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Updated: Jun 15, 2026

A Method for Quantifying Foliage-Dwelling Arthropods
Published on: October 20, 2019
Modeling spatial establishment patterns of exotic forest insects in urban areas in relation to tree cover and
Manuel Colunga-Garcia1, Robert A Haack, Roger A Magarey
1Center for Global Change and Earth Observations, Michigan State University, 204 Manly Miles Bldg., 1405 S. Harrison Rd., East Lansing, MI 48823, USA. colunga@msu.edu
Abstract:
As international trade increases so does the prominence of urban areas as gateways for exotic forest insects (EFI). Delimiting hot spots for invasions (i.e., areas where establishment is likely) within urban areas would facilitate monitoring efforts. We used a propagule-pressure framework to delimit establishment hot spots of a hypothetical generalist EFI in six U.S. urban areas: Chicago, Detroit, Houston, Los Angeles-Long Beach-Santa Ana, New York-Newark, and Seattle. We assessed how urban tree cover and propagule pressure interact to delimit establishment hot spots and compared the location of these hot spots with actual recent U.S. detections of two EFI: the Asian strain of the gypsy moth, Lymantria dispar (L.) (Lepidoptera: Lymantriidae), and Asian longhorned beetle, Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae). Using a lattice of 5-km-diameter cells for each urban area, we used the input data (urban tree cover and propagule pressure) to model establishment and Moran's I to delimit hot spots. We used urban population size and the area of commercial-industrial land use as indicators of propagule pressure in the model. Relative establishment of EFI was influenced more by the two propagule pressure indicators than by tree cover. The delimited land use-based hot spots for Los Angeles-Long Beach-Santa Ana and New York-Newark encompassed more of the actual detections of L. dispar and A. glabripennis, respectively, than the population-based hot spots. No significant difference occurred between hot spot types for A. glabripennis detections in the Chicago urban area. Implications of these findings for management and design of monitoring programs in urban areas are discussed.
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