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.

Accurately quantifying species' area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home-range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross-validation to quantify bias in empirical home-range estimates. Area requirements of mammals <10 kg were underestimated by a mean approximately15%, and species weighing approximately100 kg were underestimated by approximately50% on average. Thus, we found area estimation was subject to autocorrelation-induced bias that was worse for large species. Combined with the fact that extinction risk increases as body mass increases, the allometric scaling of bias we observed suggests the most threatened species are also likely to be those with the least accurate home-range estimates. As a correction, we tested whether data thinning or autocorrelation-informed home-range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning required an approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, not a viable solution. In contrast, autocorrelation-informed home-range estimation resulted in consistently accurate estimates irrespective of mass. When relating body mass to home range size, we detected that correcting for autocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum.

During autumn in the temperate zone of both the new and old world, bats of many species assemble at underground sites in a behaviour known as swarming. Autumn swarming behaviour is thought to primarily serve as a promiscuous mating system, but may also be related to the localization and assessment of hibernacula. Bats subsequently make use of the same underground sites during winter hibernation, however it is currently unknown if the assemblages that make use of a site are comparable across swarming and hibernation seasons. Our purpose was to characterize the bat assemblages found at five underground sites during both the swarming and the hibernation season and compare the assemblages found during the two seasons both across sites and within species. We found that the relative abundance of individual species per site, as well as the relative proportion of a species that makes use of each site, were both significantly correlated between the swarming and hibernation seasons. These results suggest that swarming may indeed play a role in the localization of suitable hibernation sites. Additionally, these findings have important conservation implications, as this correlation can be used to improve monitoring of underground sites and predict the importance of certain sites for rare and cryptic bat species.

Hybridisation between wild taxa and their domestic congeners is a significant conservation issue. Domestic species frequently outnumber their wild relatives in population size and distribution and may therefore genetically swamp the native species. The European wildcat (Felis silvestris) has been shown to hybridise with domestic cats (Felis catus). Previously suggested spatially divergent introgression levels have not been confirmed on a European scale due to significant differences in the applied methods to assess hybridisation of the European wildcat. We analysed 926 Felis spp. samples from 13 European countries, using a set of 86 selected ancestry-informative SNPs, 14 microsatellites, and ten mitochondrial and Y-chromosome markers to study regional hybridisation and introgression patterns and population differentiation. We detected 51 hybrids (four F1 and 47 F2 or backcrosses) and 521 pure wildcats throughout Europe. The abundance of hybrids varied considerably among studied populations. All samples from Scotland were identified as F2 hybrids or backcrosses, supporting previous findings that the genetic integrity of that wildcat population has been seriously compromised. In other European populations, low to moderate levels of hybridisation were found, with the lowest levels being in Central and Southeast Europe. The occurrence of distinct maternal and paternal markers between wildcat and domestic cat suggests that there were no severe hybridisation episodes in the past. The overall low (< 1%) prevalence of F1 hybrids suggests a low risk of hybridisation for the long-term genetic integrity of the wildcat in most of Europe. However, regionally elevated introgression rates confirm that hybridisation poses a potential threat. We propose regional in-depth monitoring of hybridisation rates to identify factors driving hybridisation so as to develop effective strategies for conservation.

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal's home range, and many methods of estimating home ranges have arisen over the past half century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as Kernel Density Estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian Bridge Movement Models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of applying these two classes of space use estimators appropriately and distinctly. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home-range estimates quantified using occurrence estimators are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use range estimators to estimate the area of home ranges and occurrence estimators to answer other questions in movement ecology, such as when and where an animal crosses a linear feature, visits a location of interest, or interacts with other animals.

No abstract

In the Netherlands, 69 of the 126 (55%) mink farms in total became infected with SARS‐CoV‐2 in 2020. Despite strict biosecurity measures and extensive epidemiological investigations, the main transmission route remained unclear. A better understanding of SARS‐CoV‐2 transmission between mink farms is of relevance for countries where mink farming is still common practice and can be used as a case study to improve future emerging disease preparedness. We assessed whether SARS‐CoV‐2 spilled over from mink to free‐ranging animals, and whether free‐ranging animals may have played a role in farm‐to‐farm transmission in the Netherlands. The study encompassed farm visits, farm questionnaires, expert workshops and SARS‐CoV‐2 RNA and antibody testing of samples from target animal species (bats, birds and free‐ranging carnivores). In this study, we show that the open housing system of mink allowed access to birds, bats and most free‐ranging carnivores, and that direct and indirect contact with mink was likely after entry, especially for free‐ranging carnivores and birds. This allowed SARS‐CoV‐2 exposure to animals entering the mink farm, and subsequent infection or mechanical carriage by the target animal species. Moreover, mink can escape farms in some cases, and two SARS‐CoV‐2‐positive mink were found outside farm premises. No other SARS‐CoV‐2‐RNA‐positive free‐ranging animals were detected, suggesting there was no abundant circulation in the species tested during the study period. To investigate previous SARS‐CoV‐2 infections, SARS‐CoV‐2 antibody detection using lung extracts of carcasses was set up and validated. One tested beech marten did have SARS‐CoV‐2 antibodies, but the closest SARS‐CoV‐2‐infected mink farm was outside of its home range, making infection at a mink farm unlikely. Knowing that virus exchange between different species and the formation of animal reservoirs affects SARS‐CoV‐2 evolution, continued vigilance and monitoring of mink farms and surrounding wildlife remains vital.