Biodiversity Science ›› 2018, Vol. 26 ›› Issue (7): 690-700.doi: 10.17520/biods.2018092


• Original Papers • Previous Article     Next Article

Relationship between plant functional diversity and productivity of Pinus massoniana plantations in Guangxi

Xiaorong Huang*()   

  1. Guangxi Zhuang Autonomous Region Forestry Research Institute, Nanning 530002
  • Received:2018-03-27 Accepted:2018-06-03 Online:2018-09-11
  • Huang Xiaorong
  • About author:# Co-first authors

Understanding the relationship between plant diversity and productivity can provide essential information for forest management. We surveyed plant communities in Pinus massoniana dominated plantations from four regions of Guangxi. Using correlation analysis, automatic linear modeling and variance partitioning, we assessed the effect of species diversity, functional diversity, and functional dominance on productivity. We found that productivity was extremely positively correlated with species richness, Shannon index, functional richness and functional evenness (P < 0.01). Species evenness, RaoQ, functional dispersion, functional group richness and aspect were also positively correlated with productivity (P < 0.05), while forest age was negatively correlated with productivity (P < 0.01). Four functional diversity parameters positively correlated with four species-diversity indices. No evidence of negative density-dependence was found. In the best variance partitioning model, functional diversity parameters, functional dominance and forest age explained 56%, 43% and 33% of variance in productivity respectively; and the overlap between functional diversity parameters and functional dominance was up to 27%. Functional richness and functional evenness were major contributors of complementary effects while community weighted mean (CWM) of growth form contributed to selection effects. Plots identified as dominantly shrub had higher productivity than plots identified as dominantly herbs or trees, suggesting that subordinates and transients may have important effects on ecosystem functions. The best-fit subset model built by automatic linear modeling included forest age, growth form CWM, functional richness, functional evenness and functional group richness (FGR) indescending order. We recommend that to maintain diversity and forest function, protection of understory plant species should be strengthened. Further, to enhance productivity and biodiversity we recommend planting functionally important species through compensatory photosynthesis and growth competition in understorey layers.

Key words: functional diversity, functional dominance, productivity, forest age, functional group richness

Table 1

Forest characteristics and functional dominance parameters of the plots included in this study"

Age (yr)
地上生物量 Above-
ground biomass (t·ha-1)
比叶面积优势值 CWM_ sla (mm2·mg-1) 高度优势值
个数Functional group richness (FGR)
Sum of tree abundance
Sum of shrub abundance
Sum of herb abundance
1 老虎岭 Laohuling 18 128 7.11 木姜子属 Litsea
12 30 灌木 Shrub 6 25 37 15
2 老虎岭 Laohuling 18 127 7.06 蜜茱萸属 Melicope
12 30 灌木 Shrub 6 30 37 8
3 老虎岭 Laohuling 29 161 5.55 松属

12 7 乔木
6 49 10 12
4 老虎岭 Laohuling 29 156 5.38 锥属

11 10 乔木
6 47 16 11
5 老虎岭 Laohuling 29 196 6.76 锥属

11 10 乔木
5 43 10 11
6 老虎岭 Laohuling 28 125 4.46 柯属

9.7 20 乔木
3 21 5 13
7 老虎岭 Laohuling 28 162 5.79 柯属

9.7 20 乔木
5 29 6 14
8 老虎岭 Laohuling 28 187 6.68 柯属

9.7 14.5 乔木
5 37 11 11
9 老虎岭 Laohuling 28 127 4.54 柯属

9.7 20 乔木
4 24 16 6
10 老虎岭Laohuling 28 133 4.75 柯属

9.7 20 乔木
6 29 9 11
15 三门江 Sanmenjiang 57 253 4.44 紫金牛属 Ardisia
9.7 20 乔木
4 35 14 7
16 三门江 Sanmenjiang 57 126 2.21 紫金牛属 Ardisia
9.7 20 乔木
5 39 13 7
17 三门江 Sanmenjiang 57 214 3.75 松属

9.7 30 乔木
4 44 13 13
18 三门江 Sanmenjiang 57 218 3.82 柯属

9.7 30 乔木
6 37 24 12
19 三门江 Sanmenjiang 57 249 4.37 柯属

9.7 30 乔木
5 40 15 15
23 苍梧 Cangwu 11 96 8.73 松属

15 30 灌木 Shrub 6 35 37 22
24 苍梧 Cangwu 11 97 8.82 松属

11 30 乔木
6 41 29 28
25 苍梧 Cangwu 11 83 7.55 松属

11 30 乔木
6 41 21 32
26 全州
22 114 5.18 松属

12 30 草本 Herb 4 20 15 22
27 全州
22 76 3.45 松属

12 30 草本 Herb 5 13 8 26
28 全州
22 77 3.50 松属

12 30 草本 Herb 5 13 13 21
29 全州
22 54 2.45 松属

12 30 草本 Herb 5 14 14 20
30 全州
22 95 4.32 松属

12 30 草本 Herb 5 17 20 32
31 老虎岭 Laohuling 22 151 6.86 锥属

12 30 乔木
5 47 6 7
32 老虎岭 Laohuling 22 124 5.64 松属

7.5 30 乔木
6 29 7 10

Table 2

List of functional metrics in this study"

名称 Name R代码Argument 公式 Formula 说明 Note 参考文献Reference
功能优势值Community-level weighted means functcomp (X, comm) $CWM $CWM=\sum\limits_{i=1}^{n}{{{p}_{i}}}\times trai{{t}_{i}}$ 式中pi是物种i的多度, traiti为物种i的性状值, n为物种个数。
In formula, pi is the abundance of species i and traiti is the trait value of species i.
et al, 2008
功能离散度Functional dispersion dbFD (X, comm)
$Fdis=\frac{\sum{{{a}_{j}}{{z}_{j}}}}{\sum{{{a}_{j}}}}$ 式中aj为物种j的多度, zj为物种j到加权质心的距离。
Here aj is the abundance of species j, zj is the distance of species j to the centroid.
Laliberté & Legendre, 2010
功能均匀度Functional evenness dbFD (X, comm) $FEve $E{{W}_{l}}=\frac{dist\,(i,j)}{{{w}_{i}}+{{w}_{j}}}$
$Feve=\frac{\sum\limits_{l=1}^{S-1}{\min \left( PE{{W}_{l}},\frac{1}{S-1} \right)-\frac{1}{S-1}}}{1-\frac{1}{S-1}}$
Feve为功能均匀度; EWl为加权均匀度; dist (i, j)是物种i和物种j的欧氏距离; PEWl为偏加权均匀度; S是物种数。
Feve is functional evenness; EWl is weighted evenness, dist (i, j) is the Euclidean distance between species i and j, the species involved is branch l in minimum spanning tree, and wi is the relative abundance of species i; PEWl is the partial weighted evenness of branch l; S is species in the community.
et al, 2008
功能团个数 A posteriori functional group richness dbFD (X, comm, calc.FGR = TRUE) $FG{{R}_{i}}=Grou{{p}_{present\,in\,plot\,i}}$ FGRi是样地i包含的功能团个数; 本研究指定按功能团总数为6个来分组; dbFD函数默认不计算FGR, 需指定calc.FGR = TRUE。
FGRi is the number of groups present in plot i; and in this study, total functional group was designated as 6. Default dbFD function has set FGR = FALSE and addition is needed in the argument.
Petchey & Gaston, 2006
功能丰富度Functional richness dbFD (X, comm)
$\begin{align} & Fri{{c}_{i}}=\text{Number}\,\text{of}\,\text{unique} \\ & \text{trait}\,\text{combination}\,\text{in}\,\text{plot} \\ \end{align}$ 纯数值性状情况下默认使用凸壳体算法计算功能丰富度。如果有一个性状为类型变量, 则全部性状都作为类型变量, 样地i的功能丰富度为其包含的独特性状组合的个数。
The default convex hull volume algorithm for quantitative data is suppressed as categorical traits present in ‘X’. Fric is measured as the number of unique trait combinations in this study.
et al, 2008
功能多样性Rao’s quadratic entropy dbFD (X, comm)
$RaoQ=\sum\limits_{i=1}^{S-1}{\sum\limits_{j=i+1}^{S}{{{d}_{ij}}{{p}_{i}}{{p}_{i}}}}$${{d}_{ij}}=\frac{{{u}_{ij}}}{n}$ 式中dij为物种i和j的距离, pi为物种i的多度, n为研究的性状总数, uij为物种i和j性状值不同的性状数量。
Here dij is the difference between the i-th and j-th species, and pi is abundance of species i; n = total number of traits considered, uij= number of traits with different values in species i and j.
Botta-Dukát, 2005

Table 3

Pearson correlation coefficients between productivity and diversity parameters, forest age and aspect"

功能丰富度 Functional richness (Fric) 功能均匀度 Functional evenness (Feve) 功能离散度 Functional dispersion (Fdis) 功能多样性 Rao’s quadratic entropy (RaoQ) 功能团个数 Functional group richness (FGR) Shannon指数
Shannon index (H)
Simpson指数Simpson index (D) 物种丰富度
Species richness (S)
Age -0.601**
Aspect 0.505* -0.691**
Fric 0.761** -0.342 0.333
Feve 0.577** -0.360 0.277 0.426*
Fdis 0.442* -0.367 0.422* 0.723** 0.544**
RaoQ 0.459* -0.340 0.404* 0.750** 0.545** 0.997**
FGR 0.449* -0.336 0.177 0.615** 0.231 0.597** 0.611**
H 0.608** -0.442* 0.536** 0.814** 0.626** 0.899** 0.900** 0.529**
D 0.395 -0.359 0.504* 0.614** 0.603** 0.863** 0.849** 0.389 0.948**
S 0.722** -0.388 0.371 0.974** 0.433* 0.806** 0.828** 0.654** 0.856** 0.670**
均匀度Evenness 0.441* -0.408* 0.568** 0.596** 0.659** 0.830** 0.815** 0.348 0.943** 0.990** 0.644**

Fig. 1

Effect of forest age on biomass, productivity and functional richness (means and 95% confidence intervals, varied letters indicating significant difference)"

Fig. 2

Relationship between growth form CWM (community weighted means) and productivity, functional richness and functional group richness (means and 95% confidence intervals, varied letters indicating significant difference)"

Table 4

Contributions of explanatory matrices to variance of productivity, partitioning by best-fit varpart model"

Adjusted R2
Individual fractions
1 matrix
Adjusted R2
X1 0.33 X1|X2+X3 0.22 X1|X3 0.12
X2 0.43 X2|X1+X3 0.14 X1|X2 0.31
X3 0.56 X3|X1+X2 0.08 X2|X3 0.04
X1+X2 0.74 X1*X2 -0.10 X2|X1 0.41
X1+X3 0.68 X2*X3 0.27 X3|X1 0.35
X2+X3 0.60 X3*X1 0.09 X3|X2 0.17
X1+X2+X3 0.83 X1*X2*X3 0.12
残差Residual 0.17
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