Forecasting the ecological impacts of the Hemimysis anomala invasion in North America: Lessons from other freshwater mysid introductions

The bloody red mysid shrimp Hemimysis anomala is the most recent Ponto-Caspian invader of the Great Lakes. At present, there is a paucity of data that can be used to forecast its ecological impacts in North America; however, published studies of other invasive freshwater mysids (i.e. Mysis diluviana introduced to North American lakes and Mysis relicta introduced to Scandinavian lakes) reveal potentially valuable predictive information. A frequent impact of Mysis introductions is a drastic decline in zooplankton (particularly cladoceran) abundance, which can release phytoplankton populations from grazer control and alter the abundance and growth of pelagic fishes. Other impacts include enhanced bioaccumulation of contaminants, new pathways of parasite transmission to fishes, and altered nutrient cycling. Like Mysis spp., H. anomala has the potential to affect all trophic levels in lakes. It has reduced the abundance and diversity of zooplankton in European reservoirs, where it achieved mean densities higher than those recorded for other introduced mysids elsewhere. It can colonize a broader range of habitats than Mysis throughout the year, including the nearshore areas of lakes and large rivers. Within river systems, the impacts of H. anomala will likely be most pronounced in backwater lakes, impounded areas and reservoirs. Understanding the environmental factors that limit the local abundance of H. anomala and mediate its species interactions is essential for developing predictive models of its impact.

Research highlights
► Mysid invasions can cause drastic declines in zooplankton abundance in lakes.
► Such declines can indirectly alter the abundance and growth of pelagic fishes.
► H. anomala can impact a broader range of aquatic habitats than other mysids.
► Fish predation on H. anomala may alter bioaccumulation pathways.
► Mysid impacts vary with local environmental conditions and thus are difficult to predict.