IN EX FISH. Düsseldorf, Germany. Bari, Italy.

Transcription

1 IN EX FISH Possible SST and NAO influences on the Eastern Bluefin Tuna Stock - the IN-EXFISH Approach. (BFT_SYMP/027) Christopher R. Bridges 1, Oliver Krohn 1, Michele Deflorio 2 and Gregorio De Metrio 2 1 Düsseldorf, Germany. bridges@uni-duesseldorf.de 2 Bari, Italy. g.demetrio@veterinaria.uniba.it

2 2006 Incorporating extrinsic drivers into fisheries management' is an EU funded project that aims to increase the responsiveness of fisheries management to a range of anthropogenic and non-anthropogenic forcing factors. end 2008 Recent years have seen fish stocks reach unprecedented low levels. While the fishing industry has been keen to identify pollution and global warming as the causes, it is also clear that fishing has removed large numbers of fish and altered the way the ecosystem works. Everybody would like to see the rebuilding of fish stocks and this can only be achieved if we understand all of the influences, human and natural, on fish dynamics.

3 The Workpackages 2006 Review WP1 Steering Group WP8 Coordination WP9 Metric developments WP2 Case study: North Atlantic: North Sea, ICES VIa and Icelandic Sea WP3 Case Study: Baltic Sea WP4 Case Study: Iberian Sea WP5 Case study: Mediterra -nean Sea WP6 BFT Swordfish Albacore end 2008 Synthesis & dissemination WP7 Project meetings Workshop shared information Steering committee meetings Links between work packages

4 Bluefin tuna Thunnus thynnus thynnus Large pelagic stock management is either nonexistent or not as extensive as compared to the other case study areas. Implementation of the existing regulations for the large pelagic species under study is not always observed.

5 Anthropogenic factor selected: Fisheries High fishing mortality both on juveniles and adults. Decline trends for R and SSB estimates. Anthropogenic factor under investigation: Endocrine disruption Evidence of intersexuality in the swordfish population of the Mediterranean. (17% of examined males) No data are available to evaluate the effects on the bluefin tuna and albacore populations in the Mediterranean Sea.

6 Way Forward for WP6 Large pelagics Access a) SST Data; b) NAO winter index (Hurrel). Global MED approach: access Data Banks from ICCAT and FAO and make general Catch, SSB, Recruitment vs NAO/SST comparisons / analyses. Specific approach: select spawning and 3 main fishing statistic areas (east, central and western MED). Ascertain possibility of accessing raw data from EU and at National levels (for both global MED and specific approaches).

7 Mean Weight (in kg) kg) Non-anthropogenic factors selected: (climate factors) SST 25 NAO/GSNW indices Salinity Age 3 Age 2 Age 1 Catch Influence on BFT spawning stock Influence on BFT recruitment Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Mean weights of young BFT sampled from the French purse seine fleet between 1982 and 1998.

8 C. Ravier and J.-M. Fromentin 2004 Based on data for traps over a long time scale (max ) for estimated numbers, no correlation with NAO was found.

9 Time series - General relationships Over a shorter time scale of 35 years based on weight

10 Influence of NAO (Hurrel) on Mediterranean Catch (ICCAT + FAO Data) Lag Effects 0 offset NAO NAO NAO NAO NAO Catch Catch Catch Catch Catch Accepting the limitations of understimation of catch in ICCAT FAO data and that this does not bias the data towards +ve or ve NAO data.

11 Simple linear regression Comparison for Catch Correlation between catch and NAO in the Mediterranean P value Tuna Catch 0 = (556 * NAO ) R = 0,119 Rsqr = 0,0141 Adj Rsqr = 0,000 0,484 Tuna Catch +1 = (1330 * NAO) R = 0,289 Rsqr = 0,0837 Adj Rsqr = 0,0568 0,087 Tuna Catch +2 = (1815 * NAO) R = 0,406 Rsqr = 0,165 Adj Rsqr = 0,140 0,015 Tuna Catch +3 = (1674 * NAO) R = 0,387 Rsqr = 0,150 Adj Rsqr = 0,123 0,024 Tuna Catch +4 = (1462 * NAO) R = 0,351 Rsqr = 0,123 Adj Rsqr = 0,0950 0,045 Tuna Catch +5 = (1534 * NAO) R = 0,373 Rsqr = 0,139 Adj Rsqr = 0,110 0,036 No corrrelation for Swordfish Catch and NAO No correlation for Albacore Catch and NAO

12 Simple linear regression Comparison for SSB and Recruitment SSB and influence of NAO for Eastern BFT stocks 2,8e+5 2,6e+5 2,4e+5 2,2e+5 SSB 2,0e+5 1,8e+5 1,6e+5 Influence of NAO +1 on Recruitment in Eastern BFT stock 6e+6 1,4e+5 1,2e+5 1,0e+5 8,0e+4 6,0e NAO Hurrel R = 0,0618 Rsqr = 0,00382 Adj Rsqr = 0,000 P=0,724 Recruitment (* calculated from vpa) 5e+6 4e+6 3e+6 2e+6 1e+6 0 Not significant e NAO Hurrel R = 0,452 Rsqr = 0,204 Adj Rsqr = 0,180 P =0,006 Significant

13 Rooker et al 2007 Spawning Areas and seasonal GSI changes Heinisch et al 2008 REPRODOTT Project

14 Sea Surface Temperature: AVHRR Pathfinder SST v5

15 Software tool for extraction of SST data from HDF files Import data and choose dataset (e.g. SST, chloro.) Select area of interest in a rectangular or circular shape (line, point) Area may be selected by entering coordinates alternatively. Input decimal or degrees Download and save data on your local harddisk Choosen files will be listed here Select your area of interest easily in a zoomable map Once you selected your data, dataset and area of interest: Go and get my data!

16 Mean SST ( C) Spawning Area Mean Monthly SST Month of Year Balearen Levantine Sea SST anomaly = Monthly mean SST Mean monlthly values for the period from

17 SST Anomlay SST ANOMALY ( C) Changes in SST Anom. from for Balearen 3,0 2,0 1,0 JUNE 0,0 JULY AUGUST SEPT -1,0-2,0-3,0 YEAR Changes in SST Anom. from for Levantine Sea 2 1,5 1 0,5 0 JUNE JULY AUGUST SEPT -0,5-1 -1,5 YEAR

18 Balearic Spawning Areas P-value June SST Anomaly = -144,708 + (0,0725 * YEAR) R = 0,536 Rsqr = 0,287 Adj Rsqr = 0,252 0,010 July SST Anomaly = -160,796 + (0,0806 * YEAR) R = 0,583 Rsqr = 0,339 Adj Rsqr = 0,306 0,004 August SST Anomaly = -77,303 + (0,0387 * YEAR) R = 0,354 Rsqr = 0,125 Adj Rsqr = 0,0814 0,106 September SST Anomaly = -31,602 + (0,0158 * YEAR) R = 0,135 Rsqr = 0,0183 Adj Rsqr = 0,000 0,548 Levantine Sea Spawning areas P-Value June SST Anomaly= -147,679 + (0,0740 * YEAR) R = 0,671 Rsqr = 0,450 Adj Rsqr = 0,422 <0,001 July SST Anomaly = -145,701 + (0,0730 * YEAR) R = 0,716 Rsqr = 0,513 Adj Rsqr = 0,489 <0,001 August SST Anomaly = -96,917 + (0,0486 * YEAR) R = 0,543 Rsqr = 0,294 Adj Rsqr = 0,259 0,009 Sept SST Anomaly = -79,373 + (0,0398 * YEAR) R = 0,479 Rsqr = 0,229 Adj Rsqr = 0,191 0,024 Balearic Spawning Areas SST Anom. No correlation with monthly or Hurrel NAO Levantine Sea Spawning Areas SST Anom. No correlation with monthly or Hurrel NAO Through Global warming the temperature on the spawning grounds is increasing by on Average of 1 C every 12.5 years.

19 SST Anomaly in June v Tuna Catch Tuna Catch (Metric Tonnes) SST Anomaly in June June Cyprus SST Anomaly v Tuna Catch June Balearen SST Anomaly v Tuna Catch Tuna Catch = (5720, * June SST Anom. Levantine Sea) R = 0,541 Rsqr = 0,293 Adj Rsqr = 0,257 P= 0,009 Tuna Catch= (2199 * June SST Anom. Balearen) R = 0,257 Rsqr = 0,0663 Adj Rsqr = 0,0196 P= 0,247

20 SSB and SST anomaly in June in the Balearic spawning area SSB and SST anomaly in June in the Levatine sea spawning area SSB SSB ,5-2,0-1,5-1,0-0,5 0,0 0,5 1,0 1,5 SST anomaly ( C) in June for the balearic spawning area R = 0,448 Rsqr = 0,201 Adj Rsqr = 0,159 P =0,042 1,6e+6 Recruitment and SST anomaly in the Balearic spawning area ,5-1,0-0,5 0,0 0,5 1,0 1,5 2,0 SST anomaly ( C) in June for the Levantine sea spawning area R = 0,400 Rsqr = 0,160 Adj Rsqr = 0,116 P= 0,072 1,6e+6 Recruitment and SST ( C) anomaly in June for the Levantine sea spawning area Recruitment (*calculated from vpa) 1,4e+6 1,2e+6 1,0e+6 8,0e+5 6,0e+5 Recruitment (calculated from vpa) 1,4e+6 1,2e+6 1,0e+6 8,0e+5 4,0e+5-2,5-2,0-1,5-1,0-0,5 0,0 0,5 1,0 1,5 SST Anomaly ( C) in June for the Balearic spawning area 6,0e+5-1,5-1,0-0,5 0,0 0,5 1,0 1,5 2,0 SST anomaly in June for the Levantine sea spawning area R = 0,152 Rsqr = 0,0232 Adj Rsqr = 0,000 P=0,510 R = 0,0963 Rsqr = 0,00928 Adj Rsqr = 0,000 P=0,678

21 IN SUMMARY NAO For total catch There was a positive correlation between catch and NAO (Hurrel) after a lag of +2 years. No such correlation was found for swordfish or albacore catches. *unrealiability of catch data For Spawning Stock Biomass No correlation was found for NAO (Hurrel) For Recruitment (calculated from vpa ) A positve correlation was found with NAO (Hurrel) *previous conclusions vary (ICCAT workshop 2002), some authors have made similar conclusions, however it is thought that ithat the available data on BFT recruitment at that time (1998) cannot support the hypothesis of an impact of the NAO on BFT recruitment (Fromentin 2002) to explore other mechanisms, such as the influence of the NAO on spatial distribution, migration routes and catchability Fromentin 2002

22 IN SUMMARY SST There is a positive correlation between the SST anomaly and time for both Balearic and Levantine spawning areas. This difference can be as high as +2.5 C and will amount to a 1 C change in 12.5 years on the spawning grounds. Spawning stock biomass was negatively correlated with the SST anomaly in both spawning areas. Recruitment was not correlated with the SST anomaly in both spawning areas. The SST anomaly in June was positvely correlated with catch in the levantine spawning area. but not in the balearic spawning area.

23 This study is carried out with financial support from the Commission of the European Communities, specific RTD programme Specific Support to Policies, FP SSP-4 Integrating and Strengthening the European Research Area. It does not necessarily reflect its views and in no way anticipates the Commission's future policy in this area