HRT: Home Range Tools for ArcGIS User's Manual Draft September
Contents
1. intervals To add text csv txt or dBASE dbf files as a layer to the Data View use the Add Data button or select Add Data fro ile menu Navigate to the file then click the Add button This brings your file into Arc out does not yet display the data 1 e the file name is available in the Source Table of Contents window on e open it by right clicking but nothing appears yet in the Display Table windo ight click on the file name in the Source Table of Contents and select Data In Field o dialog box select the column containing the longitude data in imilarly in the ld of the dialog box select the column containing the latitude data in UTM units on the Edit b In the Spatial Reference Properties dialog box that now appears click on the Select butt r one of the others if you know how to use them In the Browse for Coordinate System dialog box se e 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 Reference Properties dialog box where you should click the Apply button then the OK button You should now be back at the Display XY Data dialog box Click2. 1990s Worton 1989 In the context of home range analysis th ethods describe the probability of 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 A regular data and a probability density estimate is calcula volumes of the kernels A bivariate kernel probabil then calculated over the entire gric o 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 ome range estimates are derived by drawing contour lines 1 e isopleth olumes of the kernels at grid intersections These isopleths 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 1 e bandw t h more important than the c
3. T Do hand is an interactive data walkabout tool Display Travel The Display Travel option lets you moves from point to point To explore the mov would like to use in the Source or Display Table of lt to it Only one shapefile at a e processed Y right clicking on the fil ae then choosing Select want to use the entire shapefi u can use the Select Fea box around 1e Data o ubset of poin first selected fix is shown next to the main slider bar if the shapefile includes Date and Timel fields otherwise No data at No data will appear og box Figure 3 will pop up that allows you to control the Data View window as it shapefile point layer that you gt from the Selection fly out menu If you don t ure tool 88 on the Tools Toolbar to drag a dow or use one of the options on the Selection menu ve To start the process select Display Travel from the HRT Figure 3 Display Travel dialog box next to Date If the shapefile includes a Time2 field and you have
4. Tging ShapefileSQ s 5 AA OMEN EE 9 Generating Minimum Convex Polygons apertis kde RE t FE REEO IR RI ERR ERA UR UR E AER REA AMA ER MR EU EK Id aER 12 28 09 10 Area Added AAA RS 12 Fixed AMAS A IA 12 Fixed Median cccccsccsccsccecceccoscosccsccsccescscescescesccsscescscescescessssscuscascssscascuscescessescescescens 13 FINE Maida a A A Ee 1 AS A E E VM scans vies ees NIMM ME 13 Ac EEE PO A A MM pun 14 MD ie titan 14 Kernel Settings AAA A LO Output Options Tab ccccccccccccceseeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeees Literature rm 28 09 10 11 Introduction This manual is intended to accompany the Home Range Tools HRT for the ArcGIS 9 x Geographic Information System GIS Th
5. 1 x 10 so the volume of the function is thus no longer 1 itis 1 scaling factc for example the previous value would be stored as 0 00789321 Since the scali applied across the entire utilization distribution 1t has no effect on the isopleths If you increase the resolution of the raster yo crease the valu the scaling factor because the number of cells in the raster may become associated with each cell may become too small For example if 3 tudy area were 500 x 500 km in size and you reduced the Raster cell size to 10 x 1 gt raster will requ 00 000 000 2 5 billion cells and likely produce the error message Kernel cal volume of each cell in this raster would be 0 000004 1 Itiplier but following application cells would fall below 1 x 10 In without careful consideratic This reate a text file called HRT KDE ANALYSIS txt that contains diagnostics ilysis including your input pa s and many intermediate values such as the bandwidth used LSC ores and others Havir s informa
6. Michener G R 1979 Spatial relationships and social organization of adult Richardson s ground squirrels Canadian Journal of Zoology 57 125 139 Millspaugh J J R M Nielson 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 28 09 10 25 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 Sain S R K A Baggerly and D W Scott 1994 Cross validation of n v
7. 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 To start a kernel anal click the shapefile point d NET layer in 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 Toos The tion dialog box will open with 4 visible tabs Figure 6 Input Data Kernel Settings Figure 6 Kernel Density E timation Bandwi nd Output Options These tabs allow you to dialog box with Inp a d 4 i uc Band input and output file names as well as the settings to an tabs be used for a kernel ana
8. 18 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 x 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 28 09 10 11 Generating Minimum Convex Polygons The MCP Analysis option will make a minimum 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
9. 10 066 Fieberg J 2007b Utilizatio ibi estimation usir arison of least squares cross validation bandwidth options 11dlife Society Bulletin 31 823 831 Harris S W J Cressw PG 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 28 09 10 24 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 movements and home range use by female pronghorns in sagebrush steppe communities of western South Dakota Journal of Mammalogy 90 433 441 Jones M C J S Marron and S J Sheather 1996 A brief survey of b
10. Coverages ARC INFO coverages can be added to the Data View in the normal ArcGIS way using the Add Data 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 28 09 10 Editing 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 prov
11. Figure 9 Units for hef will be the same as the input units Typically h units are meters However if you rescaled to unit variance on the Kernel Settings tab units for h will not be readily interpretable the units will be standard deviations If TO RR TS EIER 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 hef If you had decided to rescale your data remember to return to the Kernel Settings tab and re click Rescale to D unit variance prior to running the kernel analysis lt Fi Kernel Density Estimation The h method is effective if the underlying dialog box with Bandwidth tab utilization distribution is unimodal i e single peaked elected showing the value of Yon for the points and the various Worton 1995 and is the default method of b lt ds available in the HRT for selection in the HRT This method may be sufficient to ba th selection describe a concentrated group of points selected in the Data Vie it will usually oversmooth the utilization distribution for an enti pefile because animals typicall
12. Remove X Y Duplicates on the HRT Remember to make a backup copy of your original Tools dropdown menu PT 1298 y 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 153_1995_6 8_utmidtime2_di result in a division by zero error that will cause ArcGIS to V Select Percentage of Points shut down Removal of duplicate locations will have no effect Percentage s ss Cancel Enter percentages separated by commas For example 3530 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 Fised M Zn Selection fly out menu To produce an MCP on a subset of Area Added Fixed Mear A pl Fixed Median points in the Data View use the Select Feature tool to Floating Mean Floating Median is drag a box around the fixes you want to include or use one of User Centre X p4904 98 Y p464844 3 the options on the 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 t
13. This method calculates the median of all points then drops ingle point The median is recalculated from the subset of points and another point is dropp is continue until the requested percentage of points remains selected User Centre This method selects the requested percentage of points closest to x longitude and y latitude co Le ordinates specified by the user 1 d The default method for calculating MCPs in the HRT is ed Mean using 95 of the fixes using all the fixes in a selected shapefile i e connect the outermost Figure 5 To generate a 10 points without removing any fixes fi he calculation select MCP Analysis on the HRT Tools A 4 dropdown menu then u icheck the box next to Select Percentage of Points or type 100 into the box next to Percentage s in the MCP o igure 5 If you want to remove a proportion of the outermost locations e use t ight be considered Occasional sallies outside the area perhaps explorat
14. 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 28 09 10 records in the file 1s indicated Fix below the date and time and 1s 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 1s encountered during processing that has an object ID FID that is out of sequence 1 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 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 v
15. distribution in home range studies Ecology 70 164 168 Worton B J 1995 Using Monte Carlo simulation to evaluate kernel based home range estimators Journal of Wildlife Management 59 794 800 28 09 10 26
16. 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 18 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 Toos Y If there are any duplicates in the file a dialog box will pop up and indicate how many duplicates were deleted from the file Note that these locations are permanently removed from the shapefile 28 09 10 8 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 8 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 By Location options on the Selection menu as previou
17. h e as described above 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 bec tilization distribution is created from sities and affect he shape and extent of the utilization distribution As a last resort you different method of choosing an appropriate smoothing parameter In part your selection of method foi sing a smooth eter will depend on your purpose in creating a utilization distribution If you are plz to use the util determine the area of a home range then you will proba int 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
18. i home range estimation D imilarities with the linear s Simulation studies well in comparisons with the LSCV and V method has not been investigated in the context of rch algorithm used by the LSCV approach the BCV method may also fail das r feter that will minimize the AMISE In these cases the HRT will again pr le a warning n essage and revert to using hey B th the LSCV an V methods o electing a smoothing parameter can run into problems if here are a number of duplicate locations recorded for an animal e g at a nest or den site because the distance b pairs of points D will be zero for all of these locations The distance between points might also become due to round off or discretization of x and y co ordinate data Silverman 1986 The result is that the e Its in the score functions equations 2 and 4 will always evaluate to 1 regardless of the value of Consequently when the distance between points is zero
19. 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 28 09 10 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 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 fil
20. 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 28 09 10 20 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 1s 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 1s 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 b ediately obvious from e entered value must not be imals in the shapefile selected in the Source or Display Table of Contents Output Options Tab The HRT provides output of kernel analyses as both rasters and polygon features Output Begin by specifying a location for the output of the raster layer and polygon features in the folder box or you c
21. outlying regions by the adaptive 28 09 10 16 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 widely 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 apt 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
22. 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 y ety of values for h in a given situation and subjectiv automated me reference to a known standard Worton 1989 1995 Calculation of this reference bandwidth h assumes 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 _ mA var var h ref 1 28 09 10 17 The value of A 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
23. HRT Home Range Tools for ArcGIS Version 1 1 June 2007 User s Manual Draft September 28 2010 Arthur R Rodgers and John G Kie Centre for Northern Forest Ecosystem Research Ontario Ministry of Natural Resources Table of Contents Introduction What is Home Range Analysis ocooocccccncnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnninnnnnns I do Foo tir TERMINER Id Aaa le in me HRT oda Minimum Convex Polygons eese UN H UY Kernel Methods ninia k Lotek MDB Files e eene RENNES 6 ARC INFO Coverages emmm Ai O A a P 7 Editing Fix Data E eee eee 8 Removing Individu P A iw id 8 Removi kgates rc apio 8 Explorato Display rota Analyzing Fix Data Calculating Interfi
24. a home range Rather than using the reference bandwidth hef or that determined by LSCV h BCV hpcv2 we suggest it might be better to incrementally decrease or 1 of hy ASSC vith individual data sets until the outermost isopleth determine a home range estimator For example decrease the vali 0 85h and so on until the outermost isopleth breaks the points up into 6 disjunct cl ns backtrack to the previous proportion of that did not cause a break up of the 1 and use that as your smoothing parameter to define the home range boundary and obtai nate This process can also be implemented from the bottom up i e start with a small propo h then increase it by some amount e g 0 05 until the outermost isopleth becomes continuous Although not fully automated the process is repeatable and therefore valid in a 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 animal using the different proportions of h e 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
25. all values of A will 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 28 09 10 19 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
26. alue 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 box next to it then choose the analysis you would like to use from the HRT Tools dropdown menu HRT Tools v 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 righ
27. an Browse to a preferred location by clicking the folder icon We strongly advise 1 Vox Vo P S you to create a new folder each time you run a kernel analysis and to use highly descriptive file names lt a you try differ nt approaches to the analyses Z w i A i because it can quickly become overwhelming anton 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 CN HRtemp EIk FilessLocationsstest 1 y i T aaa because the Unique animal ID s you specified on the Input kde SN 100 tal will be added following this prefix and ArcGIS a restricts the names of the raster layers that will be produced 1000000 to a maximum of 14 characters 7 abd The kernel methods implemented in the HRT calculate probability density estimates at the centre of each cell in a raster 1 e grid of cells The process is 7 9590 50 theoretically independent of resolution However 5 lt preliminary tests indicate that very coarse resolutions may produce home range estimates that are very different than area calculations based on higher resolutions By default Figure 10 Kernel Density Estim
28. andwidth selection for density estimation Journal of the American Statistical Association 91 401 407 Kenward R 1987 Wildlife radio tagging Academic Press Inc London 222 pp em for biological location data Wareham UK 6 Kenward R E and K H Hodder 1996 RANGES V ana Institute of Terrestrial Ecology Furzebrook Ri space use and movements Kernohan B J R A Gitzen and J J Millspaugh 2001 Pages 125 166 in J J Millspaugh and J M Marzluff e adio tracking and animal populations Academic San Diego Kie J G J Matthiopoulos J Fieberg R A Powe Moorcroft 2010 The home range concept telemetry technology Ph hical Transacti Larkin R P and D Halkin 1994 A review of software kages for estimating animal home ranges Wildlife Society Bulle 274 2 Lawson E J G and A R Rodgers 1 ifferences in home range size computed in commonly used vare programs Wildlife Society Bulletin 25 721 729
29. ariate densities Journal of the American Statistical Association 89 807 817 Schoener T W 1981 An empirically based estimate of ho 20 281 325 ation Biology Seaman D E J J Millspaugh B J Kernohan G C Brun aedeke and R A Effects of sample size on kernel home range estimates of Wildlife Management 63 739 747 Seaman D E and R A Powell 1996 An evalu ion o y el density estimators for home range analysis Ecology 77 2075 20 Silverman B W 1986 Density esti n for statistics London UK 175 p d data analysis Chapman and Hall Ltd Swihart R Testing for independence of observations in animal movements FN Swihart 5b Influence of sampling interval on estimates of home range size ment 49 1019 1025 Wand M P and M 995 Kernel smoothing Chapman and Hall Ltd London UK 212 pp White G C and R A Diego CA 383 pp arrott 1990 Analysis of wildlife radio tracking data Academic Press Inc San Worton B J 1989 Kernel methods for estimating the utilization
30. ate normal Gaussian kernel the 4 the kernel probability density estimator KDE are infinitely long so it is m ically impossible 100 of the volume under an entire utilization distribution Consequently you sho Xt specify greater than 99 99 Keep in mind that the outer isopleth of a utilization distribution does 1 to be continuous to conform to Burt s 1943 definition of nge If a kernel analysis produces a utilization distribution with several clusters that s fine it just means there is less chance of finding the animal in the spaces between clusters it does not mean the area is never used Howe f it is your objective to delineate and home re then y probably want to construct isopleths from a kernel determine the analysis by a continuous outer isopleth e g 90 95 or 99 Regardless e e area home range determined by any method provides an index but not an e measure of space use of an animal through time e isopleths then you have three options for outputting the results by ie corresp
31. ation 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 smaller 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 28 09 10 21 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 64 bits 16 significant digits such as something like 0 00000000789321 we apply a scaling factor The default multiplier in the HRT is
32. choener 1981 proposed a stati i es from independence of observations based on the ratio of the mean E a squared di ve observations and the mean squared distance from the centre of before it was located again is repeating a previous pattern of movements Swihart and Slade 1985a derived the sampling 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 70 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 28 09 10 15 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 lat
33. e 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 determine 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 car
34. each animal It is critically important that the animal ID field is defined as Text 1 e S alphanumeric and that the same ID is used for every record corresponding to each animal We made the animal ID field l alphanumeric to accommodate the hexadecimal codes used ialog box with Input Data tab selected and showing by some PS collar dependence of observations The Unique animal ID field manufacture lations must be defined as Text 1 e Y l EP i 1 gt alphanumeric and the same identify indi idua d nd bus o do not use numeric values ID must be used for every e g some researchers to give their animals names note that record corresponding to each o nal numeric values e g collar frequencies ear tags can be converted to text but not the reverse i Independence of s sive al locations is a basic assumption of many statistical methods of d Sh m home range ana OIT times called serial correlation of fix data tends to underestim t 1 ize in these models Swihart and Slade 19852 S
35. er 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 the smoothing size used are calculated and displayed on the Kernel Settings tab Figure 8 parameter is an expression of the variances or standard deviations of the x and y co ordinates around a F given point and applying a single smoothing parameter assumes each data point is scaled equally in all before any ke ethod is applied dard deviations StdDe nd StdDev y is directions Silverman 1986 it may be necessary to rescale the also shown for each case significant deviations of this ratio from a value of 1 0 i e 0 5 1 5 indicate cu ja e ja UN 2 C c p Q o O ES O un O ja pn O Cc oOo f O SS O o D S x O a gt O a Zi o O 3 F O y ec ata check the box next to Rescale the original observations will be T 240 54 1248 34 1129 74 1 1 ie Bandwidth tab see below 4 have unit variance changes the c
36. es 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 includes 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 de
37. he 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 is 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 28 09 10 12 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 method 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
38. hoice 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 28 09 10 3 for kernel density estimation and the following readings are also recommended Wand and Jones 1995 Jones et al 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 1t ourselves To install the HRT
39. hould Exclude these unsuccessful attempts to acquire a GPS fix When the import Figure 2 The Save As dialog process has finished the name of the shapefile will be displayed box showing the name of the new shapefile that will be created by the Import Lotek locations will appear in the Data View ready for use by the HRT MDB Files process in the Display and Source Table of Contents windows and the 28 09 10 6 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 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
40. how 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 hys to undersmooth utilization distributions is biased cross validation BCV In contrast with the LSCV method an integrated square BCV attempts to find a value of h that minimizes an estimate of the asymptotic me error AMISE AMISE is a large sample e g n gt 50 approximation of the MISE Wand and Jones 1995 n and the kernel density et al 1994 Wand to be minimized is Sain et al 1994 A a Aas gt 4m n 1 E n n 2 z Y amp BCV 2 h where the distance between pairs of points D LSCV method 200 values of h between 0 05h and 2h ear search algorithm to find the minimum value of BCV2 h The resulting smo XC V method pe reference methods Sain et al 1994 ever the B eee
41. ided 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 EA and select Start Editing on the Editor Editor dropdown menu Highlight the shapefile to be edited in either the Source or Display Table of Contents window Select the point to be 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 1f you want to save your edits It 1s 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
42. importantly the location data must be in UTM units meters The input data file must include a unique animal ID field column to perform Kernel Density Estimation It is critically important that the animal ID field is defined as a text field To Calculate Travel Times and Distances between successive Distances and areas in the HRT are measured in meters fixes the input data file must also include a field called Time2 see and square meters further description below Before importing data set the Data Frame respectively so location data must be in UTM units Properties on the View menu to one of the Predefined Projected meters UTM Coordinate Systems 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 button or by selecting Add Data from the File menu 28 09 10 4 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 or text editing e g WordPad program because some characters are not supported b
43. ing 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 counts 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
44. isrupt the isopleth calculation It is recommended you inspect the vector isopleth carefully to determine if it is 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 bivari
45. lysis You can save the kernel analysis settings for use wi ier files or retrieve previously saved settings using the Save Settings and ga Load Settings buttons at the bottom of the dialog box Input Data Tab A point layer alysis must first be entered drop down list Figure 7 in the box next to Point layer or selected from the 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 28 09 10 14 The Kernel Density Estimation option in the M Kernel Density Estimation HRT can process multiple animals in batch mode Simply enter a Unique animal ID field for batch processing in the box or select the corresponding field name from the drop down list Figure 7 Note that you must still specify an animal ID field even if all the points in the file to be 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
46. 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 File HRTA folder if you prefer the location of the HRT can be changed during the install process To activate the HRT following installation start ArcMap M 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 ina wide variety of formats You may already have it in an ESRI shapefile you may have it 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
47. o standard deviation Seaman and Powell 1996 The HRT allows the user to select either fixed a kernel or adaptive kernel methods of estimating a utilization distribution The fixed kernel approach assumes the width 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 outlying regions of low density because it is difficult to Figure 8 Kernel De stimation dialog box with Kernel Settings select a smoothing parameter that will accommodate these tab selected and showing sample size standard 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
48. onding polygon feature choosing Lines provides the perimeter length mete he specified isopleths Polygons provides the perimeter and the area square meters enclosed by e the specified isopleths and Donut polygons determines the areas between isopleths 1f more than one has been specified 28 09 10 23 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 Blundell G M J A K Maier and E M Debevec 2001 Linear home 1 es effects of smoothing sample size and autocorrelation on kernel estimates Ecolo Bowman A W and A Azzalini 1997 Applied smoothing te approach with S PLUS illustrations Oxford Uni Burt W H 1943 Territoriality and home range concepts as applied of Mammalogy 24 346 352 Downs J A and M A Horner 2008 Effects attern shape or e range estimates Journal of Wildlife Management 72 1813 1818 Fieberg J 20072 Kernel densi imators of hom Ecology 88
49. ork Now select Calculate Travel Times and Distances from the HRT Tools dropdown menu HRT Tools v 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 Dist 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 Wiper Fr Mile ner hour Frantlage obe automatically without the need to save them as edits You can now generate some simple summary Tes Mo statistics by right clicking on the file name choosing Open Attribute Table right clicking on Figure 4 Time Distance and Speed of Movement fieldsaddadasahe Attribute the column you would like to analyze and selecting Table when the Calculate 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
50. orted Lotek MDB files have all of the information required for full functionality of the HRT To import a Lotek MDB file select Import eg UNE 2 x Lotek MDB Files from the HRT Tools dropdown EEG HEEL ps menu An 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 000 RESET Files of type MDB Fis e Cancel name of the new shapefile that will be created by the Open as read only y import process The file will be imported and the location data will be converted from milliseconds and Figured The Open file dialos projected into the Predefined Projected Utm Coordinate box showing a selected System previously set in the Data Frame Properties Lotek MDB file on the View menu If the file includes records of unsuccessful PT 2x attempts to acquire a GPS location you will be informed that Save in POb02W v 9 ex E3 n Y our dataset may have records whose fix status is acquiring The script will ask you whether or not you want to Include or Exclude these records You may also choose to Cancel the import process With the exception of removing duplicate time or o EE location records all of the other HRT options require valid Save as type Shapefiles shp Cancel a location data to function correctly so you s
51. ory in nature sensu Burt 1943 351 5 ou can automatically select a percentage of the locations to UO CS be kept using any of the available methods by checking the box next to Select Percentage of Points entering the pe age of locations to be retained in the box next to Percentage s and choosing the Selection Style If you select the User Centre method slide bar down in the Selection Style box Figure 5 you must also the x longitude and y latitude co ordinates in UTM units of the specific 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 28 09 10 13 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
52. t clicking on the file name then choosing Select All from the Selection fly out menu you can use the Select Feature tool 18 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 1s selected see above You 28 09 10 10 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 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 w
53. termination 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 28 09 10 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 VoMCPs 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 ordering 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
54. the OK button Your data points should now be displayed in the Data View and the file name should now appear in both the Source and Display Table of Contents windows labeled as Events However the layer needs to be converted to a shapefile before you can use all of the HRT options To create a shapefile right click on the Events file in either the Source or Display Table of Contents window and select Data then Export Data In the Export Data dialog box select Use the 28 09 10 5 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 now have a shapefile to use with the HRT Lotek MDB Files Lotek MDB files are produced by their GPS Host and N4Win software using data downloaded from their early model GPS collars GPS 1000 and 2000 and differential correction data from a GPS base station These files can be imported directly to the HRT or the Lotek software can be used to output the data in a text file format csv or txt that can be edited and imported as outlined above The MDB files do not contain location data in UTM units but rather milliseconds of latitude and longitude During the import process these are projected into the Predefined Projected Utm Coordinate System set in the Data Frame Properties on the View menu before importing the data Subsequently imp
55. tion can greatly assist choosing values for subsequent analyses and for trouble shooting any problems that may arise The next section of the Output Options tab deals with the spatial extent of the output raster Isopleths of the utilization distribution 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 1 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 28 09 10 22 large enough to accommodate the utilization distribution Data truncation may therefore have occurred and this can profoundly d
56. y ArcMap such as dashes e g x coord spaces and brackets note that you can use the underscore character instead of the dash character in field names You shoul delete any records from E unsuccessful attempt to the file that do not have co ordinate data e g some GPS units may reco acquire a fix as N A and these records should be removed from the file The HRT uses a Time2 field to Calculate Travel Ti Distan etween successive fixes Thus locations in your file must be associated with a lin e scale that ides a unique value for each point otherwise this HRT option will not work Am ale can be used a h the preferred format is the number of seconds elapsed since a specific poi tin time e g the G ea of seconds since midnight 1 1 1970 or the number of seconds ele since the beginning af your study Y Y ou could simply order your data and numb calculated times and speeds of movement may
57. y have multiple centres of activity within their home range Consequentl you will probably want to try one or more of the other Bandwidth gt Selection methods available on the Bandwidth tab Figu n 1s 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 1 i I 5 e e Y Y E 2 2 mh 3 5 2 where the distance between pairs of points D is calculated as 2 2 2 X AX AA 0 t Xin 7 X 9 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 and 2h The resulting smoothing parameter that minimizes the score function is called hysc 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 28 09 10 18 hand LSCV may drastically undersmooth the utilization distribution 1f there are numerous small clusters of points 1 e centres of activity in the data Park and Marron 1990 Indeed the LSCV method has a propensity to s
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