We consider the case of age-specific ring-recovery data obtained only from recovered individual birds and modelled by conditioning a multinomial distribution on the recovery. These models may be appealing when the information about the numbers of marked individuals is missing, but they have previously been analyzed by ignoring a large set of nuisance parameters, the recovery probabilities. We investigate the consequences of this conditioning by relating the age-time specific structure of recovery probabilities to the estimation of survival.

For species that are still widespread, obtaining accurate and precise measures of population change inevitably means gathering representative sample data rather than undertaking a complete census. In the UK, a system of raising ‘alerts’ utilises stochastic models for such data to identify species in rapid (>50%) or moderate (25–50%) decline across various temporal and spatial scales. Considerable improvements in interpretation can be made by explaining annual fluctuations in terms of explicit population models (rather than trends of an arbitrary mathematical form); through the simultaneous modelling of data from a complete or partial census with those providing information on the demographic rates employed in these models; and through adopting a Bayesian rather than a frequentist statistical approach. A Bayesian approach is natural for quantifying, in the form of a probability, the support provided by the data for assigning a species to each of the categories. Based on territory mapping and ringing data for the lapwing Vanellus vanellus, we describe such an approach. Trends are estimated more precisely than those under models previously employed in the alerts context. Some smoothing is induced, but realistic responses to years of severe weather are retained, and these are expressed also via model-averaged trends in key demographic parameters. We discuss the conservation implications for this declining species, and the wider potential arising from the ability to quantify confidence that population change has exceeded a threshold either generating conservation concern or justifying a subsequent programme of action for recovery.

SummaryWe combine data from separate ring recovery and survey studies to provide indices of estimated abundance for the UK lapwing Vanellus vanellus population. Using a descriptive state space model, we demonstrate the observed decline in population size in relation to directly interpretable parameters describing the demographic characteristics of the population. The Bayesian approach readily provides information that is directly relevant to the conservation of this important bird species. The method proposed extends previous work in this area in several ways. Restrictive normality assumptions that have traditionally been imposed are removed, in addition to the assumption of constant measurement error by using information relating to the index variability across time to account fully for this source of uncertainty within the model. We also provide model-averaged inference to help to inform management policy and uses.

SummaryIn 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.