Ecography

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.

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.

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.

Introduction and AimAssessing the realized climate limits for a species based on the climate conditions (i.e., different aspects of temperature and precipitation) a species has experienced over its range enables us to determine the climatic boundaries of its existence, and thus its potential exposure to novel climate conditions in the future. We combine species range maps from IUCN and BirdLife International with global climate data from the ERA5 reanalysis and five Earth System Models (ESMs) to produce ClimLimits: a database of multivariate realized species climate limits based on historical temperature and precipitation for terrestrial and freshwater animal species worldwide. Main variables includedFor a total of 54,255 species (24,731 terrestrial, 18,182 freshwater, and 11,342 terrestrial-freshwater species), we estimated 44 species climate limits, which delineate the most extreme climate conditions experienced by a species over its entire range over the last 80 years (1940-2020). The database accounts for three aspects of species climate limits: a) maximum and minimum values of temperature and precipitation experienced over the historical reference period, b) maximum annual variability in temperature and precipitation, and c) maximum frequency, intensity, duration and severity of extreme events (heatwaves, cold-spells, and droughts). Climate data is sourced from the ERA5 reanalysis and from five different Earth System Models (ESMs), producing 6 different subsets of the ClimLimits database. Time coverageSpecies climate limits are estimated based on historical climate records from 1941-2014 (5 ESMs) and 1940-2020 (ERA5). Temperature-based limits are inferred at a daily scale, while precipitation-based limits are inferred at a monthly and yearly scale. Spatial coverageGlobal, over 24km x 24km grid-cells. TaxaTerrestrial and freshwater taxa, including amphibians, birds, mammals, reptiles, freshwater fish, and freshwater invertebrates, with shapefiles from IUCN and BirdLife International. Data is produced at the species level. ApplicationsClimLimits provides ready-to-use standardized realized climate limits for individual species across multiple aspects of climate, facilitating global-scale assessments of macroecological patterns and climate exposure risk for species.

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.

Retrospective ecological niche modeling (rENM) combines historical species occurrence records with historical environmental data to reconstruct the spatio-temporal dynamics of species-environment relationships under changing conditions. Despite growing recognition that those relationships can be nonstationary, time-series approaches to ecological niche modeling remain uncommon, and the tools to support them at scale are limited. Here, we describe the rENM Framework, an experimental, open-source suite of R packages that automates a complete rENM workflow spanning data preparation, ensemble time-series construction, trend analysis, AI interpretation, and report generation. The framework integrates eBird occurrence records with environmental variables derived from NASAs MERRA-2 reanalysis across a 45-year study period (1980-2024) and executes a complete analysis for any species with eBird data through a single function call. By treating climatic suitability as a dynamic ecological response surface rather than a static baseline, the framework produces the following analytical products that complement conventional ecological niche modeling approaches: suitability time series, long-term trend and acceleration maps, centroid displacement estimates, bioclimatic velocity metrics, variable contribution trajectories, and hotspot analyses identifying areas of accelerating suitability decline. We illustrate the frameworks outputs with a representative run for Cassins Sparrow (Peucaea cassinii), a grassland species of conservation concern in the arid southwestern United States and the focal species throughout our development work. The frameworks automated, unsupervised pipeline makes systematic application across large numbers of species tractable, with direct implications for conservation assessments, such as State Wildlife Action Plans, where species-specific analytical capacity is often limited by available resources. The rENM Framework is openly available on GitHub and archived on Zenodo.

AimElucidating the mechanisms shaping parasite diversity patterns is critical because parasites encompass about 40% of known species, and are crucial for ecosystem structure and function. In free-living species, diversity patterns in the Anthropocene are shaped by niche-breadth because specialists (narrow niche-breadth taxa) are more sensitive to environmental disturbance compared to generalists (broad niche-breadth taxa). Like free-living species, parasites too can be categorized as specialists or generalists according to their niche-breadth (i.e., diversity of hosts they can infect). However, unlike free-living species, the effects of niche-breadth on parasite diversity patterns remain unclear. Here, we used haemosporidian parasites as a model system to identify factors affecting parasite diversity patterns, and test if these patterns differ between specialist (Haemoproteus) and generalist (Plasmodium) parasites. LocationSouthern India TaxonHaemoproteus spp. and Plasmodium spp. (Haemosporida) MethodsBlood samples from wild birds were screened using molecular tools to identify haemosporidian parasite lineages. Statistical analyses, including random forest models and generalized dissimilarity models, were utilized to evaluate how environmental and host factors drive spatial patterns of parasite and {beta} diversity. ResultsOur results reveal that phylogenetic diversity is primarily shaped by host-related variables in the specialist parasites, but by numerous host- and environment-related factors in the generalists. In keeping with ecological theory, the specialist parasites showed higher diversity and lower evenness compared to the generalists. Additionally, while {beta} diversity of the specialist parasites was primarily driven by spatial differences in richness (e.g., taxon nestedness) rather than replacement (e.g., taxon turnover), the opposite pattern was found in the generalist. Main conclusionThe differential patterns and drivers of diversity in specialist vs. generalist parasites demonstrates why specialists parasites are good indicators of ecosystem health and elucidates the mechanism by which anthropogenic disturbance increases the risk of emerging infectious diseases which are primarily caused by generalist parasites.

Understanding the ecological determinants of species ranges is a central goal of ecology. Novel tools like global datasets and machine learning models allow us to describe species ranges with increasing scope and accuracy, and to develop and test ecological hypotheses about their determinants. Here, I focus on the widespread nuisance mosquito and dog heartworm vector Aedes sierrensis, a tree hole-breeding mosquito that is widespread and abundance within its native range in western North America, and develop species distribution models (SDMs) to characterize the species range and its environmental determinants. I find that the species range is highly predictable from long-term average bioclimatic variables. Temperature and precipitation were the primary determinants: suitability was highest at wet-season average temperatures of 0 - 10{degrees}C, minimum temperatures of -5 - 5{degrees}C, and summer temperatures of 8 - 22{degrees}C in environments with adequate seasonal rainfall concentrated in the winter. After accounting for climate, land cover variables showed minimal importance for prediction, but suitability was higher in forests and outside of urban areas. The results are consistent with ecological knowledge of the Ae. sierrensis life cycle from field observations and previous laboratory experiments, suggesting that individual physiological constraints scale up to determine species distributional limits.

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.

1.AimThe Species Threat Abatement and Restoration (STAR) metric was developed to support setting and measuring progress towards science-based targets for species conservation, in alignment with the Kunming-Montreal Global Biodiversity Framework. The STAR metric quantifies the potential reduction in species global extinction risk achievable through actions to abate threats (START) and restore habitat (STARR). The STAR metric is used across multiple sectors to assess contributions to nature-positive species outcomes and implement action for biodiversity. Here we present a substantially enhanced global estimate of START for terrestrial vertebrates. Developed in response to user demand, this work integrates advancements in methodology and data quality, increased spatial resolution, and addition of reptile species. LocationGlobal Time PeriodCurrent Major taxa studiedTerrestrial vertebrates MethodsSTART was estimated at 1km2 resolution for 9,100 species of threatened and Near Threatened birds, mammals, amphibians and reptiles, using IUCN Red List assessments (version 2025-1) and area of habitat (AOH) maps generated using an advanced data-driven methodology and satellite-derived land-cover data. AOH maps were validated using a two-stage protocol using species observation data. ResultsThe six countries with the highest estimated START scores, and therefore the largest individual potential to reduce global extinction risk via tackling threats nationally were Brazil, Madagascar, Indonesia, Mexico, Ecuador and Colombia (each contributing over 5% of global estimated START). The threat with the greatest individual potential to reduce extinction risk was annual and perennial non-timber crops (21.2% of global START). Main conclusionsTargeted actions to tackle a few high-impact threats in a few discrete locations, and cumulative effort across multiple areas with lower individual potential, are both required to meaningfully reduce species extinction risk through threat abatement. This global scale estimation of START enables consistent scoping of conservation opportunity over large areas and provides the critical initial data to support planning and action.

Biodiversity is predicted to stabilize ecosystems if species have different environmental responses. How this response diversity is shaped by ecological and evolutionary processes remains poorly understood. We determine the drivers of thermal response diversity of 16 Swiss peri-alpine lake-fish communities. We report the first evidence that evolutionary diversification of lineages through adaptive radiation can increase the response diversity of an ecosystem. In-situ diversification increases response diversity in the cold-deep lake environment, but non-endemic and non-native species contributed only weakly to response diversity. The loss of endemic species during historical anthropogenic eutrophication led to a negative legacy on present thermal response diversity in cold and deep lake strata. Overall, the interplay of evolutionary diversification, ecological assembly and anthropogenic impacts drives variation in response diversity. Conserving and restoring processes that generate diversity may help maintain ecosystem stability beyond the Anthropocene.

Ecosystems are rarely at steady state, yet most theory predicting universal biodiversity patterns assumes they are. Here, we test whether and how eco-evolutionary dynamics drive departures from steady state by combining arthropod community data from the geologic chronosequence of the Hawaiian Archipelago with the Maximum Entropy Theory of Ecology (METE), a minimalist steady-state framework that simultaneously predicts species abundance distributions (SADs) and individual metabolic rate distributions (IPDs). The chronosequence of the Hawaiian Archipelago has yielded insights into eco-evolutionary processes because ecosystems growing on different aged substrates offer snapshots of community assembly with different histories. We find that deviations from METE peak at geologically middle-aged sites (150 Kya-1.4 Mya), consistent with active adaptive radiation pushing communities away from statistical steady state. Within-site {beta}-diversity, which also peaks at middle-aged sites, robustly predicts deviations from METE across all sites, while the proportion of non-native species predicts deviations only after excluding the geologically youngest site. Partitioning {beta}-diversity between native and non-native species resolves this discrepancy: at the youngest site, non-native species are distributed homogeneously and do not elevate {beta}-diversity despite their high proportional representation. Together, these results are consistent with a trajectory from young, dispersal-assembled communities near statistical steady state, through an eco-evolutionary non-steady-state transition driven by diversification, to a new stable steady state at the oldest sites. Our findings suggest that periods of active diversification create windows of ecological instability that may facilitate biological invasion, with implications for understanding invasion dynamics in biodiversity hotspots.

Stratigraphic paleobiology is a newly established interdisciplinary approach, which has demonstrated that the fossil record is a joint expression of biotic and stratigraphic change, and all inferences from it must be grounded in a solid understanding of the stratigraphic context. Fossiliferous strata can be found in all depositional systems (e.g., marine, terrestrial, or lacustrine; siliciclastics or carbonates), each having a unique characteristic timescale and set of external controls, which govern the accumulation of sedimentary particles, including fossils. Consequently, the same biotic changes are preserved differently across depositional systems. While carbonate systems form a large portion of the fossil record, most studies in stratigraphic paleobiology have focused on siliciclastic systems and are not easily generalizable. As they are predominantly formed by living organisms, carbonates are both fossils and record, opening the opportunity to study the co-dependency of life and its environment. Here, we explore the stratigraphic paleobiology of carbonate systems by combining simulations of carbonate platform and ramp geometries with synthetic fossil records. We explore the preservation of extinction patterns and rates spatially and across geometries. By examining stratigraphic biases in isolation (unconformity and condensation, ecology, and abundance biases), we find characteristic differences between ramp and platform geometries due to their differential response to sea level change, spatial variability, and differences in ecological clines. Differences in the structure of the fossil record between platform geometries are traceable to the contribution and properties of the carbonate producing organisms (carbonate factories), showing that preservation of earth system data in carbonate systems will vary both latitudinally and temporally or as a result of major perturbations of the biogeosphere. Our results show that while general rules on the structure of the fossil record can be derived for entire depositional systems, accounting for the geological and ecological dynamics of a particular sedimentary basin can hugely refine interpretations of the fossil record. That is particularly true for biogenic and biologically-mediated sediments.

Invasive plants pose a major environmental problem, threatening biodiversity, altering ecosystem functions, and causing economic loss. Climate change is altering environmental conditions, potentially facilitating the spread of invasive plant species, posing challenges for ecosystem management and biodiversity conservation. Accurate predictions of invasive species distributions are therefore essential for effective monitoring and early intervention. Species distribution models (SDMs) have become an important tool for predicting species habitats, but many studies rely on traditional machine learning approaches, focus on single-species predictions and overlook uncertainty associated with future climate scenarios. This study aims to evaluate the performance of a deep learning-based SDM framework, Deepbiosphere, for predicting both native and invasive plant species distributions on a regional scale, the US state of Michigan, and to assess how climate scenario uncertainty influences spatial predictions of invasive species risk particularly on two focal invasive species. Results show that Deepbiosphere outcompeted other baseline models by on average of 10.98% with a mean AUC-ROC of 0.79 across 1553 vascular plant species. For two invasive species Rhamnus cathartica and Ailanthus altissima, Deepbiosphere respectively improved modeling accuracy by an average of 56.41% and 74.99%, suggesting its enhanced predictive capability for invasive species. Current predictions indicated that R. cathartica is already broadly suitable across much of Michigan, whereas A. altissima is currently more restricted to southern regions. Under future climate scenarios, both species were projected to expand northward, with a particularly strong expansion signal for A. altissima. Prediction uncertainty was spatially heterogeneous, where general circulation models (GCMs) were the dominant source of uncertainty across most of the state. By integrating citizen science, remote sensing, and deep learning, we produced high-resolution risk-uncertainty maps for key invasive species and highlighted the importance of explicitly mapping uncertainty to support more informed invasive species management under climate change.

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.

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

Freshwater monitoring programmes like the European Unions Water Framework Directive (WFD) provide a wealth of data on European lake status, including water quality and macrophytes (aquatic plants) as critical habitat features that support health of humans and wildlife. Easier WFD data access can enable external management and research to better safeguard human and natural freshwater use. We demonstrate a replicable workflow to easily download and process multi-year (2007-2024) observations of lake macrophytes (425 sites) and complementary water quality variables (202 sites) from Swedish WFD data. Then, we illustrate the value of improved data access to address ecological questions that drive conservation, investigating how spatial scales influence macrophyte richness and associated water quality relationships using a spatial random intercept model. Decomposing the spatial intercept links small scales (<10 km) to site-level gradients and large scales (>100 km) to biogeographical drivers. Stochastic and environmentally-structured processes coexisted at intermediate scales (10-100 km). Adding water quality rarely improved overall predictive performance of macrophyte diversity models but consistently influences the role of different spatial scales. Water quality variables showed consistent spatially structured variation at intermediate scales and unique spatial patterns in tandem, overlapping with large-scale biogeographical influences. Altogether, we show context-dependencies for spatial model interpretation and provide guidance in accounting for spatial confounding to improve inferential and predictive performance. Our workflow and results show a clear way forward for accessing high-quality macrophyte and water quality data sets and their utility for addressing ecological questions that guide macrophyte protection under the WFD. HighlightsO_LIyears Swedish of macrophyte and water quality monitoring data were extracted. C_LIO_LIrichness showed scale-specific patterns linked to geographic gradients. C_LIO_LIbest predictive models for richness had no water quality at all. C_LIO_LIoverlap in their spatial scales and must be carefully separated. C_LIO_LIpen access data and multiscale analysis can apply to many ecological questions. C_LI

Extreme heat events are becoming more frequent, intense and prolonged, making it urgent to predict how heat intensity and exposure duration combine to threaten organisms. Thermal death time (TDT) and thermal load sensitivity (TLS) models provide this link, but conventional two-stage analyses often discard uncertainty, mishandle censored or overdispersed data and limit inference. Here, we show how the four-parameter log-logistic model can recover TDT/TLS quantities, including thermal tolerance (CTmax), sensitivity (z), critical temperature (Tcrit), heat injury and survival, from one model. Simulations show the joint model reproduces classical estimates when two-stage assumptions hold and is more reliable when they fail. We introduce these workflows as Bayesian and frequentist R packages. Case studies across plant and animal taxa demonstrate this modelling framework can estimate group contrasts and predict survival from realistic field temperature-time series. This framework provides more robust inference and flexible tools for predicting organismal responses to extreme heat events.

How abundance, connectivity, and genetic diversity covary across space remains a central question in evolutionary biology. Using a global environmental DNA metabarcoding dataset targeting mitochondrial cytochrome c oxidase subunit I (COI), we examined relationships among metabarcoding-derived abundance, nucleotide diversity, and spatial genetic connectivity across marine eukaryotes. We found a widespread but incomplete decoupling between abundance and genetic diversity, particularly in unicellular lineages. By contrast, distance-decay patterns in abundance, diversity, and connectivity were broadly similar across eukaryotes and more strongly associated with cellularity. Focusing on the Iberian Peninsula, we reconstructed locality-centred haplotype networks to quantify the spatial organization of intraspecific genetic variation. Unicellular lineages showed stronger local genetic structuring, whereas multicellular organisms exhibited clearer large-scale biogeographic partitioning. Overall, our results reveal shared biogeographic organization across eukaryotes while demonstrating that abundance-based biodiversity metrics alone cannot fully capture evolutionary and demographic processes, especially in microbial organisms.

Climate influences community assembly by constraining the conditions under which species can persist, but the consistency of macroecological processes across different ecosystems remains unclear. Interspecific variation in climatic niche properties that govern community assembly across climate gradients can be gleaned from species distributions. Species-climate associations, niche properties, and resulting assembly remains poorly understood for plants in tropical dry ecosystems, and among different life-forms. We examined how species-climate associations governed community assembly of four plant life-forms across 60,000 km2 of tropical dry ecosystem in peninsular India. In the 1750 km long Eastern Ghats mountain range, we surveyed vegetation in 2500 20 x 50 m plots. Across the north-south climatic gradient from cool, wet and seasonal sites to warm, dry and less-seasonal sites, from presence-absence data of 1601 species in four life forms (trees, shrubs, herbs, climbers), we performed Bayesian Hierarchical Modelling of Species Communities to predict conditions of occurrence for each species. From this, we derived species climatic niche optima and niche width, and examined the local-level prevalence of niche properties across the climate gradient to glean assembly. Most tree, shrub and climber species associated with warm-dry conditions had wider niches than species associated with cool-wet conditions, which also resulted in assemblages having wider niches at warmer sites. Herbaceous species, by contrast, had narrower niches when associated with warmer conditions, where narrow-niche species dominated the assemblage. In all life-forms, however, assemblages in both warm-dry and cool-humid conditions consisted of species primarily associated with and preferring (having their optima in) those conditions, suggestive of stress dominance. Further evidence for stress dominance in shrubs and climbers came from assemblages in the warmest and coolest sites having low richness and comprising mainly of species with wider niches. Tree richness increased at either end of the gradient, while herb richness increased in warm-dry conditions. Overall, our results suggest that plant life-forms differ in the processes driving assembly across broad climate gradients in a tropical dry ecosystem, but many herbs were specialized to warm-dry climates. To understand life-form-specific responses to environmental gradients in tropical dry ecosystems, future work should incorporate traits and evolutionary history.