Abstract:
Concerns about the state of decline of the North American monarch butterfly (Danaus plexippus) have prompted their consideration for listing under the Endangered Species Act. Data suggest a substantial decline (> 80%) in overwintering numbers for both eastern and western monarch populations. Making an accurate status assessment is difficult due to highly variable density estimates in the eastern monarch overwintering sites. We have developed a novel application of terrestrial LiDAR scanning (TLS) which creates a scene using millions of LASER-based distance measurements in the landscape. In this technology report we discuss the use of TLS and development of Subtractive Volume Estimation (SVE) methodology for estimating overwintering monarch butterfly populations. The principle proposition of the SVE method is to compare volumetric differences between two TLS surveys, a reference scan that records roosting monarch butterflies in their overwintering environment and a derivative scan, that records the same site without butterflies. Using paired long-range laser scanners, we collected data from four overwintering sites; two in California and two in central Mexico. To help estimate the number of butterflies, we developed an accurate 3D model of an individual monarch. To test the SVE method, we created digital 3D models of bare tree trunks and distal branches, based on laser scans at two sites and combined them with our monarch model to create virtual reference and derivative point clouds. To convert from volume to number of butterflies, we introduce a scaling factor, n, which represents the estimated volume occupied by one butterfly and a correction factor, f, which accounts for variation in clustering behavior and scanner position. While work is ongoing, we confirm that TLS combined with SVE is a suitable technique for surveying clusters of overwintering monarchs at overwintering sites in Mexico and the US.
Link to full article:
Frontiers | Estimating Overwintering Monarch Butterfly Populations Using Terrestrial LiDAR Scanning (frontiersin.org)