Management thinker Peter Drucker is often quoted as saying “You can’t manage what you can’t measure.” Drucker means that you cannot know whether or not you are successful unless success is defined and monitored. Such a quote is fully applicable to fishery science because only when we can estimate the status of stocks can we provide meaningful and successful management advice: that which gets measured gets managed. However, an increasing share of fishers’ income is derived from fish from stocks whose status remains unassessed. In such situations, a simple rough model might be more useful than no model at all.

The main reasons for the lack of assessment and associated formal harvest control rules are often associated to:

However, many methods have been developed to assist in the assessment of the status of so-called data-limited stocks. Although not based on complex integrated models increasingly used in stock assessments, data-limited assessment methods, particularly when paired with precautionary harvest control rules, provide a reliable understanding of the stock status and might be used to achieve fishery sustainability.

A brief search on the Scopus database (www.scopus.com) highlighted approximately 360 documents produced between 1993 and 2023 pertaining to this area of research (TITLE-ABS-KEY [(“data-limited” OR “data poor”) AND “stock assessment”)]. The bibliographic analysis showed an exponential increase with time, especially for “data-limited” approaches (Figure 1). These studies regarded mainly northern hemisphere countries (Figure 2).

The RT included 22 papers from various countries (two from the US, five from Med, and eight from China). The works of the RT are distributed mainly around several topics:

From the analysis of the keywords used in the 22 published manuscripts, the heterogeneity in covered topics is evident. However, the most used methodologies within the data-limited paradigm are production models (cited in 16 manuscripts) and length-based approaches (cited in six manuscripts).

Overall, this Research Topic provided a ground for discussing the potential of data-poor methods to be applied in fishery assessments as well as limitations on their use. Moreover, the studies covered a management perspective with a clear objective of resource conservation, sustainable exploitation, economic viability, and a combination of these and other aims. Although many of the data-poor studies in the present RT concentrate on the assessment of the status of biological resources, the overall conclusion is that the proper management of data-limited fisheries has specific research needs to be developed in the following years. These would focus on the application of artificial intelligence in stock assessment methodologies and the implementation of data collection programs dedicated to the understanding of specific parameters (e.g., carrying capacity). Such needs have also to take into account the state of the art depicted in the 22 scientific studies collected under this RT.