scholarly journals Are N, P, and N:P stoichiometry limiting grazing exclusion effects on vegetation biomass and biodiversity in alpine grassland?

2020 ◽  
Vol 24 ◽  
pp. e01315
Author(s):  
Yan Yan ◽  
Xuyang Lu
2015 ◽  
Author(s):  
Yan Yan ◽  
Xuyang Lu

Overgrazing is considered one of the key disturbance factors that results in alpine grassland degradation in Tibet. Grazing exclusion by fencing has been widely used as an approach to restore degraded grassland s in Tibet since 2004. Is the grazing exclusion management strategy effective for the vegetation restoration of degraded alpine grasslands? Three alpine grassland types were selected in Tibet to investigate the effect of grazing exclusion on plant community structure and biomass. Our results showed that species biodiversity indicators, including the Pielou evenness index, the Shannon-Wiener diversity index, and the Simpson dominance index, did not significantly change under grazing exclusion conditions. In contrast, the total vegetation cover, the mean vegetation height of the community, and the aboveground biomass were significantly higher in the grazing exclusion grasslands than in the free grazed grasslands. These results indicated that grazing exclusion is an effective measure for maintaining community stability and improving aboveground vegetation growth in alpine grasslands. However, the statistical analysis showed that the alpine grassland type plays a more important role than grazing exclusion in which influence on vegetation in alpine grasslands because the alpine grassland type had a significant effect on vegetation indicators but grazing exclusion not. In addition, because the results of the present study come from short term (5-7 years) grazing exclusion, it is still uncertain whether these improvements will be continuable if grazing exclusion is continuously implemented. Therefore, the assessments of the ecological effects of the grazing exclusion management strategy on degraded alpine grasslands in Tibet are still need long term continued research.


2013 ◽  
Vol 57 ◽  
pp. 183-187 ◽  
Author(s):  
Xiao-Ming Shi ◽  
Xiao Gang Li ◽  
Chun Tao Li ◽  
Yu Zhao ◽  
Zhan Huan Shang ◽  
...  

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7272 ◽  
Author(s):  
Biying Liu ◽  
Jian Sun ◽  
Miao Liu ◽  
Tao Zeng ◽  
Juntao Zhu

The vegetation dynamic (e.g., community productivity) is an important index used to evaluate the ecosystem function of grassland ecosystem. However, the critical factors that affect vegetation biomass are disputed continuously, and most of the debates focus on mean annual precipitation (MAP) or temperature (MAT). This article integrated these two factors, used the aridity index (AI) to describe the dynamics of MAP and MAT, and tested the hypothesis that vegetation traits are influenced primarily by the AI. We sampled 275 plots at 55 sites (five plots at each site, including alpine steppe and meadow) across an alpine grassland of the northern Tibet Plateau, used correlation analysis and redundancy analysis (RDA) to explore which key factors determine the biomass dynamic, and explained the mechanism by which they affect the vegetation biomass in different vegetation types via structural equation modelling (SEM). The results supported our hypothesis, in all of the environmental factors collected, the AI made the greatest contribution to biomass variations in RDA , and the correlation between the AI and biomass was the largest (R = 0.85, p < 0.05). The final SEM also validated our hypothesis that the AI explained 79.3% and 84.4% of the biomass variations in the alpine steppe and the meadow, respectively. Furthermore, we found that the soils with higher carbon to nitrogen ratio and soil total nitrogen had larger biomass, whereas soil organic carbon had a negative effect on biomass in alpine steppe; however, opposite effects of soil factors on biomass were observed in an alpine meadow. The findings demonstrated that the AI was the most critical factor affecting biomass in the alpine grasslands, and different reaction mechanisms of biomass response to the AI existed in the alpine steppe and alpine meadow.


2015 ◽  
Author(s):  
Yan Yan ◽  
Xuyang Lu

Overgrazing is considered one of the key disturbance factors that results in alpine grassland degradation in Tibet. Grazing exclusion by fencing has been widely used as an approach to restore degraded grassland s in Tibet since 2004. Is the grazing exclusion management strategy effective for the vegetation restoration of degraded alpine grasslands? Three alpine grassland types were selected in Tibet to investigate the effect of grazing exclusion on plant community structure and biomass. Our results showed that species biodiversity indicators, including the Pielou evenness index, the Shannon-Wiener diversity index, and the Simpson dominance index, did not significantly change under grazing exclusion conditions. In contrast, the total vegetation cover, the mean vegetation height of the community, and the aboveground biomass were significantly higher in the grazing exclusion grasslands than in the free grazed grasslands. These results indicated that grazing exclusion is an effective measure for maintaining community stability and improving aboveground vegetation growth in alpine grasslands. However, the statistical analysis showed that the alpine grassland type plays a more important role than grazing exclusion in which influence on vegetation in alpine grasslands because the alpine grassland type had a significant effect on vegetation indicators but grazing exclusion not. In addition, because the results of the present study come from short term (5-7 years) grazing exclusion, it is still uncertain whether these improvements will be continuable if grazing exclusion is continuously implemented. Therefore, the assessments of the ecological effects of the grazing exclusion management strategy on degraded alpine grasslands in Tibet are still need long term continued research.


2021 ◽  
Vol 9 ◽  
Author(s):  
Mingxue Xiang ◽  
Junxi Wu ◽  
Jiaojiao Wu ◽  
Yingjie Guo ◽  
Duo Lha ◽  
...  

Grazing is a crucial anthropogenic disturbance on grasslands. However, it is unknown how livestock grazing affects the relationship between biodiversity and productivity of alpine grasslands in Tibet. We carried out a grazing-manipulated experiment from 2016 to 2019 with grazing intensity levels of null (control, grazing exclusion, C.K.), moderate grazing [1.65 standardized sheep unit (SSU) per hectare, M.G.], and heavy grazing (2.47 SSU per hectare, H.G.) on a typical alpine grassland in the Lhasa River Basin, central Tibet. We measured aboveground biomass (AGB), species assembly (alpha and beta diversity indices), and soil nutrients’ availability. The results showed that grazing differently affected plant community in different treatments. Notably, the total dissimilarity value between C.K. and H.G. is 0.334. Grazing decreased the Shannon–Wiener index, increased the Berger–Parker index from 2016 to 2018 significantly, and decreased AGB and total soil nitrogen (STN) significantly. Our results also showed that the grazing affected the relationship between AGB and diversity indices and soil nutrients, including soil organic carbon (SOC) and total soil phosphorus (STP). Specifically, AGB decreased with increasing SOC and STP in all treatments, and heavy grazing changed the positive relationships between AGB, STP, and Shannon–Wiener index to negative correlations significantly compared with grazing exclusion. There was a significant negative correlation between Berger–Parker and Shannon–Wiener indices under each treatment. The general linear models showed that H.G. altered the relationship between diversity and productivity of grassland in central Tibet, and AGB and Shannon–Wiener index positively correlated in C.K. but negatively correlated in H.G. Our study suggests that H.G. caused a negative relationship between plant diversity and productivity. Therefore, sustainable grazing management calls for a need of better understanding the relationship between biodiversity and productivity of alpine grassland in central Tibet.


2015 ◽  
Vol 5 (19) ◽  
pp. 4492-4504 ◽  
Author(s):  
Xuyang Lu ◽  
Yan Yan ◽  
Jian Sun ◽  
Xiaoke Zhang ◽  
Youchao Chen ◽  
...  

2012 ◽  
Vol 2 (4) ◽  
pp. 61-62
Author(s):  
Arun Sharma ◽  
◽  
K.C. Pancholi K.C. Pancholi
Keyword(s):  

2019 ◽  
Vol 67 (1) ◽  
pp. 33 ◽  
Author(s):  
Wen Jin Li ◽  
Shuang Shuang Liu ◽  
Jin Hua Li ◽  
Ru Lan Zhang ◽  
Ka Zhuo Cai Rang ◽  
...  

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