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Assessment of Potential Risks from Renewable Energy Development and Other Anthropogenic Factors to Wintering Golden Eagles in the Western United States

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Machine Learning for Ecology and Sustainable Natural Resource Management

Abstract

Wind and other energy development are expanding rapidly and on an unprecedented scale within the range of the Golden Eagle (Aquila chrysaetos) while other anthropogenic-related changes, wildfires, invasive plants, drought, and climate change are altering or destroying native habitats occupied by Golden Eagles. However, the potential effects of these factors on North American Golden Eagle populations are largely unknown and the most recent evidence indicates that the population in the western United States is declining slightly. Impediments to evaluating the potential effects of energy development projects on wintering Golden Eagles include issues of scale and a paucity of available information about eagle winter use areas and ecology. We applied a predictive model of eagle winter distribution developed for Idaho and Montana, to Idaho, Utah, Nevada and eastern Oregon to help identify potential wintering areas and identify risks that occur in those areas. The model identifies ~40% of the four state study area as potentially suitable eagle winter habitat and provides a basis for spatial assessment of possible risk factors to eagles wintering there. We used eBird and Christmas Bird Count citizen science datasets for an independent evaluation of the accuracy of our predictive distribution model. The model was robust, accurately predicting the presence of wintering Golden Eagles significantly more often than expected. We used digital environmental datasets (layers) of potential risk factors, in conjunction with model predicted eagle distribution, to better understand and estimate the extent of risks to the wintering eagle population in the study area. These layers represent available data for some of the factors previously identified as risks in the landscape to wintering Golden Eagles. The majority of predicted eagle wintering areas occurred where there was little habitat fragmentation (<10%). All predicted winter areas contained at least one potential risk factor (e.g., potential for energy development); 39.4% of predicted winter areas contained at least two known risk factors. The greatest number of risks often occurred where the human footprint was highest and where eagles were less likely to occur during winter. Our results can be used to help prioritize field surveys for identifying important Golden Eagle winter areas in the western United States and determine potential locations where energy development is least likely to have negative effects on wintering eagles. Survey efforts can be allocated in consideration of management and conservation objectives based on predicted habitat suitability and risk factors. For example, surveys for areas of high suitability and low risk can identify places to focus management for conservation of eagle winter areas. Further, sites proposed for wind energy development could be reviewed initially based on model predicted eagle wintering areas and then surveyed to determine if permitting for development is appropriate.

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Acknowledgements

Brian Millsap, Diana Whittington and other colleagues have collaborated on development and prioritization of information needs relative to Golden Eagle management. We would like to thank H. McFarland and J. & R. Craig, for their considerable contributions in earlier phases of this research and L. Dunn and L. Schueck for help with digital layers. FH acknowledges S. & J. Linke, E. Bruenning and L. Strecker for contributions while developing some of the research leading to this publication. We acknowledge the value of the publicly available GIS datasets used in this analysis and the contribution of data, access and permission for use of eBird data by the Cornell Laboratory of Ornithology and the National Audubon Society. CBC Data is provided by National Audubon Society and through the generous efforts of Bird Studies Canada and countless volunteers across the western hemisphere. This project was funded through the Science Support Partnership program of the US Geological Survey, Aquila Environmental and the Bureau of Land Management. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government.

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Authors and Affiliations

  1. Aquila Environmental, Fairbanks, AK, USA

    Erica H. Craig & Tim H. Craig

  2. Boise State University, Raptor Research Center, Boise, ID, USA

    Mark R. Fuller

  3. EWHALE Lab, Biology and Wildlife Department, Institute of Arctic Biology, University of Alaska-Fairbanks, Fairbanks, AK, USA

    Falk Huettmann

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  1. Erica H. Craig
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  2. Mark R. Fuller
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  3. Tim H. Craig
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  4. Falk Huettmann
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Correspondence to Erica H. Craig .

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Editors and Affiliations

  1. Black Bawks Data Science Ltd, Fort Augustus, Scotland, New Zealand

    Grant Humphries

  2. U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge, Soldotna, AK, USA

    Dawn R. Magness

  3. Institute of Arctic Biology, University of Alaska - Fairbanks, FAIRBANKS, AK, USA

    Falk Huettmann

Appendix

Appendix

Table A19.1 Descriptions and sources for environmental layers used to apply the grove file from the original wintering Golden Eagle model in Idaho and Montana (The Authors, Unpublished Data) to the geographic area of Idaho, Nevada, Utah and eastern Oregon1
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Table A19.2 Descriptions and source information for layers included in the analysis of risks to Golden Eagles in their predicted winter areas in Idaho, Utah, Nevada and eastern Oregon. Original source layers were clipped to the study area boundaries and reprojected, if necessary. Spatial Reference: North American Datum Albers 1983, False Easting: 0.000000, False Northing: 0.000000, Longitude of Central Meridian: -96.000000, Latitude of Origin: 23.000000, Standard Parallel 1: 29.500000 and 2: 45.500000
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Table A19.3 Chi square residuals from a comparison of 2,012 independent Golden Eagle (eBird) winter sightings that were located in predicted suitable and unsuitable Golden Eagle wintering areas (observed), with the expected distribution, based on the the percent of predicted suitable vs. unsuitable winter areas available in the Idaho, Utah, Nevada and eastern Oregon study area (χ2 = 19.1417, df = 1, p <0.001)
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Table A19.4 Comparison of the distribution of predicted suitable Golden Eagle winter habitat in CBC circles where eagles were sighted (observed), with the expected distribution in all CBC circles in the study area. Residuals of the chi-square test are shown for each category of habitat from low (unsuitable) to high suitability for winter habitat (χ2 = 25.7315, df = 2, p <0.001)
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Table A19.5 Chi square residuals for a comparison of the degree of fragmentation in predicted suitable Golden Eagle wintering areas (observed), with fragmentation of all the available (expected) area in the Idaho, Utah, Nevada and eastern Oregon study area (χ2 = 977.8901, df = 5, p <0.001)
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Table A19.6 Chi square residuals for a comparison of the intensity of the human footprint (scale 1 – 10) in predicted suitable Golden Eagle wintering areas (observed), with the intensity of the human footprint in all the available (expected) area in the Idaho, Utah, Nevada and eastern Oregon study area. Predicted winter areas were more likely to be in habitats with the lowest intensity human footprint (1 and 2) and less likely than expected in areas with greater human footprint (>3; χ2 = 826.8963, df = 5, p <0.001)
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Craig, E.H., Fuller, M.R., Craig, T.H., Huettmann, F. (2018). Assessment of Potential Risks from Renewable Energy Development and Other Anthropogenic Factors to Wintering Golden Eagles in the Western United States. In: Humphries, G., Magness, D., Huettmann, F. (eds) Machine Learning for Ecology and Sustainable Natural Resource Management. Springer, Cham. https://doi.org/10.1007/978-3-319-96978-7_19

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