Generic solutions for data-limited fishery assessments are not so simple

SMITH, ANTHONY D. M.; DICHMONT, CATHERINE M.; DOUGHERTY, DAWN T.; DOWLING, NATALIE A.; PARMA, ANA M.; WILSON, JONO R.; SMITH, DAVID C.; SAINSBURY, KEITH; COPE, JASON M.

FISH AND FISHERIES

WILEY-BLACKWELL PUBLISHING, INC

Año: 2018 p. 1 - 15

1467-2960

The majority of the world's fisheries, by number, are data-poor/limited, and there is a growing body of literature pertaining to approaches to estimate data-limited stock status.There are at least two drivers for assessing the status of data-limited fisheries.The first is to try to understand and report on the global or regional status of fisheries across many stocks. The second is to attempt to assess individual data-limited stocks, for status reporting and/or guiding management decisions.These drivers have led to attempts to find simple, generic, low-cost solutions,including the broad application of generically parameterised models, and the blanket application of a single, or limited number of possible, analytical approach(es). It is unclear that generic methods function as intended,especially when taken out of their original design context or used without care. If the intention is to resolve individual stock status for the purposes of management, there is concern with the indiscriminate application of a single method to a suite of stocks irrespective of the particular circumstances of each. We examine why caution needs to be exercised, and provide guidance on the appropriate application of data-limited assessment methods (DLMs). We recommend: (a) obtaining better data, (b) using care in acknowledging and interpreting uncertainties in the results of DLMs, (c) embedding DLMs in harvest strategies that are robust to the higher levels of uncertainty in the output of DLMs by including precautionary management measures or buffers and (d) selecting and applying DLMs appropriate to specific species' and fisheries' data and context.