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Acknowledgements: ICES Baltic Fish. Assess. WG U. Thygesen A. Visser

Incorporation of C limate- O cean I nformation in S hort- and M edium T erm S prat P redictions in the Baltic Sea. www.conwoy.ku.dk. Conference on Climate Change and North Atlantic Fish Stocks Bergen, Norway May 11-14, 2004. Acknowledgements: ICES Baltic Fish. Assess. WG

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Acknowledgements: ICES Baltic Fish. Assess. WG U. Thygesen A. Visser

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  1. Incorporation of Climate-Ocean Information in Short- and Medium Term Sprat Predictions in the Baltic Sea www.conwoy.ku.dk Conference on Climate Change and North Atlantic Fish Stocks Bergen, Norway May 11-14, 2004 Acknowledgements: ICES Baltic Fish. Assess. WG U. Thygesen A. Visser Brian MacKenzie and Fritz Köster Danish Institute for Fisheries Research DK-2920 Charlottenlund, Denmark

  2. Background and Objective: - recruitment appears to be independent of spawner biomass for present range of SSB (ICES 2004) - recruitment affected by temperature during gonadal, egg and larval development stages

  3. Recruitment – Temperature Relationship for Sprat in the Baltic Sea 1973-1999 R2 = 28%, p= 0.0029 Various processes acting ! MacKenzie & Köster 2004 Ecology 85: 784-794

  4. Background and Objective: • - recruitment appears to be independent of spawner • biomass for present range of SSB (ICES 2004) • - recruitment affected by temperature during gonadal, • egg and larval development stages • consider whether and how results can be used in • stock assessment: •  short-term predictions (1 and 2 years ahead) •  medium-term projections (10 years ahead)

  5. Desirable Characteristics ofany Prediction • Timing of prediction – earlier is better than later • 2) Quality of prediction – close to observed data  we now will address both issues

  6. Data Requirements for ICES Short-term Predictions Consider ICES 2003 assessment. WG needs estimate of recruitment for 3 years (current year, next year, following year): year 2000 2001 2002 2003 2004 2005 Age 1 1256814 474304 949243 ?? ?? ?? Age 2 209025 799757 292161 575959 ?? ?? Age 3 765188 132058 490294 168759 X ?? Age 4 107107 461827 77211 279132 X Y Age 5 215340 58016 273903 25734 X Y Age 6 184383 127247 26365 169018 X Y Age 7 28820 106248 73545 11330 X Y Age 8+ 30653 24273 111194 118992 X Y Total 2797330 2183730 2293916 1348924 X Y -X, Y from historical estimates, natural and fishing mortality (ICES 2003)

  7. Data Requirements for ICES Short-term Predictions = geometric mean for last 10 years = acoustic survey in autumn 2002 - can we provide a better prediction of recruitment in 2003 and 2004?

  8. Temperature-based 1-gr. prediction available here Timing Issues Relevant toShort-term Predictions WG meets: Estimate required of 1-gr. abundance in 2004 (born in 2003) 2002  2003  2005  2004 

  9. Application to Stock Assessment: Short-term Prediction Would be better if we could provide annual recruit estimates before the assessment WG meeting (pre-April). • identify variables that forecast both spring temperatures and recruitment

  10. --- Spring temperatures GRAS AS, http://www.gras.ku.dk +++ Sprat recruitment All links P < 0.01 Climate-Hydrography-Recruitment Links in the Baltic Sea 1955-1999 Winter climate (NAO) --- Martin Visbeck http://www.ldeo.columbia.edu/NAO Ice coverage MacKenzie & Köster 2004 Ecology 85: 784-794

  11. Desirable Characteristics of any Prediction • Timing of prediction – earlier is better than later • 2) Quality of prediction – close to observed data

  12. Quality of Sprat Recruitment Predictions • retroactively make recruitment predictions for each • yearclass 1983-1999 • - use data from 1973-1982, and increment one year • at a time, simulating WG meetings in 1983, 1984 … • ICES Assessment WG method: • recruitment = geometric mean of last 10 years ii) Use environmental-based models, with information available up to but excluding predicted yearclass

  13. Recruitment Prediction Comparisons – Time Trends

  14. Comparison of Prediction Methodologies - environmental models outperformed WG’ method (closer to observed data, less variable)

  15. Environmentally-Based Short-Term Recruitment Predictions - had lower prediction error - were less variable - available 14 months earlier than ICES’ estimates

  16. Update of Sprat Recruitment – Temperature Relationship with Year-classes 2000-2003 uncertain Does it hold ?

  17. Does it matter ? Consequences for Landings in 2005 and SSB in 2006 - as calculated in Baltic WG, April 2004:

  18. Alternative Predictions Scenario 2003 YC 2004 YC WG-SQ 0-grp., RCT3 mean 1989-2003 Env. 1 0-grp., RCT3 NAOJF 2004 Env. 2 0-grp., RCT3 Min. NAOJF Env. 3 0-grp., RCT3 Max. NAOJF Env. 4 0-grp., RCT3 Mean NAOJF Env. 5 Temp. 2003 NAOJF 2004 2100000 1800000 1500000 1200000 Spawner Biomass in 2006 900000 600000 300000 0 WG-SQ 1 2 3 4 5 Recruitment Scenario

  19. Alternative Predictions Scenario 2003 YC 2004 YC WG-SQ 0-grp., RCT3 mean 1989-2003 Env. 1 0-grp., RCT3 NAOJF 2004 Env. 2 0-grp., RCT3 Min. NAOJF Env. 3 0-grp., RCT3 Max. NAOJF Env. 4 0-grp., RCT3 Mean NAOJF Env. 5 Temp. 2003 NAOJF 2004 2100000 1800000 1500000 1200000 Spawner Biomass in 2006 900000 600000 300000 0 WG-SQ 1 2 3 4 5 Recruitment Scenario

  20. Alternative Predictions Scenario 2003 YC 2004 YC WG-SQ 0-grp., RCT3 mean 1989-2003 Env. 1 0-grp., RCT3 NAOJF 2004 Env. 2 0-grp., RCT3 Min. NAOJF Env. 3 0-grp., RCT3 Max. NAOJF Env. 4 0-grp., RCT3 Mean NAOJF Env. 5 Temp. 2003 NAOJF 2004 2100000 1800000 1500000 1200000 Spawner Biomass in 2006 900000 600000 300000 0 WG-SQ 1 2 3 4 5 Recruitment Scenario

  21. Application to Stock Assessment: Medium-Term Prediction Assessment WG produces medium-term (10-year) predictions.  used to estimate probability that stock falls below biological reference points (e.g., BPA) under different levels of fishing.

  22. Medium Term Predictions:WG’ Biological Assumptions • - nos.-at-age from tuned VPA • - age-specific natural mortality from MSVPA • - natural random variation in growth rates • - constant maturity ogive • - recruits with random variation • from Beverton-Holt model (not signif.) • constant age-specific relative fishing mortality • rates

  23. T + SD T T - SD Modification to ICES’ Methodology - include temperature influence on recruitment  choose 3 scenarios (cold, avg., warm) - develop hockey-stick recruitment model with random variation: • breakpoint = BPA • re-run the projections • 200 times at FSQ & FPA MacKenzie & Köster 2004 Ecology 85: 784-794

  24. Sprat Stock Prognoses and Biological Reference Points

  25. Medium Term Predictions: Effects of Climate & Exploitation on Sprat Biomass

  26. Summary of Medium Term Predictions: Spawner biomass will remain above BPA in warm and average temperature situations, given FSQ. Spawner biomass has ca. 20% chance of falling below BPA under low T, FPA scenario. Spawner biomass in warm scenario expected to be about double that in cold scenario for both FSQ and FPA.

  27. Conclusions 1. Environmental information (ocean-climate linkages) can be used to improve quality of recruitment predictions. 2. Environmental information (ocean-climate linkages) can be used to increase prediction leadtime without sacrificing quality of predictions. 3. Environmental information can be useful to include in medium term predictions (e.g., to identify sustainable fishing levels).

  28. Thanks for listening !

  29. Medium Term Predictions: Effects of Climate & Exploitation on Sprat Biomass Exploitation Temperature MacKenzie & Köster 2004: Ecology

  30. Sprat Recruitment and Spawner Biomass Trends ICES 2001 Spawner biomass and recruitment not related (ICES 2001).

  31. Effects of Warm Temperature on Sprat Biology • Higher egg and larval survival (lower direct mortality; • Thompson et al. 1981; Nissling 2004). • Faster growth rates in larvae and adults. • Higher food supplies for larvae and adults • (MacKenzie et al. 1996; Möllmann et al. 2000; • Voss et al. 2003). • 4. Increased / earlier egg production (Köster et al. 2003).

  32. Baltic Sprat Egg Survival and Temperature (Lab Studies) Nissling 2004 - egg survival is higher in warmer water (> 5 C)

  33. Zooplankton Concentrations Higher in Warm Springs MacKenzie et al. 1996

  34. Variability in Prey Abundance for Larval Sprat -preferred prey of larval sprat is Acartia nauplii and copepodites (Voss et al. 2003) -spring Acartia abundance has been high in 1990s (Möllmann et al. 2000): Temp. anomaly Abundance anomaly Möllmann et al. 2000

  35. Baltic Sprat Spawning Areas and Egg Vertical Distributions Parmanne et al. 1994 Köster and Möllmann 2000

  36. Spring Water Temperatures in Bornholm Basin 1955-2003 -warm conditions during 1990s-2000s MacKenzie & Köster 2004: Ecology

  37. Ice Conditions Affect... • spring water temperatures • (R2adj = 72%; P < 0.0001) • sprat recruitment • (R2adj = 24%; P = 0.0054) MacKenzie & Köster 2004 Ecology

  38. NAO Affects... • ice conditions • (R2adj = 56%; P < 0.0001) • spring water temperatures • (R2adj = 57%; P < 0.0001) • sprat recruitment • (R2adj = 22%; P = 0.0081) MacKenzie & Köster 2004 Ecology

  39. Validation of Temperature-Recruitment Relationship (1): 1955-1972 R2adj. = 37%; P = 0.0044 MacKenzie & Köster 2004 Ecology

  40. Temperature Effects on Sprat Recruitment -evident in different time periods (1973-1999; 1955-1972) -geographic evidence (north vs. south of species range) -consistent with results for other species (e.g., cod, Pacific salmon)

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