Inventorying Great Lakes survey and life history information to facilitate coregonine science, conservation, and restoration

Brian Weidel (USGS, bweidel@usgs.gov), John Sweka (USFWS), Taylor Brown (Cornell University), Helen Takade-Huemacher (USFWS), Iman Pakzad (Cornell University), Jory Jonas (MIDNR), Stephen Lenart (MIDNR), David Fielder (MIDNR), Iyob Tsehaye (WIDNR), Brad Ray (WIDNR), Ian Harding (Red Cliff Band of Lake Superior Chippewa), Jason Smith (Sault Ste Marie Tribe of Chippewa Indians), Kevin Donner (Little Traverse Bay Band of Odawa Indians), John Deller (ODNR), Edmund J. Issac (Grand Portage Band of Lake Superior Chippewa), Cory Goldsworthy (MNDNR), Michael Connerton (NYSDEC), Erin Brown (OMNRF), Jeremy Holden (OMNRF), Andy Cook (OMNRF), Eric Berglund (OMNRF), Stephen James (OMNRF)

Population models are a critical tool for informing native fish conservation and the types of models that can be developed are determined by data availability. In the Great Lakes, the size of the ecosystems and the multi-organizational management approach means population model data can come from a myriad of sources. This project, funded by the Great Lakes Restoration Initiative Coregonine Template, created two databases to synthesize the information available to develop population models with a focus on the species of the genus Coregonus (herein coregonines) . The ‘Survey of Surveys’ describes historic and contemporary long-term fishery dependent and independent surveys across the Great Lakes basin. Details include the organizations involved, the survey spatial and temporal extent, and extent the survey observations can inform coregonine modeling. We also coalesced coregonine life history parameter estimates (e.g., density, age at maturity, growth) that were published in journals and reports. Together these synthesis tools informed population modeling recommendations associated with the Great Lakes Coregonine Framework’s Population Viability Analysis team. The tools will be made publicly available so they can facilitate coregonine conservation by accelerating population model development, illustrating survey redundancy and knowledge gaps, and directing colleagues interested in providing analytical support to collaborators with survey data.