Human alteration of the global environment has triggered the sixth major extinction event in the history of life and caused widespread changes in the global distribution of organisms. These changes in biodiversity alter ecosystem processes and change the resilience of ecosystems to environmental change. This has profound consequences for services that humans derive from ecosystems. The large ecological and societal consequences of changing biodiversity should be minimized to preserve options for future solutions to global environmental problems.

Worldwide decomposition rates depend both on climate and the legacy of plant functional traits as litter quality. To quantify the degree to which functional differentiation among species affects their litter decomposition rates, we brought together leaf trait and litter mass loss data for 818 species from 66 decomposition experiments on six continents. We show that: (i) the magnitude of species-driven differences is much larger than previously thought and greater than climate-driven variation; (ii) the decomposability of a species' litter is consistently correlated with that species' ecological strategy within different ecosystems globally, representing a new connection between whole plant carbon strategy and biogeochemical cycling. This connection between plant strategies and decomposability is crucial for both understanding vegetation-soil feedbacks, and for improving forecasts of the global carbon cycle.

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▪ Abstract  Plant species differ in how they influence many aspects of ecosystem structure and function, including soil characteristics, geomorphology, biogeochemistry, regional climate, and the activity and distribution of other organisms. Attempts to generalize plant species effects on ecosystems have focused on single traits or suites of traits that strongly covary (functional groups). However, plant effects on any ecosystem process are mediated by multiple traits, and many of these traits vary independently from one another. Thus, most species have unique combinations of traits that influence ecosystems, and there is no single trait or functional-group classification that can capture the effects of these multiple traits, or can predict the multiple functions performed by different plant species. We present a new theoretical framework, the functional matrix, which builds upon the functional group and single trait approaches to account for the ecosystem effects of multiple traits that vary independently among species. The functional matrix describes the relationship between ecosystem processes and multiple traits, treating traits as continuous variables, and determining if the effects of these multiple traits are additive or interactive. The power of this approach is that the ecosystem effects of multiple traits are the underlying mechanisms determining species effects, how the effects of an individual species change across seasons and under varying environmental conditions, the nonadditive effects of plant species mixtures, and the effects of species diversity.

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Microbial communities can potentially mediate feedbacks between global change and ecosystem function, owing to their sensitivity to environmental change and their control over critical biogeochemical processes. Numerous ecosystem models have been developed to predict global change effects, but most do not consider microbial mechanisms in detail. In this idea paper, we examine the extent to which incorporation of microbial ecology into ecosystem models improves predictions of carbon (C) dynamics under warming, changes in precipitation regime, and anthropogenic nitrogen (N) enrichment. We focus on three cases in which this approach might be especially valuable: temporal dynamics in microbial responses to environmental change, variation in ecological function within microbial communities, and N effects on microbial activity. Four microbially-based models have addressed these scenarios. In each case, predictions of the microbial-based models differ-sometimes substantially-from comparable conventional models. However, validation and parameterization of model performance is challenging. We recommend that the development of microbial-based models must occur in conjunction with the development of theoretical frameworks that predict the temporal responses of microbial communities, the phylogenetic distribution of microbial functions, and the response of microbes to N enrichment.

Plant-soil interactions are the foundation of effective and sustained restoration of terrestrial communities and ecosystems. Recent advances in ecological science have greatly contributed to our understanding of the effects of soil conditions on plant community dynamics and our understanding of plant composition impacts on almost every aspect of soil structure and function. Although these theories provide important guidelines for the practice of restoration, they often fall short of providing the level of information required to make effective site-specific management decisions. This is largely because of ecology's search for simple unifying theories and the resulting tendency to generalize from studies at one or only a few sites. An average effect or broad-scale simple relationship tends to provide a ''onesize-fits-all'' (or none) prescription for managers. Plant-soil interactions can vary greatly depending on their context (e.g., environmental conditions, management practices, time, neighboring community, interaction with other organisms). The ability to predict these context-dependent interactions between plants and soils can be developed by building upon existing general frameworks for understanding plant-soil interactions. Collaborations between researchers and managers can develop conceptual tools that allow us to understand and manage the variability and complexity of plant-soil interactions, simultaneously advancing theory and applicability.

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