Statistical modelling of a new global potential vegetation distribution

The potential natural vegetation (PNV) distribution is required for several studies in environmental sciences. Most of the available databases are quite subjective or depend on vegetation models. We have built a new high-resolution world-wide PNV map using a objective statistical methodology based on multinomial logistic models. Our method appears as a fast and robust alternative in vegetation modelling, independent of any vegetation model. In comparison with other databases, our method provides a realistic PNV distribution in agreement with respect to BIOME 6000 data. Among several advantages, the use of probabilities allows us to estimate the uncertainty, bringing some confidence in the modelled PNV, or to highlight the regions needing some data to improve the PNV modelling. Despite our PNV map being highly dependent on the distribution of data points, it is easily updatable as soon as additional data are available and provides very useful additional information for further applications.

[1]  Kelly K. Caylor,et al.  Determinants of woody cover in African savannas , 2005, Nature.

[2]  T. Stocker,et al.  High-resolution carbon dioxide concentration record 650,000–800,000 years before present , 2008, Nature.

[3]  N. Viovy,et al.  Impact of CO 2 and climate on the Last Glacial Maximum vegetation: results from the ORCHIDEE/IPSL models , 2011 .

[4]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[5]  C. Amante,et al.  ETOPO1 arc-minute global relief model : procedures, data sources and analysis , 2009 .

[6]  R. Betts,et al.  Pre-industrial-potential and Last Glacial Maximum global vegetation simulated with a coupled climate-biosphere model: diagnosis of bioclimatic relationships , 2005 .

[7]  Wolfgang Lucht,et al.  Contribution of permafrost soils to the global carbon budget , 2013 .

[8]  G. Brier.  VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[9]  M. Hulme,et al.  A high-resolution data set of surface climate over global land areas , 2002 .

[10]  M. Vrac,et al.  Present and LGM permafrost from climate simulations: contribution of statistical downscaling. , 2010 .

[11]  J. Kutzbach,et al.  Impact of climate variability on present and Holocene vegetation: A model-based study , 2006 .

[12]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[13]  D. Jolly,et al.  Mid‐Holocene and glacial‐maximum vegetation geography of the northern continents and Africa , 2000 .

[14]  Sandy P. Harrison,et al.  Climate and CO2 controls on global vegetation distribution at the last glacial maximum: analysis based on palaeovegetation data, biome modelling and palaeoclimate simulations , 2003 .

[15]  R. Leemans,et al.  Comparing global vegetation maps with the Kappa statistic , 1992 .

[16]  Sandy P. Harrison,et al.  Pollen‐based reconstructions of biome distributions for Australia, Southeast Asia and the Pacific (SEAPAC region) at 0, 6000 and 18,000 14C yr BP , 2004 .

[17]  A. Belward,et al.  The IGBP-DIS global 1km land cover data set, DISCover: First results , 1997 .

[18]  Mathieu Vrac,et al.  Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe , 2007 .

[19]  W. Cramer,et al.  A global biome model based on plant physiology and dominance, soil properties and climate , 1992 .

[20]  M. Lomas,et al.  A multi-model analysis of risk of ecosystem shifts under climate change , 2013 .

[21]  S. Menard.  Coefficients of Determination for Multiple Logistic Regression Analysis , 2000 .

[22]  Robert Jacob,et al.  Simulated and Observed Preindustrial to Modern Vegetation and Climate Changes , 2005 .

[23]  B. Everitt,et al.  Large sample standard errors of kappa and weighted kappa. , 1969 .

[24]  S. P. Harrison,et al.  Palaeovegetation (Communications arising): Diversity of temperate plants in east Asia , 2001, Nature.

[25]  J. Kaplan,et al.  The prehistoric and preindustrial deforestation of Europe , 2009 .

[26]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[27]  P. Bartlein,et al.  Effects of experimental protocol on global vegetation model accuracy: A comparison of simulated and observed vegetation patterns for Asia , 2009 .

[28]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[29]  P. Jones,et al.  A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006 , 2008 .

[30]  T. Yee.  The VGAM Package for Categorical Data Analysis , 2010 .

[31]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[32]  B Huntley,et al.  Reconstructing biomes from palaeoecological data: a general method and its application to European pollen data at 0 and 6 ka , 1996 .

[33]  B. C. Hansen,et al.  Pollen-based biome reconstructions for Latin America at 0, 6000 and 18 000 radiocarbon years ago , 2009 .

[34]  N. Ramankutty,et al.  Estimating historical changes in global land cover: Croplands from 1700 to 1992 , 1999 .

[35]  Gyung-Soo Cha.  The impacts of climate change on potential natural vegetation distribution , 1997, Journal of Forest Research.

[36]  Sandy P. Harrison,et al.  Climate change and Arctic ecosystems: 1. Vegetation changes north of 55°N between the last glacial maximum, mid‐Holocene, and present , 2003 .

[37]  I. C. Prentice,et al.  BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types , 1996 .

[38]  X. Zeng,et al.  Soil microbial respiration from observations and Earth System Models , 2013 .

[39]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[40]  Sandy P. Harrison,et al.  Climate change and Arctic ecosystems: 2. Modeling, paleodata‐model comparisons, and future projections , 2003 .