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

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dc.contributor.authorMang, Thomasen_ZA
dc.contributor.authorEssl, Franzen_ZA
dc.contributor.authorMoser, Dietmaren_ZA
dc.contributor.authorKleinbauer, Ingriden_ZA
dc.contributor.authorDullinger, Stefanen_ZA
dc.contributor.editorLahoz-Monfort, Jose J.en_ZA
dc.date.accessioned2020-01-29T10:13:12Z
dc.date.available2020-01-29T10:13:12Z
dc.date.issued2018
dc.descriptionCITATION: Mang, T., et al. 2018. An integrated, spatio-temporal modelling framework for analysing biological invasions. Diversity and Distributions, 24(5):653-665, doi:10.1111/ddi.12707.en_ZA
dc.descriptionThe original publication is available at https://onlinelibrary.wiley.comen_ZA
dc.description.abstractAim: 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 explicitly tracking the species’ colonization sequence and establishment of new populations.en_ZA
dc.description.urihttps://onlinelibrary.wiley.com/doi/abs/10.1111/ddi.12707
dc.description.versionPublisher's versionen_ZA
dc.format.extent14 pages : illustrations, mapsen_ZA
dc.identifier.citationMang, T., et al. 2018. An integrated, spatio-temporal modelling framework for analysing biological invasions. Diversity and Distributions, 24(5):653-665, doi:10.1111/ddi.12707en_ZA
dc.identifier.issn1472-4642 (online)en_ZA
dc.identifier.issn1366-9516 (print)
dc.identifier.otherdoi:10.1111/ddi.12707
dc.identifier.urihttp://hdl.handle.net/10019.1/107391
dc.language.isoen_ZAen_ZA
dc.publisherWileyen_ZA
dc.rights.holderAuthors retain copyrighten_ZA
dc.subjectAmbrosia artemisiifolia -- Analysisen_ZA
dc.subjectBiological invasions -- Europeen_ZA
dc.subjectIntroduced organismsen_ZA
dc.subjectAlien plants -- Dispersalen_ZA
dc.subjectGlobal environmental changeen_ZA
dc.subjectSpatial analysis (Statistics)en_ZA
dc.subjectInvasive plants -- Effect of habitat modification onen_ZA
dc.titleAn integrated, spatio-temporal modelling framework for analysing biological invasionsen_ZA
dc.typeArticleen_ZA
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