Increasing biological realism of fisheries stock assessment: towards hierarchical Bayesian methods

Kuparinen, Anna; Mäntyniemi, Samu; Hutchings, Jeffrey A.; Kuikka, Sakari

doi:10.1139/a2012-006

Cited by 45 publications

(37 citation statements)

References 163 publications

(228 reference statements)

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“…However, employment of methods of the sophistication of those employed in fisheries and forestry harvesting analyses (Kuparinen et al . ; Yousefpour et al . ) requires both reliable and detailed information on the abundance and demographic structure of species and their potential biological responses to processes impacted by global climate change and habitat transformations.…”

Section: Future Research Directionsmentioning

confidence: 99%

Searching for sustainability: are assessments of wildlife harvests behind the times?

et al. 2012

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The unsustainable harvest of wildlife is a major threat to global biodiversity and to the millions of people who depend on wildlife for food and income. Past research has called attention to the fact that commonly used methods to evaluate the sustainability of wildlife hunting perform poorly, yet these methods remain in popular use today. Here we conduct a systematic review of empirical sustainability assessments to quantify the use of sustainability indicators in the scientific literature and highlight associations between analytical methods and their outcomes. We find that indicator type, continent of study, species body mass, taxonomic group, and socioeconomic status of study site are important predictors of the probability of reported sustainability. The most common measures of sustainability include population growth models, the Robinson and Redford model (1991), and population trends through time. Indicators relying on population-specific biological data are most often used in North America and Europe while cruder estimates are more often used in Africa, Latin America, and Oceania. Our results highlight both the uncertainty and lack of uniformity in sustainability science. Given our urgent need to conserve both wildlife and the food security of rural peoples around the world, improvements in sustainability indicators is of utmost importance.

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Section: Future Research Directionsmentioning

confidence: 99%

Searching for sustainability: are assessments of wildlife harvests behind the times?

et al. 2012

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“…Examples include ordinary least squares (Uhler, 1980), maximum likelihood and Bayesian inference (Walters and Ludwig, 1994). Some methods entail important assumptions, such as equilibrium, the existence of process and/or observation error, and prior information (Hilborn and Walters, 1992;Polacheck et al, 1993;Kuparinen et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Given the difficulties in estimation and the advice against setting too many constants, new estimation and modelling methods are still sought (Kuparinen et al, 2012). In this work, we propose a new surplus production model that facilitates parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

An alternative surplus production model

Rankin

Lemos

2015

Ecological Modelling

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a b s t r a c tIn this work we present a novel surplus production model for fisheries stock assessment. Our goal is to enhance parameter estimation and fitting speed. The model employs a production function that differs from the canonical logistic (Schaefer) and Gompertz (Fox) functions, but is still connected to the Pella-Tomlinson formulation. We embed this function in a state-space model, using observed catchper-unit-effort indices and measures of fishing effort as input. From the literature we derive Bayesian prior densities for all model hyperparameters (carrying capacity, catchability, growth rate and error variance), as well as the state (annual stock biomass). We use the well-studied Namibian hake fishery as a case study, via which we compare the Schaefer, Fox and Pella-Tomlinson models with the new model. We also develop a package for the software R, which employs a Shiny application for data exploration, model specification, and output analyses. Posterior densities of hyperparameters and reference points agree across models. Identifiability issues emerge in the more cumbersome Pella-Tomlinson model. The new model yields small but consistent improvements in precision. It also renders implementation faster and easier, with no hidden truncation of negative biomasses. We conclude by discussing theoretical and practical extensions to this new model.

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“…Millar & Meyer, 2000;McAllister et al, 2001;Yan et al, 2011). However, there is a profound need for biologically realistic models in fisheries science (Kuparinen et al, 2012) and here we aim to build a model that uses all available biological data in a case study where only aggregated catch statistics are known.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian population model to estimate changes in the stock size in data poor cases using Mediterranean bogue (Boops boops) and picarel (Spicara smaris) as an example

Juntunen¹,

Tsikliras²,

Mäntyniemi³

et al. 2014

Medit. Mar. Sci.

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The paper presents an effort to build a biologically realistic, age-structured Bayesian model for the stock assessment of data-poor fisheries where only aggregated catch data is available. The model is built using prior information from other areas and ecologically or taxonomically similar species. The modelling approach is tested with data poor fisheries on the Cyclades islands in the Greek archipelago. The two most important species in the area are selected: bogue (Boops boops) and picarel (Spicara smaris). Both are hermaphroditic. The only data available is the total catch from 1950 to 2010. Information was gathered about natural mortality, recruitment, growth, body size, fecundity, and sex ratio. There were significant problems in finding reliable prior information and a uniform prior was used for fishing mortality. The models at their present stage are not used to give management advice. The biological characteristics of the species in that area should be further studied. However, the posteriors of biological parameters reflect the best available knowledge on these species and they could be used in future studies or in simpler biomass dynamics models as priors.

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Increasing biological realism of fisheries stock assessment: towards hierarchical Bayesian methods

Cited by 45 publications

References 163 publications

Searching for sustainability: are assessments of wildlife harvests behind the times?

Searching for sustainability: are assessments of wildlife harvests behind the times?

An alternative surplus production model

A Bayesian population model to estimate changes in the stock size in data poor cases using Mediterranean bogue (Boops boops) and picarel (Spicara smaris) as an example

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