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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., Emerging infectious diseases are serious global threats. Most of these diseases originate from wildlife, particularly mammals, which face an ongoing biodiversity crisis. Using predicted distributions and abundances of 4466 mammal species, we estimated global patterns of disease risk by calculating community‐level R0. High values in temperate European, Asian, and North American locations point to risks beyond the tropics. Forecasted effects of climate change and habitat loss from 2015 to 2035 suggested many mammal assemblages will change considerably in their composition, even without local extinctions. Simultaneously, most areas were predicted to have decreased density‐dependent disease risk but increased frequency‐dependent disease risk.

Despite the scientific consensus on the extinction crisis and its anthropogenic origin, the quantification of historical trends and of future scenarios of biodiversity and ecosystem services has been limited, due to the lack of inter-model comparisons and harmonized scenarios. Here, we present a multi-model analysis to assess the impacts of land-use and climate change from 1900 to 2050. During the 20th century provisioning services increased, but biodiversity and regulating services decreased. Similar trade-offs are projected for the coming decades, but they may be attenuated in a sustainability scenario. Future biodiversity loss from land-use change is projected to keep up with historical rates or reduce slightly, whereas losses due to climate change are projected to increase greatly. Renewed efforts are needed by governments to meet the 2050 vision of the Convention on Biological Diversity.One Sentence SummaryDevelopment pathways exist that allow for a reduction of the rates of biodiversity loss from land-use change and improvement in regulating services but climate change poses an increasing challenge.

Schipper, Aafke M. et al., Scenario‐based biodiversity modelling is a powerful approach to evaluate how possible future socio‐economic developments may affect biodiversity. Here, we evaluated the changes in terrestrial biodiversity intactness, expressed by the mean species abundance (MSA) metric, resulting from three of the shared socio‐economic pathways (SSPs) combined with different levels of climate change (according to representative concentration pathways [RCPs]): a future oriented towards sustainability (SSP1xRCP2.6), a future determined by a politically divided world (SSP3xRCP6.0) and a future with continued global dependency on fossil fuels (SSP5xRCP8.5). To this end, we first updated the GLOBIO model, which now runs at a spatial resolution of 10 arc‐seconds (~300 m), contains new modules for downscaling land use and for quantifying impacts of hunting in the tropics, and updated modules to quantify impacts of climate change, land use, habitat fragmentation and nitrogen pollution. We then used the updated model to project terrestrial biodiversity intactness from 2015 to 2050 as a function of land use and climate changes corresponding with the selected scenarios. We estimated a global area‐weighted mean MSA of 0.56 for 2015. Biodiversity intactness declined in all three scenarios, yet the decline was smaller in the sustainability scenario (−0.02) than the regional rivalry and fossil‐fuelled development scenarios (−0.06 and −0.05 respectively). We further found considerable variation in projected biodiversity change among different world regions, with large future losses particularly for sub‐Saharan Africa. In some scenario‐region combinations, we projected future biodiversity recovery due to reduced demands for agricultural land, yet this recovery was counteracted by increased impacts of other pressures (notably climate change and road disturbance). Effective measures to halt or reverse the decline of terrestrial biodiversity should not only reduce land demand (e.g. by increasing agricultural productivity and dietary changes) but also focus on reducing or mitigating the impacts of other pressures

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Biodiversity and ecosystem service losses driven by land-use change are expected to intensify as a growing and more affluent lobal population requires more agricultural and forestry products, and teleconnections in the global economy lead to increasingremote environmental responsibility. By combining global biophysical and economic models, we show that, between theyears 2000 and 2011, overall population and economic growth resulted in increasing total impacts on bird diversity and carbonsequestration globally, despite a reduction of land-use impacts per unit of gross domestic product (GDP). The exceptions wereNorth America and Western Europe, where there was a reduction of forestry and agriculture impacts on nature accentuated bythe 2007–2008 financial crisis. Biodiversity losses occurred predominantly in Central and Southern America, Africa and Asiawith international trade an important and growing driver. In 2011, 33% of Central and Southern America and 26% of Africa’sbiodiversity impacts were driven by consumption in other world regions. Overall, cattle farming is the major driver of biodiversityloss, but oil seed production showed the largest increases in biodiversity impacts. Forestry activities exerted the highestimpact on carbon sequestration, and also showed the largest increase in the 2000–2011 period. Our results suggest that toaddress the biodiversity crisis, governments should take an equitable approach recognizing remote responsibility, and promotea shift of economic development towards activities with low biodiversity impacts.

A large number of diversity metrics are available to study and monitor biodiversity, and their responses to biodiversity changes are not necessarily coherent with each other. The choice of biodiversity metrics may thus strongly affect our interpretation of biodiversity change and, hence, prioritization of resources for conservation. Therefore it is crucial to understand which metrics respond to certain changes, are the most sensitive to change, show consistent responses across different communities, detect early signals of species decline, and are insensitive to demographic stochasticity. Here we generated synthetic communities and simulated changes in their composition according to 9 scenarios of biodiversity change to investigate the behaviour of 12 biodiversity metrics. Metrics showed diverse abilities to detect changes under different scenarios. Sørensen similarity index, arithmetic and geometric mean abundance, and species and functional richness were the most sensitive to community changes. Sørensen similarity index, species richness and geometric abundance showed consistent responses across all simulated communities and scenarios. Sørensen similarity index and geometric mean abundance were able to detect early signals of species decline. Geometric mean abundance, and functional evenness under certain scenarios, had the greatest ability to distinguish directional trends from stochastic changes, but Sørensen similarity index and geometric mean abundance were the only indices to show consistent signals under all replicates and scenarios. Classic abundance-weighted heterogeneity indices (e.g. Shannon index) were insensitive to certain changes or showed misleading responses, and are therefore unsuitable for comparison of biological communities. We therefore suggest that separate metrics of species composition, richness, and abundance should be reported instead of (or in addition to) composite metrics like the Shannon index., We thank R D Gregory and another anonymous reviewer for providing constructive comments on earlier versions of the manuscript. This article is based upon work from COST Action ES1101 “Harmonising Global Biodiversity Modelling” (Harmbio), supported by COST (European Cooperation in Science and Technology)

Context: Increasing the amount of green infrastructure, defined as small-scale natural landscape elements, has been named as a climate adaptation measure for biodiversity. While green infrastructure strengthened ecological networks in some studies, it is not known whether this effect also holds under climate change, and how it compares to other landscape adaptation options. Objectives: We assessed landscape adaptation options under scenarios of climate change for a dispersal-limited and climate-sensitive species: great crested newt, Triturus cristatus. Methods: A spatially-explicit modelling framework was used to simulate newt metapopulation dynamics in a case study area in the Netherlands, under alternative spatial configurations of 500 ha to-be-restored habitat. The framework incorporated weather-related effects on newt recruitment, following current and changing climate conditions. Results: Mild climate change resulted in slightly higher metapopulation viability, while more severe climate change (i.e. more frequent mild winters and summer droughts) had detrimental effects on metapopulation viability. The modelling framework revealed interactions between climate and landscape configuration on newt viability. Restoration of ponds and terrestrial habitat may reduce the negative effects of climate change, but only when certain spatial requirements (habitat density, connectivity) as well as abiotic requirements (high ground water level) are met. Conclusions: Landscape scenarios where habitat was added in the form of green infrastructure were not able to meet these multiple conditions, as was the case for a scenario that enlarged core areas. The approach allowed a deduction of landscape design rules that incorporated both spatial and abiotic requirements resulting in more effective climate adaptation options.

To prioritize conservation efforts, it is important to know which plant species are most vulnerable to extinction. Intrinsic extinction vulnerabilities depend on demographic parameters, but for many species these demographic parameters are lacking. Body size has been successfully used as proxy of such parameters to estimate extinction vulnerability of birds and mammals. For plants, not all necessary demographic parameters have been related to size yet. Here, we derived allometric relationships with maximum plant height for the intrinsic population growth rate and the carrying capacity. Furthermore, for the first time, we derived a relationship between the variance in population growth rate due to environmental stochasticity and plant height. These relationships were used to relate extinction vulnerability to maximum plant height. Extinction vulnerability was found to be most sensitive to fluctuations in the population growth rate due to environmental stochasticity. Large plant species were less susceptible to environmental stochasticity, resulting in a lower vulnerability to extinction than small plant species. This negative relationship between plant size and extinction vulnerabilities is in contrast to previous results for mammals and birds. These results increase our theoretical understanding of the relationship between plant functional traits and extinction vulnerabilities and may aid in assessments of data deficient species. The uncertainty in the allometric relationships is, however, too large to quantify true extinction vulnerabilities. Further investigation in the relationship between demographic parameters and plant traits other than height is needed to further enhance our understanding of plant species extinction vulnerabilities.

The IUCN (International Union for Conservation of Nature) Red List categories and criteria are the most widely used framework for assessing the relative extinction risk of species. The criteria are based on quantitative thresholds relating to the size, trends, and structure of species’ distributions and populations. However, data on these parameters are sparse and uncertain for many species and unavailable for others, potentially leading to their misclassification or classification as data deficient. We devised an approach that combines data on land‐cover change, species‐specific habitat preferences, population abundance, and dispersal distance to estimate key parameters (extent of occurrence, maximum area of occupancy, population size and trend, and degree of fragmentation) and hence predict IUCN Red List categories for species. We applied our approach to nonpelagic birds and terrestrial mammals globally (∼15,000 species). The predicted categories were fairly consistent with published IUCN Red List assessments, but more optimistic overall. We predicted 4.2% of species (467 birds and 143 mammals) to be more threatened than currently assessed and 20.2% of data deficient species (10 birds and 114 mammals) to be at risk of extinction. Incorporating the habitat fragmentation subcriterion reduced these predictions 1.5–2.3% and 6.4–14.9% (depending on the quantitative definition of fragmentation) for threatened and data deficient species, respectively, highlighting the need for improved guidance for IUCN Red List assessors on the application of this aspect of the IUCN Red List criteria. Our approach complements traditional methods of estimating parameters for IUCN Red List assessments. Furthermore, it readily provides an early‐warning system to identify species potentially warranting changes in their extinction‐risk category based on periodic updates of land‐cover information. Given our method relies on optimistic assumptions about species distribution and abundance, all species predicted to be more at risk than currently evaluated should be prioritized for reassessment., Article impact statement: Automatic screening to prioritize species reassessments optimizes the use of resources and keeps the IUCN Red List up to date.