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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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Figure 1.1Images of CES mist-netting†, a) disentangling a Bullfinch from a mist-net,b) fitting an individually identifying aluminium leg ring on a Greenfinch, c) mist-net on areedbed habitat, d) mist-net on a dry scrub habitat.
Figure 1.2a) Sedge Warblerscirpaceus Acrocephalus schoenobaenus †, b) Reed Warbler Acrocephalus
Table 1.4Typical clutch size, number of broods, egg incubation period, fledging period andtiming of first clutch for Sedge and Reed Warbler
Figure 2.1Classical maximum likelihood estimates and the 95% confidence intervals, de-noted by thin lines (left), and Bayesian posterior means and the 95% symmetric credibleintervals, denoted thick lines (right), of yat and yjt from Sedge Warbler (SW) and R
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CES Data Sedge and Reed Warblers The Metropolis-Hastings Algorithm Advantages of using CES Data to Monitor Productivity Models Comparing the Alternative Survival Models Chapter Summary Integrated Model with Ring-Recovery Data Including CES Live-Recapture Data Analysis of Reed Warbler Data

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