Identifying Regional Priority Areas for Focusing Conservation Actions in Streams and Grasslands: Conservation Planning

Timothy D. Lambert, Leon C. Hinz Jr., Yong Cao

Research output: Book/Report/Conference proceedingTechnical report


In the United States, many resources devoted to conservation are routed through states, but animal and plant populations do not conform to state boundaries. Consequently, neighboring states can enhance their collective conservation impact by coordinating natural resources management. In order to support managers as they review and revise state Wildlife Action Plans in Illinois, Indiana, Michigan, and Wisconsin, this project identified regional conservation priorities for streams and grasslands of the Upper Midwest. Specifically, we (1) selected stream and grassland species of common conservation interest to partnering states, (2) modeled and mapped regional distributions of these species, and (3) used predicted species occurrences to identify regional conservation focal areas.We focused on 31 native grassland and stream species: eight birds, 10 freshwater mussels, 12 fish, and one salamander. The birds, mussels, salamander, and one fish were listed as Species of Greatest Conservation Need (SGCNs) by at least two participating states (Table 1). The remaining 11 fish were reproductive hosts for the selected freshwater mussels. With the help of state Departments of Natural Resources, Natural Heritage programs, and other project partners (Table 3 and Acknowledgments), we compiled comprehensive occurrence data for all 31 species. We also downloaded environmental data for streams and grasslands across the Upper Midwest.We used the assembled species and environmental data to develop regional distribution models for all selected aquatic species and five of the selected grassland birds. The most reliable models were obtained from species with widespread survey data (e.g., fish, birds), although some rare species remained data-limited. It was more difficult to model species with presence-only observations (i.e., no absences recorded; e.g., Mudpuppy) or with surveys whose design or data availability differed among states (e.g., mussels). Predicted species distributions were used to identify regional focal areas for stream and grassland conservation. We used Marxan, a spatially explicit conservation planning software, to design reserve systems that most efficiently protect 10% of the regional populations of all selected SGCNs, which were mostly grassland birds and freshwater mussels. Marxan solutions that addressed these regional conservation priorities emphasized the importance of grassland protection in Illinois and Wisconsin and streams protection in Illinois, Indiana, and Michigan. Proposed focal areas often overlapped with Illinois and Wisconsin Conservation Opportunity Areas, Illinois State Acres For wildlife Enhancement (SAFE) Areas, and Chicago Wilderness’s Green Infrastructure Vision, but this project also points out new areas where conservation initiatives are likely to be effective. Relative to prior efforts, this project’s proposed grassland and stream reserve designs expand coverage to all of Illinois, Indiana, Michigan, and Wisconsin. 3The regional conservation priority maps for streams and grasslands can be used by natural resource managers to ensure that individual state conservation strategies contribute meaningfully to regional goals. State managers can also take a species-specific approach, identifying likely population hotspots within their state by referring to the included high-resolution maps of predicted species distributions. Conservation focal areas spanning state borders highlight opportunities where neighboring states might cooperate to more effectively manage shared natural resources. In summary, by modeling the spatial distributions of selected stream and grassland species, this project provides a regional perspective for conservation in the Upper Midwest.
Original languageEnglish (US)
PublisherIllinois Natural History Survey
StatePublished - Aug 31 2016

Publication series

NameINHS Technical Report 2016 (31)


  • INHS


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