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ABSTRACT To accurately predict earth system response to global change, we must be able to predict the responses of important properties of that system, such as the depths over which plant roots are distributed. In 2008, H. J. Schenk proposed a model for the depth distribution of plant roots based on a simple hydrological scheme and the assumptions that plants will take up the shallowest water available first and will distribute their roots in proportion to long‐term mean uptake at each depth. Here, we derive an analytical solution to the Schenk model under an idealised climate (in which infiltration events are treated as a marked Poisson process), explore properties of the result and compare with data. The solution suggests that in very humid and arid climates, the soil wetting and drying cycles induced by root water uptake are generally confined to a characteristic depth below the surface. This depth depends on the typical magnitude of rainfall events (most strongly so in arid climates), the typical total transpiration demand between rainfall events (most strongly in humid climates) and the plant‐available water holding capacity of the soil. Root water uptake (and thus predicted root density) in very humid and arid landscapes decreases exponentially with depth at a rate determined by this characteristic depth. However, in a mesic climate, soils may be wet or dry to greater depths below the near‐surface, and the duration spent in each state increases with depth. Consequently, root water uptake and root density in mesic climates more closely resemble a power law distribution. When the aridity index is exactly 1, the characteristic depth diverges and the mean rooting depth approaches infinity. This suggests that the most skewed root depth distributions might occur in mesic environments. We compared this model to another analytical solution and a compiled database of root distributions (159 combined locations). For a larger comparison dataset, we also compared 99th percentile rooting depth to rooting depths modeled by two other frameworks and a database of observed rooting depths (1271 combined locations). Results demonstrate that the analytical formulation of the Schenk model performs well as a shallow bound on rooting depths and captures something of the nonexponential form of root distributions, and its error is similar to or less than that of other modeling frameworks. Errors may be partly explained by the deviation of real climate from the idealisations used to obtain an analytical solution (exponentially distributed infiltration events and no seasonality). 

Many remarkable phenotypes have repeatedly occurred across vast evolutionary distances. When convergent traits emerge on the tree of life, they are sometimes driven by the same underlying gene families, while other times, many different gene families are involved. Conversely, a gene family may be repeatedly recruited for a single trait or many different traits. To understand the general rules governing convergence at both genomic and phenotypic levels, we systematically tested associations between 56 binary metabolic traits and gene count in 14,785 gene families from 993 Saccharomycotina yeasts. Using a recently developed phylogenetic approach that reduces spurious correlations, we found that gene family expansion and contraction were significantly linked to trait gain and loss in 45/56 (80%) traits. While 595/739 (81%) significant gene families were associated with only one trait, we also identified several “keystone” gene families that were significantly associated with up to 13/56 (23%) of all traits. Strikingly, most of these families are known to encode metabolic enzymes and transporters, including all members of the industrially relevantMALtose fermentation loci in the baker’s yeastSaccharomyces cerevisiae. These results indicate that convergent evolution on the gene family level may be more widespread across deeper timescales than previously believed. 

ABSTRACT Yeasts in the subphylum Saccharomycotina are found across the globe in disparate ecosystems. A major aim of yeast research is to understand the diversity and evolution of ecological traits, such as carbon metabolic breadth, insect association, and cactophily. This includes studying aspects of ecological traits like genetic architecture or association with other phenotypic traits. Genomic resources in the Saccharomycotina have grown rapidly. Ecological data, however, are still limited for many species, especially those only known from species descriptions where usually only a limited number of strains are studied. Moreover, ecological information is recorded in natural language format limiting high throughput computational analysis. To address these limitations, we developed an ontological framework for the analysis of yeast ecology. A total of 1,088 yeast strains were added to the Ontology of Yeast Environments (OYE) and analyzed in a machine‐learning framework to connect genotype to ecology. This framework is flexible and can be extended to additional isolates, species, or environmental sequencing data. Widespread adoption of OYE would greatly aid the study of macroecology in the Saccharomycotina subphylum. 

Many distantly related organisms have convergently evolved traits and lifestyles that enable them to live in similar ecological environments. However, the extent of phenotypic convergence evolving through the same or distinct genetic trajectories remains an open question. Here, we leverage a comprehensive dataset of genomic and phenotypic data from 1,049 yeast species in the subphylum Saccharomycotina (Kingdom Fungi, Phylum Ascomycota) to explore signatures of convergent evolution in cactophilic yeasts, ecological specialists associated with cacti. We inferred that the ecological association of yeasts with cacti arose independently approximately 17 times. Using a machine learning–based approach, we further found that cactophily can be predicted with 76% accuracy from both functional genomic and phenotypic data. The most informative feature for predicting cactophily was thermotolerance, which we found to be likely associated with altered evolutionary rates of genes impacting the cell envelope in several cactophilic lineages. We also identified horizontal gene transfer and duplication events of plant cell wall–degrading enzymes in distantly related cactophilic clades, suggesting that putatively adaptive traits evolved independently through disparate molecular mechanisms. Notably, we found that multiple cactophilic species and their close relatives have been reported as emerging human opportunistic pathogens, suggesting that the cactophilic lifestyle—and perhaps more generally lifestyles favoring thermotolerance—might preadapt yeasts to cause human disease. This work underscores the potential of a multifaceted approach involving high-throughput genomic and phenotypic data to shed light onto ecological adaptation and highlights how convergent evolution to wild environments could facilitate the transition to human pathogenicity. 

How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy. Known structural genes involved in assimilation of these sources and presence/absence patterns of growth in other sources were important features contributing to prediction accuracy. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.2%) or growth data (82.6%) but not from isolation environment data (65.6%). Prediction accuracy was even higher (93.3%) when we combined genomic and growth data. After theGALactose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol, raising the hypothesis that several species in two orders, Serinales and Pichiales (containing the emerging pathogenCandida aurisand the genusOgataea, respectively), have an alternative galactose utilization pathway because they lack theGALgenes. Growth and biochemical assays confirmed that several of these species utilize galactose through an alternative oxidoreductive D-galactose pathway, rather than the canonicalGALpathway. Machine learning approaches are powerful for investigating the evolution of the yeast genotype–phenotype map, and their application will uncover novel biology, even in well-studied traits. 

IntroductionEukaryotic life depends on the functional elements encoded by both the nuclear genome and organellar genomes, such as those contained within the mitochondria. The content, size, and structure of the mitochondrial genome varies across organisms with potentially large implications for phenotypic variance and resulting evolutionary trajectories. Among yeasts in the subphylum Saccharomycotina, extensive differences have been observed in various species relative to the model yeastSaccharomyces cerevisiae, but mitochondrial genome sampling across many groups has been scarce, even as hundreds of nuclear genomes have become available. MethodsBy extracting mitochondrial assemblies from existing short-read genome sequence datasets, we have greatly expanded both the number of available genomes and the coverage across sparsely sampled clades. ResultsComparison of 353 yeast mitochondrial genomes revealed that, while size and GC content were fairly consistent across species, those in the generaMetschnikowiaandSaccharomycestrended larger, while several species in the order Saccharomycetales, which includesS. cerevisiae, exhibited lower GC content. Extreme examples for both size and GC content were scattered throughout the subphylum. All mitochondrial genomes shared a core set of protein-coding genes for Complexes III, IV, and V, but they varied in the presence or absence of mitochondrially-encoded canonical Complex I genes. We traced the loss of Complex I genes to a major event in the ancestor of the orders Saccharomycetales and Saccharomycodales, but we also observed several independent losses in the orders Phaffomycetales, Pichiales, and Dipodascales. In contrast to prior hypotheses based on smaller-scale datasets, comparison of evolutionary rates in protein-coding genes showed no bias towards elevated rates among aerobically fermenting (Crabtree/Warburg-positive) yeasts. Mitochondrial introns were widely distributed, but they were highly enriched in some groups. The majority of mitochondrial introns were poorly conserved within groups, but several were shared within groups, between groups, and even across taxonomic orders, which is consistent with horizontal gene transfer, likely involving homing endonucleases acting as selfish elements. DiscussionAs the number of available fungal nuclear genomes continues to expand, the methods described here to retrieve mitochondrial genome sequences from these datasets will prove invaluable to ensuring that studies of fungal mitochondrial genomes keep pace with their nuclear counterparts. 

Abstract A novel budding yeast species was isolated from a soil sample collected in the United States of America. Phylogenetic analyses of multiple loci and phylogenomic analyses conclusively placed the species within the genusPichia. Strain yHMH446 falls within a clade that includesPichia norvegensis,Pichia pseudocactophila,Candida inconspicua, andPichia cactophila. Whole genome sequence data were analyzed for the presence of genes known to be important for carbon and nitrogen metabolism, and the phenotypic data from the novel species were compared to allPichiaspecies with publicly available genomes. Across the genus, including the novel species candidate, we found that the inability to use many carbon and nitrogen sources correlated with the absence of metabolic genes. Based on these results,Pichia galeolatasp. nov. is proposed to accommodate yHMH446^T(=NRRL Y‐64187 = CBS 16864). This study shows how integrated taxogenomic analysis can add mechanistic insight to species descriptions.