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. Open Research Statement Tracking data on Aepyceros melampus, Beatragus hunteri, Bycanistes bucinator, Cerdocyon thous, Eulemur rufifrons, Glyptemys insculpta, Gyps coprotheres, Madoqua guentheri, Ovis canadensis, Propithecus verreauxi, Sus scrofa, and Ursus arctos are publicly archived in the Dryad repository (Noonan et al. 2018; https://doi.org/10.5061/dryad.v5051j2), as are data from Procapra gutturosa (Fleming et al. 2014a; https://doi.org/10.5061/dryad.45157). Data on Panthera onca were taken from (Morato et al. 2018). Additional data are publicly archived in the Movebank repository under the following identifiers: Canis latrans, 8159699; Canis lupus, 8159399; Chrysocyon brachyurus, 18156143; Felis silvestris, 40386102; Gyps africanus, 2919708; Lepus europaeus, 25727477; Martes pennanti, 2964494; Panthera leo, 220229; Papio cynocephalus, 222027; Syncerus caffer, 1764627; Tapirus terrestris, 443607536; Torgos tracheliotus, 2919708; and Ursus americanus, 8170674.

Rapid, human-induced climate change has posed significant challenges to wildlife. One key strategy animals use to cope with environmental temperature fluctuations is behavioral thermoregulation. Understanding how climate change is expected to influence animal behavior is crucial for assessing its impact on species survival and informing effective conservation efforts. Giant anteaters have been found to exhibit conspicuous behavioral responses to temperature changes. Despite their broad thermal neutral zone (15 degrees C-36 degrees C), climate projections indicate that this species is increasingly likely to experience heat stress. We used GPS tracking and continuous-time analyses to investigate how environmental temperature influences the movement ecology of giant anteaters. We integrated our findings with climate change projections to link giant anteater's responses to present weather conditions with those expected under future climate scenarios. Giant anteaters' movement speed exhibited a negative quadratic response to temperature, peaking at 23.7 degrees C. 95% of their movement occurred between 15.0 degrees C and 32.3 degrees C, which aligns with their thermal neutral zone. The increasing temperature led giant anteaters to increase selection for native forests, but had no effect on selection for exotic tree plantations. This shows the importance of native forests as these thermal shelters help to mitigate the negative consequences of high temperatures on anteater's movement. However, the warmer temperatures predicted for Brazil throughout the rest of the 21st century indicate that giant anteaters may experience a reduction of up to 84% in their movement speed. This would hinder the acquisition of sufficient energy resources and threaten the species' persistence. We emphasize the need for conservation efforts that account for the impacts of climate change on species survival and stress the importance of preserving forests as essential refuges that help wildlife to cope with rising temperatures.

ABSTRACTMotivationSNAPSHOT USA is an annual, multicontributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns.Main Types of Variables ContainedSNAPSHOT USA 2019–2023 contains 987,979 records of camera trap image sequence data and 9694 records of camera trap deployment metadata.Spatial Location and GrainData were collected across the United States of America in all 50 states, 12 ecoregions and many ecosystems.Time Period and GrainData were collected between 1st August and 29th December each year from 2019 to 2023.Major Taxa and Level of MeasurementThe dataset includes a wide range of taxa but is primarily focused on medium to large mammals.Software FormatSNAPSHOT USA 2019–2023 comprises two .csv files. The original data can be found within the SNAPSHOT USA Initiative in the Wildlife Insights platform.

1. An animal's home-range plays a fundamental role in determining its resource use and overlap with conspecifics, competitors and predators, and is therefore a common focus of movement ecology studies. Autocorrelated kernel density estimation addresses many of the shortcomings of traditional home-range estimators when animal tracking data are autocorrelated, but other challenges in home-range estimation remain. 2. One such issue is known as 'spillover bias', in which home-range estimates do not respect impassable movement boundaries (e.g. shorelines and fences), and occurs in all forms of kernel density estimation. While several approaches to addressing spillover bias are used when estimating home ranges, these approaches introduce bias throughout the remaining home-range area, depending on the amount of spillover removed, or are otherwise inaccessible to most ecologists. Here, we introduce local corrections to home-range kernels to mitigate spillover bias in (autocorrelated) kernel density estimation in the continuous time movement model (ctmm) package, and demonstrate their performance using simulations with known home-range extents and distributions, and a real-world case study. 3. Simulation results showed that local corrections minimized bias in bounded home-range area estimates, and resulted in more accurate distributions when compared with commonly used post hoc corrections, particularly at small-intermediate sample sizes. 4. Comparison of the impacts of local vs. post hoc corrections to bounded home-ranges estimated from lake trout (Salvelinus namaycush) demonstrated that local corrections constrained the redistribution of probability mass within the remaining home-range area, resulting in proportionally smaller home-range areas compared with when post hoc corrections are used.

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.

Aim: The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. Location: Conterminous United States. Time Period: 2010-2021. Major Taxa Studied: Twenty-five species of mammals. Methods: We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. Results: Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east-west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main Conclusions: Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa.

Mutualisms between plants and ants are common features of tropical ecosystems around the globe and can have cascading effects on interactions with the ecological communities in which they occur. In an African savanna, we assessed whether acacia ants influence nest site selection by tree-nesting birds. Birds selected nest sites in trees inhabited by ant species that vigorously defend against browsing mammals. Future research could address the extent to which hatching and fledging rates depend on the species of ant symbiont, and why ants tolerate nesting birds but not other tree associates (especially insects).

Bergmann’s Rule—which posits that larger animals live in colder areas—is thought to influence variation in body size within species across space and time, but evidence for this claim is mixed. We tested four competing hypotheses for spatio-temporal variation in body size within bat species during the past two decades across North America. Bayesian hierarchical models revealed that spatial variation in body mass was most strongly (and negatively) correlated with mean annual temperature, supporting the heat conservation hypothesis (historically believed to underlie Bergmann’s Rule). Across time, variation in body mass was most strongly (and positively) correlated with net primary productivity, supporting the resource availability hypothesis. Climate change could influence body size in animals through both changes in mean annual temperature and in resource availability. Rapid reductions in body size associated with increasing temperatures have occurred in short-lived, fecund species, but such reductions likely transpire more slowly in longer-lived species.

Whether wild herbivores confer biotic resistance to invasion by exotic plants remains a key question in ecology. There is evidence that wild herbivores can impede invasion by exotic plants, but it is unclear whether and how this generalises across ecosystems with varying wild herbivore diversity and functional groups of plants, particularly over long‐term (decadal) time frames. Using data from three long‐term (13‐ to 26‐year) exclosure experiments in central Kenya, we tested the effects of wild herbivores on the density of exotic invasive cacti, Opuntia stricta and O. ficus‐indica (collectively, Opuntia), which are among the worst invasive species globally. We also examined relationships between wild herbivore richness and elephant occurrence probability with the probability of O. stricta presence at the landscape level (6150 km2). Opuntia densities were 74% to 99% lower in almost all plots accessible to wild herbivores compared to exclosure plots. Opuntia densities also increased more rapidly across time in plots excluding wild herbivores. These effects were largely driven by megaherbivores (≥1000 kg), particularly elephants. At the landscape level, modelled Opuntia stricta occurrence probability was negatively correlated with estimated species richness of wild herbivores and elephant occurrence probability. On average, O. stricta occurrence probability fell from ~0.56 to ~0.45 as wild herbivore richness increased from 6 to 10 species and fell from ~0.57 to ~0.40 as elephant occurrence probability increased from ~0.41 to ~0.84. These multi‐scale results suggest that any facilitative effects of Opuntia by wild herbivores (e.g. seed/vegetative dispersal) are overridden by suppression (e.g. consumption, uprooting, trampling). Synthesis. Our experimental and observational findings that wild herbivores confer resistance to invasion by exotic cacti add to evidence that conserving and restoring native herbivore assemblages (particularly megaherbivores) can increase community resistance to plant invasions.

Modern tracking devices allow for the collection of high‐volume animal tracking data at improved sampling rates over very‐high‐frequency radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under‐ or overestimate home range areas. The autocorrelated kernel density estimation (AKDE) family of estimators was designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user‐friendly guide on how they may be combined to reduce multiple biases simultaneously. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

Managing wildlife populations in the face of global change requires regular data on the abundance and distribution of wild animals, but acquiring these over appropriate spatial scales in a sustainable way has proven challenging. Here we present the data from Snapshot USA 2020, a second annual national mammal survey of the USA. This project involved 152 scientists setting camera traps in a standardized protocol at 1485 locations across 103 arrays in 43 states for a total of 52,710 trap-nights of survey effort. Most (58) of these arrays were also sampled during the same months (September and October) in 2019, providing a direct comparison of animal populations in 2 years that includes data from both during and before the COVID-19 pandemic. All data were managed by the eMammal system, with all species identifications checked by at least two reviewers. In total, we recorded 117,415 detections of 78 species of wild mammals, 9236 detections of at least 43 species of birds, 15,851 detections of six domestic animals and 23,825 detections of humans or their vehicles. Spatial differences across arrays explained more variation in the relative abundance than temporal variation across years for all 38 species modeled, although there are examples of significant site-level differences among years for many species. Temporal results show how species allocate their time and can be used to study species interactions, including between humans and wildlife. These data provide a snapshot of the mammal community of the USA for 2020 and will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, and the impacts of species interactions on daily activity patterns. There are no copyright restrictions, and please cite this paper when using these data, or a subset of these data, for publication.

Resource selection functions 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 resource selection functions 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 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 step selection functions) 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 resource selection functions. 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 resource selection functions. 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.

Diverse communities of large mammalian herbivores (LMH), once widespread, are now rare. LMH exert strong direct and indirect effects on community structure and ecosystem functions, and measuring these effects is important for testing ecological theory and for understanding past, current, and future environmental change. This in turn requires long-term experimental manipulations, owing to the slow and often nonlinear responses of populations and assemblages to LMH removal. Moreover, the effects of particular species or body-size classes within diverse LMH guilds are difficult to pinpoint, and the magnitude and even direction of these effects often depends on environmental context. Since 2008, we have maintained the Ungulate Herbivory Under Rainfall Uncertainty (UHURU) experiment, a series of size-selective LMH exclosures replicated across a rainfall/productivity gradient in a semiarid Kenyan savanna. The goals of the UHURU experiment are to measure the effects of removing successively smaller size classes of LMH (mimicking the process of size-biased extirpation) and to establish how these effects are shaped by spatial and temporal variation in rainfall. The UHURU experiment comprises three LMH-exclusion treatments and an unfenced control, applied to nine randomized blocks of contiguous 1-ha plots (n = 36). The fenced treatments are MEGA (exclusion of megaherbivores, elephant and giraffe), MESO (exclusion of herbivores ≥40 kg), and TOTAL (exclusion of herbivores ≥5 kg). Each block is replicated three times at three sites across the 20-km rainfall gradient, which has fluctuated over the course of the experiment. The first 5 years of data were published previously (Ecological Archives E095-064) and have been used in numerous studies. Since that publication, we have (1) continued to collect data following the original protocols, (2) improved the taxonomic resolution and accuracy of plant and small-mammal identifications, and (3) begun collecting several new data sets. Here, we present updated and extended raw data from the first 12 years of the UHURU experiment (2008-2019). Data include daily rainfall data throughout the experiment; annual surveys of understory plant communities; annual censuses of woody-plant communities; annual measurements of individually tagged woody plants; monthly monitoring of flowering and fruiting phenology; every-other-month small-mammal mark-recapture data; and quarterly large-mammal dung surveys. There are no copyright restrictions; notification of when and how data are used is appreciated and users of UHURU data should cite this data paper when using the data.

Homeothermy requires increased metabolic rates as temperatures decline below the thermoneutral zone, so homeotherms typically select microhabitats within or near their thermoneutral zones during periods of inactivity. However, many mammals and birds are heterotherms that relax internal controls on body temperature and go into torpor when maintaining a high, stable body temperature, which is energetically costly. Such heterotherms should be less tied to microhabitats near their thermoneutral zones and, because heterotherms spend more time in torpor and expend less energy at colder temperatures, heterotherms may even select microhabitats in which temperatures are well below their thermoneutral zones. We studied how temperature and daily torpor influence the selection of microhabitats (i.e., diurnal roosts) by a heterothermic bat (Myotis thysanodes). We (1) quantified the relationship between ambient temperature and daily duration of torpor, (2) simulated daily energy expenditure over a range of microhabitat temperatures, and (3) quantified the influence of microhabitat temperature on microhabitat selection. In addition, warm microhabitats substantially reduced the energy expenditure of simulated homeothermic bats, and heterothermic bats modulated their use of daily torpor to maintain a constant level of energy expenditure across microhabitats of different temperatures. Daily torpor expanded the range of energetically economical microhabitats, such that microhabitat selection was independent of microhabitat temperature. Our work adds to a growing literature documenting the functions of torpor beyond its historical conceptualization as a last-resort measure to save energy during periods of extended or acute energetic stress.

Photo 1. “Mega” plots in UHURU exclude megaherbivores such as (A) elephants and (B) giraffe. “Meso” plots exclude mesoherbivores such as (C) zebra and various species of antelope (impala, kudu, waterbuck, etc.). “Total” plots exclude all herbivores >5 kg. Photo credit: Courtney Reed. Photo 2. (A) Control and (B) total exclusion plots from the arid northern site of the UHURU experiment at Mpala Research Centre in central Kenya. Both understory and overstory abundance increase with large herbivore exclusion, as does acacia tree height. Trees in control plots reflect browsing from large herbivores such as elephants. Mega and meso plots (not pictured) are intermediate in vegetation abundance. Photo credit: Courtney Reed. Photo 3. Small mammals such as (A) woodland thicket rats, (B) rufous elephant shrews, and (C) fringe-tailed gerbils are important seed predators in the Kenyan savanna. Small mammal demographics are quantified using mark–recapture surveys every two months. Photo credit: Courtney Reed. These photographs illustrate the article “Ecological consequences of large herbivore exclusion in an African savanna: 12 years of data from the UHURU experiment” by Alston et al. published in Ecology. https://doi.org/10.1002/ecy.3649.

With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August-24 November of 2019). We sampled wildlife at 1,509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the United States. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as will future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.

Reproducible research is widely acknowledged as an important tool for improving science and reducing harm from the "replication crisis", yet research in most fields within biology remains largely irreproducible. In this article, we make the case for why all research should be reproducible, explain why research is often not reproducible, and offer a simple framework that researchers can use to make their research more reproducible. Researchers can increase the reproducibility of their work by improving data management practices, writing more readable code, and increasing use of the many available platforms for sharing data and code. While reproducible research is often associated with a set of advanced tools for sharing data and code, reproducibility is just as much about maintaining work habits that are already widely acknowledged as best practices for research. Increasing reproducibility will increase rigor, trustworthiness, and transparency while benefiting both practitioners of reproducible research and their fellow researchers.

The extinction of 80% of megaherbivore (>1,000 kg) species towards the end of the Pleistocene altered vegetation structure, fire dynamics and nutrient cycling world-wide. Ecologists have proposed (re)introducing megaherbivores or their ecological analogues to restore lost ecosystem functions and reinforce extant but declining megaherbivore populations. However, the effects of megaherbivores on smaller herbivores are poorly understood. We used long-term exclusion experiments and multispecies hierarchical models fitted to dung counts to test (a) the effect of megaherbivores (elephant and giraffe) on the occurrence (dung presence) and use intensity (dung pile density) of mesoherbivores (2-1,000 kg), and (b) the extent to which the responses of each mesoherbivore species was predictable based on their traits (diet and shoulder height) and phylogenetic relatedness. Megaherbivores increased the predicted occurrence and use intensity of zebras but reduced the occurrence and use intensity of several other mesoherbivore species. The negative effect of megaherbivores on mesoherbivore occurrence was stronger for shorter species, regardless of diet or relatedness. Megaherbivores substantially reduced the expected total use intensity (i.e. cumulative dung density of all species) of mesoherbivores, but only minimally reduced the expected species richness (i.e. cumulative predicted occurrence probabilities of all species) of mesoherbivores (by <1 species). Simulated extirpation of megaherbivores altered use intensity by mesoherbivores, which should be considered during (re)introductions of megaherbivores or their ecological proxies. Species' traits (in this case shoulder height) may be more reliable predictors of mesoherbivores' responses to megaherbivores than phylogenetic relatedness, and may be useful for predicting responses of data-limited species.

Data, code, and metadata for: Temperature shapes movement and habitat selection by a heat-sensitive ungulate