The study of species environmental niches underpins numerous questions in ecology and evolutionary and has increasing relevance in a rapidly changing world. Environmental niches, characterized from observations of organisms, inform about a species’ specialization in multivariate environment space and help assess their exposure and sensitivity to a changing climate. Environmental niches are also the central concept behind the species distribution models (SDMs) which assess and predict the geographic variation in environmental suitability. Despite the clear role of past evolutionary processes in driving contemporary biodiversity distribution, the assessment of multivariate or n-dimensional (where n is the number of environmental axes) niches in a phylogenetic framework has remained limited and constrained by restrictive assumptions. This hampers important existing and emerging applications, such as assessments of niche conservatism, estimates of species’ adaptive potential under changing climates, and prediction of niches in less-studied parts of the tree of life. Here we introduce a framework that extends SDMs to estimate n-dimensional environmental niches jointly with underlying evolutionary processes. Specifically, we fit the relationship between niche distance and phylogenetic distance as a latent Gaussian Process across all species in a clade. We demonstrate mathematically how the parameters of the Gaussian Process can be linked to existing traditional evolutionary models. Simulations indicate that the approach successfully recovers evolutionary parameters. Applied to two clades of hummingbirds, the presented joint framework uncovers the relationships among species’ niches in phylogenetic space and supports the quantification and hypothesis testing of niche evolution. A key advantage of the presented framework is its joint estimation of the evolutionary process alongside niches directly from species observations with uncertainty propagated to evolutionary model parameters. The proposed approach has the potential to increase the robustness of inference about niche evolution and improve understanding of how the processes of niche formation and evolution interact.

Glaciers are retreating worldwide at an ever-increasing rate, exposing new ice-free areas to ecological succession. This process leads to changes in biodiversity and potentially to species interactions. However, we still have a limited understanding of how glacier retreat influences species interaction networks, particularly the structure and robustness of mutualistic networks. After reconstructing plant--pollinator networks along a glacier foreland, we address the effects of glacier retreat on pollination network structure and robustness. Our results show that the prevalence of different network motifs changes over space-time. With glacier retreat, pollination networks shift from being highly connected with specialist interactions to loosely connected with generalist interactions. Furthermore, network robustness decreased with glacier retreat. Despite the turnover of plant species, we find that structural roles played by different plant species stay constant over space-time. Our findings suggest that glacier retreat pushes pollination networks towards a loss of specialist interactions and low robustness, leading to increased fragility in the long term. Monitoring network motifs may provide valuable insights into the ability of novel pollination networks to withstand disturbances and preserve functionality in the face of glacier extinction.

Species distribution modeling is increasingly used to describe and anticipate consequences of a warming ocean. These models often identify statistical associations between distribution and environmental conditions such as temperature and oxygen, but rarely consider the mechanisms by which these environmental variables affect metabolism. Oxygen and temperature jointly govern the rate of oxygen supply to oxygen demand, and theory predicts thresholds in these rates below which species population densities are diminished. However, parameterizing models with this joint dependence is challenging because of the paucity of experimental work for most species, and the limited applicability of experimental findings in situ. Here we ask whether the joint effects of temperature and oxygen can be reliably inferred from species distribution observations in the field, using the U.S. Pacific Coast as a model system. Through simulation testing, we found that our statistical model—which adapted the metabolic index to jointly consider oxygen and temperature by applying an Arrhenius equation and used a non-linear threshold function to link the index to fish distribution—could not precisely estimate the parameters due to inherent features of the distribution data. However, the model reliably estimated an overall metabolic index threshold effect, and provided a better fit to sablefish (Anoplopoma fimbria) spatial distribution than previously used models. This mechanistic approach may improve predictions of species distribution, even in novel environmental conditions. Further efforts to combine insights from mechanistic responses and realized species distributions will improve forecasts of species’ responses to future environmental changes.

Anticipating the effects of global change on biodiversity have become a global challenge that requires new methods. Approaches like Species Distribution Models have important limitations which have fuelled the development of Joint Species Distribution Models (JSDM). However, JSDMs rely on community data from structured surveys. Nonetheless, no assessment on the suitability of JSDMs to work with unstructured data from opportunistic databases has been performed. Here we test JSDMs performance when using opportunistic databases. Using artificial data that mimic the limitations of such databases by subsampling complete cooccurrence matrices, we analysed how the completeness of opportunistic databases affects JSDMs in terms of (a) the role of independent variables on species occurrence, (b) residual species cooccurrence (as a proxy of biotic interactions), and (c) species distributions. Moreover, we illustrate how to evaluate completeness at the pixel level of real data with a study case of forest tree species in Europe, and evaluated the role of data completeness in model estimation. Our results with artificial data demonstrate that decreasing the retention percentage increase false negatives and negative cooccurrence probabilities, leading to loss of ecological information. However, JSDMs support different levels of degradation depending on the aspect of the model being considered. Models with 50 % of missing data are valid for estimating species niches and distribution, but interaction matrices would require more complete databases with at least 75% of data retention. Furthermore, in most cases JSDMs predict the original data even better than the data from subsampled matrices, both from testing and training subsets. All those findings were confirmed in the analysis with the real study case. We conclude that opportunistic databases are a valuable data source for JSDMs, but their use require a previous analysis of data completeness for the target taxa in the study area at the spatial resolution of interest.

Remote sensing is an invaluable tool for tracking decadal-scale changes in vegetation greenness in response to climate and land use changes. While the Landsat archive has been widely used to explore these trends and their spatial and temporal complexity, its inconsistent sampling frequency over time and space raises concerns about its ability to provide reliable estimates of annual vegetation indices such as the annual maximum NDVI, commonly used as a proxy of plant productivity. Here we demonstrate for seasonally snow-covered ecosystems, that greening trends derived from annual maximum NDVI can be significantly overestimated because the number of available Landsat observations increases over time, and mostly that the magnitude of the overestimation varies along environmental gradients. Typically, areas with a short growing season and few available observations experience the largest bias in greening trend estimation. We show these conditions are met in late snowmelting habitats in the European Alps, which are known to be particularly sensitive to temperature increases and present conservation challenges. In this critical context, almost 50% of the magnitude of estimated greening can be explained by this bias. Our study calls for greater caution when comparing greening trends magnitudes between habitats with different snow conditions and observations. At a minimum we recommend reporting information on the temporal sampling of the observations, including the number of observations per year, when long term studies with Landsat observations are undertaken.

Fire plays a pivotal role in driving ecological shifts between Mediterranean-type vegetation and subtropical ecosystems in South Africa. This study investigates long-term environmental dynamics and ecological regime changes at the Mediterranean-type vegetation /subtropical boundary using a 6000-year palaeoecological sequence from the Baviaanskloof – a region of South Africa characterized by high levels of biodiversity and climate dynamism. Combining fossil pollen and microcharcoal data from a rock hyrax (Procavia capensis) midden, we analyse vegetation responses to environmental changes. Our findings reveal that Mediterranean-type vegetation resilience prevailed until ca. 2800 cal yr BP when a major fire event triggered a transition to a subtropical thicket-dominated environment. This abrupt ecological turnover underscores the significance of fire as a major driver of vegetation change at the Mediterranean-type vegetation /subtropical boundary. Our study emphasizes the vulnerability of Mediterranean-type vegetation ecosystems to global environmental change, suggesting potential implications for similar biome boundaries worldwide. By integrating multi-proxy palaeoecological evidence, we gain insights into the resilience and vulnerability of these ecosystems, aiding in understanding future responses to climate change scenarios.

Human activities drive ecological transformation, impacting island ecosystems from species diversity to ecological traits, mainly through habitat degradation and invasive species. Using two unique long-term datasets we aim to evaluate whether species traits (body size, trophic level, dispersal capacity and habitat occupancy) can predict temporal variations in the abundance of endemic, native non-endemic and exotic arthropods in the Azores Islands. We found that body size is crucial to predict arthropods abundance trends. Small-bodied herbivorous arthropods showed a decrease in abundance, while large-bodied indigenous arthropods increased in abundance, mainly in well-preserved areas. Also, large-bodied exotic arthropods increased in abundance across the entire archipelago. Moreover, endemic canopy dwellers increased in abundance, while endemic ground-dwellers decreased in abundance. Simultaneously, exotic arthropods showed the opposite result, increasing abundance in the ground while decreasing abundance in the canopy. Finally, habitat influenced both endemic and exotic spider abundance trends. Endemic spiders that occupy solely natural habitats experienced a decline in abundance, while exotic spiders in the same habitats increased in abundance. Our study underscores the significance of arthropod species traits in predicting abundance changes in island ecosystems over time, as well as the importance of monitoring species communities. Conservation efforts must extend beyond endangered species to protect non-threatened ones, given the increased extinction risk faced by even common species on islands. Monitoring and restoration programs are essential for preserving island ecosystems and safeguarding endemic arthropod populations.

Biogeography has a critical influence on how ecological communities respond to threats and how effective conservation interventions are designed. For example, the resilience of ecological communities is linked to environmental and climatic features, and the nature of threats impacting ecosystems also varies geographically. Understanding community-level threat responses may be most accurate at fine spatial scales, however collecting detailed ecological data at such a high resolution would be prohibitively resource intensive. In this study, we aim to find the spatial scale that could best capture variation in community-level threat responses whilst keeping data collection requirements feasible. Using a database of biodiversity records with extensive global coverage, we modelled species richness and total abundance (the responses) across land-use types (reflecting threats), considering three different spatial scales: biomes, biogeographical realms, and regional biomes (the interaction between realm and biome). We then modelled data from three highly sampled biomes separately to ask how responses to threat differ between regional biomes and taxonomic group. We found strong support for regional biomes in explaining variation in species richness and total abundance compared to biomes or realms alone. Our biome case studies demonstrate that there is a high variation in magnitude and direction of threat responses across both regional biomes and taxonomic group, but all groups in tropical forest showed a consistently negative response, whilst many taxon-regional biome groups showed no clear response to threat in temperate forest and tropical grassland. Our results suggest that the taxon-regional biome unit has potential as a reasonable spatial and ecological scale for understanding how ecological communities respond to threats and designing effective conservation interventions to bend the curve on biodiversity loss.

Climatic changes can affect species distributions, population abundance, and evolution. Such organismal responses could be determined by the amount and quality of available habitats, which can vary independently. In this study, we assessed changes in habitat quantity and quality independently to generate explicit predictions of the species’ responses since the Last Glacial Maximum (LGM) climatic changes. We built ecological niche models and distribution models for 21 reptile, mammal, and plant taxa from the Baja California peninsula inhabiting lowland or highland environments. Geological data suggests the CCSM global circulation model is a better representation of LGM climate for the Baja California peninsula. Significant niche divergence was detected for all clades within species, along with significant differences in niche breadth and area of distribution between northern and southern clades. Most clades showed a reduction in distribution area towards LGM. Further, niche marginality (used as a measure of habitat quality) was higher during LGM for most clades, except for northern highland species who experienced improvements in both. Our results suggest that changes in habitat quantity and quality can affect organismal response independently. This allows the prediction of genomic signatures associated with changes in effective population size and selection pressure that could be explicitly tested to support our models.

Forest litter decomposition is considered as an essential ecosystem process affecting carbon and nutrient cycling in mountains. However, there exists high uncertainty in accurately estimating the contribution of litter decomposition to terrestrial ecosystems, largely due to the incomparability of different studies and the data limitation in microclimate and non-climatic factors at spatially matched scales. Here we used the tea bag index (TBI) as a standardized protocol to evaluate spatial variations in forest litter decomposition rate (k) and stabilization factor (S) across 10 mountains spanning a wide range of subtropical and tropical forests. Based on the coordinated experiment of 6,864 teabags in 568 sampling sites along elevations, we evaluated the importance of 10 environmental factors covering soil microclimate, edaphic properties, plant diversity, and topography on k and S by using model averaging and linear-mixed effects models. Of the 10 mountains, we found a consistently decreasing pattern of k and an increasing pattern for S along elevations. And the significant effect of k with elevation was mainly found in the western and northmost mountains, while the effect of S occurred in the western and southernmost mountains. For microclimate, there was a general importance of soil temperature (coef. = 0.48) and temperature variation in the growing season (coef. = 0.36) in k, and soil temperature (coef. = -0.46) and moisture variation on S (coef. = -0.36). The dominant role of soil microclimate was mainly found in western mountains with relatively cold environments. For non-climatic drivers, a significant effect of tree diversity on k and a negative correlation of edaphic and topography with S in the western and southern mountains were detected. These findings provide a general understanding of spatial variations of driving factors in forest litter decomposition and highlight a dominant control of soil microclimate in cold forests in high elevations and latitudes.

Species distribution and habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (Antilocapra americana), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional RSF models - variability in habitat selection - into a tool to improve understanding and predicting animal distribution across space and time.

Species distribution models (SDMs) underpin a wide range of decisions concerning biodiversity. Although SDMs can be built using presence-only data, rigorous evaluation of these models remains challenging. One evaluation method is the Boyce index (BI), which uses the relative frequencies between presence points and background points within a series of bins spanning the entire range of predicted values from the SDM (the original version), or a modified version using moving windows. Obtaining accurate estimates of the index using this approach relies upon having large number of presences which is often not feasible, particularly for rarer or restricted species that are often the focus of modelling. Wider application of BI requires a method that can accurately and reliably estimate the index using small numbers of presence records. In this study we investigated the effectiveness of five statistical smoothing methods and the mean of these five methods (denoted as ‘mean’) to estimate the BI. We simulated 800 species with varying levels of prevalence, built distribution models using random forest and Maxent methods with two levels of training presences (NPT: 20 and 500) together with 2×NPT and 10000 random points for the two modelling methods respectively. We used four levels of presences (NP: 1000, 200, 50 and 10) and 5000 random points to calculate the BI. Our results indicate that both the original and the modified versions of the calculation of the BI are severely affected by the decrease of NP, but one smoothing method (i.e., the thin plate spline) and the ‘mean’ were almost not affected by the decrease of NP for most realistic situations. Hence, these methods are recommended for estimating BI for evaluating species distribution models when verified absence data are unavailable.

Climate warming has triggered shifts in plant distributions, resulting in changes within communities, characterized by an increase in warm-demanding species and a decrease in cold-adapted species - referred to as thermophilization. Researchers conventionally rely on co-occurrence data from vegetation assemblages to examine these community dynamics. Despite the increasing availability of presence-only data in recent decades, their potential has largely remained unexplored due to concerns about their reliability. Our study aimed to determine whether climate-induced changes in community dynamics, as inferred from presence-only data from the Global Biodiversity Information Facility (GBIF), corresponded with those derived from co-occurrence plot data in Norway. To assess the differences between these datasets, we quantified a Community Temperature Index (CTI) from the co-occurrence data set and compared this with CTI obtained from presence-only data. We also examined the temporal trend in CTI (i.e., thermophilization) in both datasets. To do this, we first established a species-temperature relationship based on data before climate warming. In a preliminary analysis, we assessed the performance of this relationship using three datasets: 1) Norwegian co-occurrence data, 2) presence-only data from a broader European region organized into pseudo-plots (potentially capturing more species niches), and 3) a combined dataset merging 1) and 2). The transfer function including both datasets performed best. Subsequently, we compared the CTI for the co-occurrence plots paired up spatially and temporally with presence-only pseudo-plots. The results demonstrated that presence-only data can effectively evaluate species assemblage responses to climate warming, with consistent CTI and thermophilization values in comparison to co-occurrence data. Employing presence-only data for evaluating community responses opens up better spatial and temporal resolution and much more detailed analyses of such responses, our results therefore outline how a large amount of presence-only data can be used to enhance our understanding of community dynamics in a warmer world.

Understanding the strength and predictability of changes in global biodiversity is critical for quantifying how taxa will respond to global change. By analyzing the relationships in population trends among taxa exposed to both biotic and abiotic pressures, we may be able to discern these patterns, potentially facilitating the formulation of predictive frameworks for their future shifts. However, the extent to which these pressures can describe changes in abundances over large spatial and temporal scales is vastly understudied. We use two global datasets containing abundance time-series (BioTIME) and biotic interactions (GloBI) to fit a series of hierarchical models testing whether the yearly change in abundance of any given genus is associated with the yearly change in abundance of another geographically proximal genus (i.e. genus pairs) within the same study. We then use posterior predictive modeling to assess the predictive accuracy for each genus pair from the modeled output. Finally, we test how associations and predictive accuracy are influenced by site latitude, GloBI interactions, disturbance, time-series length, and taxonomic classification to assess what ecological factors explain differences in associations and/or predictability. Generally, we find that abundance changes between genus pairs tend to be neutral to weakly positively associated over time and have good predictive accuracy as long as yearly changes in abundance are not exceedingly large (<=39%). Associations and predictive accuracy across genus pairs vary systematically across ecological factors and taxonomic identity, increasing with longer time-series, towards the equator, and in disturbed habitats. Our results show that global time-series data can illustrate meaningful, albeit variable, relationships between genera and that these patterns are shaped by known ecological factors. Overall, this suggests that by incorporating broad and accessible ecological information, we can improve forecast methods to mitigate biodiversity loss in an era of global change.

Life is not a beach for those animals that survive in the rough ecological conditions found in marine sandy beaches—and yet, microscopic animals thrive on them. We explore the drivers for meiofaunal diversity in beaches by analysing taxonomic and functional patterns of 348 flatworm communities across 116 reflective beaches in the Western Mediterranean, totalling 152 species (61.2% new to Science). First, we confirm that species richness does not differ between beach hydrodynamic levels (swash, shoaling and surf) but rather depends on the characteristics of each beach. Second, we demonstrate that species composition across those levels depends on the species traits, in addition to geographical and abiotic factors. Third, we highlight that the species functional space has a lower richness than expected and a lower redundancy in the wave-exposed swash level compared to the shoaling and subtidal levels, suggesting a trait-based ecological filtering. Finally, we show that those differences depend on the higher frequency of hydrodynamics-related traits in the species of the swash level. Our results suggest that the rough hydrodynamic conditions in the swash level favour a unique combination of species traits, which might be linked to ecological speciation in flatworms but also in other interstitial animals.

Climate change affects biodiversity in diverse ways, necessitating the exploration of multiple climate dimensions using standardized metrics. However, existing methods for quantifying these metrics are scattered and tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed “climetrics” which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure. Six widely used climate change metrics are currently implemented, including 1) Standardized Local Anomalies; 2) Changes in Probabilities of Local Climate Extremes; 3) Changes in Areas of Analogous Climates; 4) Novel Climates; 5) Changes in Distances to Analogous Climates; and 6) Climate Change Velocity. For climate change velocity, three different algorithms are implemented and available within the package including; a) Distanced-based Velocity (“dVe”); b) Threshold-based Velocity (“ve”); and c) Gradient-based Velocity (“gVe”). The package also provides additional tools to calculate the monthly mean of climate variables over multiple years, to quantify and map the temporal trend (slope) of a given climate variable at the pixel level, and to classify and map Köppen-Geiger (KG) climate zones. The climetrics R package is seamlessly integrated with the rts package for efficient handling of raster time-series data. The functions in climetrics are designed to be user-friendly, making them suitable for less-experienced R users. Detailed comments and descriptions in their help pages and vignettes of the package facilitate further customization by advanced users. In summary, the climetrics R package offers a unified framework for quantifying various climate change metrics, making it a useful tool for characterizing multiple dimensions of climate change and exploring their spatiotemporal patterns.

Many studies suggest that aside from environmental variables, such as topography and climate, species-specific ecological traits are relevant to explain the geographic distribution of intraspecific genetic lineages. Here, we investigated whether and to what extent incorporating such traits systematically improves the accuracy of random forest models in predicting genetic differentiation among pairs of localities. We leveraged available ecological datasets for birds and tested the inclusion of two categories of ecological traits: dispersal-related traits (i.e., morphology and foraging ecology) and demographic traits (such as species survival rate and generation length). We estimated genetic differentiation from published mitochondrial DNA sequences for 31 species of birds (1,801 total genetic samples, 526 localities) in the Atlantic Forest of South America. Aside from the aforementioned ecological traits, we included geographic, topographic and climatic distances between localities as environmental predictors. We then created models using all available data to evaluate model uncertainty both across space and across the different categories of predictors. Finally, we investigated model uncertainty in predicting genetic differentiation individually for each species (a common challenge in conservation biology). Our results show that while environmental conditions are the most important predictors of genetic differentiation, model accuracy largely increases with the addition of ecological traits. Additionally, the inclusion of dispersal traits improves model accuracy to a larger extent than the inclusion of demographic traits. Similar results are observed in models for individual species, although model accuracy is highly variable. We conclude that ecological traits improve predictive models of genetic differentiation, refining our ability to predict phylogeographic patterns from existing data. Additionally, demographic traits may not be as informative as previously hypothesized. Finally, prediction of genetic differentiation for species with conservation concerns may require further careful assessment of the environmental and ecological variations within the species range.

Climate change is causing geographic range shifts globally, and understanding the factors that influence species’ range expansions is crucial for predicting future changes in biodiversity. A common, yet untested, assumption in forecasting approaches is that species will shift beyond current range edges into new habitats as they become macroclimatically suitable, even though microhabitat variability could have overriding effects on local population dynamics. We aim to better understand the role of microhabitat in range shifts through its impacts on establishment by i) examining microhabitat variability along large macroclimatic gradients, ii) testing which of these microhabitat variables explain plant recruitment and seedling survival, and iii) predicting microhabitat suitability beyond species range limits. We transplanted seeds of 25 common tree, shrub, forb, and graminoid species across and beyond their current elevational ranges in the Washington Cascade Range, USA, along a large elevational gradient spanning a broad range of macroclimates. Over five years, we recorded recruitment, survival, and microhabitat characteristics rarely measured in biogeographic studies. We asked whether microhabitat variables correlate with elevation, which variables drive species establishment, and whether microhabitat variables important for establishment are already suitable beyond leading range limits. We found that only 30% of microhabitat parameters covaried in the expected way with elevation. We further observed extremely low recruitment and moderate seedling survival in our study system, and these were generally only weakly explained by microhabitat. Moreover, species and life stages responded in contrasting ways to soil biota, soil moisture, temperature, and snow duration. Microhabitat suitability predictions suggest that distribution shifts are likely to be species-specific, as different species have different suitabilities, and availabilities, of microhabitat beyond their present ranges, thus calling into question large-scale macroclimatic projections that will miss such complexities. We encourage further research on species responses to microhabitat and the inclusion of microhabitat in range shift forecasts.