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Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high-resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high-performance computing (HPC) pipeline, we developed N-SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N-SDM was built around a spatially-nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N-SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state-of-the-art SDM features embodied in N-SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithm-specific hyperparameter grid search, and the ensemble of small-models approach. N-SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and running N-SDM is openly available on the GitHub repository https://github.com/N-SDM/N-SDM., Ecography, 2023 (6), ISSN:0906-7590, ISSN:1600-0587

In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. We show that recasting multispecies distribution modeling as a ranking problem allows analyzing ubiquitous citizen-science observations with unprecedented efficiency. Based on 6.7M observations, we jointly modeled the distributions of 2477 plant species and species aggregates across Switzerland, using deep neural networks (DNNs). Compared to commonly-used approaches, multispecies DNNs predicted species distributions and especially community composition more accurately. Moreover, their setup allowed investigating understudied aspects of ecology: including seasonal variations of observation probability explicitly allowed approximating flowering phenology, especially for small, herbaceous species; reweighting predictions to mirror cover-abundance allowed mapping potentially canopy-dominant tree species nationwide; and projecting DNNs into the future allowed assessing how distributions, phenology, and dominance may change. Given their skill and their versatility, multispecies DNNs can refine our understanding of the distribution of plants and well-sampled taxa in general.

Sparse and spatiotemporally highly uneven sampling efforts pose major challenges to obtaining accurate species and biodiversity distributions. Here, we demonstrate how limited surveys can be integrated with global models to uncover hotspots and distributions of marine biodiversity. We test the skill of recent and advanced species distribution model setups to predict the global biodiversity of >560 phytoplankton species from 183,000 samples. Recent setups attain quasi-null skill, while models optimized for sparse data explain up to 91% of directly observed species richness variations. Using a refined spatial cross-validation approach to address data sparsity at multiple temporal resolutions we find that background choices are the most critical step. Predictor variables selected from broad sets of drivers and tuned for each species individually improve the models’ ability in identifying richness hotspots and latitude gradients. Optimal setups identify tropical hotspots, while common ones lead to polar hotspots disjunct from general marine diversity. Our results show that unless great care is taken to validate models, conservation areas in the ocean may be misplaced. Yet a game-changing advance in mapping diversity can be achieved by addressing data-sparse conditions that prevail for >80% of extant marine species.Authorship statementAll authors designed the research and contributed to the writing. D.R. designed the multiscale validation and predictor selection methods, developed the figures with input by M.V. and N.E.Z., performed research, and wrote the first draft.

High-resolution, downscaled climate model data are used in a wide variety of applications across environmental sciences. Here we introduce a new, high-resolution dataset, CHELSA-TraCE21k. It is obtained by downscaling TraCE-21k data, using the “Climatologies at high resolution for the earth's land surface areas” (CHELSA) V1.2 algorithm with the objective to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100-year time steps for the last 21 000 years. Paleo-orography at high spatial resolution and for each time step is created by combining high-resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases and interpolations using data from a global model of glacial isostasy (ICE-6G_C) and a coupling to mean annual temperatures from TraCE21k (Transient Climate Evolution of the last 21 000 years) based on the Community Climate System Model version 3 (CCSM3). Based on the reconstructed paleo-orography, mean annual temperature and precipitation were downscaled using the CHELSA V1.2 algorithm. The data were validated by comparisons with the glacial extent of the Laurentide ice sheet based on expert delineations, proxy data from Greenland ice cores, historical climate data from meteorological stations, and a dynamic simulation of species distributions throughout the Holocene. Validations show that the CHELSA-TraCE21k V1.0 dataset reasonably represents the distribution of temperature and precipitation through time at an unprecedented 1 km spatial resolution, and simulations based on the data are capable of detecting known LGM refugia of species.

Species interactions are influenced by the trait structure of local multi-trophic communities. However, it remains unclear whether mutualistic interactions in particular can drive trait patterns at the global scale, where climatic constraints and biogeographic processes gain importance. Here we evaluate global relationships between traits of frugivorous birds and palms (Arecaceae), and how these relationships are affected, directly or indirectly, by assemblage richness, climate and biogeographic history. We leverage a new and expanded gape size dataset for nearly all avian frugivores, and find a positive relationship between gape size and fruit size, that is, trait matching, which is influenced indirectly by palm richness and climate. We also uncover a latitudinal gradient in trait matching strength, which increases towards the tropics and varies among zoogeographic realms. Taken together, our results suggest trophic interactions have consistent influences on trait structure, but that abiotic, biogeographic and richness effects also play important, though sometimes indirect, roles in shaping the functional biogeography of mutualisms.

See 'Read me.rtf' (included in 'Data S1.rar') for a description of the files included in this dataset. Abstract of study: Early natural historians – Comte de Buffon, von Humboldt and De Candolle – established environment and geography as two principal axes determining the distribution of groups of organisms, laying the foundations for biogeography over the subsequent 200 years, yet the relative importance of these two axes remains unresolved. Leveraging phylogenomic and global species distribution data for Mimosoid legumes, a pantropical plant clade of c. 3,500 species, we show that the water availability gradient from deserts to rainforests dictates turnover of lineages within continents across the tropics. We demonstrate that 95% of speciation occurs within a precipitation niche, showing profound phylogenetic niche conservatism, and that lineage turnover boundaries coincide with isohyets of precipitation. We reveal similar patterns on different continents, implying that evolution and dispersal follow universal processes.

Human impacts such as habitat loss, climate change and biological invasions are radically altering biodiversity, with greater effects projected into the future. Evidence suggests human impacts may differ substantially between terrestrial and freshwater ecosystems, but the reasons for these differences are poorly understood. We propose an integrative approach to explain these differences by linking impacts to four fundamental processes that structure communities: dispersal, speciation, species-level selection and ecological drift. Our goal is to provide process-based insights into why human impacts, and responses to impacts, may differ across ecosystem types using a mechanistic, eco-evolutionary comparative framework. To enable these insights, we review and synthesise (i) how the four processes influence diversity and dynamics in terrestrial versus freshwater communities, specifically whether the relative importance of each process differs among ecosystems, and (ii) the pathways by which human impacts can produce divergent responses across ecosystems, due to differences in the strength of processes among ecosystems we identify. Finally, we highlight research gaps and next steps, and discuss how this approach can provide new insights for conservation. By focusing on the processes that shape diversity in communities, we aim to mechanistically link human impacts to ongoing and future changes in ecosystems., Ecology Letters, 26 (2), ISSN:1461-023X, ISSN:1461-0248

A multitude of physical and biological processes on which ecosystems and human societies depend are governed by the climate, and understanding how these processes are altered by climate change is central to mitigation efforts. We developed a set of climate-related variables at as yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We downscaled time series of near-surface relative humidity (hurs) and cloud area fraction (clt) under the consideration of orography and wind as well as near-surface wind speed (sfcWind) using the delta-change method. Combining these grids with mechanistically downscaled information on temperature, precipitation, and solar radiation, we then calculated vapor pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), the climate moisture index (cmi), and site water balance (swb) at a monthly temporal and 30 arcsec spatial resolution globally from 1980 until 2018 (time-series variables). At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models (projected variables). Time-series variables showed high accuracy when validated against observations from meteorological stations and when compared to alternative products. Projected variables were also highly correlated with observations, although some variables showed notable biases, e.g., snow cover days. Together, the CHELSA-BIOCLIM+ dataset presented here (https://doi.org/10.16904/envidat.332, Brun et al., 2022) allows improvement to our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world's ecosystems and the associated services on societies., Earth System Science Data, 14 (12), ISSN:1866-3516, ISSN:1866-3508

Current changes in the world’s climate increasingly impact a wide variety of sectors globally, from agricul-ture, ecosystems, to water and energy supply or human health. Many impacts of climate on these sectors hap-pen at high spatio-temporal resolutions that are not covered by current global climate datasets. Here we pre-sent Climatologies at high resolution for the Earth’s land surface areas - WFDE5 over land merged with ERA5 over the ocean data (CHELSA-W5E5, https://doi.org/10.48364/ISIMIP.836809.3, Karger et al., 2022): a cli-mate forcing dataset at daily temporal resolution and 30 arcsec spatial resolution for air-temperatures, precipi-tation rates, and downwelling shortwave solar radiation. This dataset is a spatially downscaled version of the 0.5° W5E5 dataset using the CHELSA V2 topographic downscaling algorithm. We show that the downscaling generally increases the accuracy of climate data by decreasing the bias, and increasing the correlation with measurements from meteorological stations. Bias reductions are largest in topographically complex terrain. Limitations arise for minimum near surface air temperatures in regions that are prone to cold air pooling, or at the upper extreme end of surface downwelling shortwave radiation. We further show that our topographically downscaled climate data compare well with the results of dynamical downscaling using the regional climate model Weather Research and Forecasting Model (WRF), as time series from both sources are similarly well correlated to station observations. This is remarkable given the lower computational cost of the CHELSA V2 algorithm compared to WRF and similar models. Overall, we conclude that the downscaling can provide high-er resolution climate data with increased accuracy. Hence, the dataset will be of value for a wide range of climate change impact studies both at global level but also as for applications that cover more than one region and benefit from using a consistent dataset across these regions.

Aim: Protected areas (PAs) play an important role in biodiversity conservation, but remain increasingly threatened by invasive alien plant species (IAPS) in conjunction with global climate change. The latter is modifying the distribution of the former, and the magnitude and direction of distributional changes are predicted to vary depending on species dispersal mode. Here, we address the question of whether clonality is expected to affect the future invasion pattern in PAs. Location: Worldwide. Time period: 1950–2100. Major taxa studied: 36 invasive alien plant species. Methods: We used ensembles of three species distribution models (GLM, GAM and Maxent) based on >70,000 occurrence records to project the distribution of 36 of the world's most invasive clonal and non-clonal plants in >20,000 PAs. Projections were based on three greenhouse gas concentration scenarios (low, medium and high) for 2080. Results: Climate change showed little impact on the global invasion pattern in PAs, and clonality showed little effect when all biomes were processed in concert. However, we discerned that the future invasion risk of clonal IAPS markedly increased in biomes located at high elevation and high latitude compared with non-clonal IAPS, while the risk decreased in lower-elevation tropical and subtropical biomes where asexual reproduction may be a less successful trait. We also showed that invasion hot spots overlapped with biodiversity hot spots and two realms (i.e. Nearctic and Palearctic), which calls for bridging the gap between invasion and conservation sciences and for more concerted management strategies. Main conclusions: We suggest that effective management of IAPS in PAs should consider in which biomes PAs are located as well as the reproductive traits of IAPS that are present or may become so., Diversity and Distributions, 27 (12), ISSN:1366-9516, ISSN:1472-4642

Early natural historians – Compte de Buffon, von Humboldt and De Candolle – established ecology and geography as two principal axes determining the distribution of groups of organisms, laying the foundations for biogeography over the subsequent 200 years, yet the relative importance of these two axes remains unresolved. Leveraging phylogenomic and global species distribution data for Mimosoid legumes, an pantropical plant clade of 3,400 species, we show that the water availability gradient from deserts to rainforests dictates turnover of lineages within continents across the tropics. We demonstrate that 95% of speciation occurs within a precipitation niche, showing profound phylogenetic niche conservatism, and that lineage turnover boundaries coincide with isohyets of precipitation. We reveal similar patterns on different continents, implying that evolution and dispersal follow universal processes. Fil: Ringelberg, Jens J. University of Zurich. Department of Systematic and Evolutionary Botany; Suiza Fil: Koenen, Erik J.M. University of Zurich. Department of Systematic and Evolutionary Botany; Suiza. Université Libre de Bruxelles. Faculté des Sciences. Evolutionary Biology & Ecology; Bélgica Fil: Sauter, Benjamín. University of Zurich. Department of Systematic and Evolutionary Botany; Suiza Fil: Aebli, Anahita. University of Zurich. Department of Systematic and Evolutionary Botany; Suiza. Abteling Umweltschutz und Energie. Departement Bau und Umwelt; Suiza Fil: Rando, Juliana G. Universidade Federal do Oeste da Bahia. Centro das Ciências Biológicas e da Saúde. Programa de Pós Graduação em Ciências Ambientais; Brasil Fil: Iganci, João R. Universidade Federal de Pelotas. Campus Universitário Capão do Leão. Instituto de Biologia; Brasil. Universidade Federal do Rio Grande do Sul. Programa de Pós-Graduação em Botânica; Brasil Fil: de Queiroz, Luciano P. Universidade Estadual de Feira de Santana. Departamento Ciências Biológicas; Brasil Fil: Murphy, Daniel J. Royal Botanic Gardens Victoria: Australia Fil: Gaudeul, Myriam. Institut de Systématique, Evolution, Biodiversité (ISYEB), MNHN-CNRS-SU-EPHE-UA: Francia Fil: Bruneau, Anne. Université de Montréal. Institut de Recherche en Biologie Végétale and Département de Sciences Biologiques; Canadá Fil: Luckow, Melissa. Cornell University. School of Integrative Plant Science. Plant Biology Section; Estados Unidos Fil: Morales, Matias. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Morón. Facultad de Agronomía y Ciencias Agroalimentarias; Argentina

Predictions from species distribution models (SDMs) that rely on presence-only data are strongly influenced by how pseudo-absences are derived. However, which strategies to generate pseudo-absences give rise to faithful SDMs in complex mountainous terrain, and whether species-specific or generic strategies perform better remain open questions.Here, across 500 plant species, we investigated comprehensively how predictions of SDMs at a 93 m spatial resolution are influenced by pseudo-absence strategies, using the complex topography of the Swiss mountains as a model system. We used five generic (random, equal-stratified, proportional-stratified, target, density) and three species-specific (target specific, density specific and geographic specific) approaches to derive pseudo-absence data. We conducted performance tests for each of our eight strategies in combination with (a) spatial bias, generated within our occurrence dataset on sites with highest sampling density, to investigate how this common bias problem influences the performance of pseudo-absence sampling strategies, and (b) a new approach to reduce model extrapolation in environmental space by including background data from all environmental conditions of the study area. SDMs were evaluated against an independent and well-sampled dataset of true presences and absences.The random, the density (generic), and the geographic specific (species-specific) strategies consistently performed best, even in cases of strong spatial sampling bias in the occurrence data. Including a background of environmentally stratified pseudo-absences improved predictions of species distributions towards environmental extremes, and significantly reduced spatial extrapolations of model predictions in environmental space.Our results indicate that both generic and species-specific pseudo-absence strategies allow estimating robust SDMs and we provide clear recommendations which strategies to choose in complex terrain and when presence data are prone to high sampling bias. In datasets with strong sampling bias, most pseudo-absence strategies produce extrapolation problems and we additionally recommend environmentally stratified pseudo-absences in these cases. Overall, in species rich datasets the use of complex and computationally demanding, species-specific pseudo-absence strategies may not always be justified compared to simpler generic approaches.

Background: We aimed to characterise the geographical distribution of Sorensen-based multi-site dissimilarity (beta(sor)) and its underlying true turnover (beta(sim)) and nestedness (beta(sne)) components for Chinese Lauraceae and to analyse their relationships to current climate and past climate change. Methods: We used ensembles of small models (ESMs) to map the current distributions of 353 Lauraceae species in China and calculated beta(sor) and its beta(sim) and beta(sne) components. We tested the relationship between beta(sor), beta s(ne) and beta(sim) with current climate and past climate change related predictors using a series of simultaneous autoregressive (SAR(err)) models. Results: Spatial distribution of beta(sor) of Lauraceae is positively correlated with latitude, showing an inverse relationship to the latitudinal alpha-diversity (species richness) gradient. High beta(sor) occurs at the boundaries of the warm temperate and subtropical zones and at the Qinghai-Tibet Plateau due to high beta(sne). The optimized SAR(err) model explains beta(sor) and beta(sne) well, but not beta(sim). Current mean annual temperature determines beta(sor) and beta(sne) of Lauraceae more than anomalies and velocities of temperature or precipitation since the Last Glacial Maximum. Conclusions: Current low temperatures and high climatic heterogeneity are the main factors explaining the high multi-site beta-diversity of Lauraceae. In contrast to analyses of the beta-diversity of entire species assemblages, studies of single plant families can provide complementary insights into the drivers of beta-diversity of evolutionarily more narrowly defined entities.