Using Machine Learning to mitigate detrimental effects of capture fisheries.

Look here for a dashboard developed in R-Shiny for additional information on this project.

Discards from commercial fisheries have been linked to detrimental effects on ecosystems and economies worldwide. Understanding spatial and temporal patterns of discards may assist in devising regulatory practices and mitigation strategies toward more sustainable fisheries practices. This study investigates data from bycatch at sea onboard monitoring programs using a machine learning approach. Machine learning has successfully revealed trends and patterns in various ecological applications, potentially providing a way forward as an alternative and complementary methodological approach in fisheries. 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) fishery. We used oceanographic, biological, spatial, and fisheries 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. This study concludes that machine learning approaches are promising in supplementing traditional methodologies, especially with the increase in data availability trends.

Bycatch, the discarded of unwanted catch, is a long-reported problem in many fisheries worldwide. It has estimated that the annual magnitude of worldwide discarded biomass averaged 7.3 million tons or around 8% of the total global catch. In that analysis it was reported that demersal finfish trawling had a relatively low discard rate but contributed substantially to the total amount of discards worldwide because of its ubiquity. The impacts of discards are both economic and ecological.

To mitigate problems with bycatch, data collection programs have been conducted at state and federal levels. That data has grown to proportions enabling alternate analytical venues to be implemented. As the volume of bycatch data increases such alternative analytical approaches supplement traditional methodologies. The process we offer in this repository is one of such approaches, commonly refered to as machine learning (ML). ML algorithms learn patterns in data to arrive at predictions. In this work, using data from the federal observer program, we investigate the ability of ML to analyze temporal and spatial patterns in the catch of incidentally caught living marine resources in a suite of mid-Atlantic fisheries. We evaluate the observer data collected by NOAA Fisheries in the federal waters of the northeastern and mid-Atlantic regions. We describe fishery-specific bycatch patterns for the Summer Flounder, Scup, Black Seabass, and Longfin Squid fisheries. We then used these data to understand the spatial and temporal characteristics that influence bycatch weight and species richness using machine learning. Our specific objectives are to (1) describe temporal and spatial patterns of bycatch in the Scup, Black Sea Bass, Longfin Squid, and Summer Flounder fisheries, and (2) to use ML techniques to understand how gear, temporal, spatial, and environmental characteristics can be used to describe contrasts in bycatch magnitude and taxonomic richness.

We used a gradient-boosting ensemble machine learning algorithm to classify the categorical outcome features for bycatch weight and taxonomic richness. Gradient boosting was used because it captures complex non-linear dependencies at a low computational cost, especially for data with a low signal-to-noise ratio. Gradient boosting was also used for transparency and ease of the interpretability of results, offered to some extent by tree-based models

Different spatial, temporal, biological, and fishery features were identified as important in classifying the magnitude of taxa-specific bycatch in the four fisheries examined. Across all models, the oceanographic feature sea surface temperature and the temporal feature year were the most important factors in classifying the median weight of bycatch. Among the spatial features, longitude was ranked among the top four important features in all models, while latitude was present but ranked lower in importance. The spatial features "inshore" and "Area Southern Massachusetts" were only significant in predicting the median weight of bycatch for the Longfin Squid fishery model. The biological features important in classifying bycatch magnitude in the Summer Flounder fishery included the presence or absence of cartilaginous fishes such as Clearnose Skate, Barndoor Skate, and Winter Skate well as Spiny Dogfish.

Shapley analysis was informative for some features we identified as important but less informative for others. Although we observed that the feature sea surface temperature consistently ranked as the most important feature in all classification models, our SHAP analysis did not indicate a clear pattern in its direction of influence on the model outcome. High and low sea surface temperature values had positive and negative impacts on the predicted outcome. Conversely, the biological features representing specific bycatch species negatively influenced the model outcome, implying a tendency for the model to predict below median bycatch weight if these taxa were also present on the trip.

In this study, we examined the bycatch composition in four commercial fisheries in the northeastern U.S. We used machine learning classification to gain insights into the spatial, temporal, biological, and fishery characteristics that describe contrasts between fishery-specific bycatch magnitude and the richness of bycatch. Our primary findings indicate that six species each accounted for at least 5% of the records, including each targeted species. The observed bycatch magnitude for the four fisheries ranged from 312 to 1,539 mt over the 17-year data duration. We found that the binary classification accuracies of the models were only moderate, never exceeding 80% classification accuracy. All classification models consistently showed that the oceanographic feature sea surface temperature and the temporal feature year are important in determining model performance. Feature importance, however, does not provide an indication of the direction of the response. The Shapley analysis indicated little consistent pattern in the value of the response. The findings of this study show the promise and challenges of using ML approaches for describing contrasts in bycatch abundance and taxonomic richness for mobile gear fisheries in the mid-Atlantic. The benefits of using an ML approach in this case is that we do not need to rely on a priori models to describe the phenomena to be studied.