Mevin B. Hooten, Devin S. Johnson, Brett T. McClintock, Juan's Animal movement : statistical models for animal telemetry PDF

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

ISBN-10: 1466582146

ISBN-13: 9781466582149

The research of animal move has continually been a key aspect in ecological technological know-how, since it is inherently associated with severe strategies that scale from participants to populations and groups to ecosystems. swift advancements in biotelemetry information assortment and processing know-how have given upward push to various statistical tools for characterizing animal circulation. The publication serves as a complete reference for the kinds of statistical types used to review individual-based animal stream.

Animal Movement is a vital reference for natural world biologists, quantitative ecologists, and statisticians who search a deeper knowing of contemporary animal move versions. a wide selection of modeling ways are reconciled within the ebook utilizing a constant notation. types are prepared into teams according to how they deal with the underlying spatio-temporal means of stream. 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 evaluation interpreting the position that animal move performs in ecology, a primer on spatial and temporal information offers a high-quality origin for the rest of the publication. each one next bankruptcy outlines a primary kind of statistical version used in the modern research of telemetry information for animal stream inference. Descriptions commence with easy conventional types and sequentially building up to basic periods of versions in each one class. very important historical past and technical info for every type of version are supplied, together with spatial aspect technique versions, discrete-time dynamic versions, and continuous-time stochastic approach versions. The ebook additionally covers the basic components for the way to deal with a number of resources of uncertainty, comparable to position blunders and latent habit states. as well as thorough descriptions of animal move versions, variations and connections also are emphasised to supply a broader standpoint of approaches.

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Extra info for Animal movement : statistical models for animal telemetry data

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6 arises because it does not matter in which order the points are observed. Thus, the indices can be permuted n! different ways. 7 yields the classic form of the log likelihood for the Poisson SPP: n l(β) = log λ(si |β) − λ(s|β)ds. 8) S Typically, β controls the relationship between λ(s|β) and a vector of spatially referenced covariates, x(s). A commonly assumed relationship between the intensity and the covariates is λ(s|β) = exp(x (s)β) such that the regression coefficients β imply the strength of a relationship.

Data) are considered random variables until they are observed, for prediction, we seek the conditional distribution of unobserved response variables given those that were observed. * In the case of interpolation (prediction within the space of the data), we are often interested in obtaining the predictions yˆ u = E(yu |yo ). A tremendously useful feature of the Gaussian distribution is that it has analytically tractable marginal and conditional distributions that are also Gaussian. 24) where the o subscript is used to denote correspondence with the observed data set and u with the unobserved data set and the associated covariance and cross-covariance matrices are indicated by the ordering of their subscripts.

P(), P(), and π() are used in other literature. The [·] has become a Bayesian convention for probability distributions. E(y) Expectation of random variable y; an integral if y is continuous and sum if y is discrete. ∝ Proportional symbol. , only differs by a scalar multiplier). 4 STATISTICAL CONCEPTS We focus mostly on parametric statistical models* in this book; thus, we rely on both Bayesian and non-Bayesian models using maximum likelihood. Occasionally, for example, in Chapters 2 through 4, we present statistical methods that are nonparametric or involve implementation methods that do not involve Bayesian or maximum likelihood approaches.

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Animal movement : statistical models for animal telemetry data by Mevin B. Hooten, Devin S. Johnson, Brett T. McClintock, Juan M. Morales

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