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      Saccharomycotina yeasts defy longstanding macroecological patterns

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          Abstract

          The Saccharomycotina yeasts (“yeasts” hereafter) are a fungal clade of scientific, economic, and medical significance. Yeasts are highly ecologically diverse, found across a broad range of environments in every biome and continent on earth 1 ; however, little is known about what rules govern the macroecology of yeast species and their range limits in the wild 2 . Here, we trained machine learning models on 12,221 occurrence records and 96 environmental variables to infer global distribution maps for 186 yeast species (~15% of described species from 75% of orders) and to test environmental drivers of yeast biogeography and macroecology. We found that predicted yeast diversity hotspots occur in mixed montane forests in temperate climates. Diversity in vegetation type and topography were some of the greatest predictors of yeast species richness, suggesting that microhabitats and environmental clines are key to yeast diversification. We further found that range limits in yeasts are significantly influenced by carbon niche breadth and range overlap with other yeast species, with carbon specialists and species in high diversity environments exhibiting reduced geographic ranges. Finally, yeasts contravene many longstanding macroecological principles, including the latitudinal diversity gradient, temperature-dependent species richness, and latitude-dependent range size (Rapoport’s rule). These results unveil how the environment governs the global diversity and distribution of species in the yeast subphylum. These high-resolution models of yeast species distributions will facilitate the prediction of economically relevant and emerging pathogenic species under current and future climate scenarios.

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          Most cited references58

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          The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications

          Abstract UNITE (https://unite.ut.ee/) is a web-based database and sequence management environment for the molecular identification of fungi. It targets the formal fungal barcode—the nuclear ribosomal internal transcribed spacer (ITS) region—and offers all ∼1 000 000 public fungal ITS sequences for reference. These are clustered into ∼459 000 species hypotheses and assigned digital object identifiers (DOIs) to promote unambiguous reference across studies. In-house and web-based third-party sequence curation and annotation have resulted in more than 275 000 improvements to the data over the past 15 years. UNITE serves as a data provider for a range of metabarcoding software pipelines and regularly exchanges data with all major fungal sequence databases and other community resources. Recent improvements include redesigned handling of unclassifiable species hypotheses, integration with the taxonomic backbone of the Global Biodiversity Information Facility, and support for an unlimited number of parallel taxonomic classification systems.
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            Hidden killers: human fungal infections.

            Although fungal infections contribute substantially to human morbidity and mortality, the impact of these diseases on human health is not widely appreciated. Moreover, despite the urgent need for efficient diagnostic tests and safe and effective new drugs and vaccines, research into the pathophysiology of human fungal infections lags behind that of diseases caused by other pathogens. In this Review, we highlight the importance of fungi as human pathogens and discuss the challenges we face in combating the devastating invasive infections caused by these microorganisms, in particular in immunocompromised individuals.
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              The global diversity of birds in space and time.

              Current global patterns of biodiversity result from processes that operate over both space and time and thus require an integrated macroecological and macroevolutionary perspective. Molecular time trees have advanced our understanding of the tempo and mode of diversification and have identified remarkable adaptive radiations across the tree of life. However, incomplete joint phylogenetic and geographic sampling has limited broad-scale inference. Thus, the relative prevalence of rapid radiations and the importance of their geographic settings in shaping global biodiversity patterns remain unclear. Here we present, analyse and map the first complete dated phylogeny of all 9,993 extant species of birds, a widely studied group showing many unique adaptations. We find that birds have undergone a strong increase in diversification rate from about 50 million years ago to the near present. This acceleration is due to a number of significant rate increases, both within songbirds and within other young and mostly temperate radiations including the waterfowl, gulls and woodpeckers. Importantly, species characterized with very high past diversification rates are interspersed throughout the avian tree and across geographic space. Geographically, the major differences in diversification rates are hemispheric rather than latitudinal, with bird assemblages in Asia, North America and southern South America containing a disproportionate number of species from recent rapid radiations. The contribution of rapidly radiating lineages to both temporal diversification dynamics and spatial distributions of species diversity illustrates the benefits of an inclusive geographical and taxonomical perspective. Overall, whereas constituent clades may exhibit slowdowns, the adaptive zone into which modern birds have diversified since the Cretaceous may still offer opportunities for diversification.

                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                31 August 2023
                : 2023.08.29.555417
                Affiliations
                [1 ]Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
                [2 ]Laboratory of Genetics, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
                [3 ]Department of Biology, Villanova University, Villanova PA 19085, USA
                [4 ]Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte NC 28223, USA
                [5 ]Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Center, South China Agricultural University, Guangzhou 510642, China
                [6 ]Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Institute of Insect Sciences, Zhejiang University, Hangzhou 310058, China
                [7 ]Westerdijk Fungal Biodiversity Institute, 3584 Utrecht, The Netherlands
                [8 ]Department of Quantitative and Computational Biology and Biological Sciences, University of Southern California, Los Angeles CA 90089, USA
                Author notes
                [* ]All correspondence should be addressed to: antonis.rokas@ 123456vanderbilt.edu
                Author information
                http://orcid.org/0000-0001-9907-789X
                http://orcid.org/0000-0002-3013-9906
                http://orcid.org/0000-0003-3224-7510
                http://orcid.org/0000-0003-0068-6703
                http://orcid.org/0000-0002-5477-4250
                http://orcid.org/0000-0002-2879-6317
                http://orcid.org/0000-0001-5765-1419
                http://orcid.org/0000-0003-0835-5925
                http://orcid.org/0000-0002-2886-3970
                http://orcid.org/0000-0001-5088-7461
                http://orcid.org/0000-0002-7248-6551
                Article
                10.1101/2023.08.29.555417
                10491267
                37693602
                0538eb24-559e-47cf-bc1e-88fbeb14d6d3

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.

                History
                Funding
                Funded by: National Science Foundation
                Award ID: DBI-1906759
                Award ID: DEB-2110403
                Award ID: DEB-2110404
                Funded by: National Science Foundation for Distinguished Young Scholars of Zhejiang Province
                Award ID: LR23C140001
                Funded by: Fundamental Research Funds for the Central Universities
                Award ID: 226-2023-00021
                Funded by: NIGMS
                Award ID: R35GM151348
                Funded by: USDA National Institute of Food and Agriculture (Hatch Project)
                Award ID: 1020204
                Funded by: DOE Great Lakes Bioenergy Research Center (DOE BER Office of Science)
                Award ID: DE-SC0018409
                Funded by: Wisconsin Alumni Research Foundation
                Funded by: National Institutes of Health/National Institute of Allergy and Infectious Diseases
                Award ID: R01 AI153356
                Funded by: Burroughs Welcome Fund
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