Optimal strategies for utilizing host plant distributions to slow the spread of plant pests
| Authors: | Adam Lampert, Andrew M. Liebhold |
| Year: | 2026 |
| Type: | Scientific Journal |
| Station: | Northern Research Station |
| DOI: | https://doi.org/10.1111/1365-2664.70312 |
| Source: | Journal of Applied Ecology |
Abstract
Invasive species are spreading globally, threatening ecosystems, biodiversity, agriculture and human health. When efforts to prevent the establishment of invasive species fail and eradication is not possible, containment becomes necessary to slow or stop the spread of established invaders. A major question is, therefore, how to allocate treatments across space and time to contain populations cost‐effectively.
Here, we examine how to optimize strategies for slowing the spread of the spongy moth (Lymantria dispar) in North America by utilizing gaps with lower densities of host plants. Around these gaps, managers can apply both bio‐pesticides and mating disruption using synthetic pheromones to disrupt male mate‐finding. We develop a spatially explicit model of the moth's population dynamics, and we develop a novel algorithm that finds the optimal spatial allocation of the two methods across landscapes with heterogeneous host‐plant distributions.
Our results show that combining pesticide application and mating disruption around host‐plant gaps significantly improves cost efficiency: Optimal treatment allocates mating disruption to low moth‐density regions nearer the moth‐free area and pesticides to higher moth‐density regions nearer the invasion front, with minimal spatial overlap in treatments.
Synthesis and applications . Containment of invasive species can be made markedly more cost‐effective by prioritizing landscape features that naturally impede spread. Targeting treatments around host‐plant gaps supports a clear operational rule: use mating disruption where densities are low to prevent establishment and concentrate pesticides where densities are high to suppress the advancing front, avoiding costly overlap. More broadly, the algorithm provides a general framework for computing optimal, landscape‐specific containment allocations over space.