Toward improved species niche modelling: Arnica montana in the Alps as a case study

Summary 1. Under the effects of rapid environmental change, such as climate change and land degradation, assessment of plant species potential distribution is becoming increasingly important for con servation purposes. Moreover, land administrators need reliable predictions of species suitability for planning a wide range of management activities. 2. In 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. 3. Maxent 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. 4. Synthesis 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.

[1]  Doug P Armstrong,et al.  Developing the Science of Reintroduction Biology , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[2]  D. Draper,et al.  Modelling bryophyte distribution based on ecological information for extent of occurrence assessment , 2007 .

[3]  Frank W. Davis,et al.  Modeling vegetation pattern using digital terrain data , 1990, Landscape Ecology.

[4]  M. Zappa,et al.  Are niche‐based species distribution models transferable in space? , 2006 .

[5]  M. Araújo,et al.  Five (or so) challenges for species distribution modelling , 2006 .

[6]  P. Hernandez,et al.  The effect of sample size and species characteristics on performance of different species distribution modeling methods , 2006 .

[7]  Jane Elith,et al.  Error and uncertainty in habitat models , 2006 .

[8]  Trevor Hastie,et al.  Making better biogeographical predictions of species’ distributions , 2006 .

[9]  Mark S. Boyce,et al.  Modelling distribution and abundance with presence‐only data , 2006 .

[10]  M. Araújo,et al.  Consequences of spatial autocorrelation for niche‐based models , 2006 .

[11]  J. Drake,et al.  Modelling ecological niches with support vector machines , 2006 .

[12]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[13]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[14]  C. Blasi,et al.  The vegetation of alpine belt karst-tectonic basins in the central Apennines (Italy) , 2005 .

[15]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[16]  R. Frondoni,et al.  Defining and mapping typological models at the landscape scale , 2005 .

[17]  Shawn W. Laffan,et al.  Effect of error in the DEM on environmental variables for predictive vegetation modelling , 2004 .

[18]  W. Thuiller.  Patterns and uncertainties of species' range shifts under climate change , 2004 .

[19]  T. Dawson,et al.  Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data , 2004 .

[20]  A. Guisan,et al.  An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data , 2004 .

[21]  D. Pegtel.  Habitat characteristics and the effect of various nutrient solutions on growth and mineral nutrition ofArnica montana L. grown on natural soil , 1994, Vegetatio.

[22]  A. Peterson.  Predicting the Geography of Species’ Invasions via Ecological Niche Modeling , 2003, The Quarterly Review of Biology.

[23]  Bianca Hörsch,et al.  Modelling the spatial distribution of montane and subalpine forests in the central Alps using digital elevation models , 2003 .

[24]  M. Austin.  Spatial prediction of species distribution: an interface between ecological theory and statistical modelling , 2002 .

[25]  Caren C. Dymond,et al.  Mapping vegetation spatial patterns from modeled water, temperature and solar radiation gradients , 2002 .

[26]  Dylan Keon,et al.  Equations for potential annual direct incident radiation and heat load , 2002 .

[27]  Uwe Schmidt,et al.  Relation between landform and vegetation in alpine regions of Wallis, Switzerland. A multiscale remote sensing and GIS approach , 2002 .

[28]  Jyrki Kangas,et al.  Integrating spatial multi-criteria evaluation and expert knowledge for GIS-based habitat suitability modelling , 2001 .

[29]  A. Guisan,et al.  Assessing alpine plant vulnerability to climate change: a modeling perspective , 2000 .

[30]  H. Pulliam.  On the relationship between niche and distribution , 2000 .

[31]  P. Poschlod,et al.  Population size, plant performance, and genetic variation in the rare plant Arnica montana L. in the Rhön, Germany , 2000 .

[32]  David R. B. Stockwell,et al.  The GARP modelling system: problems and solutions to automated spatial prediction , 1999, Int. J. Geogr. Inf. Sci..

[33]  James H. Brown,et al.  The Influence of Geomorphological Heterogeneity on Biodiversity  II. A Landscape Perspective , 1998 .

[34]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[35]  J. Franklin.  Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients , 1995 .

[36]  W. Sutherland,et al.  Managing Habitats for Conservation. , 1995 .

[37]  R. Tibshirani,et al.  Generalized additive models for medical research , 1995, Statistical methods in medical research.

[38]  Ian D. Moore,et al.  Modelling environmental heterogeneity in forested landscapes , 1993 .

[39]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[40]  P. McCullagh,et al.  Generalized Linear Models, 2nd Edn. , 1990 .

[41]  H. Ellenberg,et al.  Vegetation Ecology of Central Europe. , 1989 .

[42]  Eric Hulten,et al.  Atlas of North European vascular plants : north of the Tropic of Cancer , 1987 .

[43]  E. O'Loughlin.  Prediction of Surface Saturation Zones in Natural Catchments by Topographic Analysis , 1986 .

[44]  F. Harrell,et al.  Regression modelling strategies for improved prognostic prediction. , 1984, Statistics in medicine.

[45]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[46]  G. E. Hutchinson,et al.  An Introduction to Population Ecology , 1978 .

[47]  Ronald L. Graham,et al.  An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set , 1972, Inf. Process. Lett..

[48]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[49]  C. Stern.  CONCLUDING REMARKS OF THE CHAIRMAN , 1950 .