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Hierarchical Spatiotemporal Matrix Models for Characterizing Invasions
Author(s) -
Hooten Mevin B.,
Wikle Christopher K.,
Dorazio Robert M.,
Royle J. Andrew
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00725.x
Subject(s) - biological dispersal , ecology , invasive species , abundance (ecology) , population , fecundity , population ecology , introduced species , bayesian probability , population model , biology , computer science , artificial intelligence , demography , sociology
Summary 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.