Recognising fuzzy vegetation pattern: the spatial prediction of floristically defined fuzzy communities using species distribution modelling methods
Version of Record online: 27 MAY 2013
© 2013 International Association for Vegetation Science
Journal of Vegetation Science
Volume 25, Issue 2, pages 323–337, March 2014
How to Cite
Duff, T. J., Bell, T. L., York, A. (2014), Recognising fuzzy vegetation pattern: the spatial prediction of floristically defined fuzzy communities using species distribution modelling methods. Journal of Vegetation Science, 25: 323–337. doi: 10.1111/jvs.12092
- Issue online: 24 FEB 2014
- Version of Record online: 27 MAY 2013
- Manuscript Accepted: 25 MAR 2013
- Manuscript Received: 19 SEP 2011
- Department of Sustainability and Environment
- Boosted regression trees;
Plant communities are not necessarily spatially exclusive; a point in space can exhibit properties of multiple communities. Such variation can be described using floristically defined ‘fuzzy’ units, however these may not be easily delineated using standard remote sensing methods. Is there value in considering communities as fuzzy? Can species distribution modelling methods be used to represent fuzzy communities spatially?
Western Victoria, Australia.
Fuzzy communities were objectively identified from vegetation census quadrats with a cluster analysis of ordinated species data. Boosted regression trees were used to create models that defined relationships between the sampled communities and environmental predictor variables. These were applied to the mapped predictors to create maps of estimated fuzzy community membership for the entire study area.
Four separate fuzzy communities were identified from the sampled vegetation data. Models were created for each community and these were effectively used to generate maps of fuzzy community membership. Individual fuzzy community maps illustrated vegetation variation that could not be discerned on a discretely classified map.
Fuzzy communities were found to represent a greater proportion of species variation than discretely classified units. Species distribution modelling methods were effective in creating independent spatial maps of each floristically defined fuzzy community; however the interpretation of these maps is more complex than with a single discrete community map.