Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS

By Marc Kéry, J. Andrew Royle. 2015

747 citations recorded by the source corpus.

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

Subfields: Ecological Modeling

View Paper: https://www.semanticscholar.org/paper/1bdd4555738be1a4ac356dbeb584f197118310e6

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In Ecological Modeling

Citations in the corpus

Human development and climate affect hibernation in a large carnivore with implications for human–carnivore conflicts (2018, methodology): “Additionally, we calculated the proportion of total variation explained by each full model (including random effects) and by the fixed effects portion of each model (Kéry & Royle, 2016).”
Data Integration for Large-Scale Models of Species Distributions. (2020, methodology): “Under the Poisson point process formulation, we consider that the underlying number of individuals follows a Poisson distribution and the observation model defines the probability of observing at least one individual via the complementary loglog link function [51,52].”
Metabarcoding From Microbes to Mammals: Comprehensive Bioassessment on a Global Scale (2020, methodology): “Currently, applied hierarchical modeling can be used to build occupancy models to address questions related to species’ detection and abundance (Kery and Royle, 2015).”
Hierarchical Species Distribution Models (2016, background): “We then discuss a unified spatio-temporal point process model for count, presence-absence, and presenceonly data that has been developed within a hierarchical framework in several studies [11, 23, 31, 62].”