Hierarchical Spatiotemporal Matrix Models for Characterizing Invasions

• Published in: Biometrics Vol. 63; no. 2; pp. 558 - 567
• Main Authors: Hooten, Mevin B., Wikle, Christopher K., Dorazio, Robert M., Royle, J. Andrew
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.06.2007; International Biometric Society; Blackwell Publishing Ltd
• Subjects: Animal dispersal; Animal populations; Animals; Bayes Theorem; Bayesian analysis; biocenosis; Biometrics; Biometry; Columbidae; Data models; Detection probability; Ecological invasion; Ecological modeling; ecologists; Ecology; Ecology - statistics & numerical data; Ecosystem; fecundity; Hierarchical Bayesian models; Invasive species; Modeling; Models, Statistical; Multilevel models; Nonnative species; Parametric models; Perceptual localization; plants (botany); Population Dynamics; Population ecology; Population Growth; Population models; Population size; Research methodology; Spatiotemporal dynamics; Species Specificity; statistics & numerical data; Time Factors; uncertainty; United States; United States; USA
• ISSN: 0006-341X; 1541-0420
• DOI: 10.1111/j.1541-0420.2006.00725.x

The growth and dispersal of biotic organisms is an important subject in ecology. Ecologists are able to accurately describe survival and fecundity in plant and animal populations and have developed quantitative approaches to study the dynamics of dispersal and population size. Of particular interest are the dynamics of invasive species. Such nonindigenous animals and plants can levy significant impacts on native biotic communities. Effective models for relative abundance have been developed; however, a better understanding of the dynamics of actual population size (as opposed to relative abundance) in an invasion would be beneficial to all branches of ecology. In this article, we adopt a hierarchical Bayesian framework for modeling the invasion of such species while addressing the discrete nature of the data and uncertainty associated with the probability of detection. The nonlinear dynamics between discrete time points are intuitively modeled through an embedded deterministic population model with density-dependent growth and dispersal components. Additionally, we illustrate the importance of accommodating spatially varying dispersal rates. The method is applied to the specific case of the Eurasian Collared-Dove, an invasive species at mid-invasion in the United States at the time of this writing.