Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems

Aim The objective of this paper is to obtain a net primary production (NPP) regression model based on the geographically weighted regression (GWR) method, which includes spatial non‐stationarity in the parameters estimated for forest ecosystems in China. Location We used data across China. Methods We examine the relationships between NPP of Chinese forest ecosystems and environmental variables, specifically altitude, temperature, precipitation and time‐integrated normalized difference vegetation index (TINDVI) based on the ordinary least squares (OLS) regression, the spatial lag model and GWR methods. Results The GWR method made significantly better predictions of NPP in simulations than did OLS, as indicated both by corrected Akaike Information Criterion (AIC_c) and R². GWR provided a value of 4891 for AIC_c and 0.66 for R², compared with 5036 and 0.58, respectively, by OLS. GWR has the potential to reveal local patterns in the spatial distribution of a parameter, which would be ignored by the OLS approach. Furthermore, OLS may provide a false general relationship between spatially non‐stationary variables. Spatial autocorrelation violates a basic assumption of the OLS method. The spatial lag model with the consideration of spatial autocorrelation had improved performance in the NPP simulation as compared with OLS (5001 for AIC_c and 0.60 for R²), but it was still not as good as that via the GWR method. Moreover, statistically significant positive spatial autocorrelation remained in the NPP residuals with the spatial lag model at small spatial scales, while no positive spatial autocorrelation across spatial scales can be found in the GWR residuals. Conclusions We conclude that the regression analysis for Chinese forest NPP with respect to environmental factors and based alternatively on OLS, the spatial lag model, and GWR methods indicated that there was a significant improvement in model performance of GWR over OLS and the spatial lag model.     

Emission scenario of non-CO2 gases from energy activities and other sources in China

This paper gives a quantitative analysis on the non-CO2 emissions related to energy demand, energy activities and land use change of six scenarios with different development pattern in 2030 and 2050 based on IPAC emission model. The various mitigation technologies and policies are assessed to understand the corresponding non-CO2 emission reduction effect. The research shows that the future non-CO2 emissions of China will grow along with increasing energy demand, in which thermal power and transportation will be the major emission and mitigation sectors. During the cause of future social and economic development, the control and mitigation of non-CO2 emissions is a problem as challenging and pressing as that of CO2 emissions.This study indicates that the energy efficiency improvement, renewable energy, advanced nuclear power generation, fuel cell, coal-fired combined cycle, clean coal and motor vehicle emission control technologies will contribute to non-CO2 emissions control and mitigation.     

Spatio-temporal characteristics of vegetation water use efficiency and their relationships with climatic factors in alpine and subalpine area of southwestern China

水分利用效率(WUE)是深入理解生态系统水碳循环及其耦合关系的重要指标。为了揭示气候变化背景下区域尺度不同植被类型的响应和适应特征, 对中国西南高山亚高山地区2000-2014年的9种植被类型的WUE时空特征及其影响因素进行探究。该研究基于MODIS总初级生产力(GPP)、蒸散发(ET)数据和气象数据, 估算西南高山亚高山区植被WUE, 采用趋势分析及相关分析等方法, 分析了研究区植被WUE与气温、降水及海拔的关系。主要结果: (1)西南高山亚高山区2000-2014年植被WUE多年均值为0.95 g·m^-2·mm^-1, 整体呈显著增加趋势, 增速为0.011 g·m^-2·mm^-1·a^-1; 空间上WUE呈东南高西北低的分布, 85.84%区域的WUE呈增加趋势。(2)西南高山亚高山区各植被类型WUE多年均值表现为常绿针叶林>稀树草原>常绿阔叶林>有林草原>农田>落叶阔叶林>混交林>郁闭灌丛>草地; 时间上, 各植被类型WUE均呈上升趋势。(3)西南高山亚高山区89.56%区域的WUE与气温正相关, 92.54%区域的WUE与降水量负相关; 各植被类型中, 草地WUE与气温的相关性最高, 有林草原WUE与降水量的相关性最高。(4)西南高山亚高山区典型的地带性顶极植被常绿针叶林的WUE具有较强的海拔适应性及应对气候变化的能力。     

2008-2017年度微生物资源分类方向申请与资助情况

本文简要统计分析了2008至2017年度国家自然科学基金微生物学科在资源、分类和系统发育方向的项目申请、受理和资助情况。针对本方向项目资助与管理过程中的突出问题,对未来工作提出若干建议。     

A new trogiid genus from lower Eocene Fushun amber (Insecta: Psocodea: Trogiomorpha)

A new fossil brachypterous psocid tax on belonging to the family Trogiidae is characterized, illustrated and described from the early Eocene Fushun amber. Paralepinotus fushunensis nov. gen., nov. sp., shares several characters with the genus Lepinotus von Heyden, 1850, but differs from it mainly by the presence of tibial spurs other than apical ones and the absence of a forked sensillum on the last maxillary palp. This fossil taxon is also characterized by relatively developed elytriform forewing but still not reaching the tip of the abdomen. A checklist of all known Trogiidae is provided.     

东北地区森林凋落叶分解速率与气候、林型、林分光照的关系

在东北长白山、张广才岭、小兴安岭、大兴安岭的主要森林类型中设置26块样地,进行为期3a(2004—2006年)凋落叶分解实验,以研究气候、林型、林冠透光率对凋落叶分解速率的相对影响大小。结果表明,不同林型凋落叶分解速率依次为:落叶阔叶林针阔叶混交林落叶针叶林常绿针叶林岳桦林。对分解速率影响因素的分析表明,气候因子(热量和水分)对分解速率有较强的解释力,分别解释了分解常数k和分解95%所需时间(t95%)的55.5%和65.0%的变异。但是,气候对分解速率的影响在很大程度上是通过与林型、林冠透光率的协同作用而实现的,其独立解释力并不大(9%)。气候的变化导致林型(物种组成)的变化、进而影响分解速率,这一因素解释了分解参数变异的46.8%(k)和56.8%(t95%)。与此同时,气候和林型的变化还导致林冠透光率的变化,随着热量水平的上升林冠透光率下降、间接提高分解速率。这一因素分别解释了k值和t95%变异的23.9%和22.3%。研究结果表明,气候对凋落叶分解的影响主要是通过对物种组成、林冠结构(影响透光率)等生物因素的间接作用实现的。忽视这些生物因素、简单研究气候和分解速率的关系可能难以正确预测未来气候变化对凋落物分解的影响。     

南亚热带红椎和格木人工幼龄林土壤微生物群落结构特征

采用氯仿熏蒸浸提法和磷脂脂肪酸法(Phospholipids fatty acid,PLFA)研究了我国南亚热带地区非固氮树种红椎(Castanopsis hystrix)和固氮树种格木(Erythrophleum fordii)人工幼龄林土壤微生物生物量与微生物群落结构特征。结果表明,在旱季和雨季,红椎幼龄林土壤微生物总PLFAs量,细菌PLFAs量、放线菌PLFAs量及丛枝菌根真菌PLFAs量均大于格木幼龄林。红椎幼龄林土壤PLFA Shannon多样性指数(H_(PLFA))在旱季和雨季均大于格木幼龄林。主成分分析表明,土壤微生物群落结构组成受到林分类型和季节的双重影响。冗余分析表明,土壤硝态氮(NO_3~--N)含量、土壤含水量、p H及土壤微生物生物量氮(MBN)与特征磷脂脂肪酸之间呈显著相关关系。以上结果表明固氮树种格木与非固氮树种红椎人工幼龄林对土壤微生物生物量和群落结构的影响存在显著差异。     

1961-2013年东北三省玉米低温冷害强度的时空分布特征

基于东北三省1961-2013年气象数据、《北方春玉米冷害评估技术规范（QX/T167-2012）行业标准》,结合具有生物学意义的热量指数,给出了东北三省低温冷害强度分级,利用经验正交函数（EOF）分解法和旋转经验正交函数（REOF）分解法分析了东北三省玉米低温冷害强度时空分布特征。结果表明：东北三省玉米低温冷害强度具有明显的空间分布特点,可分为全区一致型、南北反相位分布型以及东西反相位分布型。东北三省玉米低温冷害强度的时间变化具有较好的一致性,1961-2013年东北三省低温冷害的发生强度呈波动减小趋势。低温冷害强度区域差异显著,按照低温冷害强度强弱可分为黑龙江-吉林北部区（Ⅰ区）、吉林南部-辽宁北部区（Ⅲ区）和辽宁南部区（Ⅱ区）。研究结果可为东北三省低温冷害风险区划和采取有效的减灾避灾措施提供依据。