We observed a community-wide trophic cascade in the Baltic Sea (a–c) caused by the sharp decline in cod biomass which began in the early 1980s. Since the early 1990s, the cod stock has been low and has not shown any tendency to recover (a). The severe decline of the top piscivorous fish has been followed by a drastic increase in its main prey, the zooplanktivorous sprat (a,b). Also, during the mid-1970s the sprat population was fairly high coinciding with a relatively low cod biomass. The correlations between cod biomass and sprat biomass and abundance were negative (respectively, r=−0.63 and −0.60, n=33). Since the mid-1990s, the sprat stock has been high, although rather variable (a,b). During the observed period, total zooplankton biomass first increased and then decreased following inverse sprat population development (sprat biomass–zooplankton biomass, r=−0.53, n=33; sprat abundance–zooplankton biomass, r=−0.59, n=33; b), whereas phytoplankton biomass (chlorophyll a) showed an inverse pattern to zooplankton biomass (r=−0.47, n=28; c). The negative relationships between adjacent trophic levels were also identified by the z-scores (figure S1 in the electronic supplementary material).
Figure 1 Trends of (a) cod biomass (squares) and sprat biomass (circles); (b) sprat abundance (diamonds) and zooplankton biomass (triangles) (ind., individuals); and (c) zooplankton biomass (triangles) and chlorophyll a (squares). The horizontal lines indicate (more ...)
Cross-correlation function and z-scores demonstrated that the highest correlation between cod biomass and sprat biomass occurred at a lag of +3 years (r=−0.71, n=33) and between cod biomass and sprat abundance at a lag of +5 years (r=−0.73, n=33). The delay can be explained not only by the cumulative direct effect of cod feeding on the same sprat cohort during several successive years but also by the indirect effects of cod predation on sprat reproductive output which will have a delayed effect on the sprat population. On the other hand, zooplankton and phytoplankton responded promptly (the same year) to the increase in sprat abundance. Non-adjacent trophic levels were positively correlated (cod–zooplankton at a lag of +3 years, r=0.46, n=33; sprat biomass–phytoplankton, r=0.62, n=28; sprat abundance–phytoplankton r=0.63, n=28), suggesting the existence of indirect mutualism between non-adjacent trophic levels and reinforcing the evidence of trophic cascades in the Baltic Sea.
The stock of the other main prey for cod in the Baltic Sea, the herring, decreased until the beginning of 2000 when a slight increase started to occur (a). Temperature showed a general albeit weak increasing trend, whereas salinity strongly dropped up to the early 1990s and increased afterwards (b). The NAO winter index (December–March) presented strong inter-annual oscillations, with a slight increase up to the early 1990s followed by a decrease. Total summer nutrients concentration rose from the 1960s up to the early 1990s and levelled off afterwards (c).
Figure 2 (a) Trends of herring biomass (closed circles) and abundance (open circles; ind., individuals; ICES 2007); (b) trends in temperature (circles) and salinity (triangles; average of May and August (M–A) data integrated over 0–100m (more ...)
The results of the GLM modelling showed that top-down forces played the most important role in shaping each trophic level ( and ; a–c
). Concerning the sprat models, cod biomass explained the largest proportion of models' deviance irrespective of the approach used to construct the predictors' time series. We show here the outcome of the sprat model approach (ii) (the results of the approach (i) are presented in the electronic supplementary material, table S2). Accordingly, the predictors included in the sprat biomass initial model were cod biomass, larval sprat prey biomass in May (Acartia
spp. and T. longicornis
) and NAO winter index (initial model, 59.2% of the deviance explained), since temperature and summer zooplankton biomass did not fulfil the ecological criterion (). Cod biomass and NAO were also included in sprat final model (56.6% of the deviance explained), since the elimination of either of those would have increased the Cp
of the model. Cod and NAO explained, respectively, 76.7 and 23.3% of the deviance of the sprat biomass final model (). Also in the sprat abundance model, only cod biomass and NAO were the predictors present in the final model (48.6% of the deviance explained by the model), with cod explaining almost all the deviance (; a
). The results of the GLM using the 4-year mean of the predictors (i.e. averaged at t
−4, see §2
) were very similar to those using either 3- and 5-year means (predictors averaged, respectively, at t
−3 and t
−5). Sprat fishing mortality was not included as a predictor in the sprat models because it followed a pattern similar to sprat stock development (figure S2 in the electronic supplementary material); this probably indicates that during the past three decades, the sprat stock level has influenced sprat fishing mortality (a higher population level usually allows the fishing quotas to raise) and not the opposite (ICES 2007
Table 1 Results of the GLM analyses, initial models. (Predictors, proportion of the deviance explained by the models, Cp and probability of the models are indicated. The proportion of the model deviance explained by each predictor (PED (%)) is also indicated. (more ...)
Table 2 Results of the GLM analyses, final models. (Predictors, proportion of the deviance explained by the models, Cp and probability of the models are indicated. The proportion of the model deviance explained by each predictor (PED (%)) is also indicated. M, (more ...)
Figure 3 Results of the GLM final models for (a) sprat abundance, (b) zooplankton biomass A (August) and (c) chlorophyll a A (August). The effect of each selected predictor on the response variables is shown. In the zooplankton model, we chose to show sprat abundance (more ...)
The parameters included in the zooplankton biomass initial model were sprat biomass, temperature and salinity (average May–August) and NAO winter index (40.3% of the deviance explained by the initial model), whereas chlorophyll a (average May–August) was excluded from the analysis since it did not fulfil the ecological criterion (because it was negatively correlated to zooplankton, contradicting the expectation of bottom-up regulation; ). Only sprat biomass and NAO were present in the final model (36.6% of the deviance explained), with sprat biomass explaining the largest fraction of the deviance (). The use of sprat abundance as a top-down predictor improved both the initial and final zooplankton models (41.6 and 39.1 of the deviance explained, respectively; and ; b). The same results were obtained when using chlorophyll a in May and August separated as single predictors. When herring abundance or biomass was introduced in the zooplankton GLM as a predictor, they were excluded because they did not fulfil the ecological criterion (their relation to zooplankton had slightly positive signs). Moreover, using total clupeid (sprat+herring) biomass or abundance as a top-down force, the deviance explained by both the initial and final models was lower than that in the models with only sprat (table S3 in the electronic supplementary material). Zooplankton biomasses in spring and summer were not correlated (r=−0.04, Pr=0.84, n=33).
The phytoplankton (i.e. chlorophyll a
) initial model included zooplankton biomass, temperature and salinity (which together explained 24.4% of the deviance), with zooplankton biomass explaining almost all the model deviance (). Zooplankton biomass was the only predictor present in the final model (22.2% of the deviance explained; ; c
). The same final model was obtained when using temperature, salinity and nutrients integrated over the 0–20
m depth interval, as well as with nutrients separated in nitrate and phosphate. Chlorophyll a
values in spring and summer were not correlated (r
=−0.26, Pr=0.18, n
Residuals of the final models were homogeneous and did not strongly violate normality assumption. Residuals were not autocorrelated, except for the sprat models in which they showed a very weak autocorrelation at lag 1 (figure S3 in the electronic supplementary material). GLM analyses showed results consistent with GAMs, which is a predominant importance of top-down forces on climate-hydrological forces on all the trophic levels.