That’s All I Know: Inferring the Status of Extremely Data-Limited Stocks

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 transferable to other extremely data-limited stocks to elucidate their status and inform their management.

Life history of wahoo, Acanthocybium solandri, in the Tropical Eastern Atlantic Ocean – the importance of applying a suite of methods for fisheries assessment in data-limited situations

“…This study used a length-frequency multi-package approach to estimate different growth parameters of wahoo in the Tropical Eastern Atlantic Ocean. Moncrief et al (2018) and Pantazi et al (2020) showed that the application of many methods describes growth more accurately than just a single method. This approach also allows us to identify which LFA package is optimal for determining the growth of similar species.…”

Natural mortality: Theory, estimation and application in fishery stock assessment models

“…However, this algorithm does not admit a unique solution and we thus proposed a variant (Martiradonna 2012) by imposing an adult mortality constraint that allows us to obtain explicit formulas for the parameter estimation. This variant has been applied recently in literature (Mannini et al 2020, Pantazi et al 2020) to some Mediterranean stocks as case studies of statistical catch-at-age assessments. This work is aimed at showing the gain in stability of our finding, applying the two methods to different stocks in the Mediterranean Sea.…”

Editorial: Data-limited research in stock assessment to increase the understanding of fisheries resources and inform and improve management efforts

“…The assessment and forecasting approaches for data-limited species (e.g., Armelloni et al, 2021;Pantazi et al, 2020).…”