Evaluation of the Impacts of Climate Change on Albacore Distribution in the South Pacific Ocean by Using Ensemble Forecast

Abstract

South Pacific albacore (Thunnus alalunga) is a highly migratory tuna species widely distributed throughout 0°–50°S in the South Pacific Ocean. Climate-driven changes in the oceanographic condition largely influence the albacore distribution, relative abundance, and the consequent availability by the longline fisheries. In this study, we examined the habitat preference and spatial distribution of south Pacific albacore using a generalized additive model fitted to the longline fisheries data from the Western and Central Pacific Fisheries Commission (WCPFC) and Inter-American Tropical Tuna Commission (IATTC). Future projections of albacore distributions (2020, 2050, and 2080) were predicted by using an ensemble modeling approach produced from various atmosphere-ocean general circulation models and anthropogenic emission scenarios (i.e., RCP 4.5 and RCP 8.5) to reduce the uncertainty in the projected changes. The dissolved oxygen concentration at 100 meters (DO100) and sea surface temperature (SST) were found to have the most substantial effects on the potential albacore distribution that the albacore preferred in the habitat with DO100 of 0.2–0.25 mmol L–1 and SST of 13–22°C. This study suggested that the northern boundary of albacore preferred habitat is expected to shift southward by about 5° latitudes, and the relative abundance is expected to gradually increase in the area south of 30°S from 2020 to 2080 for both RCP scenarios, especially with a higher degree of change for the RCP 8.5. Moreover, the albacore relative abundance is projected to decrease in the most exclusive economic zones (EEZs) of countries and territories in the South Pacific Ocean by 2080. These findings could lend important implications on the availability of tuna resources to the fisheries and subsequent evaluation of tuna conservation and management under climate change.

Ocean Futures for the World’s Largest Yellowfin Tuna Population Under the Combined Effects of Ocean Warming and Acidification

“…This will be an important next step for modelling the effects of climate on tuna, including longer-term forecast where both CMIP5 and CMIP6 models predict decreases in primary productivity after 2060 (Tittensor et al, 2021;Zaiss et al, 2021). Preliminary longer-term forecasts for Pacific tunas indicate that decreases in primary production are expected to be associated with potential decreases in tuna abundances at basin scales (Bell et al, 2013;Senina et al, 2018;Chang et al, 2021).…”

Impact of climate variability on moonfish (Mene maculata) catch rate in the waters off southwestern Taiwan

“…The smoothing function is represented by the variables. The data exploration and statistical analyses of GAM models were conducted using the ‘mgcv’ library in R (version 4.2.3; Chang et al, 2021).…”

Impact of climatic oscillations on marlin catch rates of Taiwanese long-line vessels in the Indian Ocean

“…GAM results are associated with “deviance explained,” a metric quantifying the proportion of variability in the dependent variable accounted for by the model, signifying the reduction in deviance compared to a null model; higher explained deviance values denote a stronger ability of the GAM to explain observed variability in the dependent variable 58 . For each species, one GAM was constructed for each climatic oscillation, with the climatic oscillation serving as the predictor variable and catch rate serving as the response variable 59 . This analysis was performed in the R environment (version 3.6.0) using the “smoothing” function of the “mgcv” package 59 .…”

“…For each species, one GAM was constructed for each climatic oscillation, with the climatic oscillation serving as the predictor variable and catch rate serving as the response variable 59 . This analysis was performed in the R environment (version 3.6.0) using the “smoothing” function of the “mgcv” package 59 . The weightage of climatic oscillation variables was ranked on the basis of deviance explained and the Akaike information criterion (AIC).…”