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Aim: To provide distribution information and preliminary conservation assessments for all species of the pineapple family (Bromeliaceae), one of the most diverse and ecologically important plant groups of the American tropics—a global biodiversity hotspot. Furthermore, we aim to analyse patterns of diversity, endemism and the conservation status of the Bromeliaceae on the continental level in the light of their evolutionary history.
Location: The Americas.
Methods: We compiled a dataset of occurrence records for 3,272 bromeliad species (93.4% of the family) and modelled their geographic distribution using either climate‐based species distribution models, convex hulls or geographic buffers dependent on the number of occurrences available. We then combined this data with information on taxonomy and used the ConR software for a preliminary assessment of the conservation status of all species following Criterion B of the International Union for the Conservation of Nature (IUCN).
Results: Our results stress the Atlantic Forest in eastern Brazil, the Andean slopes, Central America and the Guiana Highlands as centres of bromeliad diversity and endemism. Phylogenetically ancient subfamilies of bromeliads are centred in the Guiana highlands whereas the large radiations of the group spread across different habitats and large geographic area. A total of 81% of the evaluated bromeliad species are Possibly Threatened with extinction. We provide range polygons for 3,272 species, as well as newly georeferenced point localities for 911 species in the novel “bromeliad” r package, together with functions to generate diversity maps for individual taxonomic or functional groups.
Main conclusions: Diversity centres of the Bromeliaceae agreed with macroecological patterns of other plant and animal groups, but show some particular patterns related to the evolutionary origin of the family, especially ancient dispersal corridors. A staggering 2/3rds of Bromeliaceae species might be threatened with extinction, especially so in tropical rain forests, raising concerns about the conservation of the family and bromeliad‐dependent animal species.
Aim: Knowledge concerning species distribution is important for biodiversity conservation and environmental management. Fungi form a large and diverse group of species and play a key role in nutrient cycling and carbon storage. However, our understanding of fungal diversity and distribution remains limited, particularly at large spatial scales. Here, we predicted the diversity and distribution of ectomycorrhizal and saprotrophic macrofungi at relatively fine spatial resolution at a continental scale and examined the importance of variables that affect the distribution of these two functional groups. Location: Europe. Time period: 1990–2018. Major taxa studied: Macrofungi. Methods: From observations of 1,845 macrofungal species, we predicted the diversity and distribution of two functional groups of macrofungi at a resolution of 5 km across eight European countries based on 25 environmental variables using the MAXENT model. We determined the importance of variables that affect the distribution of these two functional groups of macrofungi using the built-in jackknife test in the model. Results: Analysis of the modelling results showed that eastern Denmark and southern Sweden are biodiversity hotspots for both functional groups of macrofungal species. Tree species and human disturbance (i.e., the human footprint index) were found to be the two most important predictor variables explaining the distribution of ectomycorrhizal and saprotrophic macrofungi. Main conclusions: Overall, our study demonstrates that tree species and human disturbance have played a more important role than climatic factors in determining the diversity and distribution of macrofungi at the continental scale. Our study suggests that fungal diversity and distribution might change considerably if the strongest predictors (i.e., tree species) were to be affected by climate change and/or human activity. Changes in fungal diversity might, in turn, influence other processes, because fungi are important in driving ecosystem processes, such as nutrient and carbon cycling.
Aim: Formerly introduced for their presumed value in controlling mosquito-borne diseases, the two mosquitofish Gambusia affinis and G. holbrooki (Poeciliidae) are now among the world's most widespread invasive alien species, negatively impacting aquatic ecosystems around the world. These inconspicuous freshwater fish are, once their presence is noticed, difficult to eradicate. It is, therefore, of utmost importance to assess their geographic potential and to identify their likely ability to persist under novel climatic conditions.
Location Global.
Methods We build species distribution models using occurrence data from the native and introduced distribution ranges to identify putative niche shifts and further ascertain the areas climatically suitable for the establishment and possible spread of mosquitofish.
Results We found significant niche expansions into climatic regions outside their natural climatic conditions, emphasizing the importance of integrating climatic niches of both native and invasive ranges into projections. In particular, there was a marked shift toward tropical regions in Asia and a clear niche shift of European G. holbrooki. This ecological flexibility partly explains the massive success of the two species, and substantially increases the risk for further range expansion. We also showed that the potential for additional expansion resulting from climate change is enormous—especially in Europe.
Main conclusions Despite the successful invasion history and ongoing range expansions, many countries still lack proper preventive measures. Thus, we urge policy makers to carefully evaluate the risk both mosquitofish pose to a particular area and to initiate appropriate management strategies.
Aim: Predicting future changes in species richness in response to climate change is one of the key challenges in biogeography and conservation ecology. Stacked species distribution models (S‐SDMs) are a commonly used tool to predict current and future species richness. Macroecological models (MEMs), regression models with species richness as response variable, are a less computationally intensive alternative to S‐SDMs. Here, we aim to compare the results of two model types (S‐SDMS and MEMs), for the first time for more than 14,000 species across multiple taxa globally, and to trace the uncertainty in future predictions back to the input data and modelling approach used.
Location: Global land, excluding Antarctica.
Taxon: Amphibians, birds and mammals.
Methods: We fitted S‐SDMs and MEMs using a consistent set of bioclimatic variables and model algorithms and conducted species richness predictions under current and future conditions. For the latter, we used four general circulation models (GCMs) under two representative concentration pathways (RCP2.6 and RCP6.0). Predicted species richness was compared between S‐SDMs and MEMs and for current conditions also to extent‐of‐occurrence (EOO) species richness patterns. For future predictions, we quantified the variance in predicted species richness patterns explained by the choice of model type, model algorithm and GCM using hierarchical cluster analysis and variance partitioning.
Results: Under current conditions, species richness predictions from MEMs and S‐SDMs were strongly correlated with EOO‐based species richness. However, both model types over‐predicted areas with low and under‐predicted areas with high species richness. Outputs from MEMs and S‐SDMs were also highly correlated among each other under current and future conditions. The variance between future predictions was mostly explained by model type.
Main conclusions: Both model types were able to reproduce EOO‐based patterns in global terrestrial vertebrate richness, but produce less collinear predictions of future species richness. Model type by far contributes to most of the variation in the different future species richness predictions, indicating that the two model types should not be used interchangeably. Nevertheless, both model types have their justification, as MEMs can also include species with a restricted range, whereas S‐SDMs are useful for looking at potential species‐specific responses.