Optimal and robust control of invasive alien species spreading in homogeneous landscapes

Abstract

Government agencies lack robust modelling tools to manage the spread of invasive alien species (IAS). In this paper, we combine optimal control and simulation methods with biological invasion spread theory to estimate the type of optimal policy and switching point of control efforts against a spreading IAS. We employ information-gap (info-gap) theory to assess how the optimal solutions differ from a policy that is most robustly immune to unacceptable outcomes. The model is applied to the potential invasion of the Colorado potato beetle in the UK. Under no uncertainty, we demonstrate that for many of the parameter combinations the optimal control policy corresponds to slowing down the invasion. The info-gap analysis shows that eradication policies identified as optimal under no uncertainty are robustly the best policies even under severe uncertainty, i.e. even if they are likely to turn into slowing down policies. We also show that the control of satellite colonies, if identified as optimal under no uncertainty, will also be a robust slowing down policy for IAS that can spread by long distance dispersal even for relatively ineffective control measures. The results suggest that agencies adopt management strategies that are robustly optimal, despite the severe uncertainties they face.

A structured war-gaming framework for managing extreme risks

“…Among the existing studies, two lines of research are of particular relevance to the model we developed. The first group addresses the decision-support needs of policy makers by integrating established ecological models with economic management frameworks (Barbier, 2001;Cacho et al, 2008;Carrasco et al, 2010;Ceddia et al, 2009;Crepin et al, 2011;Epanchin-Niell et al, 2014;Hyder et al, 2008;Sharov and Liebhold, 1998). The second line of research has developed spatially explicit approaches using stochastic simulations that combine environmental variables and dissemination behaviors in order to characterize uncertainty in spread patterns over time (Carrasco et al, 2012;Epanchin-Niell and Hastings, 2010;Fernandes et al, 2014;Hastings et al, 2005;Meier et al, 2014;Rafoss, 2003;Touza et al, 2010;Yemshanov et al, 2009).…”

Economics of invasive species policy and management

“…Information-gap theory, for example, selects the policy that achieves an acceptable outcome across the widest range of potential uncertainty (Ben-Haim 2006), essentially addressing the question of how much uncertainty can be tolerated before the decision would change. The approach can account for multiple forms of uncertainty simultaneously (Regan et al 2005;BenHaim 2006) and has been applied to designing cargo inspections to prevent invader introductions (Moffitt et al 2008), deciding when to declare successful eradication of an invader (Rout et al 2009), and controlling invasive species' spread across the landscape (Carrasco et al 2010a). The maximin (also referred to as minimax) approach, on the other hand, selects the invasion management policy that performs the least poorly across all potential realizations of an uncertain future (i.e., the policy under which the worst possible outcome is the least bad) (Thompson et al 2012;Mehta et al 2010) and has been applied to developing a list of priority pests (Moffitt and Osteen 2006).…”

Optimal Control of Spatial-Dynamic Processes: The Case of Biological Invasions

“…Some recent work on invasions has relaxed the assumption of perfect information to examine issues of adaptive, robust invasion management and the value of information, primarily in the context of nonspatial control of pest density (e.g., Carrasco et al 2009;Eiswerth and van Kooten 2007;Saphores and Shogren 2005;Yokomizo et al 2009). Our work contributes insight to this branch of research by suggesting that space may play an important role in adaptive management and learning.…”