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.

Evaluating the relative impacts of land use and climate change on community change is challenging – and their impact may be contextdependent. Here, we use long-term nocturnal macro-moth community data to evaluate the relative impacts of changing habitats vs. changing climates on community composition and diversity of moths in different landscape settings and for moth species associated with different traits. We used Hierarchical Modelling of Species Communities to pinpoint moth species’ responses to climate and habitat composition in 109 sites across Finland. To characterise context-dependence, we extended this framework with conditional variance partitioning analysis. We used the model predictions to evaluate the relative effect of drivers on community diversity across Finland. The landscape context (i.e. the habitat composition around the site and its changes) emerged as the dominant driver of macro-moth communities. At the site level, where forests or shrub-like vegetation dominates, variation in species occurrence was mostly explained by local habitat conditions. In heterogeneous and water-dominated habitats, both habitat and climate variability contributed equally to patterns in species occurrence. At the species level, macro-moth responses to drivers of change varied according to their host plant affinity but independently of their wingspan. Climate and habitat changes can thus contribute congruently or unequally to community change, depending on the habitat. At the community level, traits also give insights into trends in and temporal variability of biogeographic patterns. Our results underpin the importance of land-use change as a key driver of community change – even among heatsensitive ectotherms. We also demonstrate that the sensitivity of local communities to climate and land use change varies among habitat profiles. Overall, our results highlight the importance of accounting for local conditions to understand and predict community patterns under global change.

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.

A key goal in ecology is to develop more effective ways to understand species’ distributions in order to facilitate both their study and conservation. Many species distribution modeling analyses have been performed to date, using either structured survey data or unstructured citizen science data; these two pools of data have tradeoffs in terms of data density, spatiotemporal coverage, and accuracy. Recent studies have shown that combining structured and unstructured survey data can greatly improve the accuracy of species distribution models for birds, but most of this work has focused on north temperate bird species and uses bird atlas data that is much more common in the temperate zone than elsewhere. We sought to adapt a data pooling approach from the literature on north temperate bird biology to create distribution models for a selection of secretive suboscine bird species that occur in a highly diverse region of the southwestern Amazon.

Species Distribution Model (SDM) is a useful tool for conserving rare species. However, the ‘rare species modeling paradox’ presents challenges in modeling the rarest species, such as Corybas species, which have a narrow distribution and are known only from a few localities, making them difficult to model using conventional methods. An Approximate SDM (ASDMs) method was developed specifically for these species, employing a subtraction formula weighted by niche overlap metrics. This method was created in the hope that other rare species with between 5-20 occurrence records could also be modeled in the future. Our ASDMs demonstrated a good fit with directly modeled SDMs and successfully predicted the distributions of rare species with very few occurrence records. Hence, ASDMs present a promising and user-friendly approach for modeling potential distributions of rare species.

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.

Integrated distribution models (IDMs), in which datasets with different properties are analysed together, are becoming widely used to model species distributions and abundance in space and time. To date, the IDM literature has focused on technical and statistical issues, such as the precision of parameter estimates and mitigation of biases arising from unstructured data sources. However, IDMs have an unrealised potential to estimate ecological properties that could not be derived from the source datasets if analysed separately. We present a model that estimates community alpha diversity metrics by integrating one species-level dataset of presence-absence records with a co-located dataset of group-level counts (i.e. lacking information about species identity). We illustrate the ability of IDMs to capture the true community alpha diversity through simulation studies and apply the model to data from the UK Pollinator Monitoring Scheme, to describe spatial variation in the diversity of solitary bees, bumblebees and hoverflies. The simulation and case studies showed that the proposed IDM produced more precise estimates of the community diversity than the single models, and the analysis of the real dataset further showed that the alpha diversity estimates from the IDM were averages of the single models. Our findings also revealed that IDMs had a higher prediction accuracy for all the insect groups in most cases, with this performance linked to the information provided by a data source into the IDM.

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.

Forests provide essential ecosystem services that range from the production of timber to the mitigation of natural hazards. Rapid environmental changes, such as climate warming or the intensification of disturbance regimes, threaten forests and endanger forest ecosystem services. In light of these challenges, it is essential to understand forests’ demographic processes of regeneration, growth, and mortality and their relationship with environmental conditions. Specifically, understanding the regeneration process in present-day forests is crucial since it lays the foundation for the structure of future forests and their tree species composition. We used Swiss National Forest Inventory (NFI) data covering vast bio-geographic gradients over four decades to achieve this understanding. Trees that reached a diameter at breast height of 12 cm between two consecutive NFI campaigns were used to determine regeneration and were referred to as ingrowth. Employing three independent statistical models, we investigated the number, species, and diameter of these ingrowth trees. The models were subsequently implemented into a forest simulator to project the development of Swiss forests until the mid-21st century. The simulation results showed a decrease and a shift in the species composition of ingrowth, marked by a significant decrease in Norway spruce (Picea abies) and concurrent increases in broadleaves. Nevertheless, the pace of this change towards a more natural species composition is relatively slow and is likely to slow down even further as ingrowth declines in the future, in contrast to the fast-changing climatic conditions. Hence, support through adaptive planting strategies should be tested in case ingrowth does not ensure the resilience of forests in the future. We conclude that since the regeneration of forests is becoming increasingly challenging, the current level at which ecosystem services are provided might not be ensured in the coming decades.

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.

Anthropogenic climate change is creating a more flammable future by increasing the number of days when vegetation is dry enough to burn. Indices representing the percent moisture content of dead fine fuels as derived from meteorological data have been used to assess geographic patterns and temporal trends in vegetation flammability. To date, this approach has assumed a single flammability threshold, typically between 8 and 12%, controlling fire potential regardless of the vegetation type or climate domain. Here we investigate geographic variation in fuel moisture levels associated with observed fire activity among ecoregions by analysing global reanalysis data and remotely sensed burnt area products. This geospatial analysis identified a wide range of ecoregion flammability thresholds (EFTs) associated with fire activity, often well above or below the commonly used range of values. Many boreal and temperate forests, for example, can burn with much higher fuel moisture than previously identified; Mediterranean forests, on the other hand, tend to burn with consistently low fuel moisture. Bayesian modelling showed that EFTs are primarily driven by eco-climatological variables, particularly precipitation and temperature. Our analysis also identified complex associations between vegetation structure, fuel types, and climatic conditions highlighting the complexity in vegetation-climate-fire relationships globally. Our study provides a critical, necessary step in understanding and describing global pyrogeography and tracking changes in spatiotemporal fire activity.

Our knowledge of biodiversity hinges on sufficient data, reliable methods, and realistic models. Without an accurate assessment of species distributions, we cannot effectively target and stem biodiversity loss. Species range maps are the foundation of such efforts, but countless studies have failed to account for the most basic assumptions of reliable species mapping practices, undermining the credibility of their results and potentially misleading and hindering conservation and management efforts. Here, we use examples from the recent literature and broader conservation community to highlight the substantial shortfalls in current practices and their consequences for both analyses and conservation management. We detail how different decisions on data filtering impact the outcomes of analysis and provide practical recommendations and steps for more reliable analysis, whilst understanding the limits of what available data will reliably allow and what methods are most appropriate. Whilst “perfect” analyses are not possible for many taxa given limited data, and biases, ensuring we use data within reasonable limits and understanding inherent assumptions is crucial to ensure appropriate use. By embracing and enacting such best practices, we can ensure both the accuracy and improved comparability of biodiversity analyses going forward, ultimately enhancing our ability to use data to facilitate our protection of the natural world.

The present distribution of Siberian boreal forests that are dominated by larches is influenced, to an unknown extent, by the glacial history. Knowing the past treeline response to climate shifts can improve our understanding of future treeline dynamics under changing climate. Here, we study patterns in the genetic variability of Siberian larches (Larix spp.) that can help us to unravel biogeographic migration routes since the Last Glacial Maximum (LGM). We sampled 148 larch individuals from Eastern Siberia. For each individual, genome-wide single nucleotide polymorphisms (SNPs) were derived through genotyping by sequencing (GBS). We inferred the spatial distribution from 14,003 SNPs with a cluster analysis. To infer the postglacial demographic history of Larix, we applied an Approximate Bayesian Computation. The Bayesian population assignment statistically supported three to four clusters from Western to Eastern Siberia that correspond well to the geographic ranges of the main Siberian larch species Larix sibirica, L. gmelinii, and L. cajanderi. Using four plausible clusters, the tested hypotheses in DIYABC show that the existing populations seem to have been initiated long before the LGM. We presume that the different populations originate from larch populations that survived the glacial periods. From our genetic studies, we deduce that Larix was more likely to have survived the cold LGM in northern refugia, from where a fast colonization of Siberia was possible, rather than Larix completely repopulating Siberia in the postglacial spreading out from southern areas with less harsh climatic conditions. The northernmost expansion during the Holocene seems to have benefitted from refugial populations ahead of the treeline at that time, which explains the existence of Larix in the far north. We expect from our results that the present migration will be slow at first as there are currently no refugial populations far north, as there probably were in the Holocene.

Climatic and anthropogenic impacts have determined the extinction of species in the past and are also the main factors shaping their present distribution ranges. Geographic range size – a biogeographic variable commonly used to assess population abundance, survival, and conservation status – varies with latitude. According to Rapoport’s rule, range size typically increases with latitude in mammals. Bats differ from other groups of mammals with regards to numerous morphological, physiological, and behavioral adaptations of sensory and motor systems. Nevertheless, bats are a suitable group for evaluating the rule because they show a strong latitudinal gradient in species richness. Our aim was to investigate the distribution patterns of European bat fauna based on two biogeographic variables 1) geographical range size and 2) average latitude of their distribution range, and investigate whether species traits characteristic to bats, mobility and hibernation, are associated with variation in range size and latitudinal distribution. We collected geographical data and species trait data on 44 European bat species from the literature. We discovered that range size and average latitude of distribution range follows Rapoport’s rule to a high degree in bats. Additionally, traits related to hibernation and movement behavior, more specifically hibernation breadth (indicating how widely a species utilizes different types of hibernacula) and mobility (based on seasonal movements), are associated with large distribution ranges and could affect northerly ranges in European bats. Range size does not only assist in directing conservation of threatened species, but it also provides insights into fundamental processes such as dispersal and adaptation. Our results emphasize that knowledge on the relationship between traits and species distribution is important for understanding current distribution patterns and could work as background information for predictive models on the effect of future landscape changes.

Soil microbes play a key role in shaping terrestrial ecosystems. It is therefore essential to understand what drives their distributions. While multivariate analyses have been used to characterise microbial communities and drivers of their spatial patterns, few studies focused on modelling the distribution of Operational Taxonomic Units (OTUs). Here, we evaluate the potential of species distribution models (SDMs), to predict the presence-absence and relative abundance distribution of bacteria, archaea, fungi and protist OTUs from the Swiss Alps. Advanced automated selection of abiotic covariates was used to circumvent the lack of knowledge on the ecology of each OTU. ‘Presence-absence’ SDMs were successfully applied to most OTUs, yielding better predictions than null models. ‘Relative-abundance’ SDMs were less successful, yet, they were able to correctly rank sites according to their relative abundance values. Archaea and bacteria SDMs displayed better predictive power than fungi and protist ones, indicating a closer link of the latter with the abiotic covariates used. Microorganism distributions were mostly related to edaphic covariates. In particular, pH was the most selected covariate across models. The study shows the potential of using SDM frameworks to predict the distribution of OTUs obtained from environmental DNA (eDNA) data. It underscores the importance of edaphic covariates and the need for further development of precise edaphic mapping and scenario modelling to enhance prediction of microorganism distributions in the future.