Large-scale control site selection for population monitoring: an example assessing Sage-grouse trends

Product Type: 

Journal Article

Year: 

2015

Author(s): 

Fedy, B.C., O’Donnell, M.S., and Bowen, Z.H.

Suggested Citation: 

Fedy, B.C., O’Donnell, M.S., and Bowen, Z.H., 2015, Large-scale control site selection for population monitoring: An example assessing sage-grouse trends: Wildlife Society Bulletin, v. 39, no. 4, p. 700–712.

Human impacts on wildlife populations are widespread and prolific and understanding wildlife responses to human impacts is a fundamental component of wildlife management. The first step to understanding wildlife responses is the documentation of changes in wildlife population parameters, such as population size. Meaningful assessment of population changes in potentially impacted sites requires the establishment of monitoring at similar, nonimpacted, control sites. However, it is often difficult to identify appropriate control sites in wildlife populations. We demonstrated use of Geographic Information System (GIS) data across large spatial scales to select biologically relevant control sites for population monitoring. Greater sage-grouse (Centrocercus urophasianus; hearafter, sage-grouse) are negatively affected by energy development, and monitoring of sage-grouse population within energy development areas is necessary to detect population-level responses. Weused population data (1995–2012) from an energy development area in Wyoming, USA, the Atlantic Rim Project Area (ARPA), and GIS data to identify control sites that were not impacted by energy development for population monitoring. Control sites were surrounded by similar habitat and were within similar climate areas to the ARPA. We developed nonlinear trend models for both the ARPA and control sites and compared long-term trends from the 2 areas. We found little difference between the ARPA and control sites trends over time. This research demonstrated an approach for control site selection across large landscapes and can be used as a template for similar impact-monitoring studies. It is important to note that identification of changes in population parameters between control and treatment sites is only the first step in understanding the mechanisms that underlie those changes. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Related Projects

      

Bradley FedyMichael O'DonnellZack Bowen