The cyanobacterium Gloeotrichia echinulata increases the stability and network complexity of phytoplankton communities
Changes in the abundance of a taxon can have large effects on communities, particularly if that taxon is a strong interactor. These changes may arise as a consequence of environmental change, recruitment from dormant stages, or quirks of population dynamics, and have effects that ripple through a community interaction network. We hypothesized that cyanobacteria, which are increasing in many freshwater lakes globally, may be strong interactors because they can exert large and persistent effects on the biomass and composition of other phytoplankton. To test this hypothesis, we evaluated how the phytoplankton community responded to different densities of Gloeotrichia echinulata, a large colonial cyanobacterium increasingly observed in low‐nutrient lakes in northeastern North America, in an in situ mesocosm experiment. We observed that many phytoplankton taxa, especially diatoms and green algae, responded primarily to increased nutrient availability (a result of Gloeotrichia's nitrogen fixation and translocation of phosphorus from the sediments), while a few taxa (two euglenophytes, one dinoflagellate, and one cyanobacterium) responded to both the direct and indirect effects of Gloeotrichia. Surprisingly, Gloeotrichia reduced the compositional variability of the phytoplankton community relative to the non‐Gloeotrichia control treatment; there was no effect on the aggregate temporal variability of total non‐Gloeotrichia biovolume. Moreover, experimentally increased densities of Gloeotrichia coincided with increasing complexity of the phytoplankton community in network analyses of taxon co‐occurrences, as indicated by significantly greater network density and transitivity and shorter path lengths. Taken together, these findings suggest that Gloeotrichia may be a strongly interacting species in low‐nutrient lakes, with the potential to increase the resilience of phytoplankton communities to future disturbance by increasing compositional stability and network complexity.