Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean

Abstract

Changes in marine environments affect fishery resources at different spatial and temporal scales in marine ecosystems. Predictions from species distribution models are available to parameterize the environmental characteristics that influence the biology, range, and habitats of the species of interest. This study used generalized additive models (GAMs) fitted to two spatiotemporal fishery data sources, namely 1 • spatial grid and observer record longline fishery data from 2006 to 2010, to investigate the relationship between catch rates of yellowfin tuna and oceanographic conditions by using multispectral satellite images and to develop a habitat preference model. The results revealed that the cumulative deviances obtained using the selected GAMs were 33.6% and 16.5% in the 1 • spatial grid and observer record data, respectively. The environmental factors in the study were significant in the selected GAMs, and sea surface temperature explained the highest deviance. The results suggest that areas with a higher sea surface temperature, a sea surface height anomaly of approximately −10.0 to 20 cm, and a chlorophyll-a concentration of approximately 0.05-0.25 mg/m 3 yield higher catch rates of yellowfin tuna. The 1 • spatial grid data had higher cumulative deviances, and the predicted relative catch rates also exhibited a high correlation with observed catch rates. However, the maps of observer record data showed the high-quality spatial resolutions of the predicted relative catch rates in the close-view maps. Thus, these results suggest that models of catch rates of the 1 • spatial grid data that incorporate relevant environmental variables can be used to infer possible responses in the distribution of highly migratory species, and the observer record data can be used to detect subtle changes in the target fishing grounds.

An argo‐based experiment providing near‐real‐time subsurface oceanic environmental information for fishery data

“…It has been very helpful in fishery research in the open ocean. There are many direct and indirect applications of satellite measurements at the sea surface (Chassot et al, 2011;Filgueira et al, 2013;Kluger et al, 2016;Lan et al, 2017;Martínez-pita and Moreno, 2019).…”

Variation in the Catch Rate and Distribution of Swordtip Squid Uroteuthis edulis Associated with Factors of the Oceanic Environment in the Southern East China Sea

“…The techniques utilize satellite data on SST, chlorophyll-a concentration (Chl-a), sea surface height (SSH), and oceanic fronts, which are related to foraging, to delineate regions or ecological provinces in the ocean that have similar physical and biological forcing (McClain 2009;Stuart et al 2011;Lan et al 2012Lan et al , 2017Klemas 2013). Studies on oceanic front effects on marine species have postulated that discontinuities in oceanic structure serve as aggregating mechanisms for less-mobile prey because of the shear force between the distinct water masses (Olson et al 1994;Lan et al 2012).…”

Towards a Fishing Pressure Prediction System for a Western Pacific EEZ

“…Vessels were observed to fish in waters with generally low CHL, a proxy for primary production and the base of the food web. CHL is usually indicative of higher trophic levels 36,37 but this surprising negative relationship can be explained by the north-to-south gradient in CHL (Fig. 1a) and the north-to-south pattern in fishing effort (Fig.…”

“…High ADT is directly related to sea surface height, which is an indicator of the depth of the thermocline in the vicinity of Palau 35 . Notably, SST was not an important predictor, albeit SST and SST fronts are commonly a proxy for tuna habitat due to thermal tolerances and the tendency of fish to aggregate along temperature gradients 36–39 . For example, adult bigeye, yellowfin, and skipjack tuna have mean temperature tolerances between 9–26 °C, 16–27 °C, and 16–28 °C, respectively 19,40 .…”

“…Our approach is very similar to species distribution or niche modeling techniques where tagged tuna or vessel catch locations were matched to environmental features to predict species habitat preferences (ex. 36–39 ).…”