Abstract. Structural equation modeling (SEM) is increasingly being chosen by researchers as a framework for gaining scientific insights from the quantitative analyses of data. New ideas and methods emerging from the study of causality, influences from the field of graphical modeling, and advances in statistics are expanding the rigor, capability, and even purpose of SEM. Guidelines for implementing the expanded capabilities of SEM are currently lacking. In this paper we describe new developments in SEM that we believe constitute a third-generation of the methodology. Most characteristic of this new approach is the generalization of the structural equation model as a causal graph. In this generalization, analyses are based on graph theoretic principles rather than analyses of matrices. Also, new devices such as metamodels and causal diagrams, as well as an increased emphasis on queries and probabilistic reasoning, are now included. Estimation under a graph theory framework permits the use of Bayesian or likelihood methods. The guidelines presented start from a declaration of the goals of the analysis. We then discuss how theory frames the modeling process, requirements for causal interpretation, model specification choices, selection of estimation method, model evaluation options, and use of queries, both to summarize retrospective results and for prospective analyses.The illustrative example presented involves monitoring data from wetlands on Mount Desert Island, home of Acadia National Park. Our presentation walks through the decision process involved in developing and evaluating models, as well as drawing inferences from the resulting prediction equations. In addition to evaluating hypotheses about the connections between human activities and biotic responses, we illustrate how the structural equation (SE) model can be queried to understand how interventions might take advantage of an environmental threshold to limit Typha invasions.The guidelines presented provide for an updated definition of the SEM process that subsumes the historical matrix approach under a graph-theory implementation. The implementation is also designed to permit complex specifications and to be compatible with various estimation methods. Finally, they are meant to foster the use of probabilistic reasoning in both retrospective and prospective considerations of the quantitative implications of the results.

The biodiversity of microbial communities has important implications for the stability and functioning of ecosystem processes. Yet, very little is known about the environmental factors that define the microbial niche and how this influences the composition and activity of microbial communities. In this study, we derived niche parameters from physiological response curves that quantified microbial respiration for a diverse collection of soil bacteria and fungi along a soil moisture gradient. On average, soil microorganisms had relatively dry optima (0.3 MPa) and were capable of respiring under low water potentials (−2.0 MPa). Within their limits of activity, microorganisms exhibited a wide range of responses, suggesting that some taxa may be able to coexist by partitioning the moisture niche axis. For example, we identified dry‐adapted generalists that tolerated a broad range of water potentials, along with wet‐adapted specialists with metabolism restricted to less‐negative water potentials. These contrasting ecological strategies had a phylogenetic signal at a coarse taxonomic level (phylum), suggesting that the moisture niche of soil microorganisms is highly conserved. In addition, variation in microbial responses along the moisture gradient was linked to the distribution of several functional traits. In particular, strains that were capable of producing biofilms had drier moisture optima and wider niche breadths. However, biofilm production appeared to come at a cost that was reflected in a prolonged lag time prior to exponential growth, suggesting that there is a trade‐off associated with traits that allow microorganisms to contend with moisture stress. Together, we have identified functional groups of microorganisms that will help predict the structure and functioning of microbial communities under contrasting soil moisture regimes.

Stability in ecosystem function is an important but poorly understood phenomenon. Anthropogenic perturbations alter communities, but how they change stability and the strength of stabilizing mechanisms is not clear. We examined temporal stability (invariability) in aboveground productivity in replicated 18-year time series of experimentally perturbed grassland plant communities. We found that disturbed annual-dominated communities were more stable than undisturbed perennial communities, coincident with increases in the stabilizing effect of mean-variance scaling. We also found that nitrogen-fertilized communities maintained stability despite losses in species richness, probably because of increased compensatory dynamics and increased dominance by particularly stable dominant species. Among our communities, slight variation in diversity was not the strongest mechanism driving differences in stability. Instead, our study suggests that decreases in individual species variabilities and increases in the relative abundance of stable dominant species may help maintain stability in the functioning of ecosystems confronted with eutrophication, disturbance, and other global changes.

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