David Kyle T, Harrison Marie-Claire, Opulente Dana A, LaBella Abigail L, Wolters John F, Zhou Xiaofan, Shen Xing-Xing, Groenewald Marizeth, Pennell Matt, Hittinger Chris Todd, Rokas Antonis
Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA.
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.
bioRxiv. 2023 Aug 31:2023.08.29.555417. doi: 10.1101/2023.08.29.555417.
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 12,221 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.
酵母亚门酵母（以下简称“酵母”）是一类具有科学、经济和医学意义的真菌分支。酵母在生态上具有高度多样性，存在于地球上每个生物群落和各大洲的广泛环境中；然而，关于何种规则支配酵母物种的宏观生态学及其在野外的分布范围，我们所知甚少。在此，我们基于12221条出现记录和96个环境变量训练机器学习模型，以推断186种酵母（约占已描述物种的15%，来自75%的目）的全球分布图，并测试酵母生物地理学和宏观生态学的环境驱动因素。我们发现，预测的酵母多样性热点出现在温带气候的山地混交林中。植被类型和地形的多样性是酵母物种丰富度的一些最主要预测因素，这表明微生境和环境梯度是酵母多样化的关键。我们还发现，酵母的分布范围受到碳生态位宽度以及与其他酵母物种的分布范围重叠的显著影响，碳专性物种和处于高多样性环境中的物种地理范围较小。最后，酵母违背了许多长期以来的宏观生态学原则，包括纬度多样性梯度、温度依赖的物种丰富度以及纬度依赖的分布范围大小（拉波波特法则）。这些结果揭示了环境如何支配酵母亚门物种的全球多样性和分布。这些高分辨率的酵母物种分布模型将有助于预测当前和未来气候情景下具有经济相关性和新出现的致病物种。
