Figure 1.
A conceptual figure of the hypotheses.
a) At small and large scale, the relationships between species richness and nutrient supply are predicted to be unimodal or linear, respectively. b) Biomass production first increases with species richness but saturates at high richness levels. c) The causal relationships between resource availability, species richness, biomass and resource ratio. Figure modified from Cardinale et al (8).
Figure 2.
Map of Finland with the study areas marked by gray circles.
The study areas were: 1) Vantaanjoki, 2) Karjaanjoki, 3) Kokemäenjoki, 4) Upper Kymijoki, and 5) Koutajoki. On the right, small maps show geographical positions of each lake in the same study areas.
Figure 3.
The relationships between log-transformed species richness and log total P (µg/l) in a) zooplankton, b) phytoplankton, and c) bacterioplankton data sets (n = 100).
Table 1.
The regression models for the relationships between local species richness and concentrations of total P (µg/l) at five drainage systems and for the whole set of lakes for each planktonic group.
Figure 4.
The relationship between a) phytoplankton richness (n = 100), and b) NMDS1 site scores and chl a (P = 0.009, and P<0.001, respectively).
Figure 5.
The results of a Structural Equation Modeling for phytoplankton data with (5a) or without (5b) best model selection.
SEM was conducted to test whether covariance among variables collected from 100 lakes could be produced by a covariance matrix set a priori (shown in Fig. 1c). The coefficients next to arrows represent the standard deviation change between variables. R2 values indicate the amount of explained variation in species richness and phytoplankton biomass. The correlation between the resource supply and resource ratio (R) was −0.44. Dashed lines denote non-significant relationships.
Table 2.
The results of General Linear Model for the zooplankton, phytoplankton, and bacterioplankton richness for the whole set of lakes (n = 100).