COS 29-2
Assessing the population-level impacts of wind energy developmentĀ 

Tuesday, August 11, 2015: 8:20 AM
337, Baltimore Convention Center
Jessica C. Stanton, Upper Midwest Environmental Sciences Center, U.S. Geological Survey, La Crosse, WI
Wayne E. Thogmartin, Upper Midwest Environmental Sciences Center, U.S. Geological Survey, La Crosse, WI

The impact of wind energy generation on wildlife is commonly approached by monitoring the incidence of mortality resulting from turbine collisions. These mortality events may or may not scale-up to observable impacts at the population level. We present a framework for assessing population-level impacts of wind energy development using abundance time-series data. As a case study, we analyzed North American Breeding Bird Survey (NABBS) data on the horned lark (Eremophila alpestris) in conjunction with a detailed spatio-temporal dataset of wind turbine placement. Horned lark are wide-ranging and relatively common and abundant, but have been declining in recent decades. They are also one of the most frequently observed species in turbine mortality surveys. We examined whether the timing and placement of turbines on the landscape was co-incident with observed trends at local and regional scales. First, we used a dynamic factor analysis (DFA) with and without covariates relating to the regional build-out of wind energy facilities. The DFA approach also allows analysis of the underlying regional population structure. We then examined local-level impacts by comparing NABBS locations that are close to wind turbines before and after build-out with survey locations that are relatively far from any turbines using a causal impact analysis. 


The best fitting models from the DFA found two common trends underlying the observations from 15 NABBS locations in the study region. One trend described an increasing population through the 1980s with a peak in the early 1990s followed by a steep decline. The other trend described a population stable through the early 1980s with a sharp decline beginning in the late 1980s. The best fitting model did not include regional wind energy development as a predictor variable. In the best model that did include a wind energy predictor, only a single NABBS location had a significantly negative correlation coefficient to that variable. Causal impact analysis of individual NABBS locations did not show a strong relationship between the magnitude and direction of presumed impact and either the number of turbines or distance to the nearest turbine. While we did not identify wind energy development as an important driver in this case study, this approach has the potential for evaluating other species or populations from other regions, especially when the underlying population structure is unknown.