The potential of generalized additive modelling for the prediction of radial growth of Norway spruce from Central Germany |
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| Institution: | 1. Forestry Research and Competence Centre ThüringenForst AöR Jägerstr. 1, D-99867 Gotha, Germany;2. Eberswalde University for Sustainable Development, HNEE, Alfred-Möller-Str. 1, D-16225 Eberswalde, Germany;3. Public Enterprise Sachsenforst, Königsteiner Str. 6b, D-01796 Pirna, Germany;4. Forest Faculty of Applied Sciences, Erfurt Leipziger Str. 77, D- 99085 Erfurt,Germany;5. Friedrich Schiller University Jena, Institute of Ecology and Evolution Jena, Dornburger Str. 159, D-07743 Jena, Germany;1. School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China;2. Department of Geology and Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA;3. State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;4. Key Laboratory of Mountain Surface Processes and Ecological Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China;5. Pacific Northwest National Laboratory, Richland 99352, USA;6. Laboratory of Tree-Ring Research, University of Arizona, Tucson 85721, USA;1. Latvian State Forest Research Institute ‘Silava’, 111 Rigas str., Salaspils LV-2169, Latvia;2. University of Latvia, Faculty of Biology, Jelgavas str. 1, Riga LV-1010, Latvia;3. Thünen Institute of Forest Genetics, Eberswalder Chaussee 3a, D-15377 Waldsieversdorf, Germany;4. Swiss Federal Research Institute WSL, Zürcherstrasse 111, Birmensdorf CH-8903, Switzerland;5. Swiss Federal Research Institute WSL Institute for Snow and Avalanche Research SLF, Flüelastrasse 11, 7260 Davos CH-7260, Switzerland;6. Department of Silviculture and Genetics of Forest Trees, Forest Research Institute, Braci Le?nej 3, Raszyn 05-090, Poland;1. Universidade de Santiago de Compostela, Departamento de Botánica, BIOAPLIC, Escola Politécnica Superior de Enxeñaría, Campus Terra, 27002 Lugo, Spain;2. Wageningen University, Forest Ecology and Forest Management, P.O. Box 47, 6700 AA Wageningen, Netherlands;1. Beijing Key Laboratory of Traditional Chinese Medicine Protection and Utilization, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China;2. College of Resources Science and Technology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;3. College of Forestry, He’nan University of Science and Technology, Luoyang 471003, China;4. College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China;1. Faculty of Forestry, Technical University in Zvolen, T. G. Masaryka 24, 960 53, Zvolen, Slovak Republic;2. Institute of Forest Ecology, Slovak Academy of Sciences, ?túrova 2, 960 53, Zvolen, Slovak Republic;3. Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 165 00, Prague 6-Suchdol, Czech Republic;1. Instituto de Pesquisas Jardim Botânico do Rio de Janeiro, Diretoria de Pesquisa Científica, Laboratório de Botânica Estrutural, Rua Pacheco Leão, 915, 22230-030, Rio de Janeiro, RJ, Brazil;2. Departamento de Biologia Geral, Instituto de Biologia, Universidade Federal Fluminense, Campus Valonguinho, Outeiro de São João Batista, S/N, 24020-141, Niterói, RJ, Brazil |
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| Abstract: | During the past decades managed forest ecosystems in Central Europe underwent vast changes, induced by extreme climate conditions and occasionally adverse forest management. Tree ring width patterns mirror these changes and thus have been widely examined as environmental archives and reliable empirical data sources in ‘tree growth modelling’. Dendrochronologists often suppose linear co-variation among the covariates, variable independence and homoscedasticity. Conventionally, these assumptions were achieved by eliminating biological age trends (detrending) and removing the autocorrelation from the time series (pre-whitening). Particularly detrending might be biased according to the scientific problem and sometimes inflexible age models. In this study, we tackle these issues and examine the suitability of a flexible Generalized Additive Model (GAM) on recently developed tree ring width time series of 30 Norway spruce stands (Picea abies L.] H. Karst) from Central Germany.The model was established to simultaneously cope with the mentioned detrending issue, to unravel nonlinear climate-growth relationships and to predict mean ring width time series for spruce stands in the region. Particularly the latter was of primary interest, since recent forest planning relies on static yield tables that often underestimate the actual growth.The model reliably captured the empirical data, indicated by a small Generalized Cross Validation criterion (GCV = 0.045) and a deviance explained of 88.6 %. The flexible additive smoothers accounted for the social status of individual trees, captured low frequency variations of changing growth conditions adequately and displayed a rather flat biological age trend. The radial increment responded positively to summer season precipitation of the current and previous year. Positive temperature responses were found during the early vegetation period, whereas high summer season temperatures negatively affected the radial growth. The seasonal transition from spring to summer in June induced a shift in the climate response of the linear predictor, leading to a distinct negative effect of temperature and a no-role of precipitation on the linear predictor.Most important, utilizing the calibrated GAM for the purely climate-driven prediction of mean ring width time series from five independent spruce sites revealed proper coherencies. Herein, the mean ring width for sites located within the climatic-optimum for spruce growth were more exactly predicted than for sites with adverse spruce growth conditions. In addition, large mean ring widths were systematically underestimated, whereas small mean ring widths were precisely predicted. Overall, we strongly recommend GAMs as a powerful tool for the investigation of nonlinear climate-growth relationships and for the prediction of radial growth in managed forest ecosystems. |
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| Keywords: | GAM Nonlinear climate-growth response Tree-ring width modelling |
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