Ecography

Species distribution models are widely used for the evaluation and analysis of species spatial distributions and responses to environmental changes. Due to this, there's a wide diversity of tools and algorithms to perform these types of analyses. However, despite being a mature field of study, SDM still requires a large amount of research intervention and decision-making to produce reliable models. This is a natural consequence of each species unique response to environmental factors, but in practice it hinders the potential of SDMs to be used effectively and with confidence across large sets of species. Here we present the AutoMaxEnt repository, a collection of functions and tools for the automation of maximum entropy species distribution models. AutoMaxEnt offers a flexible environment to model species distribution models, automating most of the data pre-processing and preparation and giving the user the possibility of exploring multiple model scenarios automatically or with control over how those scenarios are generated. AutoMaxent offers model fit control, multiple background points generation algorithms, automatic model and variable selection, and study area configuration, among other parameters. AutoMaxEnt allows for the automation of model fitting, evaluation and model selection across different species, study areas and time periods.

Aim: To test whether individual movement mechanisms inferred from animal tracking data can be scaled up to explain landscape-scale patterns of population density and improve predictions of population redistribution under future environmental change. Location: Texas Gulf Coast, USA Time Period: 2009-2018 Major Taxa Studied: Whooping Crane (Grus americana) Methods: We parameterized individual-level step-selection functions using 14,898 GPS locations from 22 Whooping Cranes and propagated the resulting movement and habitat-selection mechanisms across the landscape to derive steady-state distributions of space use. We compared these movement-informed predictions with correlative species distribution models fitted to the same data. Predictions were evaluated using withheld tracking observations and fully independent population-level aerial-survey data. We then projected both approaches under sea-level-rise scenarios to assess how future habitat loss and fragmentation alter crane space use. Results: Movement mechanisms derived from individual-level tracking data captured the mechanistic dependence of population density on habitat quality and spatial configuration. When projected across the landscape, these mechanisms reproduced fully independently observed density patterns and outperformed conventional species distribution models. Because predictions arose from movement mechanisms rather than occurrence-environment correlations, movement-informed models also captured the effects of habitat reconfiguration under sea-level rise. They projected pronounced population redistribution and an expansion of core space-use areas of up to 80% which conventional models missed. Main Conclusions: Individual movement mechanisms derived from biologging data can scale up to explain current population structure and predict future redistribution under environmental change. By mechanistically accounting for how habitat configuration constrains accessibility, movement-informed models reveal severe impacts of fragmentation that are missed by conventional distribution models. Rapidly expanding biologging datasets therefore offer a scalable basis for connecting individual behavior to macroecological patterns and improving biodiversity forecasts in changing landscapes.

Climate affects biological processes and organisms. It is extensively recorded, and its effects are frequently studied. Climatic variables are recorded as time series of measurements, but these are often summarized as long-term averages, throwing away biologically relevant information. Subsequent analyses can therefore miss important climate effects or suffer from low predictive power. Here, we propose a new method, StatClim, inferring the most informative climatic summary from the data. Instead of taking a fixed arbitrary summary of a time series, StatClim automatically estimates the beginning and width of a climatic window and simultaneously calculates data aggregation, which best predicts a response variable. We implemented the proposed approach as a Bayesian hierarchical model in Stan. We illustrated the potential of StatClim on published data on mast seeding of Fagus sylvatica. StatClim correctly identified known climatic windows relevant for this tree species, but also uncovered that mast seeding is triggered by high temperatures within these windows. We demonstrated flexibility of the model to account for complex data structures, we ran power analysis showing the ability of the model to identify climatic effects even with modest datasets, and we evaluated its false positive rate, which is lower than 0.01. We discuss the advantages and limitations of StatClim. Compared to existing alternatives, it is suitable for hypothesis testing and offers important advances, such as estimation of the type of aggregation. In combination with existing methods, StatClim will facilitate data-driven use of climatic and microclimatic data and therefore discovering so far unknown climatic effects on biological processes and ecosystem functioning.

Species are dramatically shifting their geographical ranges, yet the consequences of these spatial redistributions on species interactions have not been examined at broad spatial and temporal scales. Here, we investigate how the redistribution of species abundance over the past 50 years affects the potential for predator-prey interactions among North American birds. We first predict potential predator-prey interactions based on existing data and functional traits, then construct a novel, abundance-weighted metric of interaction potential for each pair of predator and prey in local communities. Across North America, the potential for predator-prey interactions has been increasing over time. We further show that this widespread increasing trend in interaction potentials is mostly driven by higher abundances of predator species. The spatial redistributions of predators and prey lead to turnover of interactions within local communities, and the turnover is less associated with changes in climate but more associated with human-induced land use changes. Our novel approach and results highlight the importance of considering traits, abundance, and species interactions - including known and potential interactions - when studying the ecological consequences of species range shifts.

1. Occurrence records underpin biodiversity analyses, but their coordinates are often erroneous or missing. Misplaced coordinates distort environmental predictors and range estimates used in conservation assessment. Existing tools flag suspect records or georeference locality text, but rarely correct coordinates, provide calibrated uncertainty, or quantify downstream consequences. 2. We present GeoMend, a deterministic, rule-based method that flags and corrects suspect coordinates and georeferences records lacking coordinates from locality text, assigning each a precision tier or uncertainty radius. It uses no machine learning or artificial intelligence. We evaluate it against expert coordinates for 16,913 records and 736 species on Hispaniola, compare georeferencers using 5,000 localities from 72 countries, and measure changes in environmental predictors and the extent of occurrence. 3. On held-out Hispaniola records, GeoMend flags 92% of coordinates more than 5 km from expert truth, versus 27% for CoordinateCleaner. Among the accepted corrections with paired coordinates, the median error falls from 2.19 to 0.82 km, and 68% of the complete held-out pairs improve. On records placed by both GeoMend and GEOLocate that also have original coordinates, the median error is 0.67 km for GeoMend versus 10.69 km for GEOLocate. For records lacking coordinates, evaluated uncertainty radii contain expert truth about 92% of the time in well-supported gazetteer classes. Correction reduces the mean standardized environmental distance from expert truth by 38% and moves the extent of occurrence toward expert truth by a median of 8% (mean of 79%). Corrections and newly georeferenced records give 17 Hispaniola species their first range estimate. 4. On the global benchmark, GeoMend is more accurate than GPT-4o for two-thirds of the records placed by all four comparators, with median errors of 4.4 and 10.8 km, respectively. Applied to Global Biodiversity Information Facility (GBIF) records lacking coordinates, it places records from 82,180 previously unmapped species across 233 countries, ranging from precise locality interpretations to administrative centroids. Automated placement makes the global backlog more approachable, while explicit tiers distinguish uses supported by precise and coarse coordinates. Further georeferencing requires regional expertise to combine automated placement with local knowledge.

Short-term population dynamics are shaped by both external environmental stochasticity and endogenous ecological dynamics. The extent to which population trajectories maintain predictable temporal structure reflects underlying determinism and sets limits on inherent forecastability. Permutation entropy quantifies this intrinsic predictability using the relative frequency of short fluctuation motifs. However, requirements for long timeseries have restricted ecological applications. Here, I introduce a Bayesian hierarchical regression framework, incorporating entropy-neutral priors, that estimates expected permutation entropy from shorter, spatially-distributed ecological datasets. I validate the approach's capacity to detect the impact of increasing environmental variance through simulations. Applied to four decades of UK butterfly data, the approach identified increasing permutation entropy across 35 species, consistent with an increasing role of stochastic weather events. Outlier weather events did not have a consistent relationship with permutation entropy, highlighting the potential for environmental forcing to both disrupt and constrain trajectories. With this flexible approach, permutation entropy offers complementary perspectives on longstanding questions around forecasting and population dynamics.

1. Thermal performance curves (TPCs) are widely used to quantify temperature dependence of biological traits, but estimating them requires experiments across multiple temperatures. Because these experiments can be costly, researchers must balance the number and placement of temperature treatments against replication within treatments. Evenly spaced temperatures are a common default, but may allocate effort inefficiently when thermal responses are nonlinear or strongly asymmetric. 2. We developed TPCdesign, an R package for planning temperature-trait experiments. TPCdesign uses a pseudo-Bayesian (robust) D-optimal experimental design to identify sets of temperatures expected to provide the most information about a TPC, while allowing users to incorporate prior knowledge of thermal limits and curve shape, uncertainty in that knowledge, available numbers of treatments, and practical restrictions on temperature spacing. 3. Using simulation-based worked examples, we show that optimized designs can recover TPCs more accurately than evenly spaced designs, particularly for asymmetric curves and when the number of available temperature treatments is modest. Optimization primarily improves resolution of regions where performance changes rapidly and can substantially improve estimation of upper thermal limits while retaining information across the rest of the curve. 4. TPCdesign provides a practical framework for allocating limited experimental resources according to the biological response researchers seek to estimate. Rather than prescribing a universal set of temperatures, it allows experimental designs to reflect existing biological knowledge, uncertainty, and logistical constraints, improving the information gained from costly thermal performance experiments.

Plant phenology influences biotic interactions and the cycling of water, carbon, and nutrients, ultimately affecting global climate. Accurately quantifying phenological dynamics is therefore essential for ecological forecasting, yet estimates vary widely depending on data sources, smoothing approaches, and date-extraction techniques. For this reason, the selection of an appropriate processing pipeline requires benchmarking under controlled conditions. In this study, we use a Land Surface Model (LSM) to simulate ecosystem dynamics at four temperate forest sites under five lesser studied climate perturbations. The resulting synthetic dataset allows us to control for environmental conditions while comparing commonly used phenological detection methods (relative thresholds at 10%, 20% and 50%, highest rate of change, detection of inflection points in the curvature angle, detection of recovery and senescence points). Amongst tested methods, we find general agreement in responses to climate alterations, where inflection and low thresholds (10%) methods provide the closest match to the model predicted start of season, with a maximum discrepancy of 4 days. Higher thresholds (20%, 50%) are closer to the model predicted end of season, though discrepancies reach up to 10 days. High thresholds and rate-of-change methods also reliably detect the peak of season. We further show that some methods detect phenological responses missed by temperature-only type growing season, supporting the need for more realistic phenological representations in LSMs. Beyond evaluating extraction methods within a common framework, our study expands on the potential of using LSMs as sandboxes for ecological testing rather than solely predictive tools.

Understanding spatial and temporal variation in community composition is central to protecting and monitoring biodiversity. Similarity in the composition of two communities decreases along spatial and temporal gradients, a relationship known as distance decay. Comparing distance-decay relationships across taxonomic, functional, and bioclimatic facets of diversity provides a means of interrogating niche-based versus neutral influences on community assembly. Furthermore, the context in which these facets yield overlapping or complementary information about global change impacts remains poorly understood. In this study, we examined spatiotemporal distance-decay relationships for taxonomic, functional, and bioclimatic facets of alpine plant diversity leveraging elevation gradients on five peaks in the White Mountains, California, USA, sampled at 3-4 time points between 2007 and 2024. Using multivariate Bayesian beta regression models, we estimated the slopes of elevation distance decay over multiple surveys and across taxonomic, functional and bioclimatic diversity to understand how spatial distance-decay relationships are changing through time and across different facets of diversity. We found strong spatial distance decay of taxonomic similarity across all peaks, with the highest elevation peak showing the strongest decay. Functional and bioclimatic similarities decayed more weakly than taxonomic similarity, suggesting environmental filtering shapes trait and niche distributions. Critically, spatial decay patterns showed no temporal signal across facets of diversity, indicating community stasis rather than biotic homogenization or differentiation. This study adds further evidence that xeric alpine plant communities are resisting climate change impacts through time.

As ecosystems face rapid environmental change, understanding the interplay between landscape structure, genetic diversity, and adaptive potential is critical for predicting resilience. We investigated how biome-specific connectivity and environmental heterogeneity mediate adaptive strategies and resilience in the tropical rainbowfish (Melanotaenia splendida splendida) across contrasting rainforest and savannah ecosystems in tropical Australia. Using an integrative framework combining genomic, morphological, and environmental analyses, we inferred that hydroclimatic variation has been a key driver of genetic and phenotypic divergence. Rainforest populations exhibited strong local adaptation to fragmented, stable environments, while savannah populations showed less genomic differentiation and morphological divergence, patterns consistent with weaker local adaptation. We hypothesise that greater reliance on phenotypic plasticity may facilitate adaptive responses in savannah populations, reflecting their exposure to more connected and variable environments. Variance partitioning highlighted the contrasting roles of neutral and adaptive processes across biomes, with implications for managing resilience in freshwater systems under climate change. Our findings underscore the trade-offs between connectivity, local adaptation, and plasticity, providing a model for understanding adaptation and resilience across diverse ecosystems.

1. Plant populations at high elevation persist near their physiological limits, and treeline position is one of the clearest expressions of this constraint. In many mountain regions, however, this limit reflects historical land use as much as climate. Treeline shift is most often tracked as the displacement of a boundary line (1D) overlooking structural changes only visible when the ecotone is analysed as a surface (2D). 2. We reconstructed treeline ecotone dynamics at nine sites in the Italian Alps using aerial imagery from 1954 and 2017. At each site we detected the timberline and tree species line, quantified elevational displacement, and measured ecotonal surface change, relating them to climatic, land-use, and topographic predictors. 3. Both boundaries advanced upslope at all nine sites, with timberlines shifting faster (1.6 m/yr) than tree species lines (1.1 m/yr). This decoupled velocity caused the ecotonal surface to contract by ~17%. Land-use variables consistently explained more variance than climatic ones. 4. Land-use legacy and abandonment have a major role in shaping treeline dynamics. Boundary displacement reveals vegetation advance at both margins, while the surface view reveals concurrent narrowing of the habitat available for tree establishment. Both perspectives are needed to fully characterize how plant populations respond to shifting limiting factors at the treeline ecotone.

Information on the long-term dynamics of species distributions is essential for assessing global biodiversity change and its causes, and for informed conservation decisions. A valuable source of such historical information are atlases. They provide spatial standardisation, extensive geographic coverage, temporal replication, documented sampling protocols, and span multiple species. Here we present model-based occupancy probabilities for 1,055 bird and 58 butterfly species, derived from nine gridded atlas datasets spanning several editions over the past 50 years. These are: Japan, New Zealand, New York state (US), the states of Alberta, Quebec, and The Maritimes (Canada), Czech Republic, United Kingdom, and Europe. Despite standardisation and coordination during data collection, the raw atlas data still contain some variation in sampling effort. We accounted for this using a two-step process: (1) we estimated a proxy for sampling effort in each grid cell within each atlas period using the Frescalo Algorithm, and (2) we ran a Bayesian spatial occupancy model for each species and atlas period, using the estimated sampling effort as the single predictor of detection in the observation process of the model. As the main product, we provide occupancy probabilities for species across grid cells and atlas periods. We also report uncertainty around each probability estimate and flag ~21% (rare) species per atlas for which the model did not fit well. The probability distributions are fit for purpose and served in a user-friendly database with standardised geographic projection, metadata, and taxonomy.

1. Detecting causal relationships from ecological time series remains challenging because community dynamics are often nonlinear, high-dimensional, noisy, and strongly synchronized. These characteristics can reduce the reliability of existing causal inference methods and complicate the identification of ecological interactions in complex natural systems. 2. I introduce Forest-weighted Causal Correlation (FORCC), a machine-learning-based framework that infers directional causality from adaptive neighborhood structures learned by random forests. Rather than evaluating causal direction through cross-map reconstruction, FORCC quantifies directional dependence from the preservation of neighborhood structure using rank-based associations among forest-weight matrices. 3. Simulations of coupled nonlinear systems showed that FORCC accurately recovered causal direction while substantially reducing synchronization-induced false-positive detections relative to conventional convergent cross mapping. Chatterjee's {xi} exhibited the strongest resistance to synchronization-induced errors among the evaluated rank-based statistics. 4. Application to a 12-year marine community time series from Maizuru Bay, Japan, identified asymmetric relationships between individual taxa and community dynamics, revealing candidate driver-like and passenger-like roles that were not readily explained by seasonal synchrony alone. 5. By combining adaptive neighborhood learning with rank-based causal inference, FORCC provides a scalable framework for causal discovery in ecological time series. The method offers a practical tool for screening causal relationships, generating ecological hypotheses, and investigating complex interactions in multivariate natural systems.

Beta diversity is a fundamental component of regional biodiversity that accumulates from compositional differences between local species assemblages. Although beta diversity is increasingly applied across spatial scales, its scale dependence remains incompletely synthesized, particularly concerning variation in spatial scale used to define alpha diversity (grain size). Using tree community data from Barro Colorado Island, we find that both additive and multiplicative beta diversity are consistently higher for unequal than equal grain divisions across different spatial extents and numbers of grains. These results can be explained by Jensen's inequality based on the concave-down relationship of alpha diversity with area that in turn increases beta diversity through the scaling relationship between alpha and gamma diversity. Combined with our additional analysis showing that this prediction is robust to more general models of species richness, including species--area and species--sampling-effort curves, these results reveal a previously unrecognized, predictable range of beta diversity variation.

Metrics describing phylogenetic community structure are widely used to infer assembly processes in biological communities, but existing metrics capture only fixed phylogenetic scales and provide limited control over how rare and abundant taxa influence ecological patterns. Here we introduce q-weighted interpolated mean phylogenetic distance (qiMPD), a framework that continuously interpolates between broad evolutionary divergence and nearest-taxon distance while incorporating tunable abundance weighting. Applied to 3661 wastewater microbiomes, qiMPD revealed that abundance weighting had little effect on broad-scale phylogenetic dispersion but strongly altered local phylogenetic structure. Null-model analyses showed that within-community signals of phylogenetic overdispersion and clustering, and between-community signals of phylogenetic divergence and convergence, emerged at different combinations of phylogenetic locality and abundance weighting. Thus, the qiMPD framework enables phylogenetic scale- and abundance-resolved inference of community assembly. It is implemented in the Python package qdiv version 4.2.0.

1. Species monitoring programmes regularly encounter missing data, which complicates tasks such as estimating population size or detecting temporal trends. Selecting an imputation method suited to the properties of the data is therefore an important practical challenge, particularly for species exhibiting overdispersion and zero-inflation. 2. We compare seventeen imputation methods, comprising thirteen Poisson-based statistical models (accounting for overdispersion and zero-inflation, with fixed, random and multivariate structures) and four contrast-based approaches (LORI, MICE, missForest, correspondence analysis). Using four complete monitoring datasets of waterbird species surveyed across France and Italy over 21 years, illustrating a variety of abundance distributions, we introduced missing observations under a realistic Missing At Random mechanism, at rates from 5% to 70%. We evaluated all methods on computational burden, prediction accuracy for positive and null counts, quantification of uncertainty, and accuracy of population-size estimates. 3. No single method dominates across all criteria. Statistical models and contrast-based approaches yield similar point predictions, but only statistical models provide a genuine measure of uncertainty. Models that jointly account for overdispersion and zero-inflation perform best at predicting zero counts and achieving reliable prediction intervals, and the most suitable method ultimately depends on the abundance distribution of the target species. 4. These results provide practical guidance for ecologists selecting an imputation strategy for incomplete count data, highlighting the trade-offs between predictive accuracy, computational cost, and the ability to propagate uncertainty through subsequent ecological analyses such as population-trend estimation.

Riverine and riparian food webs are connected via 'allochthonous' resource flows from neighboring ecosystems. These resources, such as terrestrial leaf litter, contribute to benthic macroinvertebrate diets, supporting higher trophic levels. While allochthony occurs in streams globally, its sensitivity to broad environmental gradients remains poorly understood. We explored the drivers of allochthony across six river basins within the contiguous United States, including climate, stream size, and flow permanence. We used Bayesian mixing models to estimate macroinvertebrate dietary proportions in four functional feeding groups. Air temperature best predicted allochthony, with consumers relying less on allochthonous resources with increasing temperature. Our models indicate that macroinvertebrate diets become primarily autochthonous at approximately 15{degrees}C mean annual air temperature, with leaf litter-consuming shredders showing a slightly higher threshold for this shift. We also found increased allochthony in smaller, non-perennial streams. These findings highlight the complexity of stream allochthony and responses to multi-scale environmental factors.

Ecological uniqueness, commonly quantified by local contribution to beta diversity (LCBD), is important for understanding and conserving biodiversity. Its widely documented negative relationship with species richness has led to the view that species-poor communities may be disproportionately important for regional diversity. Here, we derive neutral expectations for richness-uniqueness relationships and show that negative slopes can arise from neutral assembly rather than necessarily reflecting an ecological signal of uniqueness. Neutral simulations validate the analytical expectations across abundance- and incidence-based LCBD formulations. Applying the framework to the Barro Colorado Island tropical forest and North American Breeding Bird Survey, we show that accounting for neutral sampling expectations substantially changes the ranking and spatial distribution of ecologically unique sites. By separating neutral sampling expectations from excess uniqueness, our framework provides a principled basis for interpreting ecological uniqueness and informing biodiversity conservation.

Global biodiversity recovery demands restoration at landscape scales, where ecological outcomes are shaped not only by habitat amount but also spatial arrangement. Alternative landscape restoration strategies can increase habitat amount, improve quality, and alter configuration in fundamentally different ways and may therefore vary in their efficiency. Here, we conceptualise five landscape restoration strategies and use a spatially explicit metapopulation framework to compare their ability to increase predicted species persistence across heterogeneous landscapes and trait space defined by dispersal ability and specialisation. We find that restoration outcomes are strongly trait-dependent, with restoration of existing degraded habitats yielding the highest benefits - particularly for specialists. Improvement of the landscape matrix to create low-quality habitat primarily benefits generalists, while strategies creating new habitat patches or enlarging patches provide more consistent but modest gains. These results reveal fundamental trade-offs among restoration strategies and highlight the utility of trait-explicit approaches to inform their implementation and evaluation.

Land-use and climate change are dominant drivers of terrestrial biodiversity change, yet are usually analysed separately although they co-occur and may interact. We integrated global bird assemblage records from PREDICTS with temperature anomalies and species' ecological characteristics to test how land use modifies biodiversity responses to climate warming across three dimensions: local abundance and richness, compositional dissimilarity among assemblages, and species occurrence across ecological groups. Land use modified warming associations for both abundance and richness. Relative to primary vegetation, warming associations were more positive in low-intensity agriculture for both responses, but more negative for richness in high-intensity agriculture and urban land. Predicted richness remained below the reference, defined as primary vegetation at temperature anomaly 0, across all modified land uses, with the largest declines in high-intensity agriculture (-17.9%) and urban land (-21.8%). Within land-use classes, greater warming exposure was generally associated with higher compositional similarity, consistent with warming associated biotic homogenisation. Occurrence models revealed ecological filtering: broad-niched generalists were favoured in warmer modified habitats, whereas range-restricted habitat specialists were filtered out under intensive land use and warming. Conservation assessments should therefore integrate land-use-warming interactions with changes in assemblage composition and ecological strategies, particularly in agricultural and urban landscapes where warming further disadvantaged range-restricted specialists.