Table 2. Summary of the results with biomass as response variable.
| Greifensee (N=207) | Lake Zurich (N=82) | Danube delta lakes (N=136) | |
|---|---|---|---|
| Number of models used for model averaging (out of 32) | 8 | 8 | 4 |
| Sum of weights | 98.8% | 99.5% | >99.9% |
| Species richness estimate (95% CI) | 0.008 (−0.033, 0.048) | 0.004 (−0.097, 0.104) | 0.345 (0.193, 0.497) |
| Pielou's evenness estimate (95% CI) | 0.001 (−0.023, 0.024) | −0.559 (−0.744, −0.374) | 0.006 (−0.068, 0.080) |
| TOP estimate (95% CI) | 0.073 (0.029, 0.117) | 0.070 (−0.131, 0.271) | 0.556 (0.382, 0.730) |
| TED estimate (95% CI) | −0.590 (−0.658, −0.521) | −0.437 (−0.691, −0.184) | −0.430 (−0.581, −0.278) |
| FDis estimate (95% CI) | −0.006 (−0.039, 0.027) | 0.043 (−0.137, 0.223) | −0.085 (−0.260, 0.091) |
| Mean R2 (null modela) | 0.65 (0.02) | 0.63 (0.24) | 0.65 (0.17) |
Abbreviations: CI, confidence interval; FDis, functional dispersion index; TED, trait even distribution; TOP, trait onion peeling.
The estimates of the five explanatory variables (with 95% CIs) represent standardised model-averaged regression coefficients. Values in bold are significant at the P<0.05 level.
a
Accounting for temporal and spatial heterogeneity.