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 consistently been a key aspect in ecological technological know-how, since it is inherently associated with severe procedures that scale from contributors to populations and groups to ecosystems. fast advancements in biotelemetry info assortment and processing expertise have given upward push to various statistical equipment for characterizing animal move. The ebook serves as a complete reference for the kinds of statistical versions used to check individual-based animal flow.

Animal Movement is a necessary reference for natural world biologists, quantitative ecologists, and statisticians who search a deeper knowing of contemporary animal circulation types. a wide selection of modeling ways are reconciled within the publication utilizing a constant notation. types are geared up into teams in accordance with how they deal with the underlying spatio-temporal technique of circulation. Connections between techniques are highlighted to permit the reader to shape a broader view of animal move research and its institutions with conventional spatial and temporal statistical modeling.

After an preliminary evaluate studying the function that animal flow performs in ecology, a primer on spatial and temporal statistics presents a great starting place for the rest of the e-book. each one next bankruptcy outlines a basic kind of statistical version used in the modern research of telemetry info for animal flow inference. Descriptions commence with easy conventional types and sequentially increase to normal periods of versions in every one class. vital heritage and technical information for every category of version are supplied, together with spatial element strategy types, discrete-time dynamic types, and continuous-time stochastic approach types. The publication additionally covers the basic parts for the way to house a number of assets of uncertainty, akin to situation mistakes and latent habit states. as well as thorough descriptions of animal stream types, 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 Telemetry Data

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S. state boundaries overlaid in black. Points represent measurement locations. 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.

B) Frequency histogram for the average maximum temperatures in degrees Fahrenheit. , the states of South Dakota, Nebraska, Kansas, Minnesota, Iowa, Missouri, Michigan, and Illinois). 1 MODELING AND PARAMETER ESTIMATION We begin by describing a model-based procedure for goal 1 first (inference) and then move to goal 2 (prediction). For example, suppose we wish to use the geographic position to explain temperature in the Midwestern United States. 6). , predictor variables) for temperature: (a) longitude and (b) latitude, shown as spatial maps (larger values shown in darker shade).

2000; Plummer 2003; Lindgren and Rue 2015; Carpenter et al. 2016). Furthermore, we emphasize that, even though MCMC has led to numerous breakthroughs in statistics and science, and has served as a catalyst for Bayesian methods and studies in general, new Bayesian computational approaches are regularly being developed. Depending 39 Statistics for Spatial Data on the desired inference and model, some alternative computational approaches have advantages over MCMC. However, as previously mentioned, few, if any, alternatives are as robust, intuitive, and as easy to implement as MCMC.

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