Species distribution models predict temporal but not spatial variation in forest growth

Abstract Bioclimate envelope models have been widely used to illustrate the discrepancy between current species distributions and their potential habitat under climate change. However, the realism and correct interpretation of such projections has been the subject of considerable discussion. Here, we investigate whether climate suitability predictions correlate to tree growth, measured in permanent inventory plots and inferred from tree‐ring records. We use the ensemble classifier RandomForest and species occurrence data from ~200,000 inventory plots to build species distribution models for four important European forestry species: Norway spruce, Scots pine, European beech, and pedunculate oak. We then correlate climate‐based habitat suitability with volume measurements from ~50‐year‐old stands, available from ~11,000 inventory plots. Secondly, habitat projections based on annual historical climate are compared with ring width from ~300 tree‐ring chronologies. Our working hypothesis is that habitat suitability projections from species distribution models should to some degree be associated with temporal or spatial variation in these growth records. We find that the habitat projections are uncorrelated with spatial growth records (inventory plot data), but they do predict interannual variation in tree‐ring width, with an average correlation of .22. Correlation coefficients for individual chronologies range from values as high as .82 or as low as −.31. We conclude that tree responses to projected climate change are highly site‐specific and that local suitability of a species for reforestation is difficult to predict. That said, projected increase or decrease in climatic suitability may be interpreted as an average expectation of increased or reduced growth over larger geographic scales.

[1]  G. Yohe,et al.  A globally coherent fingerprint of climate change impacts across natural systems , 2003, Nature.

[2]  Niklaus E. Zimmermann,et al.  Climate change may cause severe loss in the economic value of European forest land , 2013 .

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

[4]  A. Hamann,et al.  Tracking suitable habitat for tree populations under climate change in western North America , 2013, Climatic Change.

[5]  E. Maaten Climate sensitivity of radial growth in European beech (Fagus sylvatica L.) at different aspects in southwestern Germany , 2012, Trees.

[6]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[7]  G. Bonan Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests , 2008, Science.

[8]  D. Ackerly,et al.  Climate Change and the Future of California's Endemic Flora , 2008, PloS one.

[9]  O. Phillips,et al.  Extinction risk from climate change , 2004, Nature.

[10]  A. Thomson,et al.  The representative concentration pathways: an overview , 2011 .

[11]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[12]  A. Hampe Bioclimate envelope models: what they detect and what they hide , 2004 .

[13]  R. Knutti,et al.  Robustness and uncertainties in the new CMIP5 climate model projections , 2013 .

[14]  E. Hogg,et al.  Temporal scaling of moisture and the forest-grassland boundary in western Canada , 1997 .

[15]  Barry W. Brook,et al.  Multi‐model climate projections for biodiversity risk assessments , 2011 .

[16]  David R. B. Stockwell,et al.  Forecasting the Effects of Global Warming on Biodiversity , 2007 .

[17]  E. Cook,et al.  THE SMOOTHING SPLINE: A NEW APPROACH TO STANDARDIZING FOREST INTERIOR TREE -RING WIDTH SERIES FOR DENDROCLIMATIC STUDIES , 1981 .

[18]  Drew W. Purves,et al.  Chasing a moving target: projecting climate change‐induced shifts in non‐equilibrial tree species distributions , 2013 .

[19]  Sylvain Delzon,et al.  Climate change and European forests: what do we know, what are the uncertainties, and what are the implications for forest management? , 2014, Journal of environmental management.

[20]  A. Prasad,et al.  PREDICTING ABUNDANCE OF 80 TREE SPECIES FOLLOWING CLIMATE CHANGE IN THE EASTERN UNITED STATES , 1998 .

[21]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[22]  A. Hamann,et al.  Conservation planning under climate change: accounting for adaptive potential and migration capacity in species distribution models , 2013 .

[23]  M. Pal,et al.  Random forests for land cover classification , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[24]  M. Sykes,et al.  Predicting global change impacts on plant species' distributions: Future challenges , 2008 .

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

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

[27]  M. Schelhaas,et al.  Alternative forest management strategies to account for climate change-induced productivity and species suitability changes in Europe , 2015, Regional Environmental Change.

[28]  R. Shaw,et al.  Range shifts and adaptive responses to Quaternary climate change. , 2001, Science.

[29]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[30]  N. McDowell,et al.  A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests , 2010 .

[31]  T. D. Mitchell,et al.  Ecosystem Service Supply and Vulnerability to Global Change in Europe , 2005, Science.

[32]  A. Hamann,et al.  Strategies for Reforestation under Uncertain Future Climates: Guidelines for Alberta, Canada , 2011, PloS one.

[33]  André Berger,et al.  TREE -RINGS AND CLIMATE IN MOROCCO , 1979 .

[34]  Thomas Lengauer,et al.  ROCR: visualizing classifier performance in R , 2005, Bioinform..

[35]  Koichi Takahashi,et al.  Forecasting the effects of global warming on radial growth of subalpine trees at the upper and lower distribution limits in central Japan , 2013, Climatic Change.

[36]  S. Running,et al.  Impacts of climate change on natural forest productivity – evidence since the middle of the 20th century , 2006 .

[37]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[38]  H. Fritts,et al.  Tree Rings and Climate. , 1978 .

[39]  W. Collins,et al.  Global climate projections , 2007 .

[40]  H. Kahle,et al.  Drought sensitivity of Norway spruce is higher than that of silver fir along an altitudinal gradient in southwestern Germany , 2012, Annals of Forest Science.

[41]  M. Campioli,et al.  Climate change impacts in European forests: the expert views of local observers , 2013, Annals of Forest Science.

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

[43]  Andreas Hamann,et al.  A Comprehensive, High-Resolution Database of Historical and Projected Climate Surfaces for Western North America , 2013 .

[44]  Tongli Wang,et al.  ClimateWNA—High-Resolution Spatial Climate Data for Western North America , 2012 .

[45]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[46]  G. Nabuurs,et al.  Statistical mapping of tree species over Europe , 2011, European Journal of Forest Research.

[47]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[48]  M. Sykes,et al.  Climate change threats to plant diversity in Europe. , 2005, Proceedings of the National Academy of Sciences of the United States of America.