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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.
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Assessing impacts of bycatch policies and fishers’ heterogeneous information on food webs and fishery sustainability
Ecosystem-based fisheries management (EBFM) has emerged as a promising framework for understanding and managing the long-term interactions between fisheries and the larger marine ecosystems in which they are nested. However, successful implementation of EBFM has been elusive because we still lack a comprehensive understanding of the network of interacting species in marine ecosystems (the food web) and the dynamic relationship between the food web and the humans who harvest those ecosystems. Here, we advance such understanding by developing a network framework that integrates the complexity of food webs with the economic dynamics of different management policies. Specifically, we generate hundreds of different food web models with 20–30 species, each harvested by five different fishers extracting the biomass of a target and a bycatch species, subject to two different management scenarios and exhibiting different information in terms of avoiding bycatch when harvesting the target species. We assess the different ecological and economic consequences of these policy alternatives as species extinctions and profit from sustaining the fishery. We present the results of different policies relative to a benchmark open access scenario where there are no management policies in place. The framework of our network model would allow policymakers to evaluate different management approaches without compromising on the ecological complexities of a fishery. This article is part of the theme issue ‘Connected interactions: enriching food web research by spatial and social interactions’.
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- Award ID(s):
- 2224915
- PAR ID:
- 10556157
- Publisher / Repository:
- The Royal Society
- Date Published:
- Journal Name:
- Philosophical Transactions of the Royal Society B: Biological Sciences
- Volume:
- 379
- Issue:
- 1909
- ISSN:
- 0962-8436
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1098/rstb.2023.0181
