Impacts of climate change on the distribution of blue marlin (Makaira. nigricans) ) as inferred from data for longline fisheries in the Pacific Ocean

Transcription

1 Impacts of climate change on the distribution of blue marlin (Makaira nigricans) ) as inferred from data for longline fisheries in the Pacific Ocean Nan-Jay Su 1*, Chi-Lu Sun 1, Andre Punt 2, Su-Zan Yeh 1, and Gerard DiNardo 3 1 Institute of Oceanography, National Taiwan University, Taipei, Taiwan 2 School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USA 3 NOAA Fisheries, Pacific Islands Fisheries Science Center, Honolulu, HI, U.S.A April 2010, Sendai, Japan 1

2 Contents 1. INTRODUCTION - Background information - Objectives of this study 2. MATERIALS AND METHODS - Fishery data - Environmental data - Statistical modeling - Predict distribution 3. RESULTS AND DISCUSSION - Model fitting - Annual variation - Regional variation -Conclusions 2

3 1. Introduction Blue marlin is a large, pelagic species Source: NOAA 3

4 1. Introduction Distribution 30 N 0 30 S Source: Nakamura (1985) 4

5 1. Introduction Various fisheries Longline Harpoon Gillnet Source: TTA, CTW, MAFF 5

6 1. Introduction Catch in the three oceans Source: FAO 6

7 1. Introduction Landing in the Pacific Ocean Source: FAO 7

8 1. Introduction Migration and movement Source: Hinton (2001) 8

9 1. Introduction Migration and movement a single stock in the Pacific 4500 miles 4 years Source: Hinton (2001) 9

10 1. Introduction Tagging experiment Source: Block et al. (1992) 10

11 1. Introduction Source: Dr. Hsu HH, NTUAS 11

12 1. Introduction Blue marlin inhabit certain preferred habitats in the open ocean. The preference of this species for particular habitats may impact its distribution and vulnerability to being caught. We could infer relative density through catch-rate information. The objectives of this study To examine 1) the relationship between spatial patterns of abundance and environmental factors, 2) the impact of climate change on the distribution of blue marlin, and 3) the potential influence on the catch-rates for the longline fisheries. 12

13 Contents 1. INTRODUCTION - Background information - Objectives of this study 2. MATERIALS AND METHODS - Fishery data - Environmental data - Statistical modeling - Predict distribution 3. RESULTS AND DISCUSSION - Model fitting - Annual variation - Regional variation -Conclusions 13

14 2. Materials and Methods Two fishery data sets of longline 5x5 grids a) SPC, aggregated over longline fisheries (PLL) for b) OFDC, Taiwanese longline fisheries (TLL) for Covariates: Year, Month, Lat, Lon, Fishing effort in hooks, and number of blue marlin caught 14

15 2. Materials and Methods Four data sets of environmental factors for a) chlorophyll a concentration (CHL): SeaWiFS b) mixed layer depth (MLD): OSU c) sea surface height anomaly (SSH): AVISO d) sea surface temperature (SST): PODAAC - Average to 5x5 grids to match the fishery data 15

16 Statistical modeling 2. Materials and Methods - delta GAM (Generalized additive model) a) P/A (present/absent) model: 0 or1, assuming binominal P/A ~ s(year) + s(month) + s(chl) + s(mld) + s(ssh) + s(sst) + s(year,) + s(year,) + s(,) b) CPUE (abundance) model: catch-rates > 0, assuming lognormal CPUE ~ s(year) + s(month) + s(chl) + s(mld) + s(ssh) + s(sst) + s(year,) + s(year,) + s(,) Diagnostic analysis: residual distribution and quantile-quantile plot 16

17 2. Materials and Methods Predict relative density Consider both information from the P/A and CPUE models Prediction = probability of present relative density given presence Predict the distribution of relative density a) temporally: for each month b) spatially: 5x5 latitude and longitude grids Define the hotspot areas, the relative densities in the top 5% 17

18 2. Materials and Methods Impacts of climate change Track temporal variations of the hotspot areas - by and Remove yearly variations - medians of environment data in predict year-invariant relative density distribution Examine climate impacts, by six regions - separate the Pacific into temperate and tropical areas 18

19 2. Materials and Methods The six regions of the Pacific Ocean 40 N 20 N NW Pacific NE Pacific 0 W Trop Pacific E Trop Pacific 20 S 40 S SW Pacific SE Pacific 120 E 140 E 160 E W 140 W 120 W 100 W 80 W 19

20 Contents 1. INTRODUCTION - Background information - Objectives of this study 2. MATERIALS AND METHODS - Fishery data - Environmental data - Statistical modeling - Predict distribution 3. RESULTS AND DISCUSSION - Model fitting - Annual variation - Regional variation - Conclusions 20

21 3. Results and discussion Nominal CPUE of Pacific longline fisheries (PLL) < >

22 3. Results and discussion Nominal CPUE of Taiwanese longline fisheries (TLL) TLL for < >

23 3. Results and discussion Diagnostic analysis: residual and Q-Q plots a) PLL for Density Sample Quantiles Residuals Theoretical Quantiles b) TLL for Density Sample Quantiles Residuals Theoretical Quantiles 23

24 3. Results and discussion ANOVA table of P/A models for PLL and TLL PLL for TLL for Resid dev % dev explain AIC %AIC P(Chi) Resid dev % dev explain AIC %AIC P(Chi) PA model NULL s(year) % % < % % <0.01 +s(month) % % < % % <0.01 +s(chl) % % < % % <0.01 +s(mld) % % < % % <0.01 +s(ssh) % % < % % <0.01 +s(sst) % % < % % <0.01 +s(year,) % % < % % <0.01 +s(year, % % < % % <0.01 +s(,longitu % % < % % <0.01 Tot dev expl 33.7% 12.2% 24

25 3. Results and discussion ANOVA table of CPUE models for PLL and TLL PLL for TLL for Resid dev % dev explain AIC %AIC P(Chi) Resid dev % dev explain AIC %AIC P(Chi) CPUE model NULL s(year) % % < % % <0.01 +s(month) % % < % % <0.01 +s(chl) % % < % % <0.01 +s(mld) % % < % % <0.01 +s(ssh) % % < % % <0.01 +s(sst) % % < % % <0.01 +s(year,) % % < % % <0.01 +s(year, % % < % % <0.01 +s(,longitu % % < % % <0.01 Tot dev expl 42.0% 22.0% 25

26 3. Results and discussion Partial effects of environmental factors for PLL s(chl) CHL MLD s(ssh) s(sst) s(mld) SSH SST 26

27 3. Results and discussion Partial effects of environmental factors for TLL s(chl) CHL MLD s(ssh) s(sst) s(mld) SSH SST 27

28 3. Results and discussion Yearly variation of environmental factors (15 S-15 N) 2008 Chlorophyll a concentration Mixed layer depth E 145E E 110W 75W 110E 145E E 110W 75W 28

29 3. Results and discussion Yearly variation of environmental factors (15 S-15 N) 2008 Sea surface height anomaly Sea surface temperature E 145E E 110W 75W 110E 145E E 110W 75W 29

30 3. Results and discussion Year-invariant environmental factor (median of 10 years) Dec Chlorophyll a concentration Dec Mixed layer depth -20 Nov Nov Oct Oct -30 Sep Sep Aug Jul Jun May Apr Aug Jul Jun May Apr Mar Mar -70 Feb Feb Jan Jan E 145E E 110W 75W 110E 145E E 110W 75W 30

31 3. Results and discussion Year-invariant environmental factor (median of 10 years) Dec Sea surface height anomaly Dec Sea surface temperature 23 Nov Oct Nov Oct 24 Sep Aug Sep Aug 25 Jul Jun 0.00 Jul Jun May Apr 0.05 May Apr 28 Mar Feb 0.10 Mar Feb 29 Jan 0.15 Jan E 145E E 110W 75W 110E 145E E 110W 75W 31

32 3. Results and discussion PLL 1998 to 2007 Jan 1998 Jan 2001 Jul 1998 Jul 2001 Jan 2004 Jul 2004 Jan 2007 Jul

33 3. Results and discussion TLL 1998 to 2007 Jan 1998 Jan 2001 Jul 1998 Jul 2001 Jan 2004 Jul 2004 Jan 2007 Jul

34 3. Results and discussion Year-invariant relative density without climate change Jan Jan PLL Jul Jul PLL Jan Jan TLL Jul Jul TLL 34

35 3. Results and discussion Track the hotspot areas for PLL and TLL PLL PLL Year Year TLL TLL Year Year 35

36 3. Results and discussion Relative density by region for PLL NW Pacific NW Pacific CPUE CPUE Yr-variant Yr-invariant Year Year CPUE NW Pacific CPUE NW Pacific Year Year CPUE NW Pacific CPUE NW Pacific Year Year 36

37 3. Results and discussion Relative density by region for TLL NW Pacific NW Pacific CPUE CPUE Yr-variant Yr-invariant Year Year NW Pacific NW Pacific CPUE CPUE Year Year NW Pacific NW Pacific CPUE CPUE Year Year 37

38 Conclusions 3. Results and discussion The blue marlin population apparently moved eastwardly during the El Niño in the tropics Yearly patterns in distribution appear to be associated with the El Niño event and related to shifts in SST Future directions Increase spatial and temporal coverage of data set Include more explanatory variables, e.g. fishing gear 38

39 Thank you!! 39

40 3. Results and discussion PLL Jan 1998 Jul 1998 Jan 2001 Jul 2001 Jan 2004 Jul 2004 Jan 2004 Jul

41 3. Results and discussion TLL Jan 1998 Jul 1998 Jan 2001 Jul 2001 Jan 2004 Jul 2004 Jan 2007 Jul

42 3. Results and discussion Predicted catch = predicted CPUE x observed fishing effort W Trop Pacific E Trop Pacific Catch (1000 fish) Observed Yr-variant Yr-invariant Catch (1000 fish) Year W Trop Pacific E Trop Pacific Catch (1000 fish) Catch (1000 fish) Year Year Year 42