By Ruth King, Byron Morgan, Olivier Gimenez, Steve Brooks

Novel Statistical instruments for keeping and coping with Populations by means of collecting details on key demographic parameters, scientists can frequently are expecting how populations will increase sooner or later and relate those parameters to exterior impacts, similar to worldwide warming. due to their skill to simply include random results, healthy state-space versions, overview posterior version chances, and take care of lacking information, glossy Bayesian tools became very important during this zone of statistical inference and forecasting. Emphasising version selection and version averaging, Bayesian research for inhabitants Ecology offers updated equipment for analysing complicated ecological info. Leaders within the statistical ecology box, the authors practice the speculation to quite a lot of real case reports and illustrate the tools utilizing WinBUGS and R. the pc courses and entire info of the information units can be found at the book’s web site. the 1st a part of the booklet specializes in versions and their corresponding chance features. The authors learn classical tools of inference for estimating version parameters, together with maximum-likelihood estimates of parameters utilizing numerical optimisation algorithms. After construction this beginning, the authors improve the Bayesian strategy for becoming types to facts. additionally they examine Bayesian and standard methods to version becoming and inference. Exploring difficult difficulties in inhabitants ecology, this ebook exhibits how you can use the most recent Bayesian how you can examine info. It allows readers to use the tips on how to their very own issues of self belief.

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Additional resources for Bayesian Analysis for Population Ecology (Chapman & Hall CRC Interdisciplinary Statistics)

Sample text

This might be done in terms of comparing maximised likelihoods or appropriate information criteria, for example, and we shall see illustrations of this in Chapter 3. The first step therefore is to construct a likelihood, and we illustrate this for the simple homogeneous case, in which there is a constant probability of recapture p, which is assumed not to vary over animals, or time, or in any other fashion. When we have t sampling occasions, then if we assume independence between those occasions, the probability that any animal is caught j times is given by the simple binomial probability: t j p (1 − p)t−j .

An appealing way to combine information is through the Bayesian paradigm, discussed in Chapter 4. An example is provided by Thomas et al. (2005), who analyse data on the annual census of grey seal, Halichoerus grypus, pups in Scotland. 5. This is made easier by the publication of relevant data sets on the World Wide Web. 1 Same Animals, but Different Data As well as combining information from different studies, one may be able to perform integrated analyses of mark-recapture-recovery-resighting data measured on the same animals, and relevant work is given by Burnham (1993), Barker (1997) and Barker (1999).

Thus Var(N1,t ) = Na,t−1 ρφ © 2010 by Taylor and Francis Group, LLC MODELLING PRODUCTIVITY 35 and Var(Na,t ) = (N1,t−1 + Na,t−1 )φa (1 − φa ). We use binomial distributions when we want to represent survival or death, and Poisson distributions in order to represent birth. In both of these cases we assume that there is no overdispersion, though the model could be made more complex by the addition of overdispersion in either or both of these instances. We assume no sex effect on survival and that breeding starts at age 2.

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