1. Cycles and trends in the Iberian sardine (S. pilchardus) stock and catch series and their relationship with the environment M.B. Santos, G.J. Pierce, I. Riveiro, J.M.Cabanas, R. González-Quirós & C. Porteiro Sardine and climate

2. CANTABRIAN SEA BAY OF BISCAY GULF OF CADIZ IXa VIIIc VIIIb VIIIc-East VIIIc-West IXa-North IXa-Central North IXa-Central South IXa-South Portugal IXa-South Cadiz Sardine and climate Single stock, delimited by Spanish- French border and Strait of Gibraltar Supports important fishery in Spain and Portugal Sardine has rapid growth rate, short generation time, long spawning season; females produce high number of eggs Iberian sardine

3. Sardine and climate Iberian sardine High importance of recruitment in overall population dynamics Periods of consecutive low recruitments (+ high F) have led to “crises” in the fishery SSB F R Single stock, delimited by Spanish- French border and Strait of Gibraltar Supports important fishery in Spain and Portugal Sardine has rapid growth rate, short generation time, long spawning season; females produce high number of eggs

4. Sardine and climate López-Jamar et al, 1995 Negative correlation: R v upwelling Dickson et al, 1988 Galician upwelling v catches Roy et al., 1995 Wind strength v R Previous studies Many studies highlight apparent environmental relationships

5. Sardine and climate Stock, catch + environmental variables R, SSB series: 1978-2009 (32 y) Landings in each area 1948-2009 (62 y) Sun spots Upwelling, IPC indexes, SST, Wind, AT, CLO, etc NAO, NAO winter, AMO, GULF, EA and a series of global, regional and local environmental variables

6. 6 Sardine and climate Selection of explanatory variables Dynamic Factor Analysis (a dimension- reduction technique) to try to identify common trends in the EVs. Monthly. Best model: 1 common trend. Average value used for analysis. Exploration of collinearity in EVs + variable selection Relationships between EVs: are they correlated?

7. Time series = trend + cycle and/or AC + residual Sardine and climate Modelling approach Model each RV as function of EVs: select best EVs (GAM) Quantify AC. If AC persists in model residuals, use GAMM Decompose RVs, EVs into simple trends and residuals (GLM v time) Compare RV trends and residuals with EV trends and residuals (which components appear to be driving the relationship?)

8. Trends in Recruitment + SSB Sardine and climate Exploration of time series Possibility of linear + simple polynomial relationships with time - GLMs Linear trend in R: decreasing from high values in the 80s to low values in the 90s and now No trend in SSB SSBR

9. Cycles in Recruitment + SSB Sardine and climate Exploration of time series 0.25 cycle / y = 1 cycle every 4 years Spectral analysis (note: we are also detrending the data because we want to concentrate on the cycles) 0.1 cycle / y = 1 cycle every 10 years (short time series, only 32 years) SSBR

10. (Partial) AC in Recruitment + SSB Sardine and climate Exploration of time series Recruitment: PAC significant at time lag 1 Partial autocorrelograms SSB: PAC very significant at time lag 1

11. All response variables Sardine and climate Exploration of time series VariableCycle (years) TrendPAC (lag, years) R*4Linear ↓1 SSB10None -1 L_Total*20? Cubic ∩ 1 L_VIIIcW*≥5Cubic ↘1 L_IXaN20? Quadratic ∩ 1, 4 L_IXaCN*20?Cubic ↘1 (3) L_IXa20? Quadratic ∩ 1 L_VIIIc20? Cubic ∩ 1, 2, 16 * Log transformed

12. Explanatory variables Sardine and climate Exploration of time series VariableCycle (years) TrendPAC (lag, years) Sunspots10Linear ↓1, 2, 3 NAO4None -No AC AMO11Cubic ↗1, 9 EA4Linear ↑1, 2 UpwellingUnclearNone -1, 2, 3 IPC15Linear ↓No AC W40350_W3Linear ↑1, 2, 3 SST40350_WUnclearNone -1 AT40350_WUnclear Quadratic ∩ 1 CLU40350_W20Cubic ↘1

13. 13 Sardine and climate Results: L_IXaCN Explanatory Variable%DE in single EV model P (final model)Df (final model) SST42350_W W42350_W avspots 10.1% 21.2% 16.1% 0.0010 0.0399 0.0023 2.2 1 2.1 Landings are the most complex series to model as they will a priori contain stock, fishing and environment effects AC persists in model residuals GAMM with AR1 variance structure is an “improvement” …but AC persists and all environmental effects then become non- significant Final GAM for L_IXaCN (%DE=45.6%, AIC = 20.47) GAM/GAMM : which EVs best explain Landings?

14. 14 Sardine and climate Results: SSB Explanatory Variable%DE in single EV model P (final model)Df (final model) CLU40350_W AT40350_W AMO 26.0% 30.0% 33.9% 0.0010 0.0399 0.0023 1 2.1 2.8 No autocorrelation (confirmed by comparing AIC of best model with/without an AR1 variance structure) Final GAM for SSB (%DE=68.3%, AIC = 819.18) GAM/GAMM : which EVs best explain SSB?

15. 15 Sardine and climate Results: Recruitment Explanatory Variable%DE in single EV model P (final model)Df (final model) W40350_W SST40350_W avspots NAO 20.2% 35.3% 25.2% 3.2% 0.0260 0.0002 0.0273 0.1451 1 2.84 No autocorrelation (confirmed by comparing AIC of best model with/without an AR1 variance structure) Final GAM for LogR (%DE=64.6%, AIC = -17.44) GAM/GAMM: which EVs best explain R? VariableTrendCycle LogRLinear ↓4 y W40350_W SST40350_W avspots NAO Linear ↑ None - Linear ↓ None - 3 y Unclear 10 y 4 y

16. 16 1.GLM  Extract trend and residuals (noise) from both LogR and Wind strength 2.GAM  LogR as a function of W, W trend and W noise 3.GAM  LogR noise as function of W noise LogR v trend + noise in winter wind strength (W40350_W) Sardine and climate Results: effect of wind strength Log R v W (%DE=20.2 P=0.0143) Log R v W trend (%DE=39.2 P=0.0001) Log R v W noise (%DE=0.1 P=0.870) Log R noise v W noise (%DE=0.1 P=0.837)

17. 17 1.GLM  trend and residuals (noise) from LogR (no significant trend in SST40350_W) 2.GAM  LogR as function of SST 3.GAM  LogR trend and LogR noise as function of SST LogR v trend + noise in winter SST (SST40350_W) Sardine and climate Results: effect of SST Log R v SST (%DE=35.3 P=0.0115) Log R noise v SST (%DE=16.3 P=0.0219) Log R trend v SST (%DE=13.0 P=0.289)

18. 18 1.GLM  Extract trend and residuals (noise) from both LogR and Sun 2.GAM  LogR as a function of Sun, Sun trend and Sun noise 3.GAM  LogR noise as function of Sun noise LogR v trend + noise in average number of sunspots (avspots) Sardine and climate Results: effect of sunspots LogR v Sun (%DE=25.2 P=0.0034) LogR v Sun trend (%DE=39.2 P=0.0001) LogR v Sun noise (%DE=4.1 P=0.267) LogR noise v Sun noise (%DE=8.0 P=0.116)

19. 19 In short-lived fish, environmental relationships can be an important component of stock and fishery dynamics Iberian sardine R, SSB, catch series all show “environmental” effects: - wind strength, SST, AT, NAO, AMO, sunspots, GAMM sometimes permits removal of AC (and may not be needed) Need to investigate the nature of the relationships to understand mechanisms; separating trends and noise is useful guide Short time series remain a limitation (e.g. to detect cycles) Relationships for R: - wind, sunspots: effects due to opposite/similar linear trends - SST effect relates more to short-term variation around trend Sardine and climate Conclusions

20. We would like to thank all our Portuguese and Spanish colleagues working on sardine, all the crew and scientists in the acoustic surveys, everyone who collected the landings data, and Alain Zuur (Highland Statistics) for statistical advice Xunta de Galicia, Programa de Recursos Humanos Plan Nacional de I + D + I, Proyecto CTM 2010- 16053 (LOng-Term variability OF small-PELagic fishes at the North Iberian shelf ecosystem) Sardine and climate Acknowledgements