Animal movement : statistical models for animal telemetry 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 research of animal move has constantly been a key aspect in ecological technological know-how, since it is inherently associated with severe tactics that scale from members to populations and groups to ecosystems. speedy advancements in biotelemetry info assortment and processing expertise have given upward push to numerous statistical tools for characterizing animal circulation. The booklet serves as a complete reference for the categories of statistical types used to review individual-based animal flow.

Animal Movement is an important reference for natural world biologists, quantitative ecologists, and statisticians who search a deeper realizing of contemporary animal circulate versions. a large choice of modeling techniques are reconciled within the booklet utilizing a constant notation. types are equipped into teams in response to how they deal with the underlying spatio-temporal strategy of circulation. Connections between techniques are highlighted to permit the reader to shape a broader view of animal circulation research and its institutions with conventional spatial and temporal statistical modeling.

After an preliminary evaluate reading the function that animal move performs in ecology, a primer on spatial and temporal information presents a high-quality origin for the rest of the ebook. every one next bankruptcy outlines a basic kind of statistical version used in the modern research of telemetry info for animal move inference. Descriptions start with easy conventional varieties and sequentially increase to common periods of types in every one class. very important history and technical information for every type of version are supplied, together with spatial element technique versions, discrete-time dynamic versions, and continuous-time stochastic strategy versions. The ebook additionally covers the basic parts for a way to deal with a number of assets of uncertainty, resembling situation mistakes and latent habit states. as well as thorough descriptions of animal stream types, transformations and connections also are emphasised to supply a broader standpoint of approaches.

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Example text

N. μi is a 2 × 1 vector unless otherwise stated. Also written as: μ(ti ) in continuous-time context. , μ(t) ∈ M). , M ⊂ S ). X A “design” matrix of covariates, which will often be decomposed into rows xi for row i, depending on the context in which it is used. , β ≡ (β1 , β2 , . . , βp ) ), where p is the number of columns in X. , converts a row vector to a column). σ2 Variance component associated with the observed telemetry data, true position process, or a model parameter. Covariance matrix for either a parameter vector such as β (if subscripted) or the data or process models.

There are three other useful classes of point process models for analyzing animal telemetry data that we briefly mention here and expand upon in Chapter 4 where they are directly discussed in reference to telemetry data models. The first class of models is log Gaussian Cox process (LGCP) model. 14) 28 Animal Movement where η(s) is a random spatial process as described in the following sections. , a generalized linear mixed model [GLMM]) or, alternatively, a Poisson generalized additive model (GAM), where η(s) is a spatial spline or basis function.

X A “design” matrix of covariates, which will often be decomposed into rows xi for row i, depending on the context in which it is used. , β ≡ (β1 , β2 , . . , βp ) ), where p is the number of columns in X. , converts a row vector to a column). σ2 Variance component associated with the observed telemetry data, true position process, or a model parameter. Covariance matrix for either a parameter vector such as β (if subscripted) or the data or process models. f (·), [·] Probability density or mass function.

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