FY 05/06 | FY 04/05 | FY 03/04 | SPRING 2003 | FALL 2002 | SPRING 2002
Ecological forecasting can be defined as the process of predicting the state of ecosystems, ecosystem services, and natural capital, with fully specified and quantified uncertainties, contingent on explicit scenarios for key drivers such as climate and land use. The ability to anticipate environmental change in regions of rapid development is one of the greatest challenges to ecological forecasting. This requires observation of past activity to understand interrelationships among drivers and outcomes, and the development of models that incorporate our understanding of these interrelationships.
As a first step in improving our ability to forecast regional-scale responses to environmental change in forest ecosystems, this working group will use modeling and simulation to understand the diversity of tree populations in forests. More specifically, the group will exploit new computational methods for both inverse estimation and forward simulation to evaluate the efficacy of diversity mechanisms in these ecosystems.
Inverse estimation makes use of hierarchical Bayes model structures to test assumptions of diversity mechanisms. Forward simulation methods will be used to test predictions of diversity mechanisms. Since the underlying models are spatial and based on data for individual trees, straightforward simulation is very slow. Therefore, hierarchical data structures and approximation algorithms will be used to simulate the model efficiently.
The working group brings together scientists from several departments in an interdisciplinary effort: Jim Clark (Biology), Pankaj Agarwal (Computer Science), Michael Lavine (Institute of Statistics and Decision Science) and Dean Urban (Nicholas School of the Environment and Earth Sciences).
During Spring 2002 term, the group offered a course to graduate students on ecological data modeling and application to forecasting. More information.
Clark, J.S., M. Dietze, P. Agarwal, S. Chakraborty, I. Ibanez, S. LaDeau, and M. Wolosin. 2007. Resolving the biodiversity debate. Ecology Letters, 10: 647–662.
Clark. J.S. and A. E. Gelfand (eds). 2006. Hierarchical Modelling for the Environmental Sciences. Oxford University Press, Oxford, England.
Govindarajan, S. M. Dietze, P. Agarwal, and J.S. Clark. 2007. A scalable algorithm for dispersing populations. Journal of Intelligent Information Systems, DOI 10.1007/s10844-006-0030-z.
Clark, J.S. and A. E. Gelfand. 2006. A future for models and data in ecology. Trends in Ecology and Evolution, 21, 375-380.
Clark, J.S., G. Ferraz, N. Oguge, H. Hays, and J. DiCostanzo. 2005. Hierarchical Bayes for structured and variable populations: from capture-recapture data to life-history prediction. Ecology 86:2232-2244.
Govindarajan, S., M. Dietze, P. Agarwal, and J.S. Clark. 2004. A scalable model of forest dynamics. Proceedings of the 20th Symposium on Computational Geometry SCG, 106-115.
Clark, J.S. and O. Bjornstad. 2004. Population time series: Process variability, observation errors, missing values, lags, and hidden states. Ecology, 85:3140-3150.
Calder, K. M. Lavine, P. Mueller, and J.S. Clark. 2003. Incorporating multiple sources of stochasticity in population dynamic models. Ecology 84:1395-1402.