Environmental Factors Determine Tuna Fishing Vessels’ Behavior in Tonga

Abstract

Comprehending the spatial distribution of human fishing endeavors holds significant importance in the context of monitoring fishery resources and implementing spatial management measures. To gain insights into the spatial arrangement of tuna longline activities within the exclusive economic zones of Tonga and their correlation with the marine environment, this study utilizes data from the Tonga Tuna Longline Fisheries spanning from 2002 to 2018. The data are employed to extract information about the spatial distribution of fishing efforts and coupled with 15 marine environmental variables covering both sea surface and subsurface conditions. This study employs boosted regression trees (BRT) and general additive models (GAM) to establish the non-linear relationships between the distribution of fishing effort and marine environmental factors. Furthermore, it examines and analyzes the ecological niche occupied by tuna longline vessels in high-sea environments. The outcomes of the factor analysis indicate that the most important factors influencing the fishing efforts of tuna longliners are the dissolved oxygen content at the sea surface and latitude. These two factors contribute significantly, accounting for 19.06% and 18.62% of the fishing efforts of vessels, respectively, followed by distance to ports, longitude, and dissolved oxygen at 100 m depth, contributing 10.77%, 7.07%, and 6.30%, respectively. The sea surface chlorophyll, ocean current at 100 m depth, and mixed layer depth contributed the least, 3.63%, 2.13%, and 1.72, respectively. In terms of space and time, tuna longliners are more likely to operate in the 18–22° S latitudinal and 172–178° W longitudinal region, and fishing efforts increased in the months from March to August. The spatial distribution of the fishing efforts modeled for fishing vessels in 2018 is predicted to have good spatial distribution with the actual fishing efforts of these vessels. This research aids in comprehending the environmental impacts resulting from shifts in the spatial distribution of tuna longline vessels, offering valuable insights for the effective management of tuna longline fisheries in Tonga.

Keywords

• tuna longline vessels
• GAM model
• BRT models
• habitats
• environmental factors
• spatial distribution