An integrated, spatio-temporal modelling framework for analysing biological invasions

• Published in: Diversity & distributions 2018-05, Vol.24 (5/6), p.652-665
• Main Authors: Mang, Thomas, Dullinger, Stefan, Essl, Franz, Moser, Dietmar, Kleinbauer, Ingrid
• Format: Article
• Language: English
• Subjects: alien species; Ambrosia artemisiifolia; Bayesian analysis; Bayesian inference; BIODIVERSITY RESEARCH; biological invasion; Case studies; Colonization; common ragweed; Complexity; Computer simulation; Data processing; Dispersal; Dispersion; Environment models; Environmental conditions; Environmental effects; Filtration; Invasions; Landscape; Markov chains; Mathematical models; MCMC; Parameter estimation; propagule pressure; spatio‐temporal modelling; spread; Statistical inference
• ISSN: 1366-9516; 1472-4642
• DOI: 10.1111/ddi.12707

Aim: We develop a novel modelling framework for analysing the spatio-temporal spread of biological invasions. The framework integrates different invasion drivers and disentangles their roles in determining observed invasion patterns by fitting models to historical distribution data. As a case study application, we analyse the spread of common ragweed (Ambrosia artemisiifolia). Location: Central Europe. Methods: A lattice system represents actual landscapes with environmental heterogeneity. Modelling covers the spatio-temporal invasion sequence in this grid and integrates the effects of environmental conditions on local invasion suitability, the role of invaded cells and spatially implicit "background" introductions as propagule sources, within-cell invasion level bulk-up and multiple dispersal means. A modular framework design facilitates flexible numerical representation of the modelled invasion processes and customization of the model complexity. We used the framework to build and contrast increasingly complex models, and fitted them using a Bayesian inference approach with parameters estimated by Markov chain Monte Carlo (MCMC). Results: All modelled invasion drivers codetermined the A. artemisiifolia invasion pattern. Inferences about individual drivers depended on which processes were modelled concurrently, and hence changed both quantitatively and qualitatively between models. Among others, the roles of environmental variables were assessed substantially differently subject to whether models included explicit source-recipient cell relationships, spatio-temporal variability in source cell strength and human-mediated dispersal means. The largest fit improvements were found by integrating filtering effects of the environment and spatio-temporal availability of propagule sources. Main conclusions: Our modelling framework provides a straightforward means to build integrated invasion models and address hypotheses about the roles and mutual relationships of different putative invasion drivers. Its statistical nature and generic design make it suitable for studying many observed invasions. For efficient invasion modelling, it is important to represent changes in spatio-temporal propagule supply by populations. explicitly tracking the species' colonization sequence and establishment of new populations.