Coordinating Editor: S. Bartha.
Land use improves spatial predictions of mountain plant abundance but not presence-absence
Article first published online: 1 SEP 2009
© 2009 International Association for Vegetation Science
Journal of Vegetation Science
Volume 20, Issue 6, pages 996–1008, December 2009
How to Cite
Randin, C. F., Jaccard, H., Vittoz, P., Yoccoz, N. G. and Guisan, A. (2009), Land use improves spatial predictions of mountain plant abundance but not presence-absence. Journal of Vegetation Science, 20: 996–1008. doi: 10.1111/j.1654-1103.2009.01098.x
- Issue published online: 26 OCT 2009
- Article first published online: 1 SEP 2009
- Received 2 December 2008; Accepted 14 May 2009.
- Environmental niche;
- Logistic regression;
- Mountain flora;
- Ordinal response;
- Species distribution models (SDM);
- Variation partitioning;
- Western Swiss Alps
Question: Does a land-use variable improve spatial predictions of plant species presence-absence and abundance models at the regional scale in a mountain landscape?
Location: Western Swiss Alps.
Methods: Presence-absence generalized linear models (GLM) and abundance ordinal logistic regression models (LRM) were fitted to data on 78 mountain plant species, with topo-climatic and/or land-use variables available at a 25-m resolution. The additional contribution of land use when added to topo-climatic models was evaluated by: (1) assessing the changes in model fit and (2) predictive power, (3) partitioning the deviance respectively explained by the topo-climatic variables and the land-use variable through variation partitioning, and (5) comparing spatial projections.
Results: Land use significantly improved the fit of presence-absence models but not their predictive power. In contrast, land use significantly improved both the fit and predictive power of abundance models. Variation partitioning also showed that the individual contribution of land use to the deviance explained by presence-absence models was, on average, weak for both GLM and LRM (3.7% and 4.5%, respectively), but changes in spatial projections could nevertheless be important for some species.
Conclusions: In this mountain area and at our regional scale, land use is important for predicting abundance, but not presence-absence. The importance of adding land-use information depends on the species considered. Even without a marked effect on model fit and predictive performance, adding land use can affect spatial projections of both presence-absence and abundance models.