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Dr. James Grace

James B. Grace, Ph.D.

E-mail: jim_grace@usgs.gov

James B. Grace, Ph.D. 1980. Michigan State University. Complex systems analysis.

In recent years, Grace's research has evolved to emphasize the study of complex interrelationships in natural systems. This focus has grown out of the recognition that the study of systems (as opposed to individual processes) requires methods that are quite different from the time-honored procedures recommended for experimentation and monitoring. In brief, if we are to understand and predict the behavior of systems, we need to develop and evaluate multivariate hypotheses. One approach to the evaluation of multivariate hypotheses involves the use of structural equation modeling (SEM) and this has been the primary method we are using at present. SEM represents a methodology by which we can develop models about complex networks of relationships and subsequently evaluate the degree to which data are consistent with various models. The use of SEM allows researchers to address a whole new set of questions about how system parts interact, the relative importances of different processes, and how systems will behave under a wide variety of conditions. SEM allows for the evaluation of multivariate models using both experimental and nonexperimental data. As a model-centered approach (versus the conventional null hypothesis-centered approach), SEM provides a uniquely powerful and flexible approach for progressive learning about systems. A brief introduction to SEM can be found in Chapter 30 of McCune and Grace (2002).

While SEM holds great promise for use in the study of natural systems, there are a number of capabilities of the method that are currently underdeveloped. In particular there is a need to deal readily with complex variables and to this end, Grace has been working with other SEM specialists to develop procedures for including composite variables in structural equation models. Composite variables are particularly useful in representing nonlinear relationships as well as for representing the general relationships that emerge from complex models. Another aspect of SEM that Grace is currently studying has to do with how to best extract general predictive information out of model results. One example of this work can be found in Grace and Bollen (2005).

Finally, Grace has been collaborating with a great number of researchers to determine the range of utility of SEM in studying natural systems. Such studies include studies of plant diversity, wetland conservation, grazing, disturbance, biological control of weeds, climate change, sea level rise, fire ecology, and wildlife habitat.

In the past Grace's research program has dealt with a variety of particular applications, including the ecology of coastal plant ecosystems, especially coastal prairie and coastal wetlands. These ecosystems are currently at risk from a variety of forces, especially exotic plants but also fire suppression. Research efforts have been centered on predicting the effects of fire on coastal communities with a special emphasis on the impacts on exotic species, particularly Chinese tallow tree. Of special interest in this work have been the mechanisms of interaction among plants that determine the long-term responses to fire. For more information about the NWRC Coastal Prairie Research Program, click here.

Select Publications:

The following is only a list of recent publications (plus books published previously). For a complete list of publications, please click here (PDF, 77 KB). For assistance with obtaining reprints, please contact the NWRC Library.

Books Published Previously:

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