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

Abstract

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.

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

“…The overall trend agrees with the general pattern observed for bird populations, with survival being lower in very young and older ages. A similar pattern was observed by McCrea et al (2013) when analyzing RR data for mallards (Anas platyrhynchos) and by Jimenez-Muñoz et al (2019) for blackbirds (Turdus merula), but we note here that we have not employed a parametric curve to enforce this pattern, and instead it has been completely driven by the data. We also show the results obtained using the unconstrained approach described above.…”

“…(2013) when analyzing RR data for mallards ( Anas platyrhynchos ) and by Jimenez‐Muñoz et al. (2019) for blackbirds ( Turdus merula ), but we note here that we have not employed a parametric curve to enforce this pattern, and instead it has been completely driven by the data. We also show the results obtained using the unconstrained approach described above.…”

“…wileyonlinelibrary.com/journal/biom 2171 parameters related to EEPs are completely unconstrained, requiring two parameters to be estimated for each sampling occasion, one for entry and one for exit (Lyons et al, 2016;Pledger et al, 2009), or use parametric curves to constrain entry (Matechou et al, 2014) and exit (Jimenez-Muñoz et al, 2019;McCrea et al, 2013) patterns. However, in the former case, the model can be overparameterized and difficult to interpret, whereas in the latter case, the degree of smoothness is predefined and hence the model may not be flexible enough.…”

“…Although these models are developed for data collected using different sampling protocols, they all focus on the estimation of the EEPs of populations, where entry can correspond to arrival/birth, exit to departure/death, and corresponding length of stay (LOS) to retention/survival at the site. These existing methods either assume that parameters related to EEPs are completely unconstrained, requiring two parameters to be estimated for each sampling occasion, one for entry and one for exit (Lyons et al., 2016; Pledger et al., 2009), or use parametric curves to constrain entry (Matechou et al., 2014) and exit (Jimenez‐Muñoz et al., 2019; McCrea et al., 2013) patterns. However, in the former case, the model can be overparameterized and difficult to interpret, whereas in the latter case, the degree of smoothness is predefined and hence the model may not be flexible enough.…”

Advances in modelling demographic processes: The Euring 2017 Analytical Meeting

“…Jiménez‐Muñoz et al. () discuss how to estimate survival when age‐specific cohort ringing/banding totals are not available; a situation common to many national ringing schemes, where data historically have been submitted on paper schedules. Senar et al.…”