Effects of Climate Change in Marine Ecosystems Based on the Spatiotemporal Age Structure of Top Predators: A Case Study of Bigeye Tuna in the Pacific Ocean

Abstract

How top predators behave and are distributed depend on the conditions in their marine ecosystem through bottom−up forcing; this is because where and when these predators can feed and spawn are limited and change often. This study investigated how the catch rates of immature and mature cohorts of bigeye tuna (BET) varied across space and time; this was achieved by analyzing data on the Taiwanese longline fishery in the western and central Pacific Ocean (WCPO). We also conducted a case study on the time series patterns of BET cohorts to explore the processes that underlie the bottom-up control of the pelagic ecosystem that are influenced by decadal climate events. Wavelet analysis results revealed crucial synchronous shifts in the connection between the pelagic ecosystems at low trophic levels in relation to the immature BET cohort. Many variables exhibited decreasing trends after 2004–2005, and we followed the Pacific Decadal Oscillation (PDO) as a bottom-up control regulator. The results indicated that low recruitment into the mature cohort occurs 3 years after a decrease in the immature cohort’s food stocks, as indicated by a 3-year lag in our results. This finding demonstrated that, by exploring the connection between low-trophic-level species and top predators at various life stages, we can better understand how climate change affects the distribution and abundance of predator fish.

Effects of decadal climate variability on spatiotemporal distribution of Indo-Pacific yellowfin tuna population

“…The PDO may be a major factor affecting the physical processes and subsequent responses of zooplankton community structures 43 . Olson et al 44 and Lan et al 12 have revealed major decadal dietary shifts among tuna species over a broad region of the Pacific Ocean, suggesting that the PDO affects the pelagic ecosystem by acting as a regulator of bottom-up control. The PDO significantly influences marine environments not only in the Pacific Ocean but also in the Indian Ocean by changing SSTs and the strength of the monsoon 45 , 46 .…”

“…Multidecadal climate indices have been more useful than interannual climate indices for understanding the processes underlying the bottom-up control of the pelagic ecosystem and the various life stages of top predators, including tuna species 7 , 8 , 10 , 12 . The Pacific decadal oscillation (PDO) index is the most prominent index of decadal variability in the North Pacific Ocean, and the PDO is considered a factor contributing to the surface warming hiatus that occurred in the late twentieth century because of fluctuations in the global mean temperature 13 .…”

“…The Pacific decadal oscillation (PDO) index is the most prominent index of decadal variability in the North Pacific Ocean, and the PDO is considered a factor contributing to the surface warming hiatus that occurred in the late twentieth century because of fluctuations in the global mean temperature 13 . In the western and central Pacific Ocean, the PDO plays a bottom-up role in regulating bigeye tuna recruitment and abundance 7 , 12 . The Atlantic multidecadal oscillation (AMO) index, an index of linearly detrended North Atlantic Ocean temperatures, indicated a dominant influence of bluefin and yellowfin ( Thunnus albacores ; YFT) tuna abundance in ocean basins 7 , 8 .…”

Distribution and Catch Rate Characteristics of Narrow-Barred Spanish Mackerel (Scomberomorus commerson) in Relation to Oceanographic Factors in the Waters Around Taiwan

“…Moreover, interspecific relationships must be considered because such relationships may affect the fishing conditions of single species. The movements and distributions of top predators are governed by bottom-up processes and direct environmental effects (e.g., physiological tolerance to anoxia and thermal preferences; Lan et al, 2021). Top marine predators can have high phenotypic plasticity and adaptive capabilities that mitigate the effects of climate change on them; however, climate change may still affect these predators through their prey (Hazen et al, 2013).…”

“…All covariates were considered to be continuous, and the effective degrees of freedom were estimated for each main factor. Because the log-link function cannot handle zeros, we added a constant value of 0.1 (c) to all CRs; this value is commonly used in CR standardization processes (e.g., Manuder and Punt, 2004;Lan et al, 2018Lan et al, , 2021. Time and location were treated as interaction terms to account for the possible interannual variability engendered by environmental spatial distribution variations.…”