Wednesday, August 5, 2009
Exhibit Hall NE & SE, Albuquerque Convention Center
Paul Duffy, Neptune and Company, Inc., Bellvue, CO, David S. Schimel, Climate Sciences, Jet Propulsion Lab, California Institute of Technology, Pasadena, CA, Tom B. Stockton, Neptune and Company, Inc., Los Alamos, NM and Thompson Hobbs, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO
Background/Question/Methods One of the greatest challenges facing the ecological community lies in understanding the impacts of forecast climate change on the structure and function of ecosystems. There are two main obstacles that currently limit the ability to confidently address quantification of ecosystem change in the context climatic forcing. First, with respect to key ecosystem responses, there are limited data that have sufficient spatial-temporal resolution and extent. Second, the methods for quantifying changes in ecosystem responses that are driven by climatic forcing have not been well developed. Since the lack of methods is at least partly due to the limited availability of the necessary data, we have developed a simple simulation framework to explore the utility of different quantitative approaches. This simulation framework allows for the general specification of: the functional form of the link between climate drivers and ecosystem response variables of interest, process and measurement error, and space-time covariance structures. The goal of this simulation study is to characterize the “envelope” of values for these variables that allows for the detection of trends as well as discrimination among different functional forms that characterize the relationship between climate drivers and ecosystem response variables of interest. This work represents a first step in the ongoing effort to refine quantitative methods that identify not only climatically driven trends in ecological response variables, but also allows for the determination of the functional form of the link between climate forcing and ecosystems responses.
Results/Conclusions
Our results show that an abstract representation of the current NEON network allows for the detection of trends and functional forms linking climate drivers and ecological responses in the presence of commonly observed levels of errors and magnitude of signal.
