Developing an expert elicited simulation model to evaluate invasive species and fire management alternatives

Abstract

Invasive species can alter ecosystem properties and cause state shifts in landscapes. Resource managers charged with maintaining landscapes require tools to understand implications of alternative actions (or inactions) on landscape structure and function. Simulation models can serve as a virtual laboratory to explore these alternatives and their potential impacts on a landscape. To be useful, however, managers need to participate in model development to ensure that model structure can evaluate the response of key resources to plausible actions. Here, we detail development of a state‐and‐transition simulation model (STSM) to evaluate buffelgrass (Cenchrus ciliaris L. syn Pennisetum ciliare (L.) Link) in Saguaro National Park (SNP), Arizona, USA, through collaboration between managers and researchers. We integrate expert knowledge and research to create and parameterize a stochastic, spatially explicit STSM to evaluate specific management objectives. We also develop a dynamic link between the STSM and a fire behavior model to allow exploration of potential novel processes introduced to the ecosystem by buffelgrass invasion. Our projections show that buffelgrass can be expected to increase on the landscape and that the integration of fire into the model accelerates the projected rate of invasion and increases degradation of resources of management concern. We highlight the benefit of engaging end users in the modeling process so that the model is targeted to evaluate management objectives, in this case retention of saguaro cacti (Carnegiea gigantea (Engelm.) Britton & Rose) on the landscape. Being able to integrate an external model that can help address the unique characteristics of a problem such as the introduction of fire into the SNP desert ecosystem increases the ability of simulations to address complex ecological and management questions.

A Dynamic Modeling Framework to Evaluate the Efficacy of Control Actions for a Woody Invasive Plant, Hakea sericea

“…Similar IAS expansion after a fire has been documented for other invasive species, such as Acacia sp. (Souza-Alonso et al, 2017), Pinus radiata (Richardson and Brown, 1986), Arundo donax (Coffman et al, 2010), Chromolaena odorata (Dew et al, 2017), or Cenchrus ciliaris L. syn Pennisetum ciliare (L.) Link (Jarnevich et al, 2019). From an economic point of view, curbing such expansion will require more resources, thus increasing management costs.…”

Vegetation dynamics models: a comprehensive set for natural resource assessment and planning in the United States

“…Such an approach has been developed using STSMs to estimate the natural range of variability for the Western Cascade Range of Washington (Donato et al 2020). In addition, ST-Sim can be run spatially or linked with other models to incorporate spatial dynamics such as fire spread (Jarnevich et al 2019).…”

The Epidemiological Framework for Biological Invasions (EFBI): an interdisciplinary foundation for the assessment of biosecurity threats

“…non-native rabbits impeding woody succession), but the spatial unit itself does not usually disappear (Vilà and Hulme 2017). Even if it is transformed to another ecosystem state (Cobb et al 2017;Jarnevich et al 2019;Perry et al 2015;Stein et al 2016), the receptor may remain Susceptible to further colonisation by non-native species and it may even continue to be Infectious. Human activities can, of course, create and destroy entire ecosystems.…”

“…grazing, insect outbreaks) drivers that lead to transitions from one ecosystem state to another (Bestelmeyer et al 2017). The STM framework has increasingly been used to model shifts in ecosystem state as a result of biological invasions by non-native plants, pathogens and animals (Cobb et al 2017;Jarnevich et al 2019;Perry et al 2015;Stein et al 2016). Furthermore, STMs are also one of the most widespread modelling techniques in clinical decision analysis to capture transitions in disease status of individual patients (Siebert et al 2012).…”