Range expansion of Bythotrephes longimanus in North America: Evaluating habitat characteristics in the spread of an exotic zooplankter

摘要

Sedimentary and water column evidence from 45 boat-accessible and eight backcountry lakes was used to investigate the distribution of Bythotrephes longimanus in northeast Minnesota, USA, and adjacent Ontario, Canada. The results expand the documented range of Bythotrephes in Minnesota from Lake Superior, Island Lake, and Saganaga Lake to Flour Lake, Greenwood Lake, McFarland Lake, Pine Lake, and Caribou Lake as well as to Saganagons Lake in Ontario. The latter three lakes are located in roadless landscapes without motorized boat access. Results confirm that Bythotrephes is no longer present in Boulder Lake or Fish Lake (St. Louis County, Minnesota), providing the first evidence of range compression (extinction following introduction) of this species in North America. Distributional expansion was confined to a corridor along the international border between northeast Minnesota and Ontario. Lakes along the invasion corridor were deeper, more transparent, and had lower chlorophyll concentration, on average, compared to other lakes studied. The pattern of range expansion provided an opportunity to test the predictions of a forecasting model for Bythotrephes occurrence (MacIsaac et al. 2000 Archiv fur Hydrobiologie 149: 1-21) based on habitat characteristics. The model predicted 51% of the surveyed inland lakes to be susceptible to invasion, however, only 13% were actually invaded, implying strong dispersal constraints. Application of the forecasting model to a broader set of 179 Minnesota lakes predicted that 41% may be vulnerable to establishment by Bythotrephes based on habitat characteristics, offering an estimate of the state's overall lake susceptibility (i.e., fundamental niche) to invasion. The results of this study provide evidence for the importance of a low-light refuge where Bythotrephes can minimize vulnerability to fish predation as a key habitat feature not considered by the forecasting model.