Abstract
There is a growing number of methods to assess data-limited stocks. However, most of these methods require at least some basic data, such as commercial catches and life history information. Meanwhile, there are many commercial stocks with an even higher level of data limitation, for which the inference of stock status and the formulation of advice remain challenging. Here, we present a stepwise approach to achieve the best possible understanding of extremely data-limited stocks and facilitate their management. As a case study we use a stock of the shrimp Plesionika edwardsii (Decapoda, Pandalidae) from the eastern Mediterranean Sea, where the only available data was a sub-optimal sample of length frequencies coming from a small-scale trap fishery. We use a suite of different methods to explore and process the data, estimate the growth parameters, estimate the natural and fishing mortalities, and approximate the reference points, in order to provide a preliminary evaluation of stock status. We implement multiple methods for each step of this process, highlighting the strong and weak points of each one of them. Our approach illustrates the better insights that can be gained by applying ensembles of models, rather than a single ‘best’ model when working with limited data of poor quality. The stepwise approach we propose here is easily transferable to other extremely data-limited stocks to elucidate their status and inform their management.
Highlights
Depending on the amount of available information, fish stocks can be characterized as datarich or data-limited
The exploitation status of P. edwarsii was estimated as non-depleted in three M scenarios (Gulland, Lorenzen and Prodbiom), close to equilibrium in one M scenario (Gislason) and as in moderate or severe depletion in all other scenarios (Table 5)
This study proposes a stepwise methodological framework to assess stock status of an extremely data-limited exploited stock
Summary
Depending on the amount of available information, fish stocks can be characterized as datarich or data-limited. Categories 1 and 2 include data-rich stocks with age-structured catch and survey data allowing quantitative assessments. These assessments are considered to describe adequately the true trends of stock size and exploitation levels; as such, trends of category 1 and 2 stocks are used to monitor the effectiveness of fisheries regulations (STECF, 2020). Categories 3–6 include stocks with progressively increasing data limitations. In category 3, survey data are available which can indicate trends of mortality rates, recruitment, and biomass. In category 4, a time-series of catch data is available which allows an approximation of maximum sustainable yield (MSY).
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