Optimizing management of invasions in an uncertain world using dynamic spatial models

Abstract

Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions.

Managing invasive hybrids with pond hydroperiod manipulation in an endangered salamander system

“…Hydroperiod reduction in these agricultural ponds can be accomplished inexpensively by, for example, reducing the height of earthen berms or dams to reduce maximum depth and volume or adding standpipes that enable active manipulation of pond depth. Managing hydroperiod would have 3 major benefits: First, it would reduce the number and fitness of hybrid offspring, likely reducing the number of hybrid migrants that disperse and colonize nearby ponds, a critical factor in containing non‐native invasions (Pepin et al., 2022). Second, compared with fully draining or eliminating the pond, it allows for other endangered aquatic taxa to persist as additional hybrid salamander removal strategies are developed and deployed (Howard et al., 2015).…”

Effective use of models in intelligence-to-decision workflows within and across One Health sectors

“…Either way, a system-level model has been applied and there is some uncertainty. In contrast, predicting outcomes for new and complex phenomena, such as emerging disease spread, a biological invasion risk [5][6][7] , or climate change impacts on ecosystems is more uncertain. Here public service decision-makers may turn to mathematical models when expert opinion and experience do not resolve enough uncertainty about decision outcomes.…”

Optimizing strategies for slowing the spread of invasive species

“…For example, Pontryagin’s maximum principle [ 32 , 45 ] has been used to study certain spatially-extended bioeconomic models [ 41 , 46 ], but its application for solving more complex models requires its implementation specifically tailored to the specific model, and it might not be applicable in practice to all cases and boundary conditions, such as cases that incorporate dispersal kernel instead of diffusion. Also, methods like Dynamic Programming, which work well for finding the optimal treatment of local populations [ 47 ], often operate too slowly for solving models that incorporate large landscapes, and therefore, approximation algorithms might be necessary [ 48 ].…”