A multi-scale modelling framework to guide management of plant invasions in a transboundary context

Abstract

Background: Attention has recently been drawn to the issue of transboundary invasions, where species introduced and naturalized in one country cross international borders and become problematic in neighbouring countries. Robust modelling frameworks, able to identify the environmental drivers of invasion and forecast the current and future potential distribution of invasive species, are needed to study and manage invasions. Limitations due to the lack of species distribution and environmental data, or assumptions of modelling tools, often constrain the reliability of model predictions. Methods: We present a multiscale spatial modelling framework for transboundary invasions, incorporating robust modelling frameworks (Multimodel Inference and Ensemble Modelling) to overcome some of the limitations. The framework is illustrated using Hakea sericea Schrad. (Proteaceae), a shrub or small tree native to Australia and invasive in several regions of the world, including the Iberian Peninsula. Two study scales were considered: regional scale (western Iberia, including mainland Portugal and Galicia) and local scale (northwest Portugal). At the regional scale, the relative importance of environmental predictors sets was evaluated and ranked to determine the main general drivers for the species distribution, while the importance of each environmental predictor was assessed at the local scale. The potential distribution of H. sericea was spatially projected for both scale areas. Results: Model projections for western Iberia suggest that a large area is environmentally suitable in both Portugal and Spain. Climate and landscape composition sets were the most important determinants of this regional distribution of the species. Conversely, a geological predictor (schist lithology) was more important in explaining its local-scale distribution.

Hakea sericea Schrad. & J.C.Wendl

“…In its European invaded range, Martins et al . () showed that, at a gridded 1 km × 1 km scale, schist was an important predictor of the distribution of H. sericea ; it was not important at the larger scale of a 10 km × 10 km regional grid. In general, in its European invaded range, disturbed areas (particularly road margins), forest margins, coastal grasslands and pine forest are all highlighted as additional habitats (Fried, ; Marchante et al ., ).…”

“…The species has been known to have naturalized in the environment in the EPPO region since 1940 and has since become highly invasive in some areas (Esp ırito Santo & Ars enio, 1999;Marchante et al, 2014;Martins et al, 2016). In Spain, H. sericea is known only from Galicia (Sañudo, 2006;Xunta de Galicia, n.d.).…”

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

“…Dynamic models can capture the complexity of interactions among key ecological components by combining environmental conditions, effects of time, and stochastic factors that are difficult to understand otherwise (Jørgensen, 1999). Therefore, this type of models has the potential to support decision-making in IAS management (Santos et al, 2015;Büyüktahtakın and Haight, 2018), for example, for risk evaluation (Guisan et al, 2013), spread dynamics (Ferrari et al, 2014;Martins et al, 2016), management effectiveness (Hyder et al, 2008;Chalak et al, 2011;Portela et al, 2020) of IAS, and restoration of invaded ecosystems (Hall et al, 2020). System dynamics (SD) is a process-based and problem-oriented modeling able to study, analyze, and visualize the behavior of complex systems, in which time is a critical component (Mashaly and Fernald, 2020).…”

Effects of ecological restoration measures on the distribution of Dicranopteris dichotoma at the microscale in the red soil hilly region of China

“…Species distribution models (SDMs), also known as ‘bioclimatic envelope models’, rely on the niche concept [ 4 ]. SDMs statistically relate the distribution of a given species to environment-related factors which typically include available resources, limiting factors and disturbances, improving the understanding of the environmental factors affecting the species, and prediction of the potential distribution of the species [ 5 ]. A wide range of algorithms have been applied in SDMs including the Generalized Linear Model, Maximum Entropy, Artificial Neural Network, Support Vector Machine, Classification and Regression trees, Random Forest, and Generalized Boosted Regression [ 6 ].…”

“…The combined use of geographic information systems (GIS) and SDMs has proven to be an effective methodology for analyzing global patterns and ecological requirements of species [ 7 ]. SDMs have potential for multiple applications in land science, ecological restoration, and ecology [ 5 , 8 ]. They are often used in regional to continental scales, and frequently rely on relatively coarse-precision data sets [ 9 ].…”