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A Precise and Autonomous System for the Detection of Insect Emergence Patterns

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

Nonnative pest establishment: Spatial patterns and public detection.

Rebecca Epanchin-Niell¹, Xueting Pi²

¹Department of Agricultural and Resource Economics, 2200 Symons Hall, University of Maryland, College Park, MD 20742-5535, USA; Resources for the Future, Washington D.C., USA.

Journal of Environmental Management

|July 18, 2024

Summary

This summary is machine-generated.

Early pest detection aids rapid management. New pest establishment is more likely in populated areas near ports with suitable climates, while public detection is lower in high-value agricultural regions.

Keywords:

Biosecurity Invasive species Nonnative pest introduction Passive surveillance Pest risk Pest surveys

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Area of Science:

Agricultural Science
Ecology
Pest Management

Background:

Early detection of new pests is crucial for minimizing long-term ecological and economic impacts.
Understanding pest establishment risk and public detection rates informs effective surveillance strategies.
Formal early detection survey programs benefit from knowledge of pest invasion dynamics.

Purpose of the Study:

To identify county-level attributes associated with new pest establishment in the U.S.
To determine factors influencing whether new pests are detected by the public versus other sources.
To provide spatially explicit data for optimizing pest surveillance planning.

Main Methods:

Analysis of data from 113 new pest incursions in the United States (2010-2018).
Quantification of county-level locational, demographic, and economic factors.
Statistical modeling to assess pest establishment and detection likelihoods, controlling for sample selection bias.

Main Results:

Pest establishment likelihood increases with human population density, proximity to ports (<250 km), and favorable climate conditions.
Public detection is less likely in counties with high crop sales values and lower human populations.
High agricultural values correlate with reduced public detection, potentially due to increased professional surveillance.

Conclusions:

Spatially explicit models predict new pest establishment and public detection probabilities across U.S. counties.
Findings support targeted surveillance planning by providing quantitative inputs for decision-making.
Understanding detection sources is key to enhancing the efficiency of early pest detection programs.