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Relationships among vegetation structure, canopy composition, and avian richness patterns across an aspen-conifer forest gradientAuthor(s): Charles E. Swift; Kerri T. Vierling; Andrew T. Hudak; Lee A. Vierling
Source: Canadian Journal of Remote Sensing. 43(3): 231-243.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionEcologists have a long-term interest in understanding the relative influence of vegetation composition and vegetation structure on avian diversity. LiDAR remote sensing is useful in studying local patterns of avian diversity because it characterizes fine-scale vegetation structure across broad extents. We used LiDAR, aerial and satellite imagery, and avian field data to investigate the relative influence of vegetation structure and canopy composition on avian richness across an aspen-conifer forest gradient. Aspen enhances forest avian biodiversity but has been declining across western North America. We conducted bird surveys between 2013 and 2014 in plots with a range of aspen-conifer canopy composition. We found aspen to support higher avian richness than conifer, especially among cavity nesters. In contrast to other studies, we found weak relationships between vegetation structure and avian richness. Although primary cavity excavator richness was negatively influenced by canopy density, canopy composition was the most important variable influencing total richness and nesting guild richness. This study adds to the body of literature utilizing LiDAR-derived metrics to better understand local patterns of avian diversity, and provides perspectives on how avian communities might respond to conifer encroachment into aspen.
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CitationSwift, Charles E.; Vierling, Kerri T.; Hudak, Andrew T.; Vierling, Lee A. 2017. Relationships among vegetation structure, canopy composition, and avian richness patterns across an aspen-conifer forest gradient. Canadian Journal of Remote Sensing. 43(3): 231-243.
Keywordsvegetation composition, vegetation structure, avian diversity, LiDAR, remote sensing
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