Animal Movement: Statistical Models for Telemetry Data by Mevin B. Hooten, Devin S. Johnson, Brett T. McClintock, Juan

By Mevin B. Hooten, Devin S. Johnson, Brett T. McClintock, Juan M. Morales

The examine of animal circulate has regularly been a key point in ecological technological know-how, since it is inherently associated with serious procedures that scale from participants to populations and groups to ecosystems. swift advancements in biotelemetry information assortment and processing expertise have given upward push to numerous statistical tools for characterizing animal move. The e-book serves as a entire reference for the kinds of statistical versions used to check individual-based animal circulate.

Animal Movement is an important reference for natural world biologists, quantitative ecologists, and statisticians who search a deeper knowing of recent animal circulate versions. a wide selection of modeling methods are reconciled within the booklet utilizing a constant notation. versions are equipped into teams in keeping with how they deal with the underlying spatio-temporal technique of circulate. Connections between methods are highlighted to permit the reader to shape a broader view of animal stream research and its institutions with conventional spatial and temporal statistical modeling.

After an preliminary review studying the function that animal circulation performs in ecology, a primer on spatial and temporal information presents a great starting place for the rest of the publication. each one next bankruptcy outlines a basic form of statistical version used in the modern research of telemetry information for animal flow inference. Descriptions commence with simple conventional types and sequentially building up to basic sessions of versions in each one type. very important historical past and technical info for every type of version are supplied, together with spatial aspect method types, discrete-time dynamic types, and continuous-time stochastic procedure types. The ebook additionally covers the fundamental parts for the way to deal with a number of assets of uncertainty, comparable to position blunders and latent habit states. as well as thorough descriptions of animal circulation types, adjustments and connections also are emphasised to supply a broader viewpoint of approaches.

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Extra resources for Animal Movement: Statistical Models for Telemetry Data

Example text

Taking various sample averages of the MCMC output) because the sampling-based method for approximating integrals, called Monte Carlo (MC) integration, has excellent properties. For example, one could approximate any integral using MC samples (independent and identically distributed) θ (k) ∼ [θ] for k = 1, . . 31) for some PDF [θ] and function of theta g(θ). Therefore, coupling MC integration with MCMC output from Bayesian model fitting yields an incredibly powerful tool for finding posterior quantities of nearly any function of model parameters.

Taking various sample averages of the MCMC output) because the sampling-based method for approximating integrals, called Monte Carlo (MC) integration, has excellent properties. For example, one could approximate any integral using MC samples (independent and identically distributed) θ (k) ∼ [θ] for k = 1, . . 31) for some PDF [θ] and function of theta g(θ). Therefore, coupling MC integration with MCMC output from Bayesian model fitting yields an incredibly powerful tool for finding posterior quantities of nearly any function of model parameters.

30 Animal Movement Statistically, we can model the observed CSP as we would any other response variable in a linear or generalized linear model setting. , y ∈ ). Then we have the linear model: y(si ) = x (si )β + η(si ), where η(si ) ∼ N(0, σ 2 ) for i = 1, . . , n. The assumption of normally distributed errors is not a necessity for the estimation of β and σ 2 but it implies a decidedly model-based statistical approach and allows us to generalize the model for other purposes; thus, we retain it here.

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