Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richnessoffers 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 detectionPresents models and methods for identifying unmarked individuals and speciesWritten in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analysesIncludes companion website containing data sets, code, solutions to exercises, and further information
Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition has been greatly expanded and detail is provided regarding the estimation methods and examples of their application are given. Important study design recommendations are also covered to give a well rounded view of modeling.Provides authoritative insights into the latest in occupancy modelingExamines the latest methods in analyzing detection/no detection data surveysAddresses critical issues of imperfect detectability and its effects on species occurrence estimationDiscusses important study design considerations such as defining sample units, sample size determination and optimal effort allocation
This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems.
The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures.
The authors apply principles of hierarchical modeling to ecological problems, including
* occurrence or occupancy models for estimating species distribution
* abundance models based on many sampling protocols, including distance sampling
* capture-recapture models with individual effects
* spatial capture-recapture models based on camera trapping and related methods
* population and metapopulation dynamic models
* models of biodiversity, community structure and dynamics
* Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants)
* Development of classical, likelihood-based procedures for inference, as well as
Bayesian methods of analysis
* Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS
* Computing support in technical appendices in an online companion web site