Thomas Krishna Waring , Vera Somers, Michael McCarthy, Christopher Baker

For invasive species management, the trade-off between monitoring and control has high stakes,
and existing decision-making methods are limited in this area. In particular, most current approaches
are limited to a specific case study, or ignore the uncertainty and interaction between monitoring and control in the system. Hence, the field is missing an effective and general framework. We propose a partially observable Markov decision process (POMDP) framework to help
decision makers understand effective monitoring and control policy making. POMDPs can deal with
uncertainty in both the model and state of the system, but are more challenging to solve due to
the continuous and high-dimensional state space. We reduce the dimensionality of the state space
by restricting the belief state to a parametrised family of probability distributions, aligning the
mathematical representation of the problem with quantities of the physical process linked to human
decision making. The result of our model is a sequence of actions which minimises the expected cost incurred in managing the invasive species, where the recommendation depends on an estimate of the species’ abundance, and the uncertainty in this estimate. We demonstrate the effectiveness of our proposed framework in two generic case studies of varying complexity. Furthermore, we investigate sensitivity
of the results to the choices of control cost and efficacy, and monitoring cost and error. The framework proposed by this paper makes the powerful machinery of POMDPs available
to environmental managers. It computes the optimal course of action to manage a growing population of an invasive species, incorporating a varying time horizon and multiple control interventions. We sidestep the computational difficulties of general POMDPs to provide a clear, visual overview of decision-making recommendations, and how these decisions change in new situations. Initial results and scenario based analysis show promising results, and the framework could be extended to the related field of disease management.