Spatial optimization of invasive species control informed by management practices

Abstract

Optimization of spatial resource allocation is crucial for the successful control of invasive species under a limited budget but requires labor-intensive surveys to estimate population parameters. In this study, we devised a novel framework for the spatially explicit optimization of capture effort allocation using state-space population models from past capture records. We applied it to a control program for invasive snapping turtles to determine effort allocation strategies that minimize the population density over the whole area. We found that spatially heterogeneous density dependence and capture pressure limit the abundance of snapping turtles. Optimal effort allocation effectively improved the control effect, but the degree of improvement varied substantially depending on the total effort. The degree of improvement by the spatial optimization of allocation effort was only 3.21% when the total effort was maintained at the 2016 level. However, when the total effort was increased by 2, 4, and 8 times, spatial optimization resulted in improvements of 4.65%, 8.33%, and 20.35%, respectively. To achieve the management goal for snapping turtles in our study area, increasing the current total effort by more than 4 times was necessary, in addition to optimizing the spatial effort. The snapping turtle population is expected to reach the target density one year after the optimal management strategy is implemented, and this rapid response can be explained by high population growth rate coupled with density-dependent feedback regulation. Our results demonstrated that combining a state-space model with optimization makes it possible to adaptively improve the management of invasive species and decision-making. The method used in this study, based on removal records from an invasive management program, can be easily applied to monitoring data for wildlife and pest control management using traps in a variety of ecosystems.

Optimizing the use of suppression zones for containment of invasive species

“…We also examine how the optimal strategy depends on the demographic characteristics of the invasive species and its response to the treatment. Note that, while some previous studies have calculated the optimal spatial distribution of treatment efforts to contain a species (Baker & Bode, 2016; Bonneau et al, 2017; Nishimoto et al, 2021), the novelty in our paper is in examining the conditions that determine, based on the characteristics of the system, whether and how to cost‐effectively manage a suppression zone.…”

“…Some of these studies have examined how to allocate treatment efforts over space and time to eradicate an invasive species (Baker, 2017(Baker, , 2018. Studies focusing on containment have examined what parts of the landscape should be contained (Epanchin-Niell & Wilen, 2012), what is the optimal speed at which the species should be allowed to propagate (Menz et al, 1980;Sharov & Liebhold, 1998), and what is the optimal spatial distribution of treatment efforts (Baker & Bode, 2016;Bonneau et al, 2017;Nishimoto et al, 2021).…”

Efficacy and nontarget effects of broadcast treatments to manage spotted lanternfly (Hemiptera: Fulgoridae) nymphs

“…The targeted pests' habitat preferences, host plants, feeding strategy, phenology, and vulnerability to chemical and/or biological agents all influence the balance between control measures' effectiveness and unintended negative impacts (Kjaer et al 1998, Liebhold et al 2016). The value of a management practice also depends on its cost and the ability of managers to deploy it across the targeted area during the time periods when susceptible life stages are present (Epanchin-Niell et al 2012, Nishimoto et al 2021). Together, these considerations help identify management practices that can achieve multiple management goals, including minimizing harm to nontarget organisms.…”

When can model‐based estimates replace surveys of wildlife populations that span many discrete management units?

“…To alleviate uncertainty in population state and/or trends caused by gaps in monitoring information, alternatives to survey‐based monitoring can be used. Model‐based estimates are a useful alternative to conducting a survey and incorporating them in monitoring information can aid in the optimization of monitoring designs and management decision‐making (Hauser et al., 2006; Nishimoto et al., 2021; Westcott et al., 2018). However, model‐based estimates require prior knowledge of the conditions that affect population dynamics, which can include complex biological interactions (Ahrestani et al., 2016; Marolla et al., 2021).…”