Integrating species distribution models and interacting particle systems to predict the spread of an invasive alien plant

Abstract

Aim We demonstrate how to integrate two widely used tools for modelling the spread of invasive plants, and compare the performance of the combined model with that of its individual components using the recent range dynamics of the invasive annual weed Ambrosia artemisiifolia L. Location Austria. Methods Species distribution models, which deliver habitat‐based information on potential distributions, and interacting particle systems, which simulate spatio‐temporal range dynamics as dependent on neighbourhood configurations, were combined into a common framework. We then used the combined model to simulate the invasion of A. artemisiifolia in Austria between 1990 and 2005. For comparison, simulations were also performed with models that accounted only for habitat suitability or neighbourhood configurations. The fit of the three models to the data was assessed by likelihood ratio tests, and simulated invasion patterns were evaluated against observed ones in terms of predictive discrimination ability (area under the receiver operating characteristic curve, AUC) and spatial autocorrelation (Moran’s I). Results The combined model fitted the data significantly better than the single‐component alternatives. Simulations relying solely on parameterized spread kernels performed worst in terms of both AUC and spatial pattern formation. Simulations based only on habitat information correctly predicted infestation of susceptible areas but reproduced the autocorrelated patterns of A. artemisiifolia expansion less adequately than did the integrated model. Main conclusions Our integrated modelling approach offers a flexible tool for forecasts of spatio‐temporal invasion patterns from landscape to regional scales. As a further advantage, scenarios of environmental change can be incorporated consistently by appropriately updating habitat suitability layers. Given the susceptibility of many alien plants, including A. artemisiifolia, to both land use and climate changes, taking such scenarios into account will increasingly become relevant for the design of proactive management strategies.

Modelling the spread of European buckthorn in the Region of Waterloo

“…As mentioned above, one of the problems with using GLM to model the habitat suitability of an invasive species is that the invasive may not have had an opportunity to invade all habitat types that are suitable for it (Jeschke and Strayner, 2008;Smolik et al, 2010). Since habitat suitability is determined based on the types of habitat that are occupied, habitat that has not yet been invaded despite being suitable, may be incorrectly labelled as unsuitable (Smolik et al, 2010).…”

“…Since habitat suitability is determined based on the types of habitat that are occupied, habitat that has not yet been invaded despite being suitable, may be incorrectly labelled as unsuitable (Smolik et al, 2010). However, this problem is not significant if the species is widely dispersed within the study region and can be assumed to have reached all suitable habitat available (Smolik et al, 2010). My research has found that European buckthorn is very widely distributed in Waterloo Region and therefore this is not expected to be a large problem.…”

“…Another approach that can be used to model spread utilizes grid-based models such as interacting particle systems or cellular automata (Smolik et al, 2010;.…”

Habitat Modeling of Alien Plant Species at Varying Levels of Occupancy

“…Species distribution models (SDMs) are one such tool. They are widely used in ecology [6,7] and have broad applications in assessing the relationships between species occurrence, the environment and the impact of ecological change [8]. For invasive species, SDMs are useful for predicting species distributions and ecological niches, and also for assessing potential spread and the suitability of areas that have not yet been invaded.…”

Constructing Standard Invasion Curves from Herbarium Data—Toward Increased Predictability of Plant Invasions

“…Significant benefits can be derived from being able to monitor and predict the temporal and spatial dynamics of recent introductions of nonnative plant species. This is critical to cost-efficient management in terms of both the prioritization of control actions (e.g., Ellstrand and Schierenbeck 2000;Mack et al 2000;Smolik et al 2010) and the implementation of rapid-response techniques (e.g., Reichard and Hamilton 1997; Ricciardi et al 2011). Being able to inform these processes is critical, given that financial support for these actions is limited (see Latombe et al 2017).…”