Predicting current and future biological invasions: both native and invaded ranges matter

Olivier Broennimann Antoine Guisan Articles on similar topics can be found in the following collections Receive free email alerts when new articles cite this article - sign up in the box at the top right-hand corner of the article or click here References Subject collections Email alerting service To subscribe to Biol. Lett. go to: http://rsbl.royalsocietypublishing.org/subscriptions Global change biology Predicting current and future biological invasions: both native and invaded ranges matter Olivier Broennimann* and Antoine Guisan Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland *Author for correspondence (). The classical approach to predicting the geographical extent of species invasions consists of training models in the native range and projecting them in distinct, potentially invasible areas. However, recent studies have demonstrated that this approach could be hampered by a change of the realized climatic niche, allowing invasive species to spread into habitats in the invaded ranges that are climatically distinct from those occupied in the native range. We propose an alternative approach that involves fitting models with pooled data from all ranges. We show that this pooled approach improves prediction of the extent of invasion of spotted knapweed (Centaurea maculosa) in North America on models based solely on the European native range. Furthermore, it performs equally well on models based on the invaded range, while ensuring the inclusion of areas with similar climate to the European niche, where the species is likely to spread further. We then compare projections from these models for 2080 under a severe climate warming scenario. Projections from the pooled models show fewer areas of intermediate climatic suitability than projections from the native or invaded range models, suggesting a better consensus among modelling techniques and reduced uncertainty. 1. INTRODUCTION Biological invasions represent a growing threat to biodiversity (Pimentel et al. 2000). Once introduced species are established, they become difficult to eradicate (Genovesi 2005), thus, preventing future invasions is the most cost-effective form of management. Niche-based models ( NBM, Guisan & Thuiller 2005) are increasingly used to estimate risks of biological invasions (e.g. Thuiller et al. 2005). Two crucial assumptions are made when applying these models. First, the species is assumed to be in equilibrium with its environment in the range used to train the model, and second, the environmental niche of the species is assumed to be conserved across space and time ( Wiens & Graham 2005; Pearman et al. 2008). Electronic supplementary material is available at http://dx.doi.org/ 10.1098/rsbl.2008.0254 or via http://journals.royalsociety.org. One contribution of 12 to a Special Feature on Global change and biodiversity: future challenges. So far, NBM approaches have developed models by using observations either from the invaded range (e.g. Mau-Crimmins et al. 2006) or from the native range (e.g. Peterson & Vieglais 2001; Thuiller et al. 2005) and then predicted the potential extent of invasions. An obvious problem with the first approach is that in the invaded range, the invasion process may not be completed and thus, the invading species may not yet occupy all suitable environments ( Wilson et al. 2007). Such a violation of the equilibrium assumption can subsequently bias the model, underpredicting the full potential for invasion. A problem with the second approach is that the ecological requirements of the species might have changed during the invasion process (Pearman et al. 2008), thus violating the assumption of niche conservatism. We recently evidenced (Broennimann et al. 2007) a shift of the climatic niche of spotted knapweed (Centaurea maculosa Lam.), a weed introduced in the 1890s from Europe into western North America, where it now infests over 3!106 ha of disturbed and natural grassland habitats (see the electronic supplementary material, S2). A consequence of this difference was that models fitted in the native range could successfully predict areas of introduction in North America, but were unable to predict the full extent of invasion. Current NBM approaches may thus be inadequate for several invasive species. Moreover, distinct model training strategies may not only yield distinct predictions in the present, but also on future predictions in warmer climates (Araujo et al. 2005). Here, we assess uncertainties in predictions of spotted knapweed invasion obtained with models trained in (i) the native range, (ii) the invaded range and (iii) both ranges, as similarly explored by Kriticos & Randall (2001). We further investigate the outcome of these training strategies with regard to the outcome of predictions under a severe warming scenario by 2080 (HadCM-SRES-A1FI; Nakicenovic & Swart 2000). Attempts to predict future distributions of invasive species in a warmer climate are few (e.g. Beerling 1993). 2. MATERIAL AND METHODS (a) Species occurrence data We used the occurrence database described in Broennimann et al. (2007), complemented with occurrences from Canada (www.eflora. bc.ca), eastern USA and Russia (H. Mu ller-Scharer 2007, unpublished data), resulting in a robust cross-continental dataset of 373 occurrences for Europe and 2972 occurrences for North America. A corresponding number of pseudo-absences were sampled randomly (see Elith et al. 2006) across both native and invaded ranges. (b) Climate data We used the CRU05 climate data at 0.58 ( New et al. 2000) to derive the following set of meaningful predictors (Guisan & Thuiller 2005): mean annual temperature (tAN), annual sum of precipitation ( pAN), mean annual daily temperature range (dtrAN), minimum temperature (tMIN), maximum temperature (tMAX), total precipitation of the wettest quarter ( pWETQ), total precipitation of the driest quarter ( pDRYQ) and potential evaporation (pet; following Samani & Pessarakli 1986). The HadCM-A1FI climate scenario anomalies were retrieved from the CRU-TS2 dataset (Mitchell et al. 2004) and added to present climate maps to obtain future climate predictors for 2080. (c) Statistical modelling Models were fitted and evaluated using a standard split-sample strategy. Models were trained using three subsets (70%) of occurrence data, (i) from Europe (modEU), (ii) from North America (modNA) only and (iii) from both ranges (modEUNA). We used the same O. Broennimann & A. Guisan Current and future biological invasions Table 1. Model accuracy on evaluation dataset (30% independent data) using AUC. (Standard deviations (G) indicate the variability of model accuracy through 100 iterations. AUC values for models calibrated in Europe ( EU), North America ( NA) and both ( EUCNA) are indicated for each modelling technique. The differences in model accuracy between ranges within and between models were tested with a paired Wilcoxon signed-rank test (n.s (...truncated)