An investigation of novel population models to control aphid outbreaks via a data driven “adaptive” framework
- 1Sorbonne Université, Université Paris Cité, CNRS, Laboratoire Jacques-Louis Lions (LJLL), Paris, France
- 2Department of Mathematics, Iowa State University, Ames, Iowa, United States of America
- 3Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
Abstract
The soybean aphid (Aphis glycines) is an invasive pest that causes severe yield loss to soybeans in the North Central United States. Its populations frequently exhibit multiple, unequal-amplitude peaks within a single growing season, a pattern that classical boom-bust models cannot reproduce. We investigate and develop a sequence of population models aimed at capturing this multi-peak behaviour while remaining biologically realistic and useful for pest management. We first show that the variable carrying capacity model (VCM), in which seasonally fluctuating host-plant quality drives a time-dependent carrying capacity, can generate multiple peaks but also admits finite-time blow-up, or population explosion, rendering it unrealistic for high colonisations. We propose an adaptive behavioural model with a density-dependent switch that preserves the multi-peak dynamics while guaranteeing bounded populations. Guided by empirical evidence that abiotic stressors such as flooding alter host-plant fitness, we further devise a novel model with time-dependent, data-fitted periodic plant fitness and aphid growth rate; we prove that it produces transient multi-peak oscillations, with no sustained periodic state. We assess all models against the Economic Threshold (ET) and Economic Injury Level (EIL), and a global Sobol sensitivity analysis identifies the parameters governing outbreak frequency. Across all models, simulated aphid abundances exceed the ET and EIL, indicating that control is required. Lastly, applications of these models to improving current pest management strategies are discussed. Our work provides a stable, data-informed, and biologically grounded framework for predicting aphid outbreaks and guiding management strategies. Author summary: The soybean aphid is a major pest of soybean in the North Central United States. Farmers decide when to spray by comparing field counts with two benchmarks, the economic threshold and the economic injury level, so they need to know how aphid numbers change over a season. Field counts often show two or three separate outbreaks in one summer, yet the standard aphid model predicts a single rise and fall. An existing alternative, in which host-plant food value rises and falls through the season, gives several outbreaks, but we show that it can also predict an unlimited population explosion in finite time, which cannot happen in a field. We built two new models. In the first, aphids switch to a different growth behaviour once their numbers surge, so outbreaks can repeat while the population stays bounded. In the second, plant quality and aphid growth follow seasonal cycles like those in Iowa temperature and rainfall records; outbreaks then recur for a while and fade out. In every model, simulated aphid numbers passed both benchmarks, so control would be needed. How often environmental conditions change was among the strongest drivers of repeated outbreaks.
Related
- The stability investigation of variable viscosity control in the human‐robot interaction
- The CLEC7A Y238∗ variant and susceptibility to pneumonia: a prospective population-based cohort study and mechanistic investigation.
- Nicotinic control of dopamine release shapes striatal output and engages adaptive action selection.
- Seasonal population structure and adaptive signatures across long-term marine microbial time series
- Investigation of ferroptosis-related gene expression and oxidative stress markers in end-stage diabetic kidney disease: A case–control analysis
BibTeX
@article{banerjee2026investigation,
title = {{An investigation of novel population models to control aphid outbreaks via a data driven “adaptive” framework}},
author = {Aniket Banerjee and Urvashi Verma and Satyam Narayan Srivastava and Rana D. Parshad},
journal = {PLOS Ecosystems},
year = {2026},
month = nov,
doi = {10.1371/journal.pesy.0000039},
url = {https://doi.org/10.1371/journal.pesy.0000039},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - An investigation of novel population models to control aphid outbreaks via a data driven “adaptive” framework
AU - Aniket Banerjee
AU - Urvashi Verma
AU - Satyam Narayan Srivastava
AU - Rana D. Parshad
PY - 2026
DA - 2026/11/04
JO - PLOS Ecosystems
DO - 10.1371/journal.pesy.0000039
UR - https://doi.org/10.1371/journal.pesy.0000039
AB - The soybean aphid (Aphis glycines) is an invasive pest that causes severe yield loss to soybeans in the North Central United States. Its populations frequently exhibit multiple, unequal-amplitude peaks within a single growing season, a pattern that classical boom-bust models cannot reproduce. We investigate and develop a sequence of population models aimed at capturing this multi-peak behaviour while remaining biologically realistic and useful for pest management. We first show that the variable carrying capacity model (VCM), in which seasonally fluctuating host-plant quality drives a time-dependent carrying capacity, can generate multiple peaks but also admits finite-time blow-up, or population explosion, rendering it unrealistic for high colonisations. We propose an adaptive behavioural model with a density-dependent switch that preserves the multi-peak dynamics while guaranteeing bounded populations. Guided by empirical evidence that abiotic stressors such as flooding alter host-plant fitness, we further devise a novel model with time-dependent, data-fitted periodic plant fitness and aphid growth rate; we prove that it produces transient multi-peak oscillations, with no sustained periodic state. We assess all models against the Economic Threshold (ET) and Economic Injury Level (EIL), and a global Sobol sensitivity analysis identifies the parameters governing outbreak frequency. Across all models, simulated aphid abundances exceed the ET and EIL, indicating that control is required. Lastly, applications of these models to improving current pest management strategies are discussed. Our work provides a stable, data-informed, and biologically grounded framework for predicting aphid outbreaks and guiding management strategies. Author summary: The soybean aphid is a major pest of soybean in the North Central United States. Farmers decide when to spray by comparing field counts with two benchmarks, the economic threshold and the economic injury level, so they need to know how aphid numbers change over a season. Field counts often show two or three separate outbreaks in one summer, yet the standard aphid model predicts a single rise and fall. An existing alternative, in which host-plant food value rises and falls through the season, gives several outbreaks, but we show that it can also predict an unlimited population explosion in finite time, which cannot happen in a field. We built two new models. In the first, aphids switch to a different growth behaviour once their numbers surge, so outbreaks can repeat while the population stays bounded. In the second, plant quality and aphid growth follow seasonal cycles like those in Iowa temperature and rainfall records; outbreaks then recur for a while and fade out. In every model, simulated aphid numbers passed both benchmarks, so control would be needed. How often environmental conditions change was among the strongest drivers of repeated outbreaks.
SN - 3070-8141
ER -