While there is a clear demand for scenarios that provide alternative states in biodiversity with respect to future emissions, a thorough analysis and communication of the associated uncertainties is ...still missing. Here, we modelled the global distribution of ~11,500 amphibian, bird and mammal species and project their climatic suitability into the time horizon 2050 and 2070, while varying the input data used. By this, we explore the uncertainties originating from selecting species distribution models (SDMs), dispersal strategies, global circulation models (GCMs), and representative concentration pathways (RCPs). We demonstrate the overwhelming influence of SDMs and RCPs on future biodiversity projections, followed by dispersal strategies and GCMs. The relative importance of each component varies in space but also with the selected sensitivity metrics and with species' range size. Overall, this means using multiple SDMs, RCPs, dispersal assumptions and GCMs is a necessity in any biodiversity scenario assessment, to explicitly report associated uncertainties.
We present new global maps of the Köppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day (1980-2016) and for projected future conditions (2071-2100) under ...climate change. The present-day map is derived from an ensemble of four high-resolution, topographically-corrected climatic maps. The future map is derived from an ensemble of 32 climate model projections (scenario RCP8.5), by superimposing the projected climate change anomaly on the baseline high-resolution climatic maps. For both time periods we calculate confidence levels from the ensemble spread, providing valuable indications of the reliability of the classifications. The new maps exhibit a higher classification accuracy and substantially more detail than previous maps, particularly in regions with sharp spatial or elevation gradients. We anticipate the new maps will be useful for numerous applications, including species and vegetation distribution modeling. The new maps including the associated confidence maps are freely available via www.gloh2o.org/koppen.
Abstract
Marine phytoplankton and zooplankton form the basis of the ocean’s food-web, yet the impacts of climate change on their biodiversity are poorly understood. Here, we use an ensemble of ...species distribution models for a total of 336 phytoplankton and 524 zooplankton species to determine their present and future habitat suitability patterns. For the end of this century, under a high emission scenario, we find an overall increase in plankton species richness driven by ocean warming, and a poleward shift of the species’ distributions at a median speed of 35 km/decade. Phytoplankton species richness is projected to increase by more than 16% over most regions except for the Arctic Ocean. In contrast, zooplankton richness is projected to slightly decline in the tropics, but to increase strongly in temperate to subpolar latitudes. In these latitudes, nearly 40% of the phytoplankton and zooplankton assemblages are replaced by poleward shifting species. This implies that climate change threatens the contribution of plankton communities to plankton-mediated ecosystem services such as biological carbon sequestration.
The combination of drought and heat affects forest ecosystems by deteriorating the health of trees, which can lead to large‐scale die‐offs with consequences on biodiversity, the carbon cycle, and ...wood production. It is thus crucial to understand how drought events affect tree health and which factors determine forest susceptibility and resilience. We analyze the response of Central European forests to the 2018 summer drought with 10 × 10 m satellite observations. By associating time‐series statistics of the Normalized Difference Vegetation Index (NDVI) with visually classified observations of early wilting, we show that the drought led to early leaf‐shedding across 21,500 ± 2,800 km2, in particular in central and eastern Germany and in the Czech Republic. High temperatures and low precipitation, especially in August, mostly explained these large‐scale patterns, with small‐ to medium‐sized trees, steep slopes, and shallow soils being important regional risk factors. Early wilting revealed a lasting impact on forest productivity, with affected trees showing reduced greenness in the following spring. Our approach reliably detects early wilting at the resolution of large individual crowns and links it to key environmental drivers. It provides a sound basis to monitor and forecast early‐wilting responses that may follow the droughts of the coming decades.
We analyze the response of Central European forests to the 2018 summer drought with 10 × 10 m satellite observations. By associating time‐series statistics of the Normalized Difference Vegetation Index with aerial observations of early wilting, we show that the drought led to early leaf‐shedding across 21,500 ± 2,800 km2, in particular in Germany and the Czech Republic. Temperature and precipitation mostly explained these large‐scale patterns, with small to medium‐sized trees, steep slopes, and shallow soils being regional risk factors. Our approach provides a sound basis to monitor and forecast early wilting that may follow the droughts of the coming decades.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Aim
Statistical species distribution models (SDMs) are the most common tool to predict the impact of climate change on biodiversity. They can be tuned to fit relationships at various levels of ...complexity (defined here as parameterization complexity, number of predictors, and multicollinearity) that may co‐determine whether projections to novel climatic conditions are useful or misleading. Here, we assessed how model complexity affects the performance of model extrapolations and influences projections of species ranges under future climate change.
Location
Europe.
Taxon
34 European tree species.
Methods
We sampled three replicates of predictor sets for all combinations of 10 levels (n = 3–12) of environmental variables (climate, terrain, soil) and 10 levels of multicollinearity. We used these sets for each species to fit four SDM algorithms at three levels of parameterization complexity. The >100,000 resulting SDM fits were then evaluated under environmental block cross‐validation and projected to environmental conditions for 2061–2080 considering four climate models and two emission scenarios. Finally, we investigated the relationships of model design with model performance and projected distributional changes.
Results
Model complexity affected both model performance and projections of species distributional change. Fits of intermediate parameterization complexity performed best, and more complex parameterizations were associated with higher projected loss of current ranges. Model performance peaked at 10–11 variables but increasing number of variables had no consistent effect on distributional change projections. Multicollinearity had a low impact on model performance but distinctly increased projected loss of current ranges.
Main conclusions
SDM‐based climate change impact assessments should be based on ensembles of projections, varying SDM algorithms as well as parameterization complexity, besides emission scenarios and climate models. The number of predictor variables should be kept reasonably small and the classical threshold of maximum absolute Pearson correlation of 0.7 restricts collinearity‐driven effects in projections of species ranges.
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ABSTRACT
A fundamental goal of ecological research is to understand and model how processes generate patterns so that if conditions change, changes in the patterns can be predicted. Different ...approaches have been proposed for modelling species assemblage, but their use to predict spatial patterns of species richness and other community attributes over a range of spatial and temporal scales remains challenging. Different methods emphasize different processes of structuring communities and different goals. In this review, we focus on models that were developed for generating spatially explicit predictions of communities, with a particular focus on species richness, composition, relative abundance and related attributes. We first briefly describe the concepts and theories that span the different drivers of species assembly. A combination of abiotic processes and biotic mechanisms are thought to influence the community assembly process. In this review, we describe four categories of drivers: (i) historical and evolutionary, (ii) environmental, (iii) biotic, and (iv) stochastic. We discuss the different modelling approaches proposed or applied at the community level and examine them from different standpoints, i.e. the theoretical bases, the drivers included, the source data, and the expected outputs, with special emphasis on conservation needs under climate change. We also highlight the most promising novelties, possible shortcomings, and potential extensions of existing methods. Finally, we present new approaches to model and predict species assemblages by reviewing promising ‘integrative frameworks’ and views that seek to incorporate all drivers of community assembly into a unique modelling workflow. We discuss the strengths and weaknesses of these new solutions and how they may hasten progress in community‐level modelling.
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High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the ...earth's land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979-2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.
Understanding the processes that drive the dramatic changes in biodiversity along the productivity gradient remains a major challenge. Insight from simple, bivariate relationships so far has been ...limited. We combined >11,000 community plots in the French Alps with a molecular phylogeny and trait information for >1200 plant species to simultaneously investigate the relationships between all major biodiversity dimensions and satellite-sensed productivity. Using an approach that tests for differential effects of species dominance, species similarity and the interplay between phylogeny and traits, we demonstrate that unimodal productivity-biodiversity relationships only dominate for taxonomic diversity. In forests, trait and phylogenetic diversity typically increase with productivity, while in grasslands, relationships shift from unimodal to declining with greater land-use intensity. High productivity may increase trait/phylogenetic diversity in ecosystems with few external constraints (forests) by promoting complementary strategies, but under external constraints (managed grasslands) successful strategies are similar and thus the best competitors may be selected.
Wetland methane (CH₄) emissions are the largest natural source in the global CH₄ budget, contributing to roughly one third of total natural and anthropogenic emissions. As the second most important ...anthropogenic greenhouse gas in the atmosphere after CO₂, CH₄ is strongly associated with climate feedbacks. However, due to the paucity of data, wetland CH₄ feedbacks were not fully assessed in the Intergovernmental Panel on Climate Change Fifth Assessment Report. The degree towhich future expansion of wetlands and CH₄ emissions will evolve and consequently drive climate feedbacks is thus a question of major concern. Here we present an ensemble estimate of wetland CH₄ emissions driven by 38 general circulation models for the 21st century. We find that climate change-induced increases in boreal wetland extent and temperature-driven increases in tropical CH₄ emissions will dominate anthropogenic CH₄ emissions by 38 to 56% toward the end of the 21st century under the Representative Concentration Pathway (RCP2.6). Depending on scenarios, wetland CH₄ feedbacks translate to an increase in additional global mean radiative forcing of 0.04 W·m−2 to 0.19 W·m−2 by the end of the 21st century. Under the “worst-case” RCP8.5 scenario, with no climate mitigation, boreal CH₄ emissions are enhanced by 18.05 Tg to 41.69 Tg, due to thawing of inundated areas during the cold season (December to May) and rising temperature, while tropical CH₄ emissions accelerate with a total increment of 48.36 Tg to 87.37 Tg by 2099. Our results suggest that climate mitigation policies must consider mitigation of wetland CH₄ feedbacks to maintain average global warming below 2 °C.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Aim
Predicting the spatial distribution of species assemblages remains an important challenge in biogeography. Recently, it has been proposed to extend correlative species distribution models (SDMs) ...by taking into account (a) covariance between species occurrences in so‐called joint species distribution models (JSDMs) and (b) ecological assembly rules within the SESAM (spatially explicit species assemblage modelling) framework. Yet, little guidance exists on how these approaches could be combined. We, thus, aim to compare the accuracy of assemblage predictions derived from stacked and from joint SDMs.
Location
Switzerland.
Taxon
Birds, tree species.
Methods
Based on two monitoring schemes (national forest inventory and Swiss breeding bird atlas), we built SDMs and JSDMs for tree species (at 100 m resolution) and forest birds (at 1 km resolution). We tested accuracy of species assemblage and richness predictions on holdout data using different stacking procedures and ecological assembly rules.
Results
Despite minor differences, results were consistent between birds and tree species. Cross‐validated species‐level model performance was generally higher in SDMs than JSDMs. Differences in species richness and assemblage predictions were larger between stacking procedures and ecological assembly rules than between stacked SDMs and JSDMs. On average, predictions were slightly better for stacked SDMs compared to JSDMs, probabilistic stacks outperformed binary stacks, and ecological assembly rules yielded best predictions.
Main conclusions
When predicting the composition of species assemblages, the choice of stacking procedure and ecological assembly rule seems more decisive than differences in underlying model type (SDM vs. JSDM). JSDMs do not seem to improve community predictions compared to SDMs or improve predictions for rare species. Still, JSDMs may provide additional insights into community assembly and may help deriving hypotheses about prevailing biotic interactions in the system. We provide simple rules of thumb for choosing appropriate modelling pathways. Future studies should test these preliminary guidelines for other taxa and biogeographical realms as well as for other JSDM algorithms.
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