title: Enhancing early detection of exotic pests in agricultural and forest ecosystems using an urban-gradient framework.
abstract: Urban areas are hubs of international transport and therefore are major gateways for exotic pests. Applying an urban gradient to analyze this pathway could provide insight into the ecological processes involved in human-mediated invasions. We defined an urban gradient for agricultural and forest ecosystems in the contiguous United States to (1) assess whether ecosystems nearer more urbanized areas were at greater risk of invasion, and (2) apply this knowledge to enhance early detection of exotic pests. We defined the gradient using the tonnage of imported products in adjacent urban areas and their distance to nearby agricultural or forest land. County-level detection reports for 39 exotic agricultural and forest pests of major economic importance were used to characterize invasions along the gradient. We found that counties with more exotic pests were nearer the urban end of the gradient. Assuming that the exotic species we analyzed represent typical invaders, then early detection efforts directed at 21-26% of U.S. agricultural and forest land would likely be able to detect 70% of invaded counties and 90% of the selected species. Applying an urban-gradient framework to current monitoring strategies should enhance early detection efforts of exotic pests, facilitating optimization in allocating resources to areas at greater risk of future invasions.
authors.affiliation: Center for Global Change and Earth Observations, Michigan State University, Manly Miles Building, 1405 South Harrison, East Lansing, Michigan 48823, USA. colunga@msu.edu
authors.forename: Manuel
authors.initials: M
authors.lastname: Colunga-Garcia
authors.forename: Roger A
authors.initials: RA
authors.lastname: Magarey
authors.forename: Robert A
authors.initials: RA
authors.lastname: Haack
authors.forename: Stuart H
authors.initials: SH
authors.lastname: Gage
authors.forename: Jiaquo
authors.initials: J
authors.lastname: Qi
mesh_terms: D000383:Agriculture; D002947:Cities; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods*; D005843:Geography; D006801:Humans; D018570:Risk Assessment; D014197:Trees / Q000469:parasitology; D014481:United States