Modern Telemetry Part 10 pot

Modern Telemetry 262 to calculate KDE (ArcView version 3.x) is not directly compatible with 64-bit computer operating systems and current extensions in the newer versions of ArcMap 9.x do not offer the flexibility in several components (i.e. batch-processing, bandwidth selection) afforded by earlier versions of ArcView 3.x, are unable to handle thousands of locations and overlapping coordinates (e.g. Home Range Tools), or were incorporated into the Geospatial Modelling Environment that requires ArcMap 10.x (i.e. Animal Movement Extension, Hawth’s Tools; www.spatialecology.com). Furthermore, several studies have indicated that size of home range calculated with KDE differed with each program by as much as 20% for 95% contours (Lawson & Rodgers 1997; Mitchell 2006). Most home range programs require various input parameters or are programmed with defaults that should be considered prior to selecting the program that best suits the needs of the researcher (Lawson & Rodgers 1997; Mitchell 2006; Gitzen et al., 2006). Many new programs to estimate home range are comparable to the graphical user interface of ArcMap (e.g. Quantum GIS, www.qgis.org), require ArcMap and R (e.g. Geospatial Modelling Environment, www.spatialecology.com/gme), or considerably under-estimate home range and require further evaluation (BIOTA, www.ecostats.com; Mitchell 2006). To evaluate every program available would have been beyond the scope of our objectives, so we presented home range estimators in R that is freely available to all researchers. Fig. 6. Home range of a yearling black bear using 95% plug-in with kernel density estimation (thick line) and exploratory movements with 95% BBMM (thin line) prior to dispersal in year 2. What Is the Proper Method to Delineate Home Range of an Animal Using Today’s Advanced GPS Telemetry Systems: The Initial Step 263 Fig. 7. Comparison of 95% estimates of panther home range derived from kernel density estimation with a) h ref bandwidth selection and b) h plug-in bandwidth selection as well as c) a Brownian bridge movement model with GPS locations () in background. 6. Conclusions Our goal was to assist researchers in determining the appropriate methods to assess size and shape of home range with a variety of species and movement vectors. Although we did not set out to assess the accuracy of methods, our results suggested that BBMM and h plug-in are Modern Telemetry 264 more appropriate for today’s GPS datasets that can have >1,000 locations seasonally and up to 10,000 locations annually over a 2–3 year collection period. Of equal importance, we were not able to generate KDE with h lscv in Home Range Tools for ArcMap and, to our knowledge, no other software was suitable or reported to determine size of home range for both KDE with h plug-in and BBMM other than R. The next step of research should focus on alternate software that can be used to estimate size of home range with actual animal GPS datasets. Although all software would likely produce inconsistent home range sizes as previously indicated for earlier programs with VHF datasets (Lawson & Rodgers 1997; Mitchell 2006), the magnitude and reason for differences needs to be understood. Finally, continued assessment of accuracy of estimates of home range is necessary with simulated datasets that range from several thousand to 10,000 serial locations that have defined true utilization distributions to determine proper estimator for size of home range based on study objectives and to verify software reliability. Further assessment of third generation methods (i.e. mechanistic home-range models, movement-based kernel density estimators) and development of user-friendly packages would be beneficial. As most third generation methods are in their infancy stages of development and evaluation, we are confident that home range estimation will continue to grow and evolve to offer researchers multiple choices for each study species. Undoubtedly, the debate over the proper technique to use should continue but we caution that ecology of the study animal, research objectives, software limitations, and home range estimators should be critically evaluated from the inception of a study (i.e. prior to ordering of GPS technology) to final estimation of size of home range. 7. Acknowledgment Funding for this research was provided by the National Wildlife Research Center of the United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services. We would like to thank Dave Onorato and the Florida Fish and Wildlife Conservation Commission for use of data on the Florida panther. We would like to thank Tommy King and the USDA/APHIS/WS National Wildlife Research Center Mississippi Field Station for data on American White Pelican. We would like to thank Michael Avery and the USDA/APHIS/WS National Wildlife Research Center Gainesville Field Station for data on black and turkey vultures. We would like to thank the USDA/APHIS/WS National Wildlife Research Center, Colorado State University, and the Colorado Division of Wildlife for use of black bear data. 8. References Amstrup, S. C., McDonald, T. L. & Durner, G. M. (2004). Using satellite radiotelemetry data to delineate and manage wildlife populations. Wildlife Society Bulletin, Vol.32, No.3, pp. 661–679 Avery,M.L., Humphrey,J.S., Daughtery,T.S., Fischer,J.W., Milleson,M.P., Tillman,E.A., Bruce,W.E. & Walter,W.D., (2011). Vulture flight behavior and implications for aircraft safety. 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Journal of Wildlife Management, Vol.59, No.4, pp. 794–800 Worton, B. J. (1989). Kernel methods for estimating the utilization distribution in home- range studies. Ecology, Vol.70, No.1, pp. 164–168 13 Quantifying Wildlife Home Range Changes Trisalyn A. Nelson Spatial Pattern Analysis and Research Laboratory, Department of Geography, University of Victoria Canada 1. Introduction In wildlife research, telemetry data are often converted to home ranges. The concept of an animal’s home range can be defined as the “. . . area traversed by the individual in its normal activities of food gathering, mating and caring for young” (Burt, 1943, pg. 351). The delineation and analysis of home ranges is common in wildlife research, and several reviews of home range studies exist (Harris et al., 1990; Laver & Kelly, 2008). Site fidelity (Edwards et al., 2009), population abundance (Trewhella et al., 1988), prey-predatory abundance (Village, 1982), impacts of human disturbance (Apps et al., 2004; Berland et al., 2008; Frair et al., 2008; Rushton et al., 2000; Thiel et al., 2008), feeding strategies (Hulbert et al., 1996) and ecological correlates of critical habitat (Tufto, 1996; Fisher, 2000) are examples of topics addressed using home range as the analysis unit. Home ranges are typically delineated with polygons. Locations within the polygon are considered part of the animal’s home range, and locations outside are not. As evidenced by the large number of home range studies, such binary approaches have been useful. However, landscape use by wildlife is spatially heterogeneous (Johnson et al., 1992; Kie et al., 2002). Edges (Yahner, 1988), disturbances (i.e., roads and forest harvesting) (Berland et al., 2008), and patch size (Kie et al., 2002) are just a few landscape features that cause heterogeneity in the geographic distribution of wildlife within home ranges. To account for spatial heterogeneity within a home range, core areas, defined as those used most frequently and likely to contain homesites, along with areas of refuge and dependable food sources (Burt, 1943) are sometimes delineated to create categories of habitat use (e.g., Samuel et al., 1985). Characterizing the spatial variation in wildlife distributions should improve our understanding of habitat use, especially in conjunction with the growing spatial extents of wildlife data sets. Arguably, the two most common approaches to demarcating a home range are the minimum convex polygon and kernel density estimation (Harris et al., 1990). The minimum convex polygon tends to overestimate home range size by including all the unused areas between outermost locations and increasing in area with large sample sizes (Börger et al., 2006a; Katajisto & Moilanen, 2006). As such, kernel density estimation is often preferred when demarcating a home range (Seaman & Powell, 1996; Marzluff et al., 2004; Börger et al., 2006a; Laver & Kelly, 2008). Although used to delineate binary home ranges, kernel density estimation generates a surface of values within the home range, which is useful for characterizing spatial variability in wildlife intensity. Kernel density surfaces are often referred to as utilization distributions as they give values that indicate higher and lower utilization of locations by individuals. Modern Telemetry 270 Regardless of how the home range is calculated, there are benefits to converting point-based telemetry data to polygonal home ranges. First, unless telemetry data are collected at a very high temporal frequency, almost continuously, telemetry data represent a sample of locations visited by an individual. Conversion to a polygon is an attempt to represent the complete range of possible movements. Second, conversion to a utilization distribution has the additional benefit of being useful for integrating telemetry data with environmental data sets. Often stored within a Geographic Information System (GIS), many environmental data sets are represented using raster grids. A common example is elevation data sets, which are stored in grid cells, of varying size. Kernel density estimated values are also stored as grid cells enabling efficient integration of utilization distributions with other map-based data sets. As telemetry data sets have grown in temporal extent, it has become useful to employ home ranges to assess wildlife movement and habitat use through time. Characterizing the temporal change in home ranges has been used to study seasonal movement (Georgii, 1980), relate home range size to population abundance (Lowe et al., 2003) and land use (Viggers & Hearn, 2005), and characterize the spatial interactions of predator and prey (Village, 1982). Typically, when quantifying home range change, areal sizes are compared (e.g., Lurz et al., 1997; Lowe et al., 2003; Edwards et al., 2009) or the proportions of areal overlap enumerated (e.g., Georgii, 1980; Atwood & Weeks, 2003). In a few examples, spatial-temporal patterns of home ranges are quantified in greater detail. For instance, the multi-temporal persistence of home ranges has been related to landscape disturbance (Berland et al., 2008). Two additional approaches were identified by Kie et al. (2010) as showing potential for identifying temporal changes in home ranges. The first approach uses mixed effect models to relate temporal variation in patterns of telemetry data to climate, habitat, and age/sex variables of deer (Börger et al., 2006b). The second considers spatial variation in habitat use (represented by utilization distributions, defined below) continuous in time and representative of four dimensions (latitude, longitude, elevation, and time) (Keating & Cherry, 2009). Using a product-kernel, temporal patterns in space use were characterized using a circular time scale. Improved approaches to wildlife data collection, such as satellite and global positioning system (GPS) collars, in combination with concerns over climate change and growing anthropogenic pressures on wildlife, have increased the number of possible multi-temporal wildlife research questions. Development of new analytical approaches has begun and must continue if high temporal resolution telemetry data can be used to their full potential. Here, I present three novel approaches to quantifying spatial-temporal change in home ranges. The first method, Spatial Temporal Analysis of Moving Polygons (STAMP), uses topological relationships of home range polygons to quantify spatial-temporal patterns of home ranges. The second method detects statistically significant change between two kernel density- estimated surfaces, and is utilized to characterize statistical change in intensity of habitat use within home ranges. The third method, an integration of methods one and two, simultaneously quantifies both the spatial-temporal pattern and change in wildlife intensities within home ranges. Described below, the new methods are demonstrated on caribou (Rangifer tarandus caribou) data from western Canada, and their benefits are outlined and compared to traditional approaches. To begin, home range delineation and typical approaches to change detection are presented as the basis for comparison with these novel approaches. 2. Home range methods 2.1 Telemetry data The methods presented and compared in this chapter are applied to data on the Swan Lake woodland caribou herd, located in the southern Yukon, near Swift River (60°10'N, [...]... Change No Change Total km2 % km2 % km2 % km2 % Stable 29.17 3.73 101 .52 12.99 650.79 83.28 781.48 100 .00 Disappearance 0.00 0.00 83.58 27.92 215.80 72.08 299.38 100 .00 Contraction 0.00 0.00 453.90 49.42 464.55 50.58 918.45 100 .00 Generation 50.08 35.11 0.00 0.00 92.55 64.89 142.63 100 .00 Expansion 52.75 17.17 0.00 0.00 254.42 82.83 307.17 100 .00 Table 2 Area of statistically significant changes in kernel... between F3 and F1 was 3.2 ha F1 (♀) Telemetry locations recorded 100 % Minimum convex polygon (ha) 95% Fixed-kernel (ha) 50% Fixed-kernel (ha) F3 (♀) M2 (♂) 172 121.6 99.5 13.2 368 168.8 131.3 10. 5 63 105 .7 77.4 7.4 Table 1 Radio -telemetry data collected from three red foxes captured in Stanhope, Prince Edward Island National Park (Prince Edward Island, Canada) Use of Telemetry Data to Investigate Home... Agriculture Beach Forest Marsh Water Modern Telemetry Observations Habitat Use proportion Expected Use (# locations) proportion Average SD Preference 300 86 64 31 41 15 1 39 3 0 0.517 0.148 0. 110 0.053 0.071 0.026 0.002 0.067 0.005 0 0.077 0.054 0.048 0.034 0.039 0.024 0.006 0.038 0.011 0 0.114 0.053 0.033 0.005 0 .100 0.003 0 .109 0.503 0.060 0.019 + + + + 0 0 0 - 67 16 103 8 11 18 27 1 1 0.249 0.172 0.369... that population and its dynamics as well as to identify any potential threats to its survival (White & Garrott, 1990) It is typically used to gather data from distant, inaccessible locations, or when data collection would be dangerous or difficult for a variety of reasons Wildlife telemetry concerns the use of telemetry techniques 282 Modern Telemetry to remotely locate wild species and obtain ecological,... data It was in the 1960s, when telemetry, specifically radio -telemetry, was first used to study terrestrial wildlife (Craighhead, 1982; Hebblewhite & Haydon, 2 010) Since then, wildlife telemetry has contributed significantly to our understanding of fundamental ecological and behavioural processes of many animal species (e.g., Johnson et al., 2006) Advances in wildlife telemetry have made it possible... consumption or wild meat and were checked every day Captured foxes were anesthetized using Xylazine/Ketamine (1 :10 mg/kg) and Atipamezole (1 mg per 10 mg of Xylazine; Animal Care Protocol, University of Prince Edward Island 03-043), and then radio-collared (TS-37 Telemetry Solutions; 50 g) The radio -telemetry procedure used in this study followed recommendations made by White & Garrott (1990) During the... usage in each habitat is significantly different from expected The usage of a particular habitat type was defined as the ratio between animal locations in each habitat type and the total number of locations recorded in the study area Expected 288 Modern Telemetry usage of a habitat type was defined as the ratio of the area of the particular habitat type to the total area of the study site The study-site... contraction patterns indicate that a location is part of a home range in t but not t+1 Disappearance patterns are spatially isolated, as opposed to contraction patterns which are spatially adjacent to other home range areas that have changed in a different way Generation and expansion patterns both indicate that a location was not part of a home range in t, but became part of a home range in t+1 While generation... (Section 4), telemetry data will be used to investigate habitat selection and home range patterns of the African wild dog (Lycaon pictus) in Mkhuze Game Reserve, South Africa Contrary to the red fox, the interaction with humans has had detrimental effects on African wild-dog populations in South Africa and other parts of Africa Our objective for Case Study 2 is to illustrate the use of telemetry data... can be found during a specified time period (Harris et al., 1990; Kernohan et al., 2001) According to this definition, 284 Modern Telemetry a home range can be flexible, varying with season and overlapping with conspecifics (Harris et al., 1990), making the concept of home range particular useful for habitat selection studies In contrast, a territory, a term commonly used interchangeably with home range, . (2008). An integrated vehicle-mounted telemetry system for VHF telemetry applications. Journal of Wildlife Management, Vol.72, No.5, pp. 1241–1246 Modern Telemetry 266 Girard, I., Ouellet,. individuals. Modern Telemetry 270 Regardless of how the home range is calculated, there are benefits to converting point-based telemetry data to polygonal home ranges. First, unless telemetry. Stable 29.17 3.73 101 .52 12.99 650.79 83.28 781.48 100 .00 Disappearance 0.00 0.00 83.58 27.92 215.80 72.08 299.38 100 .00 Contraction 0.00 0.00 453.90 49.42 464.55 50.58 918.45 100 .00 Generation