JABBA: Just Another Bayesian Biomass Assessment

Winker, Henning; Carvalho, Felipe; Kapur, Maia

doi:10.1016/j.fishres.2018.03.010

Cited by 97 publications

(133 citation statements)

References 43 publications

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“…We used a noninformative normal prior for

\bar{r} \sim Normal false(0, 1000 false)

. Priors for the process error variance were

σ^{2} \sim 1 / gamma false(0.001, 0.001 false)

, or approximately uniform in log space (Winker et al., ).…”

Section: Methodsmentioning

confidence: 99%

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Estimating IUCN Red List population reduction: JARA—A decision‐support tool applied to pelagic sharks

Sherley

Winker

Rigby

et al. 2019

CONSERVATION LETTERS

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The International Union for Conservation of Nature's (IUCN) Red List is the global standard for quantifying extinction risk but assessing population reduction (criterion A) of wide-ranging, long-lived marine taxa remains difficult and controversial. We show how Bayesian state-space models (BSSM), coupled with expert knowledge at IUCN Red List workshops, can combine regional abundance data into indices of global population change. To illustrate our approach, we provide examples of the process to assess four circumglobal sharks with differing temporal and spatial datadeficiency: Blue Shark (Prionace glauca), Shortfin Mako (Isurus oxyrinchus), Dusky Shark (Carcharhinus obscurus), and Great Hammerhead (Sphyrna mokarran). For each species, the BSSM provided global population change estimates over three generation lengths bounded by uncertainty levels in intuitive outputs, enabling informed decisions on the status of each species. Integrating similar analyses into future workshops would help conservation practitioners ensure robust, consistent, and transparent Red List assessments for other long-lived, wide-ranging species. K E Y W O R D SBayesian state-space model, biodiversity conservation, demography, elasmobranch, extinction risk, population decline, threatened speciesThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…We used a noninformative normal prior for

\bar{r} \sim Normal false(0, 1000 false)

. Priors for the process error variance were

σ^{2} \sim 1 / gamma false(0.001, 0.001 false)

, or approximately uniform in log space (Winker et al., ).…”

Section: Methodsmentioning

confidence: 99%

“…Bayesian state–space models (BSSM) offer a powerful and flexible framework to model variable population trends (e.g., Boyd, DeMaster, Waples, Ward, & Taylor, ; Meyer & Millar, ; Winker, Carvalho, & Kapur, ). BSSMs also have three key properties that could help improve the objectivity of IUCN Red List assessments.…”

Section: Introductionmentioning

confidence: 99%

Estimating IUCN Red List population reduction: JARA—A decision‐support tool applied to pelagic sharks

Sherley

Winker

Rigby

et al. 2019

CONSERVATION LETTERS

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“…The model notation for relative abundance indices builds on the Bayesian state-space tool for averaging relative abundance indices by Winker et al (2018). This is based on the assumption that the mean underlying abundance trend is an unobservable state variable.…”

Section: State-space Model Formulationmentioning

confidence: 99%

“…The user definable options for the observation variance follow Winker et al (2018) with abundance indices and also informs the process variance as a portion of total variance is assigned a priori to observation variance (Winker et al 2018). .…”

Section: State-space Model Formulationmentioning

confidence: 99%

“…Winker et al 2018) and includes: (1) colour-coded lognormal residuals of observed versus predicted abundance indices i, (2) boxplots indicating the median and quantiles of all residuals available for any given year; the area of each box indicates the strength of the discrepancy between the abundance indices (larger box means higher degree of conflicting information), and (3) a loess smoother through all residuals which highlights systematically auto-correlated residual patterns (Figure 2, lower panels). In addition, the Root-Mean-Squared-Error (RMSE) and the deviance information criterion (DIC) are provided for the comparison of goodness-of-fit and model selection purposes respectively.…”

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confidence: 99%

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JARA: ‘Just Another Red-List Assessment’

Winker

Pacoureau

Sherley

2019

Preprint

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Identifying species at risk of extinction is necessary to prioritise conservation efforts. TheInternational Union for Conservation of Nature's (IUCN) Red List of Threatened Species is the global standard for quantifying extinction risk, with many species categorised on the basis of a reduction in population size (Criterion A). We introduce the Bayesian state-space framework 'JARA' (Just Another Red-List Assessment). Designed as an easy to use, rapid and widely applicable decision-support tool, JARA allows both process error and uncertainty to be incorporated into IUCN Red List assessments under criterion A. A key output of JARA is an easy to interpret graphic in which the probability distribution of the population decline is displayed against the IUCN Red List categories, where each category is assigned a probability given process and observation uncertainty. We provide fully commented R code on the global open-source platform GitHub (https://github.com/henning-winker/JARA), so that JARA can be modified and applied by conservation practitioners to their own count or relative abundance data. We illustrate the application of JARA using two real-world examples: (i) relative abundance indices from scientific trawl surveys for two elasmobranchs, Yellowspotted Skate Leucoraja wallacei and Whitespot smoothhound Mustelus palumbes; and (2) absolute abundance data (census counts) from the global Cape Gannet Morus capensis population.

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