Phyllosphere fungal diversity generates pervasive nonadditive effects on plant performance.

Plants naturally harbor diverse microbiomes that can dramatically impact their health and productivity. However, it remains unclear how fungal microbiome diversity, especially in the phyllosphere, impacts intermicrobial interactions and consequent nonadditive effects on plant productivity. Combining manipulative experiments, field collections, culturing, microbiome sequencing, and synthetic consortia, we experimentally tested for the first time how foliar fungal community diversity impacts plant productivity. We inoculated morning glories (Ipomoea hederifolia L.) with 32 phyllosphere consortia of either low or high diversity or with single fungal taxa, and measured effects on plant productivity and allocation. We found the following: (1) nonadditive effects were pervasive with 56% of fungal consortia interacting synergistically or antagonistically to impact plant productivity, including some consortia capable of generating acute synergism (e.g. > 1000% increase in productivity above the additive expectation), (2) interactions among 'commensal' fungi were responsible for this nonadditivity in diverse consortia, (3) synergistic interactions were approximately four times stronger than antagonistic effects, (4) fungal diversity affected the magnitude but not frequency or direction of nonadditivity, and (5) diversity affected plant performance nonlinearly with the highest performance in low-diversity treatments. These findings highlight the importance of interpreting plant-microbiome interactions under a framework that incorporates intermicrobial interactions and nonadditive outcomes to understand natural complexity.

Multidimensional specialization and generalization are pervasive in soil prokaryotes.

Habitat specialization underpins biological processes from species distributions to speciation. However, organisms are often described as specialists or generalists based on a single niche axis, despite facing complex, multidimensional environments. Here, we analysed 236 environmental soil microbiomes across the United States and demonstrate that 90% of >1,200 prokaryotes followed one of two trajectories: specialization on all niche axes (multidimensional specialization) or generalization on all axes (multidimensional generalization). We then documented that this pervasive multidimensional specialization/generalization had many ecological and evolutionary consequences. First, multidimensional specialization and generalization are highly conserved with very few transitions between these two trajectories. Second, multidimensional generalists dominated communities because they were 73 times more abundant than specialists. Lastly, multidimensional specialists played important roles in community structure with ~220% more connections in microbiome networks. These results indicate that multidimensional generalization and specialization are evolutionarily stable with multidimensional generalists supporting larger populations and multidimensional specialists playing important roles within communities, probably stemming from their overrepresentation among pollutant detoxifiers and nutrient cyclers. Taken together, we demonstrate that the vast majority of soil prokaryotes are restricted to one of two multidimensional niche trajectories, multidimensional specialization or multidimensional generalization, which then has far-reaching consequences for evolutionary transitions, microbial dominance and community roles.

Not that kind of tree: Assessing the potential for decision tree-based plant identification using trait databases.

PREMISE: Advancements in machine learning and the rise of accessible "big data" provide an important opportunity to improve trait-based plant identification. Here, we applied decision-tree induction to a subset of data from the TRY plant trait database to (1) assess the potential of decision trees for plant identification and (2) determine informative traits for distinguishing taxa. METHODS: Decision trees were induced using 16 vegetative and floral traits (689 species, 20 genera). We assessed how well the algorithm classified species from test data and pinpointed those traits that were important for identification across diverse taxa. RESULTS: The unpruned tree correctly placed 98% of the species in our data set into genera, indicating its promise for distinguishing among the species used to construct them. Furthermore, in the pruned tree, an average of 89% of the species from the test data sets were properly classified into their genera, demonstrating the flexibility of decision trees to also classify new species into genera within the tree. Closer inspection revealed that seven of the 16 traits were sufficient for the classification, and these traits yielded approximately two times more initial information gain than those not included. DISCUSSION: Our findings demonstrate the potential for tree-based machine learning and big data in distinguishing among taxa and determining which traits are important for plant identification.

Tripartite mutualisms as models for understanding plant-microbial interactions.

All plants host diverse microbial assemblages that shape plant health, productivity, and function. While some microbial effects are attributable to particular symbionts, interactions among plant-associated microbes can nonadditively affect plant fitness and traits in ways that cannot be predicted from pairwise interactions. Recent research into tripartite plant-microbe mutualisms has provided crucial insight into this nonadditivity and the mechanisms underlying plant interactions with multiple microbes. Here, we discuss how interactions among microbial mutualists affect plant performance, highlight consequences of biotic and abiotic context-dependency for nonadditive outcomes, and summarize burgeoning efforts to determine the molecular bases of how plants regulate establishment, resource exchange, and maintenance of tripartite interactions. We conclude with four goals for future tripartite studies that will advance our overall understanding of complex plant-microbial interactions.