Climatic and marine environmental variations associated with fishing conditions of tuna species in the Indian Ocean Kuo-Wei Lan and Ming-An Lee Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University 2nd Int. Symposium on "Effects of Climate Change on the World's Oceans" Yeosu, Korea May 15~19, 2012
The climatic variability affects the distribution and production of tuna populations Skipjack tuna catches and ENSO in the Pacific Ocean El Niño La Niña (Lu and Hseih et al., 2008) (Lehodey et al., 1997)
The climatic variability affects the distribution and production of tuna populations Yellowfin tuna CPUE and NTA in the Atlantic Ocean (a) High ST_105_Aomaly with High CPUE Climate index- North Tropical Atlantic SST index EOF analysis for the tropical Atlantic SST anomalies (region 100 W 20 E, 30 S 30 N) to predict NTA using the 20 leading Empirical Orthogonal Functions. (Penland and Matrosova, 1998) (b) Low ST_105_Aomaly with low CPUE (Lan et al., 2011)
Indian Ocean Dipole (IOD) The Indian Ocean Dipole is a coupled ocean and atmosphere phenomenon in the equatorial Indian Ocean that affects the climate of Australia and other countries that surround the Indian Ocean basin (Saji et al., 1999). The Dipole Mode Index (DMI) is measured by the difference between SST in the western (50 E to 70 E and 10 S to 10 N) and eastern (90 E to 110 E and 10 S to 0 S) equatorial Indian Ocean 3.5 2.5 1.5 DMI 0.5-0.5-1.5-2.5-3.5 1970 1975 1980 1985 1990 1995 2000 2005 Positive Dipole Mode Negative Dipole Mode SST anomalies are shaded (red color is for warm anomalies and blue is for cold). White patches indicate increased convective activities and arrows indicate anomalous wind directions during IOD events. (http://www.jamstec.go.jp/frcgc/research/d1/iod/)
IPCC AR5 Generation of GFDL Earth System Model The IPCC new scenarios referred to Representative Concentration Pathways RCPs. The RCPs will be used to initiate climate model simulations for developing climate scenarios for use in a broad range of climate-change related research and assessment and were requested to be compatible with the full range of stabilization, mitigation and baseline emissions scenarios available in the current scientific literature. (IPCC Expert Meeting Report: Towards New Scenarios, 2008)
The purpose of present study For the tropical Pacific and Atlantic oceans, the apparent abundance of tuna species related to climatic oscillations have been recognized, but in the Indian Ocean a similar interaction has not yet been found. Topic 1: The catches and distributions of tuna species in relation to the climatic and marine environmental variations in the Indian Ocean. Topic 2: The large-scale climate change effect on the longline fishing grounds in the Indian Ocean. If we can know well of fishing ground through remote sensing and assimilation data, it will helpful for fishing management. This availability of oceanographic and biological information gained herein may be useful for the LL fishery and improve fishing operations as well as management of fishery resources.
Flowchart of this dissertation Climatic and marine environmental variations associated with fishing conditions of tuna species in the Indian Ocean Data collecting Longline Fishery data IOTC 5 5 data (1960-2009) Taiwanese 5 5 data (1980-2009) Observer data (2002-2008) Climatic and Environmental data Climatic Dipole Mode Index (1960-2009) Environmental Pathfinder SST (2000-2008, 4x4 km) Net Primary Production (2000-2008, 9 9km) Monthly nominal CPUE RCPs8.5-SST and Chl-a scenarios Analysis method Time series analysis State-space time series Wavelet analysis Optimum SST and NPP Generalized additive models Histogram frequency Climatic variations effects on fishing condition Climate Change effects on fishing grounds Result and Discussion Distribution and concentration of tuna species RCPs8.5-SST increases and of 1 C, Chl-a 2 C scenarios and 4 C General summary, conclusion and recommendation
The important commercially pelagic fish species catch by Taiwanese longline fishery in the Indian Ocean Super-cold freezers (Data source: IOTC)
Cross-wavelet coherence between Dipole Mode Index (DMI) and tuna species CPUE in the Indian Ocean (1960-2009) YFT - DMI It showed a long-term negative significant coherence between the YFT CPUE and DMI with a periodicity of 4 yr. ALB - DMI BET - DMI Time The solid black contour encloses regions of >95% confidence and the black lines indicates the cone of influence where edge effects become important. The phase relationship is shown as arrows, with in-phase pointing right, anti-phase pointing left.
The distribution of yellowfin tuna nominal CPUE in the Indian Ocean Mean YFT CPUE (1970-2009) 3 2 Positive IOD events (DMI+) 1 DMI 0-1 CPUE -2 Negative IOD events (DMI-) -3 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Positive IOD events (DMI+) Negative IOD events (DMI-) CPUE (1972, 1977, 1983, 1991, 1994, 1997, 2006, 2007, 2008) (1974, 1975, 1980, 1985, 1989, 1992, 1981, 2004, 2005)
The variations of the DMI and CPUE of yellowfin tuna in the Indian Ocean 3.000 2.000 1961-63 1972 1982 1987 1994 1997 1.000 1967 1976 1991 2000-03 2006-08 DMI 0.000-1.000-2.000-3.000 1960 1960 1968-69 1970-71 1973-75 1977-81 1968 1971 1977 1964-65 1986 1965-66 1992-93 1984-86 1998 2004-05 1992-93 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 1996 1996 2004-05 35 30 1960 1965-66 YFT_CPUE_W YFT CPUE in the western Indian Ocean CPUE (fish/10 3 hooks) 25 20 15 10 5 1968 1971 1977 1986 1992-93 2004-05 1996 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
YFT catch gravity in relation to DMI during the fishing seasons in the Indian Ocean YFT catch gravity (black line) Dipole Mode Index (gray line) By matching the YFT s concentration with climate variability of DMI, the variation trend was quite similar in the Indian Ocean.
The relationship of SST, NPP and high YFT CPUE for 2002-2008 in the Indian Ocean 25 20 SST and High CPUE (>1.8) 26-29.5 C GAM- SST and CPUE Taiwanese longlone observer data (2002-2008) Percentage(%) 15 10 5 0 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 SST( C) SST( C) 14 NPP and High CPUE (>1.8) GAM- NPP and CPUE Percentage(%) 12 10 8 6 4 220-380 mg C/m 2 /d 2 0 160 200 240 280 320 360 400 440 480 NPP (mg C/m 2 /d) NPP (mg C/m 2 /d) By using the Histograms and GAM analysis indicated that high YFT CPUEs were in the areas with sea surface temperature (SST) range of 26 29.5 C and net primary production (NPP) in the range of 220 380 mg C/m 2 /d.
Predicted optimal SST and NPP areas of higher YFT abundances Pathfinder AVHRR SST map MODIS NPP map (mg C/m 2 /d) Optimal SST map Optimal NPP map High SST (>29.5 C) Optimal SST (26-29.5 C) Low SST (<26 C) High NPP >380 mg C/m2 /d Optimal NPP 220-380 mg C/m2 /d Low NPP 220-380 mg C/m 2 /d
Comparison of the optimal SST and NPP maps in negative and positive events Positive Event Optimal SST map (2007/05) Optimal NPP map (2007/05) High SST (>29.5 C) Optimal SST (26-29.5 C) Low SST (<26 C) High NPP >380 mg C/m2 /d Optimal NPP 220-380mg C/m 2 /d Low NPP 220-380 mg C/m 2 /d Negative Event Optimal SST map (2004/05) Optimal NPP map (2004/05) High SST (>29.5 C) Optimal SST (26-29.5 C) Low SST (<26 C) High NPP >380 mg C/m2 /d Optimal NPP 220-380mg C/m 2 /d Low NPP 220-380 mg C/m 2 /d
Comparisons of the potential fishing grounds in the positive and negative events Positive IOD event Negative IOD event CPUE
Positive (warm) and negative (cold) episodes associated with YFT fishing conditions in the Indian Ocean Latitude (a) Negative IOD event 2004 (Apr.-Jun.) SST images (b) Positive IOD event 2007 (Apr.-Jun.) (e) Positive IOD event Increasing of SST, decreasing NPP in the western Indian Ocean would caused lower CPUE of YFT in the western Indian Ocean (f) (c) Negative IOD event NPP images (d) ( C) Positive IOD event Latitude 2004 2007 CPUE Longitude Longitude Latitude 2004 (Apr.-Jun.) 2007 (Apr.-Jun.) Negative IOD event Decreasing of SST, increasing NPP in the western Indian Ocean would caused higher CPUE of YFT in the western Indian Ocean (mg C/m 2 /d)
Predicted Climate Change effects on the Indian Ocean By following the GFDL RCP8.5 (Clarke et al., 2010) Change of SST in the Indian Ocean Change of Chl-a in the Indian Ocean SST ( C) Chl-a (mg m -3 )
Predicted Climate Change effects on YFT longline fishing grounds
Conclusion The advanced time series analysis showed the significant coherence between the DMI and YFT CPUE with a periodicity of 2-3 yr. The DMI was also found to be negatively correlated with YFT CPUE in the western Indian Ocean. It was suggested that decreasing the optimal areas of SST and NPP during the positive IOD events would cause the lower YFT CPUE in the western Indian Ocean, while increasing the optimal areas would result in higher YFT CPUE in the negative IOD events. Furthermore, an examination of the effects of climate change on possible displacements of potential habitats of yellowfin showed that an SST increase may resulted in decreases of the most suitable areas.
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