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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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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.