Evaluating WorldClim Version 1 (1961–1990) as the Baseline for Sustainable Use of Forest and Environmental Resources in a Changing Climate

WorldClim version 1 is a high-resolution, global climate gridded dataset covering 1961–1990; a “normal” climate. It has been widely used for ecological studies thanks to its free availability and global coverage. This study aims to evaluate the quality of WorldClim data by quantifying any discrepancies by comparison with an independent dataset of measured temperature and precipitation records across Europe. BIO1 (mean annual temperature, MAT) and BIO12 (mean total annual precipitation, MAP) were used as proxies to evaluate the spatial accuracy of the WorldClim grids. While good representativeness was detected for MAT, the study demonstrated a bias with respect to MAP. The average difference between WorldClim predictions and climate observations was around +0.2 °C for MAT and −48.7 mm for MAP, with large variability. The regression analysis revealed a good correlation and adequate proportion of explained variance for MAT (adjusted R2 = 0.856) but results for MAP were poor, with just 64% of the variance explained (adjusted R2 = 0.642). Moreover no spatial structure was found across Europe, nor any statistical relationship with elevation, latitude, or longitude, the environmental predictors used to generate climate surfaces. A detectable spatial autocorrelation was only detectable for the two most thoroughly sampled countries (Germany and Sweden). Although further adjustments might be evaluated by means of geostatistical methods (i.e., kriging), the huge environmental variability of the European environment deeply stressed the WorldClim database. Overall, these results show the importance of an adequate spatial structure of meteorological stations as fundamental to improve the reliability of climate surfaces and derived products of the research (i.e., statistical models, future projections).

[1]  S. Gualdi,et al.  Decadal climate predictions with a coupled OAGCM initialized with oceanic reanalyses , 2013, Climate Dynamics.

[2]  H. Nagendra,et al.  Using spatial simulations of habitat modification for adaptive management of protected areas: Mediterranean grassland modification by woody plant encroachment , 2013, Environmental Conservation.

[3]  Donald McKenzie,et al.  Managing uncertainty in climate‐driven ecological models to inform adaptation to climate change , 2011 .

[4]  S. Stephens,et al.  Climate change and forests of the future: managing in the face of uncertainty. , 2007, Ecological applications : a publication of the Ecological Society of America.

[5]  M. Sykes,et al.  Methods and uncertainties in bioclimatic envelope modelling under climate change , 2006 .

[6]  Ana Cristina Costa,et al.  Representativeness impacts on accuracy and precision of climate spatial interpolation in data‐scarce regions , 2015 .

[7]  Marco Marchetti,et al.  Spazializzazione di dati climatici a livello nazionale tramite modelli regressivi localizzati , 2007 .

[8]  E. Campbell,et al.  Projecting future distributions of ecosystem climate niches: Uncertainties and management applications , 2012 .

[9]  M. Belda,et al.  Global warming-induced changes in climate zones based on CMIP5 projections , 2016 .

[10]  G. Beaugrand,et al.  Uncertainties in the projection of species distributions related to general circulation models , 2015, Ecology and evolution.

[11]  A. Imeson.  Desertification, Land Degradation and Sustainability , 2011 .

[12]  Maurizio Marchi,et al.  A simulation-based approach to assess forest policy options under biotic and abiotic climate change impacts: A case study on Scotland's National Forest Estate , 2017, Forest Policy and Economics.

[13]  Marco Alfò,et al.  Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale , 2007 .

[14]  P. Jones,et al.  Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset , 2014 .

[15]  Piermaria Corona,et al.  Projecting non-native Douglas fir plantations in Southern Europe with the Forest Vegetation Simulator , 2017 .

[16]  Lester L. Yuan,et al.  Comparison of spatial interpolation methods for the estimation of air quality data , 2004, Journal of Exposure Analysis and Environmental Epidemiology.

[17]  L. Salvati,et al.  Sampling strategies for high quality time-series of climatic variables in forest resource assessment , 2017 .

[18]  F. Bussotti,et al.  Traditional and Novel Indicators of Climate Change Impacts on European Forest Trees , 2017 .

[19]  Barón Marco Aurelio Azpúrua Auyanet,et al.  A Comparison of Spatial Interpolation Methods for Estimation of Average Electromagnetic Field Magnitude , 2010 .

[20]  Peter H. Verburg,et al.  Bundles of ecosystem (dis)services and multifunctionality across European landscapes. , 2017 .

[21]  J. Morton.  The impact of climate change on smallholder and subsistence agriculture , 2007, Proceedings of the National Academy of Sciences.

[22]  Giorgio Vacchiano,et al.  An improved species distribution model for Scots pine and downy oak under future climate change in the NW Italian Alps , 2014, Annals of Forest Science.

[23]  A. Nicotra,et al.  The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. , 2014, Ecology letters.

[24]  H. Spiecker,et al.  Douglas‐fir plantations in Europe: a retrospective test of assisted migration to address climate change , 2014, Global change biology.

[25]  Göran Ståhl,et al.  Adapting National Forest Inventories to changing requirements - the case of the Swedish National Forest Inventory at the turn of the 20th century , 2014 .

[26]  Edzer J. Pebesma,et al.  Multivariable geostatistics in S: the gstat package , 2004, Comput. Geosci..

[27]  Barry Gardiner,et al.  Comparing the provision of ecosystem services in plantation forests under alternative climate change adaptation management options in Wales , 2015, Regional Environmental Change.

[28]  K. Mellert,et al.  Species distribution models as a tool for forest management planning under climate change: risk evaluation of Abies alba in Bavaria , 2011 .

[29]  Hai-yan Wei,et al.  Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data , 2018, Sustainability.

[30]  B. Fady,et al.  Inferring phenotypic plasticity and local adaptation to climate across tree species ranges using forest inventory data , 2019, bioRxiv.

[31]  Tongli Wang,et al.  Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America , 2016, PloS one.

[32]  L. Iverson,et al.  Geography, topography, and history affect realized‐to‐potential tree species richness patterns in Europe , 2010 .

[33]  T. Zlatanov,et al.  Evolution-based approach needed for the conservation and silviculture of peripheral forest tree populations , 2016 .

[34]  L. Salvati,et al.  Agro-Forest Management and Soil Degradation in Mediterranean Environments: Towards a Strategy for Sustainable Land Use in Vineyard and Olive Cropland , 2018, Sustainability.

[35]  M. Santini,et al.  Likelihood of changes in forest species suitability, distribution, and diversity under future climate: The case of Southern Europe , 2017, Ecology and evolution.

[36]  M. Marchi,et al.  Delineation of seed collection zones based on environmental and genetic characteristics for Quercus suber L. in Sardinia, Italy , 2018, iForest - Biogeosciences and Forestry.

[37]  C. Daly,et al.  Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States , 2008 .

[38]  Guangyu Wang,et al.  Consensus Forecasting of Species Distributions: The Effects of Niche Model Performance and Niche Properties , 2015, PloS one.

[39]  Perdinan,et al.  Changing Human Landscapes Under a Changing Climate: Considerations for Climate Assessments , 2013, Environmental Management.

[40]  L. Perini,et al.  Assessing trends in climate aridity and vulnerability to soil degradation in Italy , 2015 .

[41]  M. Lexer,et al.  Selecting Populations for Non-Analogous Climate Conditions Using Universal Response Functions: The Case of Douglas-Fir in Central Europe , 2015, PloS one.

[42]  A. Barbati,et al.  Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems , 2010 .

[43]  Peter Carey,et al.  Environmental stratifications as the basis for national, European and global ecological monitoring , 2013 .

[44]  B. Lasserre,et al.  Natural capital and bioeconomy: challenges and opportunities for forestry , 2015 .

[45]  Ian T. Jolliffe,et al.  Empirical orthogonal functions and related techniques in atmospheric science: A review , 2007 .

[46]  Alessandro Ferrarini,et al.  Planning for assisted colonization of plants in a warming world , 2016, Scientific Reports.

[47]  Petr Máca,et al.  Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks , 2015, Comput. Intell. Neurosci..

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

[49]  Marco Bindi,et al.  Reviewing climatic traits for the main forest tree species in Italy , 2019, iForest - Biogeosciences and Forestry.

[50]  T. Dawson,et al.  Spatial scale affects bioclimate model projections of climate change impacts on mountain plants , 2008 .

[51]  R. Valentini,et al.  Hot spot maps of forest presence in the Mediterranean basin , 2016 .

[52]  Tongli Wang,et al.  Historical and projected climate data for natural resource management in western Canada , 2009 .

[53]  L. Frelich,et al.  How much does climate change threaten European forest tree species distributions? , 2018, Global change biology.

[54]  S. Wood.  ON CONFIDENCE INTERVALS FOR GENERALIZED ADDITIVE MODELS BASED ON PENALIZED REGRESSION SPLINES , 2006 .

[55]  N. Subedi,et al.  Climate‐diameter growth relationships of black spruce and jack pine trees in boreal Ontario, Canada , 2013, Global change biology.

[56]  M. Marchi,et al.  Some refinements on species distribution models using tree-level National Forest Inventories for supporting forest management and marginal forest population detection , 2018 .

[57]  Tongli Wang,et al.  Accounting for population variation improves estimates of the impact of climate change on species’ growth and distribution , 2008 .

[58]  R. Way,et al.  Underestimated warming of northern Canada in the Berkeley Earth temperature product , 2017 .

[59]  George H. Hargreaves,et al.  Reference Crop Evapotranspiration from Temperature , 1985 .

[60]  Sergio M. Vicente-Serrano,et al.  A New Global 0.5° Gridded Dataset (1901–2006) of a Multiscalar Drought Index: Comparison with Current Drought Index Datasets Based on the Palmer Drought Severity Index , 2010 .

[61]  M. Bachmaier,et al.  Variogram or Semivariogram? Variance or Semivariance? Allan Variance or Introducing a New Term? , 2011 .

[62]  M. Bozzano,et al.  Vulnerability of dynamic genetic conservation units of forest trees in Europe to climate change , 2014, Global change biology.

[63]  S. Nielsen,et al.  Velocity of climate change algorithms for guiding conservation and management , 2015, Global change biology.

[64]  T Matthew Robson,et al.  ΔTraitSDMs: species distribution models that account for local adaptation and phenotypic plasticity. , 2019, The New phytologist.

[65]  Rob H. G. Jongman,et al.  A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring , 2013 .