| Popis: |
We analysed long-term monitoring data on submerged macrophytes and water chemistry from 666 Danish lakes > 1 hectare and mean depth a, Secchi depth and depth were the strongest predictors of COV and PVI. Chlorophyll had a strong negative effect up to 50µg/l, whereas the changes related to Secchi depth and depth were more gradual and covered more of the gradient. Macrophyte species richness was best predicted by lake area and alkalinity, with chlorophylla, nutrients and colour having significant but less marked effects. For chlorophylla, 78% of the observed variance could be explained by the BRT model, with the most powerful predictors being both phosphorus and nitrogen, but with significant additional effects of plant cover and alkalinity. Our analyses revealed limited direct effect of nutrients on macrophyte abundance, but an indirect hierarchical effect of nutrients mediated through chlorophyllawith additional interactive effects by plant cover itself, alkalinity, mean depth and colour. |
