CDI-Type II: Integrating Algorithmic and Stochastic Modeling Techniques for Environmental Prediction
- Jim Clark, Nicholas School of the Environment, Duke University
- Alan Gelfand, Department of Statistical Science, Duke University
- Pankaj Agarwal, Department of Computer Science, Duke University
- Chris Woodall
- Souparno Ghosh, Department of Statistical Science, Duke University
- Thomas Mølhave, Department of Computer Science, Duke University
- Joao Montiero, Department of Statistical Science, Duke University
- Maria Terres, Department of Statistical Science, Duke University
- Kai Zhu, Nicholas School of the Environment, Duke University
Funding: National Science Foundation
Predicting biodiversity, i.e., abundance of species, in response to climate change is a goal of environmental change research. Despite recent valuable advances in understanding biodiversity and climate, the current grasp is limited. There are two widely recognized obstacles: first, because of the complexity of the underlying processes, the existing models intended for understanding and prediction are not (computationally) scalable. Second, the coarse-scale environment models fail to capture interactions among species, which control biodiversity, and the models based on fine-scale, short-term observations are unable to make long-term predictions. This research develops a prediction framework that coherently combines broad-scale pattern data with fine-scale data on species interactions and that is computationally scalable. It focuses on prediction at the geographic scale and in using geographic-scale data to better understanding at the scales where species interactions occur.
This research develops a multiscale modeling framework and design algorithms that make environmental models computationally scalable. The approach hinges upon strong interplay of algorithmic and statistical techniques. Statistical inference brings stochastic modeling sophistication in space and time, yielding improved characterization of the process and the possibility of full inference. Sophisticated algorithms makes models and processes scalable and provide trade-offs between accuracy and efficiency. The project team is composed of researchers from computer science, statistics, and environmental science, who have made significant contributions to these and related problems and who have collaborated extensively in the past.
- Clark, J.S., A.E. Gelfand, C.W. Woodall, and K. Zhu. 2013. More than the sum of the parts: forest climate response from joint species distribution models, Ecological Applications, in press.
- Clark, J.S., D.M. Bell, M.C. Kwit, and K. Zhu. 2013. Competition-interaction landscapes for the joint response of forests to climate change. Global Change Biology, in press.
- Zhu, K, C. W. Woodall, S. Ghosh, A. E. Gelfand, and J. S. Clark. 2013. Dual impacts of climate change: forest migration and turnover through life history. Global Change Biology, in press.
- Clark, J.S., D. M Bell, M. Kwit, A. Powell, And K. Zhu. 2013. Dynamic inverse prediction and sensitivity analysis with high-dimensional responses: application to climate-change vulnerability of biodiversity. Journal of Biological, Environmental, and Agricultural Statistics, 18:376-404.
- Gelfand, A.E., S. Ghosh and J. S. Clark. 2013. Scaling integral projection models for analyzing size demography. Statistical Science, in press.
- Zhu, K., C.W. Woodall, and J.S. Clark. 2012. Failure to migrate: lack of tree range expansion in response to climate change. Global Change Biology, 18, 1042-1052.
- Ghosh, S., A.E. Gelfand, K. Zhu, and J.S. Clark. 2011. The k-ZIG: flexible modeling for zero-inflated counts. Biometrics, 68, 878-885.
- Ghosh, S., A. E. Gelfand, and J. S. Clark. 2011. Inference for size demography from point pattern data using integral projection models. Journal of Agricultural, Biological and Environmental Statistics, in press.
- Agarwal, P., T. Mohave, H. Yu, and J. S. Clark. 2011. Exploiting temporal coherence in forest dynamics simulation. SCG’11 Symposium on Computational Geometry, June 13–15, 2011, Paris, France.
- Clark, J.S., P. Agarwal, D.M. Bell , P. Flikkema , A. E. Gelfand, X. Nguyen , E. Ward, and J. Yang. 2011. Inferential ecosystem models, from network data to prediction. Ecological Applications, 21,1523–1536.