SUPPLEMENTARY MATERIAL

Multi-level trophic cascades in a heavily exploited open marine ecosystem

Michele Casini1,*, Johan Lövgren1, Joakim Hjelm1, Massimiliano Cardinale1, Juan-Carlos

Molinero2 and Georgs Kornilovs3

1 Swedish Board of Fisheries, Institute of Marine Research, Box 4, 45321 Lysekil,

Sweden;

2 the Leibniz Institute of Marine Sciences IFM-GEOMAR, Marine Ecology/Experimental

Ecology, Düsternbrooker Weg 20, D-24105 Kiel, Germany;

3 Latvian Fish Resources Agency, Daugavgrivas Str. 8, LV-1048 Riga, Latvia.

*Author for correspondence ([email protected])

1 Table S1. Literature used to select the predictors utilized in the GLM analyses.

Response Predictors References Sprat cod Rudstam et al . 1994; Horbowy 1996; Harvey et al . 2001; ICES 2006 zooplankton Grauman and Yula 1989; Kalejs & Ojaveer 1989; Köster et al . 2003; Alheit et al . 2005 temperature Nissling 2004; Köster et al . 2003; MacKenzie & Köster 2004 NAO MacKenzie & Köster 2004; Alheit et al . 2005 Zooplankton sprat Cardinale et al . 2002; Casini et al . 2006 phytoplankton Larsson et al . 1985; Viitasalo et al . 1992 temperature Dippner et al . 2000; Möllmann et al . 2000 salinity Viitasalo et al . 1995; Vuorinen et al . 1998; Möllmann et al . 2000; Hänninen et al . 2003 NAO Dippner et al . 2001; Hänninen et al . 2003; Alheit et al . 2005 Phytoplankton zooplankton No direct evidence of top-down control has been presented so far for the Baltic Sea nutrients Cederwall & Elmgren 1990; Wasmund et al . 1998; Fleming & Kaitala 2006 temperature Wasmund et al . 1998; Gasiūnaitė et al . 2005; Suikkanen et al . 2007 salinity Gasiūnaitė et al . 2005

2 Table S2. Results of the GLM analyses (initial and final models) for sprat biomass and abundance, approach (i) (see Material and Methods). Predictors, proportion of the deviance explained by the models, Cp and probability of the initial (upper panel) and final (lower panel) models are indicated. The proportion of the model deviance explained by each predictor (PED %) is also indicated. The empty cells in the panel of the initial models stay to indicate that the corresponding predictor did not fulfil the ecological criterion and, thus, was discarded from the analysis. J = January, M = May; A = August.

The sign of the relationships between the responses and the predictors and the number of observations (n) are also indicated.

Predictors df Deviance Cp p PED (%) Sign n explained (%) Initial Models Sprat biomass Cod biomass 90.0 – 33 (approach (i)) Zooplankton A Preys for larvae M 7.1 + 33 Temperature J-M NAO winter index 3.0 + 33 Model 3 43.8 22.97 < 0.001 Sprat abundance Cod biomass 86.0 – 33 (approach (i)) Zooplankton A Preys for larvae M 14.0 + 33 Temperature J-M NAO winter index Model 2 42.2 22.15 < 0.001 Final Models Sprat biomass Cod biomass 92.7 – 33 (approach (i)) Preys for larvae M 7.3 + 33 Model 2 42.5 22.09 < 0.001 Sprat abundance Cod biomass 86.0 – 33 (approach (i)) Preys for larvae M 14.0 + 33 Model 2 42.2 22.15 < 0.001

3 Table S3. Results of the GLM analyses (initial and final models) for zooplankton biomass using clupeid (sprat + herring) biomass and abundance as top-down forces.

Predictors, proportion of the deviance explained by the models, Cp and probability of the initial (upper panel) and final (lower panel) models are indicated. The proportion of the model deviance explained by each predictor (PED %) is also indicated. The empty cells in the panel of the initial models stay to indicate that the corresponding predictor did not fulfil the ecological criterion and, thus, was discarded from the analysis. M = May; A =

August. The sign of the relationships between the responses and the predictors and the number of observations (n) are also indicated.

Predictors df Deviance Cp p PED (%) Sign n explained (%) Initial Models

Zooplankton biomass Clupeid biomass 44.9 – 33 Chl. a MA Temperature M-A 2.0 + 33 Salinity M-A 10.5 + 33 NAO winter 42.5 + 33 Model 4 29.4 30.64 0.02 Zooplankton biomass Clupeid abundance 80.8 – 33 Chl. a M-A Temperature M-A 5.4 + 33 Salinity M-A 4.4 + 33 NAO winter 9.4 + 33 Model 4 40.6 25.84 0.002 Final Models Zooplankton biomass Clupeid biomass 41.3 – 33 Salinity M-A 33.7 + 33 NAO winter 25.0 + 33 Model 3 28.8 29.30 0.01 Zooplankton biomass Clupeid abundance 100.0 – 33 Model 1 32.8 24.24 < 0.001

4 Figure S1. Cumulative z-scores of the biological time-series (cod biomass, sprat abundance, zooplankton biomass and phytoplankton biomass). Z-scores are standardized anomalies, i.e. deviations from the mean of the investigated time series divided by the standard deviation. Plots of the cumulative z-scores indicate periods with predominantly positive or negative anomalies in the time series (shown by upward or downward trends in the z-scores), and can be used to detect in a simple way the intensity and duration of homogenous periods within the time series (Molinero et al. 2005). Sprat abundance rather than biomass was shown here due to the strong density-dependent body growth of

Baltic sprat (Casini et al. 2006).

20 Cod biomass Sprat abundance 15 Zoopl. biomass 10 Chlorophyll a s

e 5 r o c s - 0 Z

-5

-10

-15 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 6 6 7 7 7 7 7 8 8 8 8 8 9 9 9 9 9 0 0 0 0 9 9 9 9 9 9 9 9 9 0 0 9 9 9 9 9 9 9 9 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 Year

5 Figure S2. Trends in sprat annual predation mortality (by cod) and fishing mortality rates

(averaged for sprat ages 1 to 4) in the Baltic Sea during the past three decades (ICES

2006, 2007). Sprat residual natural mortality (i.e. not due to cod predation) is not indicated in the figure because assumed to be constant (ICES 2007). ) 4 -

1 1 Predation mortality (by cod) e g a

( Fishing mortality

e 0.8 t a r

y t

i 0.6 l a t r o

m 0.4

l a u n

n 0.2 a

t a r

p 0 S 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 7 7 7 8 8 8 8 8 9 9 9 9 9 0 0 0 0 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 Year

6 Figure S3. Summary of the residual analysis of the final models: (a) sprat biomass model

(approach (ii); (b) sprat abundance model (approach (ii); (c) zooplankton model; (d) chlorophyll a model. Plots of the residuals versus predicted values, normal probability of the residuals, and autocorrelation function (ACF) of the residuals are shown.

(a)

(b)

(c)

(d)

7 Supplementary references

Cardinale, M., Casini, M. & Arrhenius, F. 2002. The influence of biotic and abiotic

factors on the growth of sprat (Sprattus sprattus) in the Baltic Sea. Aquat. Living

Resour. 15, 273-281.

Dippner, J. W., Kornilovs, G. & Sidrevics, L. 2000. Long-term variability of

mesozooplankton in the Central Baltic Sea. J. Marine Syst. 25, 23-31.

Gasiūnaitė, Z. R., Cardoso, A. C., Heiskanen, A.-S., Henriksen, P., Kauppila, P.,

Olenina, I., Pilkaitytė, R., Purina, I., Razinkovas, A., Sagert, S., Schubert, H. &

Wasmund, N. 2005. Seasonality of coastal phytoplankton in the Baltic Sea:

influence of salinity and eutrophication. Estuar. Coast Shelf S. 65, 239-252.

Grauman, G. B. & Yula, E. 1989. The importance of abiotic and biotic factors in the

early ontogenesis of cod and sprat. Rapp. P.-v. Réun. Cons. int. Explor. Mer 190,

207-210.

Kalejs, M. & Ojaveer, E. 1989. Long-term fluctuations in environmental conditions and

fish stocks in the Baltic. Rapp. P.-v. Réun. Cons. int. Explor. Mer 190, 153-158.

Köster, F. W., Hinrichsen, H.-H., Schnack, D., St. John, M. A., MacKenzie, B. R.,

Tomkiewicz, J., Möllmann, C., Kraus, G., Plikshs, M., Makarchouk, A. & Aro, E.

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stages and incorporation of environmental variability into stock-recruitment

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Larsson, U., Elmgren, R. & Wulff, F. 1985. Eutrophication and the Baltic Sea: causes

and consequences. Ambio 14, 9-14.

Rudstam, L. G., Aneer, G. & Hildén, M. 1994. Top-down control in the pelagic Baltic

ecosystem. Dana 10, 105-129.

8 Suikkanen, S., Laamanen, M. & Huttunen, M. 2007. Long-term changes in summer

phytoplankton communities of the open northern Baltic Sea. Estuar. Coast Shelf S.

71, 580-592.

Viitasalo, M. 1992. Mesozooplankton of the Gulf of Finland and Northern Baltic proper

– a review of monitoring data. Ophelia 35, 147-168.

Viitasalo, M., Vuorinen, I. & Saesmaa, S. 1995. Mesozooplankton dynamics in the

northern Baltic Sea: implications of variations in hydrography and climate. J.

Plankton Res. 17, 1857-1878.

Vuorinen, I., Hänninen, J. Viitasalo, M., Helminen, U. & Kuosa, H. 1998. Proportion of

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Sci. 55, 767-774.

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