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Understanding patterns in the spatial distribution of individuals in a population is a central question in ecology. Concurrent with advances in biotelemetry devices, development of home range estimator methods incorporating the temporal component of locational fixes are increasingly used to investigate these patterns at finer scales. However, these methods may necessitate sampling schedules that limit battery life and study period length. Practically, evaluating how home range estimator methods affect calculations of space use and habitat selection prior to deployment of biotelemetry devices could help researchers optimize data acquisition schedules. We quantified spatial overlap between a home range estimator using temporal information (dynamic Brownian bridge movement model [dBBMM]) and home range estimators not incorporating the temporal component of fixes (ad hoc and href kernel density estimator [KDE]) across differing sample schedules, and the resulting error in habitat selection ratios using data collected from wild turkeys (Meleagris gallopavo) equipped with Global Positioning Systems units in Texas, Georgia, South Carolina, and Louisiana, USA, during February–May 2015. When comparing ranges created from KDEs to dBBMM, commission errors were large (20–80%) and did not diminish with increased sampling rates. In contrast, omission error rate declined quicker and improvements were minimal when fix rates increased beyond 4/day. Compared with ranges estimated with dBBMM, KDEs poorly defined the spatial bearings of an individual’s range, overestimated areas of use, underestimated areas avoided, and showed different patterns of habitat selection. Our results suggest home range estimator methods incorporating temporal information seem capable of estimating ranges encompassing nearly all area used by an individual and should be used even at relatively low-frequency collection schedules to assess home ranges of wild turkeys. If researchers are interested in describing habitat selection of wild turkeys, we recommend a sampling schedule of ≤1 location/hour during daytime and dBBMM for range estimation.

Efficient delineation of conservation areas is a great challenge in maintaining biodiversity. Kernel density estimators (KDEs) are a powerful tool in this perspective, but they have not been applied at the population level on patch-distributed organisms. This would be particularly worthy for species that need broad habitats beyond those where they can be sampled; such as terrestrial lands for pond-breeding amphibians. The aim of this study was to compare different approaches for the identification of suitable areas for conservation: KDE, ecological niche modelling, and a combination of KDE and niche models. Paedomorphosis was chosen as a model system because this is an important form of intraspecific variation that is present in numerous taxa, but geographically localized within species and globally endangered. 277 ponds were sampled in one of the hotspots of paedomorphosis to determine the abundance and distribution of paedomorphs (i.e., individuals retaining gills at the adult stage) of the palmate newt ( Lissotriton helveticus ), with emphasis on the connections between the most valuable populations. KDEs gave insights into the surface areas required to balance the maintenance of certain number of connected ponds and the respective number of disjoint areas in which the whole population is divided. The inclusion of barriers in the models helped in accurately designing the limits of the areas to protect. Alone, habitat models were not able to successfully delineate the area to protect, but the integration between terrestrial suitable areas or barriers and KDE allowed an objective identification of areas required for conservation. Overall, the best performance was observed by the KDE integrating ecological barriers, and by the combination between KDE and niche modelling. In a broader perspective, KDEs are thus a pertinent tool in providing quantitative spatial measurements to delineate conservation areas based on patch-abundance data with a specific focus to connectivity.

The concept of an animal's home range has evolved over time, as have methods for estimating home-range size and shape. Recently, home-range estimation methods have focused on estimating an animal's utilization distribution (UD; i.e., the probability distribution defining the animal's use of space). We illustrate the importance of the utilization distribution in characterizing the degree of overlap between home ranges (e.g., when assessing site fidelity or space-use sharing among individuals). We compare several different statistics for their ability to accurately rank paired examples in terms of their degree of overlap. These examples illustrate limitations of indices commonly used to quantify home-range overlap and suggest that new overlap indices that are a function of the UD are likely to be more informative. We suggest 2 new statistics for measuring home-range overlap: (1) for a measure of space-use sharing, we suggest a generalization of Hurlbert's (1978) E/Euniform statistic, which we term the utilization distribution overlap index (UDOI), and (2) for a general measure of similarity between UD estimates, we suggest Bhattacharyya's affinity (BA; Bhattacharyya 1943). Using a short simulation study, we found that overlap indices can accurately rank pairs of UDs in terms of the extent of overlap, but estimates of overlap indices are likely to be biased. The extent of the bias depended on sample size and the degree of overlap (UDs with a high degree of overlap resulted in statistics that were more biased [low]), suggesting that comparisons across studies may be problematic. We illustrate the use of overlap indices to quantify the degree of similarity among UD estimates obtained using 2 different data collection methods (Global Positioning Systems [GPS] and very high frequency [VHF] radiotelemetry) for an adult female northern white-tailed deer (Odocoileus virginianus) in north-central Minnesota.

Understanding the home range of imperiled reptiles is important to the design of conservation and recovery efforts. Despite numerous home range studies for the Threatened timber rattlesnake ( Crotalus horridus ), many have limited sample sizes or outdated analytical methods and only a single study has been undertaken in the central midwestern United States. We report on the home range size, site fidelity, and movements of C. horridus in west-central Illinois. Using VHF telemetry, we located 29 C. horridus (13 female, 16 male) over a 5-year period for a total of 51 annual records of the species' locations and movements. We calculated annual home ranges for each snake per year using 99%, 95%, and 50% isopleths derived from Brownian Bridge utilization distributions (BBMM), and we also report 100% minimum convex polygons to be consistent with older studies. We examined the effects of sex, mass, SVL, and year on home range sizes and reported on movement metrics as well as home range fidelity using both Utilization Distribution Overlap Index (UDOI) and Bhattacharyya's affinity (BA) statistics. The home range sizes for male and non-gravid C. horridus were 88.72 Ha (CI 63.41–110.03) and 28.06 Ha (CI 17.17–38.96) for 99% BBMM; 55.65 Ha (CI 39.36–71.93) and 17.98 (CI 10.69–25.28) for 95% BBMM; 7.36 Ha (CI 5.08–9.64) and 2.06 Ha (CI 1.26–2.87) for 50% BBMM; and 78.54Ha (CI 47.78–109.30) and 27.96 Ha (CI 7.41–48.51) for MCP. The estimated daily distance traveled was significantly greater for males (mean = 57.25 m/day, CI 49.06–65.43) than females (mean = 27.55 m/day, CI 18.99–36.12), particularly during the summer mating season. Similarly, maximum displacement distances (i.e., maximum straight-line distance) from hibernacula were significantly greater for males (mean = 2.03 km, CI 1.57–2.48) than females (mean = 1.29 km, CI 0.85–1.73], and on average, males were located further from their hibernacula throughout the entirety of their active season. We calculated fidelity to high-use areas using 11 snakes that were tracked over multiple years. The mean BBMM overlap using Bhattacharyya's affinity (BA) for all snakes at the 99%, 95%, and 50% isopleths was 0.48 (CI 0.40–0.57), 0.40 (0.32–0.49), and 0.07 (0.05–0.10), respectively. The mean BBMM overlap for all snakes using the Utilization Distribution Overlap Index (UDOI) at the 99%, 95%, and 50% isopleths was 0.64 (CI 0.49–0.77), 0.32 (CI 0.21–0.47), and 0.02 (CI 0.01–0.05)), respectively. Our results are largely consistent with those of other studies in terms of the influence of sex on home range size and movements. The species also exhibits strong site fidelity with snakes generally using the same areas each summer, though there is far less overlap in specific (e.g., 50% UDOI) high-use areas, suggesting some plasticity in hunting areas. Particularly interesting was the tendency for snakes to disperse from specific hibernacula in the same general direction to the same general areas. We propose some possible reasons for this dispersal pattern.

The spatial relationship between predator and prey is often conceptualized as a behavioral response race, in which prey avoid predators while predators track prey. Limiting habitat types can create spatial anchors for prey or predators, influencing the likelihood that the predator or prey response will dominate. Joint spatial anchors emerge when predator and prey occupy similar feeding habitat domains and risk and reward become spatially conflated, confusing predictions of which player will win the space race. These spatial dynamics of risk-foraging trade-offs are often obscured by habitat heterogeneity and community complexity in large vertebrate systems, fueling ambiguity regarding the generality of predictions from predator–prey theory. To test how habitat distribution influences the predator–prey space race, we examine correlation in puma and vicuña habitat selection and space use at two sites, one of which generates a distinct risk–foraging trade-off at a joint spatial anchor. The distribution of vegetation, which serves as both forage for vicuñas and stalking cover for pumas, differs between the sites; the llano contains a single central meadow that acts as a joint spatial anchor, while the canyon is characterized by more heterogeneous vegetation. Puma–vicuña habitat selection correlation was positive in the llano and negative in the canyon, and similarly, utilization distributions were more strongly correlated in the llano than the canyon. Vicuña locations occurred at higher values of puma habitat selection and utilization in the llano than in the canyon. Similarly, puma locations in the llano occurred at higher values of vicuña habitat selection and utilization than in the canyon. Although pumas consistently selected for and utilized vegetative and topographic cover regardless of habitat distribution, vicuñas only selected against vegetation in the heterogeneous canyon site, reducing spatial correlation with pumas. Our work suggests a joint spatial anchor favors pumas in the space race due to the inability for vicuñas to avoid crucial foraging habitat. The outcome of the predator–prey space race appears to be strongly informed by the distribution of habitat, whereby corresponding predictability of predator and prey favors predators in the spatial game.

Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identically distributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs, are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology.

The rapid development of wind energy in southern Africa represents an additional threat to the already fragile populations of African vultures. The distribution of the vulnerable Cape Vulture Gyps coprotheres overlaps considerably with wind energy development areas in South Africa, creating conflicts that can hinder both vulture conservation and sustainable energy development. To help address this conflict and aid in the safe placement of wind energy facilities, we map the utilization distribution (UD) of this species across its distributional range. Using tracking data from 68 Cape Vultures collected over the last 20 years, we develop a spatially explicit habitat use model to estimate the expected UDs around known colonies. Scaling the UDs by the number of vultures expected to use each of the colonies, we estimate the Cape Vulture population utilization distribution (PUD) and determine its exposure to wind farm impacts. To complement our results, we model the probability of a vulture flying within the rotor sweep area of a wind turbine throughout the species range and use this to identify areas that are particularly prone to collisions. Overall, our estimated PUD correlates well with reporting rates of the species from the Southern African Bird Atlas Project, currently used to assess potential overlap between Cape Vultures and wind energy developments, but it adds important benefits, such as providing a spatial gradient of activity estimates over the entire species range. We illustrate the application of our maps by analyzing the exposure of Cape Vultures in the Renewable Energy Development Zones (REDZs) in South Africa. This application is a scalable procedure that can be applied at different planning phases, from strategic, nationwide planning to project-level assessments.

Home-range estimation is an important application of animal tracking data that is frequently complicated by autocorrelation, sampling irregularity, and small effective sample sizes. We introduce a novel, optimal weighting method that accounts for temporal sampling bias in autocorrelated tracking data. This method corrects for irregular and missing data, such that oversampled times are downweighted and undersampled times are upweighted to minimize error in the home-range estimate. We also introduce computationally efficient algorithms that make this method feasible with large data sets. Generally speaking, there are three situations where weight optimization improves the accuracy of home-range estimates: with marine data, where the sampling schedule is highly irregular, with duty cycled data, where the sampling schedule changes during the observation period, and when a small number of home-range crossings are observed, making the beginning and end times more independent and informative than the intermediate times. Using both simulated data and empirical examples including reef manta ray, Mongolian gazelle, and African buffalo, optimal weighting is shown to reduce the error and increase the spatial resolution of home-range estimates. With a conveniently packaged and computationally efficient software implementation, this method broadens the array of data sets with which accurate space-use assessments can be made.

1. The recently developed Brownian bridge movement model (BBMM) has advantages over traditional methods because it quantifies the utilization distribution of an animal based on its movement path rather than individual points and accounts for temporal autocorrelation and high data volumes. However, the BBMM assumes unrealistic homogeneous movement behaviour across all data. 2. Accurate quantification of the utilization distribution is important for identifying the way animals use the landscape. 3. We improve the BBMM by allowing for changes in behaviour, using likelihood statistics to determine change points along the animal's movement path. 4. This novel extension, outperforms the current BBMM as indicated by simulations and examples of a territorial mammal and a migratory bird. The unique ability of our model to work with tracks that are not sampled regularly is especially important for GPS tags that have frequent failed fixes or dynamic sampling schedules. Moreover, our model extension provides a useful one-dimensional measure of behavioural change along animal tracks. 5. This new method provides a more accurate utilization distribution that better describes the space use of realistic, behaviourally heterogeneous tracks.

Resource selection functions (RSFs) are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because RSFs assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates. Data thinning, generalized estimating equations and step selection functions (SSFs) have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data and (in the case of SSFs) scale-dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on RSFs. In this study, we demonstrate that this method weights each observed location in an animal's movement track according to its level of non-independence, expanding confidence intervals and reducing bias that can arise when there are missing data in the movement track. Ecologists and conservation biologists can use this method to improve the quality of inferences derived from RSFs. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the ctmm R package.