Ecography

AimClimate is an essential element of species niche estimates in many current ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values. Yet, climate can also be described as spatial or temporal variability for variables like temperature or precipitation. Such variability, spatial or temporal, offers additional insights into niche properties. Here, we test to what degree spatial variability and long-term temporal variability in temperature and precipitation improve SDM predictions globally. LocationGlobal. Time period1979-2013 Major taxa studiesMammal, Amphibians, Reptiles MethodsWe use three different SDM algorithms, and a set of 833 amphibian, 779 reptile, and 2211 mammal species to quantify the effect of spatial and temporal climate variability in SDMs. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). ResultsMean performance of SDMs with climatic means as predictors was TSS=0.71 and AUC=0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS=0.74, mean AUC=0.92), as does the inclusion of temporal variability (mean TSS=0.80, mean AUC=0.94). Including both spatial and temporal variability in SDMs shows similarly high TSS and AUC scores. Main conclusionsAccounting for temporal rather than spatial variability in climate improved the SDM prediction especially in exotherm groups such as amphibians and reptiles, while for endotermic mammals no such improvement was observed. These results indicate that more detailed information about temporal climate variability offers a highly promising avenue for improving niche estimates and calls for a new set of standard bioclimatic predictors in SDM research.

AimPaleoclimate reconstructions have enhanced our understanding of how past climates may have shaped present-day biodiversity. We hypothesize that habitat stability in historical Afrotropical refugia played a major role in the habitat suitability and persistence of chimpanzees (Pan troglodytes) during the late Quaternary. We aimed to build a dynamic model of changing habitat suitability for chimpanzees at fine spatio-temporal scales to provide a new resource for understanding their ecology, behaviour and evolution. LocationAfrotropics. TaxonChimpanzee (Pan troglodytes), including all four subspecies (P. t. verus, P. t. ellioti, P. t. troglodytes, P. t. schweinfurthii). MethodsWe used downscaled bioclimatic variables representing monthly temperature and precipitation estimates, historical human population density data and an extensive database of georeferenced presence points to infer chimpanzee habitat suitability at 62 paleoclimatic time periods across the Afrotropics based on ensemble species distribution models. We mapped habitat stability over time using an approach that accounts for dispersal between time periods, and compared our modelled stability estimates to existing knowledge of Afrotropical refugia. Our models cover a spatial resolution of 0.0467 degrees (approximately 5.19 km2 grid cells) and a temporal resolution of every 1,000-4,000 years dating back to the Last Interglacial (120,000 BP). ResultsOur results show high habitat stability concordant with known historical forest refugia across Africa, but suggest that their extents are underestimated for chimpanzees. We provide the first fine-grained dynamic map of historical chimpanzee habitat suitability since the Last Interglacial which is suspected to have influenced a number of ecological-evolutionary processes, such as the emergence of complex patterns of behavioural and genetic diversity. Main ConclusionsWe provide a novel resource that can be used to reveal spatio-temporally explicit insights into the role of refugia in determining chimpanzee behavioural, ecological and genetic diversity. This methodology can be applied to other taxonomic groups and geographic areas where sufficient data are available.

1Ecological demographers know that year-to-year climate variability influences the long-term growth of populations and thus their viability. Despite this, species distribution models (SDMs), widely used to project species geographic distributions based on climate, typically ignore inter-annual climate variability. Here, we show that climate variability plays a crucial role in determining current and future distributions of species. We achieved this by developing a new SDM framework, XSDM, that accounts for variability when assessing the ecological niche and distribution of species. XSDM outperforms traditional SDMs in simulation studies. Using XSDM, we assessed the impacts of variability on 10 example species. Variability reduces species potential distributions by an average of 22%, up to 45%. Moreover, sensitivities of distributions to potential changes in average temperature and in its variability were comparable in magnitude. To avoid biases, future SDMs should consider the effects of variability through a demographic approach such as XSDM. As global change alters climate variability, e.g., through increased frequency of extreme events, XSDM provides better tools for countering biodiversity losses.

In species distribution modelling (SDM), it is common practice to explore multiple machine-learning algorithms and combine their results into ensembles. This is no easy task in R: different algorithms were developed independently, with inconsistent syntax and data structures. Specialised SDM packages integrate multiple algorithms by creating a complex interface between the user (providing a unified input and receiving a unified output), and the back-end code (that tackles the specific needs depending on the algorithm). This requires a lot of work to create and maintain the right interface, and it prevents an easy integration of other methods that may become available. Here we present tidysdm, an R package that solves this problem by taking advantage of the tidymodels universe. Being part of the tidyverse, (i) it has standardised grammar and data structures providing a coherent interface for modelling, (ii) includes packages designed for fitting, tuning, and validating various models, and (iii) allows easy integration of new algorithms and methods. tidysdm allows easy, flexible and quick species distribution modelling by supporting standard algorithms, including additional SDM-oriented functions, and giving the opportunity of using any algorithm or procedure to fit, tune and validate a large number of different models. Additionally, it provides further functions to easily fit models based on paleo/time-scattered data. The package includes two vignettes detailing standard procedures for present-day and time-scattered data. These vignettes also showcase the integration with pastclim (Leonardi et al. 2023) to allow easier access to palaeoclimatic data series, if needed, but users can bring in their own climatic data in standard formats.

The latitudinal gradient of declining species richness from the Equator towards the poles is one of the most pervasive macroecological patterns on Earth today. However, the ubiquity of this trend over geological timescales remains unclear. One reason for this uncertainty is that palaeobiologists need Global Plate Models (GPMs) to estimate the latitudinal position of organisms remains at time of deposition. However, as GPMs constitute hypotheses for how tectonic plates have moved over Earths history, reconstructions of the latitudinal biodiversity gradient (LBG) might also vary based on the GPM used. Here, using the fossil record of five major marine invertebrate groups, we evaluate the impact of GPM choice on reconstructions of the LBG over the Phanerozoic. Our results show that GPM choice can lead to different conclusions about the shape and strength of LBGs in deep time, even at a coarse spatial scale. These findings suggest additional caution is needed when reconstructing deep-time biogeographic patterns and macroevolutionary events, such as the origin of the present-day LBG. We therefore advocate for future palaeobiogeographic studies to conduct sensitivity analyses investigating the impact of GPM choice on their conclusions, and for greater interdisciplinary collaboration between palaeobiologists and palaeogeographic modellers to avoid common issues in the use of GPMs.

Species Distribution Models (SDMs) are used to generate maps of realised and potential ecological niches for a given species. As any other machine learning technique they can be seen as "black boxes", due to a lack of interpretability. Advances in other areas of applied machine learning can be applied to remedy this problem. In this study we test a new tool relying on Local Interpretable Model-agnostic Explanations (LIME) by comparing its results of other known methods and ecological interpretations from domain experts. The findings confirm that LIME provides consistent and ecologically sound explanations of climate feature importance during the training of SDMs, and that the sdmexplain R package can be used with confidence.

AimThe niche is a fundamental concept in theoretical and experimental ecology and is used to describe a wide range of ecological processes from species interaction with the environment to community assemblies. A common way to represent the niche is through a multidimensional geometry known as the Hutchinsonian niche hypervolume. Ecological theory predicts that niche hypervolumes have properties such as holes with broader eco-evolutionary significance, but we lack a comprehensive empirical study of niche hypervolume properties and their evolutionary significance. LocationGlobal Time periodHolocene Major taxa studiedGymnosperms MethodsWe conducted for the first time a systematic and comprehensive test of the evolution of Hutchinsonian niche hypervolume properties (volume and holes) across 65 genera and 12 families of gymnosperms, which includes many species that are endangered or threatened. Using cutting-edge computational algorithms, we measured the evolution of geometric (i.e. volume) and topological (i.e. holes) properties of gymnosperm hypervolumes across a comprehensive calibrated phylogeny. ResultsOur comparative analysis revealed weak evidence of the non-independent evolution of niche hypervolume volume and no evidence of the non-independent evolution of hypervolume holes. We also found that genera and families with low hypervolume volume such as monotypic groups like Gingko, likely experienced shifts in hypervolume evolutionary rates. Main conclusionsOur results show that geometric and topological properties of gymnosperm climatic niche hypervolumes evolve independently. This agrees with competitive exclusion hypothesis in ecological theory where extant groups are likely to be the ones which minimise niche overlap and competition.

Predicting the geographic range of species and their response to variation in climate are entwined goals in conservation and evolutionary ecology. Species distribution models (SDMs) are foundational in this effort and used to visualize the geographic range of species as the spatial representation of its realized niche, or when based only on climate, its climate niche. SDMs can also forecast shifts in species range given climate change, but often lack of empirical support for causal links between climate and demography, yielding uncertain predictions. We addressed such uncertainties whilst also exploring the role of migration and resident life-histories in climate adaptation in mobile animals using 48 years of detailed demographic and climate data for song sparrows (Melospiza melodia), a polytypic species that varies in migratory life history. We developed SDMs representing demographic and climate niches of migratory and resident populations in western North America from California (CA) to Alaska (AK) using data from a focal population in British Columbia (BC) and 1.2 million citizen science observations. Distributions of resident and migrant populations predicted by each model agreed strongly (72.8%) in the region of our focal population, but less well in regions with dissimilar climates. Mismatches were largest in CA, smaller in AK, but in all cases supported the hypothesis that climate influences the evolution of migration and limits year-round residency. Our results imply that migrants predominated in our focal population a century ago, but that climate change has favored range expansions by non-migratory phenotypes and facilitated an upward shift in the elevational range of residents. We suggest long-term studies are crucial to evaluating the predictions of SDMs positing causal links between climatic conditions and species demography. We found such links to be robust regionally and particularly useful to elucidating the potential for migration or residence to facilitate adaptation to climate change.

Ecological networks are increasingly studied at large spatial scales, expanding their focus from a conceptual tool for community ecology into one that also adresses questions in biogeography and macroecology. This effort is supported by increased access to standardized information on ecological networks, in the form of openly accessible databases. Yet, there has been no systematic evaluation of the fitness for purpose of these data to explore synthesis questions at very large spatial scales. In particular, because the sampling of ecological networks is a difficult task, they are likely to not have a good representation of the diversity of Earths bioclimatic conditions, likely to be spatially aggregated, and therefore unlikely to achieve broad representativeness. In this paper, we analyze over 1300 ecological networks in the mangal.io database, and discuss their coverage of biomes, and the geographic areas in which there is a deficit of data on ecological networks. Taken together, our results suggest that while some information about the global structure of ecological networks is available, it remains fragmented over space, with further differences by types of eco-logical interactions. This causes great concerns both for our ability to transfer knowledge from one region to the next, but also to forecast the structural change in networks under climate change.

Reliable models of species niches and distributions depend on accurately matching occurrences to environments via spatial and temporal coordinates. For fossil occurrences, time-averaging and age uncertainty can create mismatches between fossils and their associated environments, distorting inferred niches and distributions. Using a virtual ecology approach, we assessed how temporal uncertainty ({+/-}200 years to the full late Quaternary) influences niche and distribution estimates for four virtual species centered on three periods: Holocene (6,000 y.b.p), deglacial (13,500 y.b.p.), and Last Glacial Maximum (18,000 k.y.b.p.). We compared uncertain estimates, derived by matching occurrences with environmental layers drawn from different times within each uncertainty window, against true niches and distributions. We found that during environmentally stable intervals, niches and distributions were robust to temporal uncertainty until it reached {+/-}2500 years. Higher environmental variability reduced accuracy, with the greatest mismatch occurring during the deglacial. These results demonstrate both the promise and limitations of paleodistribution reconstruction.

Geospatial modelling can give fundamental insights in the biogeography of life, providing key information about the living world in current and future climate scenarios. Emerging statistical and machine learning approaches can help us to generate new levels of predictive accuracy in exploring the spatial patterns in ecological and biophysical processes. Although these statistical models cannot necessarily represent the essential mechanistic insights that are needed to understand global biogeochemical processes under ever-changing environmental conditions, they can provide unparalleled predictive insights that can be useful for exploring the variation in biophysical processes across space. As such, these emerging tools can be a valuable approach to complement existing mechanistic approaches as we aim to understand the biogeography of Earths ecosystems. Here, we present a comprehensive methodology that efficiently handles large datasets to produce global predictions. This mapping pipeline can be used to generate quantitative, spatially explicit predictions, with a particular emphasis on spatially-explicit insights into the evaluation of model uncertainties and inaccuracies.

Anticipating biodiversity change is critical in rapidly warming regions, yet challenging because these areas often coincide with poor sampling. Data gaps are widely understood to interfere with species distribution models (SDMs), but this is difficult to detect with biased data. We test SDM bias-correction methods with a new occurrence-checklist-range (OCR) validation approach and evaluate prediction discrepancy for [~]700 Canadian terrestrial vertebrate species. We found: 1) bias-correction improves model performance against independent (checklist and range) data, but not against typical occurrence cross-validation, 2) predicted richness differed among methods (up to 2.7-fold), especially in the north, and 3) counterintuitively, future projections varied less (by 28%) because well-sampled climate space will shift north. Our findings suggest potential widespread overconfidence in SDM predictions for the unevenly sampled world, with implications for the growing reliance on biodiversity estimates for planning and policy. OCR validation and methodological discrepancy measurements are relatively easy ways to address this.

Joint Species Distribution Models (jSDM) are increasingly used to explain and predict biodiversity patterns. By accounting for species co-occurrence patterns and potentially including species-specific information, jSDMs capture the processes that shape ecological communities. Yet, factors like missing covariates or omitting ecologically-important species may alter the interpretability and effectiveness of jSDMs. Additionally, while the specific formulation of a jSDM directly affects its performances, the effects of choices related to model structure, such as inclusion, or not of phylogeny or trait information, are not well-explored. Here, we developed a multifaceted framework to comprehensively assess performances of alternative jSDM formulations at both species and community levels. We applied this framework to four alternative models fitted on presence/absence and abundance data of a polychaete assemblage sampled in two coastal habitats over 500 km and 8 years. Relative to a benchmark jSDM only capturing the effects of abiotic predictors and residual co-occurrence patterns, we explored the performance of alternative formulations that also included species phylogeny, traits, or some additional 179 non-target species, which were sampled alongside the species of interest. For both presence/absence and abundance data, explanatory power was good for all models but their interpretability and predictive power varied. Relative to the benchmark model, predictive errors on species abundances decreased by 95% or 53%, when including non-target species, or phylogeny, respectively. These differences across models relate to changes in both species-environment relationships and residual co-occurrence patterns. While considering trait data did not improve explanatory or predictive power, it facilitated interpretation of trait-mediated species response to environmental gradients. This study demonstrates trade-offs in jSDM formulation for explaining or predicting species data, highlighting the importance of using a comprehensive framework to compare models. Furthermore, our study provides some guidance for model selection tailored to specific objectives and available data.

AimTo examine the support of two ecological diversity theories- The Ecological Limits Hypothesis (ELH) and the Niche Conservatism Hypothesis (NCH) - in explaining patterns of global shark diversity. LocationGlobal scale and two ecological realms: the Tropical Atlantic and the Central Indo-Pacific. Time PeriodPast 100 years Major Taxa StudiedWe examined 534 species of sharks and chimaeras, and we performed two subclade analyses on 272 species of ground sharks and 15 species of mackerel sharks. MethodsWe compared the species richness, mean root distance (MRD), and tree imbalance patterns to those simulated under the ELH and NCH with temperate and tropical centers of origin. We used sea temperature as a proxy for energy availability. We examined the importance of biogeographic history by comparing the model fits between two taxonomic groups, ground and mackerel sharks, and two geographic regions, the Tropical Atlantic realm and Central Indo-Pacific realm. ResultsThe ELH, temperate-origin model had the best fit to the global dataset and the sub-analyses on ground sharks, mackerel sharks, and the Tropical Atlantic. The NCH temperate-origin model provided the best fit for the Central Indo-Pacific. The {beta} metric of tree symmetry showed the best potential for differentiating between the ELH and NCH models, and the correlation coefficient for temperature vs MRD performed the best at differentiating between temperate and tropical origin of ancestors. Main ConclusionsThe global and subclade analyses indicate the ELH provides the best explanation for global scale shark diversity gradients even in clades with varying ecology. However, at the realm scale, biogeographic history has an impact on richness patterns. Comparing multiple metrics in relation to a simulation model provides a more rigorous comparison of these models than simple regression fits.

AimsSpecies distributional responses to climate change can depend on ecology and phylogeny. The degree of habitat specialism is potentially important because habitat generalists with wider distributions are assumed to be less sensitive to environmental changes compared to narrowly-distributed habitat specialists. Additionally, predicting which aspects of the changing climate impacts species with different habitat associations may be particularly challenging in complex environments such as those of tropical mountains. We explore the effects of climate change on potential distributions of Muscicapidae Flycatchers in parts of the Eastern Himalayan Mountains and the Indo-Burman range, and relate predictions to the level of ecological habitat specialism, while accounting for phylogenetic relatedness. LocationBhutan and Northeast India MethodsWe used Maxent to develop ecological niche models of 60 Muscicapidae Flycatchers under different climatic scenarios by collating presences from Global Biodiversity Information Facility, Bioclimatic variables from WorldClim, and elevation from ASTER. Species were scored as habitat specialists or generalists using the Species Specialism Index. Variables with high contributions to Maxent models were extracted to explore sensitivity to climate change based on habitat specialism while testing for phylogenetic signal using Phylogenetic Generalised Least Squares. ResultsMaxent models had the highest contributions from variable bio8 (mean temperature of wettest quarter) under present climate, and tmax (maximum temperature) under future climatic scenarios. Phylogenetic Generalised Least Squares revealed that habitat generalists had higher sensitivity to climate change than specialists. We did not detect strong phylogenetic signal in sensitivity to abiotic variables under all climate scenarios. Main conclusionsPotential distributions of Muscicapidae Flycatchers were sensitive to temperature variable in the month of the highest precipitation, and to maximum temperature. Potential distributions of habitat generalists were particularly sensitive to these abiotic variables, and those of habitat specialists less so. Sensitivity to abiotic variables did not show a pattern of phylogenetic niche conservatism. climate change, Muscicapidae Flycatchers, phylogenetic niche conservatism, habitat specialism, tropical mountains, Maxent

Niche models are now widely used in many branches of the biological sciences and are often used to contrast the distribution of favourable environments between regions or under changes in environmental conditions such as climate change. Evaluating model performance and selecting optimal models is now accepted as best-practice, and a number of methods are available assist this process. One aspect of ENM application which has not received as much attention is developing methods to communicate the degree and nature of changes between model outputs (typically as raster maps). The method described in this paper, Binned Relative Environmental Change Index (BRECI), seeks to address this shortfall in communicating model results.

Anthropogenic climate change is altering the environment at unprecedented rates with severe consequences for most living organisms. As a result, species may extend, truncate, or shift their ranges in order to adapt to changing conditions. Species Distribution Models (SDMs) provide a data driven approach to predict future distributions under climate change and prioritize areas for conservation. Here, using a 10-year dataset with 4049 occurrence records of frogs from eight families and 27 genera as well as 733 occurrences of lizards from two families and 11 genera across the Western Ghats, we built SDMs to assess the changes in species distributions due to climate change. As expected, the temperature gradient across elevation and seasonality gradient across latitude contribute most to the climatic limits of species distributions. Latitudinal extents of most species were narrower in future predictions compared to the present, but there was little shift in latitudinal positions. On the other hand, most species shifted their distributions towards higher elevations, but the elevational range sizes remained the same. A total of 75 species of frogs (55%) and 15 species of lizards (45%) lose more than half of the suitable area, with few exceptions in both taxa that show an increase. In cases where a shift or increase in distribution was observed, the ability of the species to access and survive in these areas remains uncertain due to discontinuous topography and the presence of sister species. Overall, the frog and lizard fauna of the Western Ghats will be severely affected by climate change in the future due to a loss in climatic suitability.

Obtaining reliable predictions of future species distributions under global change scenarios is fundamental for identifying priority conservation sites. This requires incorporating the main drivers of distribution shifts into the modeling process. Climate change and land use change are the two primary drivers of species distributions, yet they have been considered differently in studies exploring future scenarios. Most studies focus solely on bioclimatic variables, given that climate is a major determinant of species distributions on a large spatial scale. In our study, we explored the impact of including dynamic land use variables in species distribution models, examining predicted range loss and spatial mismatch of suitable areas compared to models using only bioclimatic variables. Our findings indicate that including land cover can significantly alter model outputs. While incorporating land use and land cover variables did not enhance the predictive power for most species, it substantially affected the predicted future suitable areas for 60% of the species (mean change = 69%, range: 4% to 167%). This trend was consistent across all spatial resolutions. The two modeling approaches also differed in the spatial location of suitable areas. The level of disagreement varied across species but was generally high for both current and future scenarios, increasing with coarser resolutions. Our results underscore the significant implications of excluding land use change variables and highlight the necessity of considering these factors on a taxon-specific basis.

O_LIA central goal of biogeography is to partition the worlds biota into meaningful biogeographical regions. While naturalists have long delineated biogeographic regions using hierarchical hard clustering approaches, they have often acknowledged the existence of transition zones. However, such transition zones have not been empirically identified or distinguished from hard clusters. Transition zones are outstanding areas for understanding the processes that shape biotic assembly; however, a coherent framework for delineating them is lacking. C_LIO_LIHere, we propose a framework to empirically identify and analyze transition zones through a new metric, the "Transition Zone Index (TZI)". The TZI decomposes the proportional contribution of multiple biotas to a spatial unit (called a map cell) derived from a Grade of Membership model into a single quantitative measure per cell using Shannon entropy principles. The transition zones can then be quantified and presented as a gradient, scaled from TZI = 0 (indicating distinctive bioregions) to TZI = 1 (corresponding to the highly transitional areas with maximally admixed biotas). Finally, we implement spatial regression models to detect the predictors of these transition zones. C_LIO_LIWe demonstrate the application of our framework using vascular plants of southern Africa. Through the spatial variation of TZI, our approach represents transition zones as a continuous gradient of biotic turnover between bioregions. These transition zones were found to be either previously delineated as discrete clusters or incorporated into neighboring clusters. Our approach effectively captures both the intensity and width of transitional dynamics, showing that these patterns are primarily shaped by precipitation. C_LIO_LIBiogeographic transition zones are a measurable gradient, and our framework provides a robust quantitative foundation for detecting and analyzing the drivers of these areas. This improves our ability to understand the patterns and processes underlying biogeographic transition and provides important implications for conservation planning and biodiversity management. C_LI

AimUnderstanding whether invasive species retain or shift their ecological niches has traditionally relied on scalar overlap metrics that quantify the magnitude of niche change, but not its structure. Here, we test whether biological invasions involve a reorganisation of the environmental axes along which native and invasive ranges are differentiated, and whether the dominant axes of this reorganisation are consistently associated with invasion pathway type (intercontinental vs. within-continent). LocationGlobal (North America, Europe, Africa, Asia, Australasia). Time periodContemporary (environmental variables representing long-term averages, 1980-2021). Major taxa studiedFreshwater crayfish (Decapoda: Astacidea): Procambarus clarkii, Faxonius limosus, Pacifastacus leniusculus, Faxonius virilis, Faxonius rusticus. MethodsWe analysed native and invasive occurrences for five globally important crayfish invaders using [~]400 hydrologically resolved environmental variables from the Global Crayfish Database of Geospatial Traits. Classification models were used to quantify environmental differentiation between native and invasive ranges, and feature contributions were aggregated by environmental domain (climate, topography, soil, land cover). Patterns were evaluated across intercontinental and within-continent invasion pathways and assessed for robustness using cross-validation, permutation tests, sample-size sensitivity, and comparisons with classical niche overlap metrics. ResultsNative and invasive occurrences were consistently distinguishable across all species (accuracy 96.5-99.9%). A pathway-dependent pattern emerged: intercontinental invaders were primarily differentiated along climatic dimensions (58-76% of model importance), whereas within-continent invaders showed a more balanced contribution of climatic and topographic variables ([~]42% each), including strong signals from river network position. This contrast was stable across cross-validation folds (SD < 1.6%), and supported by permutation tests (P = 0.001). Classical niche overlap metrics (Schoeners D = 0.30-0.62) did not capture this qualitative distinction. Main conclusionsBiological invasions involve not only changes in niche position but a reorganisation of the environmental axes that distinguish species distributions. Our results suggest that the dominant axes of this reorganisation differ systematically with invasion pathway, reflecting whether species encounter novel climatic regimes or primarily shift within existing climatic space along topographic and network-position gradients. By resolving which environmental dimensions underpin native-invasive differentiation, this approach provides a complementary perspective to scalar overlap metrics and a basis for more mechanistic interpretations of invasion processes.