Evaluating distributional shifts in home range estimates

A variety of methods are commonly used to quantify animal home ranges using location data acquired with telemetry. High‐volume location data from global positioning system (GPS) technology provide researchers the opportunity to identify various intensities of use within home ranges, typically quantified through utilization distributions (UDs). However, the wide range of variability evident within UDs constructed with modern home range estimators is often overlooked or ignored during home range comparisons, and challenges may arise when summarizing distributional shifts among multiple UDs. We describe an approach to gain additional insight into home range changes by comparing UDs across isopleths and summarizing comparisons into meaningful results. To demonstrate the efficacy of this approach, we used GPS location data from 16 bighorn sheep (Ovis canadensis) to identify distributional changes before and after habitat alterations, and we discuss advantages in its application when comparing home range size, overlap, and joint‐space use. We found a consistent increase in bighorn sheep home range size when measured across home range levels, but that home range overlap and similarity values decreased when examined at increasing core levels. Our results highlight the benefit of conducting multiscale assessments when comparing distributions, and we encourage researchers to expand comparative home range analyses to gain a more comprehensive evaluation of distributional changes and to evaluate comparisons across home range levels.     

Estimating utilization distributions with kernel versus local convex hull methods

Estimates of utilization distributions (UDs) are used in analyses of home-range area, habitat and resource selection, and social interactions. We simulated data from 12 parent UDs, representing 3 series of increasingly intense space-use patterns (clustering of points around a home site, restriction of locations to a network of nodes and corridors, and dominance of a central hole in the UD) and compared the ability of kernel density estimation (KDE) and local convex hull (LCH) construction to reconstruct known UDs from samples of 10, 50, 250, and 1,000 location points. For KDE, we considered 4 bandwidth selectors: the reference bandwidth, least-squares cross-validation (LSCV), direct plug-in (DPI), and solve-the-equation (STE). For the sample sizes and UD patterns tested here, KDE achieved significantly higher volume-of-intersection (VI) scores with known parent UDs than did LCH; KDE also provided less biased home-range area estimates under many conditions. However, LCH minimized the UD volume that occurred outside the true home range boundary (V_out). Among the KDE bandwidth estimators, relative performance depended on the type and intensity of space use patterns, sample size, and the metric used to evaluate performance. Biologists should use KDE for UD and home range estimation within a probabilistic context, unless their objective is to exclude potentially unused areas by defining the area delimited by data. © 2011 The Wildlife Society.     

Comparing Global Positioning System and Very High Frequency Telemetry Home Ranges of White-Tailed Deer

ABSTRACT Use of Global Positioning System (GPS) collars on free-ranging ungulates overcomes many limitations of conventional very high frequency (VHF) telemetry and offers a practical means of studying space use and home range estimation. To better understand winter home ranges of white-tailed deer (Odocoileus virginianus), we evaluated GPS collar performance, and we compared GPS- and VHF-derived diurnal home ranges (for the same animals) and GPS-derived home range estimates for diurnal and nocturnal locations. Overall, the mean fix success rate of our GPS collars was 85% (range = 14–99%). Kernel density estimates of home range (using the 95% probability contour) derived from GPS and VHF locations were generally similar, as were GPS-derived diurnal and nocturnal home ranges. Overlap indices between GPS and VHF utilization distributions (UDs) ranged from 0.49 to 0.78 for the volume of intersection (VI) index and from 0.67 to 0.94 for Bhattacharyya's affinity (BA); overlap indices for GPS-diurnal and nocturnal UDs ranged from 0.29 to 0.81 for VI and from 0.56 to 0.94 for BA. Despite similarities of home ranges estimated from GPS versus VHF locations and GPS-diurnal versus nocturnal locations, our data also indicate that differences may have important implications for studies focused on deer use of space, habitat, and resources at a finer scale.     

Effect of GPS tagging on behaviour and marine distribution of breeding Arctic Terns Sterna paradisaea

Tracking tags have been used to map the distributions of a wide variety of avian species, but few studies have examined whether the use of these devices has impacts on the study animals that may bias the spatial data obtained. As Global Positioning System (GPS) tags small enough for deployment on terns (family: Laridae) have only recently become available, until now tracking of this group has been conducted by following unmanipulated individuals by boat, which offers a means of comparing distributions obtained from GPS‐tracking. We compared the utilization distributions (UDs) of breeding Arctic Terns Sterna paradisaea obtained by GPS‐tracking 10 individuals over 2 weeks, with UDs derived from contemporaneous visual boat tracks from 81 individuals. The 50% and 95% UDs of both methods had high similarity scores, indicating good agreement in the density distributions derived from the two methods. The footprints of the UDs of tagged birds were ~ 75–80% larger, which may reflect an effect of tagging on foraging range or the occasional inability to follow by boat individuals which roamed further from the colony. We also compared the nest attendance and chick provisioning rates of adults that were (1) fitted with a GPS tag and leg‐flag, (2) handled and marked with a leg‐flag but not tagged and (3) fitted with a leg‐flag in a previous year but unhandled in the year of the study. There was some evidence that birds fitted with both a GPS tag and leg‐flag spent slightly less time at the nest compared with unhandled birds and those fitted with a leg‐flag only. Both treatments where birds were fitted with a leg‐flag in the year of the study had similarly lower provisioning rates to those of unhandled control birds > 48 h after handling, suggesting that negative effects on provisioning are due to capture and handling or leg‐flag attachment rather than to GPS tag attachment/loading per se. Overall brood‐provisioning rate was compensated for by the increased effort by the unhandled partner. Our study suggests that despite slight effects of GPS‐tagging on behaviour, the estimates of marine density distribution obtained were very similar to those of unmanipulated birds.     

Effects of Point Pattern Shape on Home-Range Estimates

Abstract: Home-range estimators are commonly tested with simulated animal locational data in the laboratory before the estimators are used in practice. Although kernel density estimation (KDE) has performed well as a home-range estimator for simulated data, several recent studies have reported its poor performance when used with data collected in the field. This difference may be because KDE and other home-range estimators are generally tested with simulated point locations that follow known statistical distributions, such as bivariate normal mixtures, which may not represent well the space-use patterns of all wildlife species. We used simulated animal locational data of 5 point pattern shapes that represent a range of wildlife utilization distributions to test 4 methods of home-range estimation: 1) KDE with reference bandwidths, 2) KDE with least-squares cross-validation, 3) KDE with plug-in bandwidths, and 4) minimum convex polygon (MCP). For the point patterns we simulated, MCP tended to produce more accurate area estimates than KDE methods. However, MCP estimates were markedly unstable, with bias varying widely with both sample size and point pattern shape. The KDE methods performed best for concave distributions, which are similar to bivariate normal mixtures, but still overestimated home ranges by about 40–50% even in the best cases. For convex, linear, perforated, and disjoint point patterns, KDE methods overestimated home-range sizes by 50–300%, depending on sample size and method of bandwidth selection. These results indicate that KDE does not produce home-range estimates that are as accurate as the literature suggests, and we recommend exploring other techniques of home-range estimation.     

LoCoH: nonparameteric kernel methods for constructing home ranges and utilization distributions

Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: "fixed sphere-of-influence," or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an "adaptive sphere-of-influence," or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original "fixed-number-of-points," or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).     

A local nearest-neighbor convex-hull construction of home ranges and utilization distributions

We describe a new method for estimating the area of home ranges and constructing utilization distributions (UDs) from spatial data. We compare our method with bivariate kernel and α-hull methods, using both randomly distributed and highly aggregated data to test the accuracy of area estimates and UD isopleth construction. The data variously contain holes, corners, and corridors linking high use areas. Our method is based on taking the union of the minimum convex polygons (MCP) associated with the k−1 nearest neighbors of each point in the data and, as such, has one free parameter k. We propose a "minimum spurious hole covering" (MSHC) rule for selecting k and interpret its application in terms of type I and type II statistical errors. Our MSHC rule provides estimates within 12% of true area values for all 5 data sets, while kernel methods are worse in all cases: in one case overestimating area by a factor of 10 and in another case underestimating area by a factor of 50. Our method also constructs much better estimates for the density isopleths of the UDs than kernel methods. The α-hull method does not lead directly to the construction of isopleths and also does not always include all points in the constructed home range. Finally we demonstrate that kernel methods, unlike our method and the α-hull method, does not converges to the true area represented by the data as the number of data points increase.     

Population structure and spatial ecology of Kordofan giraffe in Garamba National Park,Democratic Republic of Congo

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Use of space and home range characteristics of Lepilemur mittermeieri,an endangered sportive lemur endemic to the Ampasindava peninsula,north‐west Madagascar

Lepilemur mittermeieri, a little‐studied sportive lemur of north‐west Madagascar, endemic to the Ampasindava Peninsula, faces habitat loss through forest degradation and rapid fragmentation. Understanding its habitat requirement is the first step toward preservation of this threatened forest‐dependent species. In this study, we gathered data on the use of space and home range characteristics of L. mittermeieri. We studied individuals from early March to the end of June 2015 and 2016, in three sites of the Ampasindava peninsula. We radio‐tracked 15 individuals to obtain detailed information on the size and location of home ranges (around 450 hr of tracking). Direct observation and morphometric measurements provided additional data sets. Both kernel density estimation (KDE) and minimum convex polygon (MCP) methods yielded similar home range sizes (an average of 2.01 ha with KDE method and 1.96 ha with MCP method). We did not find differences in home range size between males and females, with respect to forest type or proximity to the forest edge. Home ranges overlapped and individuals showed low levels of territoriality. We highlighted a sexually‐dimorphic trait: males have longer upper canine than females. Our results constitute the first set of ecological information on Lepilemur mittermeieri and could be the basis for a conservation strategy for this endangered species with a very small distribution area.     

The home-range concept: are traditional estimators still relevant with modern telemetry technology?

Recent advances in animal tracking and telemetry technology have allowed the collection of location data at an ever-increasing rate and accuracy, and these advances have been accompanied by the development of new methods of data analysis for portraying space use, home ranges and utilization distributions. New statistical approaches include data-intensive techniques such as kriging and nonlinear generalized regression models for habitat use. In addition, mechanistic home-range models, derived from models of animal movement behaviour, promise to offer new insights into how home ranges emerge as the result of specific patterns of movements by individuals in response to their environment. Traditional methods such as kernel density estimators are likely to remain popular because of their ease of use. Large datasets make it possible to apply these methods over relatively short periods of time such as weeks or months, and these estimates may be analysed using mixed effects models, offering another approach to studying temporal variation in space-use patterns. Although new technologies open new avenues in ecological research, our knowledge of why animals use space in the ways we observe will only advance by researchers using these new technologies and asking new and innovative questions about the empirical patterns they observe.     

Dynamic approach to space and habitat use based on biased random bridges

Background

Although habitat use reflects a dynamic process, most studies assess habitat use statically as if an animal''s successively recorded locations reflected a point rather than a movement process. By relying on the activity time between successive locations instead of the local density of individual locations, movement-based methods can substantially improve the biological relevance of utilization distribution (UD) estimates (i.e. the relative frequencies with which an animal uses the various areas of its home range, HR). One such method rests on Brownian bridges (BBs). Its theoretical foundation (purely and constantly diffusive movements) is paradoxically inconsistent with both HR settlement and habitat selection. An alternative involves movement-based kernel density estimation (MKDE) through location interpolation, which may be applied to various movement behaviours but lacks a sound theoretical basis.

Methodology/Principal Findings

I introduce the concept of a biased random (advective-diffusive) bridge (BRB) and show that the MKDE method is a practical means to estimate UDs based on simplified (isotropically diffusive) BRBs. The equation governing BRBs is constrained by the maximum delay between successive relocations warranting constant within-bridge advection (allowed to vary between bridges) but remains otherwise similar to the BB equation. Despite its theoretical inconsistencies, the BB method can therefore be applied to animals that regularly reorientate within their HRs and adapt their movements to the habitats crossed, provided that they were relocated with a high enough frequency.

Conclusions/Significance

Biased random walks can approximate various movement types at short times from a given relocation. Their simplified form constitutes an effective trade-off between too simple, unrealistic movement models, such as Brownian motion, and more sophisticated and realistic ones, such as biased correlated random walks (BCRWs), which are too complex to yield functional bridges. Relying on simplified BRBs proves to be the most reliable and easily usable way to estimate UDs from serially correlated relocations and raw activity information.     

Incorporating Movement Behavior and Barriers to Improve Kernel Home Range Space Use Estimates

Abstract: Kernel-based utilization distribution (UD) estimates are powerful tools to investigate home range space use and resource selection in many vertebrate species. By ignoring local movement information provided by the serial correlation between successive locations and the constraints to movement imposed by obvious boundaries, the classical kernel method results in loosely estimated UDs that tend to overflow into never-visited areas and eventually in possibly biased estimates of space use and habitat selection. We improved biological relevance of kernel home range space use estimates by incorporating both movement (and activity) information and boundary constraints.     

Screening Radiolocation Datasets for Movement Strategies With Time Series Segmentation

ABSTRACT Classical home range analysis is tailored to meet requirements of data with few points per individual with relatively large intervals between observations. The swift rise in Global Positioning System (GPS)-based studies requires the development of new analytical approaches because GPS data allow for more detailed analysis in time and space. The amount of data derived from GPS studies enhances the potential to more accurately separate movement strategies. We present a general, simple, conceptual approach to using large movement datasets to automatically screen and delimit spatial and temporal home ranges of individuals and movement strategies using time series segmentation. We used GPS data for moose (Alces alces) from a boreal Swedish population as an example. We tested predictions that our screening method could separate seasonal migration from dispersal and nomadic strategies by the movement profile, which includes several dimensions. Our analysis showed that broad strategies were detected using our simple analytical approach, which speeds up use of GPS data for management and research because the method can be used to calculate more objective spatial and temporal activity ranges in relation to movement strategies. Our examples illustrate the importance of using the time stamp on location data in describing home ranges and movements.     

Home‐range size of an Andean bird: Assessing the role of physical condition

Because space‐use patterns are a key aspect of the ecology and distribution of species, identifying factors associated with variation in size of territories and home ranges has been central to studies on population ecology. Space use might vary in response to extrinsic factors like habitat quality and to intrinsic factors like physical condition and individual aggressiveness. However, the role of these factors has been poorly documented in the tropics, particularly in high‐elevation bird species. We report the home‐range size of a Neotropical Andean bird, the gray‐browed brush finch (Arremon assimilis), and evaluate the role of physical condition in explaining variation in home‐range size among individuals. We performed spot mapping to estimate the home ranges of 14 territorial males in Bogotá, Colombia, using minimum convex polygons (MCP) and 95% kernel density estimators (KDE). The mean home‐range size estimated for the 100% MCP was 0.522 ± 0.305 ha (range = 0.15–1.18 ha), whereas the 95% KDE estimation was 0.504 ± 0.471 ha (range = 0.13–1.88). We calculated the real mass index of each bird as a proxy of physical condition to assess whether individuals in better physical condition had larger home ranges. Because we found no relation between our estimations of physical condition and home‐range size, we conclude that space use in this species might depend more on ecological factors such as habitat quality or neighbor density than on individual traits. Abstract in French is available with online material.     

Heading for the hills? Evaluating spatial distribution of woodland caribou in response to a growing anthropogenic disturbance footprint

Anthropogenic landscape change (i.e., disturbance) is recognized as an important factor in the decline and extirpation of wildlife populations. Understanding and monitoring the relationship between wildlife distribution and disturbance is necessary for effective conservation planning. Many studies consider disturbance as a covariate explaining wildlife behavior. However, we propose that there are several advantages to considering the spatial relationship between disturbance and wildlife directly using utilization distributions (UDs), including objective assessment of the spatially explicit overlap between wildlife and disturbance, and the ability to track trends in this relationship over time. Here, we examined how central mountain woodland caribou (Rangifer tarandus caribou) distribution changed over time in relation to (i) anthropogenic disturbance, baseline range (defined using telemetry data from 1998 to 2005), and alpine habitat; and (ii) interannual climate variation (North Pacific Index; NPI). We developed seasonal UDs for caribou in west‐central Alberta and east‐central British Columbia, Canada, monitored with GPS collars between 1998 and 2013. We mapped the cumulative annual density of disturbance features within caribou range and used indices of overlap to determine the spatial relationship and trend between caribou UDs, anthropogenic disturbance, baseline range, alpine habitat, and the NPI. Anthropogenic disturbance increased over time, but the overlap between caribou UDs and disturbance did not. Caribou use of alpine habitat during spring, fall, and late winter increased over time, concurrent with a decrease in use of baseline range. Overlap between caribou UDs and disturbance increased during spring and fall following relatively cold, snowy winters (high NPI), but overall, climate did not explain changes in caribou distribution over time. We provide evidence supporting the hypothesis that caribou populations adjust their spatial distribution in relation to anthropogenic landscape change. Our findings could have implications for population persistence if distributional shifts result in greater use of alpine habitat during winter. Monitoring long‐term changes in the distribution of populations is a valuable component of conservation planning for species at risk in disturbed landscapes.     

Effects of Telemetry Location Error on Space-Use Estimates Using a Fixed-Kernel Density Estimator

ABSTRACT Fixed-kernel density estimates using radiotelemetry locations are frequently used to quantify home ranges of animals, interactions, and resource selection. However, all telemetry data have location error and no studies have reported the effects of error on utilization distribution and area estimates using fixed-kernel density estimators. We simulated different home range sizes and shapes by mixing bivariate-normal distributions and then drawing random samples of various sizes from these distributions. We compared fixed-kernel density estimates with and without error to the true underlying distributions. The effects of telemetry error on fixed-kernel density estimates were related to sample size, distribution complexity, and ratio of median Circular Error Probable to home range size. We suggest a metric to assess the adequacy of the telemetry system being used to estimate an animal's space use before a study is undertaken. Telemetry location error is unlikely to significantly affect fixed-kernel density estimates for most wildlife telemetry studies with adequate sample sizes.     

A cautionary note on the use of hypervolume kernel density estimators in ecological niche modelling

Blonder et al. ( 2014 , Global Ecology and Biogeography, 23, 595–609) introduced a new multivariate kernel density estimation (KDE) method to infer Hutchinsonian hypervolumes in the modelling of ecological niches. The authors argued that their KDE method matches or outperforms several methods for estimating hypervolume geometries and for conducting species distribution modelling. Further clarification, however, is appropriate with respect to the assumptions and limitations of KDE as a method for species distribution modelling. Using virtual species and controlled environmental scenarios, we show that KDE both under‐ and overestimates niche volumes depending on the dimensionality of the dataset and the number of occurrence records considered. We suggest that KDE may be a viable approach when dealing with large sample sizes, limited sampling bias and only a few environmental dimensions.     

Free-Ranging Farm Cats: Home Range Size and Predation on a Livestock Unit In Northwest Georgia

This study’s objective was to determine seasonal and diurnal vs. nocturnal home range size, as well as predation for free-ranging farm cats at a livestock unit in Northwest Georgia. Seven adult cats were tracked with attached GPS units for up to two weeks for one spring and two summer seasons from May 2010 through August 2011. Three and five cats were tracked for up to two weeks during the fall and winter seasons, respectively. Feline scat was collected during this entire period. Cats were fed a commercial cat food daily. There was no seasonal effect (P > 0.05) on overall (95% KDE and 90% KDE) or core home range size (50% KDE). Male cats tended (P = 0.08) to have larger diurnal and nocturnal core home ranges (1.09 ha) compared to female cats (0.64 ha). Reproductively intact cats (n = 2) had larger (P < 0.0001) diurnal and nocturnal home ranges as compared to altered cats. Feline scat processing separated scat into prey parts, and of the 210 feline scats collected during the study, 75.24% contained hair. Of these 158 scat samples, 86 contained non-cat hair and 72 contained only cat hair. Other prey components included fragments of bone in 21.43% of scat and teeth in 12.86% of scat. Teeth were used to identify mammalian prey hunted by these cats, of which the Hispid cotton rat (Sigmodon hispidus) was the primary rodent. Other targeted mammals were Peromyscus sp., Sylvilagus sp. and Microtus sp. Invertebrates and birds were less important as prey, but all mammalian prey identified in this study consisted of native animals. While the free-ranging farm cats in this study did not adjust their home range seasonally, sex and reproductive status did increase diurnal and nocturnal home range size. Ultimately, larger home ranges of free-ranging cats could negatively impact native wildlife.     

Space Use and Habitat Selection by Bobcats in the Fragmented Landscape of South-Central Iowa

Abstract: Historically, bobcats (Lynx rufus) were found throughout the Corn Belt region, but they nearly disappeared from this area due to habitat loss and unregulated harvest that occurred during the century after European settlement. Reports of bobcat occurrences have been increasing in Iowa, USA, and biologists would like to understand the mechanisms enabling bobcats to recolonize this fragmented agricultural landscape. We determined space use and habitat selection of bobcats by radiocollaring 68 bobcats in south-central Iowa during 2003–2006. We triangulated 12,966 locations and recovered an additional 1,399 3-dimensional locations from Global Positioning System collars. We used a fixed kernel estimator to calculate 95% utilization distributions (UDs) for home ranges and 50% UDs for cores. Annual home range area of males (x̄ = 58.6 km², 95% CI = 49.2–69.9) was nearly 3 times that of females (x̄=19.9 km², 95% CI = 17.0–23.3). Females used smaller home ranges during April-September when they were suspected to have kittens with them (x̄ = 16.8 km², 95% CI = 13.7–20.7), as compared to October-March (x̄ = 24.1 km², 95% CI = 19.0–30.7), whereas home ranges of males did not differ between seasons. Similarly, core area of males (x̄ = 7.7 km², 95% CI = 6.2–9.6) was larger than that of females (x̄ = 2.3 km², 95% CI = 1.9–2.7). Females used significantly smaller cores in April-September (x̄ = 1.8 km², 95% CI = 1.4–2.3) as compared to October-March (x̄ = 2.8 km², 95% CI = 2.2–3.7), whereas males did not. For both sexes, compositional analysis indicated that forest habitat was ranked higher than all other habitat classes at both the landscape and local scale. Standardized habitat selection ratios illustrate that female and male bobcats selected forest habitat about twice as frequently as any other habitat class, including grassland and Conservation Reserve Program land. Predictive models indicated that home range and core area was smaller in landscapes where perennial forest and grassland habitats were less fragmented. Predictive models indicated home ranges were more irregular in shape in landscapes where row crop patches were less aggregated within home ranges. Our results have practical implications for wildlife managers regarding expected bobcat habitat use and distribution as the species becomes more abundant in the agricultural landscape of the Midwest.     

Detecting autocorrelation problems from GPS collar data in livestock studies

Uneven use of grasslands and savannas by livestock has a significant impact on ecosystem productivity, biodiversity, and function. In studies of livestock distribution, global positioning systems (GPS) collars are frequently used and the rapid rate of technological improvement has brought new opportunities to collect extremely large amounts of very accurate spatial information. However, these advances also pose statistical challenges associated with the analysis of large, temporally correlated, datasets. Our main goal was to find the optimal sampling time intervals for GPS collar schedules when studying livestock distribution in semi-arid ecosystems. The schedule must provide maximum spatio-temporal information while avoiding problems of autocorrelation of sequential locations to provide a methodology that is both practical and statistically valid. We used GPS collar data collected in the Southwestern region of the United States. In each study cattle were tracked and data were recorded every 5 min. Location information from the 5-min GPS fixes were subsampled into 10, 20, 30, 60, 90, 120, 150, 180, 240, 300, 360, and 420-min regular intervals. We calculated the Euclidean distance between pairs of successive locations then conducted correlation analyses to determine the degree of similarity between successive traveled distances. We then selected two correlated and two non-correlated time-interval datasets to compare estimates of kernel home range and minimum convex polygon areas. Successive Euclidean distances between GPS locations were significantly correlated when time intervals were <120 min. The calculated distance traveled was significantly reduced as time intervals between successive locations increased. Kernel home range values were smaller in correlated than in non-correlated datasets yet minimum convex polygon values were greater in correlated data than in non-correlated data sets. Our study shows the importance of considering different livestock sampling time intervals using GPS to achieve accurate and meaningful results on animal distributions especially in semi-arid ecosystems. Circumstances in which researchers may elect to use short-time interval autocorrelated data sets are also discussed.