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Statistical thinking in wildlife biology and ecology has been profoundly influenced by the introduction of AIC (Akaike's information criterion) as a tool for model selection and as a basis for model averaging. In this paper, we advocate the Bayesian paradigm as a broader framework for multimodel inference, one in which model averaging and model selection(More)
We present a hierarchical extension of the Cormack-Jolly-Seber (CJS) model for open population capture-recapture data. In addition to recaptures of marked animals, we model first captures of animals and losses on capture. The parameter set includes capture probabilities, survival rates, and birth rates. The survival rates and birth rates are treated as a(More)
In a mark-recapture study of an animal population, live-recapture information may be supplemented by resightings from marked animals obtained throughout the period of the study and the two types of data analyzed simultaneously. The resighting data can only contribute to estimates of survival probability if they are jointly modeled with the live-recapture(More)
Sampling DNA noninvasively has advantages for identifying animals for uses such as mark-recapture modeling that require unique identification of animals in samples. Although it is possible to generate large amounts of data from noninvasive sources of DNA, a challenge is overcoming genotyping errors that can lead to incorrect identification of individuals. A(More)
Quantifying long-term historical climate is fundamental to understanding recent climate change. Most instrumentally recorded climate data are only available for the past 200 years, so proxy observations from natural archives are often considered. We describe a model-based approach to reconstructing climate defined in terms of raw tree-ring measurement data(More)
We outline the use of hierarchical modeling for inference about the categorization of subjects into "responder" and "nonresponder" classes when the true status of the subject is latent (hidden). If uncertainty of classification is ignored during analysis, then statistical inference may be unreliable. An important advantage of hierarchical modeling is that(More)
Hierarchical mark-recapture models offer three advantages over classical mark-recapture models: (i) they allow expression of complicated models in terms of simple components; (ii) they provide a convenient way of modeling missing data and latent variables in a way that allows expression of relationships involving latent variables in the model; (iii) they(More)
Recently developed methods for analyzing mark–recapture data allow simultaneous modeling of information from a variety of animal encounters. Incor-poratingthis extra information in an analysis requires use of a more complicated model but can lead to improved precision of estimates. An important aspect of such joint models is that movement of animals to and(More)
Many organisms are patchily distributed, with some patches occupied at high density, others at lower densities, and others not occupied. Estimation of overall abundance can be difficult and is inefficient via intensive approaches such as capture-mark-recapture (CMR) or distance sampling. We propose a two-phase sampling scheme and model in a Bayesian(More)