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Fishery analysis is critical in maintaining the long-term sustainability ofspecies and the livelihoods of millions of people who depend on fishing forfood and income. The fishing gear, or metier, is a key factor significantlyimpacting marine habitats, selectively targeting species and fish sizes.Analysis of commercial catches or landings by metier in fishery stockassessment and management is crucial, providing robust estimates of fishingefforts and their impact on marine ecosystems. In this paper, we focus on aunique data set from the Azores' fishing data collection programs between 2010and 2017, where little information on metiers is available and sparsethroughout our timeline. Our main objective is to tackle the task of data setreconstruction, leveraging domain knowledge and machine learning methods toretrieve or associate metier-related information to each fish landing. Weempirically validate the feasibility of this task using a diverse set ofmodeling approaches and demonstrate how it provides new insights into differentfisheries' behavior and the impact of metiers over time, which are essentialfor future fish population assessments, management, and conservation efforts.

Experiential-Informed Data Reconstruction for Fishery Sustainability and Policies in the Azores