Multiâstate models: metapopulation and life history analyses

Multi-state models are designed to describe populations that move among a fixed set of categorical states.The obvious application is to population interchange among geographic locations such as breeding sites or feeding areas (e.g., Hestbeck et al., 1991;Blums et al., 2003;Cam et al., 2004) but they are increasingly used to address important questions of evolutionary biology and life history strategies (Nichols & Kendall, 1995).In these applications, the states include life history stages such as breeding states.The multi-state models, by permitting estimation of stage-specific survival and transition rates, can help assess trade-offs between life history mechanisms (e.g.Yoccoz et al., 2000).These trade-offs are also important in metapopulation analyses where, for example, the pre-and post-breeding rates of transfer among subpopulations can be analysed in terms of target colony distance, density, and other covariates (e.g., Lebreton et al. 2003; Breton et al., in review).Further examples of the use of multi-state models in analysing dispersal and life-history trade-offs can be found in the session on Migration and Dispersal.In this session, we concentrate on applications that did not involve dispersal.These applications fall in two main categories: those that address life history questions using stage categories, and a more technical use of multi-state models to address problems arising from the violation of mark-recapture assumptions leading to the potential for seriously biased predictions or misleading insights from the models.Our plenary paper, by William Kendall (Kendall, 2004), gives an overview of the use of Multi-state Mark-Recapture (MSMR) models to address two such violations.The first is the occurrence of unobservable states that can arise, for example, from temporary emigration or by incomplete sampling coverage of a target population.Such states can also occur for life history reasons, such as dormancy or the inability to capture non-breeders and in these cases, the rates of transition to and from the unobservable state provide life history insights.The second failure Kendall considers is the misclassification of states (for example in models involving states for age, sex, breeding condition, etc. where these cannot be determined without error).He reviews solutions for these that encompass three approaches: constraints on parameters to ensure identifiability (the least desireable solution); incorporating additional information; and the use of subsampling that leads to the multi-state application of the Robust design.In passing, Kendall makes reference to what are probably the 3 most significant developments in the area of multi-state models since the last Euring meeting: (1) the incorporation of tag-recovery data in addition to recapture data in MSMR models;(2) a comprehensive methodology for goodness-of-fit testing and assessing parameter identifiability in MSMR models; and (3) the development of new software to make these methods accessible.Much of (2) and ( 3) is based on the landmark thesis of Olivier Gimenez (Gimenez, 2003).Two further presentations in this session followed up the plenary theme of unobservable and misclassified states.The presentation by Roger Pradel (Pradel, in press), represented in these proceedings as a brief abstract only, dealt with the problem of errors in sexing animals; an example of what Kendall refers to as bidirectional misclassification.The presentation by Marc Kry (Kry & Gregg, 2004) is, we think, the first

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