Classification and analysis of VMS data in vertical line fisheries: incorporating uncertainty into spatial distributions
| Autor: | Robert N. M. Ahrens, Nicholas D. Ducharme-Barth |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
education.field_of_study Overfishing business.industry Process (engineering) 010604 marine biology & hydrobiology Environmental resource management Population Fishing 04 agricultural and veterinary sciences Aquatic Science 01 natural sciences Vertical bar Random forest Unit (housing) Commercial fishing Fishery 040102 fisheries 0401 agriculture forestry and fisheries Environmental science business education Ecology Evolution Behavior and Systematics |
| Zdroj: | Canadian Journal of Fisheries and Aquatic Sciences. 74:1749-1764 |
| ISSN: | 1205-7533 0706-652X |
| DOI: | 10.1139/cjfas-2016-0181 |
| Popis: | Commercial fishing fleets play a critical role in the population dynamics of exploited stocks. Understanding the spatial distribution of fleets allows managers to anticipate how fishing pressure on exploited stocks changes in response to fishing regulations or to large-scale perturbations. By anticipating how fishing pressure changes, managers can develop proactive responses to better protect stocks that are vulnerable to overfishing. Modern fisheries monitoring techniques, including vessel monitoring systems (VMS), have advanced this endeavor. This paper presents a framework for using VMS data to develop spatial distributions of catch, fishing effort, and catch per unit of effort (CPUE) as well as associated estimates of uncertainty in a vertical line fishery. VMS data are classified as fishing using a random forest (RF) model. Uncertainty is calculated using a two-step approach to account for uncertainty arising from the RF modeling process and the classification accuracy of the model. This framework is applied to investigate changes in the Gulf of Mexico reef fish fishery during a period of 6 years, including the 2010 Deepwater Horizon oil spill. |
| Databáze: | OpenAIRE |
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