Restrictions on roaming Until the past century or so, the movement of wild animals was relatively unrestricted, and their travels contributed substantially to ecological processes. As humans have increasingly altered natural habitats, natural animal movements have been restricted. Tucker et al. examined GPS locations for more than 50 species. In general, animal movements were shorter in areas with high human impact, likely owing to changed behaviors and physical limitations. Besides affecting the species themselves, such changes could have wider effects by limiting the movement of nutrients and altering ecological interactions. Science , this issue p. 466

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 data set of GPS locations from 369 individuals representing 27 species distributed across five 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 (normalNfalse^area) to quantify the information content of each data set. 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 hold‐out 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 normalNfalse^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 normalNfalse^area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an normalNfalse^area >1,000, where 30% had an normalNfalse^area <30. In this frequently encountered scenario of small normalNfalse^area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

The intestinal microbiota plays a major role in host development, metabolism, and health. To date, few longitudinal studies have investigated the causes and consequences of microbiota variation in wildlife, although such studies provide a comparative context for interpreting the adaptive significance of findings from studies on humans or captive animals. Here, we investigate the impact of seasonality, diet, group membership, sex, age, and reproductive state on gut microbiota composition in a wild population of group‐living, frugi‐folivorous primates, Verreaux's sifakas (Propithecus verreauxi). We repeatedly sampled 32 individually recognizable animals from eight adjacent groups over the course of two different climatic seasons. We used high‐throughput sequencing of the 16S rRNA gene to determine the microbiota composition of 187 fecal samples. We demonstrate a clear pattern of seasonal variation in the intestinal microbiota, especially affecting the Firmicutes‐Bacteroidetes ratio, which may be driven by seasonal differences in diet. The relative abundances of certain polysaccharide‐fermenting taxa, for example, Lachnospiraceae, were correlated with fruit and fiber consumption. Additionally, group membership influenced microbiota composition independent of season, but further studies are needed to determine whether this pattern is driven by group divergences in diet, social contacts, or genetic factors. In accordance with findings in other wild mammals and primates with seasonally fluctuating food availability, we demonstrate seasonal variation in the microbiota of wild Verreaux's sifakas, which may be driven by food availability. This study adds to mounting evidence that variation in the intestinal microbiota may play an important role in the ability of primates to cope with seasonal variation in food availability.