不同高寒生态系统甲烷通量时空差异及影响因素的量化

Spatial-temporal variations in methane flux and quantification of the influencing factors in different alpine ecosystems

使用便携式温室气体分析仪对位于玉树藏族自治州和玛多县的高寒沼泽、高寒草甸、高寒草原和高寒荒漠生态系统的CH_(4)通量进行原位观测,同时分析生物量、微生物、营养元素、土壤水分和温度等因子,旨在明确不同生态系统CH_(4)通量时空差异及其主要影响因素。结果表明:在生长季节高寒沼泽和高寒草甸是CH_(4)源,8月通量达到最大值,高寒草原和高寒荒漠是CH_(4)的汇,8月达到最小值,4种生态系统之间的CH_(4)通量差异显著(P<0.05);高寒沼泽的mcrA基因丰度最大,高寒草甸次之,而pmoA丰度则是高寒草甸最大高寒沼泽次之,高寒荒漠的mcrA和pmoA基因丰度均最小,4种生态系统之间差异显著(P<0.05);Pearson相关分析显示,生长季节高寒沼泽和高寒草甸的CH_(4)通量与土壤温度和mcrA显著正相关(P<0.05),高寒草原和高寒荒漠的CH_(4)通量与土壤温度和与pmoA显著负相关(P<0.05),不同生态系统之间CH_(4)通量则与土壤水分、有机碳、总氮、生物量、mcrA和pmoA显著相关(P<0.05);路径分析显示,土壤有机碳、mcrA和pmoA丰度直接对CH4排放产生显著影响,土壤温度和水分则是通过影响土壤微生物菌群丰度间接影响CH_(4)排放;在所有的关键影响因子中,mrcA丰度对CH4通量的相对贡献率最高,达到30.53%,其次是有机碳和生物量。总之,高寒生态系统间CH_(4)通量差异是由于微生物、有机碳、生物量等因素的不同造成的,高寒地区的CH_(4)排放模拟估算时需考虑不同生态系统CH_(4)排放的异质性。

We carried out in situ monitoring of methane(CH_(4)) fluxes of four ecosystems, alpine marsh, alpine meadow, alpine steppe, and alpine desert, in the Yushu Tibetan Autonomous Prefecture and Maduo County, with a portable greenhouse gas analyzer. Biomass, microorganism abundance, nutrients, soil moisture and temperature in each ecosystem were measured. These parameters were investigated to describe spatial-temporal variations in CH_(4) flux and quantify the key influencing factors in all the ecosystems. The results showed that alpine marsh and alpine meadow were CH_(4) sources, with the maximum flux in August. Alpine steppe and alpine desert were CH_(4) sinks, with a minimum in August. During the growing season, there were significant differences in CH_(4) flux among the four ecosystems(P<0.05). The abundance of mcrA gene in alpine marsh was highest, followed by alpine meadow, while the abundance of pmoA gene was highest in alpine meadow, followed by alpine marsh. The lowest abundances of mcrA and pmoA genes were found in alpine desert. There were significant differences in mcrA and pmoA among the four ecosystems(P<0.05). CH_(4) flux in alpine marsh and alpine meadow was significantly positively correlated with soil temperature and mcrA(P<0.05), whereas CH_(4) flux in alpine steppe and alpine desert was significantly negatively correlated with soil temperature and pmoA(P<0.05). Across all the ecosystems, CH_(4) flux was significantly correlated with soil moisture, organic carbon, total nitrogen, biomass, mcrA and pmoA(P<0.05). Results of path analysis showed that soil organic carbon and the abundance of mcrA and pmoA had significant direct effects on CH_(4) emissions, while soil temperature and water content indirectly affected it by changing soil microorganism abundances. Among all these key factors influencing CH_(4) flux, the relative contribution of mcrA abundance was the highest(up to 30.53%), followed by organic carbon and biomass. The results indicated that variations in CH_(4) flux among the different ecosystems were caused by differences in microorganism abundance, organic carbon, and biomass. It is therefore important to consider the heterogeneity of CH_(4) emissions among different ecosystems when modeling and estimating CH_(4) fluxes in alpine areas.