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Ecology --- Biogeography. --- Species diversity --- R (Computer program language) --- Bayesian statistical decision theory. --- Multilevel models (Statistics) --- Mathematical models. --- Mathematical models --- Regression analysis --- Hierarchical linear models (Statistics) --- Mixed effects models (Statistics) --- Random coefficient models (Statistics) --- Variance component models (Statistics) --- Statistical decision --- Bayes' solution --- Bayesian analysis --- Biology --- Geography --- Areography (Biology) --- Geographical distribution of animals and plants --- Species --- Species distribution --- Domain-specific programming languages --- GNU-S (Computer program language) --- Biodiversity --- Diversity, Species --- Richness, Species --- Species richness --- Geographical distribution

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Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management. This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields. Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating imperfect detection Presents models and methods for identifying unmarked individuals and species Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses Includes companion website containing data sets, code, solutions to exercises, and further information.

Mathematical statistics --- General ecology and biosociology --- Biomathematics. --- Biogeography --- Biodiversity --- R (Computer program language) --- Mathematical models. --- BUGS (Information storage and retrieval system)

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"Space plays a vital role in virtually all ecological processes (Tilman and Kareiva, 1997; Hanski, 1999; Clobert et al., 2001). The spatial arrangement of habitat can influence movement patterns during dispersal, habitat selection, and survival. The distance between an organism and its competitors and prey can influence activity patterns and foraging behavior. Further, understanding distribution and spatial variation in abundance is necessary in the conservation and management of populations"--

Spatial ecology --- Spatial behavior in animals --- Animal populations --- Demography, Wildlife --- Populations, Animal --- Wildlife demography --- Wildlife populations --- Animal ecology --- Population biology --- Animal behavior --- Ecology --- Research. --- Mathematical models.

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General ecology and biosociology