Effects of long-term nitrogen addition and precipitation changes on CH4 flux in an alpine steppe北大核心CSCD

土壤是甲烷(CH4)重要的源和汇.氮沉降和降水格局变化正在急剧改变土壤碳循环,进而可能对土壤CH4通量造成深刻影响.高寒生态系统是巨大的碳库,对氮沉降和降水变化十分敏感.然而,目前多数研究集中在短期实验上,缺乏对长期氮沉降和降水变化背景下CH4通量的响应及其调控因素的认识.以青藏高原高寒草原为研究对象,在2013年搭建模拟氮沉降和降水格局改变实验平台.基于静态箱–气相色谱法测定2020年生长季(5-10月)土壤CH4通量.结果显示,高寒草原土壤呈CH4的汇.氮添加没有显著改变生长季和植物生长高峰CH4通量.然而,降水变化显著改变了生长季和植物生长高峰CH4通量,其中降水增加(+50%降水)降低了CH4的吸收(分别为–16%和–45%),降水减少(–50%降水)增强了CH4的吸收(分别为+73%和+33%).进一步研究发现,与植物属性和功能基因丰度相比,土壤环境因子主导了CH4通量变化(解释率>90%).其中CH4通量与土壤含水量和温度显著正相关,与土壤pH显著负相关.综上所述,在未来全球变化情景下,降水格局改变更能调节青藏高原高寒草原CH4通量的变化.(图6表1参37)     

Empirical likelihood confidence intervals for adaptive cluster sampling

Adaptive cluster sampling (ACS) is an efficient sampling design for estimating parameters of rare and clustered populations. It is widely used in ecological research. The modified Hansen-Hurwitz (HH) and Horvitz-Thompson (HT) estimators based on small samples under ACS have often highly skewed distributions. In such situations, confidence intervals based on traditional normal approximation can lead to unsatisfactory results, with poor coverage properties. Christman and Pontius (Biometrics 56:503–510, 2000) showed that bootstrap percentile methods are appropriate for constructing confidence intervals from the HH estimator. But Perez and Pontius (J Stat Comput Simul 76:755–764, 2006) showed that bootstrap confidence intervals from the HT estimator are even worse than the normal approximation confidence intervals. In this article, we consider two pseudo empirical likelihood functions under the ACS design. One leads to the HH estimator and the other leads to a HT type estimator known as the Hájek estimator. Based on these two empirical likelihood functions, we derive confidence intervals for the population mean. Using a simulation study, we show that the confidence intervals obtained from the first EL function perform as good as the bootstrap confidence intervals from the HH estimator but the confidence intervals obtained from the second EL function perform much better than the bootstrap confidence intervals from the HT estimator, in terms of coverage rate.     

New aspect in seagrass acclimation: leaf mechanical properties vary spatially and seasonally in the temperate species Cymodocea nodosa Ucria (Ascherson)

Seagrasses may acclimate to environmental heterogeneity through phenotypic plasticity. In contrast to leaf morphology, which has been a central point in seagrass acclimation studies, plasticity in leaf biomechanics and fibre content is poorly understood, despite being crucial in plant ecological performance, especially regarding physical forces. We hypothesised that mechanical traits (e.g. breaking force, strength, toughness, and stiffness) and fibre content of seagrass leaves vary as morphology does under differential environments. Cymodocea nodosa was seasonally monitored at three locations around Cádiz Bay (southern Spain) with hydrodynamic regime as the most noticeable difference between them. Leaves showed plasticity in both morphology and mechanical traits, with wave-exposed individuals presenting short but extensible and tough leaves. Leaf fibre content was invariant along the year and with little spatial variability. Cross-sectional area rather than material properties or fibre content differentiates leaf mechanical resistance. Seagrass capacity to thrive under a range of mechanical forces may be dictated by their plasticity in morpho-biomechanical traits, a key element for the hydrodynamical performance and, hence, for species colonisation and distribution.