An age-structured bio-economic model of invasive species management: insights and strategies for optimal control

Controlling invasive species is a highly complex problem defined by the biological characteristics of the organisms, the landscape context, and a management objective of minimizing invasion damages given limited financial resources. While bio-economic optimization models provide a promising approach for invasive species control, current spatio-temporal optimization models omit key ecological details such as age structures—which could be essential to predict how populations grow and spread spatially over time and determine the most effective control strategies. We develop a novel age-structured optimization model as a spatial-dynamic decision framework for controlling invasive species. In particular, we propose a new carrying capacity sub-model, which allows us to take into account the biological competition among different age classes within the population. The potential use of the model is demonstrated on controlling the invasion of sericea (Lespedeza cuneata), a perennial legume threatening native grasslands in the Great Plains. The results show that incorporating age-structure into the model captures important biological characteristics of the species and leads to unexpected results such as multi-logistic population growth with multiple, sequential, and overlapping phases of logistic form. These new findings can contribute to understanding time-lags and invasion growth dynamics. Additionally, given budget constraints, utilizing control measures every 2–3 years is found to be more effective than yearly control because of the time to reproductive maturity. Results of the bio-economic optimization approach provide both ecological and economic insights into the control of invasive species. Furthermore, while the proposed model is specific enough to capture biological realism, it also has the potential to be generalized to a wide range of invasive plant and animal species under various management scenarios in order to identify the most efficient control strategies for managing invasive species.