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Plant identification is critical for a wide range of biological fields and goals, ranging from understanding ecological processes, such as community assembly, to the conservation of rare and threatened species (Thessen, 2016). Historically, species have been identified using trait-based approaches in the form of dichotomous and polyclave keys (Tilling, 1984; Edwards et al., 1987). These identification keys remain an important and widely used resource for scientists (Gaylard and Kerley, 1995; Randler, 2008), as they are convenient, inexpensive, and enable identification when tissue samples cannot be collected for molecular barcoding (Will and Rubinoff, 2004). Improving trait-based plant identification (e.g., reducing the number of traits required for identification) could be especially useful for improving the efficacy of citizen scientists in large-scale projects where the use of genetic tools is not feasible or cost-effective (Gallo and Waitt, 2011; Roy et al., 2016). Advancements in computational methods such as machine learning, in tandem with the recent rise of online, easily accessible "big data, " could provide an unprecedented opportunity to improve traitbased identification, just as it has proved useful in other important ecological areas. For instance, machine learning has been applied to large databases to predict phenomena such as global surface temperatures (Casaioli et al., 2003), and underpins some of the most

Fungi play prominent roles in ecosystem services (e.g., nutrient cycling, decomposition) and thus have increasingly garnered attention in restoration ecology. However, it is unclear how most management decisions impact fungal communities, making it difficult to protect fungal diversity and utilize fungi to improve restoration success. To understand the effects of restoration decisions and environmental variation on fungal communities, we sequenced soil fungal microbiomes from 96 sites across eight experimental Everglades tree islands approximately 15 years after restoration occurred. We found that early restoration decisions can have enduring consequences for fungal communities. Factors experimentally manipulated in 2003–2007 (e.g., type of island core) had significant legacy effects on fungal community composition. Our results also emphasized the role of water regime in fungal diversity, composition, and function. As the relative water level decreased, so did fungal diversity, with an approximately 25% decline in the driest sites. Further, as the water level decreased, the abundance of the plant pathogen–saprotroph guild increased, suggesting that low water may increase plant-pathogen interactions. Our results indicate that early restoration decisions can have long-term consequences for fungal community composition and function and suggest that a drier future in the Everglades could reduce fungal diversity on imperiled tree islands.

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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 analyzed 236 prokaryotic communities across the United States demonstrating for the first time 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 a wide range of 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 likely 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.

All plants naturally harbor diverse microbiomes that can dramatically impact their health and productivity. However, it remains unclear how microbiome diversity, especially in the phyllosphere, impacts intermicrobial interactions and consequent non-additive 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 with 32 synthetic phyllosphere communities of either low or high diversity or with single fungal taxa, and measured effects on plant productivity and allocation. We found 1) non-additive effects were pervasive with 56% of microbial communities 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 non-additivity in diverse communities, 3) synergistic interactions were ~4 times stronger than antagonistic effects, 4) fungal diversity affected the magnitude but not frequency or direction of non-additivity, and 5) diversity affected plant performance nonlinearly with highest performance in low microbial diversity treatments. These findings highlight the importance of interpreting plant-microbial interactions under a framework that incorporates intermicrobial interactions and non-additive outcomes to understand natural complexity.

Plant-associated microbiomes can improve plant fitness by ameliorating environmental stress, providing a promising avenue for improving outplantings during restoration. However, the effects of water management on these microbial communities and their cascading effects on primary producers are unresolved for many imperiled ecosystems. One such habitat, Everglades tree islands, has declined by 54% in some areas, releasing excess nutrients into surrounding wetlands and exacerbating nutrient pollution. We conducted a factorial experiment, manipulating the soil microbiome and hydrological regime experienced by a tree island native, Ficus aurea, to determine how microbiomes impact growth under two hydrological management plans. All plants were watered to simulate natural precipitation, but plants in the "unconstrained" management treatment were allowed to accumulate water above the soil surface, while the "constrained" treatment had a reduced stage to avoid soil submersion. We found significant effects of the microbiomes on overall plant performance and aboveground versus belowground investment; however, these effects depended on hydrological treatment. For instance, microbiomes increased investment in roots relative to aboveground tissues, but these effects were 142% stronger in the constrained compared to unconstrained water regime. Changes in hydrology also resulted in changes in the prokaryotic community composition, including a >20 log 2 fold increase in the relative abundance of Rhizobiaceae, and hydrology-shifted microbial composition was linked to changes in plant performance. Our results suggest that differences in hydrological management can have important effects on microbial communities, including taxa often involved in nitrogen cycling, which can in turn impact plant performance.