Trophic web structure in a shallow eutrophic lake during a dominance shift from phytoplankton to submerged macrophytes

Abstract

In Lake Krankesjön, southern Sweden, sago pondweed (Potamogeton pectinatus L.) and a stonewort (Chara tomentosa L.) expanded spatially during the second half of the 1980’s after more than a decade of phytoplankton blooms and sparse submerged vegetation. During the expansion of submerged plants the number of resting and breeding waterfowl increased. The increase was significant for herbivorous birds such as coot (Fulica atra L.) and mute swan (Cygnus olor (Gmelin)), but also for omnivorous dabbling ducks. The shift from phytoplankton to submerged macrophytes caused structural changes on higher trophic levels, and an altered trophic web developed. The density of planktonic Cladocera decreased,which is suggested to be a result of decreased phytoplankton productivity and biomass as nutrient levels dropped. The benthic macroinvertebrate assemblage changed from low diversity and biomass dominated by Chironomidae and Oligochaeta on bare sediment, to high diversity and biomass characterized by plant-associated forms like snails and isopods in areas covered by macrovegetation. The mean size of perch (Perca fluviatilis L.) increased, probably as a result of higher availability of macroinvertebrates in the vegetation. The perch reached a mean size where the species is known to shift to a fish diet, permitting an increased top down effect on the ecosystem. The results support the idea that shallow eutrophic lakes can shift between two states, each one stabilized by feed-back mechanisms including both biotic and abiotic factors. Shifts between these states are suggested to be a possible explanation for observed drastic changes in abundance of waterfowl in shallow eutrophic lakes.KeywordsSubmerged MacrophyteSubmerged VegetationAlternative Stable StateBare SedimentPotamogeton PectinatusThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.