Statistical models for the long-term monitoring of songbird populations: a Bayesian analysis of constant effort sites and ring-recovery data
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To underpin and improve advice given to government and other interested parties on the state of Britain’s common songbird populations, new models for analysing ecological data are developed in this thesis. These models use data from the British Trust for Ornithology’s Constant Effort Sites (CES) scheme, an annual bird-ringing programme in which catch effort is standardised. Data from the CES scheme are routinely used to index abundance and productivity, and to a lesser extent estimate adult survival rates. However, two features of the CES data that complicate analysis were previously inadequately addressed, namely the presence in the catch of “transient” birds not associated with the local population, and the sporadic failure in the constancy of effort assumption arising from the absence of within-year catch data. The current methodology is extended, with efficient Bayesian models developed for each of these demographic parameters that account for both of these data nuances, and from which reliable and usefully precise estimates are obtained. Of increasing interest is the relationship between abundance and the underlying vital rates, an understanding of which facilitates effective conservation. CES data are particularly amenable to an integrated approach to population modelling, providing a combination of demographic information from a single source. Such an integrated approach is developed here, employing Bayesian methodology and a simple population model to unite abundance, productivity and survival within a consistent framework. Independent data from ring-recoveries provide additional information on adult and juvenile survival rates. Specific advantages of this new integrated approach are identified, among which is the ability to determine juvenile survival accurately, disentangle the probabilities of survival and permanent emigration, and to obtain estimates of total seasonal productivity. The methodologies developed in this thesis are applied to CES data from Sedge Warbler, Acrocephalus schoenobaenus, and Reed Warbler, A. scirpaceus.
Thesis, PhD Doctor of Philosophy