Saccharomycotina yeasts defy long-standing macroecological patterns

Author:

David Kyle T.12ORCID,Harrison Marie-Claire12ORCID,Opulente Dana A.34ORCID,LaBella Abigail L.125ORCID,Wolters John F.3ORCID,Zhou Xiaofan6ORCID,Shen Xing-Xing7ORCID,Groenewald Marizeth8ORCID,Pennell Matt910ORCID,Hittinger Chris Todd3ORCID,Rokas Antonis12ORCID

Affiliation:

1. Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235

2. Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235

3. Laboratory of Genetics, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, Department of Energy (DOE) Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726

4. Department of Biology, Villanova University, Villanova, PA 19085

5. Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223

6. 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

7. Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Institute of Insect Sciences, Zhejiang University, Hangzhou 310058, China

8. Westerdijk Fungal Biodiversity Institute, Utrecht 3584, The Netherlands

9. Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089

10. Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089

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; however, little is known about what rules govern the macroecology of yeast species and their range limits in the wild. Here, we trained machine learning models on 12,816 terrestrial occurrence records and 96 environmental variables to infer global distribution maps at ~1 km 2 resolution 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 diversity. 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 long-standing macroecological principles, including the latitudinal diversity gradient, temperature-dependent species richness, and a positive relationship between latitude and 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.

Funder

National Science Foundation

HHS | NIH | National Institute of General Medical Sciences

HHS | NIH | National Institute of Allergy and Infectious Diseases

U.S. Department of Agriculture

DOE Great Lakes Bioenergy Research Center

Publisher

Proceedings of the National Academy of Sciences

Reference80 articles.

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