Biological invasions pose a major threat to biodiversity, yet their nonlethal, behaviorally mediated effects on native wildlife remain poorly understood, particularly in tropical systems. Domestic dogs (Canis familiaris) and wild boars (Sus scrofa) rank among the world’s worst invasive species and are recognized for their significant negative impacts on the ecosystem. Using long-term camera-trap data from two protected areas in Brazilian Cerrado, we investigated how invasive species influence native mammals across temporal and spatial dimensions. The effects of both invasive species were evaluated on seven native mammals with contrasting ecological traits: two canids, an apex predator, and four prey species. We assessed (i) changes in activity patterns through kernel density overlap, (ii) short-term attraction or avoidance using piece-wise exponential additive mixed models, and (iii) spatial responses via abundance-mediated multi-species occupancy models. Our results reveal that native species responded primarily through temporal adjustments rather than spatial exclusion. Xenarthrans were particularly affected, exhibiting activity pattern shifts that could carry energetic costs given their physiological constraints as imperfect homeotherms. Crab-eating foxes and opossums showed flexible, context-dependent responses. We found an exploratory hypothesis based on spatial trends, suggesting that apex predators may create spatial refugia that buffer native species from invasive species pressure. These findings highlight the urgency of domestic dog control, underscore that apex predator conservation could be a critical component of effective invasive species management, and that their management must be context-dependent.

1. Human population growth has long driven biodiversity loss. Yet, some places are now experiencing depopulation, which can facilitate the recovery of large mammals. These species-both herbivores and carnivores-not only play important ecological roles but can also come into conflict with humans. 2. Here, we draw a clear link between rural depopulation and large mammal recovery in Europe and Japan. We discuss the social and ecological consequences of recovery, including how recovery has led to conflict, like crop damage, attacks on livestock, attacks on people, vehicle collisions and disease spread. We then discuss how to minimize these conflicts in the context of rural depopulation through non-lethal approaches, managed hunting and understanding the human dimensions. 3. Large mammal recovery presents new hunting and ecotourism opportunities, which could economically benefit rural communities. There are also ecosystem services stemming from recovery (e.g. trophic regulation via predation and herbivory), although there is more to learn about community-level effects and how to best track recovery. 4. Other factors can influence large mammal recovery, including climate change, human immigration and the agricultural economy, complicating our ability to predict where recovery might occur in the future. 5. Synthesis and applications. Depopulation and large mammal recovery will likely occur in more regions in the coming years. Countries and institutions can maximize the benefits and reduce potential conflict by (1) tracking recovery; (2) treating recovery as a natural experiment; (3) investing in non-lethal conflict mitigation; (4) incentivizing hunting; and (5) embracing the benefits of recovery. We should anticipate a need for adaptive management of recovering species: protect them as they recover but be prepared to suppress their populations if they become overabundant.

Detecting competitive interactions is important for predicting species responses to environmental change but remains challenging, especially over large scales. Modern coexistence theory predicts that reduced ecological trait overlap promotes coexistence through stabilizing mechanisms, while fitness differences generate competitive asymmetries. However, as an alternative to pure coexistence theory, dominant entities may emerge: highly abundant species with broad ecological tolerances and fast life history traits that override stabilizing mechanisms and dominate community dynamics. We tested these contrasting hypotheses in small mammal communities (n = 68 species) at 44 sites in 18 regions across the United States using the US National Ecological Observatory Network (NEON). We based inference on changes in abundance over time while accounting for weather and habitat factors using a dynamic generalized joint attribute modeling framework. Under the coexistence hypothesis, we predicted that pairwise interaction strengths inferred from our model would correlate with ecological and life history trait differences. Under the dominance hypothesis, we predicted that one (or a small set of) species would meet thresholds for dominance, including >50% of site abundance, >= 45% of total interaction strength, and also have disproportionately high numbers of strong competitive interactions. Predictions of competition based on coexistence theory were not well correlated with model results (mean correlation = 0.25, SE = 0.03), but we found more sites with dominant entities than were predicted by coexistence theory. In particular, compared with prediction from coexistence theory, our model identified three generalist Peromyscus species as the dominant entities at more sites (53.1% vs. 0%), associated with higher mean interaction strength at a site (58.0% [SE = 7.5%] vs. 13.8% [SE = 1.4%]), and a higher proportion of strong interactions with other species at a site (46.2% [SE = 4.4%] vs. 26.7% [SE = 1.7%]). Empirically inferred species interactions more closely matched large-scale observed abundance patterns than did coexistence theory predictions, underscoring the need to account for dominant entities and temporal changes in abundance when characterizing competitive interactions and predicting community responses to environmental change.

Trait-based approaches are key to understanding eco-evolutionary processes but rarely account for animal behaviour despite its central role in ecosystem dynamics. We propose integrating behaviour into trait-based ecology through movement traits—standardised and comparable measures of animal movement derived from biologging data, such as daily displacements or range sizes. Accounting for animal behaviour will advance trait-based research on species interactions, community structure and ecosystem functioning. Importantly, movement traits allow for quantification of behavioural reaction norms, offering insights into species’ acclimation and adaptive capacity to environmental change. We outline a vision for a ‘living’ global movement trait database that enhances trait data curation by (1) continuously growing alongside shared biologging data, (2) calculating traits directly from individual-level data using standardised, consistent methodology and (3) providing information on multi-level (species, individual, within-individual) trait variation. We present a proof-of-concept ‘MoveTraits’ database with 52 mammal and 97 bird species, demonstrating calculation workflows for 5 traits across multiple timescales. Movement traits have significant potential to improve trait-based global change predictions and contribute to global biodiversity assessments as Essential Biodiversity Variables. By making animal movement data more accessible and interpretable, this database could bridge the gap between movement ecology and biodiversity policy, facilitating evidence-based conservation.

To survive climate change, forest trees will have to shift seed production poleward. However, warming will not stimulate tree fecundity in the north if it is limited by other habitat variables. We evaluated the responses of tree fecundity to climate change for 292 tree species in North America and Europe, using response velocity, defined as (climate sensitivity) × (climate-change rate). The sensitivities to climate were estimated for each species and combined with rates of climate change to quantify how temperature, moisture deficits, and late freeze are influencing biogeographic shifts in tree reproduction. The results show that moisture deficit and late freeze, not annual temperature, drive changing seed production. Unlike annual temperature, which is increasing generally, change in these climate variables is not driving poleward shifts in seed production. These findings do not challenge the expectation that forests might eventually shift poleward. Rather, they show why current efforts offer divergent interpretations. The changes happening now are not consistent with annual temperature trends. As warming continues, fecundity changes can best be anticipated from temperature interactions with precipitation and extremes that impact flowering and fruiting in winter and spring.

iNaturalist data are growing rapidly, and researchers are increasingly interested in their utility for ecology and conservation. Yet because iNaturalist data are collected opportunistically, they have taxonomic and geographic unevenness that researchers should be aware of. Here, we describe the taxonomic, geographic, and temporal patterns in the global iNaturalist mammal data; evaluate whether residents or visitors contribute more observations; and illustrate the potential utility and pitfalls for IUCN Endangered or Data-Deficient mammals. As of early 2025, there were 5.29 million observations of mammals on iNaturalist, with >10 K observations from most orders. Larger species were more likely to be observed across the globe, but effects from other species traits on observations varied across countries. Observations of smaller species also tended to be identified by the community less frequently, highlighting a need for additional engagement by taxonomic experts. Geographically, over 20% of the data are from within North America, but observation rates are growing in all countries. Some of this growth is likely driven by tourism, as we estimated that visitors contribute the majority of observations for 63% of countries. iNaturalist data now vastly outnumber museum records for Data-Deficient species and could improve knowledge about species distributions. We pass along several lessons we learned from working with this dataset, including best practices for quality control related to the taxonomic and positional uncertainty of observations. We also highlight 3 key ways in which the utility of iNaturalist can continue to grow for mammals: (i) more observations, particularly for small species and in places with few observations; (ii) more identifications of existing observations by local and non-local experts; and (iii) development of robust methods to estimate (relative) abundance, which could be used for trend analyses. Ultimately, we hope to inspire additional engagement with iNaturalist, both on the platform and within the research community.

Movement ecology-the study of how and why animals move within their environments-stands to offer transformative insights into our rapidly changing world, with benefits for both nature and people. Here, we present the first global horizon scan for movement ecology, engaging leading experts to identify innovations likely to shape the field over the next two decades. These include: engineering breakthroughs, such as long-lived miniature tags with enhanced sensing capacities, non-invasive attachment mechanisms and real-time data processing; analytical advances to predict movement trajectories and scale individual data to population-level patterns; and targeted coordination to mobilize data, scale collaborative infrastructure and expand participation in underrepresented regions. Strategic investment in these priorities would advance understanding of wildlife biology and ecosystem functions, providing mechanistic insights that could help address planetary-scale challenges from biodiversity loss to global health. To highlight these opportunities, we map alignment between identified innovations, movement ecology applications and key multilateral environmental frameworks, including the Kunming-Montreal Global Biodiversity Framework and the Sustainable Development Goals. Our analyses fill a gap at a critical juncture in the evolution of movement ecology as a discipline, offering a community-driven agenda that calls attention to the wide-reaching implications of the advancements on the horizon today.

Acceleration of large-scale solar energy deployment can pose competition for land with biodiversity conservation areas. Solar suitability analyses (SSAs) help identify low-conflict zones for solar development, yet limited work defines which biodiversity-relevant criteria (BRCs) are essential for SSAs or whether supporting data are available. We convened a United States-based Delphi panel of practitioners with expertise in biodiversity and renewable energy to identify BRCs that are essential across SSAs ('core') and data- or scale-limited ('peripheral'). Practitioners identified 16 core and 13 peripheral BRCs. Core criteria primarily aligned with regulatory frameworks, while peripheral BRCs reflected context-dependent ecological attributes lacking consistent and scalable data. Open-access data were available for 14 core criteria across 10 databases. Our assessment of US-based SSAs revealed that 10 included core BRCs. Our findings indicate a need for improved access to fine-scale biodiversity data and coordination with agencies to improve SSAs.

Sustainable human–wildlife coexistence requires a mechanistic understanding of the many ways that humans affect animals. However, progress is hampered by the lack of accessible data measuring the dynamic presence of people. Here, we leverage mobile-device data to disentangle how human presence and landscape modification differentially influence the use of geographic and environmental space for 37 mammal and bird species across the United States. Human presence affected more than 65% of species, with substantial variation across species. For ~60% of species that responded to human activities, the effects were interdependent—animals tended to react more strongly to human presence in less modified habitats. Our results demonstrate that human presence and landscape modification have complex combined effects on wildlife, which need to be considered for effective management.

Over the past two decades, coyotes have colonized North Carolina’s barrier-island chain where they have been documented depredating protected shore-nesting birds and sea turtles. Following their arrival, federal agencies have trapped and removed >170 coyotes, yet the species persists. We combined non-invasive genetic mark-recapture with GPS tracking to describe coyote population and behavior within Cape Hatteras and Cape Lookout National Seashores and Pea Island National Wildlife Refuge. We implemented a multistate robust design model to provide estimates of island-specific abundance, apparent survival, inter-island movement, and detection probability. Average site densities ranged from 0.05 to 0.86 coyotes/km2, with Bodie having the highest average density, followed by Pea Island, Shackleford Banks, South Core Banks, Hatteras, Ocracoke, and North Core. We used tracking data to quantify space use, characterize inter-island movement, and evaluate the efficacy of fladry, a non-lethal deterrent, at reducing entry in sensitive nesting areas. While most monitored coyotes remained on the island where they were first detected, we documented inter-island movement with both datasets: genetic recaptures revealed a resident female moving from North Core Banks to Ocracoke and back (~4 km), and another female on Cape Lookout made >50 crossings among Shackleford Banks, Morgan Island, and South Core Banks, predominately at night and often near low tide. While these events are uncommon, they demonstrate functional connectivity which appears to be at a level sufficient to refill vacancies caused by lethal removal. Additionally, pack sizes on Hatteras and Bodie remained stable across seasons despite over a dozen animal removals. Furthermore, we did not find clear population reduction in our population due to removal. Our model showed that apparent survival tended to be lower where predator management occurred, but the effect size was small and highly uncertain with credible intervals overlapping zero, and abundance did not consistently decline following culling events. The fladry experiment coincided with an 83% reduction in crossings by the individual that most frequently used the protected area prior to deployment, demonstrating its ability to reduce incursions into sensitive nesting areas. Overall, our results demonstrate that sustained suppression or eradication of coyote populations on the Outer Banks is unlikely, and that management aimed at modifying their behavior has a greater potential to reduce their impacts on vulnerable nesting species.

Motivation: SNAPSHOT USA is an annual, multi-contributor 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 Contained: SNAPSHOT USA 2024 contains 377,427 records of camera trap image sequence data and 3127 records of camera trap deployment metadata. Spatial Location and Grain: Data were collected across the United States of America in 49 states, 12 ecoregions and many ecosystems. Time Period and Grain: Data were collected between 1 August and 19 December in 2024. Major Taxa and Level of Measurement: The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format: SNAPSHOT USA 2024 comprises two.csv files. The original data can be found within the SNAPSHOT USA 2024 project on the Wildlife Insights platform.

Community platforms for animal tracking data store harmonized information on the movements of more than a half-million individuals, documenting behaviors across regions and decades. Software compatible with these platforms offer the possibility of automated monitoring of migration and reproductive phenology and landscape connectivity in near-real time, as well as assessment of spatiotemporal trends in relation to changing environmental conditions. Already widely used for local and regional decision-making, emerging pipelines offer possibilities to develop policy-relevant data products to support multilateral environmental agreements. We will present three examples of such pipelines integrated with Movebank, a global platform for animal-borne sensor data. First, ECODATA offers a suite of apps for manipulating large environmental datasets and linking them to species occurrence data, including through custom animations of wildlife movements. These apps make complex datasets and expert interpretation more accessible to decisionmakers. Second, MoveApps is a no-code platform for adaptable, sharable workflows that can be scheduled to run and deliver output at desired intervals. These workflows support responsive wildlife management and efficient reporting within and across institutions. We will demonstrate the use of these software pipelines to study caribou populations through responsive monitoring of calving biology across herds and jurisdictions. Third, the R package movepub translates data from Movebank to Darwin Core archives for publication on OBIS and GBIF. Currently under development, a GBIF-hosted portal for wildlife tracking data will support cross-platform data integration and make movement data more widely accessible to biodiversity researchers and policymakers. We will show initial results of tracking data published in comparable Darwin Core formats from Movebank and the Ocean Tracking Network. Each of these software pipelines is open for use and further development, including linking to other tracking databases and species occurrence data. To make these pipelines relevant to multinational policy frameworks, we encourage collaboration to understand design requirements to integrate animal tracking data within biodiversity metrics and indicators.

Urbanization is one of the most extreme forms of land cover change. Many studies have shown that mesopredators respond positively to urbanization, possibly benefiting from reduced abundance of larger carnivores. However, free-roaming domestic dogs and cats may act as dominant predators in many cities of the Global South, potentially competing with native mesopredators. We hypothesized that domestic carnivores mediate native carnivore occurrence and behavior in urban areas, with stronger negative interactions expected between species of the same family (i.e., canid-canid and felid-felid). We conducted a camera trap study across rural-urban gradients in southern Chile, with 177 camera sites distributed across two landscapes. Using a continuous-time, two-species occupancy modeling framework we evaluated the spatiotemporal interactions between native carnivores-chilla fox (Lycalopex griseus) and kodkod (Leopardus guigna)-and free-roaming dogs and cats. Of seven wild carnivores detected, five occurred within urban areas. Domestic carnivores were ubiquitous in urban areas, whereas native carnivores showed a decline in occupancy and activity with higher urbanization. Our results suggest that native carnivores respond to domestic carnivore presence, with stronger effects within taxonomic families. Chilla exhibited temporal activity change in response to dogs, increasing nocturnality, while kodkods showed spatial segregation from cats. Further research across the Global South is needed to evaluate the role of domestic carnivores as amplifiers of human disturbance and to guide urban planning efforts that promote coexistence with native wildlife.

Camera traps have become a core tool in ecological research, enabling large-scale, noninvasive monitoring of wildlife populations and behavior. By automatically recording animals as they pass within view, these devices generate massive image datasets with minimal field effort. Yet this data richness introduces a new bottleneck when translating the images into usable information due to time and effort required for human annotation. Recently, artificial intelligent (AI) has been integrated into the workflow to improve this efficiency. However, the data procured from AI approaches are of a different nature, necessitating new statistical methods in order to obtain inference, make predictions, and quantify uncertainty. We propose a new Bayesian hierarchical data-fusion model which combines the strengths of human annotations and AI predictions. The benefits of our approach are an ability to provide uncertainty quantification as well as improved inference and prediction power, which we demonstrate using a simulation study. We apply our model to an AI analysis of the body condition of white-tailed deer (Odocoileus virginianus) from camera trap images from North Carolina to study the relationship between health and their environment. We find that bucks in rut have higher body condition than other deer and that green, open habitats are correlated with high body condition. Our new model derived novel ecological inference compared to a traditional approach using the same data.

As an invasive species, domestic dogs can impact wildlife both directly (through predation and disease transmission) and indirectly (by altering species behavior). In this study, we analyzed camera trap data from Cerrado and Atlantic Forest areas to examine the interactions between prey species, natural predators, and domestic dogs to understand whether prey temporally avoid dogs. We used approaches to assess how dogs affect prey activities by comparing activity patterns with kernel density estimates and calculating attraction-avoidance ratios. Giant anteaters exhibited a clear temporal shift towards more nocturnal activity in areas with consistent presence of dogs, likely as an avoidance strategy, although this change may impose additional energetic costs due to their imperfect homeothermy. Agoutis showed subtle adjustments, whereas armadillos and pacas maintained consistent activity patterns regardless of dog presence. This absence of avoidance suggests either low perceived predation risk or limited behavioral flexibility, which may increase their vulnerability. Prey species also showed no avoidance toward dogs when compared to natural predators or non-predatory species. This lack of response may result from (1) the high energy costs of altering activity patterns for homeotherms; (2) effective anti-predation strategies already used against native predators; and (3) prey avoiding dogs spatially rather than temporally. Overall, our findings reinforce the negative impacts of domestic dogs on wildlife and underscore the need for more effective management strategies to control dog populations in and around protected areas.

1. If niche differences contribute to biodiversity, then landscapes must vary and species must respond differently to that variation. Terrain, through its effects on solar radiation and moisture, is an important contributor to habitat variation, but its effects on demography are largely unknown. Understanding fecundity responses to this landscape heterogeneity could provide insights on niche differences and on how terrain buffers climate change effects. Here, we use a hierarchical Bayesian state-space model to quantify how topographic features buffer tree fecundity from intensifying climate change. We estimate the effects of slope, aspect and drainage on tree fecundity across North America and Europe while accounting for individual condition and climate. 2. We analysed 2,874,955 tree-years from 292 species across forest inventory plots spanning mountainous regions in North America and Europe. We fitted species-specific fecundity responses to terrain variables and climate interactions, then constructed predictive distributions of terrain effects at landscape scales. We quantified community-level hypervolume to assess how diversity in fecundity responses across species relates to topographic heterogeneity. 3. Topography influences tree fecundity, with terrain as an important contributor for most species. In dry portions of their ranges, tree species in southeastern North America and south-central Europe increase fecundity towards northeast aspects, while highest fecundity of species in southwestern North America shifts towards south-facing aspects. Terrain-sensitive species (terrain explains >20% of fecundity variance) are most abundant in southwestern North America (similar to 42% of species), but southeastern North America shows the strongest terrain effects on fecundity and highest per-species fecundity variance, despite having the gentlest slopes. Community hypervolume is exceptionally high in southeastern United States. Whether this variation maintains diversity depends on how fecundity combines with other demographic rates. 4. Synthesis: Fine-scale terrain variation creates reproductive differences through species-specific fecundity responses. The effectiveness of topographic heterogeneity as a climate refuge depends on regional landscape structure and species sensitivity to terrain. In topographically diverse regions, species may buffer climate change through fine-scale redistribution to favourable microsites. Quantifying these differences helps identify factors limiting reproduction and habitat features with greatest potential as local refuges.

The capacity and footprint of large, ground-mounted photovoltaic solar facilities (GPVs) in the United States (U.S.) has grown rapidly in the early twenty-first century, introducing the potential for conflict with other place-based considerations such as biodiversity conservation. One critical gap in our understanding of the relationship between GPVs and biodiversity is how their infrastructure may affect animal movement. Here, we present a case study demonstrating the value of movement data for the simulation of animal responses to GPVs. We tracked a free-ranging bobcat through a landscape with GPVs, developed an integrated step selection function to quantify its response to solar facility infrastructure and other landscape factors, and used model results to create an agent-based model that simulates bobcat responses to different GPV siting and design scenarios. This bobcat was slightly less likely to select for locations closer to the GPV in her home range, and the facility fencing appeared to be a meaningful barrier to her movement. Our simulations indicated that (1) decreasing the barrier effect of GPV fencing could increase bobcat usage of the area within a GPV; (2) the presence of a corridor within a GPV likely facilitates bobcat movement around the facility; and (3) different GPV spatial arrangements can produce different patterns of habitat use. As GPV development in the U.S. expands and the GPV footprint burgeons nationwide, models such as these will be critical for meaningful consideration of biodiversity concerns in GPV siting and design.

Abstract Models for estimating animal density from camera traps require four parameters informing detection: movement speed, daily activity level, staying time (duration animals remain within the detection zone), and effective detection distance. These parameters traditionally come from labor-intensive manual measurements and auxiliary telemetry. Recent advances in computer vision can provide the positions of animals in camera trap images, which have been used for distance sampling. We extend this approach to extract all four parameters from imagery, providing the first AI-derived estimates of movement speed and staying time from automated coordinate tracking. We also introduce a new joint multi-species hierarchical distance function that estimates deployment-specific effective detection distances while borrowing strength across species through partial pooling. Our pipeline integrates MegaDetector for animal detection, the Segment Anything Model for segmentation, and Dense Prediction Transformers for monocular depth estimation. From frame-level coordinates, we reconstruct movement trajectories across burst sequences to estimate speed with size-biased distribution corrections, calculate staying time through bounding box interpolation, and estimate activity levels from detection timestamps. The joint hierarchical distance function decomposes the detection scale parameter into a shared deployment-level effect and species-specific offsets, so species effects represent deviations from the multi-species average, allowing data-rich species to inform detection conditions where rare species have few observations. AI-derived scene depth enters the model as a covariate on detection range, providing a vegetation openness metric from the same pipeline. To address position errors from depth estimation, we apply data quality filters. We processed 122,574 frames from 181 deployments across montane forests in Washington and Montana, generating parameter estimates for 12 species without manual annotation. Automated speed estimates produced day ranges 2.7 to 4.3 times GPS telemetry-derived daily distances, reflecting differences between encounter velocity within detection zones and landscape-scale displacement. Deployment-level variation in detectability exceeded species-level differences 3:1, with scene depth strongly predicting detection range; mean effective detection distances ranged from 4.1 to 7.6 m. Applied to a Random Encounter Model, these parameters yielded a white-tailed deer density estimate of 21.4 animals/km² and the Random Encounter Staying Time model yielded 11.6animals/km² in Montana. This pipeline enables scalable density estimation across large camera trap networks.

Ecologists show growing interest in observational data generated by citizen scientists. For mammals, the largest citizen science platform is iNaturalist, which has more than 5 million Research Grade observations globally represented through images of living animals, dead animals, tracks, and scat. These different types of evidence could give insight into the underlying sampling paradigm for an observation (e.g., dead animals might be more likely to be reported near roads) and thus may be useful for scientific applications of these data. However, while iNaturalist allows users to annotate observations by evidence type, many observations are not annotated. We use machine learning to classify the evidence types associated with observations of North American mammals in iNaturalist, adding metadata that can be used to subset data or to model multiple observation processes. Here, we present a dataset containing metadata augmenting 1.33 million North American mammal iNaturalist observations with evidence type. Each observation is categorized as either live animal, dead animal, tracks, scat, or other sign, and an associated confidence score is provided.