Multisite and Statespace Models

  • George A. F. SeberEmail author
  • Matthew R. Schofield
Part of the Statistics for Biology and Health book series (SBH)


This is a large and complex chapter which, with recent computational developments and matrix methods, is becoming a fundamental model as it includes many of the previous models as special cases (e.g., the dead recovery and CJS models). A multisite model involves applying the capture–recapture method to a number of interconnecting geographical areas (strata or sites) where animals can move from one area to another. This topic has a history going back to 1956. By changing the word “site” to “state” we see that multisite models can now be considered as a special case of statespace models. For example, an individual could be in one of two states, breeding and nonbreeding, with the possibility of changing from one state to the other and back again. The basic statespace model is developed along with several modifications including when there are some unidentifiable species or sex, capture probabilities being dependent on the previous stratum, and then a Bayesian method along with an extension to multiple recaptures. The statespace model can also be incorporated into a dynamic model using stochastic processes and stochastic matrices under the title of integrated population models. Because of the large number of parameters involved with multistate models, we extend the model to include more types of data such as recaptures, resightings, and dead recoveries to increase the efficiency of parameter estimation and develop more hypotheses. Another extension includes recaptures, recoveries, and age data. A number of models are described involving some state uncertainty such as Pradel’s hidden Markov model, so-called memory models, models allowing for partially observable states, and an omnibus model combining the hidden Markov model with the robust model of Pollock described in a previous chapter. Multivariate extensions are also given, as well as considering heterogeneity of reporting, and estimating dispersal components. We see then that statespace models are very versatile.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of AucklandAucklandNew Zealand
  2. 2.Department of Mathematics and StatisticsUniversity of OtagoDunedinNew Zealand

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