Impacts of spatial uncertainty on performance of age structure-based harvest strategies for blue eye trevalla (Hyperoglyphe antarctica)

Fay, Gavin; Punt, André E.; Smith, Anthony D. M.

doi:10.1016/j.fishres.2011.04.015

Cited by 28 publications

(19 citation statements)

References 18 publications

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“…Moreover, while the treatment species for the assessments span the majority of those assessed in the main fishery in the region (the SESSF), they are not exhaustive, as they do not include herbivores, short‐lived or sedentary invertebrates, or forage fish. The individual species level results are consistent with results from single‐species MSE testing of the data‐rich management strategies (e.g., Fay, Punt, & Smith, ; Klaer, Wayte, & Fay, ; Little et al., ; Wayte & Klaer, ). Nonetheless, confidence in these results would be much greater if repeated using other modelling frameworks, more socioecological systems of different types (so not just different ecosystem structure, but systems with alternative cultural expectations, demographics, livelihood make up, etc.…”

Section: Discussionsupporting

confidence: 78%

Ecosystems say good management pays off

et al. 2018

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Understanding the strengths and weaknesses of alternative assessment methods, harvest strategies and management approaches are an important part of operationalizing single‐species and ecosystem‐based fisheries management. Simulations run using two variants of a whole‐of‐ecosystem model for the Southern and Eastern Scalefish and Shark Fishery (SESSF) area shows that (a) data‐rich assessments outperform data‐poor assessments for target species and that this performance is reflected in the values of many system‐level ecosystem indicators; (b) ecosystem and multispecies management outperforms single‐species management applied over the same domain; (c) investment in robust science‐based fisheries management pays dividends even when there are multiple jurisdictions, some of which are not implementing effective management; and (d) that multispecies yield‐oriented strategies can deliver higher total catches without a notable decline in overall system performance, although the resulting system structure is different to that obtained with other forms of ecosystem‐based management.

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Section: Discussionsupporting

confidence: 78%

Ecosystems say good management pays off

et al. 2018

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“…Full technical specifications for the generalised version of the operating model are detailed in Fay et al . (). This operating model has been used extensively within the SESSF to evaluate the performance of assessment methods and management strategies (Wayte & Klaer ; Fay et al .…”

Section: Methodsmentioning

confidence: 97%

Integrating recreational fisheries data into stock assessment: implications for model performance and subsequent harvest strategies

Griffiths

Fay

2015

Fisheries Management Eco

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Understanding the impacts of recreational fishing on commercially fished stocks is becoming increasingly relevant for fisheries managers. However, data from recreational fisheries are not commonly included in stock assessments of commercially fished stocks. Simulation models of two assessment methods employed in Australia's Commonwealth fisheries were used to explore how recreational fishery data can be included, and the likely consequences for management. In a data-poor management strategy for blue eye trevalla, Hyperoglyphe antarctica (Carmichael), temporal trends in recreational catch most affected management outcomes. In a data-rich age-structured stock assessment for striped marlin, Kajikia audax (Philippi), estimates of stock status were biased when recreational catches were large or when the recreational fishery targeted different size classes than the commercial fishery and these data were not integrated into the assessment. Including data from recreational fishing can change perceptions of stock status and impact recommendations for harvest strategies and management action. An understanding of recreational fishery dynamics should be prioritised for some species.

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“…where O y,a is the observed proportion of catch at age a in year y, n y is the sample size in year y, andÑ a is the predicted proportion of catch at age a (Fay et al, 2009a):…”

Section: Estimation Of Current Fishing Mortalitymentioning

confidence: 99%