Effective and timely use of models to inform on-the-ground management of invasive plants

Invasive plant management is a costly endeavor, with new areas constantly being invaded and new invaders perpetually appearing. Managers need to be efficient in how they use the limited resources they have for management activities, especially when managing large areas or extensive invasions. While a large body of developed mathematical models predicting plant invasion spread and impact can help determine optimal strategies to achieve the maximum return from management investment, we still lack adequate knowledge to generalize optimal strategies in the field since little empirical work has been done to validate existing conclusions from the modeling approaches. To address critical gaps between invasive plant management and modeling, we discuss how a variety of developed models can be used to inform on-the-ground management and identify major challenges that inhibit field application of modeling findings. We propose potential solutions to these challenges, including (1) developing models that share mutual objectives with field managers; (2) using model input parameters that are easily measured in the field to improve communication and understanding between modelers and managers; (3) moving away from models that rely on data- and time-intensive individual-based measurements and towards models that are more patch-based and/or hierarchical in nature; and (4) encouraging managers to better integrate model output/results into management activities by developing field-applicable models with output parameters that are familiar to land managers, e.g., socioeconomic or logistical factors. These solutions are expected to facilitate communication and create a culture in which long-term interactions and interdisciplinary teams between managers and modelers can be built, which will ultimately contribute to more efficient incorporation of relevant modeling results into management activities and improve our ability to manage invasive plant species.