Statistical guidelines for assessing marine avian hotspots and coldspots: A case study on wind energy development in the U.S. Atlantic Ocean

Journal Article

Title: Statistical guidelines for assessing marine avian hotspots and coldspots: A case study on wind energy development in the U.S. Atlantic Ocean

Author:

Zipkin, E.; Kinlan, B.; Sussman, A.; Rypkema, D.; Wimer, M.; O'Connell, A.

Publication Date:

November 1, 2015

Journal:

Biological Conservation

Volume:

191

Pages:

216-223

Publisher:

Elsevier

Affiliation

Michigan State University, National Oceanic and Atmospheric Administration (NOAA), Stanford University, Patuxent Wildlife Research Center

Technology:

Wind Energy, Fixed Offshore Wind

Receptor:

Seabirds, Birds

Document Access

Website:

External Link

Citation

Zipkin, E.; Kinlan, B.; Sussman, A.; Rypkema, D.; Wimer, M.; O'Connell, A. (2015). Statistical guidelines for assessing marine avian hotspots and coldspots: A case study on wind energy development in the U.S. Atlantic Ocean. Biological Conservation, 191, 216-223. https://doi.org/10.1016/j.biocon.2015.06.035

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Abstract

Estimating patterns of habitat use is challenging for marine avian species because seabirds tend to aggregate in large groups and it can be difficult to locate both individuals and groups in vast marine environments. We developed an approach to estimate the statistical power of discrete survey events to identify species-specific hotspots and coldspots of long-term seabird abundance in marine environments. We illustrate our approach using historical seabird data from survey transects in the U.S. Atlantic Ocean Outer Continental Shelf (OCS), an area that has been divided into “lease blocks” for proposed offshore wind energy development. For our power analysis, we examined whether discrete lease blocks within the region could be defined as hotspots (3 × mean abundance in the OCS) or coldspots (1/3 ×) for individual species within a given season. For each of 74 species/season combinations, we determined which of eight candidate statistical distributions (ranging in their degree of skewedness) best fit the count data. We then used the selected distribution and estimates of regional prevalence to calculate and map statistical power to detect hotspots and coldspots, and estimate the p-value from Monte Carlo significance tests that specific lease blocks are in fact hotspots or coldspots relative to regional average abundance. The power to detect species-specific hotspots was higher than that of coldspots for most species because species-specific prevalence was relatively low (mean: 0.111; SD: 0.110). The number of surveys required for adequate power (> 0.6) was large for most species (tens to hundreds) using this hotspot definition. Regulators may need to accept higher proportional effect sizes, combine species into groups, and/or broaden the spatial scale by combining lease blocks in order to determine optimal placement of wind farms. Our power analysis approach provides a general framework for both retrospective analyses and future avian survey design and is applicable to a broad range of research and conservation problems.