HRT: Home Range Tools for ArcGIS User's Manual Draft July 27, 2011
Contents
1. 27 07 11 1 Introduction This manual is intended to accompany the Home Range Tools HRT for the ArcGIS 9 x Geographic Information System GIS The manual has been written for novice GIS users who already understand basic wildlife telemetry issues and who are familiar with the concept of a home range The HRT contains software that extends ArcGIS to analyze home ranges of animals This is accomplished within a GIS which provides a common and relatively familiar interface for analyses performed on telemetry fixes and home range polygons The user should be able to perform all the analyses of their point data within ArcGIS and the HRT What is Home Range Analysis Field studies of animals commonly record the locations where individuals are observed In many cases these point data often referred to as fixes are determined by radio telemetry These data may be used in both basic and applied contexts The information may be used to test basic hypotheses concerning animal behaviour resource use population distribution or interactions among individuals and populations Location data may also be used in conservation and management of species The problem for researchers is to det2. Worton 1989 To simplify comparisons the Ratio of the star nd StdDev y is 4 also shown for each case significant deviations of this 1 5 indicate data should be rescaled before any kernel method is applied T To rescale the data check the box next to Rescale to unit variance otherwise the original observations will be P F ulate the smoothing parameter by one of the ethe e Bandwidth tab see below 240 54 1248 34 1129 74 1 1 ing the data to have unit variance changes the co PM of the fixes by dividing each value of x and y by kernel or adaptive kernel methods of estimating a utilization distribution The fixed kernel approach assumes the width p Gaussian bivariate normal of the standard bivariate normal kernel placed at each observation is the same throughout the plane of the utilization distribution This can be problematic if there are hemes Keme De S outlying regions of low density because it is difficult to dialog box with Kernel Settings select a smoothing parameter that will accommodate these tab selected and showing sample size s
3. moves from point to point To explore the movements in a would like to use in the Source or Display Table o to it Only one shapefile at a e processed II the points in the active shapefile by right clicking on the file name then choosing Select All from the Selection fly out menu If you don t x gt want to use the entire shapefile you cat he Select Feature tool SS on the Tools Toolbar to drag a box around a subset f the points in the Data Vie ndow or use one of the options on the Selection menu to select subset of points as outline ve To start the process select Display Travel from the HRT Tools dr n menu A dialog box Figure 3 will pop up that allows you to control the display and provides information about the fixes When th isplay Travel dialog Figure 3 fir mi Display Travel appears the date and time
4. Exploratot Display ETENE Analyzing Fix Data Calculating Interfi reing Shapefiles aa TRER NR EAA AEEA N AEE TASA EETA 11 Generating Minimum Convex POPODSunnsenemdnesmiikodeanbnde niende 14 27 07 11 Area Added ccccsccsccescoscosccsccuscesceccescescesscsscuscescescessesccuscescescessescssscascscescescesscescuscscescess 14 Fixed Mean ccccescoscosccsccsccecceccescesccsscescescescssscascccescescessssscescesceccescescssscascuscescessescescescess 14 Fixed Median cccccsccsccsccesceccoscosccsccsccescescescescesccsscescuscescescessssscuscesccsscascascescessescuscescess 15 Qe LCM EEE TRE ME fe GERE EEE NT erani EE OE EEE TERESE TR ne Bre Ko ge ERE EE TE GE GE ERE ETE ENE SENESTE SERT 18 ake Output Options Tab rrrrrnnnrrrrrorernnnnrrrnnrsrnnnnnnnrrnnnssnrnnnnvnrnnnnsnennn ide Literature REE se Appendix 3D View of Utilization Distributions 06 sa
5. hef or that determined by LSCV h BCV hpcy2 We suggest it might be better to incrementally decrease or in ae proportion of h e asso vith individual data sets until the outermost isopleth breaks down or become inuous to determine a home range estimator For example decrease the value 0 o 0 95h then 0 9h 0 85 and so on until the outermost isopleth breaks the points up into disjunct clumps Once that happens backtrack to the previous proportion of h r that did not cause a break up of the range isopleth and use that as your smoothing parameter to define the home range boundary and obtain an area estimate This process can also be implemented from the bottom up i e start with a small proportion of h then increase it by some amount e g 0 05 until the outermost isopleth scientific sense It does not require the removal of duplicate locations Further the HRT is set up to facilitate the process on the Bandwidth tab check the radio button next to Proportion s of reference bandwidth type in several values for the proportions you would like to test or you can use the default values already in the box and the program will produce multiple outputs for each anim
6. between successive fixes so locations in your file must be associated with a linear time scale that provides a unique value for each point see Text Files and dBASE Files above and these values must be in a numeric field called Time2 otherwise this HRT option will not work Now select Calculate Travel Times and Distances from the HRT Tools dropdown menu HRT Toals This will add 6 new columns to the Attribute Table of the shapefile Figure 4 including elapsed time between consecutive Add Time and Distance x fixes cumulative time over which the points were recorded distances between consecutive fixes This will add the Following Fields to the selected layer cumulative distances and speed of movement T_Obs_Sec Time since last observation in seconds Oo T Cumul Time cumulative in seconds between locations in both m sec and mph Note that Bist Distance From last observation in meters Dist Cumul Distance cumulative in meters these fields will be added to the shapefile Me Per Sec Meters per second From last obs vi per Fr Mile ner hour Frantlageebe automatically without the need to save them as edits You can now generate some simple summary Yes Mo statistics by right clicking on the file name choosing Open Attribute Table right clicking on Figure 4 Time Distance and Speed of Movement teldsaddsdaashe Arbus the column you would like to analyze and selecting Table when the Calcul
7. J A and M A Horner 2008 Effect attern shape or e range estimates Journal of Wildlife Management 72 1813 1818 Fieberg J 2007a Kernel densi imators of hom Ecology 88 10 066 Fieberg J 2007b Utilizatio ibi estimation usin arison of least squares cross validation bandwidth options ildlife Society Bulletin 31 823 831 Harris S W J Cresswell P G Forde W J Trewhella T Woollard and S Wray 1990 Home range analysis using radio tracking data a review of problems and techniques particularly as applied to the study of mammals Mammal Review 20 97 123 Hemson G P Johnson A South R Kenward R Ripley and D Macdonald 2005 Are kernels the mustard Data from global positioning system GPS collars suggests problems for kernel home range analyses with least squares cross validation Journal of Animal Ecology 74 455 463 27 07 11 26 Horne J S and E O Garton 2006 Likelihood cross validation versus least squares cross validation for choosing the smoothing parameter in kernel home range analysis Journal of Wildlife Management 70 641 648 Jacques C N J A Jenks and R W Klaver 2009 Seasonal mov
8. Table 1 Attributes of data in a shapefile imported by the HRT to ArcGIS from a Lotek MDB file Attribute Description FID A unique ArcGIS identification number for each record Shape The type of ArcGIS feature represented by each record i e points CollarID GPS collar hexadecimal address for data downloading can be used by the HRT as a unique animal ID field Timel Time of day in AM PM format Date Date in day month year format Time2 Seconds since midnight 1 1 1970 FixStatus Type of GPS fix 2 dimensional or 3 dimensional differentially corrected DOP GPS Dilution of Precision ReceiverSt GPS receiver status Convergenc GPS satellite convergence Activity Average activity in predefined time period from sensor in GPS collar TTFF Time to acquire signals from at least 3 GPS satellites Temperatur Ambient temperature in C from sensor in GPS collar ARC INFO Coverages ARC INFO coverages can be added to the Data View in the normal ArcGIS way using the Add Data gt button or by selecting Add Data from the File menu Find the coverage and highlight it in the Add Data dialog box then click the Add button Other Information All sorts of other information can be imported into ArcGIS in a variety of ways The ArcGIS Desktop Help file and link to ArcGIS Desktop Help Online available on the Help menu are useful resources and a good place to start if you are not already familiar with ArcGIS 27 07 11 Editi
9. Why use ArcGIS Just as there has been a proliferation of home range analysis models there has been a proliferation of home range analysis software Characteristics of many software programs used to estimate animal home ranges are summarized by Larkin and Halkin 1994 and Lawson and Rodgers 1997 Most of these are older DOS based programs with a cumbersome interface that requires batch files or data manipulation to be carried out with text editors or database programs This results in a multi stage procedure that is time consuming and has the potential for error at each step in the process Because of their age most of the existing programs do not include some of the more recent home range models e g kernel methods Many of these programs do not allow export of home range polygons to a GIS for habitat analyses Of those that do some are limited to fewer than 1 000 animal locations in the home range analysis Although these limitations may be acceptable to studies involving conventional radio tracking of animals automated equipment such as GPS based telemetry systems Rodgers et al 1996 Rodgers 2001 can easily generate enormous quantities of data that cannot be entirely analyzed by these previous software programs The ability to use large data sets and carry out all required home range analyses within a single software environment was a primary reason for developing the HRT within ArcGIS Techniques Available in the HRT The HRT include
10. Selection menu You can now apply one of the methods available in the HRT Figure 5 to automatically Figure 5 MCP Dialog Box select the outer points to be used to generate an MCP Area Added This method drops points based on the amount of area they add to the home range polygon White and Garrott 1990 To begin a 100 MCP is calculated The points that form the polygon are then deleted one at a time After a point is removed a new MCP is constructed and its area is calculated The difference in area between the new MCP and the 100 MCP is determined The point is then restored and the next point is deleted After all boundary points are tested the polygon that had the greatest difference in area from the 100 MCP 1s identified and the associated point is dropped The polygon constructed without this point becomes the new polygon against which to test the remaining points This process continues until the requested percentage of points remains selected This procedure can be excruciatingly slow especially with many points Fixed Mean This method calculates the arithmetic mean of all x longitude and y latitude co ordinates then selects the requested percentage of points closest to that arithmetic mean point 27 07 11 14 Fixed Median This method calculates the median of all x longitude and y latitude co ordinates then selects the requested percentage of points closest to that median point Floating Mean This metho
11. box next to it then choose the analysis you would like to use from the HRT Tools dropdown menu HRT Tools When using the MCP Analysis option you will also need to select the points to include in the analysis you can analyze all the points in the active shapefile by right clicking on the file name then choosing Select All from the Selection fly out menu you can use the Select Feature tool 3 on the Tools Toolbar to drag a box around a subset of the points in the Data View window or use the Select By Attributes or Select By Location options on the Selection menu to select a subset of points as outlined above Calculating Interfix Times and Distances Although the Display Travel option allows you to step through selected points and visualize movement patterns you may want to calculate and save interfix distances and elapsed times as well as cumulative values for these variables With these data speed of movement can be determined and displayed along with the movement path when the Display Travel option is selected see above You 27 07 11 12 could also calculate average distance moved between fixes average elapsed time between fixes total distance moved in a given period and so on Select the shapefile that you would like to use and make it active by clicking on its name in the Source or Display Table of Contents The HRT uses a Time2 field to Calculate Travel Times and Distances
12. following steps outline the process of converting Geographic to UTM coordinates in ArcGIS Open ArcCatalog and navigate to the file that contains the locations to be converted Right click on the file name in the Contents window and select Create Feature Class then From XY Table In the X Field of the dialog box select the column containing the longitude data and in the Y Field of the dialog box select the column containing the latitude data Next click Coordinate System of Input Coordinates to specify the format of the original location data In the Spatial Reference Properties dialog box that now appears click on the Select button In the Browse for Coordinate System dialog box select the geographic coordinate system used for the original location data using the predefined Geographic Coordinate Systems e g click the Geographic Coordinate Systems folder then the North America folder and select North American Datum 1983 prj then click the Add button This brings you back to the Spatial Reference Properties dialog box where you should click the Apply button then the OK button Now Specify output shapefile or feature class by providing a path and name for the Output shapefile then click Advanced Geometry Options to specify the format of the location data in UTM units Check that the button next to Use a different spatial reference is selected then click Edit In the Spatial Re
13. isopleths Polygons provides the perimeter and the area square meters enclosed by each of the specified isopleths and Donut polygons determines the areas between isopleths if more than one has been specified 27 07 11 25 Literature Cited Ackerman B B F A Leban M D Samuel and E O Garton 1990 User s manual for program HOME RANGE Second ed Forest Wildlife and Range Experiment Station University of Idaho Moscow ID Tech Rep 15 80 pp Berger K M and E M Gese 2007 Does interference competition with wolves limit the distribution and abundance of coyotes Journal of Animal Ecology 76 1075 1085 SR Blundell G M J A K Maier and E M Debevec 2001 Linear home ranges effects of smoothing sample size and autocorrelation on kernel estimates Ecological M raphs 71 469 489 efor data a is the kernel rsity New York New Yor USA Bowman A W and A Azzalini 1997 Applied smoothing te approach with S PLUS illustrations Oxford Uni f y Burt W H 1943 Territoriality and home range concepts as applied to mals Journal of Mammalogy 24 346 352 Downs
14. lo reate a jle called HRT KDE ANALYSIS txt that contains diagnostics for eac nput pa ters and many intermediate values such as the bandwidth Isopleths of the utilizat ibution are created from the raster and outer isopleths e g 95 or 99 will typically extend beyond the boundaries of the area covered by the location data so it is usually necessary to add a buffer The HRT will estimate the extent of the required buffer and make a default suggestion In most cases the suggested value will be satisfactory but you can enter a new value in the original units i e meters if you wish You can also check the radio button next to Minimize the extent of each UD and no buffer will be added Instead the minimum and maximum x and y co ordinates of the input point layer will be used to define the extent of the output raster Combined with the Raster cell size and Scaling factor previously specified minimizing the extent of the utilization distribution or reducing the size of the buffer can cause a warning message to appear i e Warning It is possible that the extent of the raster was not 27 07 11 24 large enough to accommodate the utilization distribution Data truncation may therefore have occurred and this can profoundly disrupt the isopleth calculation It is recommended you inspect the vector isopleth carefully to determine if it is
15. of the first selected fix is shown next to the main slider bar if the shapefile includes Date and Timel fields otherwise No Figure 3 Display Travel dialog box data at No data will appear next to Date If the shapefile includes a Time2 field and you have calculated travel times and distance see below the speed of travel and distance between points will be displayed above the date and time as locations are processed The object ID FID of the starting location in relation to the total number of 27 07 11 11 records in the file 1s indicated Fix below the date and time and is incremented as locations are processed You can change the starting location within the range of selected points by dragging the main slider bar to the right This will automatically update the values associated with the starting location By default an animal s travel path is displayed automatically when the Display Travel button is clicked If you prefer the travel path can be displayed manually by checking the box next to One Step at a Time then clicking the Step Forward or Step Back buttons It is assumed that the records selected from the file are in the sequence they are to be processed If a fix is encountered during processing that has an object ID FID that is out of sequence i e less than the previous value or greater than the previous value 1 you will be notified that the track segment is out of sequence and asked if you want
16. on how your date and time data are formatted Below is an example that can be modified as needed Assuming your date and time look like the following example where date is day month year and time is displayed as hours minutes seconds 10 5 2010 13 30 00 10 5 2010 13 30 10 01 1 1900 13 30 00 Now reformat the date column to number with 0 decimal places 40456 13 30 00 40456 13 30 10 l 13 30 00 You now have a column with a running number of days since the start of the 20th century Now reformat the time column to number shown here with 6 decimal places The result is a proportion of a 24 hour day 40456 0 562500 40456 0 562616 l 0 562500 There are 86 400 seconds in a day so simply add the new date and time columns together then multiply by 86 400 to get a new Time2 field 40456 0 562500 40456 562500 x 86 400 3495447000 40456 0 562616 40456 562616 x 86 400 3495447010 1 0 562500 1 5625 x 86 400 135000 You can see that the difference in times between the first 2 dates and times is 10 seconds which is what we expected Time2 calculated this way is a very big number so in ArcMap you should specify the field format as double rather than long integer In fact it would be advisable in the above example to subtract 3400000000 from each of the larger two values to reduce the number of digits and avoid potential problems as you can see this would have no effect on the time differences To add text c
17. or text editing e g WordPad program because some characters are not supported by ArcMap such as dashes e g x coord spaces and brackets note that you can use the 27 07 11 5 underscore character instead of the dash character in field names You should also delete any records from the file that do not have co ordinate data e g some GPS units may record an unsuccessful attempt to acquire a fix as N A and these records should be removed from the file The HRT uses a Time2 field to Calculate Travel Times and Distances between successive fixes Thus locations in your file must be associated with a linear time scale that provides a unique value for each point otherwise this HRT option will not work Any linear scale can be used although the preferred format is the number of seconds elapsed since a specific point in time e g the GPS time standard of seconds since midnight 1 1 1970 or the number of seconds elapsed since the beginning of your study You could simply order your data and number the sequence from first to last in the Time2 column but calculated times and speeds of movement may be nonsensical unless locations were obtained at equal time intervals Creating a Time2 Field The HRT requires a field labeled Time2 representing continuous time in seconds for most time and distance calculations as well as display travel options This field can be created in Microsoft Excel Obviously specifics depend
18. removed using the Select Feature tool Multiple points can be removed by dragging a box around a group of points or by holding the Shift key while selecting individual points with the Select Feature tool You could also use the Select By Attributes or Select By Location options on the Selection menu to remove single or multiple points Press the Delete key on the keyboard to remove the selected points When you are finished removing points select Stop Editing from the Editor dropdown menu You will be asked if you want to save your edits It is important to realize that if you choose to save your edits the original shapefile will be overwritten Removing Duplicates Rather than manually editing an Attribute Table there is an easy way to remove time or location duplicates from a shapefile using the HRT Highlight the shapefile that you want to check for duplicates in either the Display or Source Table of Contents window Select the points you want to check for duplicates using the Select Feature tool EE on the Tools Toolbar or one of the options on the Selection menu as outlined above You can select all the points in a shapefile by right clicking on the file name then choosing Select All from the Selection fly out menu After selecting the points choose Remove X Y Duplicates or Remove Time2 Field Duplicates from the HRT Tools dropdown menu HRT Tools Y If there are any duplicates in the file a di
19. to draw the track segment anyway click Yes or skip it click No The travel path is displayed as a line with an arrowhead indicating the direction of travel between consecutive points that have been selected The speed at which the path is drawn can be decreased by entering a value in the box or dragging the slider bar to the right in the Display Speed section of the dialog box Figure 3 The Display Speed can be increased by dragging the slider bar to the left or by entering a smaller proportion in the box e g 0 001 The display can be stopped and settings adjusted at any time by clicking the Stop button To retain all lines connecting the selected points in the display check the box next to Keep Travel Path otherwise the line connecting two points will be deleted as the next line is drawn The travel path can be saved as a shapefile by clicking the Save Travel button To clear the travel path from the display click on Delete Travel Path before closing the Display Travel dialog Note that if you want to clear the travel path later after closing the Display Travel dialog you will have to reopen it and click on Delete Travel Path Analyzing Fix Data Generating interfix distances and elapsed times or home range polygons from animal locations is a simple procedure All you need to do is click the shapefile point layer in the Source or Display Table of Contents that you would like to analyze and make it visible by checking the
20. valid You will then need to increase the size of the buffer or adjust your settings for the Raster cell size and Scaling factor A very useful option is the ability for the user to specify an existing raster layer as the extent of the raster calculated in the HRT by selecting Every UD is calculated at the same extent as this raster layer For example specifying a habitat raster layer derived from Landsat imagery will provide a grid cell structure of 30 x 30 m and the utilization distribution will be calculated for each of those cells This can be extremely valuable in resource selection studies Alternatively prior to your analysis of multiple animals if you create a single study wide raster the utilization distribution for each animal will be calculated based on that command raster which will facilitate overlap comparisons The final section of the Output Options tab deals wit of polygon features If you want sopleths You cal X the HRT to calculate isopleths check the radio button ne the default isopleths 1 e 95 99 50 or you can ch commas for example to do both a total home range analys because the HRT uses a bivariate normal Gaussian kernel the i the kernel probability density estimator KDE are infinitely long so it i
21. 1996 Gitzen and Millspaugh 2003 Hemson et al 2005 Gitzen et al 2006 Horne and Garton 2006 Fieberg 2007a b Downs and Horner 2008 and Kie et al 2010 Installing and Removing the Home Range Tools The HRT requires ArcGIS 9 x running under Windows XP as far as we know it will also run under Windows Vista and Windows 7 but we have not tested it ourselves To install the HRT navigate to the folder on your hard drive where you stored the downloaded HRT9 zip file and extract the contents Run double click the setup exe program and the HRT will be installed by default to a c Program Files HRT folder if you prefer the location of the HRT can be changed during the install process To activate the HRT following installation start ArcMap click on the Tools menu and choose Customize On the Toolbars tab in the dialog box that appears place a check mark in the box beside Home Range Tools Arcgis 9 Alternatively click on the View menu choose Toolbars and select Home Range Tools Arcgis 9 You can also right click on any toolbar in the ArcMap window and select Home Range Tools Arcgis 9 Any of these methods will add the HRT Tools dropdown menu to the Data View window after which it can be dragged to a toolbar To uninstall the HRT use the Add or Remove Programs utility in the Control Panel Importing Fix Data Fix data come in wide variety of formats You may already have it in an ESRI shapefile you may have it
22. HRT Home Range Tools for ArcGIS Version 1 1 June 2007 User s Manual Draft July 27 2011 Arthur R Rodgers and John G Kie re for Northern Forest stem Research amp Ontario Ministry of Natural Resources Table of Contents Introduction What is Home Range Analysis rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrsssssssssssssnssssnssnnssnsnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnne l UTE A S a RE re SE in me HRT REE EE EE Minimum Convex Polygons rrrrrrrrrrrrrrrrrrrrnrrrrrrrnrnnnnnnnrnnnnsnnene M e OD Kernel NCGS i isivscicivnsssicivcdsedeisaxtvinusimersavduiuassonmiveanedwics k Lotek MDB FileS ccccccccccccccsceccses N Daenen C EENET AATETTA EIEE 7 ARC INFO Coverages esessssesesesesesesseses N Go ME A LE 9 Editing Fix Data AR ee eee oe AAEREN 10 Removing Individual Pi CO EE A ETE TR 10 Remov1i Tc C SOR TE RD ET 10 pes
23. X AX Xin X io t Xin X ivy 3 h g h The minimum value of CV h is found using a linear search algorithm that tests 200 values of h between 0 05h r and 2h r The resulting smoothing parameter that minimizes the score function is called hyse In situations where the utilization distribution is not unimodal the LSCV method has been shown to overcome the problem of oversmoothing associated with the use of h Worton 1989 On the other 27 07 11 20 hand LSCV may drastically undersmooth the utilization distribution if there are numerous small clusters of points i e centres of activity in the data Park and Marron 1990 Indeed the LSCV method has a propensity to show structure in the data when none exists Sain et al 1994 Further the LSCV method is not always successful in finding a smoothing parameter that will minimize the MISE In these cases the HRT will provide a warning message and will revert to using h e 1 e Warning the LSCV function failed to minimize between 0 05 HREF and 2 00 HREF The bandwidth defaulted to HREF A technique that may strike a balance between the tendency of h to oversmooth and hjsey to undersmooth utilization distributions is biased cross validation BCV In contrast with the LSCV method estimate However it is computationally faster and easier to calc 1 and Jones 1995 Jones et al 1996 In the HRT the functio d D2 gt S i o be minimized is Sai
24. al using the different proportions of h Be warned however that the program could take a while to run and will produce a lot of files depending on the number of proportions you enter the number of animals in the file and the numbers of locations per animal If you run animals individually keep in mind that you can use the 27 07 11 22 Save Settings and Load Settings buttons at the bottom of the dialog box so you don t have to type everything in again for each animal The method we have suggested is referred to as the ad hoc method by Berger and Gese 2007 and Jacques et al 2009 and is further discussed by Kie et al 2010 In the event that the automated methods do not provide a suitable value for the smoothing parameter the HRT allows you to input a subjective choice by checking the radio button next to User defined and entering a value This feature is particularly useful for data exploration and hypothesis generation In addition to helping you choose the best smoothing parameter these subjective choices may allow you to highlight various features of a home range dataset that may not be immediately obvious from the entered value must not be a simple plot of animal locations or one of the automated methods Note that D scaled i e it is in the original units and the same value will be used for mimals in the shapefile selec
25. alog box will pop up and indicate how many duplicates were deleted from the file Note that these locations are permanently removed from the shapefile 27 07 11 10 Merging Shapefiles To merge shapefiles such as multiple downloads from the same animal s GPS collar or location files collected at intervals use the Merge Tool found by clicking on the ArcToolbox icon amp then selecting Data Management Tools General and Merge The Merge dialog box allows selection of two or more files to be merged and will create a new shapefile containing all the features from the input files The attribute types of the shapefiles to be merged should be identical to ensure subsequent functionality of the HRT You should check the new shapefile for X Y duplicates or Time2 Field duplicates as outlined above Exploratory There are two major exploratory data analysis tools available in the basic ArcGIS queries and Display Travel The queries use the Identify tool and the Select By Attributes or Select By Location options on the Selection menu as previ on the other hand is an interactive data walkabout tool b Display Travel Le The Display Travel option lets you 1 Data View window as it
26. ate Travel Times Statistics lt and Distances option is selected If you want to calculate interfix times distances and speed of movement on a subset of the data or between non consecutive fixes you will have to create a new shapefile Use the Select Feature tool Eh on the Tools Toolbar to drag a box around a subset of the points in the Data View window or use one of the options on the Selection menu to select the points you want to include You could also select points manually by opening the Attribute Table and choosing the points to be used with the Select Elements tool LX a range of points can be selected by holding the Shift key down while selecting the first and last records to be included or individual points can be selected by holding down the Ctrl key while selecting each point After selecting all the points you want to include right click on the file name in the Display Table of Contents window and select Data then Export Data In the Export Data dialog verify that Export Selected features appears in the box then select Use the same Coordinate System as the data frame specify the path and file name for the Output shapefile or feature class then click OK Click Yes to add the exported data to the map as a layer You can now Calculate Travel Times and Distances for the subset of data as above 27 07 11 13 Generating Minimum Convex Polygons The MCP Analysis option will make a mi
27. be entered in the box next to Point layer or selected from the drop down list Figure 7 You have the choice of using all the points in the file or only those pre selected using one of the techniques described above To use a pre selected subset of points check the box next to Use selected features only otherwise all locations in the specified shapefile will be used 27 07 11 16 The Kernel Density Estimation option in the r HRT can process multiple animals in batch mode Simply ss02 enter a Unique animal ID field for batch processing in the box or select the corresponding field name from the drop RADIO down list Figure 7 Note that you must still specify an animal ID field even if all the points in the file to be 240 54 050578 1 92584 included in the analysis are from the same animal If you need to create an animal ID field open the attribute table associated with the shapefile and click the Options button then select Add Field to create a Text field and populate it with the same string for each animal It is critically important that the animal ID field is defined as Text 1 e 4 s amp s alphanumeric and that the same ID is used for every recorc Fig Kernel Density imation alphanumeric to accommodate the hexadecimal codes used ialog box with Input Data tab selected and showing dependence of obs
28. d calculates the arithmetic mean of all points then drops the farthest single point The mean is recalculated from the subset of points and another point is dropped This continues until the requested percentage of points remains selected Floating Median This method calculates the median of all points then drops th gle point The median is percentage of points remains selected User Centre d This method selects the requested percentage of points closest to x longitude and y latitude co ordinates specified by the user The default method for calculating MCP IRT ed Mean using 95 of the fixes gt apefile 1 e connect the outermost Figure 5 To generate a 100 MCP using all the in a selected select MCP Analysis on the HRT Tools points without removin fixes from the calculatio ESS dropdown menu en uncheck the box nex Select Percentage of Points or type 100 into igure 5 If you want to remove a proportion of the outermost ions e g be LUS night be considered Occasional sallies outside th
29. dely used in kernel analyses but others e g Epanechnikov might also be applied Silverman 1986 so we have provided a window for choosing other kernel shapes in future releases of the software Bandwidth Tab h rtant step in Choosing an appropriate smoothing parameter 1 deriving a kernel density estimator Worton 1989 but ment on how to apr this problem Silverman 1986 Wand and Jones 1995 Jones et al et al 2005 Gitzen et al 2006 Horne and Garton 2006 Fieberg 2 al 2010 The smoothing parameter A deter ie spread of the k that is centred over each observation If the value of h is small individual kern KDE at a given point will be based on only a fe may obscure the fir d ety of values for h in a given situation and subjectiv automated me reference to known standard Worton 1989 1995 Calculation of this reference bandwidth A ef assume
30. e One possible way out of this situation is to shift duplicate points or points that are very close together some randomly selected distance from their original values but we are not aware of any studies that have determined the implications of doing this Another choice might be to use the Remove X Y Duplicates option on the HRT Tools dropdown m nu to get rid of identical points However this is not a good solution becs tilization dist ution is created from extent of the utilization distribution As a last resort you different method of choosing an appropriate smoothing parameter In part your selection of method i Si eter will depend on your purpose in creating a utilization distribution If you are ple to use the util determine the area of a home range then you will proba t to construct isopleths from a kernel e g 90 95 or 99 However analysis and determine the area enclosed by a conti lization distributions with continuous the reference and cross validatior ds do not always produce outer isopleths from w estimate the area of a home range Rather than using the reference bandwidth
31. e area perhaps exploratory Burt 1943 ou can automatically select a percentage of the locations to be kept usit le methods by checking the box next to Select Percentage of Points entering the perce as ocations to be retained in the box next to Percentage s and choosing the User Centre method slide bar down in the Selection Style box location Note that you can enter multiple percentages in the Percentage s box by separating each value with a comma you will be asked for a file name to save each MCP polygon before it is calculated MCP polygons are automatically added to the Data View after they are calculated To determine the area square meters enclosed by an MCP open its corresponding Attribute Table 27 07 11 15 Generating Kernel Polygons The Kernel Density Estimation option produces a set of polygons based on isopleths derived from the calculation of the standard bivariate normal 1 e Gaussian kernel probability density estimator 1 e utilization distribution or KDE Isopleths are calculated from the summed kernel volumes under different portions of the utilization distribution A chosen smoothing parameter is calculated automatically or you can enter a value manually and the option provides both fixed and adaptive methods of smoothing the kernel probability density estimator click the shapefile point n To start a kernel anal d bo Rog layer i
32. ements and home range use by female pronghorns in sagebrush steppe communities of western South Dakota Journal of Mamrn 90 433 441 alogy Jones M C J S Marron and S J Sheather 1996 A brief survey of bandy election for density estimation Journal of the American Statistical Association 91 40 Kenward R 1987 Wildlife radio tagging Academic Press Inc Lo Kenward R E and K H Hodder 1996 RANGES V an S S cation data Institute of Terrestrial Ecology Furzebrook Res Kernohan B J R A Gitzen and J J Millspaugh 2001 Anal movements Pages 125 166 in J J Millspaugh ar d J M Marzluff editors Radio tracking and animal populations Academic Press Inc Sa 0 California US Kie J G J Matthiopoulos J Fieberg R A Por Moorcroft 2010 The home range concep ditior ators still relevant with modern telemetry technology Phi hical Tran of the Royal Society B 365 2221 2231 Larkin R P and D Halk Wildlife Society Bulle Miche
33. ermine which data points are relevant to their needs and how to best summarize the information Researchers are rarely interested in every point that is visited or the entire area used by an animal during its lifetime Instead they focus on the animal s home range which is defined as that area traversed by the individual in its normal activities of food gathering mating and caring for young Occasional sallies outside the area perhaps exploratory in nature should not be considered as in part of the home range Burt 1943 Thus in its simplest form home range analysis involves the delineation of the area in which an animal conducts its normal activities This can often be accomplished through subjective evaluation To maintain scientific integrity 1 e repeatability or for comparisons with other studies however objective criteria must be used to select movements that are normal White and Garrott 1990 The obvious difficulty is in the definition of what should be considered normal Because of this difficulty there has been a proliferation of home range analysis models Depending on the general treatment of point location estimates home range analysis models can be classified into four fundamentally different approaches minimum convex polygons bivariate normal models Jennrich Turner estimator weighted bivariate normal estimator multiple ellipses Dunn estimator nonparametric models grid cell cou
34. ervations corresponding to each animal We made the animal ID fi by some GPS collar The Unique animal ID field manufac o ulations must be defined as Text ae i e alphanumeric and the identify individual and other users who do not use numeric values q same ID must be used for e g some researchers like to give their animals names note that every record corresponding i to each animal eric values e g collar frequencies ear tags can be converted to q text but not the rever N 4 Independence P iccessive animal locations is a I p a Te e home range analysis I 1utocorrelation sometimes called serial correlation of fix data tends to underestima t ize i se models Swihart and Slade 1985a Schoener 1981 proposed from independence of observations based on the ratio of the mean NG squared di between succe e observations and the mean squared distance from the centre of before it was located again or it is repeating a previous pattern of movements Swihart and Slade 1985a derived the sampl
35. ew and the file name should now appear in both the and Display Table of Contents windows labeled as Events However the layer need con apefile before you can use all of the HRT options To create a shapefile right click on the Eve in either the Contents window and select Data then Export Data In thi ort Data dialog box select Use the same Coordinate System as the data frame specify the path and ume for the Output shapefile or feature class then click OK Click Yes to adc ported data to the map as a layer You now have a shapefile to use with the HRT Lotek MDB Files I Lotek MDB rodu heir GPS Host and N4Win software using data downloaded from their early model G station These files data in a text file forms dv n be edited and imported as outlined above The MDB files do not cc location data in units ailliseconds of latitude and longitude During the import process the e projected
36. ference Properties dialog box click on the Select button In the Browse for Coordinate System dialog box choose one of the predefined Projected Coordinate Systems to convert your data to a UTM coordinate system e g click the Projected Coordinate Systems folder then Utm Nad 1983 and select NAD 1983 UTM Zone 15N prj then click the Add button This will again bring you back to the Spatial Reference Properties dialog box where you should click the Apply button then the OK button Click OK in the Advanced Geometry Options dialog box as well as the Create Feature Class from XY Table dialog box To verify that the new shapefile has been created select Refresh from the View dropdown menu in ArcCatalog Now Open ArcMap and Add the new shapefile you just created in ArcCatalog ESRI Shapefiles ESRI shp files can be added as a layer to the Data View window in the normal ArcGIS way using the Add Data gt button or by selecting Add Data from the File menu Text Files and dBASE Files Text files comma separated values tab delimited ASCII and dBASE files are treated identically To use all of the HRT features the file must include a text field that uniquely identifies the animal s a numeric Time2 field and separate numeric fields for latitude and longitude in UTM units Before attempting to use one of these file types you should check and if necessary edit the field names in the file using a spreadsheet e g Excel
37. g criterion based on the amount of are point contributes to the MCP White and Garrott 1990 Kernel Methods Kernel analysis is a nonparametric statistical method nsities from a set of points Kernel probability density estimation is well srstood by statisticians havit P explored since the 1950s However kernel methods have or en us 1990s Worton 1989 In the context of home range analysis thi ethods describe the probability of oO finding an animal in any one place The mett egins by centering ariate probability density function with unit volume 1 e the kernel over each oint regular data and a probability density estimate is calcula volumes of the kernels A bivariate kernel probabil then calculated over the entire gric the probability densi ates at each grid intersection The resulting kernel probab nsity estimator will have ly large values in areas with many observations and low values as lome range estimates are derived by drawing contour lines 1 e isopletk olumes of the kernels at grid intersections These isopleth
38. in a spreadsheet or database file or a simple text file Since the HRT was developed at a site using Lotek GPS collars we have included an import filter for data files produced by their GPS Host and N4Win software MDB files Regardless of the original format files must be converted to shapefiles point layers and certain types of information must be included for full functionality of the HRT Most importantly the location data must be in UTM units meters The input data file must include a Distances and areas in the HRT are measured in meters unique animal ID field column to perform Kernel Density and square meters Estimation It is critically important that the animal ID field is respectively so location l l data must be in UTM units defined as a text field To Calculate Travel Times and Distances meters between successive fixes the input data file must also include a field called Time2 see further description below Before importing data set the Data Frame Properties on the View menu to one of the Predefined Projected UTM Coordinate Systems 27 07 11 4 Converting Geographic to UTM coordinates in ArcGIS Location data are often recorded in a geographic coordinate system e g decimal degrees but distances and areas in the HRT are measured and calculated in meters and square meters respectively so location data must be converted to UTM units meters before they can be used in the HRT The
39. ing distribution of Schoener s index and provided a test of independence that can be used to determine the time to independence between observations 1 e the minimum time interval between successive observations that allows them to be considered independent They later provided their own bivariate measure of autocorrelation which included terms for both serial correlation and cross correlations Swihart and Slade 1985b High values of the Swihart and Slade index 1 e gt 0 6 indicate significant autocorrelation Ackerman et al 1990 By pressing the Calculate button on the Input Data tab Schoener s 1981 index and the Swihart and Slade 1985b index will be calculated from the locations corresponding to each animal ID Figure 7 27 07 11 17 If either or both of these indices suggest your data are autocorrelated you might consider randomly deleting locations until these indices are no longer significant Ackerman et al 1990 or more objectively use the method developed by Swihart and Slade 1985a to determine the time to independence and remove the intervening locations For later reference these independence statistics may be saved as a simple text file csv format by pressing the Save As button Kernel Settings Tab Standard deviations of the x and y values corresponding to each animal ID including the sample size used are calculated and displayed on the Kernel Settings tab Figure 8 Since the smoothing
40. into ed Projected Utm Coordinate System set in the Data Frame Properties 0 View menu before importing the data Subsequently imported Lotek MDB files have all of the information required for full functionality of the HRT 27 07 11 7 To import a Lotek MDB file select Import Lotek MDB Files from the HRT Tools dropdown I POb2029W sje 2 POb2029W mdb menu n Open file dialog box will pop up Figure 1 Navigate to the folder containing the Lotek MDB file s highlight the file you want and click Open In the Save As dialog box that appears Figure 2 type the name of the new shapefile that will be created by the ro import process The file will be imported and the location data will be converted from milliseconds and Figure 1 T a den Medialog projected into the Predefined Projected Utm Coordinate box ag a selected Lotek M System previously set in the Data Frame Properties on the View menu gt rds of unsuccessful j attempts to acquire a GPS lo cation you will be i ormed that whose fix status acquiring y a lt E Your dataset m er or not you want to Include or gt The script will ask y Exclude these rec
41. ion distribution the view on the left used a value of 10 000 and the view on the right used a value of 20 000 5 You are now ready to explore the utilization distribution in 3D You can use the scroll button on your mouse to zoom in and out or use some of the tools on the Tools toolbar to navigate the view In addition to the Zoom and Field of View buttons on the Tools toolbar the main tools are The Pan tool ET allows you to move the entire scene left right up or down The Navigate tool cS lets you rotate the entire scene in any direction by holding down the left mouse button while dragging the cursor The Fly tool Ea allows you to navigate through a scene with the ability to move continually in a particular direction Click the tool move the cursor near the middle of the scene then click the left mouse button to start If you want to go faster double click the left mouse button If you want to go slower double click the right mouse button When you want to stop click the Escape Esc key on your keyboard Clicking Full Extent D will get you back to viewing the entire scene in the View window 27 07 11 32
42. n BCV 2 h ry v i dl 4 TA n 1 oS D n 2h r where the distance between pairs of points D ain calculated as ation 3 above Similar to the LSCV method 200 values of h between 0 05h and 2h ested using ear search algorithm to find the minimum value of BCV2 h The resulting smoothing par IS C led Npey Simulation studies CV method orms quite well in comparisons with the LSCV and reference methods Sain et al 1994 ever the BCV method has not been investigated in the context of home range estimation D similarities with the linear search algorithm used by the LSCV approach the BCV meth 1a y also fail las eter that will minimize the AMISE In these cases the HRT will again pre essage and revert to using he V met f selecting a smoothing parameter can run into problems if there are lt um plic te locations recorded for an animal e g at a nest or den site because the distance eai Wi n D will be zero for all of these locations The distance between points regardless of the value of _ Consequently when the distance between points is zero all values of h will
43. n the Source or Di Table of Contents containing the fixes for which you would like to generate polygons and make the points visible by checking the box next to it Only ut the file may ow To in sin the Data rel Density Estimation lown menu HRT Tools Y The tion dialog box will open with 4 visible tabs Figure 6 Input Data Kernel Settings Figure 6 Kernel Density E timati Bandwidth and Output Options These tabs allow you to dialog box with Inp a 4 D l crac Band i input and output file names as well as the settings to an tabs be used for a kernel analysis You can save the kernel analysis settings for use w1 jer files or retrieve previously saved settings using the Save Settings and oo Load Settings buttons at the bottom of the dialog box Input Data Tab A point layer alysis must first
44. ndow then choose your desired Options in the Symbol Selector dialog and click the OK button when done 27 07 11 30 4 Click on the View menu in ArcScene choose Scene Properties selec General tab Now specify the Vertical Exaggeration for the utilization distribution T ompletely arbitrary the intent is to visualize the utilization distribution in way that will allow you pare areas of high peaks and low valleys use Keep in mind th cell in proportion of the utilization distribution so the value you e g gt 10 000 You can also change the Background color o AN color ramp you selected previously 20000 27 07 11 raster represent e small 4 probably need to be very large cene to gain better contrast with the d l Ef p ere fom ren be Ous SENECTA TETEE OM Pism Lem fP kes a gt z Effect of choosing different values for the Vertical Exaggeration ofa utilizat
45. ner G I Canadian Millspaugh J J R son L McDonald J M Marzluff R A Gitzen C D Rittenhouse M W Hubbard and S L Sheriff 2006 Analysis of resource selection using utilization distributions Journal of Wildlife Management 70 384 395 Mohr C O 1947 Table of equivalent populations of North American small mammals American Midland Naturalist 37 223 249 Park B U and J S Marron 1990 Comparison of data driven bandwidth selectors Journal of the American Statistical Association 85 66 72 27 07 11 21 Powell R A 2000 Animal home ranges and territories and home range estimators Pages 65 110 in L Boitani and T Fuller editors Research techniques in animal ecology controversies and consequences Columbia University Press New York New York USA Rodgers A R 2001 Recent telemetry technology Pages 79 121 in J J Millspaugh and J M Marzluff editors Radio tracking and animal populations Academic Press San Diego California USA Rodgers A R R S Rempel and K F Abraham 1996 A GPS based telemetry system Wildlife Society Bulletin 24 559 566 ariate densities Journal of the Sain S R K A Baggerly and D W Scott 1994 Cross validation of American Statistical Association 89 807 817 Schoener T W 1981 An empirically based estimate of ho nge Theo
46. ng Fix Data Sometimes there are irrelevant fixes that need to be removed from data files before further analyses can proceed For instance locations recorded during the process of initializing GPS collars prior to deployment on animals Another problem is the occurrence of duplicate points in a data file This might occur due to transcription errors or when files are merged see below These records can be identified and removed from text or dBase files using a spreadsheet e g Excel or Before you start editing you should make a backup text editing e g WordPad program before importing to ArcGIS copy of your shapefile s Otherwise you may choose to edit the shapefiles created in ArcGIS Removing individual points including duplicates will after data are imported as described below In addition to the editing overwrite the original shapefile features provided by ArcGIS the HRT includes a couple of options for removing duplicate data Removing Individual Points There are many ways to select individual points for editing in ArcGIS using options on the Selection menu and the Editor Toolbar However a simple interactive method is to make use of the Select Feature tool Eh on the Tools Toolbar Activate the Editor Toolbar 22 and select Start Editing on the Editor Editor dropdown menu 27 1 Highlight the shapefile to be edited in either the Source or Display Table of Contents window Select the point to be
47. ng dialog box with Bandwidth tab utilization distribution is unimodal i e single peaked elected showing the value of for the points and the various Worton 1995 and is the default method of bandwidt ds available in the HRT for ba idth selection selection in the HRT This method may be sufficie i i usually oversmooth the describe a concentrated group of points selected in tl pefile because animals typically have multiple centres of activity utilization distribution for an enti within their home range Consequentl you will probably want to try one or more of the other Bandwidth yy d Selection methods available on the Bandwidth tab Figure 9 l n is the most common method for automatically calculating d attempts to find a value of h that minimizes the mean integrated ng asc nction CV h for the estimated error between the true density timate Worton 1995 l Di I an e f e s 2 2 mh g 2 where the distance between pairs of points D is calculated as 2 2 2
48. nge estimators Journal of Wildlife Management 59 794 800 27 07 11 28 Appendix 3D View of Utilization Distributions Sometimes it may also be useful to explore a utilization distribution created from a kernel analysis in the HRT by rendering it in 3 dimensions The following steps demonstrate how this can be done in ArcGIS using the raster output from the kernel analysis 1 Open ArcScene and add the raster output for the utilization distribution that you saved in the file folder specified on the Output Options tab of the Kernel Density Estimation dialog using the Add Data 1 button or select Add Data from the File menu You can also add the animal locations used to generate the utilization distribution and or the resulting isopleths from the kernel analysis NOTE if you want to superimpose these layers on the 3D view of a utilization distribution you must add these layers before proceeding to the next steps if you try to add these layers later a quirk in ArcScene will prevent overlaying them k Untitled ArcScene ArcInfo File Edit View Selection Tools Window Help OSHS SEX MAlGO nv Qe QsgQqguuMesr ona 3D Analyst Layer 8 kdeo V R i Scene layers E P062146W Value High 0 160227 Bh iow 0 2 Right click on the raster file name in either the Source or Display Table of Contents window and select Properties then the Base Heights tab in the Layer Properties dial
49. nimum convex polygon MCP using the outer points in a group of selected fixes Before proceeding with an MCP analysis Remove duplicate locations you should remove any duplicate locations see Removing Duplicates before an MCP analysis section above by choosing Remove X Y Duplicates on the HRT Remember to make a backup copy of your original Tools dropdown menu HAT 198 This is necessary because shapefile s before you p ry remove duplicates calculation of MCPs involves determination of distances between points and if the difference in x or y co ordinates is zero it can MCP for 15a 1995 6 8 utmidtime up result in a division by zero error that will cause ArcGIS to Select Percentage of Points shut down Removal of duplicate locations will have no effect Percentage s 35 Cancel Enter percentages separated by commas For example 35 90 85 To construct an MCP from all the points in a shapefile on the construction of MCPs point layer following removal of duplicate locations right Selection Style click on the file name then choose Select All from the Fixed M EE Selection fly out menu To produce an MCP on a subset of Area Added Fixed Mear i 18 Fixed Median points in the Data View use the Select Feature tool _ to Floating Mean Floating Median el drag a box around the fixes you want to include or use one of User Centre X 54904538 Y 54648443 the options on the
50. nt digits such as something like 0 00000000789321 we apply a scaling factor Jefault multiplier in the HRT is previous value would be stored as 0 00789321 Since the scali utilization distribution it has no effect on the isopleths If you increase the resolution of the raster so need to increase the valu e scaling factor because the number of cells in the raster may become to r the proportion of the total volume associated with each cell may become too small For example if your study area were 500 x 500 km in size and you reduced the Raster cell size to 10 x 1 gt raster will requi gt 2 500 00 and likely produce the error message Kernel calculatc JO Out of mem y If evenly distributed the volume of each cell in this raster would be 0 000004 using the c ultiplier but following application of the smoothing parameter i likely that the volume of somi cells would fall below I x 107 In general you should use the default value of the scaling factor or increase the value but do not reduce it without careful consideratic ault yc ld check the radio button next to Write verbose analysis
51. nts Fourier series smoothing harmonic mean and contouring models peeled polygons kernel methods hierarchical incremental cluster analysis All of these methods can be used to estimate areas occupied by animals but some have been developed to specifically elucidate characteristics of home range shape e g bivariate normal models or structure e g contouring models If home range size is combined with information about home range shape then it is possible to estimate resources available to individuals in a population Consideration of home range shape 27 07 11 1 may also allow identification of potential interactions among individuals Analytical models developed to examine home range structure may be useful in the identification of areas within home ranges that are used by individuals for specific purposes such as nest sites or food caches However home range analysis may involve more than just estimating the characteristics of areas occupied by animals Researchers often want to know about the distances headings times and speed of animal movements between locations They may also want to assess interactions of animals based on areas of overlap among home ranges or distances between individuals at a particular point in time Thus home range analysis comprises a wide variety of techniques and approaches Most of these methods and their limitations have been reviewed by Harris et al 1990 White and Garrott 1990 and Powell 2000
52. og box that appears Now click the radio button next to Obtain heights for layer from surface and check that the path leads to the same raster file as in the previous step Click the Apply button then the OK button to close the dialog box Follow the same procedure and make the same choices for any additional layers e g animal locations isopleths that you may have added to the Data View 27 07 11 29 Layer Properties C Documents and Settings RODGERSAR My Documents 4AT SHARES and GyMan S e Layer features have Z values Use them f r heights 3 Reopen the Layer Properties dialog box by right clicking on the raster file name in either the Source or Em Display Table of Contents window and select Prope Click the Symbology ta select a color ramp that will apply different colors to the heights id cells in the raster Click the Apply button then the OK button to close the dialog box d Display Low 0 Standard Deviations 2 If you have added any additional layers you may want to change the shape size and or colors of points or lines in the Data View You can do this by clicking on the colored symbol or line under the layer name in the Source or Display Table of Contents wi
53. olutions may produce home range estimates that are very different than area calculations based on higher resolutions By default Figure 10 Kernel Density Estimation dialog box with Output Options tab selected showing the various system units meters when using a UTM projection Finer options for raster and polygon output of the kernel analysis results the original units but the value must not be larger than the the HRT uses a raster cell size of 100 x 100 coordinate resolutions can be specified in the Raster cell size box in 27 07 11 23 bandwidth Increasing the resolution of the raster may require an increase in the scaling factor see below and could dramatically increase computing time The Scaling factor multiplier is needed for the raster output To create the raster the volume of the entire utilization distribution is scaled to a value of 1 and the proportion of the total volume associated with each cell is assigned Depending on the extent of the utilization distribution and the distribution of points over the area and especially if there are duplicate locations or numerous points that are very close together some cells may have extremely small values The default Grid structure in ArcGIS uses single precision 32 bits for the sake of argument think of this as 8 significant digits so to avoid major loss of information due to truncation of extremely small double precision numbers bits 16 significa
54. ords re process With the exception ME location records Il of the other Z unsuccessful attempts to acquire a GPS fix When the import ISave As also choose to Cancel the import ing duplicate time or RT options require valid location data function corre tly so you should Exclude these d Figure 2 The Save lialog box showing the n of the process has finished the name of the shapefile will be displayed new shapefile that w the Display a Source Table of Contents windows and the created b Import oc will appear in the Data View ready for use by the HRT MDB P S The list of attributes associated with the data revealed by right clicking on the shapefile and selecting Open Attribute Table is shown in Table 1 27 07 11 3
55. produce the same value for the score functions and no single value of h will be found that minimizes the MISE or AMISE functions As noted above the HRT will provide a warning message and will revert to using h e when no single value of h can be found that minimizes the MISE or AMISE functions Even if the LSCV or BCV method is successful in finding a smoothing parameter duplicate locations or numerous points that are very close together can have a disproportionately large influence 1 e bias on the overall estimate of the MISE or AMISE functions because of the summation component in the score functions equations 2 and 4 This may produce very small values of h that minimize the 27 07 11 21 MISE or AMISE functions and drastically undersmooth the utilization distribution Indeed duplicate locations or numerous points that are very close together could end up producing the degenerate value h 0 Silverman 1986 as the solution If the HRT encounters duplicate points or points that are very close together a warning message will appear i e Warning Overlapping points were detected for the following animals This may prevent reasonable estimation of the bandwidth and you will have the option to proceed or not If you choose to proceed you may encounter another warning message if the process fails to find a value of h that will minimize the MISE or AMISE functions If this happens the HRT will revert to using h e as described abov
56. retical Population Biology Seaman D E J J Millspaugh B J Kernohan G C Brund Effects of sample size on kernel home range estimates 747 Seaman D E and R A Powell 1996 An evaluation of tl ry 77 2075 2085 home range analysi Silverman B W 198 London UK 175 p Swihart R K Ecology 66 117 ut R K and N A Slade 1985b Influence of sampling interval on estimates of home range size Wildlife Management 49 1019 1025 Journa Wand M P and M 1es 1995 Kernel smoothing Chapman and Hall Ltd London UK 212 pp White G C and R A Garrott 1990 Analysis of wildlife radio tracking data Academic Press Inc San Diego CA 383 pp Worton B J 1989 Kernel methods for estimating the utilization distribution in home range studies Ecology 70 164 168 Worton B J 1995 Using Monte Carlo simulation to evaluate kernel based home ra
57. s 2 home range analysis models minimum convex polygons MCPs and kernel methods Although they have been severely criticized MCPs have been included because they are easy to compare among studies and they are the most frequently used Harris et al 1990 White and Garrott 1990 Whereas MCPs do not indicate how intensively different parts of an animal s range are used kernel methods allow determination of centres of activity Worton 1989 1995 Seaman and Powell 1996 Because different computer software programs may produce large differences in home range estimates based on these models Lawson and Rodgers 1997 we have attempted to provide all of the options offered in earlier programs for calculation of the estimators and values input for various parameters 27 07 11 2 Minimum Convex Polygons Minimum convex polygons MCPs are constructed by connecting the peripheral points of a group of points such that external angles are greater than 180 Mohr 1947 Percent minimum convex polygons MCPs Michener 1979 sometimes referred to as probability polygons Kenward 1987 restricted polygons Harris et al 1990 or mononuclear peeled polygons Kenward and Hodder1996 can be generated for a subset of fixes using one of several percentage selection methods available in the HRT These methods include both the exclusion of points from a calculated e g mean or user specified e g nest site location and an orderin
58. s define home range t probability levels whose areas can be calculated xed and adaptive kernel methods The kernel probability density function usec in th ndard bivariate normal i e Gaussian curve Other kernel functions are described in Silv have not been implemented The choice of an appropriate smoothing factor i e bandw t h more important than the choice of a kernel Worton 1989 and there should be little difference between the estimates of home range produced by different kernel functions compared to the dietes caused by the choice of smoothing factor Wand and Jones 1995 Several automated and subjective methods of finding the best smoothing factor are provided in the HRT Kernel methods in the home range literature are derived from Worton 1989 His paper was primarily based on Silverman 1986 Both of these works are essential reading but much has been written since and although not an exhaustive list we also recommend the following as an introduction to kernel methods and their application Wand and Jones 1995 Worton 1995 Seaman and Powell 1996 Bowman and Azzalini 1997 Seaman et al 1999 Powell 2000 Blundell et al 2001 Kernohan et al 2001 and Millspaugh et al 2006 There have also been numerous studies of smoothing parameter choice 27 07 11 3 for kernel density estimation and the following readings are also recommended Wand and Jones 1995 Jones et al
59. s m ically impossible 100 of the volume under an entire utilization distribution Consequently you sho yt specify greater than 99 99 Keep in mind that the outer isopleth of a utilization distribution does r gt to be continuous to conform to Burt s 1943 definition of ge If a kernel analysis produces a utilization distribution with several clusters that there is less chance of finding the animal in the spaces between clusters it does not mean 1 f it is your objective to delineate and determine the area bly want to construct isopleths from a kernel analysis and determine ea y a continuous outer isopleth e g 90 95 or 99 Regardless you must always recognize t me range determined by any method provides an index but not an absolute measure of space use of an animal through time If you gt chosen to calculate isopleths then you have three options for outputting the results by checking the radio button next to the corresponding polygon feature choosing Lines provides the perimeter length meters e specified
60. s your data are normally distributed in biva ace Silverman 1986 Worton 1989 1995 Since the HRT uses a standard bivariate normal probat sity function to estimate the utilization distribution h e is calculated equation 1 as the square root of the mean variance in x var and y var co ordinates divided by the sixth root of the number of points Worton 1995 _ r var var h ref 1 27 07 11 19 The value of h r for the locations corresponding to each animal ID in the shapefile selected in the Source or Display Table of Contents or a subset of points selected in the Data View is shown on the Bandwidth tab Figure 9 Units for i h r Will be the same as the input units Typically h g units p are meters However if you rescaled to unit variance on the Kernel Settings tab units for h r will not be readily interpretable the units will be standard deviations If TREE SEE TESS you wish to record h e with meaningful units simply return to the Kernel Settings tab unclick Rescale to unit variance then go back to the Bandwidth tab and record her If you had decided to rescale your data remember to S return to the Kernel Settings tab and re click Rescale to gt unit variance prior to running the kernel analysis Fi Kernel Density stimation The h r method is effective if the underlyi
61. sv txt or dBASE dbf files as a layer to the Data View use the Add Data gt button or select Add Data from the File menu Navigate to the file then click the Add button This brings your file into ArcMap but does not yet display the data 1 e the file name is available in the Source Table of Contents window on the left and you can open it by right clicking but nothing appears yet in the 27 07 11 6 Display Table of Contents window Right click on the file name in the Source Table of Contents and select Display XY Data In the X Field of the dialog box select the column containing the longitude data in UTM units Similarly in the Y Field of the dialog box select the column containing the latitude data in UTM units Click on the Edit button In the Spatial Reference Properties dialog box that now appears click on the Select button or one of the others if you know how to use them In the Browse for Coordinate System dialog box select the same UTM system as for the Data Frame Properties using the predefined Projected Coordinate Systems e g click the Projected Coordinate Systems folder then Utm Nad 1983 and select NAD 1983 UTM Zone 15N prj then click the Add button This brings you back to the Spatial he OK button You should Reference Properties dialog box where you should click the Apply button tl now be back at the Display XY Data dialog box Click the OK button points should now be displayed in the Data Vi
62. tandard deviations of the x and y values corresponding to distribution The adaptive kernel method on the other hand each animal ID and Kernel parameter options outer areas without oversmoothing the core of the allows the width of the kernel to vary such that regions with low densities of observations are smoothed more than areas of high concentration This can produce oversmoothing of the outlying regions of the distribution Whereas Worton 1989 found the adaptive kernel approach provided better estimates of home range size derived from a utilization distribution than the fixed kernel method Seaman and Powell 1996 found the opposite One explanation is that the widening of kernels in outlying regions by the adaptive 27 07 11 18 approach may produce unacceptable expansion of the utilization distribution Kenward and Hodder 1996 It is more likely however that differences in performance of the two methods noted by these authors are simply a consequence of the different sets of observations used in each study Therefore we suggest the choice of which smoothing approach to use depends on the original observations and is left up to the user to determine through exploration of their data The HRT uses a bivariate normal or Gaussian kernel referring to the shape of the probability distribution placed over each observation point in bivariate space No other choices are available in the HRT at this time Gaussian kernels are wi
63. ted in the Source or Display Table of Contents Output Options Tab The HRT provides output of kernel analyses Figure 10 h Begin by specifying a location for the output of the raster la gt Output folder box or you can Browse to a preferred location by clicking der A icon We strongly advise you to create a new folder each time you run a kernel hly descriptive file names Vin Z because it can quickly become overwhelming i a IN Yn r UA wh p p of your data Next you will need to provide a Raster name prefix or you can use the default prefix kde already in the box You should keep this prefix as short as possible C AX HRtempiElk FilesyLocationsttest 01 i eks because the Unique animal ID s you specified on the Input kde V 100 tab will be added following this prefix and ArcGIS ER P gt restricts the names of the raster layers that will be produced 1000000 to a maximum of 14 characters g 2141 The kernel methods implemented in the HRT calculate probability density estimates at the centre of each cell in a raster i e grid of cells The process is 7 NET theoretically independent of resolution However lt preliminary tests indicate that very coarse res
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