Ecography

Quantifying niche divergence is crucial to understanding the ecological and evolutionary processes underlying range limits, coexistence, speciation, biogeography, and macroevolution. Yet available approaches rely on low-dimensional climate summaries, are vulnerable to multiple biases, or struggle with high-dimensional collinear data. We introduce the R package NicheDiv, which adapts discriminant analysis of principal components (DAPC) to quantify pairwise niche divergence across any number of abiotic and biotic environmental variables associated with occurrence records. Our method first addresses correlations among environmental variables through principal component analysis. It then identifies a single discriminant axis that maximizes separation between predefined groups (species/lineages/populations), summarizing multivariate niche structure into one dimension. Significance is assessed by a permutation test that reshuffles group identities to mimic a shared niche. To characterize ecological differentiation, NicheDiv calculates Schoeners D as an overlap index and extends the niche divergence plane to multivariate space, providing metrics such as niche dissimilarity and exclusivity. Extracted variable contributions from the discriminant axis identify environmental variables that contribute most to divergence. Using simulations and empirical data together with a large set of environmental layers, we demonstrate that NicheDiv is computationally scalable, detects subtle divergence in high-dimensional space despite multicollinearity, distinguishes different forms of niche divergence (weighted, nested, soft, hard), and identifies the variables that potentially drive divergence. Compared with alternative divergence tests (PCA-env, hypervolumes, MVNH, PERMANOVA, PCA-space, and logistic regression), NicheDiv generally retains more variation, scales more consistently with increasing divergence, and returns more interpretable effect sizes. NicheDiv automatically extracts such environmental data from preconfigured and user-supplied GIS layers and implements a preprocessing pipeline that reduces known biases: delimiting accessible background space, spatially thinning occurrences, balancing sample sizes, filtering low-information variables, and screening predictors for between-group environmental analogy. We test our framework with empirical analyses of Hemileuca buck moths and demonstrate that their niches are structured by a range of seasonal abiotic and biotic variables rather than annual climatic averages. Overall, NicheDiv offers a robust framework for characterizing niche divergence across multiple environmental axes in support of species delimitation, local adaptation, community ecology, biogeography, and macroevolution.

Anthropogenic climate and land-use change are driving an emerging extinction crisis that is expected to intensify in the future. Species climatic niche limits shape their sensitivity to these pressures, potentially leading to disproportionate extinction risk among more climatically vulnerable species. We test whether realized climatic niche limits are associated with current and projected extinction risk across >23,000 terrestrial vertebrate species. We assessed the phylogenetic structure of thermal and aridity niche limits and related them to IUCN threat status and simulated future extinctions. We show that realized niche limits are phylogenetically conserved, indicating evolutionary clustering of climatic tolerances. Species with colder upper thermal limits were more likely to be classified as threatened across taxa. Aridity niche limits show weaker and less consistent relationships with current threat status. Simulated extinction scenarios reveal taxon-specific patterns of climatic niche loss compared to random species extinctions. We also show significant reductions in phylogenetic diversity relative to randomized expectations based on simulated species extinctions. We find that extinction risk is systematically associated with species climatic niche limits, reflecting evolutionary constraints on environmental tolerance. These results indicate that future extinctions will disproportionately affect climatically vulnerable lineages, with cascading consequences for phylogenetic diversity and ecosystem functioning.

Commonly shared patterns of introduction and spread into new environmental conditions are often poorly understood, even though a better understanding of invasion history and niche dynamics among closely related invasive species could give practitioners valuable information to prevent and mitigate the impact of biological invasions. For this study, we investigate the invasion history and niche patterns among congeneric invasive species. We synthesize public occurrence data for five invasive alien anurans (Eleutherodactylus coqui, E. planirostris, E. johnstonei, E. antillensis, and E. martinicensis) to reconstruct their historic introductions and evaluate evidence for climatic niche shifts between their native and established non-native ranges. By pairing these data with current and future climate projections, we compare patterns of range shifts under future climate scenarios. Our results highlight different temporal and geographic introduction histories in invasive Eleutherodactylus, but a strong signal of colonizing broader invasive climatic niches, specifically into colder environmental conditions. Under future climate scenarios, suitable habitats for most of the non-native regions are likely to increase, although this increase is restricted under scenarios with high greenhouse gas emissions. Our results reveal that despite different invasion histories, the ability to spread into colder regions may be a conserved trait among the most widespread Eleutherodactylus anurans. This study ultimately shows that commonalities among closely related invasive species can provide clues about their ability to expand into areas with particular abiotic conditions, a pattern likely to be widespread, offering a potentially valuable opportunity to deploy targeted prevention strategies.

AO_SCPLOWBSTRACTC_SCPLOWMountain gradients facilitate our understanding of species range limits, competition dynamics, stress-resilience trade-offs, and determinants of vegetation zone boundaries. Forest compositional models often use altitude as the main predictor, a proxy for temperature that is defensible where floristic transitions are gradual and climate relationships are linear. However, mountains with distinct assemblages, representing tropical gradients or areas with complex biogeographic history, require a modeling framework that reflects non-linear dynamics or interactions between environmental factors, including outlier events (rather than mean conditions). Our study system encompasses both tropical and temperate forests along a broad ([~]3000 m) altitudinal gradient, positioned within a narrow latitudinal band (< 1{degrees}) and composed of mature, continuous forest in the Bhutan Himalaya. To represent the breadth of climatic conditions experienced over a trees lifetime, we used a Bayesian modeling paradigm and integrated multi-generational field knowledge to develop a priori hypotheses and informed priors, with consideration of monsoon seasonality and possible ecophysiological thresholds. Our approach followed three stages (the Pattern, the Mechanism, the Test). Specifically, we interpolated microclimate data and derived custom metrics based on thermodynamics, propagating uncertainty into subsequent models to test whether climate posteriors outperformed altitude in explaining growth form partitioning. For spatial patterns, we identified six distinct vegetation zones (encompassing 145 species from 57 families), with a mid-gradient peak in richness at the tropical-temperate transition zone, and convergence of deciduousness at either end of the gradient. For individual growth forms, abundance was tied to different ecological mechanisms, explained by adaptations to climatic stressors and competition trade-offs. For instance, evergreen broad-leaved dominance was linked to ephemeral cloud immersion, whereas tropical deciduous species were affiliated with higher vapor pressure deficit at lower altitudes. Most importantly, compositional (between-group) models showed that the interaction between frost events and fog probability (air saturation prior to the dry season) governed growth form partitioning more than any single factor; temperate deciduous species, confined to a narrow altitudinal band, exemplified this finding. Our methodological approach is transferable to other data-sparse mountain systems, and our results highlight the vulnerability of unique habitat types and montane endemics under climate change scenarios that alter the fog-frost dynamics. Second abstract in DzongkhaTo see the second abstract in Dzongkha, the official language of Bhutan, please visit our Zenodo site: https://doi.org/10.5281/zenodo.19081441.

O_LIRapid climate warming threatens mountain biodiversity, particularly species with narrow climatic niches and limited dispersal capacity. Mountain ecosystems are especially vulnerable because steep environmental gradients restrict opportunities for species redistribution. The Kashmir Himalaya, a globally important biodiversity hotspot experiencing accelerated warming, has already undergone an increase of approximately 0.8 {degrees}C during the 20th century and is projected to warm by 2.5-2.8 {degrees}C by the 2050s. Despite climatic changes, future persistence of many threatened plants remains poorly understood. C_LIO_LIHere, we evaluate present and future habitat suitability for two Himalayan taxa, Gentiana cachemirica, an endemic species, and Gentiana kurroo, a critically endangered species. Specifically, we quantify how climate change and topographic variability influence species distribution and persistence under multiple emission scenarios. C_LIO_LIWe applied species distribution models (SDMs) to presence data of both species and forecasted habitat suitability under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). We hypothesized that G. cachemirica, a narrow-niche, low-dispersal species, is expected to lose habitat due to thermal sensitivity, while G. kurroo may persist or expand under favorable scenarios because of broader tolerance and higher dispersal. We also expected microclimatic refuges to buffer populations, whereas extreme warming would accelerate habitat decline. C_LIO_LIThe predictions of our SDMs under current conditions indicate a highly restricted and fragmented habitat for G. cachemirica, covering 651 km{superscript 2}, but a broader suitable area (2,452 km{superscript 2}) for G. kurroo. In agreement with our hypotheses, our forecasts indicate severe habitat contraction (55-70%) for G. cachemirica across all SSPs, but scenario-dependent responses for G. kurroo, including modest expansion under low-emission scenarios and severe declines under high-emission scenarios. Centroid analyses suggest pronounced climate-driven range shifts, with G. kurroo projected to migrate up to 33 km toward the east-southeast by 2100, while G. cachemirica is projected to display limited dispersal capacity. C_LIO_LISynthesis. Our findings suggest that climatic niche breadth, dispersal limitation, and topographic buffering strongly mediate species responses to warming in mountain ecosystems. Endemic specialists are projected to experience disproportionate habitat fragmentation and range restriction, highlighting the importance of conserving climatic refugia and elevational connectivity under rapid environmental change. C_LI

Aim: To forecast near-future arrivals of non-native terrestrial and freshwater vertebrates at the regional level. Location: Global (geopolitical regions worldwide, including countries and main administrative divisions). Methods: We compiled first regional record data and assembled functional and macroecological variables for 1,931 non-native vertebrate species. For each region, we identified recently arrived non-native species using retrospective windows of thirty and twenty years ending in 2015 (1986-2015; 1996-2015). We then fitted region-specific random-forest models classifying recently arrived species versus those not yet arrived using as predictors: (i) harmonised species traits (e.g., habitat, diet, body size and native-range attributes) and (ii) spread history, capturing time since first record elsewhere. Predictive performance was evaluated using leave-one-out cross-validation, comparing full models with trait-only and spread-only variants. We also assessed relationships between predictive accuracy, predictor importance, and the geographic positioning and trade connectedness of regions. Finally, we predicted region-specific probabilities of arrival for species not yet recorded. Results: Forecasting accuracy was consistently high across regions and taxa, with AUC values above 0.9 in more than half of the focal regions. Full models substantially outperformed models using either predictor set alone, and spread-history-only models typically exceeded trait-only models. Relative importance of spread-history predictors declined with geographic distance to the focal region, whereas predictability was lower in highly trade-connected regions. Predicted near-future high-risk arrivals were dominated by birds and freshwater fishes and showed strong regional structuring. A small set of species ranked highly across many regions (e.g., birds: Phasianus colchicus, Acridotheres tristis, Amandava amandava, Colinus virginianus, Corvus splendens and Lonchura malacca; fishes: Coregonus peled and Oreochromis mossambicus; mammal: Oryctolagus cuniculus), suggesting substantial unrealised spread potential. Main conclusions: Near-future regional arrivals of non-native vertebrates are predictable from spread history and species traits. This enables scalable, updateable regional watchlists to support prevention, early detection and horizon scanning.

O_LIThermal performance curves (TPCs) predict species vulnerability to climate change, but standard respirometry assumes that measured oxygen consumption reflects physiological state. Sessile invertebrates that retract their tentacles and contract under thermal stress violate this assumption, with unmeasured consequences for thermal limit estimates. C_LIO_LIWe tested this behavioral confound in an undescribed cold-water intertidal anemone (Urticina sp.) in the Northwest Atlantic by integrating a negative binomial encounter-rate regression, a maximum entropy species distribution model (both from effort-corrected iNaturalist data), and closed-chamber respirometry across seven temperatures (1-30{degrees}C, 18 individuals, 126 trials). C_LIO_LIThe strongest distributional predictors were cloud cover and coastal urbanization, with a weaker association with winter minimum SST; direct evidence for warm-edge thermal limitation came from the experiment. Anemone expansion state (scored 0-1 from fully closed to fully expanded) was variable and without a clear trend across the coldest treatments but declined above 20{degrees}C before collapsing at the 30{degrees}C treatment, which proved lethal to all individuals. C_LIO_LIStandard TPC models extrapolated the thermal maximum far beyond the lethal bracket ([~]74{degrees}C symmetric Gaussian; 45.9{degrees}C asymmetric). A Bayesian multiplicative model that separated physiology from behavior showed that physiology continued to track temperature while expansion state declined above 20{degrees}C; a fully expanded anemone respired about twice as fast as a fully closed one at the same temperature. The decline in measured respiration is therefore both behavioral and physiological, and disentangling the two requires recording expansion state alongside oxygen consumption. C_LIO_LIBecause a closed anemone cannot feed or exchange gases, the ecologically relevant thermal limit is the temperature at which the animal can no longer maintain its normal expanded posture, not a curve-fitted thermal maximum. That behavioral threshold leaves warm-edge populations within a few degrees of functional thermal failure. C_LIO_LIFuture thermal physiology studies of organisms capable of modulating oxygen consumption through behavior should incorporate quantitative behavioral covariates to separate physiological from behavioral components of the metabolic response. C_LI

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

Climate is a strong predictor of global species richness, but the effects of climatic conditions are difficult to separate from the geography of the climates themselves. Recent work in climate space has shown that the area and isolation of discrete climatic conditions explain broad-scale richness gradients, yet the internal spatial cohesion of those climates remains poorly characterized. Here, we introduce climate percolation as a complementary descriptor of climate geography, measuring the degree to which the total area of a climate bin is concentrated within effectively connected fragments. Using global range maps for amphibians, birds, mammals and reptiles, we quantified species richness across a two-dimensional climate space defined from 12 climatic variables and evaluated the independent and joint effects of climate area, climate isolation and climate percolation across multiple climate-space resolutions. Climate isolation and percolation were strongly coupled: their first joint axis explained, on average, more than 95% of their shared variation, revealing a dominant gradient of climate fragmentation along which geographically isolated climates are also internally subdivided. Despite this collinearity, percolation consistently outperformed isolation in cross-validation across all four vertebrate groups, with particularly strong predictive gains for birds and mammals. The largest improvements, however, came from the shared isolation-percolation axis, indicating that vertebrate richness in climate space is more strongly associated with the integrated geographical structure of climates than with either inter-fragment distance or internal cohesion alone. These results suggest that climate fragmentation is a multidimensional property of environmental space, combining both the distance among climate fragments and the dominance structure of connected areas. By extending climate-space approaches from area and isolation to percolation, our framework provides a more complete description of how the geography of climate may shape global richness gradients and offers a structural basis for anticipating how future changes in climate connectivity could alter biodiversity patterns.

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.

The effect that environmental conditions have on community and network assembly processes remains unclear, in part because these processes operate at multiple scales. Because marine primary producers and microinvertebrates have limited mobility, are susceptible to multiple stressors, and can be observed interacting in situ, their habitat-based interactions provide an informative system for disentangling network organising processes. We sampled 646 habitat-use networks, quantifying interactions involving habitat-users and biogenic habitat-formers over 12 months at 9 sites within Te Ihutai/Avon-Heathcote estuary in Christchurch, Aotearoa New Zealand. Using generalised dissimilarity mixed-effect models, we examined whether changes to species interactions - deconstructed into species turnover and interaction rewiring - were modulated by environmental covariates, including freshwater discharge, elevation, temperature, spatial location and season. We found that with increasing dissimilarity in sites proximity to freshwater, interaction change was more driven by rewiring, whereas differences in elevation (i.e., between channels and non-channel habitats) were driven by species turnover, with more sessile species inhabiting tidal channels. The proximity of habitats also played a strong role, with nearby networks comprising more similar interactions, and species turnover becoming more prevalent with increasing distance. Our results highlight that the relative influence and magnitude of rewiring and species turnover in controlling estuarine interaction networks was affected by the individual species distributions across the estuary and their responses to separate, but co-occurring, environmental factors. Quantification of habitat-former/user interaction networks offers robust, albeit understudied, measures of processes that can underpin community assembly, highlighting their potential importance in research, management and conservation.

Species distribution models (SDMs) increasingly inform conservation and biosecurity decisions in freshwater systems, where the reliability of its uncertainty estimates matters as much as its point predictions. Ensemble SDMs derive prediction intervals from across-replicate variance, but this variance captures systematic error only when replicates disagree about it, an assumption that fails when training data are contaminated with low-accuracy records, the norm in citizen-science datasets. Whether this failure is spatially uniform or concentrates in identifiable parts of a range is unknown. Using a panel of European freshwater crayfish spanning native headwater-associated species and invasive lowland colonizers, we show that contamination-induced calibration failure is strongly spatially structured: it concentrates at stream-network headwaters, the topological tops of the network, where upstream-aggregated predictors are structurally undefined, and scales with contamination severity, replicated across four species and both dominant ensemble protocols (replicate and consensus). The failure is driven by upward prediction bias, not by intervals failing to widen: contaminated ensembles overpredict suitability in headwaters, and because the bias is shared across ensemble members, the intervals do not flag it. This is a conservation-relevant blind spot, because headwaters are both refugia for threatened native crayfish and front lines for invasion; an SDM that silently overpredicts suitability there misdirects survey and management effort toward the segments where its predictions are least trustworthy. Standard leave-one-basin-out conformal calibration, the recommended panel-wide remedy, repairs marginal coverage but leaves headwaters undercovered, because a single calibration threshold is dominated by the abundant non-headwater segments. A group-conditional (Mondrian) variant, calibrating the two populations separately, restores reliable coverage in both at no extra cost and reallocates width where it is needed. We recommend network-position-stratified calibration as a default for ensemble SDMs in dendritic freshwater systems.

Knowledge of species interactions unlocks our understanding of how ecological communities respond to climate change or habitat loss, explaining their resilience and robustness. Such knowledge requires inferring the presence, sign, and per capita strength of species interactions, as well as species intrinsic growth rates. While various studies have attempted to infer these parameters in isolation, none have successfully inferred them simultaneously. Here, we solve this grand challenge using an integrative approach combining ecological mechanistic models and statistical inference to simultaneously infer these parameters across time, capturing environmental variation and seasonality. We validate our approach on synthetic data in constant and changing environments, highlighting its ability to detect high-probability weak interactions - the key contribution of our method, and proving our ability to detect environmental changes. Applied to empirical data, it recovers the expectations from biological knowledge and unveils network rewiring. Our approach takes one step further to bridge the gap between mechanistic models and empirical ecology. It advances the understanding of ecological networks and their dynamics, thereby helping to validate existing hypotheses, spark new theories, and help guide ecological management and conservation.

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.

Identifying what makes species vulnerable to extinction requires accounting for complex biological and environmental interactions. Due to their high predictive accuracy, machine learning methods have been widely used for these assessments; however, relying on black-box models offers limited interpretability. Here, using a comprehensive dataset of anthropogenic, ecological, morphological, demographic, and biogeographical variables from 9,053 species (81% of birds worldwide), we applied Inductive Logic Programming (ILP), an explainable artificial intelligence framework, to generate explicit and quantitative IF-THEN rules with confidence scores for bird extinction risk. Our approach revealed that extinction vulnerability follows a hierarchical structure, shaped by interactions among range size, morphological traits, and human pressures. The framework recovered well-established knowledge, while also revealing previously undescribed extinction patterns. For example, consistent with prior evidence, species with geographic ranges below [~]13,500 km{superscript 2} were identified as higher risk (88% confidence). Nevertheless, this threshold shifted to [~]3,270 km{superscript 2} when human impacts were removed, revealing quantitatively how anthropogenic activities expand the pool of vulnerable species beyond those at risk due to biological and biogeographical traits alone. Beyond established patterns, species with tail length >304 mm were identified as higher risk (82% confidence), a pattern not previously documented. ILP models achieved 91% overall accuracy, slightly lower than Random Forest (93%), but notably better than Neural Networks (83%). These results show that ILP can offer high accuracy results with full interpretability, also providing quantitative transition thresholds that clarify the structural architecture of extinction risk, and translate complex ecological interactions into actionable tools for conservation.

Habitat loss driven by climate and anthropogenic pressures alters patch morphology, with critical consequences for population persistence. Geometric and mechanistic metrics are commonly used to quantify degradation, yet their respective limitations remain poorly understood. Here, we address this gap using a reaction-diffusion framework for population growth and dispersal in a viable patch embedded in a hostile environment. We compare geometric descriptors of patch shape with a mechanistic metric derived from population growth near the extinction threshold. Along degradation trajectories, we find that geometric metrics systematically overestimate persistence, suggesting moderate and decelerating impacts, whereas mechanistic indicators reveal rapid, accelerating approaches to extinction. These results highlight fundamental limitations of geometric approaches and underscore the need for mechanistic assessments when evaluating biodiversity loss in complex landscapes.

Understanding global biodiversity patterns and their drivers is a prerequisite for countering the biodiversity crisis. In this paper, we introduce a novel generalized linear model, Hubbell regression, to estimate a key biodiversity descriptor, the fundamental biodiversity number. This can be converted into a set of biodiversity descriptors, including Shannon and Simpson indices, and more. Hence, quantifying the impact of environmental conditions on the fundamental biodiversity number allows us to predict the general properties of local biodiversity in any setting. In addition to having a strong mathematical foundation, Hubbell regression consistently outperformed current state-of-the-art models in predicting global biodiversity. We apply the method to arthropods, which account for the majority of terrestrial biodiversity. By parameterizing the models using samples of 1.78 million arthropods from 2415 samples collected at 135 sites spanning all continents, we pinpoint the drivers of arthropod biodiversity and its features at the global scale. We find that actual evapotranspiration is the single largest predictor of arthropod diversity and explains nearly 30% of the variation in richness. Moreover, we infer that high human activity has led to a 21.3 % and 29.2% decrease in potential insect richness in tropical and dry zones, respectively, but increased insect richness in polar regions. These insights bring a new foundation for biodiversity research and action.

As climate changes, habitat suitability for multiple taxa are also expected to change. In recent years, mangrove latitudinal range expansion has been linked to increasing temperatures and reduced freezing in temperate regions, happening mainly through encroachment into saltmarshes. The range limit of mangrove forests in Eastern South America has not seen drastic changes in the last four decades, despite trends of increased temperature and the seemingly favorable direction of the Brazilian Current. Here, we investigate if and how the distribution of South-Atlantic mangrove forests may respond to different scenarios of climate change. To do this, we combine ecological niche modelling with propagule dispersal simulations to understand the roles of climate and ocean currents in defining the austral limits of South-Atlantic American mangroves. Our results indicate that minimum sea surface temperature strongly constrains habitat suitability beyond the current distribution of mangroves (28{degrees}2868" S), while dispersal processes heavily limit propagule stranding beyond 35{degrees} S. The Brazil-Malvinas currents confluence zone creates steep temperature gradients and an oceanographic barrier that makes the latitudinal expansion of mangroves unlikely in this region, even in future scenarios of heating climate. We found no evidence of current nor future poleward expansion of mangroves, but total mangrove area has increased in Brazil over the last decades, likely due to landward migration, but anthropogenic interference and urban expansion may restrict this process, leading to coastal squeeze. Under scenarios where both landward and poleward migration are limited, South American mangroves may face increasing vulnerability, with potential impacts on the several ecological, biogeochemical and social cycles they support. Our results contribute to leading hypotheses of climate restriction and shed light on the role of ocean currents in South America, helping to explain why the poleward expansion reported in other regions has not yet been observed in the South-Atlantic mangrove range limit.

Identifying how and why species vary in their ability to adjust to rapidly changing climates is a key challenge in ecology. While phenological shifts are well documented for birds and often studied in the context of tracking resource availability, less is known about the extent to which adjustments in phenology allow populations to track a consistent thermal niche. In particular, there has been little examination of how the extent of phenological thermal niche tracking compares over time versus space; a comparison that has the potential to inform on the underlying mechanisms. Here, we use data on breeding phenology derived from BTO Nest Record Scheme data, to examine the extent to which 13 passerine bird species track a consistent incubation thermal niche across years (both interannually and a year gradient) and along latitudinal and elevational gradients, and whether migrant and resident species differ in their tracking ability. Overall, we found support across species for partial tracking, with all species showing trends consistent with partial tracking across one or more axis, though for one species we could not reject the null hypothesis of no tracking. When we looked at average trends across species, we found significant tracking across interannual variation, latitude, and elevation, but not across a year trend. However, we found no evidence that tracking differs between residents and migrants, and for only a few species did we found evidence that species incubation thermal niche impacts on fitness. Taken together, our findings highlight the extent to which shifts in phenology can allow birds to track a thermal niche in a changing climate. The timing of a thermal niche provides a useful and widely-applicable yardstick to examine how changes in climate will impact on the abiotic conditions that populations experience.

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.