Are tag-based integrated stock assessments robust to IUU fishing?

2021 ◽  
Vol 243 ◽  
pp. 106098
Author(s):  
Brett Stacy ◽  
Paul Burch ◽  
Philippe E. Ziegler ◽  
Katherine A. Cresswell ◽  
Klaas Hartmann ◽  
...  
2021 ◽  
Vol 13 (11) ◽  
pp. 6101
Author(s):  
Rishi Sharma ◽  
Henning Winker ◽  
Polina Levontin ◽  
Laurence Kell ◽  
Dan Ovando ◽  
...  

Catch-only models (COMs) have been the focus of ongoing research into data-poor stock assessment methods. Two of the most recent models that are especially promising are (i) CMSY+, the latest refined version of CMSY that has progressed from Catch-MSY, and (ii) SRA+ (Stock Reduction Analysis Plus) a recent developments in field. Comparing COMs and evaluating their relative performance is essential for determining the state of regional and global fisheries that may be lacking necessary data that would be required to run traditional assessment models. In this paper we interrogate how performance of COMs can be improved by incorporating additional sources of information. We evaluate the performance of COMs on a dataset of 48 data-rich ICES (International Council for the Exploration of Seas) stock assessments. As one measure of performance, we consider the ability of the model to correctly classify stock status using FAO’s 3-tier classification that is also used for reporting on sustainable development goals to the UN. Both COMs showed notable bias when run with their inbuilt default heuristics, but as the quality of prior information increased, classification rates for the terminal year improved substantially. We conclude that although further COM refinements show some potential, most promising is the ongoing research into developing biomass or fishing effort priors for COMs in order to be able to reliably track stock status for the majority of the world’s fisheries currently lacking stock assessments.


2011 ◽  
Vol 68 (5) ◽  
pp. 972-981 ◽  
Author(s):  
André E. Punt ◽  
David C. Smith ◽  
Anthony D. M. Smith

Abstract Punt, A. E., Smith, D. C., and Smith, A. D. M. 2011. Among-stock comparisons for improving stock assessments of data-poor stocks: the “Robin Hood” approach. – ICES Journal of Marine Science, 68: 972–981. An approach is outlined for conducting stock assessments in which parameters are estimated for multiple stocks at the same time. Information from data-rich stock assessments, e.g. trends in fishing mortality, and values for parameters of selectivity functions are provided to data-poor assessments in the form of penalties on the estimated parameters, which leads to stock assessments for the most data-poor stocks being informed by those for the most data-rich stocks. The method is applied for example purposes to data for nine stocks in Australia's southern and eastern scalefish and shark fishery. The results of the application confirm that results for data-rich stocks are little impacted by being assessed in conjunction with data-poor stocks and that the results for data-poor stocks can be qualitatively different when information for data-rich stocks is taken into account.


2021 ◽  
Vol 236 ◽  
pp. 105844
Author(s):  
Catherine M. Dichmont ◽  
Roy A. Deng ◽  
Natalie Dowling ◽  
André E. Punt

2014 ◽  
Vol 72 (3) ◽  
pp. 1057-1068 ◽  
Author(s):  
Enric Cortés ◽  
Elizabeth N. Brooks ◽  
Kyle W. Shertzer

Abstract We review three broad categories of risk assessment methodology used for cartilaginous fish: productivity-susceptibility analysis (PSA), demographic methods, and quantitative stock assessments. PSA is generally a semi-quantitative approach useful as an exploratory or triage tool that can be used to prioritize research, group species with similar vulnerability or risk, and provide qualitative management advice. Demographic methods are typically used in the conservation arena and provide quantitative population metrics that are used to quantify extinction risk and identify vulnerable life stages. Stock assessments provide quantitative estimates of population status and the associated risk of exceeding biological reference points, such as maximum sustainable yield. We then describe six types of uncertainty (process, observation, model, estimation, implementation, and institutional) that affect the risk assessment process, identify which of the three risk assessment methods can accommodate each type of uncertainty, and provide examples mostly for sharks drawn from our experience in the United States. We also review the spectrum of stock assessment methods used mainly for sharks in the United States, and present a case study where multiple methods were applied to the same species (dusky shark, Carcharinus obscurus) to illustrate differing degrees of model complexity and type of uncertainty considered. Finally, we address the common and problematic case of data-poor bycatch species. Our main recommendation for future work is to use Management Strategy Evaluation or similar simulation approaches to explore the effect of different sources of uncertainty, identify the most critical data to satisfy predetermined management objectives, and develop harvest control rules for cartilaginous fish. We also propose to assess the performance of data-poor and -rich methods through stepwise model construction.


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