Species distribution models (SDMs) are widely used to understand and predict how species respond to changes in their environment. Hence, it is critical to understand how SDMs are influenced by methodological aspects and choices. The choice of spatial resolution is an understudied subject, especially for freshwater fish SDMs. Here, we aimed to analyse how the choice of spatial resolution affects the predictive accuracy, predictor variable importance and spatial predictions of SDMs of freshwater fish species in Europe. We fitted SDMs for 49 freshwater fish species based on point occurrence records combined with environmental and anthropogenic predictor variables aggregated at five spatial resolutions as represented by nested hydrological basins (level 8 to 12 in the HydroSHEDS database). Following an ensemble modelling approach, we employed nine algorithms to establish the SDMs at each of the resolutions, using temperature, topography, streamflow, land use, human population and dam density as predictor variables. We analysed the differences in predictive performance by testing the models on a geographically independent subset of the data. Predictive performance and variable importance were highly similar across the resolutions, with a median TSS between 0.37 and 0.39 and temperature and topography being the most important variables. In contrast, predicted range size decreased towards higher resolutions, while the difference between predicted and observed range size increased. Our results indicate that the choice of spatial resolution has a small effect on the performance and predictor importance of continental freshwater fish SDMs, while significantly influencing predicted range size. The latter may have important consequences for conservation and extinction risk assessments, which often rely on estimates of range size. From a precautionary principle, establishing SDM at the highest resolution possible may help to prevent the risk of overestimating range size hence underestimating extinction risk.

Motivation Wetland restoration is expected to play an important role in helping Europe achieve its climate and biodiversity targets as stated in the European Green Deal. Restored wetlands sequester atmospheric carbon and provide habitat for a wide range of plant and animal species. To design effective, evidence-based restoration targets and guide future projects, it is critical to understand the impacts of wetland restoration on a diverse range of ecosystem functions. Existing knowledge, however, is scattered across empirical studies assessing restoration outcomes on a limited set of indicators, in specific settings and single locations. This fragmentation limits our ability to assess the effectiveness of wetland restoration across different indicators and contexts. To address this gap, we present a comprehensive dataset on the effects of wetland restoration across Europe. The dataset compiles 2534 entries representing changes in carbon stocks, greenhouse gases (GHG) fluxes, and biodiversity indicators across different wetland types, pre-restoration land use and restoration strategies. The database allows comparisons between restored and both degraded and pristine sites.Main Types of Variables The database contains data on carbon stock, GHG fluxes and biodiversity indicators in restored and control wetlands, with standard error, sampling methodology, wetland type, location, climate, restored site area, water table depth, soil characteristics, pre-restoration land use, time since restoration started, and restoration strategy.Spatial Location and Grain Europe.Time Period and Grain Original data were published between 2000 and 2024. Restoration age ranges from a few months to 139 years.Major Indicators and Level of Measurement Carbon stocks, GHG fluxes and biodiversity indicators in restored wetlands and two controls: degraded and pristine sites.Software Format The dataset is available at https://doi.org/10.17026/LS/H5MFEH as a .xlsx file and as a .shp file, with a metadata file.

Ecosystem restoration is increasingly recognized as a sustainable climate change mitigation strategy, yet global estimates of its carbon sequestration potential widely vary. Modeling-based studies differ in assumptions over key restoration aspects, including restorable areas and restoration outcomes. Many assume recovery of carbon stocks to pristine levels, an expectation not supported by empirical evidence. They also focus primarily on forests and biomass, with limited attention to soil organic carbon (SOC). Here, we estimate the global SOC sequestration potential of forest and grassland restoration by combining current SOC levels on degraded land areas available for restoration with empirically derived SOC increase factors at the ecosystem scale. We provide spatially explicit estimates of SOC sequestration potential, absolute and per hectare. We also assess the carbon sequestration potential achievable under national forest restoration pledges across four major resolutions. With 1223 million hectares (Mha) of degraded land globally, the SOC sequestration potential is 38.5 GtC, of which 35.1 GtC (IQR 30.4-39.3 GtC) in forests and 3.4 GtC (IQR 2.6-4.2) in grasslands. National pledges cover 133 Mha, whose restoration could sequester 4-5.5 Gt of SOC. We show that there is a large unexplored theoretical climate mitigation potential of restoration globally. Environmental policies targeting Southeast Asia and South America, where potential is high and pledges are low, are particularly promising.

Ecosystem restoration can contribute to climate change mitigation, as recovering ecosystems sequester atmospheric CO2 in biomass and soils. It is, however, unclear how much soil organic carbon (SOC) stocks recover across different restored ecosystems. Here, we show SOC recovery in different contexts globally by consolidating 41 meta-analyses into a second-order meta-analysis. We find that restoration projects have, since their inception, led to significant SOC increases compared to the degraded state in 12 out of 16 ecosystem-previous land-use combinations, with mean SOC increases thus far that range from 25% (grasslands; 10-39%, 95% CI) to 79% (shrublands; 38-120% CI). Yet, we observe a SOC deficit in restored ecosystems compared to pristine sites, ranging from 14% (forests; 12-16% CI) to 50% (wetlands; 14-87% CI). While restoration does increase carbon sequestration in SOC, it should not be viewed as a way to fully offset carbon losses in natural ecosystems, whose conservation has priority.

ABSTRACT Species Distribution Models (SDMs) represent a common method for predicting biodiversity responses to environmental changes. SDMs are typically established for and applied to large areas, while little is known about the transferability of large‐scale SDMs to smaller spatial extents. This study aims to fill this gap by fitting country‐level ensemble SDMs for the seven native reptile species occurring in the Netherlands, and testing their predictive ability against independent occurrence data in 10 municipalities. For each of the species, we trained an ensemble SDM using bioclimatic variables, land cover fractions and soil type as predictors. We then evaluated model performance in 10 municipalities that were not included in model training. Despite performing well at national extent, with a mean cross‐validated True Skill Statistic (TSS) value of 0.85 (range of 0.76–0.94) across the species, the ensemble SDMs generally yielded poor to moderate performance scores for the municipalities. Averaged per species, TSS values ranged from 0.05 to 0.57, while mean TSS values per municipality ranged from −0.07 to 0.61. The general decrease in model performance from national to local extent was mainly due to a decline in model specificity (i.e., the ability to predict absence). A two‐way ANOVA showed model performance varied significantly among both municipalities and species (p < 0.001). Our results suggest that while ensemble SDMs fitted across large spatial extents can provide a rough indication of the potential distribution of a species, caution should be applied when informing local conservation decisions based on broad‐scale SDM outcomes.

Intermediate producers bear responsibility for emissions embedded in their supply chains. Here, we quantified greenhouse gas (GHG) emissions associated with 63 sectors in 264 regions of the European Union in 2017. We used a multi-regional input-output (MRIO) database with subnational European information on production and trade structures, accounting for trade inside and outside Europe. We added a subnational environmental extension for GHG emissions from an air emission database at the NUTS 2 level. We focused on quantifying the subnational variation of sector GHG footprints. We also identified spatial and sectoral hotspots and quantified the share of indirect emissions. We found that environmentally extended MRIO (EEMRIO) datasets with national coverage instead of subnational coverage in the EU miss out on 33 % of the variation in sector GHG footprints. The largest subnational variation in sector GHG footprints was found in Italy and Germany, particularly for metal manufacturing. The EEMRIO dataset can support targeted measures, on regions and on sectors, to drive climate change mitigation efforts in the EU.

As animal home range size (HRS) provides valuable information for species conservation, it is important to understand the driving factors of HRS variation. It is widely known that differences in species traits (e.g. body mass) are major contributors to variation in mammal HRS. However, most studies examining how environmental variation explains mammal HRS variation have been limited to a few species, or only included a single (mean) HRS estimate for the majority of species, neglecting intraspecific HRS variation. Additionally, most studies examining environmental drivers of HRS variation included only terrestrial species, neglecting marine species. Using a novel dataset of 2800 HRS estimates from 586 terrestrial and 27 marine mammal species, we quantified the relationships between HRS and environmental variables, accounting for species traits. Our results indicate that terrestrial mammal HRS was on average 5.3 times larger in areas with low human disturbance (human footprint index [HFI] = 0), compared to areas with maximum human disturbance (HFI = 50). Similarly, HRS was on average 5.4 times larger in areas with low annual mean productivity (NDVI = 0), compared to areas with high productivity (NDVI = 1). In addition, HRS increased by a factor of 1.9 on average from low to high seasonality in productivity (standard deviation (SD) of monthly NDVI from 0 to 0.36). Of these environmental variables, human disturbance and annual mean productivity explained a larger proportion of HRS variance than seasonality in productivity. Marine mammal HRS decreased, on average, by a factor of 3.7 per 10 degrees C decline in annual mean sea surface temperature (SST), and increased by a factor of 1.5 per 1 degrees C increase in SST seasonality (SD of monthly values). Annual mean SST explained more variance in HRS than SST seasonality. Due to the small sample size, caution should be taken when interpreting the marine mammal results. Our results indicate that environmental variation is relevant for HRS and that future environmental changes might alter the HRS of individuals, with potential consequences for ecosystem functioning and the effectiveness of conservation actions. Relationships between home range size (HRS) and environmental variables for terrestrial and marine mammals. Relationships between HRS and environmental variables for terrestrial mammals in different body mass the ranges are shown in separate plots. Lines show relationship between HRS and each environmental variable based on coefficient estimates from the most parsimonious model. Lines are shown only for the environmental variables retained in the most parsimonious model.image

Based on an extensive model intercomparison, we assessed trends in biodiversity and ecosystem services from historical reconstructions and future scenarios of land-use and climate change. During the 20th century, biodiversity declined globally by 2 to 11%, as estimated by a range of indicators. Provisioning ecosystem services increased several fold, and regulating services decreased moderately. Going forward, policies toward sustainability have the potential to slow biodiversity loss resulting from land-use change and the demand for provisioning services while reducing or reversing declines in regulating services. However, negative impacts on biodiversity due to climate change appear poised to increase, particularly in the higher-emissions scenarios. Our assessment identifies remaining modeling uncertainties but also robustly shows that renewed policy efforts are needed to meet the goals of the Convention on Biological Diversity.

Large-scale introduction of green hydrogen is envisioned to play an important role in reaching net-zero greenhouse gas emissions. The production and transport of green hydrogen itself is, however, not free from emissions. Here we assess the life-cycle greenhouse gas emissions for 1,025 planned green hydrogen facilities, covering different electrolyser technologies and renewable electricity sources in 72 countries. We demonstrate that the current exclusion of life-cycle emissions of renewables, component manufacturing and hydrogen leakage in regulations gives a false impression that green hydrogen can easily meet emission thresholds. Evaluating different hydrogen production configurations, we find median production emissions in the most optimistic configuration of 2.9 kg CO2 equivalents (CO2e) kg H2-1 (0.8-4.6 kgCO2e kg H2-1, 95% confidence interval). Including 1,000 km transport via pipeline or liquid hydrogen shipping adds another 1.5 or 1.8 kgCO2e kg H2-1, respectively. We conclude that achieving low-emission green hydrogen at scale requires well-chosen production configurations with substantial emission reductions along the supply chain. This study assesses the life-cycle greenhouse gas emissions for 1,025 planned green hydrogen facilities covering diverse technologies and renewable electricity sources in 72 countries, noting that well-chosen production configurations are central to green hydrogen.

Global biodiversity is projected to further decline under a wide range of future socio-economic development pathways, even in sustainability-oriented scenarios. This raises the question how biodiversity can be put on a path to recovery, the core challenge for the implementation of the CBD Kunming-Montreal Global Biodiversity Framework. We designed two ambitious global conservation strategies, ‘Half Earth’ (HE) and ‘Sharing the Planet’ (SP), and evaluated their ability to restore terrestrial and freshwater biodiversity and to provide nature's contributions to people (NCP), while also limiting global warming below 2 degrees and ensuring food security. We applied the integrated assessment framework IMAGE with the GLOBIO biodiversity model, using the ‘Middle of the Road’ Shared Socio-economic Pathway (SSP2) with its projected human population growth as baseline. We found that the HE strategy performs generally better for terrestrial biodiversity (biodiversity intactness (MSA), Area of Habitat, Living Planet Index, Red List Index) in currently still natural regions. The SP strategy yields more improvements for biodiversity in human-used areas, for freshwater biodiversity and for regulating NCP (pest control, pollination, erosion control, water quality). However, both strategies were insufficient to restore biodiversity and corresponded with considerable increases in food security risks and global temperature. Only when we combined the conservation strategies with a portfolio of ‘integrated sustainability measures’, including climate change mitigation and reductions of food waste and animal product consumption, our scenarios resulted in a restoration of biodiversity and NCP while keeping global warming below two degrees and food security risks below the baseline projection.

Land use is a major cause of biodiversity decline worldwide. Agricultural and forestry diversification measures, such as the inclusion of natural elements or diversified crop types, may reduce impacts on biodiversity. However, the extent to which such measures may compensate for the negative impacts of land use remains unknown. To fill that gap, we synthesised data from 99 studies that recorded mammal populations or assemblages in natural reference sites and in cropland and forest plantations, with or without diversification measures. We quantified the responses to diversification measures based on individual species abundance, species richness and assemblage intactness as quantified by the mean species abundance indicator. In cropland with natural elements, mammal species abundance and richness were, on average, similar to natural conditions, while in cropland without natural elements they were reduced by 28% and 34%, respectively. We found that mammal species richness was comparable between diversified forest plantations and natural reference sites, and 32% lower in plantations without natural elements. In both cropland and plantations, assemblage intactness was reduced compared with natural reference conditions, but the reduction was smaller if diversification measures were in place. In addition, we found that responses to land use were modified by species traits and environmental context. While habitat specialist populations were reduced in cropland without diversification and in forest plantations, habitat generalists benefited. Furthermore, assemblages were impacted more by land use in tropical regions and landscapes containing a larger share of (semi)natural habitat compared with temperate regions and more converted landscapes. Given that mammal assemblage intactness is reduced also when diversification measures are in place, special attention should be directed to species that suffer from land use impacts. That said, our results suggest potential for reconciling land use and mammal conservation, provided that the diversification measures do not compromise yield.

Halting the alarming rate of species extinction, driven primarily by habitat destruction, motivated the in-ternational community to adopt the Global Biodiversity Framework (2022) and its targets aimed at reversing habitat and species loss. Because of urgency and resource constraints, a key challenge is meeting targets effectively and efficiently. Here we conduct a global prioritization linking 70,492 unique population maps and life history characteristics for 861 threatened terrestrial mammal species. Incorpo-rating individual population data to identify priority areas for conservation nearly doubled the likely long-term persistence of species for the same amount of land compared with a typical approach based on species distributions alone. We map and rank global mammal persistence priority areas and assess how well the current protected area (PA) system captures these important regions. Our results offer a clearer, quantifiable link between conservation actions and global extinction risk than previously possible at a global scale.

Zip folder conaining the data and code that support the findings of Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology. The Data_code.zip folder contains four subfolders with the following files: Data_raw AgriDiv_data.csv AgriDiv_metadata.docx Species_data.csv Species_metadata.docx Landscape_data.csv Landscape_metadata.docx Data_derived RIA_RSR_effect_sizes.csv MSA_effect_sizes.csv Data_output Response_estimation.csv R_scripts 01_Effect_size_calculation.R 02_Null_model_analysis.R 03_Model_selection.R 04_Model_analysis.R 05_Response_estimation.R 06_Figures.R README.md

New Guinea is one of the last regions in the world with vast pristine areas and is home to many endemic species. However, extensive road development plans threaten the island's biodiversity. We quantified habitat fragmentation due to existing and planned roads for 139 terrestrial mammal species in New Guinea. For each species, we calculated the equivalent connected area (ECA) of habitat, a metric that takes into account the area and connectivity of habitat patches in 3 situations: no roads (baseline situation), existing roads (current), and existing and planned roads combined (future). We assessed the effect of roads as the proportion of the ECA remaining in the current and future situations relative to the baseline. To examine whether there were patterns in these relative ECA values, we fitted beta-regression models relating these values to 4 species characteristics: taxonomic order, body mass, diet, and International Union for the Conservation of Nature Red List status. On average across species, current ECA was 89% (SD 12) of baseline ECA. Shawmayer's coccymys (Coccymys shawmayeri) had the lowest amount of current ECA relative to the baseline (53%). In the future situation, the average remaining ECA was 71% (SD 20) of baseline ECA. Future remaining ECA was below 50% of the baseline for 28 species. The montane soft-furred paramelomys (Paramelomys mollis) had the lowest future ECA relative to the baseline (36%). In general, currently nonthreatened carnivorous species with a large body mass had the greatest reductions of ECA in the future situation. In conclusion, future road development plans imply extensive additional habitat fragmentation for a large number of terrestrial mammal species in New Guinea. It is therefore important to limit the impact of planned roads, for example, by reconsidering the location of planned roads that intersect habitat of the most threatened species, or by the implementation of mitigation measures such as underpasses.

Aim:Macroecological studies that require habitat suitability data for many species often derive this information from expert opinion. However, expert-based information is inherently subjective and thus prone to errors. The increasing availability of GPS tracking data offers opportunities to evaluate and supplement expert-based information with detailed empirical evidence. Here, we compared expert-based habitat suitability information from the International Union for Conservation of Nature (IUCN) with habitat suitability information derived from GPS-tracking data of 1,498 individuals from 49 mammal species. Location:Worldwide. Time period:1998-2021. Major taxa studied:Forty-nine terrestrial mammal species. Methods:Using GPS data, we estimated two measures of habitat suitability for each individual animal: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN's classification into suitable, marginal and unsuitable habitat types. Results:IUCN habitat suitability data were in accordance with the GPS data (> 95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a > 50% probability of agreement based on proportional habitat use and selection ratios, respectively. Main conclusions:We show how GPS-tracking data can be used to evaluate IUCN habitat suitability data. Our findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, we show that GPS-tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data.

Biodiversity is severely threatened by habitat destruction. As a consequence of habitat destruction, the remaining habitat becomes more fragmented. This results in time-lagged population extirpations in remaining fragments when these are too small to support populations in the long term. If these time-lagged effects are ignored, the long-term impacts of habitat loss and fragmentation will be underestimated. We quantified the magnitude of time-lagged effects of habitat fragmentation for 157 nonvolant terrestrial mammal species in Madagascar, one of the biodiversity hotspots with the highest rates of habitat loss and fragmentation. We refined species' geographic ranges based on habitat preferences and elevation limits and then estimated which habitat fragments were too small to support a population for at least 100 years given stochastic population fluctuations. We also evaluated whether time-lagged effects would change the threat status of species according to the International Union for the Conservation of Nature (IUCN) Red List assessment framework. We used allometric relationships to obtain the population parameters required to simulate the population dynamics of each species, and we quantified the consequences of uncertainty in these parameter estimates by repeating the analyses with a range of plausible parameter values. Based on the median outcomes, we found that for 34 species (22% of the 157 species) at least 10% of their current habitat contained unviable populations. Eight species (5%) had a higher threat status when accounting for time-lagged effects. Based on 0.95-quantile values, following a precautionary principle, for 108 species (69%) at least 10% of their habitat contained unviable populations, and 51 species (32%) had a higher threat status. Our results highlight the need to preserve continuous habitat and improve connectivity between habitat fragments. Moreover, our findings may help to identify species for which time-lagged effects are most severe and which may thus benefit the most from conservation actions.

Global biodiversity is increasingly threatened by anthropogenic environmental change. While there is mounting evidence that habitat loss is a key threat to biodiversity, global assessments typically ignore additional threats from habitat fragmentation. Here, we present a species-area model that integrates habitat size and connectivity, considering species habitat preference and dispersal capacity, patch size, inter-patch distances, and landscape matrix permeability. We applied the model to predict threats from habitat loss and fragmentation to non-volant mammal diversity across the world's ecoregions. We predict that, on average, 10 mammal species are committed to extinction due to habitat loss and fragmentation (range 0-86). On average, 9% of loss is due to fragmentation (range 0%-90%). Considering both habitat loss and fragmentation, our model can be used for large-scale explorative assessments to inform and evaluate strategies for minimizing biodiversity loss and for optimizing habitat conservation and restoration.

Although there is mounting evidence that habitat loss is a key driver of biodiversity loss, global assessments typically ignore the additional effects of habitat fragmentation. Here, we assessed the combined effect of habitat loss and fragmentation on non-volant mammal species richness in 804 of the world’s terrestrial ecoregions. To that end we used a species-area model that integrates differences in both habitat suitability and habitat connectivity, accounting for species habitat preference, patch size, inter-patch distances, landscape matrix permeability and species’ dispersal capacity. On average across the ecoregions, 10 mammal species are committed to extinction due to habitat loss and fragmentation combined, up to a maximum of 86. On average, 9% of the estimated species loss is caused by fragmentation, yet this can be up to 90%. Our results imply that comprehensive strategies for meeting international biodiversity targets require not only combatting habitat loss, but also measures to reduce fragmentation.

As a source of emerging infectious diseases, wildlife assemblages (and related spatial patterns) must be quantitatively assessed to help identify high-risk locations. Previous assessments have largely focussed on the distributions of individual species; however, transmission dynamics are expected to depend on assemblage composition. Moreover, disease-diversity relationships have mainly been studied in the context of species loss, but assemblage composition and disease risk (e.g. infection prevalence in wildlife assemblages) can change without extinction. Based on the predicted distributions and abundances of 4466 mammal species, we estimated global patterns of disease risk through the calculation of the community-level basic reproductive ratio R0, an index of invasion potential, persistence, and maximum prevalence of a pathogen in a wildlife assemblage. For density-dependent diseases, we found that, in addition to tropical areas which are commonly viewed as infectious disease hotspots, northern temperate latitudes included high-risk areas. We also forecasted the effects of climate change and habitat loss from 2015 to 2035. Over this period, many local assemblages showed no net loss of species richness, but the assemblage composition (i.e. the mix of species and their abundances) changed considerably. Simultaneously, most areas experienced a decreased risk of density-dependent diseases but an increased risk of frequency-dependent diseases. We further explored the factors driving these changes in disease risk. Our results suggest that biodiversity and changes therein jointly influence disease risk. Understanding these changes and their drivers and ultimately identifying emerging infectious disease hotspots can help health officials prioritize resource distribution.

In conservation decision-making, it is important to have information not only on the likely effectiveness of conservation actions, but also on the corresponding costs. Reintroduction of wildlife is a commonly applied 'last resort' conservation measure. However, a quantitative approach to predict the costs of reintroduction for sustaining a wildlife population under the influence of time-varying anthropogenic stress is lacking. Here, we fill this gap by quantifying the costs of reintroduction as a function of exposure to an environmental stressor and the size of the wildlife population to be maintained. Our approach combines quantitative stressor-response relationships for vital rates (reproduction and survival) with a wildlife demographic model to compute the impacts of the stressor on the size of the target population. Subsequently, cost estimates are obtained by quantifying the number of captive-reared individuals needed per year in order to maintain a user-defined population size, given the exposure to the stressor of concern. We applied our approach to calculate the reintroduction costs required to restore a minimum viable population (MVP) of peregrine falcons (Falco peregrinus) in California over the period 1970-1994, when the population was exposed to the toxicant dichlorodiphenyldichloroethylene. Assuming a gradual yearly increase of 150% in the availability of captive-reared young, 1,753 captive-reared young were required to restore and maintain a MVP of 238 adults. The corresponding reintroduction costs were in total similar to$3,023,000. Assuming lower reintroduction efforts (in terms of the availability of captive-reared young), the projected reintroduction costs decreased by similar to 33%. However, the population then reached the minimum viable size only 9 years later, thus reflecting a trade-off between costs and population viability. Synthesis and applications. The approach presented in this study ensures an adequate prediction of the costs of maintaining a wildlife population at a user-defined size through reintroduction. It can be applied to any wildlife population in order to obtain the number of individuals and corresponding costs required to sustain a population under current and future influence of an anthropogenic stressor. This type of information provides important input for decision-making necessary to conserve biodiversity.