Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model Export

@article{citeulike:3728465, abstract = {Accomodation of important sources of uncertainty in ecological models is essential to realistically predicting ecological processes. The purpose of this project is to develop a robust methodology for modeling natural processes on a landscape while accounting for the variability in a process by utilizing environmental and spatial random effects. A hierarchical Bayesian framework has allowed the simultaneous integration of these effects. This framework naturally assumes variables to be random and the posterior distribution of the model provides probabilistic information about the process. Two species in the genus Desmodium were used as examples to illustrate the utility of the model in Southeast Missouri, USA. In addition, two validation techniques were applied to evaluate the qualitative and quantitative characteristics of the predictions.}, author = {Hooten, Mevin B. and Larsen, David R. and Wikle, Christopher K.}, citeulike-article-id = {3728465}, citeulike-linkout-0 = {http://dx.doi.org/10.1023/A:1026001008598}, citeulike-linkout-1 = {http://www.springerlink.com/content/j5t71t59w68458p5}, day = {1}, doi = {10.1023/A:1026001008598}, journal = {Landscape Ecology}, keywords = {bayesian-stats, multilevel-modeling}, month = {July}, number = {5}, pages = {487--502}, posted-at = {2008-11-30 03:45:36}, priority = {2}, title = {Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model}, url = {http://dx.doi.org/10.1023/A:1026001008598}, volume = {18}, year = {2003} }

TY - JOUR ID - citeulike:3728465 L3 - citeulike-article-id:3728465 N2 - Accomodation of important sources of uncertainty in ecological models is essential to realistically predicting ecological processes. The purpose of this project is to develop a robust methodology for modeling natural processes on a landscape while accounting for the variability in a process by utilizing environmental and spatial random effects. A hierarchical Bayesian framework has allowed the simultaneous integration of these effects. This framework naturally assumes variables to be random and the posterior distribution of the model provides probabilistic information about the process. Two species in the genus Desmodium were used as examples to illustrate the utility of the model in Southeast Missouri, USA. In addition, two validation techniques were applied to evaluate the qualitative and quantitative characteristics of the predictions. IS - 5 JF - Landscape Ecology EP - 502 TI - Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model VL - 18 SP - 487 KW - bayesian-stats KW - multilevel-modeling AU - Hooten, Mevin AU - Larsen, David AU - Wikle, Christopher PY - 2003/07/01/ UR - http://dx.doi.org/10.1023/A:1026001008598 ER -