Assessing the Effectiveness of Coar Areas for Greater Sage-Grouse Conservation: A Spatially-Explicit Demographic Approach Using Management and Resource Development Scenarios

Project ID: G13AC00359

Federal Agency: U.S. Geological Survey

Partner Institution: University of Washington

Fiscal Year: 2013

Initial Funding: $48,175

Total Funding: $48,175

Project Type: Technical Assistance

Project Disciplines: Biological

Principal Investigator: Lawler, Joshua

Agreement Technical Representative: Aldridge, Cameron

Abstract: This cooperative agreement will provide support for the project titled: ”Assessing the Effectiveness of Core Areas for Greater Sage-Grouse Conservation: A Spatially-Explicit Demographic Approach Using Management and Resource Development Scenarios”. The overall goal is to develop a population viability modeling framework to enable quantitative investigation of the long-term dynamics and persistence of Sage-grouse populations, directly assisting managers in prioritizing limited resources for conservation and management. The modeling framework will serve as a data-driven decision support tool for prioritizing and managing Sage­ grouse populations and their habitats, synthesizing current habitat and population information in a spatially explicit individual-based population model to predict Greater Sage-grouse abundance, productivity, and persistence in Wyoming. With the availability of similar base layers across other regions (some already under development), this modeling framework could be transported, modified, and applied to Sage-grouse populations throughout the western US. The completed modeling framework will be used to assess a range of current and future scenarios including habitat (e.g., core areas) and population management, and resource development (e.g., oil and gas). This comprehensive approach is unlike any other Sage-grouse management resource available today because it will: 1) use data-driven habitat models for the species, 2) directly incorporate fitness components across life stages, 3) evaluate demographic and environmental variability within the models, 4) provide spatially explicit models as a tool for managers, 5) account for population structure and inherent site fidelity of individuals, 6) forecast future
trajectories, predicting population numbers into the future, and 7) directly incorporate key factors
(energy development as an initial concept) affecting landscape change.