Biodiversity Science ›› 2018, Vol. 26 ›› Issue (6): 545-553.doi: 10.17520/biods.2017320

• Original Papers • Previous Article     Next Article

Relationships between species richness and biomass/productivity depend on environmental factors in secondary forests of Dinghai, Zhejiang Province

Chuping Wu1, Wenjuan Han2, Bo Jiang1, Bowen Liu3, Weigao Yuan1, Aihua Shen1, *(), Yujie Huang1, Jinru Zhu1   

  1. 1 Zhejiang Academy of Forestry, Hangzhou 310023
    2 College of Chemistry and Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang 321000
    3 Ministry of Agriculture, Forestry and Fisheries of Dinghai District, Zhoushan City, Zhejiang Province, Dinghai, Zhejiang 316100
  • Received:2017-12-01 Accepted:2018-04-02 Online:2018-09-11
  • Shen Aihua
  • About author:

    # Co-first authors

Experimental studies of the relationship between biodiversity and ecosystem functioning (BEF) have mainly been conducted in artificial ecosystems with randomly assembled species, highlighting the need of testing this relationship in real world. In particular, these studies did not consider the effects of environmental factors on the relationship between biodiversity and ecosystem functioning. Hence, we used secondary forests, which are widely distributed in the subtropics, as research objects. We used model fitting methods to study the relationship between species richness and biomass/productivity. Meanwhile, we also explored the effects of environmental factors (e.g., elevation, aspect, slope, and soil depth) on biomass and productivity and on BEF relationships. Our results showed that there was a significant linear correlation between species richness and biomass, and a quartic correlation between species richness and productivity (i.e., humped curve) when environmental factors were not considered. Considering that biomass was significantly affected by stem density and soil depth, while productivity was not affected by environmental factors, we found that only under the environmental conditions characterized by steep slopes, south aspects, or higher soil depths, was species richness significantly correlated with biomass. The relationship between species richness and productivity showed a significant quadratic correlation under higher elevation, but not under any other environmental conditions. These results suggest that the relationship between species richness and productivity is different from the relationship between species richness and biomass, and both these relationships depend on environmental factors.

Key words: biodiversity, ecosystem functioning, subtropical forest, environmental gradient, Zhejiang

Table 1

Analysis of the optimal model between species richness and biomass/productivity"

模型 Models F R2 P AICc
BIO~S 5.436 0.110 0.02 818.941
BIO~ S + S 2 2.700 0.116 0.07 821.261
∆BIO~S 0.545 0.012 0.46 -34.944
∆BIO ~ S + S 2 3.075 0.125 0.05 -38.123

Fig. 1

Relationships between species richness and biomass (a) / productivity (b). The biomass in (a) means the total biomass in each plot (20 m × 20 m) in 2015, and the productivity in (b) means the ration of biomass between 2012 and 2015 in each plot. Dotted lines refer to the 95% confidence interval. Solid lines fitted by the model with lowest AICc value in the Table 1."

Table 2

The significant environmental and community factors influencing the biomass and productivity"

估计值 Estimate 标准误 SE t P
Biomass (BIO)
截距 Intercept 8.141 0.073 111.036 < 0.001
个体密度 Density (ind./ha) 0.221 0.076 2.900 < 0.01
土壤厚度 Soil depth (cm) 0.136 0.075 1.815 0.07
物种丰富度 Species richness 0.126 0.076 1.646 0.10
Productivity (∆BIO)
截距 Intercept 0.147 0.020 7.665 < 0.001
物种丰富度 Species richness 0.265 0.103 2.590 < 0.05
物种丰富度的平方 Square of species richness -0.249 0.102 -2.434 < 0.05

Fig. 2

Variance of biomass and productivity between different environmental gradients. The biomass means the total biomass in each plot (20 m × 20 m) in 2015, and the productivity means the ration of biomass between 2012 and 2015 in each plot."

Table 3

Relationships between species richness and biomass/productivity in different environmental gradients"

No. of plots
Linear equation
R2 二次方程
Quadratic equation
Elevation (m)
≤ 65 23 BIO 423.583 421.439 + 0.07 426.317 423.997 +, - 0.08 2.558
∆BIO -3.744 -5.887 + 0.02 -3.257 -5.577 +, - 0.12 0.310
> 65 23 BIO 403.055 400.912 + 0.20* 405.131 402.811 -, + 0.23# 1.899
∆BIO -23.754 -25.897 + 0.02 -26.458 -28.778 +, - 0.24# -2.881
≤ 20° 33 BIO 596.410 592.748 + 0.01 598.983 594.426 +, - 004 1.678
∆BIO -15.746 -19.408 + 0.03 -14.324 -18.882 +, - 0.09 0.526
> 20° 13 BIO 227.108 228.079 + 0.59** 227.930 230.671 -, + 0.64** 2.592
∆BIO -10.178 -9.206 + 0.01 -11.306 -8.566 +, - 0.24 0.640
South, Southwest
21 BIO 370.428 368.706 + 0.19* 371.462 369.784 -, + 0.26# 1.078
West, Southeast
∆BIO -25.380 -27.102 + 0.01 -26.650 -28.328 +, - 0.19 -1.226
East, Northeast
25 BIO 456.261 453.747 + 0.07 457.746 454.871 +, - 0.14 1.124
North, Northwest
∆BIO -4.620 -7.133 + 0.02 -3.648 -6.524 +, - 0.10 0.609
Soil depth
< 40 26 BIO 454.222 451.539 + 0.01 457.463 454.335 +, - 0.01 2.996
∆BIO -14.875 -17.559 + 0.04 -11.992 -15.119 +, - 0.06 2.440
≥ 40 20 BIO 367.197 365.710 + 0.14# 369.428 368.112 +, - 0.17 2.402
∆BIO -12.855 -14.342 + 0.03 -13.070 -14.386 +, - 0.17 -0.044
Forest age
≤ 15 22 BIO 404.116 402.176 + 0.05 406.959 404.948 -, + 0.06 2.772
∆BIO -13.638 -15.578 - 0.01 -14.995 -17.006 +, - 0.14 -1.428
> 15 24 BIO 422.094 419.760 + 0.20* 425.249 422.642 -, + 0.20# 2.882
∆BIO -9.905 -12.239 + 0.02 -8.569 -11.176 +, - 0.09 1.063
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