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An Assessment of Factors Affecting the Spatial Distribution of Audubon’s Shearwater (Puffinus l. lherminieri) throughout the Caribbean

Chatfield-Taylor, William
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Abstract
This study aims to better understand the factors that contribute to Audubon’s Shearwater (Puffinus l. lherminieri) nesting sites on islands across the Caribbean region. Using locational presence and absence data of their breeding colonies a Geographical Information System (GIS) is used to determine the proximity and presence of a variety of marine (SST, bathymetry and derived bathymetry data) and terrestrial (elevation derived statistics) environmental variables that may influence nesting locations. For each location in the dataset, a set of nearshore (within 50 km) and offshore (50 and 300 km) metrics are calculated. Each selected variable is tested for statistical significance both in the nearshore and offshore locations. Logistic regression analysis is used to predict the presence and absence of sites. It is determined that a combination of bathymetry, sea surface temperature (SST), and ocean front proxies are the best variables for predicatively modeling Audubon’s Shearwater nesting locations. A different subset of SST metrics and SST front proxies predict colony presence and absence when considering the offshore data. Both models have a predicative accuracy of 62.72%, with a degree of uncertainty arising from the quality of the presence and absence data. It is likely the relative success of both nearshore and offshore logistic regression analyses is linked to the respective, and differing, ecological roles that males and females play in the pre-laying exodus in this species. Despite the difficulty of detecting true absence data for this study, the results suggest that there is a great need to better understand the differential sex roles of Audubon’s Shearwater and their breeding behavior to assist in future conservations efforts of the species.
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Date
2015-05-31
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University of Kansas
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Keywords
Geography, Biology, Audubon's Shearwater, breeding distribution, GIS, logistic regression, pre-laying exodus
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