Conditional modelling of ring‐recovery data

McCrea, Rachel S.; Morgan, Byron J. T.; Brown, Diana L.; Robinson, Robert A.

doi:10.1111/j.2041-210x.2012.00226.x

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…As can be seen, the estimates for older ages for the model using the Weibull centering distribution are smoother, with narrower credible intervals and avoid boundaries, typically seen as a result of data sparseness at older ages, a result with further highlights the benefit of a BNP model centered on a parametric distribution. Finally, the posterior mean of the recovery probability is equal to 0.123 (95% PCI: 0.121–0.125), which is in line with findings of similar studies (McCrea et al., 2012). Further details regarding the MCMC runs are given in the Supporting Information.…”

Section: Case Studiessupporting

confidence: 90%

A General Modeling Framework for Open Wildlife Populations Based on the Polya Tree Prior

Diana¹,

Matechou²,

Griffin³

et al. 2022

Biometrics

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Wildlife monitoring for open populations can be performed using a number of different survey methods. Each survey method gives rise to a type of data and, in the last five decades, a large number of associated statistical models have been developed for analyzing these data. Although these models have been parameterized and fitted using different approaches, they have all been designed to either model the pattern with which individuals enter and/or exit the population, or to estimate the population size by accounting for the corresponding observation process, or both. However, existing approaches rely on a predefined model structure and complexity, either by assuming that parameters linked to the entry and exit pattern (EEP) are specific to sampling occasions, or by employing parametric curves to describe the EEP. Instead, we propose a novel Bayesian nonparametric framework for modeling EEPs based on the Polya tree (PT) prior for densities. Our Bayesian nonparametric approach avoids overfitting when inferring EEPs, while simultaneously allowing more flexibility than is possible using parametric curves. Finally, we introduce the replicate PT prior for defining classes of models for these data allowing us to impose constraints on the EEPs, when required. We demonstrate our new approach using capture-recapture, count, and ring-recovery data for two different case studies.

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Section: Case Studiessupporting

confidence: 90%

A General Modeling Framework for Open Wildlife Populations Based on the Polya Tree Prior

Diana¹,

Matechou²,

Griffin³

et al. 2022

Biometrics

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“…In the case of unknown ringing totals, the conditional model, which conditions on the numbers of recovered individuals only, can be considered as an alternative to the historical data model. The conditional probabilities for birds ringed in year i that are recovered in year t in both age classes are

P_{j, i, t}^{C} = \frac{P_{j, i, t}}{{false\sum}_{t = i}^{n_{2}} P_{j, i, t}} and P_{a, i, t}^{C} = \frac{P_{a, i, t}}{{false\sum}_{t = i}^{n_{2}} P_{a, i, t}}

(McCrea et al., ). The likelihood function for the conditional model for the fledged birds is

L_{C} = {false\prod}_{i = 1}^{n_{1}} {false\prod}_{t = i}^{n_{2}} {(P_{j, i, t}^{C})}^{N_{j, i, t}} {(P_{a, i, t}^{C})}^{N_{a, i, t}} .

…”

Section: Methodsmentioning

confidence: 99%

“…If annual total numbers are unknown, it is possible to use a model that is conditional on the number of birds recovered. The most commonly used model assumes a constant probability of reporting after death for all members of the cohort (Seber, 1971), but this can result in biased parameter estimates (McCrea, Morgan, Brown, & Robinson, 2012).…”

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confidence: 99%

Estimating age‐dependent survival from age‐aggregated ringing data—extending the use of historical records

Jiménez-Muñoz

Cole

Freeman

et al. 2019

Ecology and Evolution

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Bird ring‐recovery data have been widely used to estimate demographic parameters such as survival probabilities since the mid‐20th century. However, while the total number of birds ringed each year is usually known, historical information on age at ringing is often not available. A standard ring‐recovery model, for which information on age at ringing is required, cannot be used when historical data are incomplete. We develop a new model to estimate age‐dependent survival probabilities from such historical data when age at ringing is not recorded; we call this the historical data model. This new model provides an extension to the model of Robinson, 2010, Ibis, 152, 651–795 by estimating the proportion of the ringed birds marked as juveniles as an additional parameter. We conduct a simulation study to examine the performance of the historical data model and compare it with other models including the standard and conditional ring‐recovery models. Simulation studies show that the approach of Robinson, 2010, Ibis, 152, 651–795 can cause bias in parameter estimates. In contrast, the historical data model yields similar parameter estimates to the standard model. Parameter redundancy results show that the newly developed historical data model is comparable to the standard ring‐recovery model, in terms of which parameters can be estimated, and has fewer identifiability issues than the conditional model. We illustrate the new proposed model using Blackbird and Sandwich Tern data. The new historical data model allows us to make full use of historical data and estimate the same parameters as the standard model with incomplete data, and in doing so, detect potential changes in demographic parameters further back in time.

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