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In this article, we consider the U.K. Common Birds Census counts and their use in monitoring bird abundance. We use a state-space modeling approach within a Bayesian framework to describe population level trends over time and contribute to the alert system used by the British Trust for Ornithology. We account for potential overdispersion and excess zero counts by modeling the observation process with a zero-inflated negative binomial, while the system process is described by second-order polynomial growth models. In order to provide a biological motivation for the amount of smoothing applied to the observed series the system variance is related to the demographic characteristics of the species, so as to help the specification of its prior distribution. In particular, the available information on productivity and survival is used to formulate prior expectations on annual percentage changes in the population level and then used to constrain the variance of the system process. We discuss an example of how to interpret alternative choices for the degree of smoothing and how these relate to the classification of species, over time, into conservation lists.

A number of methods for joint inference from animal abundance and demographic data have been proposed in recent years, each with its own advantages. A new approach to analyzing panel survey and demographic data simultaneously is described. The approach fits population-dynamics models to the survey data, rather than to a single index of abundance derived from them and thus avoids disadvantages inherent in analyzing such an index. The methodology is developed and illustrated with British Lapwing data, and the results are compared with those obtained from existing approaches. The estimates of demographic parameters and population indices are similar for all methods. The results of a simulation study show that the new method performs well in terms of mean squared error.

In this paper we propose a strategy for analysing recovery data from birds ringed as nestlings. The approach advocated starts with a global model, involving calendar year dependence of both reporting and first-year survival rates, and age-dependence of survival rates for older birds. Likelihood ratio tests are then used to choose between a range of submodels. The strategy is illustrated through application to three data sets, on mallards, herring gulls, and blue-winged teal. The effect of age-dependence operating also on reporting rates is examined through matched simulations, since a model with age-dependent reporting rates cannot be fitted directly. This reveals an underestimation of the first-year survival rates, when the probability of recovery for first-year birds is greater than that for older birds. It is argued that this bias may not be serious and indeed may be allowed for in practice. For mallards and teal, comparisons are drawn with the results from other models that additionally analyse recoveries of birds ringed as adults; the same general conclusions are reached.