Chin J Plant Ecol ›› 2018, Vol. 42 ›› Issue (8): 818-830.doi: 10.17521/cjpe.2018.0056

• Research Articles • Previous Articles     Next Articles

Response of plant biomass to nitrogen addition and precipitation increasing under different climate conditions and time scales in grassland

DIAO Li-Wei1,2,*,LI Ping2,3,*,LIU Wei-Xing2,XU Shan4,QIAO Chun-Lian5,ZENG Hui1,6,LIU Ling-Li2,3,**()   

  1. 1 School of Urban Plan and Design, Shenzhen Graduate School, Peking University, Shenzhen, Guangdong 518055, China
    2 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
    3 University of Chinese Academy of Sciences, Beijing 100049, China
    4 South China Botanical Garden, Chinese Academy of Science, Guangzhou 510650, China
    5 Institute for Conservation and Utilization of Agro-bioresources in the Dabie Mountains, Xinyang Normal University, Xinyang, Henan 464000, China; and 6College of Urban and Environmental Sciences, Peking University, Beijing 100084, China
    6 College of Urban and Environmental Sciences, Peking University, Beijing 100084, China
  • Received:2018-03-07 Online:2018-11-03 Published:2018-08-20
  • Contact: Li-Wei DIAO,Ping LI,Ling-Li LIU E-mail:liu@ibcas.ac.cn
  • Supported by:
    Supported by the Chinese National Key Development Program for Basic Research(2014CB954003);the National Natural Science Foundation of China(31522011)

Abstract:

Aims Plant biomass accounts for the main part of grassland productivity. The productivity of grassland regarded as one of important ecosystem function is always co-limited by nitrogen and water availability, therefore, how grasslands respond to atmosphic nitrogen (N) addition and precipitation increasing need to be systematically and quantitatively evaluated at different climate conditions and temporal scales.
Methods To investigate the impact of nitrogen addition and precipitation increasing on grassland biomass over climate conditions and temproal scales, a meta-analysis was conducted based on 46 papers that were published during 1990-2017 involving 1 350 observations.
Important findings Results showed that: (1) N addtion, precipitation increasing and the combinations of these two treatments significantly increased the aboveground biomass (37%, 41%, 104%), total biomass (32%, 23%, 60%) and the ratio of aboveground biomass to belowground biomass (29%, 25%, 46%) in grassland ecosystem. Belowground biomass showed no response to single N addtion, but could be significantly enhanced together with increaseing precipitation; (2) The response of grassland biomass under these N addtion and the increasing of precipitation showed obvious spatial pattern under different climate conditions. The N addition tended to increase more aboveground biomass, total biomass and the ratio of aboveground biomass to belowground biomass under high sites with high mean annual air temperature (MAT) and mean annual precipitation (MAP) while precipitation increasing tended to simulate more belowground biomass and total biomass under low MAT and MAP sites; (3) In addition, the response of grassland biomass under these two global change index showed obvious temporal pattern. With the increase of duration of N addition, the belowgound biomass tended to decrease, while the aboveground biomass, total biomass and the ratio of aboveground biomass to belowground biomass tended to increase under N addition. With the increase of duration of precipitation manipulation, the total biomass showed no response to precipitation increasing, while aboveground biomass, belowground biomass and the ratio of aboveground biomass to belowground biomass tended to be enhanced. The results indicated that aboveground biomass was more likely to be enhanced than belowground biomass under N addition or precipitation increasing in the long term.

Key words: grassland biomass, nitrogen addition, precipitation increasing, response ratio, meta-regression, climate condition, temporal scale

Fig. 1

The map of distribution of all the field experiments in this meta-analysis."

Table 1

Database structure and parameters"

氮添加 Nitrogen addition
实验数量 Study numbers 地上生物量 AGB 地下生物量 BGB 总生物量 TB 地上地下生物量比 AGB/BGB
年均 Annual mean 109 56 59 42
年际 Inter-annual 169 79 74 37
增雨处理 Precipitation increasing
实验数量 Study numbers 地上生物量 AGB 地下生物量 BGB 总生物量 TB 地上地下生物量比 AGB/BGB
年均 Annual mean 77 82 71 51
年际 Inter-annual 62 90 98 32
增氮增雨处理 Nitrogen addition and precipitation increasing
实验数量 Study numbers 地上生物量 AGB 地下生物量 BGB 总生物量 TB 地上地下生物量比 AGB/BGB
年均 Annual mean 19 21 17 14
年际 Inter-annual 21 32 27 11

Fig. 2

Effects of N addition (in circle), precipitation increasing (in square) and N addition plus precipitation increasing (in rhombus) on aboveground biomass (AGB)(A), belowground biomass (BGB)(B), total biomass (TB)(C) and the ratio of aboveground biomass to belowground biomass (AGB/BGB)(D) (mean ± 95%CI). Numbers in the parentheses represent study number."

Fig. 3

Bubble plots of the meta-regression results between the response of aboveground biomass (AGB)(A, E), belowground biomass (BGB)(B, F), total biomass(TB)(C, G) and the ratio of aboveground biomass to belowground biomass (AGB/BGB)(D, H) to the treatment level of N addition and precipitation increasing. A-D represent the N addition treatment; E-H represent the precipitation increasing treatment. The size of the bubble is the relative weight of the effect size (response ratio, RR) in the random-effects meta- regression. Larger bubbles indicate study outcomes that contribute a great overall weight in meta-regression. The y-direction error bars of the black dots represent the standard error of the means of response ratio; the x-direction error bars represent the standard error of treatment level under N addition and precipitation increasing."

Fig. 4

Bubble plots of the meta-regression results between the response of aboveground biomass (AGB)(A, E), belowground biomass (BGB)(B, F), total biomass (TB)(C, G) and the ratio of aboveground biomass to belowground biomass (AGB/BGB)(D, H) to the mean annual temperature (MAT) in the study sites. A-D represent the N addition treatment; E-H represent the precipitation increasing treatment. The size of the bubble is the relative weight of the effect size (response ratio, RR) in the random-effects meta-regression. Larger bubbles indicate study outcomes that contribute a great overall weight in meta- regression. The y-direction error bars of the black dots represent the standard error of the means of response ratio; the x-direction error bars represent the standard error of the means of mean annual temperature (MAT)."

Fig. 5

Bubble plots of the meta-regression results between the response of aboveground biomass (AGB)(A, E), belowground biomass (BGB)(B, F), total biomass(TB)(C, G) and the ratio of aboveground biomass to belowground biomass (AGB/BGB)(D, H) to the mean annual precipitation (MAP) in the study sites. A-D represent the N addition treatment; E-H represent the precipitation increasing treatment. The size of the bubble is the relative weight of the effect size (response ratio, RR) in the random-effects meta-regression. Larger bubbles indicate study outcomes that contribute a great overall weight in meta-regression. The y-direction error bars of the black dots represent the standard error of the means of response ratio; the x-direction error bars represent the standard error of the means of mean annual precipitation (MAP)."

Fig. 6

Bubble plots of the meta-regression results between the response of aboveground biomass (AGB)(A, E), belowground biomass (BGB)(B, F), total biomass(TB)(C, G)and the ratio of aboveground biomass to belowground biomass (AGB/BGB)(D, H) to the study year. A-D represent the N addition treatment; E-H represent the precipitation increase treatment. The size of the bubble is the relative weight of the effect size (response ratio, RR) in the random-effects meta- regression. Larger bubbles indicate study outcomes that contribute a great overall weight in meta-regression. The y-direction error bars of the black dots represent the standard error of the means of response ratio; the x-direction error bars represent the standard error of the means of treatment time (a)."

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