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

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

Recruitment – Temperature Relationship for Sprat in the Baltic Sea Various processes acting ! R 2 = 28%, p= MacKenzie & Köster 2004 Ecology 85:

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)

Desirable Characteristics of any Prediction 1)Timing of prediction – earlier is better than later 2) Quality of prediction – close to observed data  we now will address both issues

Data Requirements for ICES Short-term Predictions WG needs estimate of recruitment for 3 years (current year, next year, following year): Consider ICES 2003 assessment. -X, Y from historical estimates, natural and fishing mortality (ICES 2003) year Age ?? Age ?? Age X?? Age XY Age XY Age XY Age XY Age XY Total XY

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

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

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

Climate-Hydrography-Recruitment Links in the Baltic Sea Winter climate (NAO) Ice coverage --- MacKenzie & Köster 2004 Ecology 85: Spring temperatures --- GRAS AS, Martin Visbeck Sprat recruitment +++ All links P < 0.01

Desirable Characteristics of any Prediction 1)Timing of prediction – earlier is better than later 2) Quality of prediction – close to observed data

Quality of Sprat Recruitment Predictions i)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 - retroactively make recruitment predictions for each yearclass use data from , and increment one year at a time, simulating WG meetings in 1983, 1984 …

Recruitment Prediction Comparisons – Time Trends

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

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

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

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

Scenario2003 YC2004 YC WG-SQ0-grp., RCT3mean Env. 10-grp., RCT3NAOJF 2004 Env. 20-grp., RCT3 Min. NAOJF Env. 30-grp., RCT3 Max. NAOJF Env. 40-grp., RCT3Mean NAOJF Env. 5Temp. 2003NAOJF 2004 Recruitment Scenario WG-SQ12345 Spawner Biomass in Alternative Predictions

Scenario2003 YC2004 YC WG-SQ0-grp., RCT3mean Env. 10-grp., RCT3NAOJF 2004 Env. 20-grp., RCT3Min. NAOJF Env. 30-grp., RCT3Max. NAOJF Env. 40-grp., RCT3Mean NAOJF Env. 5Temp. 2003NAOJF 2004 Recruitment Scenario WG-SQ12345 Spawner Biomass in Alternative Predictions

Scenario2003 YC2004 YC WG-SQ0-grp., RCT3mean Env. 10-grp., RCT3NAOJF 2004 Env. 20-grp., RCT3Min. NAOJF Env. 30-grp., RCT3Max. NAOJF Env. 40-grp., RCT3Mean NAOJF Env. 5Temp. 2003NAOJF 2004 Recruitment Scenario WG-SQ12345 Spawner Biomass in Alternative Predictions

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., B PA ) under different levels of fishing.

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

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

Sprat Stock Prognoses and Biological Reference Points

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

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

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).

Thanks for listening !

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

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

Effects of Warm Temperature on Sprat Biology 1.Higher egg and larval survival (lower direct mortality; Thompson et al. 1981; Nissling 2004). 2.Faster growth rates in larvae and adults. 3.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).

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

MacKenzie et al Zooplankton Concentrations Higher in Warm Springs

Möllmann et al 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 Variability in Prey Abundance for Larval Sprat

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

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

spring water temperatures (R 2 adj = 72%; P < ) sprat recruitment (R 2 adj = 24%; P = ) Ice Conditions Affect... MacKenzie & Köster 2004 Ecology

ice conditions (R 2 adj = 56%; P < ) NAO Affects... spring water temperatures (R 2 adj = 57%; P < ) sprat recruitment (R 2 adj = 22%; P = ) MacKenzie & Köster 2004 Ecology

Validation of Temperature-Recruitment Relationship (1): R 2 adj. = 37%; P = MacKenzie & Köster 2004 Ecology

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