AbstractHome range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive dataset of GPS locations from 369 individuals representing 27 species distributed across 5 continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function (AKDE), Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ($\hat{N}\mathrm{area}$) to quantify the information content of each dataset. We found that AKDE 95% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the holdout sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing $\hat{N}\mathrm{area}$. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small $\hat{N}\mathrm{area}$. While 72% of the 369 empirical datasets had \textgreater1000 total observations, only 4% had an $\hat{N}\mathrm{area}$ \textgreater1000, where 30% had an $\hat{N}\mathrm{area}$ \textless30. In this frequently encountered scenario of small $\hat{N}\mathrm{area}$, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

AbstractAnimal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.

Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive dataset of GPS locations from 369 individuals representing 27 species distributed across 5 continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function (AKDE), Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ($\hat{N}\mathrm{area}$) to quantify the information content of each dataset. We found that AKDE 95% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the holdout sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing $\hat{N}\mathrm{area}$. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small $\hat{N}\mathrm{area}$. While 72% of the 369 empirical datasets had \textgreater1000 total observations, only 4% had an $\hat{N}\mathrm{area}$ \textgreater1000, where 30% had an $\hat{N}\mathrm{area}$ \textless30. In this frequently encountered scenario of small $\hat{N}\mathrm{area}$, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

Ontogenetic changes in mammalian skulls are complex. For a very few species (i.e. some Sorex shrews), these also include seasonally driven, bidirectional size changes within individuals, presumably to reduce energy requirements during low resource availabilities. These patterns are poorly understood, but are likely most pronounced in high-metabolic species with limited means for energy conservation. We used generalized additive models to quantify the effect of location, Julian day, age and sex on the length and depth of 512 and 847 skulls of stoat (Mustela erminea) and weasel (M. nivalis) specimens collected throughout the northern hemisphere. Skull length of both species varies between sexes and geographically, with stoat skull length positively correlated with latitude. Both species demonstrate seasonal and ontogenetic patterns, including a rare, absolute growth overshoot in juvenile braincase depth. Standardized braincase depths of both species peak in their first summer, then decrease in their first winter, followed by a remarkable regrowth that peaks again during their second summer. This seasonal pattern varies in magnitude and timing between geographical regions and the sexes, matching predictions of Dehnel's phenomenon. This suggests implications for the evolution of over-wintering strategies in mammals, justifying further research on their mechanisms and value, with implications for applied osteology research.