Tree Biomass Allocation and Its Model Additivity for Casuarina equisetifolia in a Tropical Forest of Hainan Island, China

PLoS One. 2016 Mar 22;11(3):e0151858. doi: 10.1371/journal.pone.0151858. eCollection 2016.

Abstract

Casuarina equisetifolia is commonly planted and used in the construction of coastal shelterbelt protection in Hainan Island. Thus, it is critical to accurately estimate the tree biomass of Casuarina equisetifolia L. for forest managers to evaluate the biomass stock in Hainan. The data for this work consisted of 72 trees, which were divided into three age groups: young forest, middle-aged forest, and mature forest. The proportion of biomass from the trunk significantly increased with age (P<0.05). However, the biomass of the branch and leaf decreased, and the biomass of the root did not change. To test whether the crown radius (CR) can improve biomass estimates of C. equisetifolia, we introduced CR into the biomass models. Here, six models were used to estimate the biomass of each component, including the trunk, the branch, the leaf, and the root. In each group, we selected one model among these six models for each component. The results showed that including the CR greatly improved the model performance and reduced the error, especially for the young and mature forests. In addition, to ensure biomass additivity, the selected equation for each component was fitted as a system of equations using seemingly unrelated regression (SUR). The SUR method not only gave efficient and accurate estimates but also achieved the logical additivity. The results in this study provide a robust estimation of tree biomass components and total biomass over three groups of C. equisetifolia.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomass*
  • China
  • Magnoliopsida / physiology*
  • Models, Theoretical
  • Plant Leaves
  • Plant Roots
  • Rainforest*
  • Trees / physiology*

Grants and funding

This study was supported by the State Forestry Bureau research and public service industry (No. 201304320), Beijing Natural Science Foundation (No. 8152033), and the National Natural Science Foundation of China (No. 31400538).