  Fishery analysis is critical in maintaining the long-term sustainability of
species and the livelihoods of millions of people who depend on fishing for
food and income. The fishing gear, or metier, is a key factor significantly
impacting marine habitats, selectively targeting species and fish sizes.
Analysis of commercial catches or landings by metier in fishery stock
assessment and management is crucial, providing robust estimates of fishing
efforts and their impact on marine ecosystems. In this paper, we focus on a
unique data set from the Azores' fishing data collection programs between 2010
and 2017, where little information on metiers is available and sparse
throughout our timeline. Our main objective is to tackle the task of data set
reconstruction, leveraging domain knowledge and machine learning methods to
retrieve or associate metier-related information to each fish landing. We
empirically validate the feasibility of this task using a diverse set of
modeling approaches and demonstrate how it provides new insights into different
fisheries' behavior and the impact of metiers over time, which are essential
for future fish population assessments, management, and conservation efforts.
