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Flying under the LiDAR: relating forest structure to bat community diversity

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Date Issued:
2016
Abstract/Description:
Bats are important to many ecological processes such as pollination, insect (and by proxy, disease) control, and seed dispersal and can be used to monitor ecosystem health. However, they are facing unprecedented extinction risks from habitat degradation as well as pressures from pathogens (e.g., white-nose syndrome) and wind turbines. LiDAR allows ecologists to measure structural variables of forested landscapes with increased precision and accuracy at broader spatial scales than previously possible. This study used airborne LiDAR to classify forest habitat/canopy structure at the Ordway-Swisher Biological Station (OSBS) in north central Florida. LiDAR data were acquired by the National Ecological Observatory Network (NEON) airborne observation platform in summer 2014. OSBS consists of open-canopy pine savannas, closed-canopy hardwood hammocks, and seasonally inundated basin marshes. Multiple forest structural parameters (e.g., mean, maximum, and standard deviation of canopy height) were derived from LiDAR point clouds using the USDA software program FUSION. K-means clustering was used to segregate each 5x5 m raster across the ~3765 ha OSBS area into six different clusters based on the derived canopy metrics. Cluster averages for maximum, mean, and standard deviation of return heights ranged from 0 to 19.4 m, 0 to 15.3 m, and 0 to 3.0 m, respectively. To determine the relationships among these landscape-canopy features and bat species diversity and abundances, AnaBat II bat detectors were deployed from May to September in 2015 stratified by these distinct clusters. A statistical regression model selection approach was performed in order to evaluate how forest structural attributes such as understory clutter, vertical canopy structure, open and closed canopy, etc. and landscape metrics influence bat communities. The most informative models showed that a combination of site-specific (e.g., midstory clutter and entropy) and landscape level attributes (e.g., area of water and service road length) contributed to bat community patterns. This knowledge provides a deeper understanding of habitat-species interactions to better manage survival of these species and provides insight into new tools for landscape management as they apply to specific species.
Title: Flying under the LiDAR: relating forest structure to bat community diversity.
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Name(s): Butterfield, Anna, Author
Weishampel, John, Committee Chair
Noss, Reed, Committee Member
King, Joshua, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2016
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Bats are important to many ecological processes such as pollination, insect (and by proxy, disease) control, and seed dispersal and can be used to monitor ecosystem health. However, they are facing unprecedented extinction risks from habitat degradation as well as pressures from pathogens (e.g., white-nose syndrome) and wind turbines. LiDAR allows ecologists to measure structural variables of forested landscapes with increased precision and accuracy at broader spatial scales than previously possible. This study used airborne LiDAR to classify forest habitat/canopy structure at the Ordway-Swisher Biological Station (OSBS) in north central Florida. LiDAR data were acquired by the National Ecological Observatory Network (NEON) airborne observation platform in summer 2014. OSBS consists of open-canopy pine savannas, closed-canopy hardwood hammocks, and seasonally inundated basin marshes. Multiple forest structural parameters (e.g., mean, maximum, and standard deviation of canopy height) were derived from LiDAR point clouds using the USDA software program FUSION. K-means clustering was used to segregate each 5x5 m raster across the ~3765 ha OSBS area into six different clusters based on the derived canopy metrics. Cluster averages for maximum, mean, and standard deviation of return heights ranged from 0 to 19.4 m, 0 to 15.3 m, and 0 to 3.0 m, respectively. To determine the relationships among these landscape-canopy features and bat species diversity and abundances, AnaBat II bat detectors were deployed from May to September in 2015 stratified by these distinct clusters. A statistical regression model selection approach was performed in order to evaluate how forest structural attributes such as understory clutter, vertical canopy structure, open and closed canopy, etc. and landscape metrics influence bat communities. The most informative models showed that a combination of site-specific (e.g., midstory clutter and entropy) and landscape level attributes (e.g., area of water and service road length) contributed to bat community patterns. This knowledge provides a deeper understanding of habitat-species interactions to better manage survival of these species and provides insight into new tools for landscape management as they apply to specific species.
Identifier: CFE0006177 (IID), ucf:51151 (fedora)
Note(s): 2016-05-01
M.S.
Sciences, Biology
Masters
This record was generated from author submitted information.
Subject(s): remote sensing -- lidar -- community ecology -- bats -- Florida
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0006177
Restrictions on Access: campus 2017-05-15
Host Institution: UCF

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