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 2011 International Workshop on Climate and Ocean Fisheries Rarotonga, Cook Island 3-5 October, 2011
The environmental and climatological variability affects the distribution and production of tuna populations Skipjack tuna catches and ENSO in the Pacific Ocean CPUE(G) 29 C SOI El Niño El Nino La Niña El Nino La Nino (Lehodey et al., 1997) (Lu and Hseih et al., 2008)
High ST_105_Aomaly with High CPUE (a) 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
Indian Ocean Dipole (IOD) and Dipole Mode Index (DMI) 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 1961 1967 1972 1976 1982 1987 1991 1994 1997 2006-08 1960 1984 2004-05 1998 1964 1992 1996 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/)
The relationship of Indian Ocean Dipole ENSO Is there any connection between the ENSO and the IOD? It is shown by several researchers that the IOD in the Indian Ocean can evolve without the ENSO forcing from the tropical Pacific. (Saji et al.,1999) (Yamagata et al.,2003) However, some researchers argue positive phases of the IOD tend to co-occur with El Niño, and negative phases with La Niña. Izumo et al. (2010) indicated negative (positive) IODs tend to precede the development of El Niño (La Niña) events, whereas ENSO has no significant predictive skill of the IOD at one-year lead.
The Years of El Niño, La Niña, and Interactions with the Tropical Indian Ocean Composite averages of the SST anomaly (Meyers et al.,2007)
The Years of El Niño, La Niña, and Interactions with the Tropical Indian Ocean Percentage of years rainfall (Meyers et al.,2007)
Long-term trends and cycles of the standardized CPUE of the regular, deep longline, SST, Chl-a, D20 and DMI after seasonal adjustments Cross-wavelet coherence between environmental variables and standardized CPUE of the regular, deep longline
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.
The important commercially pelagic fish species catch by Taiwanese longline fishery in the Indian Ocean Super-cold freezers (Data source: IOTC)
The longline catch percentages of YFT, BET and ALB in the Indian Ocean Five sub fishing areas divided by Indian Ocean Tuna Commission (IOTC) in 2002 Yellowfin tuna (YFT) Bigeye tuna (BET) Albacore (ALB)
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 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 SST increases of 1 C, 2 C and 4 C General summary, conclusion and recommendation
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 variations of the DMI and CPUE of yellowfin tuna in the Indian Ocean DMI 1962 1961-63 1967 1970 1972 1975 1976 1982 1987 1991 1994 1995 1997 1999 2003 2000-03 2008 2006-08 1960 1960 1964-65 1965-66 1968-69 1970-71 1968 1971 1973-75 1977-81 1977 1984-86 1986 1992-93 1992-93 1996 1996 1998 2004-05 2004-05 35 YFT_CPU E_W 1960 YFT CPUE in the western Indian Ocean 1965-66 30 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 16 14 1962 YFT_CPUE_E YFT CPUE in the eastern Indian Ocean CPUE (fish/10 3 hooks) 12 10 8 6 4 2 1970 1975 1995 1999 2003 2008 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
The distribution of yellowfin tuna nominal CPUE in the Indian Ocean YFT CPUE Mean (1960-2009) 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 CPUE (1962,1986,1972,1975,1982,1987,1991,1994,1997,1999, 2003,2008) (1960, 1965, 1966, 1968, 1971, 1977, 1986, 1992, 1993, 1996, 2004, 2005)
The monthly mean CPUE of YFT in the western and eastern Indian Ocean Western Indian Ocean 12 Negative events Mean (1960-2009) CPUE (fish/10 3 hooks) 10 8 6 4 2 Positive events Eastern Indian Ocean CPUE (fish/10 3 hooks) 0 12 10 8 6 4 2 1 2 3 4 5 6 7 8 9 10 11 12 Month Negative events Mean (1960-2009) Positive events 0 1 2 3 4 5 6 7 8 9 10 11 12 Month
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.
YFT catch gravity in relation to zonal average of SST, NPP and DMI during the fishing seasons in the Indian Ocean SST zonal average YFT catch gravity (purple line) NPP zonal average CPUE ( C)
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 220-380 Low NPP mg C/m 2 /d 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 220-380 mg 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 CPUE
Positive (warm) and negative (cold) episodes associated with YFT fishing conditions in the Indian Ocean Positive DMI Event Increasing of SST, decreasing NPP in the western Indian Ocean Caused lower CPUE of YFT in the western Indian Ocean Negative DMI Event Decreasing of SST, increasing NPP in the western Indian Ocean Caused higher CPUE of YFT in the western Indian Ocean
Climate variability and purse seine catch rates in the West tropical Indian Ocean (Marsac, 2008)
Comparison of yellowfin tuna CPUE in the Atlantic and Indian Ocean Atlantic Ocean Indian Ocean 500000 Catch (N) CPUE 20 Catch (kg) 400000 300000 200000 15 10 CPUE 100000 5 0 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 0 Pacific Ocean?
Predicted Climate Change effects on YFT longline fishing grounds High SST (>29.5 C) Optimal SST (26-29.5 C) Low SST (<26 C)
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. The areas with high CPUE were deceased in the western Indian Ocean during the positive IOD events and increased in the negative IOD events. Catch gravity of YFT would also migrated to the eastern (western) site of tropical Indian Ocean during the positive (negative) IOD events. It was suggested that decreasing the optimal areas of SST and NPP during the positive IOD events would cause the decrease YFT CPUE in the western Indian Ocean, while increasing the optimal areas would result in increasing YFT CPUE in the negative IOD events.
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