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Natural history collections (NHCs) represent an enormous and largely untapped wealth of information on the Earth's biota, made available through GBIF as digital preserved specimen records. Precise knowledge of where the specimens were collected is paramount to rigorous ecological studies, especially in the field of species distribution modelling. Here, we present a first comprehensive analysis of georeferencing quality for all preserved specimen records served by GBIF, and illustrate the impact that coordinate uncertainty may have on predicted potential distributions. We used all GBIF preserved specimen records to analyse the availability of coordinates and associated spatial uncertainty across geography, spatial resolution, taxonomy, publishing institutions and collection time. We used three plant species across their native ranges in different parts of the world to show the impact of uncertainty on predicted potential distributions. We found that 38% of the 180+ million records provide coordinates only and 18% coordinates and uncertainty. Georeferencing quality is determined more by country of collection and publishing than by taxonomic group. Distinct georeferencing practices are more determinant than implicit characteristics and georeferencing difficulty of specimens. Availability and quality of records contrasts across world regions. Uncertainty values are not normally distributed but peak at very distinct values, which can be traced back to specific regions of the world. Uncertainty leads to a wide spectrum of range sizes when modelling species distributions, potentially affecting conclusions in biogeographical and climate change studies. In summary, the digitised fraction of the world's NHCs are far from optimal in terms of georeferencing and quality mainly depends on where the collections are hosted. A collective effort between communities around NHC institutions, ecological research and data infrastructure is needed to bring the data on a par with its importance and relevance for ecological research.

AimThe impact of climate change on forest biodiversity and ecosystem services will be partly determined by the relative fortunes of invasive and native forest trees under future conditions. Aotearoa New Zealand has high conservation value native forests and one of the world's worst invasive tree problems. We assess the relative effects of habitat redistribution on native Nothofagus and invasive conifer (Pinaceae) species in New Zealand as a case study on the compounding impacts of climate change and tree invasions.LocationAotearoa New Zealand.MethodsWe use species distribution models (SDMs) to predict the current and future distribution of habitat for five native Nothofagus species and 13 invasive conifer species under two 2070 climate scenarios. We calculate habitat loss/gain for all species and examine overlap between the invasive and native species now and in future.ResultsMost species will lose habitat overall. The native species saw large changes in the distribution of habitat with extensive losses in North Island and gains mostly in South Island. Concerningly, we found that most new habitat for Nothofagus was also suitable for at least one invasive species. However, there were refugia for the native species in the wetter parts of the climate space.Main ConclusionIf the predicted changes in habitat distribution translate to shifts in forest distribution, it would cause widespread ecological disruption. We discuss how acclimation, adaptation and biotic interactions may prevent/delay some changes. But we also highlight that the poor establishment capacity of Nothofagus, and the contrasting ability of the conifers to invade, will present persistent conservation challenges in areas of both new habitat and forest retreat. Pinaceae are problematic invaders globally, and our results highlight that control of invasions and active native forest restoration will likely be key to managing forest biodiversity under future climates.

Multiple statistical algorithms have been used for species distribution modelling (SDM). Due to shortcomings in species occurrence datasets, presence‐only methods (such as MaxEnt) have become increasingly widely used. However, sampling bias remains a challenging issue, particularly for density‐based approaches. The Isolation Forest (iForest) algorithm is a presence‐only method less sensitive to sampling patterns and over‐fitting because it fits the model by describing the unsuitable instead of suitable conditions. Here, we present the itsdm package for species distribution modelling with iForest, which provides a workflow wrapper for the algorithms in iForest family and convenient tools for model diagnostic and post‐modelling analysis. itsdm allows users to fit and evaluate an iForest SDM using presence‐only occurrence data. It also helps the users to understand relationships between species and the living environment using Shapley values, a suggested technique in explainable artificial intelligence (xAI). Additionally, itsdm can make spatial response maps that indicate how species respond to environmental variables across space and detect areas potentially affected by a changing environment. We demonstrated the usage of the itsdm package and compared iForest with other mainstream SDMs using virtual species. The results enlightened that iForest is an advantageous presence‐only SDM when the actual distribution range is unclear.

Aim Grasslands cover a third of Earth's landmass and provide critical ecosystem services. Anticipating how perennial C3 (cool‐season) and C4 (warm‐season) grasses respond to climate change will be key to predicting future composition and functioning of grasslands. Here, we evaluate environmental drivers of C3 and C4 perennial distributions and assess how C3 and C4 grass distributions shift in response to future climate change. Location Western United States. Methods We developed integrated species distribution models to identify climate and soil drivers of relative abundance of C3 and C4 perennial grasses. We then created projections of species abundances under future climate and evaluated when and where projected shifts in relative abundance were robust across climate models. Results Historically, C3 grasses occupied areas with lower temperature and more variable precipitation regimes, while C4 grasses occupied areas of higher temperature, greater temperature variability and greater warm‐season precipitation. C4 species also occupied narrower soil texture niches. In response to future climate change, C3 grass abundance declined across 74% of areas, while C4 abundance increased across 66% of areas. C3 grasses expanded in mid‐ to higher‐latitude areas with increasing temperature and decreasing seasonality of precipitation. In contrast, C4 grasses increased in higher‐latitude regions, but declined in lower‐latitude, dryer regions. Results were surprisingly robust across climate scenarios, suggesting high confidence in the direction of these future changes. Main Conclusions Findings imply C3 and C4 perennial grasses will have highly divergent responses to climate change that may result in grassland functional compositional changes. Increasing temperatures and precipitation variability may favour some C4 grasses, but C4 habitat expansion may be constrained by soil conditions in western USA. Results provide actionable insights for anticipating the impacts of climate change on grass‐dominated and co‐dominated ecosystems and improving large‐scale conservation and restoration efforts.

Pteridophytes are excellent ecological indicators of habitat quality. In this study, we built a model that predicts the habitat suitability of Elaphoglossum beddomei Sledge, an epiphytic or lithophytic and endemic pteridophyte in Southern Western Ghats, by using the technique of species distribution modelling. The occurrence data of E. beddomei from field explorations as well as from various herbaria were collected during 2018–2022. These occurrence data along with climatic data were processed by R packages. The processed data were further analysed using MaxEnt software to project the distribution of E. beddomei in future climatic scenarios. After correlation analysis, five bioclimatic variables – Mean Temperature of Wettest Quarter (bio8), Precipitation of Driest Quarter (bio17), Precipitation of Warmest Quarter (bio18), Precipitation of Wettest Quarter (bio16) and Temperature Annual Range (BIO5-BIO6) (bio7) – were selected from 19 bioclimatic variables with less correlation. Precipitation of Warmest Quarter (bio18) had the most influence in determining the distribution of E. beddomei, with a permutation importance of 83%. Conversely, Temperature Annual Range (BIO5-BIO6) (bio7) and Precipitation of Driest Quarter (bio17) showed least influence in determining the distribution of E. beddomei, and hence, the models created without these variables are considered for prediction. The habitat suitability predictions of the model indicate that the potential habitats of the species may get reduced in Southern Western Ghats in future climatic scenarios. It is in tune with the predicted expansion of drier climatic zones in Southern Western Ghats, which may reduce the suitable habitats for the E. beddomei in near future. So, it demands formulating suitable strategies for reducing the emission of greenhouse gases, regenerating forests and conserving forests by implementing more stringent policies on the environment to protect such highly habitat-specific evergreen elements.

Freshwater ecosystems are increasingly imperiled by the dual pressures of biological invasions and climate change, necessitating robust predictive frameworks for effective management. This study integrates advanced ensemble machine learning (EML) within a species distribution modeling (SDM) framework to assess the current and future invasion risk of Carassius species ( C. auratus , C. gibelio , and C. langsdorfii ) across Iranian inland waters. A comprehensive dataset of 486 occurrence records was analyzed alongside eight rigorously selected environmental predictors encompassing climatic, topographical, hydrological, and anthropogenic variables. The BIOMOD2 R package facilitated the construction of an EML-based SDM, leveraging six algorithms weighted by AUC to maximize predictive accuracy. Model performance, evaluated via AUC and true skill statistic (TSS), demonstrated high discriminatory power. Projections under two CMIP6 climate scenarios (SSP 126 and SSP 585) reveal significant potential for range expansion and spatial redistribution of Carassius species, particularly under high-emission trajectories, highlighting increased invasion risks in ecologically sensitive basins. Variable importance analysis underscored the primacy of temperature, precipitation, terrain ruggedness, and human footprint in shaping invasion potential. Additionally, using kernel density estimation (KDE) analysis, the Caspian basin emerged as a critical invasion region for Carassius populations. These findings underscore the urgent need for targeted monitoring and management strategies and demonstrate the utility of EML-SDMs in anticipating biological invasions under global change. The integrative approach presented here provides a scalable framework for proactive biodiversity conservation and policy development in freshwater systems facing multifaceted anthropogenic threats and provides a replicable framework for forecasting biological invasions in other vulnerable freshwater systems.

ContextLight pollution can alter animal movements and landscape connectivity. This is particularly true in urban landscapes where a need to incorporate conservation issues in urban planning is urgent.ObjectivesWe investigated how potential light-reduction scenarios at conurbation scale change landscape connectivity for bats.MethodsThrough random stratified sampling and species distribution modelling, we assessed the relative importance of light pollution on bat presence probability and activity. We recorded bats during one entire night on each 305 sampling points in 2015. In 2016, we surveyed 94 supplementary points to evaluate models performance. We used our spatial predictions to characterize landscape resistance to bat movements. Then we applied a least-cost modelling approach to identify nocturnal corridors and estimated the impact of five light-reduction scenarios on landscape connectivity for two light non-tolerant bat species.ResultsWe found that light pollution detected from satellite images was a good predictor of bat presence and activity up to 700 m radius. Our results exhibited contrasting responses to average radiance: M. daubentonii responded negatively, P. nathusii had a positive response for low values then a negative response after a threshold radiance value of 20 W.m−2.sr−1 and E. serotinus responded positively. Five and four light-reduction scenarios significantly improved landscape connectivity for M. daubentonii and P. nathusii respectively.ConclusionsLight-reduction measures should be included in urban planning to provide sustainable conditions for bats in cities. We advocate for the use of our methodological approach to further studies to find the best trade-off between conservation needs and social acceptability.

Molluscs are the proverbial examples of slow movement. In this review, dispersal distances and speed were assessed from literature data. Active upstream movement can occur both individually and in groups; and depends on traits such as size, sex and reproductive status, and on external factors such as flow velocity, temperature, sediment structure, and food availability. The potential for active dispersal follows the sequence Pulmonata ≥ Prosobranchia > Bivalvia, although data for Pulmonata originated from short-term experiments that likely overestimated dispersal capabilities. Active upstream movement may be 0.3 to 1.0 km per year for most snails and is probably well below 0.1 km per year for bivalves. Natural passive upstream dispersal increases the range 10-fold (snails) to 100-fold (bivalves), and anthropogenic vectors can increase upstream dispersal more than 100-fold (snails) to 1000-fold (bivalves). Three km seems to be the maximal within-stream distance at which many species display regular population mixing, and at which re-colonisation or successful restoration can be expected within 3–10 years. Lateral dispersal between unconnected water bodies is passive and mostly known from observational reports, but potential distances depend on vectors, climate and geomorphology. In general, active dispersal seems insufficient to furnish a compensatory mechanism, e.g., for the rate of projected climate change. We provide an overview on dispersal strategies in the light of applied issues. More rigorous field surveys and an integration of different approaches (such as mark-recapture, genetic) to quantify distances and probabilities of lateral dispersal are needed to predict species distributions across space and time.

The global change threats significantly alters the ecological distribution of species across different ecosystems. Species distribution models (SDMs) are considered a widely used tool for assessing the global impact on biodiversity. Recently, remote sensing data have been used in a growing number of studies to predict species distribution and improve SDMs performance. This study evaluates the contribution of spectral indices in species distribution modeling using MaxEnt. We compared models based on spectral indices data (RS-only), environmental variables (EN-only), and their combination (CM) to predict the distribution of three key Mediterranean native species: Thymelaea hirsuta , Ononis vaginalis , and Limoniastrum monopetalum . The combined models (CM) demonstrated superior performance with excellent accuracy measures values compared to other models. Jackknife tests revealed both environmental factors (e.g., distance to coastline, mean temperature of wettest and driest quarters) and spectral indices (e.g., NDWI, LST) contributed substantially to predicting the studied species. The findings emphasize the importance of integrating diverse data sources to improve the accuracy of SDMs, particularly in heterogeneous landscapes like the Mediterranean region. This integrated approach provides a more comprehensive understanding of species spreading patterns and is critical for effective management and conservation strategies.

Effective biodiversity conservation requires knowledge of species' distributions across large areas, yet prevalence data for marine sessile species is scarce, with traditional variables often unavailable at appropriate temporal and spatial resolutions. As marine organism distributions generally depend on terrain heterogeneity, topographic variables derived from digital elevation models (DEMs) can be useful proxies in ecological modelling, given appropriate spatial resolutions. Here, we use three reef‐building Acropora coral species across the Great Barrier Reef, Australia, in a case study to (1) assess high‐resolution bathymetry DEM sources for accuracy, (2) harness their derived topographic variables for regional coral species distribution models (SDMs), and (3) develop a transferable framework to produce, select and integrate multi‐resolution variables into marine spatial models. For this, we obtained and processed three distinct bathymetric digital depth models that we treat as DEMs, which are available across the GBR extent: (i) Allen Coral Atlas (ACA) at 10 m, (ii) DeepReef at 30 m and (iii) DeepReef at 100 m. We generalised the three DEMs to multiple nested spatial resolutions (15 m–120 m) and derived the same eight topographic variables to assess SDM sensitivity to bathymetry source and spatial resolution. The ACA and DeepReef DEMs shared similar vertical accuracies, each producing topographic variables relevant to marine SDMs. Slope and vector ruggedness measure (VRM), capturing hydrodynamic movement and shelter or exposure, were the most relevant variables in SDMs of all three species. Interestingly, variables at the finest resolution (15 m) were not always the most relevant for producing accurate coral SDMs, with optimal resolutions between 15 and 60 m depending on the variable type and species. Using multi‐resolution topographic variables in SDMs provided nuanced insights into the multiscale drivers of regional coral distributions. Drawing from this case study, we provide a practical and transferable framework to facilitate the adoption of multiscale SDMs for better‐informed conservation and management planning. We investigate the application of multiscale topographic variables obtained from freely available bathymetric digital elevation models (DEMs) for ecological modelling in seascape environments. Using a case study of three Acropora coral species in the Great Barrier Reef, we demonstrate that various spatial resolutions can effectively model coral distributions, thus allowing us to identify the key topographic variables in these models. Integrating multiscale frameworks into generating environmental predictor variables has the potential to improve ecological models, supporting the development of robust and effective marine conservation and management planning.