index of 2 is exceeded by 3 consecutive locations. The time where a Schoeners value of 2 is exceeded is put into the output file as TTISchoeners2(hours) – this is the "Time To Independence" estimator proposed by [[Bibliography|Swihart & Slade 1985]]. The output file is automatically loaded into the Statistics window.<br />
<br />
== Dynamic interactions ==<br />
<br />
This analysis routine gives a single "cohesion" index, for the tendency of pairs of animals to be close together at the same time. Animals shown to share large areas in [[Overlap Analysis|overlap analyses]] may seldom encounter each other because they rarely visit the same place at the same time. [[Bibliography|Macdonald et al (1980)]] called the analysis of overlapping range outlines "static interaction" and they proposed the examination of "dynamic interaction" by looking at locations taken at the same time. The original publication provided a test of whether 2 individuals showed significant attraction or avoidance, but this depended on two assumptions. Firstly, that locations are statistically independent and secondly, that their distribution fitted a parametric model. Ranges avoids assumptions about independence and distributions of locations between single pairs of individuals. <br />
<br />
The programme provides a single statistic for each range as described in [[Bibliography|Kenward et al (1993)]]. The observed and possible distances between animals are compared. The mean, geometric mean and median distances are estimated between the n observed pairs of same-time locations for animal 1 and 2. Then the equivalent values are estimated for the n x n possible distances if animal 2 could be at any of its n used positions when animal 1 was at each of its used positions. The observed and possible distances are compared using Jacob's Index ([[Bibliography|Jacobs 1974]]). This gives a value of 0 if the observed and possible distances were the same, rising towards +1 if observed distances were small relative to possible distances (because the animals were usually together) or falling towards -1 if animals tended to avoid each other. This gives a single index for each pair of animals, which tends to be most consistent if based on the geometric mean distances ([[Bibliography|Walls & Kenward 2001]]).<br />
<br />
==== Individual selection ====<br />
<br />
''all''<br />
<br />
Use this if all the animals share overlapping ranges, for example through being a single family. <br />
<br />
''overlapping quadrats''<br />
<br />
This uses a rectangular edge around the W E S and N limits of ranges to select animals which have overlapping quadrats for estimation of range overlaps.<br />
<br />
''overlapping ranges''<br />
<br />
This examines animals with ranges which overlap, and requires the input of a range overlap matrix created in [[Overlap Analysis|overlap analysis]].<br />
<br />
''the same focal site''<br />
<br />
If many families are combined in a file, you can use this to confine the analysis to those at the same den or other focal site. <br />
<br />
==== Same time observations defined by ====<br />
<br />
''sequence of locations''<br />
<br />
This is appropriate if there are equal numbers of locations for each animal, recorded at roughly the same time on each occasion (i.e. by repeated sampling round all the tagged animals). The analysis will still work if a few animal locations were scored as missing (-9,-9). The missing values merely reduce n for all pairs involving that animal. <br />
<br />
''time attributes of locations''<br />
<br />
For this to work the location file must contain correctly labelled [[File Types#Location qualifying variables (LQVs)|location-qualifying variables]] which define the times that locations were recorded. You will need to input a ‘threshold between same time observations’, to make allowance for records that were actually recorded 5 minutes apart but can be considered simultaneous. Locations further apart than this threshold will be treated as missing and excluded from the analysis.<br />
<br />
==== Maximum randomisation sample ====<br />
<br />
The set of all possible pairs of locations is n x n, where n is the maximum locations recorded for any individual. This number can become very large for large sample sizes, which makes the analyses slow, due to the ranking process that determines median values, and also require excessive memory. In this case, very little extra variation will result if the total is boot-strapped, to select at random a smaller sample within the set n x n. This is useful to make analyses of large datasets run faster. Selection of a sample from n x n occurs automatically to limit sample size if n x n is greater than 5000. The user-interface will accept values between 500 & n x n.<br />
<br />
== Location-point distances ==<br />
<br />
This option allows you to estimate whether an animal has been tending to approach or avoid sites associated with other animals, such as nests or scent marks. To use it, you require a location file and a site file. The latter can either be a file of locations or of habitat points. Habitat shape files may be used to refine the analysis by defining envelopes for random sampling operations.<br />
<br />
The routine starts by producing a single sample of nearest-site distances for a set of random locations within a [[#Envelope|defined envelope]]. It then estimates the observed distances of each location from the nearest site for each set of locations. The observed distances are then compared to the expected distances (from the random locations) in a process very similar to that used for [[#Dynamic interactions|dynamic interactions]] to give a Jacobs' Index of avoidance or cohesion. The arithmetic mean, geometric mean and median distances are also displayed (or exported in a statistics file) so that the size of the difference can be seen. <br />
<br />
It is important to note that the results of the analysis are boundary-dependent. Imagine setting an extensive envelope for random locations, far beyond the area used by animals. The animal locations would then tend always to be closer than random locations to the point-sites. On the other hand, setting a envelope smaller than the size of home ranges may create an opposite effect. It is therefore not really possible to test for absolute cohesion or avoidance in this analysis, but it is useful for comparing different categories of animal or times of year if the same analysis criteria are used throughout. For further explanation and use of this analysis, see [[Bibliography|Walls & Kenward (2001)]].<br />
<br />
==== Envelope ====<br />
<br />
The envelope within which random locations are thrown, and within which observed locations are used, can either be a polygon around all the points (Inclusive or User-defined), or a circle around each point (Point-centred circle). <br />
<br />
''inclusive polygon''<br />
<br />
This places a envelope around the outside to include all the points. In this case a point file will have been entered, either as a location file or habitat point file. Then you will be asked to choose the buffer strip, which is the distance outside all the locations that the polygon is drawn. This should be relevant to the distribution of the points and therefore it is based on the nearest neighbour distances (please see below).<br />
<br />
[[File:interaction_envelope1.gif|inclusive polygon]]<br />
<br />
''user-defined envelope''<br />
<br />
The envelope can be defined by a vector shape or an edge file, for example to delimit the study area [[Input & Graphics#New|create a vector shape file]] containing the corners of the study area. <br />
<br />
[[File:interaction_envelope2.gif|user-defined envelope]]<br />
<br />
''point-centred circles''<br />
<br />
This analysis is really useful for isolated points between which there are areas that the animals are unlikely to use. If random locations are thrown into areas that the animals themselves are unlikely to use it will obviously produce an unwanted bias to the expected distances.<br />
<br />
[[File:interaction_envelope3.gif|point-centred circles]]<br />
<br />
The size of the buffer strip in the last two methods is based on the nearest-neighbour distances of the points, so that it is biologically meaningful.<br />
<br />
==== Buffers ====<br />
<br />
The choice here depends on whether you are testing for attraction to sites, or avoidance of sites. When testing for attraction, the conservative approach is to use a narrow buffer to create a small envelope around sites, perhaps using the minimum nearest neighbour distance or the very conservative boundary suppression of the final option. When testing for avoidance, the conservative approach is to use a large buffer, the maximum nearest neighbour distance, to create a large envelope. Using the mean nearest-neighbour distance may be slightly biased in either direction, but is useful for an initial run, to check whether significant attraction or avoidance is likely.<br />
<br />
''mean n-n distance / 2''<br />
<br />
Half of the mean nearest-neighbour distance.<br />
<br />
''max n-n distance / 2''<br />
<br />
Half of the maximum nearest-neighbour distance.<br />
<br />
''min n-n distance / 2''<br />
<br />
Half of the minimum nearest-neighbour distance.<br />
<br />
''suppressed''<br />
<br />
No buffers.<br />
<br />
==== Maximum randomisation sample ====<br />
<br />
Select the size of the "randomisation sample" (the number of random locations used in the analysis). Small random samples increase the speed of runs (e.g. for a preliminary assessment), whereas large random samples minimise the sampling variation in the results. The minimum sample permitted is 500.<br />
<br />
== Range centre spacing ==<br />
<br />
The final interaction analysis provides tests for spacing of range centres, using conventional nearest neighbour analysis ([[Bibliography|Clarke & Evans 1954]]). The minimum input required is a range-edge file containing a single core of convex polygons, ellipses or contours (concave polygons and cluster analysis outlines lack centres). Go to [[Location Analysis|location analyses]] for details on creating these polygon files. The results indicate whether centres are more regularly spaced both than an expected normal distribution, and also than spacing based (by default) on 1000 random locations. In each case, as in the original publication, Student's t is used to test for significantly regular spacing. These results are displayed on the final screen and can be filed.<br />
<br />
When using random points, note that the results of the analysis could be envelope-dependent, especially if the distribution is being estimated within an irregular envelope. Setting an extensive envelope for random locations means that higher regularity will occur in the random sample than when an area is, say, long and narrow.<br />
<br />
The way in which buffers [[#Envelope|define envelopes]] can be seen above.</div>Adminhttps://ranges-support.anatrack.com/wiki/Import_LocationsImport Locations2014-11-06T13:24:06Z<p>Admin: </p>
<hr />
<div>The window for importing a location files has four main sections : [[#Summary table| Summary table]] displaying the first ten rows of the file, [[#Option boxes for range variables|Option boxes for range variables]], [[#Option boxes for location-qualifying variables (LQVs)|Option boxes for location-qualifying variables (LQVs)]] and [[#Location file attributes|Location file attributes]].<br />
<br />
Location files are organised as a number of ranges that are usually identified by a description of the animal and when tracking began (i.e. ID number, Age, Sex, Month and Year).<br />
<br />
=== Summary table ===<br />
<br />
The table at the top of the window displays the first 10 lines of the file. If it detects that the first value in the first column is not a number it assumes that the first row is a header row and uses it to add titles to each column. The header row can be switched on and off using the tick box at the top right. The data in this table is not editable, but allows you to check what values are in which columns.<br />
<br />
=== Options boxes for range variables ===<br />
<br />
On the middle left of the window there are 9 choice boxes with titles E, N, ID, Age, Sex, Month, Year, FocalE, FocalN (location coordinates followed by the seven range attribute variables). The selections you make in these boxes will determine which columns in the input file the data will be obtained from. If the file has a header row the choice boxes will contain the column titles, if not they will contain the column numbers. Using an input file with a header row reduces the risk of importing the wrong columns.<br />
<br />
All of the choice boxes, with the exception of E and N, have ''absent'' as the last option. This is used to indicate that the file does not contain any data for that attribute. The ‘absent’ option is not available for E and N as it would be meaningless to create a location file lacking one or both coordinates. If the last seven choice boxes (ID to FocalN) are all set to ‘absent’ the data will be read in as a single range with no attribute information.<br />
<br />
The ID determines which range that row of data will be read into. Data for the same range need not be adjacent, so it will read in data that has been collected for multiple individuals in sequence. e.g. if the following column file was imported:<br />
<br />
{|<br />
| style="width: 60px;" | * || style="width: 60px;" | * || style="width: 60px;" | 1<br />
|-<br />
|* || * || 2<br />
|-<br />
|* || * || 3<br />
|-<br />
|* || * || 1<br />
|-<br />
|* || * || 2<br />
|-<br />
|* || * || 3<br />
|}<br />
<br />
where columns 1 and 2 contain data values, and column 3 is specified as the ID, the result would be two locations in each of three ranges. If the ID choice box is set to ‘absent’ all of the locations will be assigned to the same range. This provides one method of amalgamating all of the ranges in a multi-range file. Using different ID field can also be used to sub-divide your data.<br />
<br />
ID number, Age, Sex, Month, Year, FocalE and FocalN are all range attributes. Thus there is only one value stored for each range. The program imports the value obtained from the first location in each range. It does NOT check whether the values for subsequent locations in the same range are the same as this. Thus it is important that range attributes are correct for the first location in a range, subsequent locations could contain any character in the range attribute columns (but the columns cannot be left empty because then the column structure of the file would be lost). <br />
<br />
Age, Sex, Month and Year need to be imported as integers. ID can contain letters as well as numeric characters. Age and Sex values have corresponding labels which are defined in the Age Labels and Sex Labels sections lower in the table. FocalE and FocalN coordinates can be integers or decimals.<br />
<br />
The choice boxes automatically detect certain default location column file formats and are set up accordingly, but they can then be changed.<br />
<br />
=== Option boxes for location qualifying variables (LQVs) ===<br />
<br />
On the middle right of the window (to the right of where it says Location Qualifying Variables) there is a choice box with the title ''No.''. This determines the number of LQVs that will be in the location file to be created. Beneath this will appear two components for each LQV. The first is a choice box allowing you to select the column that contains the values for that LQV. The second is a text box allowing you to type a label for that LQV; this defaults to the column title (which is just the column number if the input file has no header).<br />
<br />
The components automatically detect certain default location column file formats and are set up accordingly, but they can then be changed. <br />
<br />
=== Location file attributes === <br />
<br />
The lower portion of the window contains five other components that allow you to alter the file attributes of the location file to be created.<br />
<br />
=== Default location column file formats === <br />
<br />
On importing location column files, the import procedure makes assumptions about the location of data elements, dependent upon the number of columns in the file. The initial set up in the import window is based upon these assumptions, as shown below. If you set up your import file in these ways you should just need to press OK to accept the default settings.<br />
<br />
{| class="wikitable"<br />
! style="text-align:left;width:120px"| No. columns<br />
! style="text-align:left;"| Assumed contents of data columns<br />
|- <br />
|2 || E, N<br />
|- <br />
|3 || E, N, ID<br />
|-<br />
|4 || E, N, ID, lqv1<br />
|-<br />
|5 || E, N, ID, lqv1, lqv2<br />
|-<br />
|6 || E, N, ID, lqv1, lqv2, lqv3<br />
|-<br />
|7 || E, N, ID, lqv1, lqv2, lqv3, lqv4<br />
|-<br />
|8 || E, N, ID, lqv1, lqv2, lqv3, lqv4, lqv5<br />
|-<br />
|9 || ID, age, sex, month, year, focalE, focalN, E, N<br />
|-<br />
|10+ || ID, age, sex, month, year, focalE, focalN, E, N, lqv1+<br />
|}<br />
<br />
Location files are exported from Ranges in the same format as the last of these, i.e. with nine columns containing the range and coordinate data followed by 1 extra column for each location-qualifying variable.<br />
<br />
=== Using different ID fields to sub-divide your data ===<br />
<br />
Importing a file specifying a different ID column provides a means of subdividing the data in different ways. For example if you have data for multiple animals over multiple years you could create columns that specify a) year and animal, and b) year, month and animal, in order to look at how use of space changes over different time intervals. These identifier columns can be set up easily in Excel, for example for a) ‘=year*100+animal_id’ would give you space for 100 different animals, and the ID 200107 would indicate animal 7 in 2001. The lion data in the sample file lion\lions.loc was subdivided in this way, the identifiers were set up as ‘year*1000 + month*10 + animal_id’, so 2001101 is animal 1 in October 2001.<br />
<br />
Note that the maximum value allowed for an integer value in Ranges is 2,147,483,647.</div>Adminhttps://ranges-support.anatrack.com/wiki/Habitat_AnalysisHabitat Analysis2014-11-06T12:03:54Z<p>RobertKenward: /* Habitat preference in ranges */</p>
<hr />
<div>== Introduction ==<br />
<br />
Habitat analyses in Ranges can use data from vector maps (shapes, points) or raster maps (e.g. remote sensed imagery). [[#Habitat content of a map|Habitat content of a map]] can be assessed for the whole map or in rectangular or circular sub-sections. Maps can also be used together with edge files to estimate [[#Habitat content of ranges|Habitat content of ranges]] as alternative indices of habitat availability or of habitat use, both for areas of habitat and for important [[#Points within ranges|Points within ranges]]. The most precise estimates of habitat use come from analysing [[#Habitat at locations|Habitat at locations]], with combined [[#Habitat preference in ranges|Location & range habitat analyses]] to estimate preference analyses within the same run. All results can be saved to output files that are suitable for further processing in spreadsheets. For analyses using raster data there are different [[#Count cell centres, corners or partial cell areas|options]] for estimating habitat areas in partial cells around shape edges. The [[Selections|make selections]] button can be used to restrict the analyses to a subset of the ranges and/or locations within the input files.<br />
<br />
== Habitat content of a map ==<br />
<br />
This option leads to a submenu which gives the opportunity to assess the whole map, or sections of it.<br />
<br />
=== map rectangle ===<br />
<br />
This allows you to enter the north, south east and western limits of a rectangular sample of the map. Ranges then estimates the habitat available within that rectangle. <br />
<br />
=== map circle === <br />
<br />
You can manually enter coordinates for the centre of a circle and its radius. <br />
<br />
== Habitat content of ranges ==<br />
<br />
This requires a range edge file (created in [[Location Analysis|location analysis]]). It estimates the habitat composition of each range and finishes with a summary of the content of all ranges. The percentage of each habitat in each core is available in the Stats Viewer and output files. There are two output files: <br />
<br />
# ''xxx_Hab_mapname.csv'' shows the area of the core followed by the percentage of each habitat within the range core<br />
# ''xxx_Hab_mapnameareas.csv'' shows the area of the core followed by the area of each habitat within that core. <br />
<br />
These ''.csv'' files with column headers can be double-clicked to open in Microsoft Excel, imported to an alternative spreadsheet, or loaded back into Statistics at a later time.<br />
<br />
=== output clip file ===<br />
<br />
If you have chosen a vector shape (''.ves'') map file, then an output "clip file" will be created. The clip file will be the intersection of the edge and vector shape files. The file is output in edge format. This can be used to "clip" range edges, to exclude areas that individuals are unable to visit. For example a river outline can be used to excluded areas outside the river from fish home ranges. For example using ''fish.loc'' in the ''fish'' directory of sample data, you could create an edge file using one of the home-range estimators, then you could load ''\fish\river.ves'' into this option in habitat analysis and use it to create a new clipped edge file that does not contain areas outside of the river. This clipped edge file could then be used for further analyses such as overlap. <br />
<br />
Note that if you clip an edge file using a more complex vector shapefile (e.g. ''<RangesFolder>\samples\blackbird\blackbird_map.ves''), the resultant file will contain all of the overlapped polygon boundaries from the vector file, and hence may not be very useful.<br />
<br />
== Points within ranges ==<br />
<br />
This requires a vector points file (e.g. of nests or feeding sites) and a range edge file (created in [[Location Analysis|location analysis]]). It calculates which points/sites are within each range and finishes with the mean number of points contained in all of the ranges. <br />
<br />
== Habitat at locations ==<br />
<br />
This requires a location file and a vector or raster map file. It estimates the habitat at each location point or in an ellipse around each location.<br />
<br />
=== location points ===<br />
<br />
This calculates the percentage of the locations in each range that are in each habitat type and produces a summary of the percentage of all of the locations that fall within each habitat type. If locations fall on a habitat boundary, it shows this on the display as "habitat1label/habitat2label" and attributes a half-share of each habitat to the location in the summary.<br />
<br />
===buffers around locations ===<br />
<br />
For a conventional location file, you will be prompted to '''Input circle radius (m)'''. This will determine the radius of a circle around each location. The default value is set to half of the tracking resolution defined in the location file, thus generating circles with a diameter equal to the tracking resolution.<br />
<br />
The buffer option is particularly useful for providing an average value round locations on raster maps. Individual raster cells may have been categorised inaccurately but the general area may be representative, if an appropriate radius is used. The routine could also be used for a simple form of edge detection near locations, by gradually increasing the assessment circle round each location. When more than one habitat is included in the results then a location is less than the radius of the circle from that edge.<br />
<br />
The percentage of each habitat type within the buffers surrounding all of the locations within each range is output to a statistics file (''.csv'') and automatically loaded in the Statistics window. A "location buffers" edge file showing the positions of the buffers around each location is also created and is automatically loaded to the main window.<br />
<br />
== Habitat preference in ranges ==<br />
<br />
This assesses habitat in ranges and at locations during the same run. After the analysis run, the display shows the preference or avoidance of habitat at locations, with [[Bibliography|Jacob's (1974)]] index, (values +1 to -1), compared with habitat in the range as a whole.<br />
<br />
== Calculating areas ==<br />
<br />
When calculating areas within raster maps, you will be offered one of the following 3 options. Your choice will depend on your need for speed and the scale of the raster cells relative to the areas you are using. The different methods deal differently with raster cells that are partially included in your areas. If your areas contain hundreds of raster cells then one of the counting estimation methods should be fine. It is only when your areas are closer to the size of the raster cells that you should need to use the ‘partial areas’ option (and then you should be careful that you are not overestimating what information is contained within the raster data). <br />
<br />
=== count cell centres(fast) ===<br />
<br />
Counts only those raster cells for which the cell centre is within the shape.<br />
<br />
=== count cell corners ===<br />
<br />
For partial cells, counts the number of cell corners and divides by 4.<br />
<br />
=== partial cell areas(slow) ===<br />
<br />
Calculates the area of partial cells.</div>Adminhttps://ranges-support.anatrack.com/wiki/GlossaryGlossary2014-11-06T11:30:38Z<p>RobertKenward: </p>
<hr />
<div>'''Accuracy ellipses''' are generated round Locations from bearing data by some packages (e.g. LOCATE II). <br />
<br />
'''Activity centre''' is calculated as the Arithmetic mean, Recalculated Arithmetic mean or Harmonic mean centre for a set of Locations (a Range).<br />
<br />
'''Arithmetic mean''' is the mean of x and y coordinates for a Range.<br />
<br />
'''Autocorrelation analysis''' estimates the degree of spatio-temporal dependence of Locations.<br />
<br />
'''ASCII''' is American Standard Code, used to turn bytes into text characters.<br />
<br />
'''Bivariate ellipses''' are based on Location distributions along a major and a minor axis.<br />
<br />
'''Boundary strips''' round Locations in Polygons have a width of half the Tracking resolution.<br />
<br />
'''Byte arrays''' are Ranges storage format for Raster maps; each byte codes for 1 raster.<br />
<br />
'''Centroid distance''' in Cluster analysis is from an Arithmetic mean to all the Locations in a Cluster.<br />
<br />
'''Cluster analysis''' joins Locations in groups based on the Nearest Neighbour distances between them.<br />
<br />
'''Contour analysis''' plots Isolines across a Density Matrix to represent either on a probabilistic or Location-inclusive Utilisation distribution.<br />
<br />
'''Core''' denotes one or more areas of high Location density in a set of Locations.<br />
<br />
'''CSV files''' are composed of Comma Separated Values (other separators are Break/Space and Tab).<br />
<br />
'''Density Matrix''' values are estimated at intersections of an arbitrary grid in Contour analyses.<br />
<br />
'''Dispersal detection''' provides an objective estimate of when animals leave an area.<br />
<br />
'''Dispersion''' (in contour analyses) the peak density value (at the range centre location) divided by the standard deviation of the density value across all the Locations.<br />
<br />
'''Diversity of areas''' in Cluster analysis are Simpson's index for the areas within all the cluster Polygons in a Range.<br />
<br />
'''Diversity of locations''' in Cluster analysis are Simpson's index for the numbers of Locations within all the cluster Hulls in a Range.<br />
<br />
'''Edge''' denotes an outline estimated round locations as a polygon or by contouring.<br />
<br />
'''Ellipses''' include circles.<br />
<br />
'''Ellipse asymmetry''' is the ratio of the standard deviations along the major and minor axes.<br />
<br />
'''End date''' in a Kaplan Meier Survival analysis is the date to which survival should be estimated; by default the last date for any animal, it may be set earlier to ensure an adequate sample in the last interval or to analyses a particular season; see also Start date.<br />
<br />
'''Focal site''' denotes an attraction point in a range, such as a den or nest.<br />
<br />
'''ESRI''' is the Environmental Systems Research Institute, which supplies GIS software.<br />
<br />
'''Grid cells''' are as wide as the Location resolution.<br />
<br />
'''GIS''' is a Geographic Information System.<br />
<br />
'''Grid edges''' are the eastmost, westmost, northmost and southmost coordinates in sets of locations.<br />
<br />
'''Gridascii''' files are of ASCII values used by ESRI soft for transferring raster map data.<br />
<br />
'''Habitat points''' are x,y coordinates associated with habitat codes for different trees, etc.<br />
<br />
'''Habitat shapes''' are formed from a clockwise set of x,y values with the same start and end point.<br />
<br />
'''Harmonic Mean analyses''' are based on the inverse reciprocal mean of distances to Locations.<br />
<br />
'''Harmonic Mean centre''' is the Location in a Range at which the inverse reciprocal mean of distances to all other Locations is minimal.<br />
<br />
'''hRef''' is the reference Smoothing parameter in Kernel analyses ( SD / sixth root N )<br />
<br />
'''Hull''' is a Polygon with vertices fitted to a set of Locations; a Convex Hull has all external angles greater than 180°.<br />
<br />
'''Incremental analysis''' estimates and plots the change in Range area as successive Locations are added.<br />
<br />
'''Isolines of equal location density''' are created during Contour analysis and converted to Polygons.<br />
<br />
'''Jacob's index''' has values between -1 and +1 to indicate attraction versus avoidance.<br />
<br />
'''Kaplan Meier Survival''' plots survival in intervals between Start dates and End dates in .srv files and tests for significance between cohorts.<br />
<br />
'''Kernel analyses''' are based on estimating Location density as functions of distance from all the Locations in a Range.<br />
<br />
'''Kurtosis''' is an index of spread in the density distribution during Harmonic Mean & Kernel contouring.<br />
<br />
'''Location''' denotes x,y coordinates of an observation, often with associated qualifying variables.<br />
<br />
'''Location centring''' is computed during Harmonic Mean contouring to remove Location resolution effects.<br />
<br />
'''Location resolution''' is the smallest distance that can be recorded between adjacent locations.<br />
<br />
'''Location Qualifying Variables''' (LQVs) are time, activity, habitat, values associated with x,y coordinates.<br />
<br />
'''Nearest Neighbour distance''' is the minimum distance between spatially separate Locations.<br />
<br />
'''Neighbour Linkage analyses''' estimate Polygons round Locations whose individual or summed Nearest Neighbour distances do not exceed a certain value.<br />
<br />
'''Nuclei''' are the number of separate Polygons defined by Cluster Analaysis. <br />
<br />
'''Objective Cores''' exclude Locations more isolated than a criterion distance based on the distribution of all Nearest Neighbour distances.<br />
<br />
'''OREP''' Objective Restrictive Edge Polygons have maximum peripheral edge distances less than a criterion based on the distribution of all Nearest Neighbour distances.<br />
<br />
'''Outlier exclusion distances''' (OEDs) are derived from the distribution of all Nearest Neighbour distances as either the distance that excludes the outermost 5% of the distribution, or by iterative exclusion of each distances beyond an alpha level until no locations exceed that level.<br />
<br />
'''Outlier locations''' are those more isolated than a criterion distance based on the distribution of all Nearest Neighbour distances.<br />
<br />
'''Overlap matrices''' are formed as % overlaps of range A on B and B on A.<br />
<br />
'''Partial area''' is the summed area of Clusters divided by a single area encompassing all Clusters.<br />
<br />
'''Peeled Polygons''' are formed as Convex Hulls by excluding Locations furthest from an Activity Centre.<br />
<br />
'''Polygons''' are formed by lines round a set of vertices, which may be Locations, corners on Vector maps or equal values interpolated on a Density Matrix.<br />
<br />
'''RADA''' Resource Area Dependence Analysis uses negative correlations of (log) Range Outline area with (log) resource metric (e.g. habitat proportion in the outline) to detect resources that are important enough for animals to expand the area they cover in order to get an adequate supply.<br />
<br />
'''Range''' is an area defined by analysis of a set of animal Locations observed in a particular period.<br />
<br />
'''Range Core''' denotes one or more areas of high Location density in a set of Locations.<br />
<br />
'''Range variables''' are seven values that code ID, age, sex, month, year and focal site coordinates for a Range.<br />
<br />
'''Raster maps''' are composed of equal-size rectangles with different habitat codes.<br />
<br />
'''Range outline''' denotes one or more Polygons (including Hulls and Contours) fitted to some or all the Locations in a Range.<br />
<br />
'''Recalculated Arithmetic mean''' is the mean x and y coordinates, recalculated after each Location of a Peeled Polygon is excluded.<br />
<br />
'''Schoener's index''' increases from 0 with decreasing spatio-temporal dependence between Locations (= mean squared distances between Locations / mean squared distance from each Location to the Arithmetic mean activity centre).<br />
<br />
'''Shape files''' are the standard Polygon format in an ESRI GIS.<br />
<br />
'''Skew''' in the Location density distribution is estimated during Harmonic Mean and Kernel contouring (the Euclidean distance between the Arithmetic mean centre and the Location with the peak density value, divided by the standard deviation of the density value across all the Locations).<br />
<br />
'''Simpson's index''' increases from 1 with increasing diversity between Polygons in Cluster analysis.<br />
<br />
'''Smoothing factor''' modulates the density function in Kernel analyses to improve fit of Contours.<br />
<br />
'''Span''' of a Range is the maximum diagonal dimension of a Convex Hull enclosing all the Locations.<br />
<br />
'''Spread''' of a Range is the grand mean of distances between all the Locations.<br />
<br />
'''Start date''' in a Kaplan Meier Survival analysis is the date from which survival should be estimated; by default the first date for any animal, it may be set later to ensure an adequate sample in the first interval or to analyses a particular season; see also End date.<br />
<br />
'''Tracking resolution''' is the smallest distance that can be recorded between adjacent Locations.<br />
<br />
'''Usual area''' is the single Polygon that encloses all the separate Cluster Polygons on a Range Core.<br />
<br />
'''Utilisation plots''' are of Range area against percentages of Location-inclusion or Location-density.<br />
<br />
'''UTM''' stands for Universal Transverse Mercator and is a two-dimensional Cartesian coordinate system to give locations on the surface of the Earth. <br />
<br />
'''Vector maps''' are composed of lines and closed shapes defined by a sequence of x,y coordinates.</div>Adminhttps://ranges-support.anatrack.com/wiki/File_TypesFile Types2014-11-06T11:25:02Z<p>RobertKenward: /* Statistics Files */</p>
<hr />
<div>== Introduction ==<br />
<br />
All data files in Ranges contain text, and can therefore be viewed in text editors, except for raster maps (''.rst'') and image files (''.ima'') which are byte arrays. Locations files (''.loc''), vector files (''.vep'', ''.vel'', & ''.ves'' , for point, line and shape maps), edge files (''.edg'', of range outlines), utilisation files (''.uti''), incremental files (''.inc'') and survival files (''.srv'') are mainly to be used as inputs for other Ranges analyses. They can also be viewed in a spreadsheet, but the data are arranged to save space rather than for ease of interpretation. Outputs from modelling analyses, RADA files (''.rda'') and Kaplan Meier Graph files (''.kms'') have no map data and are only useful for displaying output data and plots within Ranges. Ranges also outputs files for use in other software, e.g. ‘.csv’ files that can be used spreadsheets (simply double click on them to open in Excel), and ESRI shapefiles that can be opened in ArcView or ArcGIS.<br />
<br />
== Location Files ==<br />
<br />
Location files (''.loc'') are the main starting point for analyses. They contain the point location data and associated attribute information. Locations are stored in a series of ranges, which may represent an individual, a social group or a particular time period. Location files can be [[Input & Graphics#New|created]] from scratch, or [[Input & Graphics#Import|imported]]. To ensure that Ranges is suitable for tracking all animals (from ants to elephants), each file must include information about the [[#Tracking Resolution|tracking resolution]], [[#Scale|scale]] and whether a focal site (e.g. nest, den etc.) should be included in the range.<br />
<br />
=== Location coordinates ===<br />
<br />
The locations themselves must be recorded in a flat projection, such as UTM ( or for Britain the OS National Grid ). Ranges can convert to UTM from latitude-longitude during the [[Input & Graphics#Import|import procedure and an Excel spreadsheet for doing this is available cheaply from [[http://www.dmap.co.uk/ll2tm.htm|http://www.dmap.co.uk/ll2tm.htm]]. GPS receivers usually have the option to output coordinates in UTM rather than Lat/Long.<br />
<br />
Location coordinates can be decimals, but they cannot be negative. If you have locations with negative coordinates you will need to first transform your data by adding a number to the Eastings and or Northings such that all of the coordinates become positive. (The added number will have to be greater than or equal to the magnitude of the most negative easting or northing). This can be accomplished in a spreadsheet such as Excel. <br />
<br />
=== File attribute variables ===<br />
<br />
==== Tracking Resolution ====<br />
<br />
The resolution depends on the accuracy of your tracking. Accuracy is sometimes cited as the standard deviation of locations obtained in a trail. Greater comparability with other statistical selection criteria would be to use the 95% confidence distance. For tracking with a 2-3 element Yagi, a 1-in-10 rule is acceptable: if you make triangulations at up to 10m from a small animal, the tracking resolution is 1m, or 10m at 100m. For Yagi antennas with more elements, a 1-in-20 rule can apply, i.e. a resolution of 100m for triangulations at up to 2km from an animal. The resolution with GPS may vary from 10-50m depending on terrain and other conditions. For ARGOS satellite tracking it may be closer to 1 km and depend on location class. Tracking resolution is used to:<br />
<br />
# Set the width of the boundary strip that is included in polygon edges and areas. The boundary strip is half the value of the resolution e.g. 0.5m if the resolution is 1m. This means that a coordinate with a real position between 10.5 and 11.5, can be entered as 11. If you wish to suppress the boundary strip you can set the tracking resolution to 0. This is useful if you need analysis results comparable with a system lacking boundary strip estimation.<br />
# Estimate the size of a grid cell plotted around single outlying locations in concave polygon analyses, and multiple locations at one site in cluster analyses. The width of the grid cell is equal to the resolution. <br />
# To define the truncation distance in one type of harmonic mean contouring. In this case, the allocation of an inappropriate resolution can substantially affect range areas and statistics.<br />
<br />
==== Scale ====<br />
<br />
Scale is the number of metres represented by each coordinate unit. A scale of 10m means that each coordinate unit (e.g. 8,8) is 10m from the next (e.g. 8,9). Scaling greater than 1 allows you to type in data with fewer digits. For example, you can use a scale of 100m and type 213 instead of 21300. <br />
<br />
Warning! The scale parameter must match the scale parameter in any [[#Scale of coordinate units|vector]] or [[#Raster Files|raster]] maps with which you wish to compare your data.<br />
<br />
The scale parameter does not influence the map display in Input & Graphics (which uses the untransformed coordinate values), so a location file may appear to overlay a vector map file, but if they have different scale parameters they may not overlay in, for example, habitat analysis. <br />
<br />
If a file is [[Input & Graphics#Export|exported]] to an ArcView shapefile the coordinates are multiplied by scale to convert them to metres. If a file is exported to a text file the coordinates are not multiplied by the scale parameter and the scale parameter will have to be re-entered if the text file is re-imported into Ranges.<br />
<br />
==== Include Nest/Focal Site In Locations ====<br />
<br />
If you choose yes, the focal site for each range will be included as a location in later analyses.<br />
<br />
==== Coordinate System Datum Ellipsoid ====<br />
<br />
If your data is in UTM (Universal Transverse Mercator) coordinates, it can be converted to latitude-longitude coordinates for display on Google Maps or for export to KML. The '''Coordinate System Datum Ellipsoid''', the ellipsoid used to model the shape of the Earth, must be set to achieve this. Most UTM systems use WGS84.<br />
<br />
==== UTM Latitude Zone and UTM Longitude Zone ====<br />
<br />
These must be set to convert UTM coordinates to latitude-longitude.<br />
<br />
==== Range attribute variables ====<br />
<br />
A file may contain thousands of ranges from different individuals, or from the same individual during different tracking sessions, and these must be easily identified. Saving multiple ranges in the same file can make analyses much easier as you only need to run once per file. It is easy to [[Selections|select the ranges]] you wish to look at during the analysis (e.g. Only the males). You can define how you want to identify the ranges, but the defaults are ID number, Age, Sex, Month and Year. These are explained below:<br />
<br />
All seven Range attribute variables are stored as numeric values (for sorting purposes). The first five variables (ID, Age, Sex, Month and Year) must be integers, whilst the focal site coordinates can be decimals. The Age and Sex variables can be given one letter labels to identify animals on the screen. <br />
<br />
The default Range attribute variable labels are as follows:<br />
<br />
{| class="wikitable" <br />
| style="width: 200px;" |Age || 1=J, 2=Y, 3=A (for Juvenile, Yearling, Adult)<br />
|-<br />
| Sex || 1=M, 2=F (for Male, Female)<br />
|}<br />
<br />
There are default values for each range variable, newly created ranges will start with these values.<br />
<br />
{| class="wikitable" <br />
| style="width:200px;" | ID || sequence number, which increments by one for each consecutive range<br />
|-<br />
|AGE || ? (coded as 0)<br />
|-<br />
|SEX || ? (coded as 0)<br />
|-<br />
|MONTH || (missing, coded as -9)<br />
|-<br />
|YEAR || (missing, coded as -9)<br />
|-<br />
|FOCAL SITE COORD E || (missing, coded as -9)<br />
|-<br />
|FOCAL SITE COORD N || (missing, coded as -9)<br />
|}<br />
<br />
<br />
A typical label might be displayed as AF21 6/01 for Adult Female 21 tracked from (or during) June 2001. Missing variables appear on results screens as ''?''. If you only define IDs, the same label would be ??21 ?/?.<br />
<br />
ID should be unique for each range ( e.g. you shouldn’t have a male and female both with ID 21). By using import and export and changing the ID you can [[Import Locations|subdivide your data]] in different ways, e.g. to look at range changes over time.<br />
<br />
==== Location qualifying variables (LQVs) ====<br />
<br />
It is often helpful to know what an animal is doing or what habitat an animal is in at each location, or when the location was recorded, or even a score of how accurate the location was. This is achieved by using Location Qualifying Variables, of which there can be up to 50. It is easy to [[Selections|select the locations]] you wish to look at during the analysis (e.g. excluding locations when the animal is sleeping).<br />
<br />
==== Time LQVs ====<br />
<br />
Labels for times must be capital letters, in combinations of 2, 4 or 6 letters of the following : YY, MO, DD, HH, MI, SS. e.g. YYDD, HHMI, YYMODD, DDMOYY. There is a limit of 6 characters in the time LQVs so that 010417 is OK for April 17 2001, but 20010417 is not. A useful tip. If time variables are entered as "combinations" in descending order, (eg. YYMODD or HHMI), you can use these in '''Make Selections''' to select runs of locations that cross period boundaries. Using single labels, it is difficult to select locations between 15 December 1994 and 1 February 1995 while excluding locations between 1-14 December 1994 and those in January 1994. However, combination labels make it easy to select locations between 941215 and 950201. <br />
<br />
==== Accuracy ellipse LQVs ====<br />
<br />
LQVs can also be used to represent accuracy ellipses in which case the labels must be defined by capitals as MARAD, MIRAD, THETA or X-VAR, Y-VAR, COVAR, which represent either the maximum radius, minimum radius and inclination of an ellipse or the variance and covariance of its distribution (see the output statistics from location analysis, ellipses for more details). <br />
<br />
Labels for other LQVs can be upper or lower case and should ideally have 5 letters (e.g. ACTIV) for them to show best in displays. Please note that although LQV labels and their range of values are listed to help make selections during analyses, you still need to record (outside of Ranges) the numerical codes of these 5-letter labels (eg. ACTIV, 1=resting, 2=feeding, 3=preening). <br />
<br />
== Vector Files ==<br />
<br />
Vector files (''.vep'', ''.vel'', ''.ves'') can contain either point, line or shape information and are used for mapping habitats. Coordinates are stored in groups, each group representing a single shape, line or point group. Each group is assigned to a category which has an associated label and colour. Groups may have the same No. and ID, this is useful for the representation of holes and secondary polygons within shapes. A [[#Scale of coordinate units|scale]] parameter defines the m per coord unit.<br />
<br />
Vector file attributes can be set in the vector properties window, which is displayed when '''new...vector''' or ‘modify’ is pressed from the [[Input & Graphics|main panel]]. Vector files can be [[Import Vectors|imported]] as text files with columns containing point coordinates or from ArcView shapefiles.<br />
<br />
Vector files can be used in the following ways in Ranges:<br />
<br />
# Background maps in location analyses: Point, line or shape files can be used as [[Input & Graphics#Background maps|backgrounds]] for location analyses in which case they are plotted in grey. Grey is used for maps in this option to avoid confusion with the several colouring options for range edges. <br />
# In Habitat analyses: Vector shapes and points are also used in colour in habitat analyses. <br />
# In Interaction analyses: Shapes can be used to define sampling areas in interaction analyses. Points can be used to see, for example, how close a bird goes to nest sites or feeding areas, allowing objective assessments of association or avoidance.<br />
# In Location Analyses: Vector line files formatted as midline files can be used in [[Location Analysis#Midline Analyses (Interlocation, Linear Ranges and Clusters)|midline analyses]] to define potential routes between locations (e.g. for fish in rivers).<br />
<br />
==== On-screen digitising to create Vector files ==== <br />
<br />
Use '''new''' to create an empty vector file.Points can be [[Input & Graphics#On-screen digitising|digitised]] by holding down the CTRL key and clicking the left mouse button, perhaps following an image background.<br />
<br />
==== Vector file type - points, lines or shapes ====<br />
<br />
Points files have no connections between coordinates. In lines files, lines are drawn between the coordinates within each line. In shapes files, coordinates in the same shape are connected by lines and the resultant shape is filled with colour. <br />
<br />
Coordinates for shapes must be entered in a clockwise sequence, without lines that cross and must finish by repeating the first set of coordinates. <br />
<br />
Holes can be added to shapes: first select the boundary shape and click '''hole'''. The ID and category of a hole is the same as its shell shape and cannot be edited. <br />
<br />
Vector shapefiles including holes can be [[Input & Graphics#On-screen digitising|digitised]] and the coordinates copied to the following shape to fill in the hole. <br />
<br />
==== Scale of coordinate units ====<br />
<br />
The scale specifies the number of metres that each coordinate unit represents. For example if the scale is 1, a location at 1,1 will be 5m from a location at 1,5, with a scale of 10 this distance would be 50m.<br />
<br />
==== Coordinate System Datum Ellipsoid ====<br />
<br />
If your data is in UTM (Universal Transverse Mercator) coordinates, it can be converted to latitude-longitude coordinates for display on Google Maps or for export to KML. The '''Coordinate System Datum Ellipsoid''', the ellipsoid used to model the shape of the Earth, must be set to achieve this. Most UTM systems use WGS84.<br />
<br />
==== UTM Latitude Zone and UTM Longitude Zone ====<br />
<br />
These must be set to convert UTM coordinates to latitude-longitude.<br />
<br />
==== Vector categories ====<br />
<br />
The '''add category''' button can be used to add new categories. Within the table the category label can be edited by double clicking in its cell. Category labels can be words or numbers. If the labels are numbers (e.g. density values), they can be decimals of any length. If you use numbers, for example to denote density of food in particular areas, [[Habitat Analysis#Habitat content of ranges|Habitat content of ranges]] will produce an average value from the proportion of the different areas in each range. <br />
<br />
Colour can be edited by a single click within the cell that will bring up a colour chooser. You will be forced to choose another colour if the colour chosen is too close to that of an existing category. <br />
<br />
There is a limit of 50 vector categories within Ranges. <br />
<br />
For ArcView shapefiles that have > 50 categories, in ArcView you can create two or more maps with a subset of the categories with the following procedure:<br />
<br />
# Select the theme containing the whole map<br />
# Theme, Query, category < 50th category<br />
# Theme, Start editing<br />
# Edit, Delete features<br />
# Theme, Save edits as, new filename<br />
# Repeat the above, changing the query in step 2) to >= 50th category<br />
<br />
The maps can then be imported separately into Ranges.<br />
<br />
== Raster Files ==<br />
<br />
Raster files (''.rst'') are files that store information, usually habitat, as a grid of cells, each containing a single map value. They are generally used for land cover information, particularly when it is derived from remotely sensed data from satellites or aerial photography. The size of each raster cell is defined and cannot be altered, which means that cells are displayed and analysed as large squares relative to fine-scaled range polygons, whereas vector maps retain angular shapes at any scale. However, analyses that use raster maps are faster than shapes over large areas. Moreover, makers of mapping systems tend not to export vectors in a convenient format for other packages, but will export raster files. Be careful to choose a raster cell size that provides adequate detail for analysis but remember that large raster maps are memory-hungry.<br />
<br />
Ranges stores raster data as a byte array with a text appendix (these files are not text files and so cannot be viewed using a text editor). Although Ranges currently only handles 15 raster categories during analysis, maps with many more categories can be used. Categories can be easily combined, e.g. Orchard could be combined with deciduous woodland, and most analyses end up being based on less than 10 combinations. It is also simple to set categories to be unclassified, so that one set of 15 types are used for a run, and another set for a repeat run. <br />
<br />
Raster files can be created from scratch or imported from gridascii files. The byte format in Ranges is typically about a third the size of the equivalent gridascii file, and therefore requires less memory.<br />
<br />
Older versions of Ranges used to limit the area of a raster file you could view at a time. With increased computer memory this is no longer necessary however if you open a raster file with more than 10 million cells, the cell values table will not be displayed.<br />
<br />
If you have problems loading large rasters, it is likely that you are running Ranges in a Java Runtime Environment (JRE) with limited memory. It is straight forward to [[Large Data And Java Memory|increase the memory reserved for Ranges]].<br />
<br />
Further instructions on setting up raster files can be found here: [[Raster File Setup|Raster File Setup]].<br />
<br />
== Edge Files ==<br />
<br />
Edge files (''.edg'') store the coordinates of home-range shapes generated in [[Location Analysis|location analyses]].<br />
<br />
They can store multiple ranges (which may represent different individuals or different tracking sessions), and for each range can store multiple [[Convex Polygons#Selected cores|cores]] representing different levels of use. <br />
<br />
Edge files can be viewed and exported from the [[Input & Graphics|main window]], but edge shapes cannot be edited there. When opened in Input & Graphics (as either the primary file or a background map), if you hold the mouse pointer over the filename a description of the analysis used to create the analysis will be displayed.<br />
<br />
Edge files are the principal input for [[Overlap Analysis|Overlap analyses]].<br />
<br />
== Utilisation Files ==<br />
<br />
Utilisation files (''.uti'') are created in location analysis and contain the areas of range cores at 5% intervals (e.g. for [[Convex Polygons#Cores at 5%|convex polygons]]). [[Input & Graphics#Utilisation plots|Utilisation plots]] can be viewed and printed from Input & Graphics, by selecting the '''open...Utilisation files''' option.<br />
<br />
== Incremental Files ==<br />
<br />
Incremental files (''.inc'') are created in location analysis and contain the areas of ranges created from sub-samples of locations, starting from the first three and finishing with the entire set (e.g. for [[Clusters#Incremental area|clusters]]). Incremental plots can be viewed and printed from Input & Graphics, by selecting the '''open...Incremental file''' option.<br />
<br />
== Statistics Files ==<br />
<br />
Many analyses have an option for outputting a statistics file. They are in CSV (comma-separated variable) format and are stored with the ''.csv'' extension. They have column headers and can be opened in the Ranges Statistics window, double-clicked to open in Microsoft Excel or imported to an alternative spreadsheet.<br />
<br />
== Esri Shapefiles ==<br />
<br />
Esri shapefiles can be [[Input & Graphics#Export|exported]] from Input & Graphics, and can be opened in ArcView, ArcGIS and other GIS packages. The ESRI shapefiles produced each consist of a minimum three files with the same root, ''.shp'', ''.shx'' and ''.dbf'', all three files are needed for it to be opened in another package. Shapefiles can also be imported into Ranges from Input & Graphics. <br />
<br />
== Image Files ==<br />
<br />
Image files (''.ima'') can be used as [[Input & Graphics#Background maps|backgrounds]]. They are created from standard image files, JPEG, PNG, GIF or bitmap, by importing into Ranges and aligning with a coordinate system.<br />
<br />
== RADA Files ==<br />
<br />
RADA files (''.rda'') are the output of RADA analyses and contain habitat core data for the RADA plots. They cannot be edited.<br />
<br />
== Survival Files ==<br />
<br />
Survival files (''.srv'') contain animal survival data and are used as the input to Kaplan Meier Survival analyses. Survival files have no location data, and therefore no map, but a number of extra range attribute variables in order to make survival analysis possible. These are Start Day, Start Month and Start Year, End Day, End Month and End Year, and Fate Code, the fate of the animal on the end date, which can be ? (Unknown), Lost, Lived or Died.<br />
<br />
== Kaplan Meier Survival Graph Files ==<br />
<br />
Kaplan Meier Survival Graph files (''.kms'') are the output of Kaplan Meier analyses and contain data for the Kaplan Meier plots. They cannot be edited.</div>Adminhttps://ranges-support.anatrack.com/wiki/Demo_TutorialDemo Tutorial2014-11-06T09:19:34Z<p>Admin: </p>
<hr />
<div>This is a tutorial for the free demonstration version of Ranges. It allows the entry, viewing, editing and exporting of location and other map data, and the creation of convex polygons around locations. It demonstrates the Log window for displaying analyses progress and the Statistics window for inspecting output from the analyses.<br />
<br />
To compare functionality in the different versions of Ranges, please visit the [[http://www.anatrack.com/ranges_compare_versions.php|Anatrack web site]]. This page describes a short tutorial to introduce you to the Demo version. This wiki contains the Help for the full version of Ranges and can give you an idea of the additional functionality it offers. <br />
<br />
== Quick Start ==<br />
<br />
To help you with this demonstration some files have been pre-loaded. These files can be changed for running the other example files or to run Minimum Convex Polygons (MCPs) on your own data.<br />
<br />
# Start Ranges and click the '''Demo''' button at the bottom of the ''About'' form. You will see an animated display representing 4 blackbirds moving around, with their MCPs as a background map.<br />
# Use the dropdown at the top of the map to change from '''animate locations by sequence'' to other views.<br />
# Click on the '''Location''' button tab at the top of the screen; some data for blackbirds has been pre-loaded. <br />
# Click the red '''Run Analysis''' button on the lower right side. <br />
# When the analysis completes the main form will be loaded with 100% convex polygon (MCP) analysis of blackbird home ranges and the statistics for the analysis will be displayed in a new window. <br />
# Close the Statistics window to see the main form. The screen shows the outline around the locations (as a Background map). To see other ranges click on the different ranges in the Edge Shapes table. <br />
# View the progress of all analyses run in this session by clicking the '''Log''' button.<br />
# Click '''open''' in in the Background panel to find ''<RangesFolder>\samples\blackbird\blackbird_map.ves''. This will show the habitat content of the MCP outlines. Change the ''clipped'' option in the Background panel to ''all'' to see the surrounding habitat too. <br />
<br />
== A Second Analysis ==<br />
<br />
To see how easy it is to load files, output spreadsheet-ready statistics and change other options, please follow the following procedure.<br />
<br />
# Click on the '''Location''' button tab at the top of the screen<br />
# Click on ''convex polygons''<br />
# Use the '''browse button''' to navigate to the file blackbird.loc (or another location file)<br />
# Click on ''selected cores'' and edit the '''Core %s''', e.g. 50 70 90.<br />
# Click on ''arithmetic mean centre (Ac)'' from the '''Choose Peel Centre''' menu.<br />
# Press the '''Make Selections''' button. To select ranges 2 and 4, click on range 2, hold down CTRL and click on range 4, then press the OK button.<br />
# Tick the '''Output edge file''' checkbox.<br />
# Tick the '''Output stats & areas file''' checkbox.<br />You can edit the automatic filenames if you are doing multiple runs, but the automatic name makes analysis much quicker and easier, recording exactly what you are doing.<br />
# Click the '''Run Analysis''' button to start the analysis.<br />
# On completion the Statistics window will opwn. See the each range in a row with the birds Age, Sex etc. and the Areas of each core at the end of the row. This data can also be accessed in a csv file (e.g. the automatically named ''<RangesFolder>\samples\blackbird\blackbird_xa_50%75%95%.csv'').<br />
# Close the Statistics window. On the main form, you will see the first range with the cores. The core in blue is the one highlighted in the Edge Shapes box on the left side of the screen. Clicking on the different ranges will reveal the different core sizes. <br />
# Use the pulldown menu at the top of the map panel to select '''display cores same as 1st selected''' and see all ranges with the same core. Try the other display options there.<br />
<br />
For more information explore other areas of the help files or email [[mailto:info@anatrack.com info@anatrack.com]].</div>Adminhttps://ranges-support.anatrack.com/wiki/Input_%26_GraphicsInput & Graphics2014-11-04T19:41:44Z<p>RobertKenward: /* Survival file data tables */</p>
<hr />
<div>== Introduction ==<br />
<br />
The main Ranges panel allows you to [[#Open|open]] and visualize existing Ranges files, create [[#New|new]] files, [[#Import|import]] text files, [[#Modify|modify]] file contents, [[#Export|export]] and [[#Save|save]] files. The [[#Data Tables|data tables]] for files are editable (with the exception of edge files), and the [[#Map Display|map display]] is mouse sensitive allowing selection and creation of locations and vector points as well as zooming.<br />
<br />
== Open ==<br />
<br />
Use this to open existing Ranges format files, allowing you to visualize the data, edit data points, modify file attributes and export to text files. <br />
<br />
On pressing the open button, the file filter will show files of any Ranges type. List specific Ranges file types using the '''Files of type''' drop down. Location files contain the locations of animals whereas the Vector files and Raster files are two different ways of storing maps. Location files contain location data in groups which may represent individuals or ranges, and have the potential to store associated information on both the locations and the groups. Vector files store point information which may define points, lines or shapes. Raster files contain grid based data, where each cell in the grid is represented by a value, usually representing a habitat. Edge files store the coordinates of home-range shapes generated in [[Location Analysis|location analyses]] they cannot be edited but may be exported. For edge files, if you hold the pointer over the filename a description of the analysis used to create the analysis will be displayed. [[File Types#Utilisation Files|Utilisation files]] are created in location analyses and contain the areas of range cores at 5% intervals (e.g. for [[Convex Polygons|convex polygons]]), if you select Utilisation files (providing there is a ''.uti'' and ''.edg'' file with the same prefix), a [[#Utilisation plots||utilisation plot]] will be displayed. [[File Types#Incremental Files |Incremental files]] are also created in location analyses and contain the areas of ranges created from sub-samples of locations, starting from the first three and finishing with the entire set (e.g. for [[Clusters|clusters]]). Opening them here will display the location file and corresponding [[#Utilisation plots||Incremental plots]]. Ranges Image files are images aligned for display with other map files. RADA files contain the output of a resource area dependency analysis. Survival files contain dates and fate data for use with Kaplan Meier analysis; this analysis outputs Kaplan Meier Survival graph files. <br />
<br />
See [[File Types|File Types]] for more details on the difference between these.<br />
<br />
On clicking '''Open''', the file will be opened in the panel. Depending on the type, the left-hand column tables will populated with data, a map will be shown and a chart will be displayed in a new window.<br />
<br />
== New ==<br />
<br />
Use '''New''' to create new Location, Vector or Raster map or Survival files, either by entering data directly from the keyboard or by copying and pasting from another application such as a spreadsheet or word processor. Vector files can be added to using the right mouse button for [[#On-screen digitising||on-screen digitising]]. Before entering the data itself, you will have to enter attribute information that will be used in the creation of the file. <br />
<br />
=== Location file ===<br />
<br />
You will be prompted to enter location file parameters. This allows you to determine the attributes of the file to be created, including the age and sex labels, the number of location-qualifying variables (LQVs), map scale and tracking resolution and UTM (Universal Transverse Mercator coordinate system) ellipsoid and zone data to place the data in a latitude-longitude system. Location file attributes are explained [[File Types#File attribute variables|here]]. After OK is pressed, a file with a single range and no locations is created. Refer to [[#Data Tables|data tables]] for details on how to add data to this minimal file. <br />
<br />
=== Vector file ===<br />
<br />
You will be prompted to set vector file attributes (more details [[File Types#Vector Files|here]]). This determines the attributes of the file to be created, including whether it is a point, line or shape file, UTM data, the scale and the number, name and colour of vector map categories. After OK is pressed, an empty file is created, you may add to this using on-screen digitising or other methods outlined in [[#Data Tables|data tables]]. <br />
<br />
=== Raster file ===<br />
<br />
You will be prompted to set raster file attributes (more details [[File Types#Raster Files|here]]). This determines the attributes of the file to be created, including its size, edge coordinates, habitat names and colours. After OK is pressed, an empty file is created. Refer to [[#Data Tables|data tables]] for details on how to add data to the file. <br />
<br />
=== Survival file ===<br />
<br />
[[File Types#Survival Files|Survival files]] do not contain any map data. You will only be prompted to choose age and sex labels.<br />
<br />
== Import ==<br />
<br />
On pressing the import button, you will be prompted to choose between the following import file types:<br />
<br />
[[#Location file from column text file|Location file from column text file]]<br /><br />
[[#Vector map from column text file|Vector map from column text file]]<br /><br />
[[#Vector map from ArcView shapefile|Vector map from ArcView shapefile]]<br /><br />
[[#Location file from ArcView points shapefile|Location file from ArcView points shapefile]]<br /><br />
[[#Raster map from ArcInfo ASCII Grid format|Raster map from ArcInfo ASCII Grid format]]<br /><br />
[[#Raster map from Idrisi ASCII format|Raster map from Idrisi ASCII format]]<br /><br />
[[#Image file from JPEG/GIF/PNG/BMP|Image file from JPEG/GIF/PNG/BMP]]<br /><br />
[[#Survival file from Ranges location file|Survival file from Ranges location file]]<br />
<br />
=== Location file from column text file ===<br />
<br />
Use this to import a text column file, as exported from a spreadsheet, into a Ranges [[File Types#Location Files|location file]]. The minimum requirement is a file with two columns, one containing coordinate eastings and the other containing coordinate northings. Files may also contain columns specifying range attribute variables such as ID, age, sex, month, year and focal site coordinates (such as a nest) and up to 50 location-qualifying variables (LQVs). So a typical file to be imported could start like this:<br />
<br />
{| <br />
| style="width: 60px;" | ID || style="width: 60px;" | Age || style="width: 60px;" | Sex || style="width: 60px;" | Month || style="width: 60px;" | Year || style="width: 60px;" | FocalE || style="width: 60px;" | FocalN || style="width: 60px;" | E || style="width: 60px;" | N || style="width: 60px;" | lqv1 || style="width: 60px;" | lqv2<br />
|-<br />
|A456 ||2 ||1 ||7 ||02 ||1003 ||586 ||1000 ||500 ||2 ||5<br />
|-<br />
|A456 ||2 ||1 ||7 ||02 ||1003 ||586 ||1052 ||510 ||3 ||10<br />
|-<br />
|A456 ||2 ||1 ||7 ||02 ||1003 ||586 ||1068 ||513 ||2 ||6<br />
|-<br />
|A456 ||2 ||1 ||7 ||02 ||1003 ||586 ||1009 ||525 ||0 ||5<br />
|-<br />
|B563-2 ||1 ||2 ||7 ||02 ||987 ||556 ||972 ||583 ||3 ||15<br />
|-<br />
|B563-2 ||1 ||2 ||7 ||02 ||987 ||556 ||988 ||532 ||4 ||2<br />
|-<br />
|B563-2 ||1 ||2 ||7 ||02 ||987 ||556 ||955 ||551 ||9 ||7<br />
|-<br />
|B563-2 ||1 ||2 ||7 ||02 ||987 ||556 ||997 ||533 ||2 ||5<br />
|}<br />
<br />
Try [[#Export|exporting]] and re-importing a location file to see how the data is assessed. You can look at the exported file in a text editor or spreadsheet such as notepad or excel.<br />
<br />
The first prompt is to choose a file for importing. Initially, only files with a .txt extension will be displayed; however, you can alter the ''Files of Type'' choice box at the bottom of the dialog to display all files. <br />
<br />
Once you have chosen a file, providing that it is in an appropriate format, an [[Location Import|import screen]] will appear. This will display the first 10 lines of the file to be imported, so that you can select which data are in which columns and set other attributes of the location file to be created. If the first row of data is the table header information, check the '''Header Row''' checkbox so that ranges does not attempt to import this as data. The '''Input Data Summary''' will reflect this,<br />
<br />
Ranges is unable to use non-Cartesian coordinate systems. If the location data are latitude/longitude coordinates, you can convert these to metres in the UTM coordinate system by selecting ''latitude-longitude to utm'' in the '''Coordinate System Conversion''' list then selecting a '''Reference Ellipsoid''', usually ''WGS84'' but it will depend on your coordinate capturing device.<br />
<br />
Set the '''Scale of Coordinates''' which is the number of metres a coordinate value of 1 represents and the '''Tracking Resolution''' which is important for drawing boundary strips around calculated ranges. A value of 10 is an appropriate resolution in most situations.<br />
<br />
Setting '''Include Nest/Focal Site In Locations''' means that the coordinates for the range defined by FocalE and FocalN will be used in the analyses.<br />
<br />
Click OK to import the data. You will be prompted for a folder and file name for the new Ranges location file. Once saved, the data will be displayed in the [[#Data Tables|data tables]] and on the [[#Map Display|map display]].<br />
<br />
=== Vector map from column text file ===<br />
<br />
Use this to import a text column file, as from a spreadsheet, into a Ranges [[File Types#Vector Files|vector file]]. The minimum requirement is a file with two columns, one containing coordinate eastings and the other containing coordinate northings. Files may also contain columns specifying ID, label and colour information.<br />
<br />
Each ID can only have one label and colour, the one associated with the first coordinates with a particular ‘No.’ will be used.<br />
<br />
Holes and other secondary polygons (for shapes) should be separated from the primary polygon by a blank row (blank rows are not needed between the primary polygons themselves). Holes should have an ID which is the negative of that of the primary polygon (and secondary polygons should have the same ID as the primary polygon).<br />
<br />
The file below would import as one shape with a hole and another shape that fills the hole:<br />
<br />
{| <br />
| style="width: 60px;" | E || style="width: 60px;" | N || style="width: 60px;" | Id || style="width: 60px;" | Label<br />
|-<br />
|0 || 4 || 7 || habitat1<br />
|-<br />
|4 || 4 || 7 || habitat1<br />
|-<br />
|4 || 0 || 7 || habitat1<br />
|-<br />
|0 || 4 || 7 || habitat1<br />
|- <br />
| &nbsp;||&nbsp; ||&nbsp; ||&nbsp;<br />
|- <br />
|2 || 3 || -7 || habitat1<br />
|-<br />
|3 || 3 || -7 || habitat1<br />
|-<br />
|3 || 2 || -7 || habitat1<br />
|-<br />
|2 || 3 || -7 || habitat1<br />
|-<br />
|2 || 3 || 10 || habitat2<br />
|-<br />
|3 || 3 || 10 || habitat2<br />
|-<br />
|3 || 2 || 10 || habitat2<br />
|-<br />
|2 || 3 || 10 || habitat2<br />
|}<br />
<br />
For shapes the polygons should close by repeating the first coordinate as above.<br />
<br />
The first prompt is to choose a file for importing. Initially only files with a ''.txt'' extension will be displayed. However, you can alter the '''Files of Type''' choice box at the bottom of the dialog to display all files. <br />
<br />
Once you have chosen a file, providing that it is in an appropriate format an import screen will appear. This will display the first 10 lines of the file to be imported, so that you can select which data are in which columns and set other attributes of the vector file to be created.<br />
<br />
==== Input Data Summary ====<br />
<br />
The table at the top of the import vector window displays the first 10 lines of the file. If it detects that the first value in the first column is not a number it assumes that the first row is a header row and uses it to add titles to each column. The header row can be switched on and off using the tick box at the top right. The data in this table is not editable, but allows you to check what values are in which columns.<br />
<br />
==== Attribute Mapping ====<br />
<br />
On the middle left of the window there are 5 choice boxes with titles E, N, ID, Label, Colour. The selections you make in these boxes will determine which columns in the input file the vector data will be obtained from. If the file has a header row, the choice boxes will contain the column titles, if not they will contain the column numbers. Using an input file with a header row reduces the risk of importing the wrong columns.<br />
<br />
All of the choice boxes, with the exception of E and N, have ''absent'' as the last option. This is used to indicate that the file does not contain any data for that attribute. The ''absent'' option is not available for E and N as it would be meaningless to create a vector file lacking one or both coordinates. <br />
<br />
The ID determines which point group, line or shape that row of data will be read into. Data with the same ID need not be adjacent.<br />
<br />
Category label and colour are variables associated with a particular point group, line or shape. The program assigns the label and colour obtained from the first coordinate of each ID. It does NOT check whether the label and colour assigned for subsequent coordinates with the same ID are the same as the first.<br />
<br />
Finally choose the vector file type, point, line or shape, from the list and set the '''Scale of Coordinates''' which is the number of metres a coordinate value of 1 represents. <br />
<br />
Click OK to import the data. You will be prompted for a folder and file name for the new Ranges location file. Once saved, the data will be displayed in the [[#Data Tables|data tables]] and on the Map Display. The file is not saved in Ranges format until the [[#Save|'''save'''] button is pressed. <br />
<br />
=== Vector map from ArcView shapefile ===<br />
<br />
Use this to import an ArcView shapefile to a Ranges [[File Types#Vector Files|vector file]]. Note: to reverse the process see [[#Export|export]], but note that the data format will be changed such that whilst the location and areas of shapes will remain the same the shapefile produced will not be identical to the original.<br />
<br />
The following shapefile types are supported: point, polyline, polygon and multipoint. Point and multipoint files are imported as Ranges vector points, polyline files as vector lines and polygon files as vector shapes. The [[#Modify|'''modify''']] button can be used to convert between these formats after import. <br />
<br />
==== Choose category column ====<br />
<br />
ArcView shapefiles consist of 3 files with the suffixes ''.shp'', ''.shx'' and ''.dbf''. If the ''.dbf'' file is not present the points will be imported with no attribute information. If the .dbf file is present you will be presented with a choice box, allowing you to choose which column to get the habitat labels from (note that if the attribute fields in the shapefile contain spaces these are replaced by '_' ). You probably want to choose the field/column that contains the habitat information; in shapefiles that are exported from Ranges this field is called ''Label''. If you choose ID as the category column each shape will be put into a separate category.<br />
<br />
==== Polygon and polyline details ====<br />
<br />
In polygon and polyline shapefiles, multiple parts (shapes or lines) can be stored in a single record. In contrast, Ranges [[File Types#Vector Files|vector files]] contain a single shape or line for each record; this conversion takes place during the import process.<br />
<br />
=== Location file from ArcView points shapefile ===<br />
<br />
Use this to import an ArcView point or multipoint shapefile to a Ranges location file.<br />
<br />
The coordinates from the ''.shp'' file, and any attribute information from the ''.dbf'' file are displayed in an [[Import Locations|import locations]] window, allowing you to choose which fields to import.<br />
<br />
=== Raster map from ArcInfo ASCII Grid format ===<br />
<br />
Use this to import a gridascii file into a Ranges raster file. A gridascii file is a text file that can be exported from ArcView and other GIS packages. Alternatively it can be created in a spreadsheet or text editor.<br />
<br />
To export a gridascii file from ArcView, choose '''File...Export data source'''. When you '''Select export file type''' to be ''ASCII Raster'' and you will be prompted to choose a Grid file. You can first convert an ArcView shapefile to a Grid file by choosing '''Theme...Convert to Grid'''.<br />
<br />
A gridascii file needs to have a header in the following format :<br />
<br />
{| <br />
| style="width: 60px;" | ncols || style="width: 60px;" | 200<br />
|-<br />
|ncols || 200<br />
|-<br />
|nrows || 200<br />
|-<br />
|xllcorner || 392000<br />
|-<br />
|yllcorner || 89000<br />
|-<br />
|cellsize || 25<br />
|-<br />
|NODATA_value || -9999<br />
|}<br />
<br />
''ncols'' and ''nrows'' specify the number of columns and rows. ''xllcorner'' and ''yllcorner'' specify the minimum eastings and northings. ''cellsize'' specifies the number of coordinate units per cell. ''NODATA_value'' specifies the value that indicates there is no data for that cell. <br />
<br />
This can easily be seen by exporting buzzard\buzmap.rst and looking at the exported file in a text editor. However, please be aware that viewing text files in some text editors may add hidden characters to the file which may make the file unsuitable for re-importing. <br />
<br />
Following the header, the cell data needs to be in rows separated by line feeds. Within rows, values should be separated by spaces.<br />
<br />
Once you have chosen a gridascii file, Ranges will read the data and display with a screen offering options to set raster file attributes. This is the same screen that you will see when creating a raster file from scratch using '''new''' or modifying an existing raster file using '''modify'''. Large raster files, in excess of a million cells, will take a while to import, for more details see [[File Types#Raster Files|raster files]].<br />
<br />
=== Raster map from Idrisi ASCII format ===<br />
<br />
Export a raster map from idrisi choosing the ascii option and it will create two files one .rst and the other .rdc or .doc (depending which version of Idrisi you have). To import to Ranges select the .rst file and Ranges will automatically look for the associated file. Be careful that Idrisi and Ranges raster files have the same extension (''.rst'') but are not in the same format, so make sure that you don’t overwrite one with the other.<br />
<br />
Once you have chosen an Idrisi raster file, Ranges will read the data and present you with a screen offering options to set raster file attributes. This is the same screen that you will see when creating a raster file from scratch using '''new''' or modifying an existing raster file using '''modify'''. <br />
<br />
=== Image file from JPEG/GIF/PNG/BMP ===<br />
<br />
Images can be loaded as backgrounds in Ranges with other file type data displayed in the foreground. In order for the image to aligned properly, a Ranges image file, ''.ima'', containing the alignment data needs to be created by importing a JPEG, GIF, PNG or bitmap file.<br />
<br />
To import the image, select the file; it will be loaded in to the alignment tool. As for the main map display, yuo can pan and zoom around the image using the controls in the top right, you can zoom to an area by right click and dragging to create the area and you can zoom in and out of a point in the image using the mouse wheel or equivalent.<br />
<br />
Alignment requires that four points, A, B, C and D, on image correspond to points in the real world. These points are entered into the grid in the top right. '''Image Coords''' are pixel position with 0,0 being the top left of the image and '''World Coordinates''' are the position in metres of these points in the world (note that in the northern hemispere, eastings will be lower at the bottom of the image. You can type directly into the grid to set these.<br />
<br />
The image coordinates are indicated by orange flags on the image. The flags can be dragged around the image as an alternative way of specifying image coordinates. By default the image coordinates, and the flags, are in the four corners of the image.<br />
<br />
Once the alignment coordinates are defined, click OK to save the image file and to show it on the Map Display. It is more usefully loaded as the '''Background'''.<br />
<br />
=== Survival file from Ranges location file ===<br />
<br />
Survival files require more range qualifying variables than lcoation files but do not contain any location data. They can be created from existing location files by simply selecting the .loc file to import from.<br />
<br />
If the location file has date definitions as LQVs these dates will be used to define the start date and end date of each range in the survival file. If they do not exist, the start date will use the month and year from the range labels and you will need to fill out the remaining date details. <br />
<br />
Survival data also requires a fate code for each animal; this is not imported but must be input manually. Once imported, the data will be displayed in the [[#Data Tables|data tables]].<br />
<br />
== Modify ==<br />
<br />
You can modify locations, edges, vectors, rasters, images and survival files. Click '''modify''' with the location file loaded to display the file settings for editing. Note that modified files are not saved until the '''save''' button is clicked. <br />
<br />
==== Sampling locations and edges ====<br />
<br />
Generally it is useful to have all the data in the file, so that any corrections can be made in one place. However, there are times when subsets of the data are being repeatedly run, so rather than make those choices all the time it is better to create a file of just that subset. Additionally, using a subset can reduce analysis time. <br />
<br />
To create a subset, click '''Sample''' on the '''modify''' window. This will allow you to select a subsample of your data by its attributes (e.g. age, sex, time, activity), and to save this to a file with a new name. The selection frame allows you to select ranges, cores and locations depending on the file type. When you click OK, you will first be prompted for a new filename; it is important to save the new file under a different name to avoid losing the original data. <br />
<br />
==== Random subsampling locations ====<br />
<br />
There are also times when you may not want to use an entire location dataset, e.g. to avoid [[Interaction Analysis#Autocorrelation|autocorrelation]] or to investigate how much the analyses are affected by the number of locations. Use the '''Random Subsampling''' section, where you can determine the percentage of locations to select, or specify a particular number of locations to use and specify if you want a minimum time between each location. Press the OK button once the appropriate choices have been entered.<br />
<br />
==== Merging location and edge files ====<br />
<br />
Merge a second file with the one that is loaded by clicking the '''Merge''' button on the '''modify''' window. Location files can only be merged if they have the same number of age and sex labels, have the same scale, tracking resolution and have the same number of location qualifier variables. Edge files need to have the cores too. When you click OK, you will be prompted for a new filename; it is important to save the new file under a different name to avoid losing the original data.<br />
<br />
== Export ==<br />
<br />
You can export the active file to a chosen file type by clicking '''export'''.<br />
<br />
=== Location files ===<br />
<br />
==== Text file ====<br />
<br />
Exports the location file to a plain text file in column format. For each location there are seven columns specifying the range attributes, two columns for the location coordinates and as many columns as there are location qualifying variables (LQVs).<br />
<br />
The column headings will be as follows : <br />
<br />
{| <br />
| style="width: 60px;" | Id || style="width: 60px;" | Age|| style="width: 60px;" | Sex || style="width: 60px;" |Month || style="width: 60px;" | Year || style="width: 60px;" | FocalE || style="width: 60px;" | FocalN || style="width: 60px;" | E || style="width: 60px;" | N || style="width: 60px;" | LQV1 || style="width: 60px;" | LQV2 || style="width: 60px;" | ...<br />
|}<br />
<br />
==== ArcView Shapefile ====<br />
<br />
Creates the 3 files that make up an ESRI shapefile (''.shp'', ''.shx'' and ''.dbf'').<br />
<br />
Coordinates will be multiplied by the [[File Types#Scale|scale]] parameter to convert them to metres.<br />
<br />
==== Text file for display in Excel ====<br />
<br />
Creates a text file in a format for display in Excel. In the resulting CSV (comma separated variable) file, each range has two columns, for the easting and northing; these columns are headed with a string containing the range labels.<br />
<br />
==== KML ====<br />
<br />
Creates a KML (Keyhole Markup Language) file containing the location data. KML files can be displayed in third party applications such as Google Earth.<br />
<br />
=== Vector files ===<br />
<br />
==== Text file ====<br />
<br />
This exports the vector file to a plain text file in column format. For each coordinate pair there are five columns. <br />
<br />
{| <br />
| style="width: 60px;" | E || style="width: 60px;" | N || style="width: 60px;" | ID || style="width: 60px;" | Label || style="width: 60px;" | Colour<br />
|}<br />
<br />
where E and N are the coordinates, ID is the identity of the feature (point group, line or shape) to which the coordinates belong, Label is the habitat label for the feature and Colour is the colour for the feature (in shorthand hexadecimal RGB)<br />
<br />
==== ArcView Shapefile ==== <br />
<br />
This creates the three files that make up an Esri ArcView shapefile (''.shp'', ''.shx'' and ''.dbf'').<br />
<br />
Coordinates will be multiplied by the [[File Types#Scale|vector scale]] parameter to convert them to metres.<br />
<br />
==== Text file for display in Excel ====<br />
<br />
Creates a text file in a format for display in Excel. In the resulting CSV (comma separated variable) file, each shape has two columns, for the easting and northing; these columns are headed with a string containing the shape labels.<br />
<br />
=== Raster files ===<br />
<br />
This exports raster files in gridascii format (see [[#Raster map from ArcInfo ASCII Grid format|Import Raster]] for details) that can be imported to Esri software.<br />
<br />
=== Edge files ===<br />
<br />
==== ArcView Shapefile Polyline ====<br />
<br />
Creates the three files that make up an Esri ArcView polyline shapefile (''.shp'', ''.shx'' and ''.dbf''). In ArcView polyline files, shapes are displayed as unfilled lines. Coordinates will be multiplied by the scale parameter from the location file that they were derived to convert them to metres.<br />
<br />
==== ArcView Shapefile Polyline - 1 per range ====<br />
<br />
As in the previous option, but creates one shapefile for each range in your edge file. This can be useful if you want to choose which ranges to display within ArcView. A warning message is generated because this could create a very large number of files if your edge file has lots of ranges. The filenames will be based on the name you choose with _r[range_num] added to each, e.g. if you chose the name fox.shp, the files would be named fox_r0.shp, fox_r1.shp, fox_r2.shp etc. Any existing files with the same names will be overwritten without prompting.<br />
<br />
==== ArcView Shapefile Polygon ====<br />
<br />
Creates the 3 files that make up an Esri ArcView polygon shapefile (''.shp'', ''.shx'' and ''.dbf''). In ArcView polygon files, shapes are displayed as filled polygons. Coordinates will be multiplied by the scale parameter from the location file that they were derived to convert them to metres.<br />
<br />
==== Text file for display in Excel ====<br />
<br />
Creates a text file in a format for display in Excel. In the resulting CSV (comma separated variable) file, each edge has two columns, for the easting and northing; these columns are headed with a string containing the edge labels.<br />
<br />
==== Separate Ranges Edge files - 1 per range ==== <br />
<br />
Subdivides your edge file, creating one edge file for each range. (This can be useful for viewing individual ranges against the locations used to produce them within the Input & Graphics display, loading the location file as the primary file, and the individual range edge file as the background file.) A warning message is generated because this could create a very large number of files if your edge file has lots of ranges. The filenames will be based on the name you choose with _r[range_num] added to each, e.g. if you chose the name ''fox.edg'', the files would be named ''fox_r0.edg'', ''fox_r1.edg'', ''fox_r2.edg'' etc. Any existing files with the same names will be overwritten without prompting.<br />
<br />
==== Export to KML ====<br />
<br />
Creates a KML (Keyhole Markup Language) file containing the edge polygons. KML files can be displayed in third party applications such as Google Earth.<br />
<br />
=== Survival files ===<br />
<br />
This exports survival file data to a plain text file in column format. For each range pair there are ten columns: <br />
<br />
{| <br />
| style="width: 60px;" | Id || style="width: 60px;" | Age || style="width: 60px;" | Sex || style="width: 60px;" |StartDay || style="width: 60px;" |StartMonth || style="width: 60px;" |StartYear || style="width: 60px;" |EndDay || style="width: 60px;" |EndMonth || style="width: 60px;" |EndYear || style="width: 60px;" |FateCode<br />
|}<br />
<br />
== Save ==<br />
<br />
This saves the active file in Ranges format. You will be prompted with a dialog box allowing you to browse your files, if you enter a filename with no extension then the appropriate file type extension will be added. If you try to overwrite an existing file you will be warned.<br />
<br />
== Data Tables ==<br />
<br />
The two data tables on the left hand side of the '''Input & Graphics''' panel, display and allow editing of the currently loaded file. The tables differ according to the file type as follows<br />
<br />
{| class="wikitable"<br />
! style="text-align:left;width:120px"| File Type<br />
! style="text-align:left;width:120px"| Top Table<br />
! style="text-align:left;width:120px"| Bottom Table<br />
|-<br />
|location || ranges || locations<br />
|-<br />
|vector points || point groups || points<br />
|-<br />
|vector lines || lines || line vertices<br />
|-<br />
|vector shapes || shapes || shape vertices<br />
|-<br />
|raster || map categories || raster cell values<br />
|-<br />
|edge || edge shapes || edge vertices<br />
|-<br />
|utilisation || edge shapes || edge vertices<br />
|-<br />
|incremental || ranges || locations<br />
|-<br />
|image || - || -<br />
|-<br />
|RADA || RADA habitat cores || -<br />
|-<br />
|survival || survival ranges || -<br />
|-<br />
|Kaplan Meier survival graph || - || -<br />
|} <br />
<br />
The following features are common to each table: <br />
<br />
* column widths can be changed by clicking and dragging in the header row<br />
* white cells are editable grey cells are not<br />
* click 3 times in editable cells to overwrite, twice to add. <br />
* multiple rows can be selected by using Ctrl click, or shift click to select a series<br />
<br />
Any editing does not effect the opened file (and is not saved) until the '''save''' button is pressed. <br />
<br />
=== Location file data tables and incremental plots ===<br />
<br />
==== Viewing ====<br />
<br />
Within [[File Types#Location Files|location files]], locations are grouped into ranges. The upper table displays the attributes of each range. The lower table displays the locations and location qualifying variables (LQVs) for the range that is selected in the upper table. <br />
<br />
Selecting different rows in the ranges table will cause different sets of locations to be displayed in the locations table. To step through all of the ranges in the file, select the first row in the ranges table and then use the down cursor key to display each range in turn. Locations in the selected range are displayed in blue in the map display.<br />
<br />
Selecting a row within the locations table will cause that location to be circled red within the map display.<br />
<br />
Clicking with the SHIFT key held down on a blue location within the map display causes the row associated with that location to be selected (if there are multiple locations at the same point then all of the relevant rows will be selected).<br />
<br />
==== Editing ====<br />
<br />
Data are non-editable if the table cell is grey and editable if the table cell is white. In the ranges table Age, Sex and Month cells have pull down menus with available options, these are activated by a left mouse click.<br />
<br />
For other editable cells, use the left button of the mouse to click once or twice within the cell and you will be able to add to or delete from the existing cell contents. Click three times and you can overwrite the cell contents. <br />
<br />
In the locations table, you can use CTRL+C to copy and CTRL+V to paste blocks of data between portions of the table or between the table and a spreadsheet such as Microsoft Excel. Please note, if you wish to copy more than one column you have to select them by moving the cursor right or left, even though that does not alter the look of the cell.<br />
<br />
In the ranges table use the '''add''' button to add a blank range and the '''delete''' to remove selected ranges. <br />
<br />
To add locations, simply type additional locations into the blank cells immediately following the existing locations in the locations table. If data are pasted in the number of rows in the table will increase to accommodate them. Use the '''delete''' button to delete selected locations.<br />
<br />
=== Incremental area plots ===<br />
<br />
If you have opened an Incremental file (''.inc'', created in [[Convex Polygons#Incremental area analysis|location analysis]]), an incremental plot is displayed in a new window. This allows you to examine how the range area changes as successive locations are added. The plot will display the range that is selected in the upper table; click on rows in the upper table to display other ranges. If the chart window is closed, it can be reopened by clicking on the '''display''' button below the locations table. For more details see [[Range Use Plots#Incremental area plots|Incremental area plots]].<br />
<br />
=== Vector file data tables ===<br />
<br />
==== Viewing ==== <br />
<br />
For [[File Types#Vector Files|vector files]], groups of coordinates are stored in a similar way to location files. For points, the upper table displays point groups and the lower table points. For lines, the upper table displays lines and the lower table line vertices. For shapes, the upper table displays shape polygons and the lower table shape vertices.<br />
<br />
As for location file data, tables changing the row that is selected in the upper table changes which data are displayed in the lower table. The selected point group, line or shape is displayed in blue in the map display.<br />
<br />
Selecting a row in the upper table will highlight the point, line or shape by circling it in black within the map display. Selecting a row within the points, line vertices or shape vertices table will cause that location to be circled red.<br />
<br />
Clicking on a blue location within the map display causes the row associated with that point, line vertex or shape vertex to be selected (if there are multiple features at the same point then all of the relevant rows will be selected).<br />
<br />
==== Editing ====<br />
<br />
The upper table (point groups, lines or shapes) contains ''No.'', ''ID'' and ''Category'' columns. ''No.'' is not editable. ''ID'' is editable but must contain an integer. ''Category'' is editable with a pull down list of available categories. Choose [[#Modify|'''modify''']] to add categories .<br />
<br />
Holes can be added to shapes: first select the boundary shape and click '''hole'''. The ID and category of a hole is the same as its shell shape and cannot be edited. <br />
<br />
Use the '''add''' button to create a new feature and '''delete''' to remove a feature. The feature will be inserted below the currently selected one. You cannot add a new shape between a hole and its shell.<br />
<br />
The lower table (points, line vertices or shape vertices) contains the coordinate data. <br />
<br />
In the locations table, you can use CTRL+C to copy and CTRL+V to paste blocks of data between portions of the table or between the table and a spreadsheet such as Microsoft Excel. Please note, if you wish to copy more than one column you have to select them by moving the cursor right or left, even though that does not alter the look of the cell.<br />
<br />
Use the '''delete''' button to delete selected coordinates.<br />
<br />
==== On-screen digitising ====<br />
<br />
You can add points to the selected point group, line or shape by left-clicking on the map with the CTRL key held down (you are in ''draw'' mode when the the cursor over the map becomes a pen. You can remove points from an object, from last to first by right-clicking on the map with the CTRL key held down.<br />
<br />
For vector files, using the right or middle mouse buttons when the cursor is within the map display will add the corresponding point to the currently selected shape. You can use this to create new vector files by tracing around an existing file loaded as a [[#Background maps|background]]. This is a good way of creating a vector shape file that can be used in habitat analyses from an image file (perhaps from a scanned map or aerial photograph). For vector shape files, coordinates should always be added in a clockwise direction. To add a shape that fills a hole, simply add another shape and then copy all the coordinates (CTRL+C) from the table for hole into that for the new shape (CTRL+V).<br />
<br />
=== Raster file data tables ===<br />
<br />
==== Viewing ====<br />
<br />
For Raster files, the upper table contains the map categories and the lower table the raster cell values. <br />
<br />
Clicking on a cell in the raster cell values table draws a white box at that point in the map display.<br />
<br />
==== Editing ====<br />
<br />
The map categories table is not editable; press the [[#Modify|'''modify''']] button to edit attributes of the raster map.<br />
<br />
The raster cell values table is editable and changes made will be shown in the map display. Use CTRL+C to copy and CTRL+V to paste blocks of data between portions of the table or between the table and a spreadsheet such as Microsoft Excel.<br />
<br />
=== Edge file data tables and utilisation plots ===<br />
<br />
==== Viewing ====<br />
<br />
For Edge files, the upper table contains the edge shapes and the lower table the coordinates specifying the edge vertices. Edge file data is not editable. In the upper table, columns ''ID'', ''Age'', ''Sex'', ''Month'' and ''Year'' are the same as for locations and ''core'' displays the core %.<br />
<br />
Note that a core may contain multiple shapes e.g. following cluster analysis or if there is a hole generated by contouring. In this case subsequent shapes will have the same ID and category.<br />
<br />
As for location file data tables, changing the row that is selected in the upper table changes which data are displayed in the lower table. The selected edge shape is displayed in blue in the map display.<br />
<br />
==== Utilisation plots ====<br />
<br />
If you have opened an Utilisation file (''.uti'', created in location analysis), an incremental plot is displayed in a new window. The plot will display the range that is selected in the upper table; click on rows in the upper table to display other ranges. If the chart window is closed, it can be reopened by clicking on the '''display''' button below the locations table. For more details see [[Range Use Plots#Utilisation plots|Utilisation plots]].<br />
<br />
Examination of utilisation plots ([[Bibliography|Ford & Krumme 1979]]) provides a method of deciding on the percentage of locations that define a core range. In Ranges, utilisation plots display the area of estimated home range cores at 5% intervals from 20-100%. If there are a few locations far from the range centre, the slope of the plot is initially steep, but becomes shallower when only the core locations remain. This slope discontinuity, if present, is a useful indicator of how many locations constitute the core range. For more details see Range Use Plots.<br />
<br />
==== Editing ====<br />
<br />
Edge files are not editable in Input & Graphics.<br />
<br />
=== RADA file data tables ===<br />
<br />
==== Viewing ====<br />
<br />
RADA files contain the output of RADA analyses. There is a row in the top table for each habitat for each core specified in the analysis. See RADA analysis for more details of this table.<br />
<br />
Clicking on a row in the table will display the RADA plot for that habitat core. If the plot is hidden, open it again by clicking the '''display''' button below the table.<br />
<br />
==== Editing ====<br />
<br />
RADA files are not editable.<br />
<br />
=== Survival file data tables === <br />
<br />
<br />
==== Viewing ====<br />
<br />
Survival files contain survival data for Kaplan Meier analysis. There is a row in the top table for each survival record, usually for each animal. As well as ID, Sex and Age labels used to identify the animal there are labels for the tagging Start Date and End Date as well as the Fate Code. <br />
<br />
==== Editing ====<br />
<br />
The survival data are editable. In the ranges table Age, Sex, the Month cells and Fate Code have pull down menus with available options, these are activated by a left mouse click.<br />
<br />
For other editable cells, use the left button of the mouse to click once or twice within the cell and you will be able to add to or delete from the existing cell contents. Click three times and you can overwrite the cell contents.<br />
<br />
=== Graphs from location analyses ===<br />
<br />
Other location analyses produce graphs, e.g. the distance between locations over time. These are also displayed in a new window. The plot will display the range that is selected in the upper table; click on rows in the upper table to display other ranges. If the chart window is closed, it can be reopened by clicking on the '''display''' button below the locations table. For more details see Incremental area plots. <br />
<br />
== Map Display ==<br />
<br />
The Map Display is on the right of the window. The size of the window can be changed relative to the Data Tables panel by click on the dividing bar between them and dragging it to the left and right.<br />
<br />
The panel displays the contents of the loaded location, vector or raster file and is sensitive to mouse clicks allowing selection of features, zooming and drawing of vector objects.<br />
<br />
Display options can be selected from the pull down boxes at the top of the panel.<br />
<br />
[[#Background maps|Background files]] can be chosen by clicking '''open''' on the upper right.<br />
<br />
Colour options for Location and Edge files can be chosen from the '''range colours''' drop down in the top right.<br />
<br />
=== Panning and zooming ===<br />
<br />
Controls in the top left of the Map Display control the panning and zooming. <br />
<br />
The arrow keys allow panning up down left and right but the easiest way to position the map is to hold down the left mouse button and drag the map.<br />
<br />
Buttons in the top left allow zooming in and out and zoom to fit, fitting both foregrounds and background to the Map Display panel. You can also ''zoom to selection'' by holding the right mouse button down and dragging right and down to draw a red rectangle; when you release the mouse button, panel will be filled with the contents of the rectangle. Finally, zoom quickly in and out to the mouse cursor using the mouse scroll wheel or its equivalent.<br />
<br />
=== Point selection ===<br />
<br />
You can select a point in the map display in two ways:<br />
<br />
* select the row in the lower table of the Data Tables panel<br />
* left click the mouse on the map display while holding down the SHIFT key on the keyboard.<br />
<br />
This second option will select the closest point to the click in the currently selected range; the point group, line or shape and the row containing it in the lower table will be highlighted. If there are multiple features at the same point then all of the relevant rows will be selected. Note that it is not possible to select coordinates from an un-selected range, point group, line or shape.<br />
<br />
=== Focal points and range centres ===<br />
<br />
Focal points such as Nests, defined by FocalE and FocalN for the range are indicated by an '''x''' on the map. Ranges centres, calculated during polygon-building location analyses are marked with a '''+'''. <br />
<br />
=== Display options ===<br />
<br />
For location, vector and edge files, a pull down list at the top of the map display allows you to select whether to display all of the coordinates in the file or just those in the selected group. The options are dependent on the file type.<br />
<br />
==== Location Files ====<br />
<br />
Display options for location files are as follows:<br />
<br />
{| class="wikitable"<br />
! style="text-align:left;width:160px"| Option<br />
! style="text-align:left;"| Function<br />
|-<br />
|''display selected'' || Displays only those features selected in the upper table.<br />
|-<br />
|''display all'' || Displays all ranges or shapes in the file.<br />
|-<br />
|''animate locations by sequence'' || Animates the map for all ranges, animates locations according to their sequence in the file. Additional animation options are made available at the top of the map display panel.<br />
|-<br />
|''animate locations by time'' || Animates the map for all ranges, animates locations according to time variables stored in the location qualifying variables (lqvs). This option is only offered if appropriate time variables are stored in the file. Additional animation options are made available at the top right corner of the user interface.<br />
|-<br />
|''display selected as path'' || Joins the locations within each selected range into a path. <br />
|-<br />
|''display all as paths'' || Joins the locations within each range into a path and displays them all. <br />
|}<br />
<br />
==== Edge files ====<br />
<br />
Display options for edge files are as follows:<br />
<br />
{| class="wikitable"<br />
! style="text-align:left;width:160px"| Option<br />
! style="text-align:left;"| Function<br />
|-<br />
|''display selected'' || Displays only those features selected in the upper table.<br />
|-<br />
|''display 1st selected range'' || Displays all of the cores from the range selected in the upper table. The first selected core% is displayed in blue and others in black. If more than one range is selected, those after the first are displayed in grey.<br />
|-<br />
|''display cores same as 1st selected'' || Displays all the cores for all ranges that have the same value as the first selected one. e.g. if the first selected core is 50%, then the 50% cores for all ranges will be shown.<br />
|-<br />
''display all'' || Displays all ranges or shapes in the file.<br />
|}<br />
<br />
==== Vector map files ====<br />
<br />
Display options for vector files are as follows:<br />
<br />
{| class="wikitable"<br />
! style="text-align:left;width:160px"| Option<br />
! style="text-align:left;"| Function<br />
|-<br />
|''display selected'' || Displays only those features selected in the upper table.<br />
|-<br />
|''display all, selection colours'' || Displays all ranges or shapes in the file. Selected coordinates in red, selected ranges in blue, others in black.<br />
|-<br />
|''display all, range or category colours'' || Displays all the shapes in the colours defined in the file (and can be altered using the '''modify''' button). For location and edge files a single colour is applied for each range from a set colour scheme. <br />
|}<br />
<br />
=== Line colour ===<br />
<br />
Line colour is only available for location and edge files. Colour options are as follows:<br />
<br />
{| class="wikitable"<br />
! style="text-align:left;width:120px"| Option<br />
! style="text-align:left;"| Colour<br />
|-<br />
|selection || red=selected locations, blue=selected ranges or shapes, black=others<br />
|-<br />
|black || all shown in black<br />
|-<br />
|pale || all shown in light grey<br />
|-<br />
|sex || red=F, blue=M, black=?<br />
|-<br />
|pale sex || pink=F, cyan=M, light grey=?<br />
|-<br />
|range || coloured according to range number, 24 potential colours<br />
|-<br />
|age || 12 potential colours<br />
|-<br />
|range month || 12 colours, spring green, summer red, autumn purple, winter blue<br />
|-<br />
|range year || 24 colours<br />
|-<br />
|lqv || the option to colour locations by any of the Location Qualifying Variables in the file. If an LQV is chosen containing HH (hour) data then the colour scheme is set for 24 hours (morning green, midday red, afternoon purple, night blue, midnight nearly black). If not then a colour scheme of 24 colours is used.<br />
|}<br />
<br />
=== Animation attributes ===<br />
<br />
Two option boxes control the animation, the first determines the number of locations displayed at one time, the second controls the speed of the animation.<br />
<br />
=== Background maps ===<br />
<br />
Any Ranges spatial file can be used as a background (location, edge, vector, raster or image). Ranges raster maps, vector shape files and images will be displayed in colour, other files will be displayed in grey. Note that a raster map cannot be used as a background for another raster map.<br />
<br />
Open the background by clicking the '''open''' button in the '''Background''' panel and selecting a file. The background can be removed with the '''close''' button.<br />
<br />
Background maps can be used to compare range edges to the locations they were created from, or for comparing range edges created by different methods.<br />
<br />
==== Background opacity ====<br />
<br />
This option box allows you to choose how bright a background map should be displayed. There are four options: ''full'' displays the map with its full colours, ''faded'' and ''faint'' display it progressively fainter and ''hide'' hides the background altogether.<br />
<br />
==== Background selected only ====<br />
<br />
This option is available for edge and location files and allows you to display just those parts of the background file relating to the currently selected range. For example if you open an edge file as the primary file and the location file used to create it as the background, then choose ‘display 1st selected range’, tick ‘selected only in background’, you will be able to scroll down through the range edges, and see just the locations used to create each. Similarly you can load an edge file created by a different method as the background, and look at the differences for each range in turn.<br />
<br />
This is the default option following location analyses that create edge files, however it only becomes visible if the two files contain the same number of ranges, so will not be available if you used range selections in the creation of your edge file.<br />
<br />
==== Background clipping ====<br />
<br />
This option is available for edge files with a raster or vector shape background map. When ticked only those areas of habitat within the ranges are displayed. This is the default option following analysis of ‘habitat in ranges’.<br />
<br />
=== Map snapshots ===<br />
<br />
The map image can be copied to the clipboard or saved as an image file. To do this, size the Map Display panel to the size you require the image by dragging the main window borders and the central divider into position then click '''Save map''' button (with a picture of a floppy disk). An option window will appear allowing you to select to copy to the clipboard (for pasting into a Word document or elsewhere) or to save to an image file, PNG, JPEG, GIF or bitmap. PNG (portable network graphics) files have the best quality to size ratio. If you chose to save as a file you will be prompted to provide a file name. You can also chose to remove the scale bar from the image (all other Map Display furniture such as the option boxes and zoom/pan controls will be removed from the image).</div>Adminhttps://ranges-support.anatrack.com/wiki/BibliographyBibliography2014-11-04T15:06:26Z<p>RobertKenward: </p>
<hr />
<div><b>Aebischer, N.J., Robertson, P.A. and Kenward, R.E. 1993.</b> Compositional analysis of habitat use from animal radio-tracking data. <i>Ecology 74 (5): 1313-1325.</i> <br />
<br />
<b>Anderson, D.J. 1982.</b> The home range: a new non-parametric estimation technique. <i>Ecology 63:103-112.</i><br />
<br />
<b>Burt, W.H. 1943.</b> Territoriality and home range concepts as applied to mammals. <i>Journal of Mammalogy 24, 346-352.</i><br />
<br />
<b>Calhoun, J.B. and Casby, J.U. 1958.</b> Calculation of Home Range and Density of Small Mammals. <i>United States Public Health Service, Public Health Monograph 55.</i><br />
<br />
<b>Clarke, P.J. and Evans, F.C.1954.</b> Distance to nearest neighbour as a measure of spatial relationships in populations. <i>Ecology 35:445-453.</i><br />
<br />
<b>Cox, D.R. and Oakes, D. 1984.</b> <i>Analysis of survival data. Chapman and Hall, New York.</i><br />
<br />
<b>Dalke, P.D. and Sime, P.R. 1938.</b> Home and seasonal ranges of the eastern cottontail in Connecticut. <i>Transcripts of the North American Wildlife Conference 3: 659-669.</i><br />
<br />
<b>Dixon, K.R. and Chapman, J.A. 1980.</b> Harmonic mean measure of animal activity areas. <i>Ecology 61:1040-1047.</i><br />
<br />
<b>Don, B.A.C. and Rennolls, K. 1983.</b> A home range model incorporating biological attraction points. <i>Journal of Animal Ecology 52:69-81.</i><br />
<br />
<b>Ford, G. and Krumme, D.W. 1979.</b> The analysis of space use patterns. <i>Journal of Theoretical Biology 76:125-155.</i> <br />
<br />
<b>Getz, W.M. and Wilmers, C.C. 2004.</b> A local nearest-neighbor convex-hull construction of home ranges and utilization distributions. <i>Ecography 27: 489-505.</i><br />
<br />
<b>Getz, W.M., Fortmann-Roe, S., Scott, P.C., Lyons, A.J., Ryan, S.J. and Wilmers, C.C. 2007.</b> LoCoH: nonparameteric kernel methods for constructing home ranges and utilization distributions. <i>PLoS ONE 2(2): e207. doi:10.1371/journal.pone.0000207.</i><br />
<br />
<b>Glendinning, R.H. 1991.</b> The convex hull of a dependent vector-valued process. <i>Journal of Statistical Computation and Simulation 38: 219-237.</i><br />
<br />
<b>Hartigan, J.A. 1987.</b> Estimation of a convex density contour in two dimensions. <i>Journal of the American Statistical Association 82:267-270.</i><br />
<br />
<b>Harvey, M.J. and Barbour, R.W. 1965.</b> Home range of Microtus ochrogaster as determined by a modified minimum area method. <i>Journal of Mammalogy 46:398-402.</i><br />
<br />
<b>Harris, S., Cresswell, W.J., Forde, P.G., Trewella, W.J., Woollard, T. and Wray, S. 1990.</b> Home-range analysis using radio-tracking data - a review of problems and techniques particularly as applied to the study of mammals. <i>Mammal Review 20:97-123.</i><br />
<br />
<b>Harrison, J.L. 1958.</b> Range and movements of some Malayan rats. <i>Journal of Mammalogy 39: 190-206.</i><br />
<br />
<b>Hayne, D.W. 1949.</b> Calculation of size of home range. <i>Journal of Mammalogy 30:1-18.</i> <br />
<br />
<b>Hemson, G., Macdonald, D.W., Ginsberg, J., Kenward, R.E., Ripley, R. and South, A.B. 2005.</b> Are Kernels the mustard, an assessment of kernel home range estimators using GPS data from lions. <i>Journal of Animal Ecology. 74 (3), 455-463.</i><br />
<br />
<b>Hodder, K. H., Kenward, R. E., Walls, S. S. and Clarke, R. T. 1998.</b> Estimating core ranges: a comparison of techniques using the common buzzard (Buteo buteo). <i>Journal of Raptor Research 32(2): 82-89.</i><br />
<br />
<b>Hodder, K. H., Masters J.E.G., Beaumont W.R.C., Welton, J.S., Kenward, R.E., Pinder A C. and Gozlan R.E. 2007.</b> Techniques for evaluating the spatial behaviour of river fish. <i>Hydrobiologia 582:257-269.</i><br />
<br />
<b>Jacobs, J. 1974.</b> Quantitative measurements of food selection. <i>Oecologia 14:413-417.</i><br />
<br />
<b>Jamsa, K. 1991</b> DOS: the complete reference. <i>3rd Ed. Osbourne McGraw-Hill, London.</i><br />
<br />
<b>Jennrich, R.J. and Turner, F.B. 1969.</b> Measurement of non-circular home range. <i>Journal of Theoretical Biology 22:227-237.</i> <br />
<br />
<b>Johnstone, I.G. 1992.</b> Home range utilization and roost selection by non-breeding territorial European robins (Erithacus rubecula). <i>In "Wildlife Telemetry - Remote Monitoring and Tracking of Animals" (I.G. Priede, and S.M. Swift, eds), 495-509. Ellis Horwood, Chichester, UK.</i><br />
<br />
<b>Kaplan, E.L. and Meier, P. 1958.</b> Nonparametric estimation from incomplete observations. <i>Journal of the American Statistical Association 53, 457-481.</i><br />
<br />
<b>Kenward, R.E. 1982.</b> Goshawk hunting behaviour, and range size as a function of habitat availability. <i>Journal of Animal Ecology 51:69-80.</i><br />
<br />
<b>Kenward, R.E. 1987.</b> <i>Wildlife radio tagging: equipment, field techniques and data analysis. Academic Press, London, UK.</i><br />
<br />
<b>Kenward, R.E. 1992.</b> Quantity versus quality: programmed collection and analysis of radio-tracking data. <i>pp. 231-246 in I.G. Priede and S.M.Swift (Eds). Wildlife telemetry: remote monitoring and tracking of animals. Ellis Horwood, Chichester, UK.</i><br />
<br />
<b>Kenward, R.E. 2001.</b> <i>A Manual for Wildlife Radio Tagging. Academic Press, London, UK.</i><br />
<br />
<b>Kenward, R. E., Clarke, R. T., Hodder, K. H. and Walls, S. S. 2001.</b> Distance and density estimators of home range: Defining multi-nuclear cores by nearest neighbor clustering. <i>Ecology 82(7): 1905-1920.</i> <br />
<br />
<b>Kenward, R.E., Marcström, V. and Karlbom, M. 1993.</b> Post-nestling behaviour in goshawks, Accipiter gentilis. II. Sex differences in sociality and nest switching. <i>Animal Behaviour 46:371-378.</i><br />
<br />
<b>Kenward, R.E., Marcström, V. & Karlbom, M. 1999.</b> Demographic estimates from radio-tagging: models of age-specific survival and breeding in the goshawk. <i>Journal of Animal Ecology 68:1020-1033.</i><br />
<br />
<b>Knight, C.M., Kenward, R.E., Gozlan, R.E., Hodder, K.H., Walls, S.S. & Lucas, M.C. 2009.</b> Home range estimation within a restricted linear environment: importance of method selection in detecting seasonal change. <i>Wildlife Research 36: 213–224.</i><br />
<br />
<b>Larkin, R.P. and Halkin, D. 1994.</b> A review of software packages for estimating animla home ranges. <i>Wildlife Society Bulletin 22:274-287.</i><br />
<br />
<b>Macdonald, D.W., Ball, F.G. and Hough, N.G. 1980.</b> The evaluation of home range size and configuration using radio tracking data. <i>A Handbook on Biotelemetry and Radio Tracking (C.J. Amlaner and D.W. Macdonald, eds), 405-424. Pergamon Press, Oxford, UK.</i><br />
<br />
<b>Michener, G.R. 1979.</b> Spatial relationships of adult Richardson’s ground squirrels. <i>Canadian Zoology 57, 125-139.</i><br />
<br />
<b>Otis, D.L. and White, G.C. 1999.</b> Autocorrelation of location estimates and the analysis of radiotracking data. Journal of Wildlife Management 63, 1039-1044.</i><br />
<br />
<b>Pollock, K.H., Winterstein, S.R., Bunck, C.M. and Curtiss, P.D. 1989.</b> Survival analysis in telemetry studies: the staggered entry design. <i>Journal of Wildlife Management 53, 7-14.</i><br />
<br />
<b>Robertson, P.A., N.J. Aebischer, R.E. Kenward, I.K. Hanski and N.P. Williams. (1998).</b> Simulation and jack-knifing assessment of home-range indices based on underlying trajectories. <i>Journal of Applied Ecology 35, 928-940.</i><br />
<br />
<b>Spencer, W.D. and Barrett, R.H. 1984.</b> An evaluation of the harmonic mean method for evaluating carnivore activity areas. <i>Acta Zoologica Fennica 171:255-259.</i> <br />
<br />
<b>Swihart, R.K. and Slade, N.A. 1985.</b> Testing for independence of observations in animal movements. <i>Ecology 66:1176-1184.</i><br />
<br />
<b>Voight, D.R. & Tinline, R.R. 1980.</b> Strategies for analysing radio-tracking data. <i>pp.387-404 in Amlaner, C.J. & Macdonald, D.W. (eds) A Handbook on Biotelemetry and Radio Tracking, Pergamon Press, Oxford, UK.</i><br />
<br />
<b>Walls S.S. and Kenward R.E., 1995.</b> Movements of radio-tagged Common Buzzards Buteo buteo in their first year. <i>Ibis 137: 177-182.</i><br />
<br />
<b>Walls S.S. and Kenward R.E., 1998.</b> Movements of radio-tagged common buzzards in early life. <i>Ibis 140: 561-568.</i><br />
<br />
<b>Walls S.S. and Kenward R.E. 2001.</b> Spatial consequences of relatedness and age in buzzards. <i>Animal Behaviour 61: 1069-1078.</i> <br />
<br />
<b>White, G.C. and Garrott, R.A. 1990.</b> <i>Analysis of wildlife tracking data. Academic Press, New York, USA.</i><br />
<br />
<b>Worton, B.J. 1989.</b> Kernel methods for estimating the utilisation distribution in home range studies. <i>Ecology 70:164-168.</i><br />
<br />
<b>Worton, B.J. 1995a.</b> A convex hull-based estimator of home-range size. <i>Biometrics 51, 1206-1215.</i><br />
<br />
<b>Worton, B.J. 1995b.</b> Using MonteCarlo simulation to evaluate kernel-based home-range estimators. <i>Journal of Wildlife Management 59, 794-800.</i><br />
<br />
<b>Wray, S., Cresswell, W.J. and Rogers, D. 1992.</b> Dirichlet tessellations: a new, non-parametric approach to home range analysis. <i>pp 247-255 in Priede & Swift.</i></div>Adminhttps://ranges-support.anatrack.com/wiki/ContentsContents2014-11-04T13:51:28Z<p>Admin: </p>
<hr />
<div># [[Main Page|Anatrack Ranges]]<br />
## [[Main Page#Introduction|Introduction]]<br />
## [[Main Page#New Features|New Features]]<br />
## [[Main Page#Installing Ranges|Installing Ranges]]<br />
## [[Main Page#Licensing Ranges|Licensing Ranges]]<br />
## [[Main Page#Using Ranges|Using Ranges]]<br />
## [[Main Page#Citing Ranges|Citing Ranges]]<br />
# [[Ranges Overview|Ranges Overview]]<br />
# [[Input & Graphics|Input & Graphics]]<br />
## [[Input & Graphics#Introduction|Introduction]] <br />
## [[Input & Graphics#Open|Open]] <br />
## [[Input & Graphics#New|New]] <br />
## [[Input & Graphics#Import|Import]]<br />
## [[Input & Graphics#Modify|Modify]]<br />
## [[Input & Graphics#Export|Export]]<br />
## [[Input & Graphics#Save|Save]]<br />
## [[Input & Graphics#Data Tables|Data Tables]]<br />
## [[Input & Graphics#Map Display|Map Display]]<br />
# [[Location Analysis|Location Analysis]] <br />
## [[Location Analysis#Introduction|Introduction]] <br />
## [[Location Analysis#Inter-location Measures|Inter-location Measures]] <br />
## [[Location Analysis#Convex Polygons|Convex Polygons]] <br />
## [[Location Analysis#Concave Polygons|Concave Polygons]] <br />
## [[Location Analysis#Neighbour Linkage|Neighbour Linkage (OREP)]] <br />
## [[Location Analysis#Ellipses|Ellipses]] <br />
## [[Location Analysis#Harmonic Mean Contours|Harmonic Mean Contours]] <br />
## [[Location Analysis#Kernel Contours|Kernel Contours]] <br />
## [[Location Analysis#Midline Analyses (Interlocation, Linear Ranges and Clusters)|Midline Analyses]] <br />
# [[Range Use Plots|Range Use Plots]] <br />
## [[Range Use Plots#Introduction|Introduction]] <br />
## [[Range Use Plots#Utilisation Plots|Utilisation Plots]] <br />
## [[Range Use Plots#Incremental Area Analysis Plots|Incremental Area Analysis Plots]]<br />
# [[Overlap Analysis|Overlap Analysis]] <br />
## [[Overlap Analysis#Introduction|Introduction]]<br />
## [[Overlap Analysis#Range Overlap|Range Overlap]] <br />
## [[Overlap Analysis#Overlap Of Ranges On Locations|Overlap Of Ranges On Locations]] <br />
# [[Interaction Analysis|Interaction Analysis]] <br />
## [[Interaction Analysis#Introduction|Introduction]] <br />
## [[Interaction Analysis#Autocorrelations|Autocorrelation Analysis]]<br />
## [[Interaction Analysis#Dynamic interactions|Dynamic Interaction Analysis]] <br />
## [[Interaction Analysis#Location-point distances|Location-point Distances]] <br />
## [[Interaction Analysis#Range centre spacing|Range Centre Spacing]]<br />
# [[Habitat Analysis|Habitat Analysis]]<br />
## [[Habitat Analysis#Introduction|Introduction]]<br />
## [[Habitat Analysis#Habitat content of a map|Habitat Content Of A Map]]<br />
## [[Habitat Analysis#Habitat content of ranges |Habitat Content Of Ranges]]<br />
## [[Habitat Analysis#Points within ranges|Points Within Ranges]]<br />
## [[Habitat Analysis#Habitat at locations|Habitat At Locations]]<br />
## [[Habitat Analysis#Habitat preference in ranges|Habitat Preference Within Ranges]]<br />
## [[Habitat Analysis#Calculating areas|Calculating Areas]]<br />
# [[Modelling Analysis|Modelling Analysis]]<br />
## [[Modelling Analysis#Introduction|Introduction]]<br />
## [[Modelling Analysis#Resource Area Dependence Analysis|Resource Area Dependence Analysis]]<br />
## [[Modelling Analysis#Kaplan-Meier Survival|Kaplan-Meier Survival]]<br />
# [[Selections|Selections]]<br />
## [[Selections#Introduction|Introduction]] <br />
## [[Selections#Selecting Ranges|Selecting Ranges]] <br />
## [[Selections#Selecting Cores|Selecting Cores]] <br />
## [[Selections#Selecting Locations|Selecting Locations]] <br />
# [[Output Files|Output Files]] <br />
## [[Output Files#Introduction|Introduction]] <br />
## [[Output Files#Output filename codes for location analyses|Output Filename Codes]]<br />
# [[Large Data And Java Memory|Large Data And Java Memory]] <br />
# [[Tutorial|Tutorials]] <br />
## [[Demo Tutorial|Demo Tutorial]] <br />
## [[Tutorial|Full Tutorial]] <br />
# [[Glossary|Glossary]] <br />
# [[Bibliography|Bibliography]]</div>Adminhttps://ranges-support.anatrack.com/wiki/Inter-location_MeasuresInter-location Measures2014-10-14T07:40:48Z<p>RobertKenward: </p>
<hr />
<div>This allows you to calculate simple measures between locations or groups of locations. You need to load a location file, choose a measure, select between which locations you want that to be calculated and then press the ‘Run location analysis’ button. The analysis will run – producing a summary of its progress and output the results to a file. What happens after the run is finished depends on the options you have selected.<br />
<br />
If you select [[#FirstToLast|first to last]] or a [[#LocationInterval|location interval]] other than 1 then the output file will be displayed in the Statistics window.<br />
<br />
For other options, after running the analysis a plot for the first range will appear in a separate window, and the user interface will switch to Input & Graphics and display the location file with an extra column added containing the newly calculated attribute. To show later ranges in the plot, select them in the Ranges table. The currently selected location in the Locations table is displayed in red in the plot. Running subsequent analyses on the same file will result in multiple plot windows which can be viewed at the same time. This helps to discover how and when a juvenile disperses or an animal migrates, whether the distance moved is constant all day etc.<br />
<br />
==Headings==<br />
<br />
Results are given as headings in 360 degrees, [[#Between Which Locations|between a user-defined number of locations]]. <br />
<br />
==Distances==<br />
<br />
Results are given in metres either between locations, or from a focal site (e.g. a nest) to the location. When [[#FocalSiteToLocation|focal site to location]] is selected for distances the [[Inter-location Measures#Dispersal Detection|dispersal detection]] option is enabled.<br />
<br />
==Times==<br />
<br />
Note that this option is only enabled if your input file has [[File Types#Location Qualifying Variables|location qualifying variables]] defining times for each location (these must have labels containing combinations of YY, MO, DD, HH, MI, SS). Results are given in minutes. <br />
<br />
==Speeds==<br />
<br />
Note that this option is only enabled if your input file has [[File Types#Location Qualifying Variables|location qualifying variables]] defining times for each location (these must have labels containing combinations of YY, MO, DD, HH, MI, SS). Results are given in metres per hour. <br />
<br />
==Between Which Locations==<br />
<br />
<span id="FirstToLast"><I>span first to last</I></span> <br />
<br />
Calculate the chosen metric between the first and last location of each range. The value calculated is the sum of the result between each consecutive location in the range (with the exception of headings, which just calculates the heading between the first and last locations).<br />
<br />
<span id="LocationInterval"><I>location interval</I></span><br />
<br />
Allows you to input the location interval between which the metric is calculated. Entering 1 will result in calculations being made between all locations. For values greater than 1 the result will be the sum of the calculation between each consecutive location, e.g. if locations were taken at hourly intervals, a location interval of 24 could be used to estimate the daily distance moved.<br />
<br />
<span id="FocalSiteToLocation"><I>focal site to location</I></span><br />
<br />
Calculate the chosen metric between the focal site and each location. This option is not offered for times and speeds because the focal site doesn’t have an associated time value.<br />
<br />
When focal site to location is selected for distances the [[Inter-location Measures#Dispersal Detection|Dispersal detection]] option is enabled.<br />
<br />
==Dispersal Detection==<br />
<br />
This option is only available when distances and focal site to location are selected, and when your input file has [[File Types#Location Qualifying Variables|location qualifying variables]] defining times for each location (these must have labels containing combinations of YY, MO, DD, HH, MI, SS). This latter requirement is because the question you are asking is "when did dispersal occur?".<br />
<br />
In this options two attribute columns will be added to the loc file, the first contains the distance from the site to the location, and the second whether the individual is classed as having dispersed yet (0=no, 1=yes). This enables easy splitting of files into before and after dispersal using [[Input & Graphics#Modify|modify]], or estimation of home range areas before and after dispersal by using the [[Selections|Make Selections]] button in Location Analysis.<br />
<br />
<b>Minimum dispersal distance (m)</b> <br />
<br />
Set minimum distance to recognise dispersal. It is important to set a value such as 10 m for birds with initial locations in a nest, to prevent premature triggering when they leave the nest. Alternatively, if you would not classify a bird within 1000 m of the nest as a disperser, set this distance (see [[Bibliography|Walls & Kenward 1995, 1998]]).<br />
<br />
<b>Alpha for dispersal detection</b><br />
<br />
<i>none</i><br />
<br />
Dispersal is classified as having occurred when the distance from the focal site is exceeded.<br />
<br />
<i>1%, 5% or 10%</i><br />
<br />
The stochastic dispersal detection starts with the first 3 locations, and estimates their arithmetic mean centre (Ac), and their confidence limit for distance from the Ac, using the relevant probability level. It then estimates the arithmetic mean for the next n locations in the data set, where n is usually 3 (but see below), and defines the "dispersal" vector (i.e. average direction of dispersal) between these. Dispersal is recognised if the orthogonal distances of the n locations along this vector are all outside the confidence limit for the first N (i.e. 3). If not, N increments by 1, and the process repeats through the set of locations. <br />
<br />
The use of a dispersal vector means that dispersal will not be recognised erroneously if an animal makes consecutive excursions in different directions. However, dispersal may be recognised late if the animal makes an initial movement in one direction, and then departs in another direction. The estimation is only rigorous with N+n locations where n > 3. Missing values in the dataset (recorded as -9, -9), are assumed to represent occasions when the animal was sought but not found, and was outside the confidence limit for the current N. Dispersal is, therefore, recognised with a minimum of 4 (3+1) locations if coordinates for the last location are -9, -9. It is advisable to set a minimum distance as well as a probability level. This will prevent premature detection of dispersal (e.g. when a bird leaves a nest after several records there, instead of leaving the natal area).<br />
<br />
After running Dispersal detection, the screen will display a distance/time plot with a vertical red line just after the appropriate location.<br />
<br />
==Add Stats To Loc File==<br />
<br />
If you select the <b>Add stats to loc file option</b> in '''Output Files''' the new location file with the added attribute column will be saved with a new name. This enables the attribute to be used as a [[File Types#Location Qualifying Variables|location qualifying variables]] in subsequent analyses.</div>Adminhttps://ranges-support.anatrack.com/wiki/Location_AnalysisLocation Analysis2014-10-14T07:18:29Z<p>RobertKenward: </p>
<hr />
<div>This panel provides a number of analyses of location data, which are used either to provide simple [[#Inter-location Measures|inter-location measures]], including dispersal detection, or to estimate home ranges. A wide variety of different methods of range analysis have been published. Ranges implements those that have been used to produce refereed publications beyond a first description, including [[#Convex Polygons|convex polygons]] (sometimes called minimum convex polygons, MCPs), [[#Concave Polygons|concave polygons]] (with restricted edges), polygons in [[#Clusters|clusters]], [[#Ellipses|ellipses]] and contouring from [[#Harmonic Means|harmonic means]] and other density [[#Kernels|kernels]]. Grid cells may be plotted by selecting options within [[#Concave Polygons|concave polygons]]. <br />
<br />
The pros and cons of these different techniques are discussed in more detail in the [[Review Of Home Range Analyses|Review Of Home Range Analyses]] and for a more comprehensive recent review, see “A Manual for Wildlife Radio Tagging” ([[Bibliography|Kenward 2001]]) and Kenward et al. ([[Bibliography|2001]]). <br />
<br />
Below you will find a brief outline of each technique and a link to a description of how it is implemented in Ranges.<br />
<br />
==Inter-location Measures==<br />
<br />
These are useful for providing plots and summary statistics from continuous recording sessions or from tags that regularly record GPS locations. Other applications include the estimation of indices of daily distances and speeds for animals tracked intermittently, and the investigation of dispersal, including a dispersal detector. <br />
<br />
More on this topic can be found here: [[Inter-location Measures|Inter-location Measures]].<br />
<br />
==Convex Polygons==<br />
<br />
Convex polygons have external angles which are all greater than 180º. Minimum convex polygons (MCPs) are the smallest of such polygons which can be drawn around a set of locations. The outermost of these, which includes 100% of the locations, has been widely used to define ranges. It is therefore useful for comparisons, even though its area and shape are heavily influenced by outlying locations.<br />
<br />
More on this topic can be found here: [[Convex Polygons|Convex Polygons]].<br />
<br />
==Concave Polygons==<br />
<br />
Concave polygons (or "restricted edge polygons"), can be used to eliminate large areas that are not visited, such as lakes at the edges of areas used by terrestrial animals. Lines are only drawn between edge locations if they are shorter than a selected fraction of the range width. This makes the range concave where linkages between edge fixes are long.<br />
<br />
More on this topic can be found here: [[Concave Polygons|Concave Polygons]].<br />
<br />
==Ellipses==<br />
<br />
Ellipses, usually estimated to include 95% of the location density distribution, are another long-standing technique. They do not define range shape well but require few locations to reach a maximum area estimate and are therefore useful for identifying habitat available to animals that cannot be tracked frequently. <br />
<br />
More on this topic can be found here: [[Ellipses|Ellipses]].<br />
<br />
==Neighbour Linkage (Cluster analysis, OREPs)==<br />
<br />
Neighbour linkage or cluster analysis is particularly good for eliminating outliers and separating range cores. This can identify patchiness in range use, for instance where the study animal forages in several separate areas. Convex polygons are used to provide an outline around these cores, that are calculated by looking at the distances between all locations rather than the distances from one centre.<br />
<br />
More on this topic can be found here: [[Clusters|Neighbour Linkage]].<br />
<br />
==Harmonic Mean Contours==<br />
<br />
The harmonic mean model ([[Bibliography|Dixon & Chapman 1980]]) estimates the location density distribution (equivalent to the probability of encountering the animal) at intersections of an estimation matrix. The density function used is the reciprocal of the mean inverse distances of all the locations from each intersection. Contours containing a specified percentage of actual locations or estimated location density are then interpolated across the matrix. Harmonic mean contours are sensitive to intersection spacing unless the analysis centres locations between intersections or has at least two intersections per unit of tracking resolution, but provide contours that are least sensitive to outlying locations and most precise in fitting core locations.<br />
<br />
More on this topic can be found here: [[Harmonic Mean Contours|Harmonic Mean Contours]].<br />
<br />
==Kernel Contours==<br />
<br />
Strictly speaking, kernel analyses include the harmonic mean approach. Worton ([[Bibliography|1989]]) introduced the concept of using a function with a negative exponential term for distances of locations from intersections of the estimation matrix. This is a mathematically robust kernel that produces more consistent results than harmonic mean contouring, but is more sensitive to outlying locations<br />
<br />
Location densities at matrix intersections are derived using a bivariate normal kernel estimator. This is less matrix-dependent than the harmonic mean function, so there is no need to centre locations between intersections or have very large matrices: the result is very similar for 20x20 and 100x100 grids. With relatively low smoothing, stable size estimates can be obtained with 15-20 locations.<br />
<br />
More on this topic can be found here: [[Kernel Contours|Kernel Contours]].<br />
<br />
==Midline Analyses (Interlocation, Linear Ranges and Clusters)==<br />
<br />
Midline analyses assess the distance between locations using the [[Midline Analysis#How does Ranges calculate midline distances ?|route along a user-defined line]], rather than the shortest route between them as in all other analyses. This was developed principally for fish in rivers but could be useful for riparian species or other situations where movements are restricted to a simple network. The analyses require a [[Midline Analysis#Creating a midline file|midline file]] along which all distances will be measured, this needs to be a Ranges vector line file formatted correctly. [[Midline Analysis#Midline inter-location measures|Midline inter-location]] measures distances and speeds along the line. [[Midline Analysis#Midline linear ranges|Midline linear ranges]] finds the maximum linear extents from all of the locations in a range, and produces a linear range which is a subsection of the input midline file. [[Midline Analysis#Midline cluster analysis|Midline clusters]] was developed as a potential means of estimating the home range areas of fish in rivers using the same methodology as for cluster analysis, but with all distances calculated along the midline rather than the shortest route between locations. Publications using midline techniques include [[Bibliography|Hodder et al. (2007]]) and [[Bibliography|Knight et al. (2009]]).<br />
<br />
More on this topic can be found here: [[Midline Analysis|Midline Analysis]].</div>Adminhttps://ranges-support.anatrack.com/wiki/Ranges_OverviewRanges Overview2014-10-14T07:11:47Z<p>RobertKenward: </p>
<hr />
<div>Ranges now opens with what was known as the [[Input & Graphics|Input & Graphics]] screen in previous versions where users can create, import, modify and export data and view both input and output data numerically and graphically. Types of files that can be viewed in Ranges are summarised here: [[File Types|File Types]]<br />
<br />
Buttons along the top allow the user to access the other areas of Ranges, opening windows that can be moved and resized, as follows:<br />
<br />
* '''About''' gives details on your licence, the Ranges version and how to contact us. <br />
* [[Location Analysis|'''Location''']] performs analyses on location files, including inter-location measurements and estimates of home ranges based on concave and convex polygons, ellipses, cluster analysis and harmonic mean and kernel contouring. It also provides [[Midline Analysis|midline analysis]] used particularly for analysing the movements of fish in rivers.<br />
* [[Overlap Analysis|'''Overlap''']] analyses and displays the overlap of range edges created in Location Analysis. There are options to create and view an overlap matrix which records the overlap of the ranges within an edge file.<br />
* [[Interaction Analysis|'''Interaction''']] analyses spatial interactions between animals or between patterns of locations (spatial and temporal) for individual animals.<br />
* [[Habitat Analysis|'''Habitat''']] analyses the habitat content of maps, map sections, home ranges created in ‘Location analysis’, and in the areas immediately around locations. <br />
* [[Modelling Analysis|'''Modelling''']] allows resource area dependence and Kaplan Meier survival analyses.<br />
* '''Statistics''' displays the statistics output file for the most recently run analysis. Other statistics files, in comma-separated variable (''.csv'') format can be opened here using the '''open''' button.<br />
* '''Log''' shows the log for all analyses since the session started.<br />
* '''Help|Help''' links to these on-line help files. Note that a set of help files is also packaged with Ranges but these are not as up-to-date as those online.<br />
<br />
When an analysis is run, a '''Progress''' window will popup giving progress messages, calculation summaries, warnings and errors. The progress window scrolls down automatically by default as it is filled; if you want to read an entry earlyier in the window, check the '''Disable autoscroll''' box to enable the scrollbars. If the analysis runs successfully, the progress window will close and the results, usually '''Statistics''', often map data in [[Input & Graphics|the main window]] and sometimes a chart, will be displayed. The contents of the Progress window can be accessed after the analysis in the '''Log'''.<br />
<br />
Settings changed in analysis windows and files opened in the statistics window will remain unchanged if the window is closed and then re-opened. <br />
<br />
Work through the [[Tutorial|tutorial]] for an introduction to getting your data into Ranges and starting to analyse it.</div>Adminhttps://ranges-support.anatrack.com/wiki/Main_PageMain Page2014-08-28T13:26:54Z<p>Admin: /* Offline Support */</p>
<hr />
<div>Ranges is a comprehensive system for viewing, editing and analysing spatial location data. The current version is Ranges 9.<br />
<br />
== Introduction ==<br />
<br />
The Ranges suite of software has been evolving since the early 1980s, not merely as a convenient way to analyse the extensive data that can be gathered from radio-tagged, and increasingly gps-tagged, animals, but also to assist planning that makes the effort of data-gathering as efficient as possible. Early versions were written for the BBC micro but Ranges now has Windows and Apple Macintosh interfaces to help you to set up and run analyses. <br />
<br />
Ranges includes comprehesive tools to allow you to input, view and export your animal location data. Not only does it have a long list of <b>location</b> analyses for estimating home range outlines but also supports analyses of how these outlines <b>overlap</b> and the dynamic <b>interaction</b> of location records with those of other animals or of sites that are important for feeding or sociality. It allows you to investigate availability and use of <b>habitat</b> in different ways and more recently adds <b>modelling</b> techniques to its tool box. <br />
<br />
== New Features ==<br />
<br />
Ranges 9 includes<br />
<br />
* Resource Area Dependence analysis<br />
* Kaplan-Meier Survival analysis<br />
* An up-to-date look with a more user-friendly interface but without loosing the familiar Ranges feel<br />
* Better data and map split positioning on screen with an adjustable divider to optimise space for each<br />
* Map rendering improvements: both faster and with fewer artefacts<br />
* Improved map furniture: coordinates, scale bar, zoom and pan controls, etc.<br />
* Zoom to selection with right mouse button<br />
* Zoom in and out to cursor with mouse scroll button<br />
* Ability to create locations and vector points with CTRL + left mouse click, remove from the end with CTRL + right mouse click<br />
* Improved data charts for utilization and incremental files, autocorrelation & interlocation analyses with axis-scaling, and better labels & headings<br />
* Ability to make map backgrounds paler to improve visibility of foreground material <br />
* Faster file loading <br />
* Huge file handling including large location files from GPS devices and large raster maps<br />
* Importing locations in latitidue-longitude format<br />
* Exporting to KML using lat-lng<br />
* Location file merging; edge file sampling and merging<br />
* Display locations and analysis maps on Google Maps<br />
* Save maps and plots to image file<br />
* Range overlap analysis map output<br />
<br />
For new features added to each version of Ranges see: [[New Features|New Features]]<br />
<br />
== Installing Ranges ==<br />
<br />
Ranges can be installed and run without purchasing a licence. Until it is licensed it will run in <b>demo</b> mode.<br />
<br />
To install Ranges:<br />
<br />
1. Install the latest version of the Java Runtime Environment (JRE). To do this, download the version suitable for your operating system here:<br />
<br />
https://java.com/en/download/manual.jsp<br />
<br />
<div style="color: red; font-weight:bold;border: solid 0px grey; padding-left:0px;">Note that if you are using a 64 bit version of Windows, you must install the 64 bit version of Java.</div><br />
<br />
2. Install Ranges by downloading and running the version suitable for your operating system from:<br />
<br />
http://www.anatrack.com/download_ranges.php<br />
<br />
Uninstall Windows Ranges through the '''Control Panel...Programs''' and Mac Ranges by deleting the folder in Programs.<br />
<br />
== Licensing Ranges ==<br />
<br />
To unlock and use the analyses in Ranges, you must purchase a licence depending on whether the software is to be used by only you (Single User licence) or by a number of people in the same organisation (Site licence).<br />
<br />
<b>Single User</b> licences allows Ranges to be installed on two computers, e.g. for use on one in a lab and another in the field, provided both computers are not to be used at the same time.<br />
<br />
A <b>Site</b> licence allows Ranges to be installed on up to ten computers. If you need to install Ranges on more than ten computers, please [http://www.anatrack.com/contact.php contact us].<br />
<br />
Licences can be purchased here:<br />
<br />
http://www.anatrack.com/buy_ranges.php<br />
<br />
after which we will supply a licence key.<br />
<br />
Once you have a licence key and have installed Ranges, navigate to the Ranges folder in Anatrack Ltd in your applications folder. Click on the License Ranges shortcut and follow the instructions on screen. When prompted enter the licence number.<br />
<br />
== Using Ranges ==<br />
<br />
An overview of Ranges functionality with links to more detailed descriptions of analyses and features can be found here: [[Ranges Overview|Ranges Overview]]<br />
<br />
If you are new to Ranges, please work through [[Tutorial|the tutorial]].<br />
<br />
== Citing Ranges ==<br />
<br />
Please use the following citation in publications:<br />
<br />
<i><b>Kenward, R.E., Casey, N.M., Walls, S.S. & South A.B. (2014) Ranges9 : For the analysis of tracking and location data. Online manual. Anatrack Ltd. Wareham, UK.</b></i><br />
<br />
== Offline Support ==<br />
<br />
If you will be using Ranges where you do not have access to the Internet, this support wiki can be downloaded to your computer here:<br />
<br />
http://www.anatrack.myzen.co.uk/Ranges9/Anatrack_Ranges9_Support.zip<br />
<br />
To use it, unzip it to a suitable location, keeping the folder structure as it is. The wiki pages are accessed through <br />
<br />
<i><InstallationFolder>\Anatrack Ranges 9 Support\ranges_support.html</i></div>MediaWiki default