More info about the project can be found below. More info about me, including my previous research and CV can be found on my website.
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Website: cameroncosgrove.github.io
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E-poster version high res download 2.2 mb
The marbled murrelet (Brachyramphus marmoratus) is an elusive tree-nesting sea bird found along the Pacific coast of North America whose old-growth nesting habitat has declined in size over the last century. Mapping the remaining nesting habitat is a core step in the conservation of this Species at Risk with fine-scale mapping efforts expected to be enhanced by the inclusion of Airborne Laser Scanning (ALS) data, which can provide quantitative measures of forest structure for birds. This study presents results from an ALS informed habitat model for marbled murrelet nest sites on the Sunshine Coast of British Columbia, using ecologically relevant forest structure predictors. Two different modeling approaches - ensembles of small models (ESMs) and maximum entropy modeling (Maxent) - were used to link 58 nest locations with ALS data at a 100 m2 resolution after masking known non-habitat and disturbed areas. The best performing Maxent model showed good discrimination from random background points with an AUC scores of 0.767 for the training area and AUC of 0.631 for the independent test area. High Boyce indexes showed that higher-ranked habitat was preferentially selected for in the landscape. These results align with other ALS marbled murrelet habitat models in the United States with similar accuracy and ecological conclusions. This study offers a quantitative way to predict sites with potential nesting habitat for an elusive Species at Risk using a parsimonious set of ecologically relevant predictors and a modeling framework that can accommodate low sample sizes.
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