Saccharomycotina yeasts defy long-standing macroecological patterns

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 km² 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.

Keywords: AI; biogeography; fungi; latitudinal species gradient; macroecology.

Fig. 1.

Global yeast diversity. (A) Heat map of the distributions of 186 yeast species inferred through random forest machine learning models. (B) Average species richness per grid cell for each latitude band or line.

Fig. 2.

Yeast species richness is concentrated in temperate, mixed forests. Average species richness per grid cell for each Köppen-Geiger climate class (A) and biome (B).

Fig. 3.

Traditional predictors of species diversity are poor indicators of yeast species diversity. (A) Variables that scale with diversity in other clades, such as tropical climates (Left), temperature (Center), and area (Right), did not scale with yeast species diversity. (B) Three select variables that were among the best predictors of yeast species diversity: temperate climates (Left), vegetation diversity (Center), and geomorphic class diversity (Right). All graphs represent the same regression analysis with the following summary statistics; FDR: false discovery rate of the negative binomial regression. m: scaled slope of linear regression. Black curves represent locally estimated scatterplot smoothing.

Fig. 4.

Yeast species range size scales negatively with species richness and latitude, positively with carbon niche breadth. (A) Specialist species that grew on only a few carbon sources had significantly smaller geographic ranges than nonspecialists. P-values represent a phylogenetic ANOVA test. Size of inferred ranges for each species included in this study, compared to (B) species richness and (C) absolute latitude. Summary statistics represented phylogenetic generalized least squares tests. All tests use the same underlying time-calibrated tree from Opulente et al. (2).