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| 1 | 高寒半湿润沙地草本修复期土壤微生物变化研究显示文摘土壤微生物变化对生态恢复过程中土壤养分循环具有重要作用,但高寒半湿润沙地生态修复过程中土壤微生物及养分变化研究较为缺乏。为明确该环境下沙化生态系统的修复过程中土壤环境的变化,选用燕麦、垂穗披碱草、中华羊茅混播修复高寒半湿润沙地,并以未修复沙地为对照,测定0~10 cm和10~20 cm两土层修复4年过程中土壤微生物及土壤养分含量的变化,分析探讨土壤微生物和养分随植被修复的动态,及微生物与土壤养分变化的相关性。结果表明,随修复年限增加,土壤中主要微生物类群以细菌为主,沙地修复4年后土壤中微生物总量由8.30 nmol·g^-1增加到10.58 nmol·g^-1,但是细菌依然占微生物总量的50%以上,植被恢复并未改变细菌占微生物总量比例。两个土壤层土壤微生物生物量(碳、氮、磷)、多样性(细菌、真菌、放线菌、G^-菌、G^+菌)、土壤养分(有机质、全氮、全磷)含量均呈现先降低后增加趋势,且0~10 cm始终大于10~20 cm土层。草本修复第1年,0~10 cm和10~20 cm土层微生物及养分各组分含量显著降低(P<0.05);修复2年后,0~10 cm土层含量开始增加;到修复第3和4年,土壤微生物与土壤养分中各组分含量均恢复到未治理水平,且有机质、微生物量氮(MBN)含量显著高于未治理沙地(P<0.05)。土壤养分中各组分含量与土壤微生物生物量、微生物总量和细菌含量呈极显著的正相关关系(P<0.01),与G^+、G^-菌、真菌、放线菌均呈不同程度的正相关关系。因此,在沙地进行人工草本种植能够提高土壤养分和微生物含量,对帮助高寒地区生态系统修复和稳定具有重要意义。 | 帅林林 周青平 陈有军 苟小林 周蓉 | 2019 | 草业学报2019，28，9： | 6 |
| 2 | Spatial and temporal change patterns of freeze-thaw erosion in the three-river source region under the stress of climate warming显示文摘The three-river source region(TRSR), located in the Qinghai-Tibet Plateau in China, suffers from serious freeze-thaw(FT) erosion in China. Considering the unique eco-environment and the driving factors of the FT process in the TRSR, we introduce the driving force factors of FT erosion(rainfall erosivity and wind field intensity during FT period) and precipitation during the FT period(indicating the phase-changed water content). The objective was to establish an improved evaluation method of FT erosion in the TRSR. The method has good applicability in the study region with an overall precision of 92%. The spatial and temporal changes of FT erosion from 2000 to 2015 are analyzed. Results show that FT erosion is widely distributed in the TRSR, with slight and mild erosion being the most widely distributed, followed by moderate erosion. Among the three sub-regions, the source region of the Yellow River has the slightest erosion intensity, whereas the erosion intensity of the source region of Yangtze River is the most severe. A slight improvement can be observed in the condition of FTerosion over the whole study region from 2000 to 2015. Vegetation coverage is the dominant factor affecting the intensity of FT erosion in the zones with sparse vegetation or bare land, whereas the climate factors play an important role in high vegetation coverage area. Slopes>28° also have a significant effect on the intensity of FT erosion in the zones. The results can provide a scientific basis for the prevention and management of the soil FT erosion in the TRSR. | GUO Bing LUO Wei WANG Dong-liang JIANG Lin | 2017 | Journal of Mountain Science2017，14，6： | 4 |
| 3 | The Response of Vegetation Biomass to Soil Properties along Degradation Gradients of Alpine Meadow at Zoige Plateau显示文摘Alpine grassland of the Tibetan Plateau has undergone severe degradation, even desertification. However, several questions remain to be answered, especially the response mechanisms of vegetation biomass to soil properties. In this study, an experiment on degradation gradients was conducted in an alpine meadow at the Zoige Plateau in 2017. Both vegetation characteristics and soil properties were observed during the peak season of plant growth. The classification and regression tree model(CART) and structural equation modelling(SEM) were applied to screen the main factors that govern the vegetation dynamics and explore the interaction of these screened factors. Both aboveground biomass(AGB) and belowground biomass(BGB) experienced a remarkable decrease along the degradation gradients. All soil properties experienced significant variations along the degradation gradients at the 0.05 significance level. Soil physical and chemical properties explained 54.78% of the variation in vegetation biomass along the degradation gradients. AGB was mainly influenced by soil water content(SWC), soil bulk density(SBD), soil organic carbon(SOC), soil total nitrogen(STN), and pH. Soil available nitrogen(SAN), SOC and p H, had significant influence on BGB. Most soil properties had positive effects on AGB and BGB, while SBD and p H had a slightly negative effect on AGB and BGB. The correlations of SWC with AGB and BGB were relatively less significant than those of other soil properties. Our results highlighted that the soil properties played important roles in regulating vegetation dynamics along the degradation gradients and that SWC is not the main factor limiting plant growth in the humid Zoige region. Our results can provide guidance for the restoration and improvement of degraded alpine grasslands on the Tibetan Plateau. | LIU Miao ZHANG Zhenchao SUN Jian XU Ming MA Baibing TIJJANI Sadiy Baba CHEN You-jun ZHOU Qingping | 2020 | Chinese Geographical Science2020，30，3： | 3 |
| 4 | 三江源草地植被群落与土壤性质对不同鼠兔密度的响应显示文摘为了探究高原鼠兔(Ochotona curzoniae)对高寒草地的影响,2016年在三江源高寒草原选取4个不同高原鼠兔密度梯度的采样点取样,分别对植被群落特征、地上地下生物量、土壤理化性质与高原鼠兔密度梯度进行相关分析。结果表明,1)植被群落的物种丰富度整体上随鼠兔密度的增加呈现显著下降的趋势,但适量的高原鼠兔能够改变草甸植物群落的组成。2)地上生物量和地下生物量都随着高原鼠兔密度的增加而减少,但其根茎比整体随高原鼠兔种群密度梯度呈逐渐升高的趋势。3)不同土壤属性及其在不同土壤深度上对鼠兔种群密度梯度的响应差异显著,在0–20 cm的土壤中,多数土壤属性值随鼠兔种群密度梯度的增加呈现波动状递减趋势;无鼠兔采样点与高密度和低密度鼠兔采样点的土壤属性整体上差异显著,而与中密度鼠兔采样点的土壤属性值趋于近似。4)通过对所有采集的土壤性质指标与根茎比做热度图分析,发现土壤因子是影响植物根茎比变化的主要驱动力。结果表明,适度的高原鼠兔密度能够改变草原的植物群落结构,改善土壤营养,为草原生态系统的可持续发展做出一定贡献。 | 刘碧颖 王毅 刘苗 曾涛 | 2019 | 草业科学2019，36，4： | 3 |
| 5 | 基于遥感数据和机器学习算法的草地地上生物量估算研究显示文摘草地生态系统作为自然生态系统的重要组成部分,为畜牧经济发展提供了重要的牧草资源,对调节气候变化和维持生态系统平衡等起着非常重要的作用。草地地上生物量(aboveground biomass,AGB)是草地植被生理状态的重要指标,它的大小体现着草地初级生产力水平,是衡量草地生态系统中能量循环和物质流动的重要指标,在陆地生态系统的碳循环中起着重要的作用。近几十年来,伴随畜牧业经济快速发展和全球气候变暖,草地生态系统的稳定性降低,生态环境发生退化,草地地上生物量和固碳能力势必受到影响。大尺度、动态化、高精度的草地地上生物量监测对草地碳储量核算和畜牧业可持续发展具有重要意义,而遥感技术凭借高时空探测能力恰好为其提供了解决思路。机器学习算法凭借其优越性、高效性、稳健和精确性已被广泛应用于各个研究领域,使用机器学习算法快速、准确、大范围监测草地地上生物量是目前的研究热点。因此,构建准确的草地地上生物量估算模型,精确估算草地地上生物量及分析其空间分布特征能够有效地衡量草地生态系统的稳定性和维持草地生态资源的可持续发展利用,为该区域草地资源的可持续利用和科学管理提供依据,对该地区的生态安全保护和畜牧业可持续发展具有重要意义。本研究以青海省兴海县草地为研究区,基于野外实测的草地地上生物量数据,结合高空间分辨率的遥感数据、气候数据、地形数据和土壤数据等,利用随机森林(random forest,RF)和极端梯度提升决策树(extreme gradient boosting,XGBoost)方法构建兴海县草地地上生物量估算模型,采用决定系数R^(2)和均方根误差(Root Mean Square Error,RMSE)两个精度验证指标评价两种草地地上生物量估算模型的精度,实现草地地上生物量高精度模拟和制图,并分析其空间分布格局特征。结果表明:基于XGBoost模型的草地地上生物量估算精度(R^(2)=0.75,RMSE=44.64)高于RF的模拟精度(R^(2)=0.72,RMSE=46.36),并且XGBoost模型估算的草地地上生物量与实测的草地地上生物量值更接近。基于两种机器学习模型估算的草地地上生物量数据制作空间分布图,其空间特征与实测草地地上生物量的空间分布相似,草地地上生物量高值区位于研究区的东部,西部地区草地地上生物量值最低,但是模型模拟能更好地揭示草地地上生物量分布的空间异质性。在空间分布特征上,XGBoost模型估算的草地地上生物量空间变异细节更加详细,尤其在研究区东部。本研究基于两种机器学习算法实现草地地上生物量的高精度(30 m空间分辨率)估算和数字制图,并分析其空间分布格局,可为草地生态环境监测和草地资源可持续利用提供科学依据,对于维持生态系统平衡和预测未来气候变化对草地生态系统的影响具有十分重要的理论和实践意义。 | 王婷 周伟 肖洁芸 谢利娟 | 2023 | 冰川冻土2023，45，2： | 0 |
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