Statistical Inference from Band Recovery Data: A Handbook

Hailman, Jack P.; Brownie, Cavell; Anderson, David R.; Burnham, K. P.; Robson, D. S.

doi:10.2307/4512398

Cited by 10 publications

(31 citation statements)

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“…‘indirect’ recoveries). We then defined the probabilities of being recovered in each year as a function of survival probability ( S ) and band‐recovery probability ( f ) for juvenile (Ψ) and adult (Ω) female mallards following Brownie (1978). The cell probabilities were therefore,

\begin{matrix} left \begin{matrix} true \\ left ψ_{i, j} = \{\begin{matrix} left 0, & left i < j \\ left f_{jv, i}, & left i = j \\ left S_{jv, i} f_{ad, j}, & left i + 1 = j \\ left S_{jv, i} (\prod_{k = i + 1}^{k = j - 1} S_{ad, k}) f_{ad, j}, & left i + 1 < j < T + 1 \\ left 1 - \sum_{k = 1}^{k = T} ψ_{i, k}, & left j = T + 1 \end{matrix}, \\ left ω_{i, j} = \{\begin{matrix} left 0, & left i < j \\ left f_{ad, i}, & left i = j \\ left \prod_{k = i}^{k = j - 1} S_{ad, k} (f_{ad, normalj}), & left i < j < T + 1 \\ left 1 - \sum_{k = 1}^{k = T} ω_{i,} \end{matrix} \end{matrix} \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography

Riecke

Sedinger

Arnold

et al. 2022

Journal of Animal Ecology

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The management of sustainable harvest of animal populations is of great ecological and conservation importance. Development of formal quantitative tools to estimate and mitigate the impacts of harvest on animal populations has positively impacted conservation efforts. The vast majority of existing harvest models, however, do not simultaneously estimate ecological and harvest impacts on demographic parameters and population trends. Given that the impacts of ecological drivers are often equal to or greater than the effects of harvest, and can covary with harvest, this disconnect has the potential to lead to flawed inference. In this study, we used Bayesian hierarchical models and a 43‐year capture–mark–recovery dataset from 404,241 female mallards Anas platyrhynchos released in the North American midcontinent to estimate mallard demographic parameters. Furthermore, we model the dynamics of waterfowl hunters and habitat, and the direct and indirect effects of anthropogenic and ecological processes on mallard demographic parameters. We demonstrate that density dependence, habitat conditions and harvest can simultaneously impact demographic parameters of female mallards, and discuss implications for existing and future harvest management models. Our results demonstrate the importance of controlling for multicollinearity among demographic drivers in harvest management models, and provide evidence for multiple mechanisms that lead to partial compensation of mallard harvest. We provide a novel model structure to assess these relationships that may allow for improved inference and prediction in future iterations of harvest management models across taxa.

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\begin{matrix} left \begin{matrix} true \\ left ψ_{i, j} = \{\begin{matrix} left 0, & left i < j \\ left f_{jv, i}, & left i = j \\ left S_{jv, i} f_{ad, j}, & left i + 1 = j \\ left S_{jv, i} (\prod_{k = i + 1}^{k = j - 1} S_{ad, k}) f_{ad, j}, & left i + 1 < j < T + 1 \\ left 1 - \sum_{k = 1}^{k = T} ψ_{i, k}, & left j = T + 1 \end{matrix}, \\ left ω_{i, j} = \{\begin{matrix} left 0, & left i < j \\ left f_{ad, i}, & left i = j \\ left \prod_{k = i}^{k = j - 1} S_{ad, k} (f_{ad, normalj}), & left i < j < T + 1 \\ left 1 - \sum_{k = 1}^{k = T} ω_{i,} \end{matrix} \end{matrix} \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography

Riecke

Sedinger

Arnold

et al. 2022

Journal of Animal Ecology

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“…A common notation for this model structure would be S age,year f age,year ; we exclude “sex” from the notation as we analyzed the female and male data separately. With this model formulation, the recovery parameter ( f ) is the joint probability of being shot, retrieved, and reported (Brownie et al, 1985). We note the models specified by Kéry and Schaub (pp.…”

Section: Methodsmentioning

confidence: 99%

“…We parameterized the multinomial formulation of the we exclude "sex" from the notation as we analyzed the female and male data separately. With this model formulation, the recovery parameter (f) is the joint probability of being shot, retrieved, and reported (Brownie et al, 1985). We note the models specified by implement the Seber r parameterization in place of the Brownie f parameterization, which is a difference regarding the estimation of recovery probability (Cooch et al, 2014 pp.…”

Section: Correlation Modelsmentioning

confidence: 99%

“…For our case study, we use banding and recovery data from the midcontinent mallard ( Anas platyrhychos ) population, one of the largest tag‐recovery data sets in the world, for the years 1961–1996. We focus on tag‐recovery data and the widely implemented model of Brownie et al (1985) given its frequent application to bird and fish populations experiencing harvest or other documentable forms of anthropogenic mortality. Specifically, we evaluate model performance when choosing Wishart or Uniform priors for multivariate normal distributions (Riecke et al, 2019), along with Gamma priors like those implemented in Program MARK for multivariate normal distributions (White & Burnham, 1999).…”

Section: Introductionmentioning

confidence: 99%

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Prior choice and data requirements of Bayesian multivariate hierarchical models fit to tag‐recovery data: The need for power analyses

Deane

Carlson

Cunningham

et al. 2023

Ecology and Evolution

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Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with hierarchical Bayesian multivariate models fit to tag-recovery data. In these applications, increasingly negative correlation between survival and recovery has been interpreted as evidence for increasingly additive harvest mortality. The power of these hierarchal models to detect nonzero correlations has rarely been evaluated, and these few studies have not focused on tag-recovery data, which is a common data type. We assessed the power of multivariate hierarchical models to detect negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit hierarchical effects models to a mallard (Anas platyrhychos) tag-recovery data set and to simulated data with sample sizes corresponding to different levels of monitoring intensity. We also demonstrate more robust summary statistics for tagrecovery data sets than total individuals tagged. Different priors led to substantially different estimates of correlation from the mallard data. Our power analysis of simulated data indicated most prior distribution and sample size combinations could not estimate strongly negative correlation with useful precision or accuracy. Many correlation estimates spanned the available parameter space (−1,1) and underestimated the magnitude of negative correlation. Only one prior combined with our most intensive monitoring scenario provided reliable results. Underestimating the magnitude of correlation coincided with overestimating the variability of annual survival, but not annual recovery. The inadequacy of prior distributions and sample size combinations previously assumed adequate for obtaining robust inference from tag-recovery data represents a concern in the application of Bayesian hierarchical models to tagrecovery data. Our analysis approach provides a means for examining prior influence and sample size on hierarchical models fit to capture-recapture data while emphasizing transferability of results between empirical and simulation studies.

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“…We collated recoveries of birds found dead in Britain during successive annual periods between 1 May and 31 August, separately for the two age-classes (n = 69 for birds ringed as first-years, 546 for adults). We use dead recovery models (Brownie 1978, Williams et al 2002 rather than multistate models (which require (live) recapture or resight data), and modelled survival as annual deviations from a long-term mean, and annual recovery probability as a 5-year running mean (truncated for the first 2 years and last 2 years). Annual survival was estimated separately for birds marked as firstyears and adults, but recovery probability was shared between the two age classes.…”

Section: Trends In Annual Survivalmentioning

confidence: 99%

Demography of Common Swifts (Apus apus) breeding in the UK associated with local weather but not aphid biomass

Finch

Bell

Robinson

et al. 2022

Ibis

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In common with many other insectivorous birds, the Common Swift Apus apus is undergoing population declines, with an estimated 57% reduction in abundance between 1995 and 2017 in the UK. The ultimate drivers of this decline are poorly understood but links have been drawn to reductions in the abundance and availability of insect prey, as well as loss of nesting sites. The aim of this study is to improve understanding of the demographic and environmental drivers of Swift decline in Great Britain, with a particular focus on weather and aphid biomass. We explore spatio‐temporal trends in the biomass of aphids, an important source of food for these aerial feeders, from a network of 17 suction‐traps, each 12.2 m in length. We then use data from national nest recording and ringing schemes to describe temporal trends in, and covariates of, Swift breeding success and annual survival. Demographic data are sparse prior to the mid‐1990s but show stable adult survival, a reduction in first‐year survival and an increase in nest failure rate since the mid‐1970s. Despite marked declines in aphid biomass across much of southern and eastern England during this period, we find no association between aphid biomass and Swift demography. Weather was a stronger correlate of variation in Swift demography, with increased precipitation associated with smaller brood size, higher nest failure rate and lower first‐year survival. Our data suggest that falling first‐year survival, partly linked to wetter summers, may be the most likely demographic driver of population decline. Given that the only convincing correlates of Swift demography were weather‐related, we suggest it would be precautionary for conservation efforts to continue to focus on ensuring that safe and productive nesting sites are in sufficient supply. Further research to identify habitats and land management practices which provide foraging resources, particularly during periods of inclement weather, is required, and future demographic modelling exercises would benefit from increased nest recording and ringing efforts.

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Statistical Inference from Band Recovery Data: A Handbook

Cited by 10 publications

References 0 publications

A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography

A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography

Prior choice and data requirements of Bayesian multivariate hierarchical models fit to tag‐recovery data: The need for power analyses

Demography of Common Swifts (Apus apus) breeding in the UK associated with local weather but not aphid biomass

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