Developing an Abundance Index of Skipjack Tuna (Katsuwonus pelamis) from a Coastal Drifting Gillnet Fishery in the Southern Waters of Indonesia

Abstract

Skipjack tuna is targeted by various types of fishing gear in coastal countries. Due to itsresilience, it has withstood heavy fishing pressure in the past few decades. Coastal drifting gillnetfleets also mark skipjack as their main target, but it is often overlooked in terms of stock assessment.This study provides new information on an abundance index based on fishery-dependent data from2010 to 2017. Generalized linear models (GLMs) were used to standardize the catch-per-unit-ofeffort(CPUE) using year, quarter, and gross tonnage as the prediction variables. Model goodnessof-fit and model selection were based on the Akaike Information Criterion (AIC), the pseudocoefficient of determination (R2), and model diagnostics with a residual analysis. The finalestimation of the abundance index was calculated by least square means or marginal means. Theresults showed that the index was heavily influenced by the year and quarter, but it did not relateto the vessel’s capacity. While the CPUE series fluctuated greatly, it showed a declining trend overthe years of observation.

Seasonal Migration Zone of Skipjack Tuna (Katsuwonus pelamis) in the South Java Sea Using Multisensor Satellite Remote Sensing

“…The production of skipjack tuna (Katsuwonus pelamis) caught in the Indian Ocean is 13,000 tons each year out of the total national catch, making it the species that produces the most than other tuna species. In addition, in 2012-2016, skipjack tuna dominated the catch of tuna species with fishing grounds in the Indian Ocean, which reached 51.4% of 150,062 tons [2].…”

Identification of skipjack tuna (Katsuwonus pelamis) pelagic hotspots applying a satellite remote sensing-driven analysis of ecological niche factors: A short-term run

“…The ENFA approach is a presence-only data model, which does not use absence data, often associated with false absences and insufficient sampling effort [22]. For tuna, which are highly mobile, and whose majority of available distribution datasets are obtained from fisheries, eliminating biases associated with fishing strategies and the assumptions that null catches represent species absences can be challenging [23]. The ENFA is in a family of species distribution models [24] that can be used to explain habitat utilization of a species, by estimating its niche using occurrence records and environmental predictor layers [21].…”