%0 Journal Article %1 hooten_hierarchical_2007 %A Hooten, Mevin B. %A Wikle, Christopher K. %D 2007 %J Environmental and Ecological Statistics %K Bayesian hierarchical inference, mixed model model, %N 1 %P 59--70 %R 10.1007/s10651-007-0040-1 %T A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove %U http://www.springerlink.com/index/10.1007/s10651-007-0040-1 %V 15 %X The spread of invasive species is a long studied subject that garners much interest in the ecological research community. Historically the phenomenon has been approached using a purely deterministic mathematical framework (usually involving differential equations of some form). These methods, while scientifically meaningful, are generally highly simplified and fail to account for uncertainty in the data and process, of which our knowledge could not possibly exist without error. We propose a hierarchical Bayesian model for population spread that accommodates data sources with errors, dependence structures between population dynamics parameters, and takes into account prior scientific understanding via non-linear relationships between model parameters and space-time response variables. We model the process (i.e., the bird population in this case) as a Poisson response with spatially varying diffusion coefficients as well as a logistic population growth term using a common reaction-diffusion equation that realistically mimics the ecological process. We focus the application on the ongoing invasion of the Eurasian Collared-Dove.

@article{hooten_hierarchical_2007, abstract = {The spread of invasive species is a long studied subject that garners much interest in the ecological research community. Historically the phenomenon has been approached using a purely deterministic mathematical framework (usually involving differential equations of some form). These methods, while scientifically meaningful, are generally highly simplified and fail to account for uncertainty in the data and process, of which our knowledge could not possibly exist without error. We propose a hierarchical Bayesian model for population spread that accommodates data sources with errors, dependence structures between population dynamics parameters, and takes into account prior scientific understanding via non-linear relationships between model parameters and space-time response variables. We model the process (i.e., the bird population in this case) as a Poisson response with spatially varying diffusion coefficients as well as a logistic population growth term using a common reaction-diffusion equation that realistically mimics the ecological process. We focus the application on the ongoing invasion of the Eurasian Collared-Dove.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Hooten, Mevin B. and Wikle, Christopher K.}, biburl = {https://www.bibsonomy.org/bibtex/2fd998cbcad6244054a37ae16f8c12d90/yourwelcome}, doi = {10.1007/s10651-007-0040-1}, interhash = {898d80166c72865765bfd43e4d821569}, intrahash = {fd998cbcad6244054a37ae16f8c12d90}, issn = {1352-8505, 1573-3009}, journal = {Environmental and Ecological Statistics}, keywords = {Bayesian hierarchical inference, mixed model model,}, month = sep, number = 1, pages = {59--70}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {A hierarchical {Bayesian} non-linear spatio-temporal model for the spread of invasive species with application to the {Eurasian} {Collared}-{Dove}}, url = {http://www.springerlink.com/index/10.1007/s10651-007-0040-1}, urldate = {2012-03-08}, volume = 15, year = 2007 }