Predicting the climate change induced yield potential changes of sessile oak stands

Illés Gábor

Bulletin of Forestry Science / Volume 8 / Issue 1 / Pages 105-118
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Gábor Illés

Correspondence

Correspondence: Illés Gábor

Postal address: H-9600 Sárvár, Várkerület 30/A.

e-mail: illes.gabor[at]erti.naik.hu

Abstract

Growth of forest stands is a central question in the field of forest research. Climate change impact assessment also assigns significance to this question. The growing conditions of forests are changing in Central Europe and the impacts of changes are generally considered to be disadvantageous. Increasing frequencies and duration of heat waves and droughts constrain the growing potential of industrially important species. For this reason a statistical evaluation of growth of sessile oak (Quercus petraea, Liebl) was conducted using site (bioclimate and soil) describing predictor variables. The study involved 4594 geo-referenced species records from the National Forestry Database. We focused on practically monoculture stands of seed origin. Climate variables were represented by the Climate EU database. The period of 1961-1990 was considered as climatic baseline. The future, altered climate conditions were represented by the RCP 4.5 scenario based climate models for the period of 2041-2070. Soil and non-climate site data were added from the most recent spatial soil database of Hungary. The statistical random forest package of R was used to build classifiers for yield class predictions based on soil and bioclimatic variables for the reference period. The results of the model series were tested on test sites taken from the permanent yield monitoring plots and forestry database. It was found that predictions reached a relatively high 62-83% correct classification rates by yield classes performing on 77% as an average. Models were run using future climate datasets for the period of 2041-2070 in order to assess changes in future yield classes of forests. Results showed that the extent of the area of best yield classes will decrease, and the most suitable areas show a slight shift to west and to north. In the Pre-Alps region, in the South-Transdanubian region, and in the Transdanubian Mountainous region the well growing sessile oak areas will probably turn into medium or even poorly growing ones. In the same time in the Northern Mountainous region models did not predict significant changes in yield potentials. Overall growing conditions of sessile oak seem to be slightly worsening in the next decades.

Keywords: forest growth, yield assessment, climate change, multivariate statistics

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Béky A. 1981: Mag eredetű kocsánytalantölgyesek fatermése. Erdészeti Kutatások 74: 309-320.

Boisvenua C. & Running S.W. 2006: Impacts of climate change on natural forest productivity - evidence since the middle of the 20th century. Global Change Biology 12: 862–882. DOI: 10.1111/j.1365-2486.2006.01134.x

Breiman L. 2001: Random forests. Machine Learning 45: 5–32. DOI: 10.1023/A:1010933404324

Fournier R.A., Guindon L., Bernier P.Y., Ung C.H. & Raulier F. 2000: Spatial implementation of models in forestry. The Forestry Chronicle 76: 929–940. DOI: 10.5558/tfc76929-6

Führer E., Horváth L., Jagodics A., Machon A. & Szabados I. 2011: Application of a new aridity index in Hungarian forestry practice. Időjárás 115: 205–216.

Geßler A., Keitel C., Kreuzwieser J., Matyssek R., Seiler W. & Rennenberg H. 2006: Potential risks for European beech (Fagus sylvatica L.) in a changing climate. Trees 21: 1–11. DOI: 10.1007/s00468-006-0107-x

Hamann A., Wang T., Spittlehouse D.L. & Murdock T.Q. 2013: A comprehensive, high-resolution database of historical and projected climate surfaces for western North America. Bulletin of the American Meteorological Society 94: 1307–1309. DOI: 10.1175/BAMS-D-12-00145.1

Hartl-Meier C., Dittmar C., Zang C. & Rothe A. 2014: Mountain forest growth response to climate change in the Northern Limestone Alps. Trees 28: 819–829. DOI: 10.1007/s00468-014-0994-1

Hlasny T., Barcza Z., Fabrika M., Balázs B., Churkina G., Pajtík J. et al. 2011: Climate change impacts on growth and carbon balance of forests in Central Europe. Climate Research 47: 219–236. DOI: 10.3354/cr01024

Illés G. & Fonyó T. 2016: A klímaváltozás fatermésre gyakorolt várható hatásának becslése az AGRATÉR projektben. Erdészettudományi Közlemények 6(1): 25–34. DOI: 10.17164/EK.2016.003

Illés G., Fonyó T., Pásztor L., Bakacsi Zs., Laborczi A., Szatmári G. et al. 2016: Az Agrárklíma 2 projekt eredményei: Magyarország digitális talajtípus térképének előállítása. Erdészettudományi Közlemények 6(1): 17–24. DOI: 10.17164/EK.2016.002

Jump A.S., Hunt J.M. & Peñuelas J. 2006: Rapid climate change-related growth decline at the southern range edge of Fagus sylvatica. Global Change Biology 12(11): 2163–2174. DOI: 10.1111/j.1365-2486.2006.01250.x

Lexer M.J., Hönninger K., Scheifinger H., Matulla Ch., Groll N., Kromp-Kolb H. et al. 2002: The sensitivity of Austrian forests to scenarios of climatic change: a large-scale risk assessment based on a modified gap model and forest inventory data. Forest Ecology and Management 162: 53–72. DOI: 10.1016/S0378-1127(02)00050-6

Mátyás Cs. & Sun G. 2014: Forests in a water limited world under climate change. Environmental Research Letters 9: 085001. DOI: 10.1088/1748-9326/9/8/085001

Mátyás Cs., Vendramin G.G. & Fady B. 2009: Forests at the limit: evolutionary — genetic consequences of environmental changes at the receding (xeric) edge of distribution. Report from a research workshop. Annals of Forest Science 66: 800. DOI: 10.1051/forest/2009081

McDowell N.G. & Allen C.D. 2015: Darcy’s law predicts widespread forest mortality under climate warming. Nature Climate Change 5: 669–672. DOI: 10.1038/nclimate2641

Nothdurft A., Wolf T., Ringeler A., Böhner J., Saborowski J. 2012: Spatio-temporal prediction of site index based on forest inventories and climate change scenarios. Forest Ecology and Management 279: 97–111. DOI: 10.1016/j.foreco.2012.05.018

Pásztor L., Laborczi A., Bakacsi Zs., Szabó J., Illés G. 2018: Compilation of a national soil-type map for Hungary by sequential classification methods. Geoderma 311: 93–108. DOI: 10.1016/j.geoderma.2017.04.018

Rasztovits E., Berki I., Mátyás Cs., Czimber K., Pötzelsberger E., Móricz N. 2014: The incorporation of extreme drought events improves models for beech persistence at its distribution limit. Annals of Forest Science 71(2): 201-210. DOI: 10.1007/s13595-013-0346-0

Sáenz-Romero C., Lamy J-B., Ducousso A., Musch B., Ehrenmann F., Delzon S. et al. 2017: Adaptive and plastic responses of Quercus petraea populations to climate across Europe. Global Change Biology 23(7): 2831–2847. DOI: 10.1111/gcb.13576

Savva Y., Oleksyn J., Reich P.B., Tjoelker M.G., Vaganov E.A., Modrzynski J. 2006: Interannual growth response of Norway spruce to climate along an altitudinal gradient in the Tatra Mountains, Poland. Trees 20: 735–746. DOI: 10.1007/s00468-006-0088-9

Wang T., Hamann A., Spittlehouse D. & Carroll C. 2016: Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS One 11: e0156720. DOI: 10.1371/journal.pone.0156720

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Cite this article as:

Illés, G. (2018): Predicting the climate change induced yield potential changes of sessile oak stands. Bulletin of Forestry Science, 8(1): 105-118. (in Hungarian) DOI: 10.17164/EK.2018.007

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Volume 8, Issue 1
Pages: 105-118

DOI: 10.17164/EK.2018.007

First published:
29 May 2018

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