A dynamic approach to tailoring plant pest surveillance design to spatial outbreak characteristics
Abstract
Official guidelines and methodological recommendations are available for the design and implementation of plant pest monitoring surveys. A key element of survey design is the specification of the design prevalence, defined as the minimum prevalence at which a survey should detect the pest with a specified confidence level. In practice, this value is often fixed a priori, based on a rule of thumb, without accounting for the underlying spatial distribution patterns of the pest. Here, we introduce a general method that adjusts the design prevalence according to the level of spatial aggregation in the pest distribution. The approach uses geometric properties represented through the expected perimeter of affected locations, to provide a common criterion for calibrating design prevalence across aggregation levels. For each level of aggregation, our approach selects the design prevalence that preserves the same geometric summary across aggregation levels. When compared against the standard practice, this method improved October 16, 2026 1/35 performance according to three evaluation criteria. The proposed framework is conceptually simple and readily applicable in real-world pest surveillance settings
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BibTeX
@misc{dalayer2026dynamic,
title = {{A dynamic approach to tailoring plant pest surveillance design to spatial outbreak characteristics}},
author = {François d'Alayer and Edith Gabriel and Samuel Soubeyrand and Antonio Vicent and Martina Cendoya},
howpublished = {HAL},
year = {2026},
month = oct,
url = {https://hal.inrae.fr/hal-05820816v1},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - A dynamic approach to tailoring plant pest surveillance design to spatial outbreak characteristics
AU - François d'Alayer
AU - Edith Gabriel
AU - Samuel Soubeyrand
AU - Antonio Vicent
AU - Martina Cendoya
PY - 2026
DA - 2026/10/20
PB - HAL
UR - https://hal.inrae.fr/hal-05820816v1
AB - Official guidelines and methodological recommendations are available for the design and implementation of plant pest monitoring surveys. A key element of survey design is the specification of the design prevalence, defined as the minimum prevalence at which a survey should detect the pest with a specified confidence level. In practice, this value is often fixed a priori, based on a rule of thumb, without accounting for the underlying spatial distribution patterns of the pest. Here, we introduce a general method that adjusts the design prevalence according to the level of spatial aggregation in the pest distribution. The approach uses geometric properties represented through the expected perimeter of affected locations, to provide a common criterion for calibrating design prevalence across aggregation levels. For each level of aggregation, our approach selects the design prevalence that preserves the same geometric summary across aggregation levels. When compared against the standard practice, this method improved October 16, 2026 1/35 performance according to three evaluation criteria. The proposed framework is conceptually simple and readily applicable in real-world pest surveillance settings
ER -