Spotted lanternfly predicted to establish in California by 2033 without preventative management.

Autor: Jones C; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA. cmjone25@ncsu.edu., Skrip MM; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA., Seliger BJ; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA., Jones S; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA., Wakie T; Animal and Plant Health Inspection Service (APHIS) US Department of Agriculture (USDA) Riverdale, Riverdale, MD, USA., Takeuchi Y; Center for Integrated Pest Management, North Carolina State University, Raleigh, NC, USA., Petras V; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA., Petrasova A; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA., Meentemeyer RK; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA.; Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA.
Jazyk: angličtina
Zdroj: Communications biology [Commun Biol] 2022 Jun 08; Vol. 5 (1), pp. 558. Date of Electronic Publication: 2022 Jun 08.
DOI: 10.1038/s42003-022-03447-0
Abstrakt: Models that are both spatially and temporally dynamic are needed to forecast where and when non-native pests and pathogens are likely to spread, to provide advance information for natural resource managers. The potential US range of the invasive spotted lanternfly (SLF, Lycorma delicatula) has been modeled, but until now, when it could reach the West Coast's multi-billion-dollar fruit industry has been unknown. We used process-based modeling to forecast the spread of SLF assuming no treatments to control populations occur. We found that SLF has a low probability of first reaching the grape-producing counties of California by 2027 and a high probability by 2033. Our study demonstrates the importance of spatio-temporal modeling for predicting the spread of invasive species to serve as an early alert for growers and other decision makers to prepare for impending risks of SLF invasion. It also provides a baseline for comparing future control options.
(© 2022. The Author(s).)
Databáze: MEDLINE
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