Abstract
Analysis tools that combine large spatial and temporal scales are necessary for efficient management of wildlife species, such as the burrowing owl (Athene cunicularia). We assessed the ability of Ripley’s K-function analysis integrated into a geographic information system (GIS) to determine changes in burrowing owl nest clustering over two years at NASA Ames Research Center. Specifically, we used these tools to detect changes in spatial and temporal nest clustering before, during, and after conducting management by mowing to maintain low vegetation height at nest burrows. We found that the scale and timing of owl nest clustering matched the scale and timing of our conservation management actions over a short time frame. While this study could not determine a causal link between mowing and nest clustering, we did find that Ripley’s K and GIS were effective in detecting owl nest clustering and show promise for future conservation uses.
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Acknowledgments
We thank C. Alderete, T. Anderson, S. Clubb, C. Johnson, J. H. Lee, C. Levitan, Y. Malhi, and E. Watkins for help and assistance with this research. This work was supported by NASA Headquarters under the Earth System Science Fellowship Grant NGT5-30473, by NASA Ames Research Center funding for summer internships, and by a contract from NASA Ames Research Center and administered by PAI Corporation.
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Fisher, J.B., Trulio, L.A., Biging, G.S. et al. An Analysis of Spatial Clustering and Implications for Wildlife Management: A Burrowing Owl Example. Environmental Management 39, 403–411 (2007). https://doi.org/10.1007/s00267-006-0019-y
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DOI: https://doi.org/10.1007/s00267-006-0019-y