Environmental Science Division (EVS)a Division of Argonne National Laboratory
Predictive environmental understanding
 

Impacts of Wind Energy Development on the Greater Sage-Grouse

EVS developed a model for examining the cumulative impacts of wind energy development on the greater sage-grouse.

EVS staff members have developed a spatially explicit, individual-based model (IBM, also known as agent-based model) for examining the cumulative impacts of wind energy development on populations and habitat of the greater sage-grouse (Centrocercus urophasianus), a species whose population has declined significantly, is considered near threatened by the International Union for Conservation of Nature, and has been considered for listing under the U.S. Endangered Species Act. Model development was initiated with funding from DOE's Wind and Water Power Program.

Concern over the sustainability of sage-grouse populations in the face of increasing development has led to widespread restrictions on development in the species' core areas, but an incomplete understanding of the birds' response to the development of wind farms and other structures could lead to unnecessary or ineffective restrictions. The EVS work is intended to facilitate smart development that minimizes impact by synthesizing available information into a predictive model.

The sage-grouse has a complex life history that includes seasonal migrations to meet life history needs. Local disruption of any part of the annual cycle could result in long-term impacts in a wider portion of the species range. To improve understanding of the possible effects of wind energy projects and other impacts on populations, the EVS model incorporates the species' requirements and movements.

The model was developed using Albany County, Wyoming as a case study. Albany County supports important populations of greater sage‑grouse and has high potential for wind energy development. This early model prototype demonstrated the utility of the approach in assessing direct, indirect, and cumulative effects by modeling changes in habitat suitability, reproduction, and survivorship of individual birds. With this tool, users are able to determine the proximate causes of sage-grouse population changes (e.g., changes in seasonal habitat suitability, survivorship, and reproduction,). This information can be used to design focused mitigation strategies that address underlying impact drivers.

The greater sage-grouse IBM interface. User-defined parameter settings (a), instantaneous status of numerical information (b), instantaneous species' spatial distribution (c), and population level trajectories (d-g). Videos: <a href='https://www.youtube.com/watch?v=7ioQjh1xzEQ'>Baseline Year 5</a>, <a href='https://www.youtube.com/watch?v=4rtsKb3CkA4'>Wind development scenario Year 27</a> (<a href='/downloads/Sage-Grouse-IBM-key-features.pdf'>more about the interface and videos</a>).
The greater sage-grouse IBM interface. User-defined parameter settings (a), instantaneous status of numerical information (b), instantaneous species' spatial distribution (c), and population level trajectories (d-g). Videos: Baseline Year 5, Wind development scenario Year 27 (more about the interface and videos).
Modeled seasonal population distributions when applying wind development scenario alone (left), wind development and habitat improvement scenarios (center), and wind development and both habitat improvement and degradation scenarios (right) 40 and 50 years after initialization. Population expansion (A and B) compared with the wind development scenario alone; a newly established habitat (C); noticeable decrease in population density and habitat extent (D); and newly established habitat (E and F).
Modeled seasonal population distributions when applying wind development scenario alone (left), wind development and habitat improvement scenarios (center), and wind development and both habitat improvement and degradation scenarios (right) 40 and 50 years after initialization. Population expansion (A and B) compared with the wind development scenario alone; a newly established habitat (C); noticeable decrease in population density and habitat extent (D); and newly established habitat (E and F).

This modeling approach could be applied to evaluate impacts of other developments on sage-grouse or other wildlife species. Future work will focus on incorporating climate change scenarios and natural hazard risks into the model, and developing the model into a fully functioning, user-friendly tool that land management agencies, planners, developers, and other stakeholders can use to evaluate the effects of energy development on important wildlife species.

 Key Features: Greater Sage-Grouse Individual-Based Model (IBM).

 Report: LaGory, K.; Hamada, Y.; Tarpey, P.; Weber, C. “A Spatially Explicit Individual-Based Modeling Approach to Evaluate the Cumulative Effects of Wind Energy Development on the Greater Sage-Grouse: Sensitivity Analysis and Validation of Key Parameters,” ANL/EVS/R-113/3, 2021.

 Report: LaGory, K.; Hamada, Y.; Tarpey, P.; Levine, E.; Weber, C.; LePoire, D.; Walston, L. “A Spatially Explicit Individual-Based Modeling Approach to Evaluate the Cumulative Effects of Wind Energy Development on the Greater Sage-Grouse,” ANL/EVS/R-12/3, 2012.

 Poster: LaGory, K.; Hamada, Y.; Tarpey, P.; Weber, C. An Individual-Based Model to Assess the Cumulative Impacts of Wind Energy Development on Greater Sage-Grouse. Poster presented at: AWEA Wind Power 2012 Conference and Exhibition; June 3-6, 2012; Atlanta, GA.

Related Research Areas

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photo of Yuki Hamada
Biophysical Remote Sensing Scientist
Capabilities: Applications of optical and infrared remote sensing and geospatial modeling approaches for analyzing and monitoring terrestrial ecosystem functions and processes; application of plant spectroscopy to hyperspectral image analysis for terrestrial ecosystem research; development of novel image processing algorithms to extract and characterize land surface and aquatic features and properties; use of geospatial information technologies in development of a framework for data interpolation, extrapolation, and scaling from fine-resolution local scale to coarse-resolution regional scale.