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A climate-sensitive aboveground biomass model for three larch species in northeastern and northern China

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A climate-sensitive aboveground biomass model developed by combining a nonlinear mixed-effects model and dummy variable approach led to higher prediction accuracy of biomass than those without climatic variables for three larch species.

Abstract

As native species and being widely distributed in northeastern and northern China, larch forests play a pivotal role in maintaining forest ecosystem functions and mitigation of carbon concentration at the atmosphere. However, the spatial sensitivity of growing and aboveground biomass (AGB) of larch species to climate change is not known. In this study, a climate-sensitive AGB model was developed by combining nonlinear mixed-effects modeling and a dummy variable approach to account for the spatial sensitivity of AGB of three larch species including Dahurian larch (Larix gmelinii (Rupr.) Kuzen.), Korean larch (Larix olgensis Henry.) and Prince Rupprecht larch (Larix principis rupprechtii Mayr.) to climate change in northeastern and northern China. We examined the AGB values of 256 larch trees growing in five secondary climate zones: mid-temperature humid climatic zone, mid-temperature sub-humid climatic zone, mid-temperature semi-arid climatic zone, cool temperature humid climatic zone and warm temperature sub-humid climatic zone in northeastern and northern China. The results showed that the long-term averages of growing season temperature and total growing season precipitation, and the mean temperature and precipitation of wettest quarter, had significant (α = 0.05) effects on AGB. The prediction accuracy of the developed model was much higher than that of the model at the population average level and its base model. Excessive rain and high mean temperature during the growing season increased the AGB values of larch trees, while abundant precipitation and high mean temperature in the wettest quarter led to the decrease of AGB. The AGB of the larch species from the mid-temperature semi-arid climatic zone had the greatest values, which indicated that climate conditions were more favorable in this zone than in the other four secondary climate zones. It is expected that the findings from this study can be combined with the knowledge of adaptive management to reduce the risks and uncertainties associated with forest management decisions.

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Acknowledgements

We thank the Forestry Public Welfare Scientific Research Project of China (no. 201404417), the Central Public-interest Scientific Institution Basal Research Fund (grant no. 2014QB017) and the Chinese National Natural Science Foundations (nos. 31270679, 31470641, 31300534, 31570628) for the financial support of this study. We also appreciate the valuable comments and constructive suggestions from two anonymous referees and the associate editor.

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Authors and Affiliations

  1. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, People’s Republic of China

    Liyong Fu

  2. Center for Statistical Genetics, Pennsylvania State University, Loc T3436, Mailcode CH69, 500 University Drive, Hershey, PA, 17033, USA

    Liyong Fu

  3. College of computer and Information Engineering, Xinjiang Agricultural University, Xinjiang, 830052, China

    Wei Sun

  4. Department of Geography and Environmental Resources, Southern Illinois University at Carbondale, Carbondale, IL, 62901, USA

    Guangxing Wang

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Correspondence to Guangxing Wang.

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Communicated by R. Grote.

Appendix

Appendix

An R program for evaluating the base allometric aboveground biomass (AGB) Model (1), the AGB model without inclusion of climate variables Model (2), the base Model (9) and the nonlinear mixed-effects climatic-sensitive AGB Model (12) in two cases: at the population average (PA) level and province level using the leave-one-out cross-validation approach is given as follows:

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Fu, L., Sun, W. & Wang, G. A climate-sensitive aboveground biomass model for three larch species in northeastern and northern China. Trees 31, 557–573 (2017). https://doi.org/10.1007/s00468-016-1490-6

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