Manuela Huso is a Research Statistician with the USGS Forest and Rangeland Ecosystem Science Center in Corvallis, OR with a courtesy faculty appointment in the Department of Statistics at Oregon State University (OSU). Before coming to the USGS 6 years ago, she spent more than 20 years as a consulting statistician at OSU. Since 2004, she has been involved in design and analysis of post-construction fatality monitoring studies as well as deterrent and curtailment studies at several wind-power generation facilities. Her current ecological research looks at predicting fatality from weather variables; investigating the hypothesis that some bat species might be attracted to wind turbines; and testing the potential for reducing bat fatality through deterrent or other adaptive management techniques. On the statistical side, she and colleagues are exploring approaches to increase the efficiency of monitoring, particularly by searching only roads and pads, in order to reduce the financial burden imposed on industry without sacrificing accuracy and precision of the estimated fatality. Her recent statistical work has focused on developing unbiased estimators of turbine-caused fatality, both for understanding general patterns of fatality in large groups, e.g. raptors, passerines or bats, and also for the problematic case of rare or endangered species.
Wind Wildlife Fatality: How we know what we know and how we might mislead ourselves
The main purpose of CWW is to exchange information regarding the impacts of wind power development on wildlife. One concern, among many, is direct fatality caused by collision with rotating blades. Estimating fatality from observed carcasses has been an active topic of research for several years and much has been done to advance the accuracy of the estimates. Nonetheless, this is only a part of the story. Meaningful interpretation of the data and resulting estimates is needed to guide management and societal decisions regarding this form of energy production. I will briefly outline the processes typically used to collect fatality data, then give examples of where misguided interpretation of limited data can lead to inference that is not justified and decisions that might have unintended consequences. I will follow the doom and gloom with a discussion of where we can go from here to improve both data collection and sharing and ultimately, inference regarding fatality impacts from wind.