Integrating regional climate change into allometric equations for estimating tree aboveground biomass of Masson pine in China

  • Liyong Fu
  • Xiangdong Lei
  • Zongda Hu
  • Weisheng Zeng
  • Shouzheng Tang
  • Peter Marshall
  • Lin Cao
  • Xinyu Song
  • Li Yu
  • Jingjing Liang
Original Paper

Abstract

Key message

A climate-sensitive aboveground biomass (AGB) equation, in combination with nonlinear mixed-effects modeling and dummy variable approach, was developed to examine how climate change may affect the allometric relationships between tree diameter and biomass. We showed that such changes in allometry need to be taken into account for estimating tree AGB in Masson pine.

Context

As a native species and being widely distributed in subtropical China, Masson pine (Pinus massoniana Lamb.) forests play a pivotal role in maintaining forest ecosystem functions and mitigation of carbon concentration increase at the atmosphere. Traditional biomass allometric equations do not account for a potential effect of climate on the diameter–biomass relationships. The amplitude of such an effect remains poorly documented.

Aims

We presented a novel method for detecting the long-term (2041–2080) effects of climate change on the diameter–biomass relationships and the potential consequences for long-term changes of biomass accumulation for Masson pine.

Methods

Our approach was based on a climate-sensitive AGB model developed using a combined nonlinear mixed-effects model and dummy variable approach. Various climate-related variables were evaluated for their contributions to model improvement. Heteroscedasticity was accounted for by three residual variance functions: exponential function, power function, and constant plus function.

Results

The results showed that diameter at breast height, together with the long-term average of growing season temperature, total growing season precipitation, mean temperature of wettest quarter, and precipitation of wettest quarter, had significant effects on values of AGB. Excessive rain during the growing season and high mean temperature in the wettest quarter reduced the AGB, while a warm growing season and abundant precipitation in the wettest quarter increased the AGB.

Conclusion

Climate change significantly affected the allometric scale of biomass equation. The new climate-sensitive allometric model developed in this study may improve biomass predictions compared with the traditional model without climate effects. Our findings suggested that the AGB of Masson pine trees with the same diameter at breast height under three climate scenarios including representative concentration pathway (RCP) 2.6, RCP 4.5, and RCP 8.5 in the future period 2041–2080 would increase by 24.8 ± 32.7% (mean ± standard deviation), 27.0 ± 33.4%, and 27.7 ± 33.8% compared with the constant climate (1950–2000), respectively. As a consequence, we may expect a significant regional variability and uncertainty in biomass estimates under climate change.

Keywords

Masson pine Subtropical China Climate change Climate-sensitive aboveground biomass model Nonlinear mixed-effects model Dummy variable approach 

Notes

Acknowledgements

The article was funded by the Forestry Public Welfare Scientific Research Project (No. 201504303) and the Chinese National Natural Science Foundation (Nos. 31470641, 31300534, and 31570628). We appreciate the valuable comments from the chief editor Dr. Erwin Dreyer, Dr. Jean-Michel Leban, the handling editor, and two anonymous reviewers who improved the manuscript.

Compliance with ethical standards

The biomass data used in this study were collected according to the protocol of data collection for tree biomass modeling which was drafted by the State Forestry Administration of China (SFA 2014).

Supplementary material

13595_2017_636_MOESM1_ESM.docx (17 kb)
ESM 1 (DOCX 16 kb)

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Copyright information

© INRA and Springer-Verlag France 2017

Authors and Affiliations

  • Liyong Fu
    • 1
  • Xiangdong Lei
    • 1
  • Zongda Hu
    • 2
  • Weisheng Zeng
    • 3
  • Shouzheng Tang
    • 1
  • Peter Marshall
    • 4
  • Lin Cao
    • 5
  • Xinyu Song
    • 1
    • 6
  • Li Yu
    • 1
  • Jingjing Liang
    • 7
  1. 1.Research Institute of Forest Resource Information TechniquesChinese Academy of ForestryBeijingPeople’s Republic of China
  2. 2.College of ResourcesSichuan Agricultural UniversityChengduPeople’s Republic of China
  3. 3.Academy of Forest Inventory and PlanningState Forestry AdministrationBeijingPeople’s Republic of China
  4. 4.Department of Forest Resources ManagementUniversity of British ColumbiaVancouverCanada
  5. 5.Co-Innovation Center for Sustainable Forestry in Southern ChinaNanjing Forestry UniversityNanjingPeople’s Republic of China
  6. 6.College of Computer and Information TechniquesXinyang Normal UniversityXinyangPeople’s Republic of China
  7. 7.Division of Forestry and Natural ResourcesWest Virginia UniversityMorgantownUSA

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