Ecography

A major goal of community ecology lies in the deciphering of the processes underlying species distribution. A widespread approach to this question is to identify patterns in species community data and relate them to possible processes. Joint Species Distribution Models (JS-DMs) offer one way to do so through the infernece of association networks that describe patterns of statistical correlations and dependencies between species, but it is unclear what processes can explain the presence of such correlations. While it has now been established that there is no equivalence between JSDM-inferred associations and biotic interactions, the later remain one possible explanation, among others, for the former. However, to our knowledge, there is no specific study of the statistical patterns induced by different types of interactions or of the conditions under which they may or may not appear as statistical correlations / dependencies in species communities. To explore these questions, we propose a "virtual ecologist" approach that consists in simulating community data based on abiotic and biotic processes with the VirtualCom model that emulates the effects of environmental processes and of competition and facilitation interactions. Then, we study to what extent JSDMs retrieve correlations between species that match the simulated interactions. We show that these interactions are better identified when using JSDMs that model partial correlations between species rather than marginal ones. We further demonstrate how critical it is to correctly model abiotic effects in order to identify biotic ones and that the "correct modelling" of these effects depend on the type of interactions at stake.

AimA comprehensive understanding of the spatial distribution of biodiversity is hindered by fragmented datasets, sampling biases, and inconsistent observation protocols. Here, we present a workflow that integrates disparate datasets to produce large scale maps of biodiversity metrics as a basis for management-relevant information tools. We use integrated species distribution modeling (iSDM) to account for sampling biases and disparate data collection techniques, taking advantage of the vast numbers of open datasets available in data aggregators like GBIF. LocationNorway (excluding Svalbard and Jan Mayen) TaxonVascular plants MethodsThe workflow consists of four main steps: data acquisition, data integration, integrated species distribution modelling (iSDM), and the production of derived outputs. Input data include structured surveys, opportunistic observations, and environmental covariates. These are standardised and integrated into a point-processed based iSDM framework to produce species richness maps, associated uncertainties, and sampling effort maps. The outputs are further processed to identify biodiversity hotspots or to summarise species-environment relationships. The workflow used vascular plant data from Norway, combining occurrence-only and presence-absence datasets with environmental covariates. Outputs were generated at a spatial resolution of 500 x 500 meters, balancing accuracy, computational feasibility and relevance for management decisions. High-performance computing resources were utilized for model fitting and predictions. A subset of available data was used to validate the species richness maps. ResultsWe produced detailed maps of species richness, uncertainties and sampling intensity across Norways heterogeneous landscape, incorporating 1218 species in our final results. The species richness patterns highlight patterns consistent with previous mapping efforts. Validation showed an increase in model accuracy when compared to models which did not use an iSDM framework. The workflow highlights limitations in the infrastructure of the currently openly accessible data, particularly the need for more structured presence-absence datasets and standardized metadata. Main conclusionsThis study underscores the potential of workflows that integrate disparate datasets for biodiversity modeling. To maximize accuracy and utility, future efforts should focus on improving data standardization, the publication and collection of more structured data, and fostering data-sharing collaborations. Advances in the workflow itself, including optimising modelling covariates and integrating more comprehensive spatio-temporal aspects, will also increase the relevance of the outputs. These advances will increase our ability to estimate species richness with a precision and accuracy that can reliably inform conservation and management decisions.

The ongoing decline in biodiversity highlights the need for understanding the causes of population changes. This study uses 25-year, large-scale monitoring dataset to investigate the influence of climate and landscape structure on the annual population growth rates of 84 bird species across Poland. Our methodological framework involves the spatiotemporal decomposition of these environmental drivers to decouple demographic effects of long-term carrying capacities from the short-term effects of environmental perturbations. Using species-specific demographic models followed by a community-wide meta-analysis, we evaluated how individual species responses scale up to shape community-level dynamics. The results reveal significant variation in species-specific responses to individual drivers. At the community level, our findings suggest that bird populations are mainly regulated by the long-term spatial constraints rather than short-term disturbances. Persistent environmental heterogeneity had the strongest positive demographic effect on birds, followed by temperature, forest dominance over croplands, and precipitation. In contrast, rapid temporal shifts in environmental heterogeneity and precipitation anomalies negatively affected population growth, whereas urbanisation consistently exerted a negative effect across both spatiotemporal dimensions. Our results highlight the significance of protecting existing heterogeneous and ecotonal habitats, as well as the need to incorporate features that enhance habitat heterogeneity into urban development. Article impact statementPreserving heterogeneous habitats is essential for the conservation of bird populations.

Effective conservation planning increasingly relies on species distribution models (SDMs) to guide where actions deliver the greatest biodiversity benefits through spatial conservation prioritization. However, SDMs are inherently uncertain, and this uncertainty propagates through prioritization processes, affecting the identification of priority areas and influencing conservation decisions. Here, we evaluate whether correcting SDM overprediction reduces uncertainty propagation into spatial conservation prioritization. Using two large European datasets of vertebrates and invertebrates, we compared unconstrained SDMs with models corrected for overprediction through a Bayesian integration of occurrences, expert range maps, and habitat suitability. We found that overprediction correction reduced spatial and performance uncertainty, with uncertainty strongly structured by model and algorithm choice and amplified when overprediction was not corrected. Although no single modelling adjustment fully eliminates uncertainty propagation from SDMs into prioritization, we demonstrate that overprediction correction consistently reduces it across datasets, taxa, and modelling approaches, highlighting its importance for robust conservation planning.

AimHuman land-use change contributes to biodiversity declines, but also creates new niches that facilitate novel biotic interactions. These interactions can reshape ecological communities and ecosystem function, yet remain poorly understood. Swiftlets and swallows in Southeast Asia present a classic example: coexistence is facilitated by fine-scale diet partitioning, with population sizes historically limited by available nesting substrates. However, several species now nest on manmade structures, particularly "nest farms" built to harvest edible swiftlet nests. We evaluated whether land-use change, especially the spread of nest farms, is leading to breakdowns in niche partitioning and increased competition among six sympatric swiftlets and swallows. LocationNorthern Borneo MethodsWe calculated geographic niche overlap using species distribution models (SDMs) with different environmental predictors, hypothesizing greater overlap when land-use variables were included. We then implemented joint species distribution models (JSDMs) to partition shared environmental responses from potential biotic interactions, predicting that competition would emerge as negative residual correlations. We used sightings from citizen-science datasets and structured surveys to evaluate the influence of climate, land-use, nest farms, morphology, and foraging behavior on species occurrences. ResultsSDMs that included land-use variables showed high niche overlap, suggesting that human activity homogenizes niches. The optimal JSDM, based on structured survey data, identified distance to nest farms as the strongest predictor of occurrence for all species, with species showing both positive and negative responses. Morphology and behavior had small effects, and residual correlations were weak, indicating limited unexplained biotic interactions. Main conclusionsHuman activity, through the creation of artificial nesting sites, broadly drives co-occurrence of swallows and swiftlets across our study region. These effects appear to operate primarily through environmental filtering rather than direct competition. Our findings reveal substantial and complex impacts of land-use change and anthropogenic nest sites on the distribution and composition of aerial insectivore communities.

O_LIAnimal-mediated seed dispersal is key for the maintenance and functioning of tropical ecosystems. Specifically, in the Cerrado, the largest Neotropical savanna and a global biodiversity hotspot, nearly 60% of plant species rely on animals for dispersal. C_LIO_LIClimate change threatens these interactions by affecting species distributions, reshaping communities, and potentially decoupling plants from their dispersers. Anticipating how such disruptions may alter seed dispersal networks is particularly relevant for understanding the resilience of future tropical ecosystems. C_LIO_LIHere, we combined empirical data on 139 pairwise plant-frugivore interactions with species distribution forecasts to build probabilistic interaction matrices under present and future climate scenarios, which were then used to construct 6,221 local seed dispersal networks. Using ecological niche modelling, we tested how climate change influences species range size and centroid displacement. Then, we evaluated whether such changes translate into losses of pairwise plant-frugivore co-occurrence. Finally, we investigated how these changes in occurrence overlap may affect key structural properties of future local seed dispersal networks. C_LIO_LIWe forecast that by the 2070s, under a business-as-usual climate scenario, species are likely to contract their ranges by 56 {+/-} 33% and shift their distribution centroids by 88 {+/-} 57 km within the Cerrado, leading to a 27 {+/-} 29% loss in plant-frugivore co-occurrence mainly driven by reductions in plant species distributions. At the community level, these losses will lead to smaller and more nested networks and specialized, indicating a structural simplification of seed dispersal systems in the Cerrado. C_LIO_LISynthesis: By combining empirical data on animal-mediated seed dispersal with forecasts of species distributions, we found that climate change may simplify frugivore-plant interaction networks in the Cerrado by decreasing species ranges and co-occurrence of partners. Our study demonstrates that future climate may pose a threat not only to species distributions but also to ecological interactions, such as seed dispersal, that are key to enabling climate-tracking by plants. Thus, preventing the simplification of interaction networks will be essential to conserve biodiversity in species-rich regions. C_LI

The small mammals in the tropical savannas of northern Australia, have undergone a degree of change in recent decades, best documented in the Northern Territory. Data is limited from northern Queensland and though the same trends are assumed, the topographic and climatic features differ substantially. In this study we examined data systematically collected from 725 sites between 1998-2012 in three bioregions representing a climatic gradient: from semi-arid to monsoon tropical savannas. We investigated via information-theoretic models and model averaging, the relationship between five mammal groupings and three landscape variables (fractional cover green, elevation and vegetation diversity) to elucidate any consistent or different patterns in the mammal fauna. Key patterns included relationships with increasing elevation (critical weight range species richness positively associated with elevation, rodent species richness negatively associated), increasing rodent and dasyurid species richness with vegetation diversity, and lower macropod and dasyurids abundance with increasing fractional cover green. These relationships underscore a need to consider mammal conservation in Queensland with more nuance than in the more topographically inert Northern Territory. Management strategies need to be more attuned to taxonomic and regional differences, to prevent perverse outcomes.

Winter can affect animal population dynamics by limiting resource availability and increasing energetic costs of movement caused by deep snow. Given the rapid alteration of snowpack properties due to climate change, quantifying how snow characteristics influence reproduction and physical condition is critical. We evaluated how snow cover duration, depth, and density affect spring body mass, reproduction probability, and subsequent autumn body mass of bighorn sheep (Ovis canadensis) using 45 years of individual-based data at Ram Mountain, Alberta, Canada, along with historical snow records reconstructed via the SNOWPACK model. Using Bayesian structural equation modeling, we quantified the direct and indirect effects of snow across different sex and age classes. Long and deep snow covers reduced spring body mass across all demographic groups, with yearlings, especially males, losing up to 0.12 kg per additional cm of snow depth. Harsh snow conditions reduced the probability of reproduction for adult females and generated a compensatory indirect effect on mass by avoiding the energetic costs of reproduction. In contrast, yearlings showed no compensatory responses and entered the following autumn in poor condition (up to 14% lighter for males and 8% for females following the deepest snow years). The impact of snow density on autumn mass of adult males was density-dependent, shifting from beneficial at low density (+0.09 kg per kg/m3) to detrimental at high density (-0.04 kg per kg/m3). The effects of snow conditions generate persistent, context-dependent carry-over effects across seasons. Our study suggests that distinct demographic groups rely on different mechanisms to cope with environmental constraints, highlighting complex, time-lagged consequences of changing winter climate on alpine herbivore populations.

The release of tens of millions of common pheasants (Phasianus colchicus) across the UK for shooting may pose an ecological risk to native species and sensitive habitats, particularly if the birds move into protected areas (PAs) such as Special Areas of Conservation (SAC), Special Protection Areas (SPA), and Sites of Special Scientific Interest (SSSI). The extent of this ecological risk depends on the abundance of pheasants in these sensitive sites, especially if they are attracted there after the shooting season when game management efforts to retain the birds cease. We used relative pheasant abundance measures derived from British Trust for Ornithology bird atlas data from 3793 2km tetrads across four English counties (Berkshire, Cornwall, Devon, and Hertfordshire) to determine if pheasants preferentially disperse into or reside in areas with greater PA coverage. We analysed relative abundance in both the winter shooting season and the breeding season using a Bayesian occupancy-abundance model, controlling for habitat type and diversity. Our results showed a strong influence of habitat on pheasant abundance, consistent with known habitat preferences. However, we found no evidence of a relationship between relative pheasant abundance and the proportion of ecologically relevant PA coverage in a tetrad. This lack of a relationship was consistent across all four counties and across both the winter and breeding seasons. Our finding suggests that common pheasants do not preferentially disperse into or reside in protected areas compared to surrounding, unprotected land, suggesting that the ecological impacts caused by released pheasants are no more likely to occur in protected areas than in non-protected areas.

BackgroundWintering migratory birds must balance energetic requirements, resource availability, and disturbance in increasingly human-modified landscapes. However, individual-level variability in daily movement and winter space use remains poorly understood in South Asian populations of the common crane. We investigated how seasonal dynamics, landscape composition, and individual differences structure winter movement ecology in a semi-arid agro-wetland system in western India. MethodsWe analysed high-resolution GPS telemetry data from multiple tagged cranes tracked across three consecutive winters. Daily movement distances were modelled using mixed-effects approaches to partition variance within and among individuals and among winters. Daily movement trajectories were evaluated using non-linear temporal terms. Landscape predictors, including cropland proportion, built-up area, and habitat heterogeneity, were incorporated to assess environmental drivers. Winter range distributions were estimated using autocorrelation-informed kernel density estimation within a continuous-time movement modelling framework. ResultsMost variation in daily movement occurred within individuals rather than among them, indicating strong behavioural flexibility. Interannual differences explained substantial variance, suggesting sensitivity to changing environmental conditions. Daily movement distance followed a non-linear seasonal pattern consistent with shifts in the profitability of agricultural resources over winter. Cropland proportion and landscape evenness were negatively associated with movement distance, whereas a high proportion of built-up areas increased daily movement distance, reflecting a trade-off between resource concentration and anthropogenic disturbance. Winter range distribution size varied markedly both within individuals across years and among individuals within seasons. ConclusionWinter movement and space use in common cranes are predominantly context-dependent and environmentally driven. Seasonal dynamics, agricultural landscapes, and human disturbance jointly structure movement patterns, with limited but consistent individual differences. Multi-year, individual-based telemetry provides a comprehensive understanding of winter spatial strategies in dynamic semi-arid agro-wetland systems.

Mark-recapture analyses on the delineation of natural populations between areas often assume random sampling, with a between/within (B/W) area resighting ratio that declines towards zero as the population components of two areas become more-and-more isolated from one another, with fewer-and-fewer individuals mixing between areas. I use an individual based population model split in two areas to simulate this result, analysing also for the potential effects of the space-time fidelity of the mark-recapture sampling in the areas. I find that small B/W resighting ratios--that traditionally is taken as evidence of population isolation--can easily be observed within a completely mixing population if a random sampling scheme is restricted in space and/or time. Random sampling within restricted areas and time windows is not sufficient to estimate mixing rates and population isolation between areas, unless the resighting rates are analysed by a method that accounts both for the space-time fidelity of the scientific sampling scheme and the space-time fidelity of the distributional behaviour of the individuals in the population.

Despite substantial global commitments to expand protected-area networks, the strategic allocation of limited resources remains challenging. Spatial conservation planning helps identify priority regions that maximise conservation benefits per unit area. Yet, they also tend to neglect two fundamental aspects of conservation: climate-driven range shifts and the representation of environmentally distinct populations within species. Here, we propose a continental-scale conservation planning framework that explicitly accounts for both processes through novel routines implemented in the conservation planning software CAPTAIN. We apply this framework to European crop wild relatives (CWR), for which niche coverage is a focal priority, as it underpins their potential to support agricultural adaptation to future environmental stressors through breeding programs. Comparative analyses on a subset of 186 CWR associated with five focal crops show that accounting for range shifts and niche coverage leads to substantially different conservation priorities from those obtained with a baseline model based on current distributions only. These additions reduced the number of non-protected species by 64%, increased the average protected distribution range by 43%, increased mean niche coverage from 75.8% to 84.5% and reduced the number of species with less than half of their niche protected from 35 to 10. Applied to a more comprehensive checklist of 1,140 European CWRs, the final framework identifies continental-scale priority areas representing 93.5% of these taxa and includes 94.4% of its critically endangered species. Our results highlight the importance of incorporating both temporal dynamics and within-species environmental representation when designing conservation strategies under climate change. RepositoryThe repository will be made publicly accessible after publication at doi: https://10.5281/zenodo.19855597

Explainable machine learning (ML) methods are gaining increasing attention in environmental and ecological research for their ability to reveal relationships between environmental drivers and population dynamics. However, there remain questions on the reliability of these tools, especially given recent research shows that these explanations can be highly sensitive to model architecture. In ecology, it is typical to use a single ML model, and a comparative evaluation of sensitivity of explainability for different ML approaches is overlooked. In this paper, we develop a novel framework that quantifies explanation consistency between multiple ML model architectures. This framework provides a discrepancy measure for each model prediction, with high discrepancy indicating substantive explanation disagreement across models and low discrepancy indicating strong consensus in explanations across models. We then demonstrate that low explanation discrepancy aligns well with ground truth mechanism. Furthermore, high explanation discrepancy provide a mechanism to identify areas for model refinement and further investigation by domain experts. We do this by using a simulation study based on synthetic coral cover data that incorporate spatio-temporal variability driven by known disturbance effects. Our method provides a quantitative approach to assess the sensitivity of explainable ML in the absence of ground truth. As a result, this enhances the utility of ML approaches in conservation and ecological management. While we focus primarily on ecological modelling for coral reefs, our methods are generally applicable to other ecological and environmental modelling settings.

Aimto characterize the evolution of climatic niches during the diversification of the Phyllotis darwini species group, in order to assess the extent to which divergences involved in radiation were associated with patterns of conservatism or divergence of climatic niches, and whether the differentiation found among climatic niches correlated with species phylogenetic relationships. Locationsouth-central Andes, surrounding lowlands, and Patagonia, South America. Methodsspecies climatic niches were characterized by sampling contemporaneous precipitation and temperature conditions across occurrence locations and entire distributional ranges. Climatic niches were analyzed and modeled using multivariate statistics (PCA, PERMANOVA), a maximum entropy-based algorithm, and novel methods developed to explore levels of differentiation (niche overlap test) and divergence (niche divergence test) between realized and fundamental niches. Comparative phylogenetic methods were applied using a time-calibrated phylogeny and integrating climate niche data to estimate ancestral environmental niches within geographic and environmental spaces. Resultscomparisons revealed low levels of climatic niche overlap, both among species realized niches and among their fundamental niches, suggesting high levels of niche differentiation during the diversification of Phyllotis species. Quantifications of niche overlap further showed that observed differences among species lay primarily in the multidimensional nature of climatic niches, as unidimensional quantifications exhibited higher levels of overlap. Evolved differences among species climatic niches were better fitted to a Brownian motion model of evolution, but lacked phylogenetic signal and showed no significant association with species phylogenetic distances. Main conclusionslow levels of differentiation between ancestral climatic niches suggest that the early radiation of species in the Phyllotis darwini species group was promoted by geographic isolation, whereas the more recent diversification of extant species was accompanied by climatic niche differentiation, possibly involving local adaptation to regional ecoclimatic changes associated with Quaternary glacial cycles. The spatial separation of sister species, the complete divergence of their climatic niches, and the lack of phylogenetic signal in niche differences suggest a scenario of diversification in which divergences were prompted by the spatial isolation, but also by the divergent selection exerted by regional climatic differences.

AimHome range size is a fundamental aspect of animal spatial ecology, and understanding the factors that shape it is important for conservation purposes. Several hypotheses, based on energy needs or competition, assume that home range size negatively correlates with population density. However, this pattern has been little investigated on a global scale, and it remains unclear whether it would stand at both intra- and interspecific levels. To fill this gap, we conducted a global exploration of this relationship at the level of an animal family. Location: Global. Time period: Contemporary. Major taxa studied: Wild Felidae. MethodsIndividual home range size records (n = 1022) and population density estimates (n = 1061) were retrieved from the literature for 23 felid species across the world. We first investigated the interspecific relationship by modelling the median home range size of a species as a function of its median population density. To study the intraspecific relationship, we spatially merged data points based on their spatial or temporal proximity. We then applied a mixed-effects linear model using species as a random factor. ResultsWe found that home range size was negatively associated with population density, at both interspecific (-1.323 {+/-} 0.180, p < 0.001) and intraspecific levels (-0.569 {+/-} 0.201 to - 0.537 {+/-} 0.201 depending on the merging approach, p < 0.01). Landscape features were also predictors of home range size, without confounding the effect of population density. Main conclusionsSeveral processes likely govern the relationship between home range size and population density: differences in body mass between species may drive the interspecific relationship, whereas the intraspecific pattern is probably explained by conspecific competition. Although more research is needed to quantify their relative contribution, our study highlights a worldwide ecological pattern that exists at multiple biological levels in the wild.

BackgroundThe Andean bear (Tremarctos ornatus), South Americas only ursid, is one of the worlds most elusive large mammals, making movement data collection exceptionally rare. Addressing this gap, we present the largest telemetry dataset ever assembled, spanning 19 individuals tracked across three Ecuadorian National Parks over two decades, paired with a novel analytical approach. MethodsWe integrated Continuous-Time Movement Models (CTMM), Auto-correlated Kernel Density Estimators (AKDEs), Hidden Markov Models (HMM) and a diel niche theoretical framework to mitigate biases previously unaccounted for the species in telemetry studies. Fine-scale AKDEs and non-linear movement metrics were calculated to understand seasonal space use and movement behaviors. Speed and diffusion from CTMM and behavioral states from HMM were modelled with environmental covariates to investigate which conditions shape diel and seasonal activity. ResultsPopulation mean home range was 138.2 km2 (95% Confidence Intervals 78.7-225.5), with males (239.8 km2; 182.8-307.5), significantly exceeding females (58.5 km2; 35.5-90.3). Notably, three females exhibited ranges comparable to some males. Weekly and monthly AKDEs uncovered cyclic home range dynamics potentially driven by resource availability, with contractions around corn harvests, mortino and achupalla fruiting, and expansions during paramo transitions. Decoupling speed from diffusion rates showed region-specific behaviors: intensive patch exploitation in Llanganates, broad exploratory ranging in Cayambe-Coca, and suppressed female locomotion in Cotacachi-Cayapas. Statistical analyses identified temperature as a key diel modulator and precipitation as the seasonal driver. Foraging probability increased between 2:00-6:00, large displacements between 7:00-14:00, and nocturnal movement rose significantly under colder conditions. Across diel hypothesis frameworks, bears were classified as cathemeral rather than strictly diurnal, corroborated by camera-trap records from Colombia, Ecuador, and Peru. ConclusionsWe propose a cathemeral diel phenotype that responds to thermal fluctuations and situates Andean bears within a broader ursid context of thermoregulatory niche plasticity. This dataset reveals unprecedented resolution of regional and sex specific behaviors that will facilitate and accelerate comparative studies in rapidly changing Andean landscapes. By releasing this long-term dataset as an open resource, we provide a foundation for climate-resilient conservation strategies. More broadly, we advocate for data democratization and invite collaboration.

ContextAnimals balance resource acquisition with risk mitigation. These trade-offs are rarely uniform, being mediated by spatial scale, demographic traits, and environmental constraints. Understanding these divergent spatial behaviors is critical for management across human-dominated landscapes. ObjectivesWe investigated how sexual dimorphism and ontogeny interact with landscape structure to influence scale-dependent resource selection. Specifically, we sought to determine how these demographic factors mediate spatial trade-offs between optimal foraging habitats, top-down intraguild predation risk, and bottom-up severe winter weather. MethodsWe examined the spatial ecology of a solitary carnivore, the bobcat (Lynx rufus), across a heterogeneous, human-modified landscape in northern Minnesota, USA. Using spatial data derived from harvested adult and juvenile individuals, we evaluated multi-scale selection relative to land cover, structural ecotones, intraguild predator activity, and winter severity. ResultsHabitat selection was scale-dependent and partitioned demographically. Whereas bobcats universally selected for ecotones and avoided homogeneous open habitats at fine scales, responses to other features diverged by sex and age. Females actively avoided areas with high coyote activity and freezing temperatures; males exhibited high risk tolerance, apparently indifferent to coyote activity and tolerant of freezing temperatures. We identified a distinct ontogenetic spatial shift among females. Subordinate juveniles were competitively excluded from optimal natural ecotones, forcing them into riskier, anthropogenic agricultural edges. In contrast, adult females optimized foraging opportunities by selecting productive ecotones at the intersection of woody vegetation and semi-natural grasslands. ConclusionsOur findings demonstrate that habitat selection is not a static species-level trait, but instead a dynamic process resulting from the interaction between ontogeny, sex, and landscape heterogeneity. The reliance of vulnerable demographic groups on marginal or anthropogenic habitats highlights how human land-use changes can inadvertently produce ecological winners and losers within the same species. Consequently, landscape management and conservation planning for solitary carnivores must shift from broad, population-wide habitat prescriptions to strategies that explicitly accommodate the divergent spatial requirements of specific demographic cohorts.

O_LIClassifying animal movement strategies from GPS tracking data is essential for understanding space use, population dynamics and conservation planning. However, existing approaches either require strong parametric assumptions about trajectory shape, large labelled datasets (i.e. expert-annotated) for machine learning, or lack formal uncertainty quantification. These limitations create barriers for researchers working with novel species or limited sample sizes. C_LIO_LIWe present a profile-based classification framework consisting of three steps. First, trajectories are segmented using breakpoint detection applied to Net Squared Displacement (NSD) time series. Movement metrics are then extracted from each segment and classified by comparing them to empirically derived behavioural profiles via Z-score distances transformed to softmax probabilities. Bootstrap resampling quantifies uncertainty in the resulting classifications from both training and test data. We validated the framework through simulation experiments and applied it to GPS tracking data from two ecologically contrasting species: gray wolf (Canis lupus;43 individuals) and northern lapwing (Vanellus vanellus;15 individuals). C_LIO_LISimulations showed that 5-10 training segments per movement strategy suffice for reliable classification, with overall accuracy of 91.1%across residential, floating and dispersal strategies. Segment duration of 30-60 days was required for confident discrimination of residential and floating behaviour. For wolves, the framework clearly distinguished residency, floating or dispersal (91.2%of segments classified with >50%probability). For lapwings, migration was identified with high confidence, while residential-floating discrimination reflected genuine ecological ambiguity confirmed by domain experts, with bootstrap confidence intervals transparently flagging uncertain cases. C_LIO_LIThe profile-based framework provides an accessible, interpretable alternative to parametric NSD fitting and machine learning approach, requiring modest training data while delivering probabilistic classifications with honest uncertainty estimates. An R package (moveprofile) implementing the complete workflow is freely available. The framework is applicable to any tracked species where distinct movement strategies can be identified by experts knowledge. C_LI

Climate change drives shifts in species composition, but turnover in many communities lags behind the current pace of change. Anticipating the impact of the resulting community-climate disequilibria on ecosystem functioning is critical. Present-day communities may already be out of equilibrium with climate, providing an opportunity to estimate the effects of disequilibrium before they become more widespread. We analyzed plant community composition and function data from [~]60,000 rangeland monitoring sites across the western US to measure how community-climate disequilibrium contributes to spatial and temporal variation in net primary productivity (NPP) - a key ecosystem function. We found that communities were already substantially out of equilibrium with climate and accounting for this disequilibrium helped explain patterns of NPP. Communities farthest from equilibrium were less productive than those that were closely matched with climate. Our findings suggest that future increases in community-climate disequilibrium may further impair ecosystem functioning.

Theory predicts that demographic performance should peak at the core of species ranges and decrease toward their limits. Yet, empirical correlations between population growth rate and species distribution remain weak for most tree species. Part of the problem may arise from the difficulty of integrating multiple demographic processes across the complex life cycle of a forest, and from the significant variability among individuals and locations. It remains unclear if the mismatch between performance and distribution arises from modelling limitations or if climate is simply a poor predictor of species performance across distributions. Here, rather than asking whether demographic performance correlates with species distributions, we ask how climate and competition jointly shape population growth rate for 31 tree species across eastern North America. By combining flexible nonlinear hierarchical models for growth, survival, and recruitment with explicit uncertainty propagation, we use Integral Projection Models to address key gaps in previous studies. Perturbation analyses revealed that population growth rate was consistently more sensitive to mean annual temperature than to conspecific or heterospecific competition across all species. We further examined how sensitivities to climate and competition varied across species thermal ranges. The dominance of climate over competition increased toward both cold and hot range limits, while sensitivity to competition generally declined from cold to hot limits. Notably, these patterns emerged along the continental thermal gradient shared across species rather than within each species individual range, suggesting that range-edge demographic responses may arise as a community-level phenomenon. Across species, the largest source of variability remained the local plot conditions captured by random effects, likely reflecting differences in soil conditions, drainage, and disturbance history. Together, these results may provide a mechanistic pathway underlying the performance declines predicted by range-limit theories, and offer a basis for understanding how forest populations and communities may reorganize in response to ongoing climate change and shifting disturbance regimes.