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Discards from commercial fisheries have been linked to detrimental effects on ecosystems and stocks of living marine resources. Understanding spatial and temporal patterns of discards may assist in devising regulatory practices and mitigation strategies and promote sustainable management policies. This study investigates data from bycatch monitoring programs using a machine learning approach. We used a gradient boosting classifier for describing catch and bycatch patterns in the U.S. Mid-Atlantic Black Seabass (Centropristis striata), Summer Flounder (Paralichthys dentatus), Scup (Stenotomus chrysops), and Longfin Squid (Doryteuthis pealeii) fisheries. We used oceanographic, biological, spatial, and fisheries data as explanatory model features. We found positive associations between target species volume and bycatch. Although we found that sea surface temperature and year were important model features, the direction of impact of those predictors was variable. From our findings, we conclude that machine learning approaches are promising in supplementing traditional methodologies, especially with the increase in data availability trends. 

The Atlantic chub mackerel (Scomber colias) stock is commercially exploited throughout the Atlantic and Mediterranean and has been recently targeted by a small, but emerging, fishery off the Northeast coast of the United States. Recent efforts by the Mid-Atlantic Fishery Management Council to manage the Northwest Atlantic stock have necessitated the description of its life-history characteristics. The objectives of this study were to evaluate the utility of ageing methods, describe the length-at-age and weight-at-length relationships, and compare estimated growth parameter values to those reported from other regions. We found that whole otoliths provided the most precise method for age determination of Atlantic chub mackerel. Age estimates were derived for adult (n = 422) and larval fish (n = 60). Parameter estimates of individual growth models were determined using a Bayesian framework. The length-at-age relationship was described using four non-linear candidate growth models, which were fit to total length (TL, cm) and age estimates (y). We found that the three-parameter VBGF (L∞ = 33.56 cm TL, k = 1.75 y-1, t0 = 0.07 y) was the best candidate model to describe the length-at- age relationship. A power function was used to describe the weight-at-length relationship from 1 136 individuals (a = 0.0258, b = 2.72). We found that individuals exhibit a greater rate of growth and reach smaller average maximum length when compared to published estimates in other regions. The rate of increase of weight relative to length was found to be significantly lower than that reported in other regions. These results can be used to inform assessment of the Atlantic chub mackerel stock in the Northwest Atlantic.