SUPPLEMENTARY MATERIAL
Multi-level trophic cascades in a heavily exploited open marine ecosystem
Michele Casini1,*, Johan Lövgren1, Joakim Hjelm1, Massimiliano Cardinale1, JuanCarlos 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 (michele.casini@fiskeriverket.se)
1
Table S1. Literature used to select the predictors utilized in the GLM analyses.
Response
Sprat
Predictors
cod
zooplankton
temperature
NAO
Zooplankton sprat
phytoplankton
temperature
salinity
NAO
Phytoplankton zooplankton
nutrients
temperature
salinity
References
Rudstam et al . 1994; Horbowy 1996; Harvey et al . 2001; ICES 2006
Grauman and Yula 1989; Kalejs & Ojaveer 1989; Köster et al . 2003; Alheit et al . 2005
Nissling 2004; Köster et al . 2003; MacKenzie & Köster 2004
MacKenzie & Köster 2004; Alheit et al . 2005
Cardinale et al . 2002; Casini et al . 2006
Larsson et al . 1985; Viitasalo et al . 1992
Dippner et al . 2000; Möllmann et al . 2000
Viitasalo et al . 1995; Vuorinen et al . 1998; Möllmann et al . 2000; Hänninen et al . 2003
Dippner et al . 2001; Hänninen et al . 2003; Alheit et al . 2005
No direct evidence of top-down control has been presented so far for the Baltic Sea
Cederwall & Elmgren 1990; Wasmund et al . 1998; Fleming & Kaitala 2006
Wasmund et al . 1998; Gasiūnaitė et al . 2005; Suikkanen et al . 2007
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
explained (%)
Cp
p
PED (%)
Sign
n
90.0
–
33
7.1
+
33
3.0
+
33
86.0
–
33
14.0
+
33
92.7
7.3
–
+
33
33
86.0
14.0
–
+
33
33
Initial Models
Sprat biomass
(approach (i))
Sprat abundance
(approach (i))
Cod biomass
Zooplankton A
Preys for larvae M
Temperature J-M
NAO winter index
Model
Cod biomass
Zooplankton A
Preys for larvae M
Temperature J-M
NAO winter index
Model
3
2
43.8
42.2
22.97
22.15
< 0.001
< 0.001
Final Models
Sprat biomass
(approach (i))
Cod biomass
Preys for larvae M
Model
Sprat abundance
(approach (i))
Cod biomass
Preys for larvae M
Model
2
2
42.5
42.2
22.09
22.15
< 0.001
< 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
explained (%)
Cp
p
PED (%)
Sign
n
44.9
–
33
2.0
10.5
42.5
+
+
+
33
33
33
80.8
–
33
5.4
4.4
9.4
+
+
+
33
33
33
41.3
33.7
25.0
–
+
+
33
33
33
100.0
–
33
Initial Models
Zooplankton biomass
Zooplankton biomass
Clupeid biomass
Chl. a MA
Temperature M-A
Salinity M-A
NAO winter
Model
Clupeid abundance
Chl. a M-A
Temperature M-A
Salinity M-A
NAO winter
Model
4
4
29.4
40.6
30.64
25.84
0.02
0.002
Final Models
Zooplankton biomass
Zooplankton biomass
Clupeid biomass
Salinity M-A
NAO winter
Model
Clupeid abundance
Model
3
28.8
29.30
0.01
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
15
Z-scores
10
Cod biomass
Sprat abundance
Zoopl. biomass
Chlorophyll a
5
0
-5
-10
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
-15
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
1
Predation mortality (by cod)
Fishing mortality
0.8
0.6
0.4
2006
2002
2004
1998
2000
1994
1996
1988
1990
1992
1984
1986
1980
1982
0
1976
1978
0.2
1974
Sprat annual mortality rate (age 1-4)
indicated in the figure because assumed to be constant (ICES 2007).
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)
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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.
2003. Recruitment of Baltic cod and sprat stocks: identification of critical life
stages and incorporation of environmental variability into stock-recruitment
relationships. Sci. Mar. 67(Suppl. 1), 129-154.
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.
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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
copepod biomass declines with decreasing salinity in the Baltic Sea. ICES J. Mar.
Sci. 55, 767-774.
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