Pest control at a regional scale: Identifying key criteria using a spatially explicit, agent‐based model

Abstract

1. Invasive mammals are a major threat to biodiversity. Understanding how their distributions and abundance could be affected by different temporal and spatial control strategies is fundamental for planning effective management programs.2. We developed a spatially explicit, agent-based model to test the impacts of different spatiotemporal management strategies on a pest population. As a case study, we used the common brushtail possum (Trichosurus vulpecula) population in the Cape-to-City treatment area in Hawke's Bay, New Zealand.3. We found striking differences in the effectiveness of different spatial control strategies -a well designed spatial control strategy could be up to twice as cost effective as poorly designed ones.4. The optimal spatial control strategy may depend on the total control effort. At low control effort, habitat-targeted control was more effective; at high control effort, homogeneously distributed control was more effective.5. Immigration rather than in situ breeding is likely to initiate the population recovery in treated areas after an initial knockdown. Therefore, increasing the size of treatment areas and maximizing the use of natural barriers to immigration could prolong treatment persistence. 6. Synthesis and applications. We have demonstrated how a spatially explicit, agentbased model can be used to identify key criteria for a control strategy of open pest populations. The integration of available information on pest habitat use, population dynamics and actual levels of control allowed us to assess the deployment of control sites and assisted the choice of spatial control strategies. Important roles for this type of model are to help predict hotspots of mammalian pest activities, to suggest the most effective control strategy and to identify important parameters and data for improving predictions. Ultimately, further integration of spatial and temporal analyses is critical for updating and optimizing management strategies. K E Y W O R D S agent-based modelling, dispersal, heterogeneous landscape, immigration, landscape patterns, Mammalian pests, population dynamics, spatiotemporal management strategies 1516 | Journal of Applied Ecology LUSTIG eT aL. | 1517 Journal of Applied Ecology LUSTIG eT aL.

Mechanistic invasive species management models and their application in conservation

“…Both individual and agent-based models simulate decision-making individuals; however, agent-based models consider individuals or "agents" to be adaptive and autonomous (Grimm et al, 2005). Therefore, individual-based models have been used to simulate nonautonomous invasive species (Tonini et al, 2014), while agent-based models can represent self-governing organisms (Lustig, James, Anderson, & Plank, 2019) or human behaviors involved in species spread or removal (Rebaudo & Dangles, 2013). Here, we refer to both individual and agent-based models as IBMs because they have similar mathematical foundations.…”

“…The model integrated social learning of farmers and found that negative public opinions on invasive species control communicated by media outlets contributed to control program failure. Lustig et al (2019) built an IBM to test the success of management strategies for invasive brushtail possums (Trichosurus vulpecula) in New Zealand and incorporated sophisticated species-habitat interactions and spatially explicit carrying capacities. They showed that the optimal spatial control strategy depended on management effort.…”

Habitat-specific densities of urban brushtail possums

“…This research was conducted with the aim of gathering baseline data of urban possums in New Zealand in order to effectively plan and achieve future possum control and biodiversity outcomes. These data would also improve predictive possum models, which to date have made the unsupported assumption that possum density in urban areas is zero (Warburton et al 2009;Shepherd et al 2018;Lustig et al 2019).…”

abmR: an R package for agent-based model analysis of large-scale movements across taxa

“…Thus, ABM has found applications in many study areas including biology, disease risk, social sciences, and economics (Polhill et al, 2008; Grimm & Railsback, 2013; Kilmek et al, 2015; Willem et al, 2017) with the unifying goal of investigating and predicting the dynamics of complex systems (Grimm et al, 2005). In particular, wildlife studies have adopted the ABM approach to simulate population growth, reproduction, mortality rate, energy budget, and migration ecology, just to cite a few (Brown & Robinson, 2006; Lustig et al, 2019; Aurbach et al, 2020; Goldstein et al, 2021). However, we currently lack a robust and spatially explicit ABM coding platform for the implementation of large-scale animal movement investigations (but see Thiele et al (2012) or Chubaty and McIntire (2021)).…”