Applying Geospatial Semantic Array Programming for a Reproducible Set of Bioclimatic Indices in Europe

Bioclimate-driven regression analysis is a widely used approach for modelling ecological niches and zonation. Although the bioclimatic complexity of the European continent is high, a particular combination of 12 climatic and topographic covariates was recently found able to reliably reproduce the ecological zoning of the Food and Agriculture Organization of the United Nations (FAO) for forest resources assessment at pan-European scale, generating the first fuzzy similarity map of FAO ecozones in Europe. The reproducible procedure followed to derive this collection of bioclimatic indices is now presented. It required an integration of data-transformation modules (D-TM) using geospatial tools such as Geographic Information System (GIS) software, and array-based mathematical implementation such as semantic array programming (SemAP). Base variables, intermediate and final covariates are described and semantically defined by providing the workflow of D-TMs and the mathematical formulation following the SemAP notation. Source layers to derive base variables were extracted by exclusively relying on global-scale public open geodata in order for the same set of bioclimatic covariates to be reproducible in any region worldwide. In particular, two freely available datasets were exploited for temperature and precipitation (WorldClim) and elevation (Global Multi-resolution Terrain Elevation Data). The working extent covers the European continent to the Urals with a resolution of 30 arc-second. The proposed set of bioclimatic covariates will be made available as open data in the European Forest Data Centre (EFDAC). The forthcoming complete set of D-TM codelets will enable the 12 covariates to be easily reproduced and expanded through free software.

[1]  Daniele de Rigo,et al.  Toward Open Science at the European Scale: Geospatial Semantic Array Programming for Integrated Environmental Modelling , 2013 .

[2]  Miroslav Kubásek,et al.  Environmental Software Systems. Fostering Information Sharing , 2013, IFIP Advances in Information and Communication Technology.

[3]  Daniele de Rigo,et al.  Semantic Array Programming for Environmental Modelling: Application of the Mastrave Library , 2012 .

[4]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[5]  D. Gesch,et al.  Global multi-resolution terrain elevation data 2010 (GMTED2010) , 2011 .

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

[7]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[8]  Daniele de Rigo,et al.  Continental-Scale Living Forest Biomass and Carbon Stock: A Robust Fuzzy Ensemble of IPCC Tier 1 Maps for Europe , 2013, ISESS.

[9]  A. Hirzel,et al.  Habitat suitability modelling and niche theory , 2008 .

[10]  Ron Store,et al.  A GIS-based multi-scale approach to habitat suitability modeling , 2003 .

[11]  Dawn M. Lawson,et al.  The Roles of Dispersal, Fecundity, and Predation in the Population Persistence of an Oak (Quercus engelmannii) under Global Change , 2012, PloS one.

[12]  Mingtian Xu,et al.  The thermodynamic basis of entransy and entransy dissipation , 2011 .

[13]  M. Araújo,et al.  Presence-absence versus presence-only modelling methods for predicting bird habitat suitability , 2004 .

[14]  J. Oldeland,et al.  Modelling potential distribution of the threatened tree species Juniperus oxycedrus: how to evaluate the predictions of different modelling approaches? , 2011 .

[15]  O. Nordenskiöld,et al.  The geography of the polar regions , 1928 .

[16]  G. Brent Hall,et al.  Open Source Approaches in Spatial Data Handling , 2008 .

[17]  XinGang Liang,et al.  Entransy—A physical quantity describing heat transfer ability , 2007 .

[18]  Daniele de Rigo,et al.  Supporting EFSA assessment of the EU environmental suitability for exotic forestry pests: Final Report , 2014 .

[19]  Frank Warmerdam,et al.  The Geospatial Data Abstraction Library , 2008 .

[20]  Amit K. Srivastava,et al.  Climate Impacts in Europe - The JRC PESETA II Project , 2014 .

[21]  P. Thomas,et al.  Biological Flora of the British Isles: Fagus sylvatica , 2012 .

[22]  D. Spittlehouse,et al.  GENETIC RESPONSES TO CLIMATE IN PINUS CONTORTA: NICHE BREADTH, CLIMATE CHANGE, AND REFORESTATION , 1999 .

[23]  Markus Metz,et al.  GRASS GIS: A multi-purpose open source GIS , 2012, Environ. Model. Softw..

[24]  J. Hofierka,et al.  GRASS GIS manual: r.sun , 2007 .

[25]  Yanbing Zheng,et al.  Movement of outbreak populations of mountain pine beetle: influences of spatiotemporal patterns and climate , 2008 .

[26]  D. Kluza,et al.  Potential distribution of emerald ash borer: What can we learn from ecological niche models using Maxent and GARP? , 2012 .

[27]  John W. Eaton,et al.  GNU Octave and reproducible research , 2012 .