GRASSLAND BIOMASS OF COMMUNITIES ALONG GRADIENTS OF THE INNER MONGOLIA GRASSLAND TRANSECT

doi:10.17521/cjpe.2006.0073

Abstract

Abstract:

Background and Aims Vegetation transect is an effective method to study the relationship between global change and terrestrial ecosystem. However there are few researches on biomass gradient characters of grassland vegetation and their environmental driving mechanism. The transect measurement was used in this study to analyze biomass gradient change of grassland vegetation in Inner Mongolia. The following questions were tried to answer: (a) How does grassland biomass change along with longitude and latitude as well as water and thermal gradients? (b) Which climate factors will produce pivotal effect to grassland biomass change? (c) How do climate factors control material production process of grassland ecosystem?
Methods The selection of 49 sample sites was principally defined by a 1 900 km long×150 km wide transect across the grasslands of Inner Mongolia. Aboveground and belowground biomasses were measured in the sample sites. The correlativity between biomass and climate factors was analyzed using China climate grid database which constructed by Chinese Ecosystem Research Network.
Key results Grassland biomass was found to be negatively correlated to mean annual temperature, ≥0 ℃ accumulated temperature, ≥10 ℃ accumulated temperature and annual hours of sunshine. Contrarily it was positively correlated with mean annual precipitation and mean annual relative humidity. Among climate factors, mean annual temperature and mean annual relative humidity effected grassland biomass the most obvious.
Conclusions Aboveground and belowground biomasses present visible change regulation along with longitude and latitude as well as climate gradients. Mean annual precipitation was shown as a key role to grassland biomass although its effect ways were complicated. The study suggested combined and integrated relationships of environment factors should be considered in researches of biomass forming and changing.

Key words: Inner Mongolia, Grassland transect, Aboveground biomass, Belowground biomass, Below-aboveground biomass ratio, Gradient

HAN Bin, FAN Jiang-Wen, ZHONG Hua-Ping. GRASSLAND BIOMASS OF COMMUNITIES ALONG GRADIENTS OF THE INNER MONGOLIA GRASSLAND TRANSECT[J]. Chin J Plant Ecol, 2006, 30(4): 553-562.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2006.0073

https://www.plant-ecology.com/EN/Y2006/V30/I4/553

Figures/Tables 11

Fig.1 Location of the sample sites

Table 1 Descriptive statistics of meteorological elements for all sampling sites

	最小值 Minimum value	最大值 Maximum value
海拔高度Altitude(H) (m)	586	1 634
年平均气温Mean annual temperature(T) (℃)	-2.61	9.05
年降水量Annual precipitation(P) (mm)	103.92	394.21
≥0 ℃年积温Accumulated temperature above zero(A₀) (℃)	2 215.16	4 065.01
≥10 ℃年积温Accumulated temperature above ten degree(A₁₀) (℃)	1 728	3 696
年平均相对湿度Mean annual relative humidity(R)(%)	38.75	70.24
年实际日照总时数Annual hours of sunshine(S)(h)	2 617.77	3 322.9

Table 2 Tests of between-subjects effects

rep\|com	总生物量 Total biomass	地下生物量/地上生物量 Below-aboveground biomass ratio	地上生物量 Aboveground biomass	地下生物量 Belowground biomass
F检验计算值 Computed value of F test	80.086 \| 0.326	15.482 \| 0.218	23.660 \| 10.474	78.219 \| 0.236
显著性水平 Significant level (p)	0.722 \| 0.000	0.805 \| 0.000	0.232 \| 0.000	0.790 \| 0.000

Fig.2 Changing trend of belowground biomass with that of precipitation, temperature along gradients of longitude or latitude

Fig.3 Changing trend of aboveground biomass with that of precipitation, temperature along gradients of longitude or latitude

Fig.4 Changing trend of total biomass with that of precipitation, temperature along gradients of longitude or latitude

Fig.5 Changing trend of below-aboveground biomass ratio along gradients of factors

Fig.6 Biomass on different grassland type

Fig.7 Biomass distribution on different grassland type

Fig.8 Below-aboveground biomass ratio on different grassland type

Table 3 Statistics of regression for biomass with given factors by backward elimination

α=0.05 回归方法 Regression method	总生物量 Total biomass			地下生物量/地上生物量 Below-aboveground biomass ratio			地上生物量 Aboveground biomass			地下生物量 Belowground biomass
α=0.05 回归方法 Regression method	全回归法 Enter	向后剔除法 Backward		全回归法 Enter	向后剔除法 Backward		全回归法 Enter	向后剔除法 Backward		全回归法 Enter	向后剔除法 Backward
复相关系数 Mutiple correlation coefficient (R)	0.790		0.767	0.489		0.404	0.642		0.603	0.792	0.769
判定系数 Determination coefficient (R²)	0.624		0.588	0.239		0.163	0.412		0.363	0.627	0.591
调整的判定系数 Adjusted determination coefficient ( $R a 2$ )	0.537		0.551	0.064		0.087	0.276		0.305	0.541	0.554
显著性水平 Significant level (p)	0.000		0.000	0.238		0.091	0.008		0.000	0.000	0.000
选入的参数 Variables entered	全部All		E,T,P,A₀	全部All		E,T,P,A₀	全部All		N,T,P,S	全部All	E,T,P,A₀

Table 3 Statistics of regression for biomass with given factors by backward elimination

α=0.05 回归方法 Regression method	总生物量 Total biomass			地下生物量/地上生物量 Below-aboveground biomass ratio			地上生物量 Aboveground biomass			地下生物量 Belowground biomass
α=0.05 回归方法 Regression method	全回归法 Enter	向后剔除法 Backward		全回归法 Enter	向后剔除法 Backward		全回归法 Enter	向后剔除法 Backward		全回归法 Enter	向后剔除法 Backward
复相关系数 Mutiple correlation coefficient (R)	0.790		0.767	0.489		0.404	0.642		0.603	0.792	0.769
判定系数 Determination coefficient (R²)	0.624		0.588	0.239		0.163	0.412		0.363	0.627	0.591
调整的判定系数 Adjusted determination coefficient ( $R a 2$ )	0.537		0.551	0.064		0.087	0.276		0.305	0.541	0.554
显著性水平 Significant level (p)	0.000		0.000	0.238		0.091	0.008		0.000	0.000	0.000
选入的参数 Variables entered	全部All		E,T,P,A₀	全部All		E,T,P,A₀	全部All		N,T,P,S	全部All	E,T,P,A₀

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rep\|com	总生物量 Total biomass	地下生物量/地上生物量 Below-aboveground biomass ratio	地上生物量 Aboveground biomass	地下生物量 Belowground biomass
F检验计算值 Computed value of F test	80.086 \| 0.326	15.482 \| 0.218	23.660 \| 10.474	78.219 \| 0.236
显著性水平 Significant level (p)	0.722 \| 0.000	0.805 \| 0.000	0.232 \| 0.000	0.790 \| 0.000

rep\|com	总生物量 Total biomass	地下生物量/地上生物量 Below-aboveground biomass ratio	地上生物量 Aboveground biomass	地下生物量 Belowground biomass
F检验计算值 Computed value of F test	80.086 \| 0.326	15.482 \| 0.218	23.660 \| 10.474	78.219 \| 0.236
显著性水平 Significant level (p)	0.722 \| 0.000	0.805 \| 0.000	0.232 \| 0.000	0.790 \| 0.000