A review of marine environmental contaminant issues in the North Pacific: The dangers and how to identify them

2003 ◽  
Vol 11 (2) ◽  
pp. 103-139 ◽  
Author(s):  
Robie W Macdonald ◽  
Brian Morton ◽  
Sophia C Johannessen

Chemical contaminants in the North Pacific Ocean include hydrocarbons, persistent organic pollutants, metals, persistent solids, and domestic pollutants. Here, we review contaminant research conducted over the past decade, finding that the effects of contaminants cannot be considered in isolation from other major factors causing change to North Pacific ecosystems. Climate change, over-fishing, habitat destruction, eutrophication, and the introduction of exotic species interact with one another and alter contaminant pathways. Climate change and over-fishing are perceived as the main threats to the remote northern marginal seas, the central North Pacific, and the west coast of North America, with contaminants engendering local concern, especially in semi-enclosed bodies of water. Climate change receives less attention in Asian waters, probably because widespread habitat destruction and contamination provide, by themselves, an impending ecological disaster. A systematic approach is urgently required to recognize and prioritize the threats to North Pacific coastal ecosystems. This should include box models, case studies, proxy records, and time series. The ocean should be monitored as a system, including physical media (water, sediment) and the full trophic range of the food web, and tissues should be preserved in archives to provide a resource for understanding emerging concerns. Finally, the development of ecological indicators is urgently required to provide a robust warning system based on the health of the marine ecosystems themselves. It is time to conduct a multi-national assessment of the North Pacific Ocean to develop a common, factual awareness of the threats looming over our coastal waters. Key words: contaminants, climate change, ecosystem change, monitoring, North Pacific, trends.

2010 ◽  
Vol 3 (11) ◽  
pp. 762-765 ◽  
Author(s):  
E. Di Lorenzo ◽  
K. M. Cobb ◽  
J. C. Furtado ◽  
N. Schneider ◽  
B. T. Anderson ◽  
...  

2013 ◽  
Vol 70 (5) ◽  
pp. 1013-1022 ◽  
Author(s):  
Nan-Jay Su ◽  
Chi-Lu Sun ◽  
André E. Punt ◽  
Su-Zan Yeh ◽  
Gerard DiNardo ◽  
...  

Abstract Su, N.-J., Sun, C.-L., Punt, A. E., Yeh, S.-Z., DiNardo, G., and Chang, Y.-J. 2013. An ensemble analysis to predict future habitats of striped marlin (Kajikia audax) in the North Pacific Ocean. – ICES Journal of Marine Science, 70: 1013–1022. Striped marlin is a highly migratory species distributed throughout the North Pacific Ocean, which shows considerable variation in spatial distribution as a consequence of habitat preference. This species may therefore shift its range in response to future changes in the marine environment driven by climate change. It is important to understand the factors determining the distribution of striped marlin and the influence of climate change on these factors, to develop effective fisheries management policies given the economic importance of the species and the impact of fishing. We examined the spatial patterns and habitat preferences of striped marlin using generalized additive models fitted to data from longline fisheries. Future distributions were predicted using an ensemble analysis, which represents the uncertainty due to several global climate models and greenhouse gas emission scenarios. The increase in water temperature driven by climate change is predicted to lead to a northward displacement of striped marlin in the North Pacific Ocean. Use of a simple predictor of water temperature to describe future distribution, as in several previous studies, may not be robust, which emphasizes that variables other than sea surface temperatures from bioclimatic models are needed to understand future changes in the distribution of large pelagic species.


Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 388
Author(s):  
Hao Cheng ◽  
Liang Sun ◽  
Jiagen Li

The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.


2021 ◽  
Author(s):  
R. J. David Wells ◽  
Veronica A. Quesnell ◽  
Robert L. Humphreys ◽  
Heidi Dewar ◽  
Jay R. Rooker ◽  
...  

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