James B. Grace

Effects of Invasive Alien Plants on Fire Regimes

Abstract Plant invasions are widely recognized as significant threats to biodiversity conservation worldwide. One way invasions can affect native ecosystems is by changing fuel properties, which can in turn affect fire behavior and, ultimately, alter fire regime characteristics such as frequency, intensity, extent, type, and seasonality of fire. If the regime changes subsequently promote the dominance of the invaders, then an invasive plant–fire regime cycle can be established. As more ecosystem components and interactions are altered, restoration of preinvasion conditions becomes more difficult. Restoration may require managing fuel conditions, fire regimes, native plant communities, and other ecosystem properties in addition to the invaders that caused the changes in the first place. We present a multiphase model describing the interrelationships between plant invaders and fire regimes, provide a system for evaluating the relative effects of invaders and prioritizing them for control, and recommend ways to restore pre-invasion fire regime properties.

The factors controlling species density in herbaceous plant communities: an assessment

Abstract This paper evaluates both the ideas and empirical evidence pertaining to the control of species density in herbaceous plant communities. While most theoretical discussions of species density have emphasized the importance of habitat productivity and disturbance regimes, many other factors (e.g. species pools, plant litter accumulation, plant morphology) have been proposed to be important. A review of literature presenting observations on the density of species in small plots (in the vicinity of a few square meters or less), as well as experimental studies, suggests several generalizations: (1) Available data are consistent with an underlying unimodal relationship between species density and total community biomass. While variance in species density is often poorly explained by predictor variables, there is strong evidence that high levels of community biomass are antagonistic to high species density. (2) Community biomass is just one of several factors affecting variations in species density. Multivariate analyses typically explain more than twice as much variance in species density as can be explained by community biomass alone. (3) Disturbance has important and sometimes complex effects on species density. In general, the evidence is consistent with the intermediate disturbance hypothesis but exceptions exist and effects can be complex. (4) Gradients in the species pool can have important influences on patterns of species density. Evidence is mounting that a considerable amount of the observed variability in species density within a landscape or region may result from environmental effects on the species pool. (5) Several additional factors deserve greater consideration, including time lags, species composition, plant morphology, plant density and soil microbial effects. Based on the available evidence, a conceptual model of the primary factors controlling species density is presented here. This model suggests that species density is controlled by the effects of disturbance, total community biomass, colonization, the species pool and spatial heterogeneity. The structure of the model leads to two main expectations: (1) while community biomass is important, multivariate approaches will be required to understand patterns of variation in species density, and (2) species density will be more highly correlated with light penetration to the soil surface, than with above-ground biomass, and even less well correlated with plant growth rates (productivity) or habitat fertility. At present, data are insufficient to evaluate the relative importance of the processes controlling species density. Much more work is needed if we are to adequately predict the effects of environmental changes on plant communities and species diversity.

Habitat Partitioning and Competitive Displacement in Cattails (Typha): Experimental Field Studies

The aquatic plants Typha latifolia and T. angustifolia are observed to be strongly segregated along a gradient of increasing water depth with T. latifolia restricted to depths of less than 80 cm and T. angustifolia to depths greater than 15 cm. Transplantation of both species along the gradient in the absence of competitors showed that T. latifolia was little affected by the presence of T. angustifolia but T. angustifolia was capable of growing over the entire gradient. The loss of precompetitive distribution was not statistically significant for T. latifolia compared to a 39.6% loss for T. angustifolia. It was further observed that overlap was reduced by 43.5% during the course of the growing season. Rhizomes transplanted into natural stands failed to survive, further demonstrating that competition was actively operating to maintain zonation between species. The basis for habitat partitioning appears to be a difference in morphology whereby T. latifolia was prevented from growing in deep water because of the higher cost of producing broader leaves but better able to compete for light in shallow water because of its greater leaf surface area. Coexistence is largely the result of a deep-water refuge for the competitively inferior T. angustifolia although other factors may be involved.

On the specification of structural equation models for ecological systems

The use of structural equation modeling (SEM) is often motivated by its utility for investigating complex networks of relationships, but also because of its promise as a means of representing theoretical concepts using latent variables. In this paper, we discuss characteristics of ecological theory and some of the challenges for proper specification of theoretical ideas in structural equation models (SE models). In our presentation, we describe some of the requirements for classical latent variable models in which observed variables (indicators) are interpreted as the effects of underlying causes. We also describe alternative model specifications in which indicators are interpreted as having causal influences on the theoretical concepts. We suggest that this latter nonclassical specification (which involves another variable type—the composite) will often be appropriate for ecological studies because of the multifaceted nature of our theoretical concepts. In this paper, we employ the use of meta-models to ...

The relationship between species richness and community biomass: the importance of environmental variables

Several studies have used plant community biomass to predict species richness with varying success. In this study we examined the relationship between species richness and biomass for 36 marsh communities from two different watersheds. In addition, we measured several environmental variables and estimated the potential richness (the total number of species known to be able to occur in a community type) for each community. Above-ground living and dead biomass combined was found to be weakly correlated with species richness (R 2 = 0.02). Instead, a multiple regression model based on elevation (R 2 = 0.30) soil organic matter (R 2 = 0.18), and biomass was able to explain 82 % of the variance in species richness (...)

A Structural Equation Model of Plant Species Richness and Its Application to a Coastal Wetland

Studies of plant species richness have often emphasized the role of either community biomass (as an indicator of density effects) or abiotic factors. In this article we present a general model that simultaneously examines the relative importance of abiotic and density effects. General and specific models were developed to examine the importance of abiotic conditions, disturbance, and community biomass on plant species richness. Models were evaluated using structural equation modeling based on data from 190 plots across a coastal marsh landscape. The accepted model was found to explain 45% of the observed variation in richness, 75% of biomass, and 65% of light penetration. Model results indicate that abiotic conditions have both direct effects on the species pool and indirect effects on richness mediated through effects on biomass and shading. Effects of disturbance were found to be indirect via biomass. Strong density effects on richness were indicated by the results, and canopy light penetration was found to be a better predictor of richness than was biomass. Overall, it appears that richness in this coastal landscape is controlled in roughly equal proportions by abiotic influences on the species pool and density effects, with disturbance playing a lesser role. The structure of the general model presented should be applicable to a wide variety of herbaceous plant communities.

Guidelines for a graph‐theoretic implementation of structural equation modeling

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.

Integrative modelling reveals mechanisms linking productivity and plant species richness

How ecosystem productivity and species richness are interrelated is one of the most debated subjects in the history of ecology. Decades of intensive study have yet to discern the actual mechanisms behind observed global patterns. Here, by integrating the predictions from multiple theories into a single model and using data from 1,126 grassland plots spanning five continents, we detect the clear signals of numerous underlying mechanisms linking productivity and richness. We find that an integrative model has substantially higher explanatory power than traditional bivariate analyses. In addition, the specific results unveil several surprising findings that conflict with classical models. These include the isolation of a strong and consistent enhancement of productivity by richness, an effect in striking contrast with superficial data patterns. Also revealed is a consistent importance of competition across the full range of productivity values, in direct conflict with some (but not all) proposed models. The promotion of local richness by macroecological gradients in climatic favourability, generally seen as a competing hypothesis, is also found to be important in our analysis. The results demonstrate that an integrative modelling approach leads to a major advance in our ability to discern the underlying processes operating in ecological systems.

REGIONAL AND LOCAL SPECIES RICHNESS IN AN INSULAR ENVIRONMENT: SERPENTINE PLANTS IN CALIFORNIA

We asked how the richness of the specialized (endemic) flora of serpentine rock outcrops in California varies at both the regional and local scales. Our study had two goals: first, to test whether endemic richness is affected by spatial habitat structure (e.g., regional serpentine area, local serpentine outcrop area, regional and local measures of outcrop isolation), and second, to conduct this test in the context of a broader assessment of environmental influences (e.g., climate, soils, vegetation, disturbance) and historical influences (e.g., geologic age, geographic province) on local and regional species richness. We measured endemic and total richness and environmental variables in 109 serpentine sites (1000-m 2 paired plots) in 78 serpentine-containing regions of the state. We used structural equation modeling (SEM) to simultaneously relate regional richness to regional- scale predictors, and local richness to both local-scale and regional-scale predictors. Our model for serpentine endemics explained 66% of the variation in local endemic richness based on local environment (vegetation, soils, rock cover) and on regional endemic richness. It explained 73% of the variation in regional endemic richness based on regional environment (climate and productivity), historical factors (geologic age and geographic province), and spatial structure (regional total area of serpentine, the only significant spatial variable in our analysis). We did not find a strong influence of spatial structure on species richness. However, we were able to distinguish local vs. regional influences on species richness to a novel extent, despite the existence of correlations between local and regional conditions.

Representing general theoretical concepts in structural equation models: the role of composite variables

Structural equation modeling (SEM) holds the promise of providing natural scientists the capacity to evaluate complex multivariate hypotheses about ecological systems. Building on its predecessors, path analysis and factor analysis, SEM allows for the incorporation of both observed and unobserved (latent) variables into theoretically-based probabilistic models. In this paper we discuss the interface between theory and data in SEM and the use of an additional variable type, the composite. In simple terms, composite variables specify the influences of collections of other variables and can be helpful in modeling heterogeneous concepts of the sort commonly of interest to ecologists. While long recognized as a potentially important element of SEM, composite variables have received very limited use, in part because of a lack of theoretical consideration, but also because of difficulties that arise in parameter estimation when using conventional solution procedures. In this paper we present a framework for discussing composites and demonstrate how the use of partially-reduced-form models can help to overcome some of the parameter estimation and evaluation problems associated with models containing composites. Diagnostic procedures for evaluating the most appropriate and effective use of composites are illustrated with an example from the ecological literature. It is argued that an ability to incorporate composite variables into structural equation models may be particularly valuable in the study of natural systems, where concepts are frequently multifaceted and the influence of suites of variables are often of interest.