Optimizing surveillance and management of emerald ash borer in urban environments

Abstract

Emerald ash borer (EAB), a wood-boring insect native to Asia, was discovered near Detroit in 2002 and has spread and killed millions of ash trees throughout the eastern United States and Canada. EAB causes severe damage in urban areas where it kills highvalue ash trees that shade streets, homes, and parks and costs homeowners and local governments millions of dollars for treatment, removal, and replacement of infested trees. We present a multistage, stochastic, mixed-integer programming model to help decision-makers maximize the public benefits of preserving healthy ash trees in an urban environment. The model allocates resources to surveillance of the ash population and subsequent treatment and removal of infested trees over time. We explore the multistage dynamics of an EAB outbreak with a dispersal mechanism and apply the optimization model to explore surveillance, treatment, and removal options to manage an EAB outbreak in Winnipeg, a city of Manitoba, Canada. Recommendation to Resource Managers • Our approach demonstrates that timely detection and early response are critical factors for maximizing the number of healthy trees in urban areas affected by the pest outbreak.

Optimal invasive species surveillance in the real world: practical advances from research

“…Contemporary work on developing surveillance strategies for invasive species has focused on determining optimal levels of survey effort, sometimes in combination with eradication or other control activities [ 11 , 21 , 44 , 47 , 49 , 51 , 52 ]. Other work has explored optimal survey selection in spatial settings [ 23 , 50 , 53 , 54 ] and together in both spatial and temporal domains [ 17 , 22 , 45 , 46 , 55–57 ]. In these studies, the survey manager's main objective may have been to minimize the total expected costs associated with an invader (i.e.…”

“…the economic value of anticipated host losses plus control and mitigation costs) [ 17 , 22 , 53 ], the expected number of new infestations [ 23 ] or the time to first detection of an incipient population [ 57 ]. Instead, the objective may have been to minimize the number of invaded or potentially invaded host plants [ 50 ] or maximize the number of uninvaded hosts [ 55 ]. Alternately, the goal may have been to maximize the expected number of detections [ 45 ] or minimize the number of survey sites with false negatives (i.e.…”

“…mixed-integer programming [ 23 , 50 , 60 ]), facilitated by increasingly efficient optimization software, have made it possible to compute optimal solutions in a reasonable amount of time, usually a matter of hours [ 43 ]. This is true even though many optimization studies involving invasive species surveillance have added complexity by incorporating uncertainty in one or more parameters, such as dispersal [ 23 , 50 , 55 , 57 , 60 ], the probability of detection [ 67 ] or the current invasion extent [ 49 ].…”

Risk-Averse Multi-Stage Stochastic Programming to Optimizing Vaccine Allocation and Treatment Logistics for Effective Epidemic Response

“…Therefore, many previous studies considered mean-risk models with CVaR in stochastic programming models (Ahmed, 2006;Miller and Ruszczyński, 2011;Rockafellar and Uryasev, 2002;Schultz and Tiedemann, 2006). CVaR-based mean-risk stochastic programming has been studied in various applications, such as supply chain management (Alem and Morabito, 2013), reverse logistic network design problem (Soleimani and Govindan, 2014), solid waste management system (Dai et al, 2014), water resources allocation (Zhang et al, 2016), and forestry invasive species control planning (Bushaj et al, 2020(Bushaj et al, , 2021.…”

Guiding large-scale management of invasive species using network metrics

“…In real management contexts, managers are challenged by making control decisions across numerous infested and noninfested sites. To meet these needs, researchers across disciplines are increasingly incorporating spatial data into mathematical approaches for optimizing invasion management (e.g., using optimal control, mixed integer linear and nonlinear programming, and simulations (e.g., Chades et al, 2011;Epanchin-Niell andWilen, 2012, 2015;Aadland et al, 2015;Baker, 2017;Bushaj et al, 2021;Fischer et al, 2021)). However, each optimization approach must make simplifying assumptions along spatial, temporal, biological, or economic dimensions, due to the complexity of real world landscapes and invasion and economic processes (Büyüktahtakın and Haight, 2018;Kıbış et al, 2020).…”