Submerged macrophytes in Danish lakes: impact of morphological and chemical factors on abundance and species richness

We analysed long-term monitoring data on submerged macrophytes and water chemistry from 666 Danish lakes > 1 hectare mean depth < 3 m, encompassing a total of 1447 lake years. Our aim was to describe how plant cover (COV), volume inhabited (PVI) species richness related physical chemical environmental variables. Boosted regression tree (BRT) analyses revealed that chlorophyll a, Secchi were the strongest predictors COV PVI. Chlorophyll had strong negative effect up 50 µg/l, whereas changes more gradual covered gradient. Macrophyte best predicted by area alkalinity, with nutrients colour having significant but less marked effects. For 78% observed variance could be explained BRT model, most powerful being both phosphorus nitrogen, additional effects alkalinity. limited direct macrophyte abundance, an indirect hierarchical mediated through interactive itself, colour.