El Niño events were first perceived several centuries ago as a dramatic change in the marine resources along the Peruvian coast. It is now recognized as part of the world's largest natural climate fluctuation: the El Niño Southern Oscillation (ENSO). There is a rapidly growing body of scientific literature showing that ENSO has physical and ecological impacts throughout the Pacific Ocean and more broadly across the other oceanic basins through atmospheric teleconnections. This review details a range of these examples in all major ecosystems impacted by ENSO in the Pacific Ocean. Teleconnections with other basins are also discussed, as are the diversity of changes associated with ENSO phases and their consequences on fisheries sustained by these ecosystems. Information is provided on the emerging complexity of the connection between ENSO and the ocean ecosystems, and particularly the diversity of El Niño types, characterized by eastern and central spatial patterns and differences in intensity. As these mechanisms become better understood, useful predictive capacity for ecosystem and fisheries management will result. However, growing evidences suggest that climate change may have already started interacting with ENSO dynamics and effects, complicating mechanistic understanding.
The vertical distribution of juvenile skipjack tuna Katsuwonus pelamis was investigated in the tropical western Pacific (0-25°N and 130-160°E) based on midwater trawl sampling from October to December in 1992 to 1996. Most juveniles were sampled between depths of 40 and 120 m, that is, at depths ranging immediately above and below the thermocline, and at temperatures between 20 and 30°C. Relatively lower temperatures were observed in the pelagic zone of the research area from 1992 to 1994 (period of a shallow thermocline), in contrast to relatively higher temperatures from 1995 to 1996 (with a deep thermocline). The vertical distribution of skipjack juveniles became shallower from 1992 to 1993, whereas it became deeper in 1995 and 1996. These findings suggest that the vertical distribution of skipjack tuna during the juvenile period changed annually relative to the vertical temperature profile. Moreover, fluctuations in vertical temperature are believed to affect the expansion or contraction of the vertical habitat of skipjack juveniles in the pelagic zone. The mean standard length of juveniles collected in 1994 at a depth of 80-100 m in the North Equatorial Counter Current area was significantly larger than that of juveniles collected at 40-60 m. These findings suggest that the vertical distribution of juvenile skipjack tuna becomes deeper in line with their growth.
Annual catches of Todarodes pacificus in Japan have gradually increased since the late 1980s. Paralarval abundances have also been higher since the late 1980s compared to the late 1970s and mid-1980s. Here is proposed a possible scenario for the recent stock increase based on changing environmental conditions. Based on trends in annual variations in stock and in larval abundances, catches are reviewed and potential spawning areas inferred, assuming that egg masses and hatchlings occur over the continental shelf at temperatures between 15 and 23°C. Changes are then inferred in the spawning areas during 1984–1995, based on GIS data. Since the late 1980s, the autumn and winter spawning areas in the Tsushima Strait and near the Goto Islands appear to have overlapped, and winter spawning sites seem to have expanded over the continental shelf and slope in the East China Sea.
Abundance indices derived from fisheries-dependent data (catch-per-unit-effort or CPUE) are known to have potential for bias, in part because of the usual non-random nature of fisheries spatial distributions. However, given the cost and lack of availability of fisheries-independent surveys, fisheries-dependent CPUE remains a common and informative input to fisheries stock assessments. Recent research efforts have focused on the development of spatiotemporal delta-generalized linear mixed models (GLMMs) which simultaneously standardize the CPUE and predict abundance in unfished areas when estimating the abundance index. These models can include local seasonal environmental covariates (e.g. sea surface temperature) and a spatially varying response to regional annual indices (e.g. the El Niño Southern Oscillation) to interpolate into unfished areas. Spatiotemporal delta-GLMMs have been demonstrated in simulation studies to perform better than conventional, non-spatial delta-generalized linear models (GLMs). However, spatiotemporal delta-GLMMs have rarely been evaluated in situations where fisheries spatial sampling patterns change over time (e.g. fisheries expansion or spatial closures). This study develops a simulation framework to evaluate 1) how the nature of fisheries-dependent spatial sampling patterns may bias estimated abundance indices, 2) how shifts in spatial sampling over time impact our ability to estimate temporal changes in catchability, and 3) how including seasonal environmental covariates and/or regional annual indices in spatiotemporal delta-GLMMs can improve the estimation of abundance indices given shifts in spatial sampling. Spatiotemporal delta-GLMMs are then applied to a case study example where the spatial sampling pattern changed dramatically over time (contraction of the Japanese pole-and-line fishery for skipjack tuna Katsuwonus pelamis in the western and central Pacific Ocean). Results from simulations indicate that spatial sampling in proportion to the underlying biomass can produce similar abundance indices to those produced under random sampling. Though estimated abundance indices were not perfect, spatiotemporal GLMMs were generally able to disentangle shifts in spatial sampling from temporal changes in catchability when shifts in spatial sampling were not too extreme. Lastly, the inclusion of seasonal environmental covariates and/or regional oceanographic indices in spatiotemporal GLMMs did not improve abundance index estimation and in some cases resulted in degraded model performance.
We developed a new statistical spatiotemporal model for chlorophyll-a (chl-a) distribution over the Sea of Japan, derived from the satellite-based Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Because preliminary analysis showed that the SeaWiFS data exhibit anisotropy in space and autocorrelation in time, we propose a new spatiotemporal model for chl-a distribution and its predictor. Numerical prediction experiments applying the SeaWiFS data showed that the predictor could forecast chl-a distributions in summer and early fall well, although further changes in the model structure will be necessary to predict aspects of the spring and late fall blooms.
