Toward improved species niche modelling: Arnica montana in the Alps as a case study
*Correspondence author. E-mail: firstname.lastname@example.org
- 1Under the effects of rapid environmental change, such as climate change and land degradation, assessment of plant species potential distribution is becoming increasingly important for conservation purposes. Moreover, land administrators need reliable predictions of species suitability for planning a wide range of management activities.
- 2In this study, we used the recent Maxent algorithm for modelling the niche of Arnica montana within a Site of Community Importance in the Alps, with the ultimate aim of providing a rigorous evidence base for management of this locally threatened species. We built a final suitability map taking into account (i) the minimization of spatial autocorrelation through the use of a constrained random split of sampled data; (ii) the use of a stepwise selection of predictors in order to obtain a reduced model containing only meaningful variables; (iii) the comparison of the predictive power of three sets of environmental predictors; (iv) the identification of the most suitable areas by overlaying predictions of three competing models; (v) the use of divergence maps as a complement to conventional performance comparison assessments.
- 3Maxent improved accuracy both on training and test data sets. Elevation, geomorphology and hosting habitats performed as effective primary predictors. A reduced model based on the outcomes of a preliminary stepwise selection analysis of predictors gave the best accuracy score on test data. Two parts of the study area have been selected for management as a result of areas of agreement between the three competing models.
- 4Synthesis and applications. There remain important methodological issues that need to be improved in order to increase confidence in niche modelling and ensure that reintroduction and management activities for threatened or rare plant species are based on reliable distribution models. Modellers can improve predictions of plant distribution by addressing methodological topics that are often overlooked, as demonstrated for A. montana in this study.