ABSTRACTBackgroundSpatial scale is important when studying ecological processes. The Greater sage-grouse (Centrocercus urophasianus) is a large sexually dimorphic tetraonid that is endemic to the sagebrush biome of western North America. The impacts of oil and gas development at individual leks has been well-documented. However, no previous studies have quantified the population-level response.MethodsHierarchical models were used to estimate the effects of the areal disturbance due to well pads as well as climatic variation on individual lek counts and Greater sage-grouse populations (management units) over 32 years. The lek counts were analyzed using General Linear Mixed Models while the management units were analyzed using Gompertz Population Dynamic Models. The models were fitted using frequentist and Bayesian methods. An information-theoretic approach was used to identify the most important spatial scale and time lags. The relative importance of oil and gas and climate at the local and population-level scales was assessed using information-theoretic (Akaike’s weights) and estimation (effect size) statistics.ResultsAt the local scale, oil and gas was an important negative predictor of the lek count. At the population scale, there was only weak support for oil and gas as a predictor of density changes but the estimated impacts on the long-term carrying capacity were consistent with summation of the local impacts. Regional climatic variation, as indexed by the Pacific Decadal Oscillation, was an important positive predictor of density changes at both the local and population-level (particularly in the most recent part of the time series).ConclusionsAdditional studies to reduce the uncertainty in the range of possible effects of oil and gas at the population scale are required. Wildlife agencies need to account for the effects of regional climatic variation when managing sage-grouse populations.
Oil and gas development exposure and conservation scenarios for Greater sage-grouse: Combining spatially explicit modeling with GIS visualization provides critical information for management decisions
