4.4 Article

Predicting individual-tree growth of central European tree species as a function of site, stand, management, nutrient, and climate effects

期刊

EUROPEAN JOURNAL OF FOREST RESEARCH
卷 137, 期 1, 页码 29-44

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SPRINGER
DOI: 10.1007/s10342-017-1087-7

关键词

Basal area increment; Empirical growth models; Growth function; National forest inventory; Tree growth; Switzerland

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资金

  1. Swiss Federal Office for the Environment FOEN
  2. Swiss Federal Institute for Forest, Snow and Landscape Research WSL as part of the research programme 'Forests and Climate Change'

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We used data from representatively sampled trees to identify key drivers of tree growth for central European tree species. Nonlinear mixed models were fitted to individual-tree basal area increments (BAI) from the Swiss national forest inventory. Data from 1983 to 2006 were used for model fitting and data from 2009 to 2013 for model evaluation. We considered 23 potential explanatory variables specifying individual-tree characteristics, site and stand conditions, management, climate, and nitrogen deposition. Model selection was processed separately for Picea abies, Abies alba, Pinus sp., Larix sp., other conifers, Fagus sylvatica, Quercus sp., Fraxinus sp./Acer sp., and other broadleaves. The selected models explained 56-70% of the BAI variance in the model fitting dataset and 21-64% in the evaluation dataset. While some variables were relevant for all species, the combination of further variables differed among the species, reflecting their physiological properties. In general, BAI was positively related to DBH and temperature and negatively related to basal area of larger trees, stand density, mean DBH of the 100 thickest trees per ha, slope, and soil pH. For most species, harvesting had a positive effect on BAI. In general, nitrogen deposition was positively related to BAI, except for spruce and fir, for which the inverse effect was found. Increasing drought reduced BAI for most species, except for pine and oak. These BAI models incorporate many influencing factors while representing large spatial extents, making them useful for both nationwide scenario analyses and deepening the understanding of the main drivers modulating tree growth throughout central Europe.

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