How to overcome the rare species modelling paradox? A test on 109 species of conservation concern Frank Breiner1*, Michael Nobis1, Antoine Guisan2, Ariel Bergamini1 1Swiss Federal Research Institute WSL, Birmensdorf, Switzerland , 2University of Lausanne, Lausanne, Switzerland * Contact: Frank Breiner, Swiss Federal Research Institute WSL, Zürcherstr. 111, 8903 Birmensdorf, [email protected], +41 44 7392 418 Methods cont. Introduction • 3 SDM techniques (GLM, GBM, Maxent) and the AUC based averaged mean Ensemble Forecast (EF) Species Distribution Models (SDMs) are widely used within basic and applied research. In nature conservation SDMs for rare species which are characterised by low sample sizes are of particular interest. Unfortunately, model accuracy is usually decreasing with decreasing sample size, i.e. rare species and many predictor variables boost model over-fitting. Species which are most in need for conservation purposes are the most difficult to model ('Rare Species Modelling Paradox’). Recently, Lomba et. al (2010) introduced a new SDM strategy, the so-called ‘Ensemble Bivariate Modelling’ (EBM) to overcome the ‘Rare Species Modelling Paradox’. Here, we test different SDM strategies on 109 rare and under-sampled vascular plant species. • 3 SDM strategies: − ‘Reduced set’: standard strategy for variable selection (e.g. BIC stepwise for GLMs) − ‘PCA set’: using the first 6 components of a PCA as predictors − ‘EBM’: new strategy by fitting all possible bivariate models for a given set of environmental variables and average them using the AUC based weighted average (Fig. 1) Fig. 1: Framework of the ‘Ensemble Bivariate Modelling’-Strategy. Results • • • Methods • 109 species with number of occurrences ranging between 10 and 377 (median 37) EBM clearly outperforms the other SDM strategies (Fig. 2) For very rare species (n<25) EBM is especially advantageous While there is considerable variation between SDM techniques for the ‘Reduced’ and the ‘PCA’ set, there hardly is any variation for the EBM strategy • Red list categories are represented in our target species as following: 5 critically endangered, 25 endangered, 21 vulnerable, 26 near threatened and 32 least concern • 50-fold split sampling with 90% train and 10% test data for the evaluation with AUC and Boyce Index • 11 low correlated (|r| ≤ 0.7) climatic, topological and geological variables and their quadratic terms were used to calibrate the models Fig.2: Model evaluation using AUC (top) and Boyce Index (bottom) for three groups with increasing sample size. The EBM-strategy outperforms standard strategies especially for rare species. Conclusion Discussion All predictor variables could be included in the EBM-Strategy, i.e. there is more information available for modelling species’ distributions. Interestingly, variability between SDM techniques is marginal for the EBM-strategy in contrast to the standard strategies. It is thus not so important which technique is used. Literature A ‘double-ensemble framework’ (EBM and EF) does not improve model performance and is thus not recommended also due to its high computational effort and complexity. Lomba, A., Pellissier, L., Randin, C., Vicente, J., Moreira, F., Honrado, J., Guisan, A., 2010. Overcoming the rare species modelling paradox: A novel hierarchical framework applied to an Iberian endemic plant. Biological Conservation 143, 2647–2657. Funding We recommend to use the ‘EBMStrategy’ especially for rare species (approx. <25 presences). We are convinced that its use in future modelling is worth the higher effort to implement it. Support Supervision Session Spatial Ecology | 43rd Annual Meeting of the Ecological Society GFÖ 2013 | Potsdam