Prediction models perform better when including transition zones Sophie Vermeersch Plant Science and Nature Plant Science and Nature Management, Department of Biology, Vrije Universiteit Brussel Prediction models of vegetation communities perform better when including transition zones Environmental gradients: incorporation or omission? - limited number of factors that account for the major proportion of variability in vegetation composition - at least one explanatory variable for each environmental gradient identified by preliminary explanatory analyses - environmental gradients expressed as resource gradients vegetation gradients: species or communities? - similar species compositions correspond to similar environments within a geographical area - satisfying weighting mechanisms for diagnostic species cutting mowing Alnion glutinosae Filipendulion Calthion Phragmition Vegetation data training set N = 324 validation set N = 50 quadrat size: 225m nomenclature by Schamine et al. (1995), Schamine et al. (1996) and Stortelder et al. (1999) Environmental data water content (%) pH (CaCl 2 glass/calomel electrode) nutrients: available phosphate, nitrate and ammonium (calorimetric methods) K +, Na +, Ca 2+, Mg 2+ (AAS) Eigenvalues: 1st axis: 0,585 2nd axis: 0,324 3rd axis: 0,227 Modelling procedure vegetation gradients: CCA to attribute weights to vegetation communities - pairwise CCA of homogeneous vegetation communities - introduction of the heterogeneous samples in the canonical space - calculation of Euclidean distance between the community centroid and the heterogeneous sample model precision - bootstrapping (leave-one-out) evaluation of attributed weights - comparison of 2 models constructed through multiple logistic regression: (weighted and unweighted models) - % variance explained by the models - ability of calculating the estimates of occurrences of vegetation communities for an independent data set Cumulative % variance explained: 1st axis: 10,4 2nd axis: 17,3 3rd axis: 23,5 A AB B Carici elongatae- Alnetum Carici remotae- Fraxinetum INPUT Soil variables (% moisture, pH, available fractions of PO4, NO3, NH4, K, Na, Ca, Mg) INPUT Relevs and classification PREDICTION VALIDATION Ecological interpretation Adaptation of input PREDICTION OF VEGETATION COMMUNITIES OMITTING GRADIENTS INTERVENTION INPUT Soil variables in homogeneous environment INPUT Relevs in homogeneous environment and classification PREDICTION OF VEGETATION COMMUNITIES IN GRADIENT SITUATIONS INTERVENTION INPUT Relevs in gradients and classification INPUT Soil variables in gradients PAIRWISE REFERENCES CCA Multiple regression of standardised canonical coefficients and soil variables WEIGHTS for relevs in gradients PREDICTIONS Ecological interpretation VALIDATION Adaptation of input Limitations if important explanatory variables are not incorporated lower amount of correct predictions for communities with less extreme preferences low number of relevs time dependent responses to the fertility gradient prediction of the potentiallity of occurrence of vegetation communities, NOT the actual occurrence Conclusions the used set of edaphic factors in the CCA proved to be significant and able to validate model predictions incorporation of the communities in gradient situations leads to a better model fit, especially when these variables are weighted to environmental variables - higher % of variance (increase of +/- 5%) - larger proportion of corrected samples (78% vs. 56%)