A Generalized Estimator for Estimating Bird and Bat Mortality at Renewable Energy Facilities - GenEst Completed

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GenEst

• Provides unbiased estimates of mortality to inform development and operational decisions and allow meaningful comparisons across time, sites and regions
• Is easy to use
• Provides easy-to-interpret results

GenEst does not require

• Changes to monitoring protocols
• Increased monitoring effort
• New prescriptive monitoring objectives

GenEst allows end-users to test assumptions regarding input parameters and select an approach that best reflects their situation and data. Flexible parameter inputs allow GenEst to yield statistically valid, unbiased results across a wide spectrum of study designs with greatly reduced potential for user error.

GenEst provides

• Guidance on study design to increase efficiency and reduce costs of fatality studies
• Information with which to meaningfully interpret regional impacts and temporal trends
• A way to standardize carcass searches and perform data analyses
• Reduction of bias to improve accuracy of fatality estimates, or rates, generated from carcass searches

GenEst is not an evidence of absence-type estimator and is not intended for use when few carcasses are found.

Download GenEst Software and Example Data Sets

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Fatality estimates provide basic information for studying impacts of renewable energy development on wildlife and how to minimize such impacts. Only accurate estimators that don’t introduce inconsistent bias will produce estimates that can be compared across time, sites and regions. Multiple methods for statistically estimating bird and bat fatalities at wind energy facilities have been developed over the last 20 years. Several of those methods are still widely used today.

They are:

Shoenfeld (2004) | Huso (2010) | Huso et al. (2012) | Korner-Nievergelt et al (2011) | Perón et al (2013) | Wolpert (2013)

These methods adjust raw survey data to account for instances where carcasses are not counted because they’ve been scavenged by other animals, lost to natural decay, located in unsearchable areas, missed by searchers, and others.

Guidance regarding which is the appropriate estimating method - estimator – to use in a situation is not always available and may not be followed when available. Industry and regulatory specialists must decide on their own the most appropriate estimators to use for their situation. These different estimators can provide different results that could lead to confusion and conflict between the renewable energy developer or operator and the regulatory body.

Time and resources are limited. Not every facility or regulatory body has a statistician available to advise which method is most appropriate for each project or study design scenario. GenEst replaces earlier estimators, eliminating confusion about which estimator is most applicable for a given situation.

View a webinar on GenEst and how it can be used to estimate mortality.

Download presentation slides from the Intro to GenEst, A Generalized Estimator of Mortality, Workshop held at the National Wind Coordinating Collaborative's Wind Wildlife Research Meeting XII on November 26, 2018 in St. Paul Minnesota. 

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Other Resources

Simonis, J., Dalthorp, D.H., Huso, M.M., Mintz, J.M., Madsen, L., Rabie, P., Studyvin, J., 2018, GenEst User Guide—Software for a Generalized Estimator of Mortality: U.S. Geological Survey Techniques and Methods, v. 7, no. C19, p. 72, https://doi.org/10.3133/tm7C19.

Dalthorp, D.H., Simonis, J., Madsen, L., Huso, M.M., Rabie, P., Mintz, J.M., Wolpert, R., Studyvin, J., Korner-Nievergelt, F., 2018, Generalized Estimator of Mortality (GenEst) - R Package: U.S. Geological Survey Software Release, https://doi.org/10.5066/P9O9BATL.

Dalthorp, D.H., Madsen, L., Huso, M.M., Rabie, P., Wolpert, R., Studyvin, J., Simonis, J., Mintz, J.M., 2018, GenEst statistical models—A generalized estimator of mortality: U.S. Geological Survey Techniques and Methods, v. 7, no. A2, p. 13, https://doi.org/10.3133/tm7A2.

Wolpert, R., and J. Coleman. 2015. ACME: A partially periodic estimator of avian and chiropteran mortality at wind turbines: Cornell University Library, https://arxiv.org/abs/1507.00749v1.

Péron, Guillaume, and Hines, J.E., 2014, fatalityCMR—Capture-recapture software to correct raw counts of wildlife fatalities using trial experiments for carcass detection probability and persistence time: U.S. Geological Survey Techniques and Methods 7–C11, 14 p., http://dx.doi.org/10.3133/tm7C11.

Wolpert, R. 2013. Appendix B: A partially periodic equation for estimating avian mortality rates. Pages A1-A20 in W. Warren-Hicks, J. Newman, R. Wolpert, B. Karas, and L. Tran, editors. Improving Methods for Estimating Fatality of Birds and Bats at Wind Energy Facilities. California Wind Energy Association, Berkely, California.

Huso, M.M., Som, N., Ladd, L., 2012, Fatality estimator user’s guide (ver. 1.2, December 2018): U.S. Geological Survey Data Series 729, p. 22, https://doi.org/10.3133/ds729.

Korner-Nievergelt, F., P. Korner-Nievergelt, O. Behr, I. Niermann, R. Brinkmann, and B. Hellriegel. 2011. A new method to determine bird and bat fatality at wind energy turbines from carcass searches: Wildlife Biology, v. 17, no. 4, p. 350-363, https://doi.org/10.2981/10-121.

Huso, M.M., 2010, An estimator of wildlife fatality from observed carcasses: Environmetrics, v. 22, no. 3, p. 218-239, https://doi.org/10.1002/env.1052.

Shoenfeld, P. 2004. Suggestions regarding avian mortality extrapolation. Report for the West Virginia Highlands Conservancy.