Potential for a Simple GPS-Based Binary Logit Model to Predict Fishing Effort in a Vertical Hook-and-Line Reef Fish Fishery

摘要

Accurate fishing effort information is fundamental to the successful management of fisheries resources. Automated, independent, and reliable methods for quantifying fishing effort are needed. The use of vessel speed from Global Positioning System (GPS) data to identify fishing activity has worked well for trawl fisheries but has been less successful in stationary fisheries. Therefore, five trips on four vessels from a vertical hook-and-line reef fish fishery were used to examine the efficacy of GPS (speed and time) and electronic video monitoring (EVM) sensor (drum and video) data to corroborate an observer's account of effort using binary logistic regression classification (logit) models as well as a simple speed and time filter (filter). One minute was the minimum data collection interval examined that documented 100% of fishing events. As no fishing occurred at night, opportunistically defined as the 7 h between 2200 and 0500 hours, these records were excluded from analyses. During the day, vessels spent on average 45.2% of the time fishing. Classification success of the approaches examined ranged from 82.4% to 89.5%. Models that included both GPS and EVM sensor data outperformed the filter and GPS-only models. In general, the filter and most model results can be used as a proxy for observer effort data, at least for the trips examined here. The GPS-based speed + time logit model was chosen as the preferred approach because of its discriminatory power compared with the filter and the existing widespread use and lower costs of GPS data collection relative to EVM systems and sensors. The speed + time logit model outlined here may have broad utility in this and similar vertical-line fisheries, including the offshore marine recreational fishing sector.