1Present address: School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.
Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change
Article first published online: 22 JUN 2009
© 2009 Blackwell Publishing Ltd
Global Change Biology
Volume 16, Issue 1, pages 24–35, January 2010
How to Cite
CHEUNG, W. W. L., LAM, V. W. Y., SARMIENTO, J. L., KEARNEY, K., WATSON, R., ZELLER, D. and PAULY, D. (2010), Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Global Change Biology, 16: 24–35. doi: 10.1111/j.1365-2486.2009.01995.x
- Issue published online: 2 DEC 2009
- Article first published online: 22 JUN 2009
- Received 21 December 2008; revised version received 13 April 2009 and accepted 6 May 2009
Figure S1. Brief summary of the structure of the dynamic bioclimate envelope model developed in this study which was implemented in Visual Basic.Net environment.
Figure S2. Distribution of relative abundance (A) and the inferred temperature preference profile (TPP) (B) of the Small yellow croaker (Larimichthys polyactis).
Figure S3. A series of cells at the same latitude within the same ocean basin. T is the sea surface temperature at each particular cell.
Figure S4. Map showing the upwelling indexes assigned to each upwelling region.
Figure S5. Projected zonal (latitudinal) change in 10 years average maximum catch potential calculated between 2005 and 2055 using the three primary production estimation algorithms: B & F – Behrenfeld & Falkowski, 1997; Marra – Marra et al., 2003; Carr – Carr, 2002. Globally, projected change in maximum catch potential is not sensitivity to the different primary production estimation algorithms (A, top). Regionally, the tropical region (between 30o north and south) is relatively more sensitivity to the different algorithms (B, bottom) with the B & F algorithm predicts slightly stronger decrease in maximum catch potential than the predictions using the Carr and Marr algorithms. However, the selection of particular algorithm does not affect the overall conclusion of the analysis.
Table S1. List of environmental and species distribution variables and the sources of data that the dynamic bioclimate envelope model accounts for in predicting the current and future distributions of marine fish and invertebrate.
Table S2. Upwelling index computed from the SST anomalies data or wind-induced upwelling data. Regions with upwelling index equal to 6 have the strongest upwelling. Regions would have the weakest upwelling within the upwelling region if their index value is 1.
Table S3. List of families of the 1066 species of marine fish and invertebrates that are included in the analysis.
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Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.