鼓翅皱膝蝗生态学特性研究

在甘肃省夏河县对鼓翅皱膝蝗的种群结构、数量变动、空间格局及动脉进行了系统研究。鼓翅皱膝蝗1a发生1代，以卵在土中越冬，翌年5月中旬开始孵化出土，6月上旬达到出土高峰，此期蝗虫种群仅由1龄和2龄蝗蝻组成，其中1龄蝗蝻占81.8％，2龄蝗蝻占18.2％，成虫于7月上旬开始羽化，8月上旬达到羽化高峰，此期的成虫数量达61.3％，而蝗蝻仅占38.7％，蝗蝻期约为72d，成虫寿命54d左右，每雌平均产卵32.6粒。鼓翅皱膝蝗在草地上属聚集分布，蝗蝻发生期种群的空间动态主要表现出扩散趋势，但初孵化出土时有短暂的聚集行为。取食量随龄期增大而增大，每头鼓翅皱膝蝗取食牧草量蝗蝻期平均为1.5g，成早期约为5.8g，成早期的食量是蝗蝻期的3.7倍。表3参12     

A new synergetic paradigm in environmental numerical modeling: Hybrid models combining deterministic and machine learning components

A new type of environmental numerical models, hybrid environmental numerical models (HEMs) based on combining deterministic modeling and machine learning components, is introduced and formulated. Conceptual and practical possibilities of developing HEM, as an optimal synergetic combination of the traditional deterministic/first principles modeling (like that used for solving PDEs on the sphere representing model dynamics of global climate models) and machine learning components (like accurate and fast neural network emulations of model physics or chemistry processes), are discussed. Examples of developed HEMs (hybrid climate models and a hybrid wind–wave ocean model) illustrate the feasibility and efficiency of the new approach for modeling extremely complex multidimensional systems.     

川西亚高山白桦林小气候的时空动态特征

对川西亚高山白桦林(海拔2 540 m)内太阳辐射、空气温湿度以及土壤5 cm和15 cm层温度等进行了连续的定位观测.结果表明:1)林冠下的太阳辐射日进程与林冠上的不同,但辐射强度随着太阳高度角的变化而变化;林冠下太阳辐射日总量不仅受林冠上辐射日总量的影响,也受森林群落生长季节的影响;春、夏和秋季,林冠下平均辐射日总量分别占林冠上平均辐射日总量的53.1%、39.4%和55.8%.2)夏季,白天空气温度高于土壤温度,而夜间则相反;空气温度和土壤温度的日极端温度出现的时间不同步,空气温度对太阳辐射强度的敏感性比土壤温度的敏感性高.3)春季,土壤表层>4℃的积温高于空气和土壤底层>4℃的积温;夏季,>4℃的积温由高到低的次序为:林冠下空气>土壤5 cm层>土壤15 cm层,而秋季的比较结果与之相反;空气日平均温度与土壤日平均温度有显著的线性关系(P<0.001).4)林冠下的空气相对湿度(RH)比林冠上的高,林冠下的空气RH日变幅和季节性变幅比林冠上的小.图7表1参19     

A Bayesian strategy for combining predictions from empirical and process-based models

We present a strategy for using an empirical forest growth model to reduce uncertainty in predictions made with a physiological process-based forest ecosystem model. The uncertainty reduction is carried out via Bayesian melding, in which information from prior knowledge and a deterministic computer model is conditioned on a likelihood function. We used predictions from an empirical forest growth model G-HAT in place of field observations of aboveground net primary productivity (ANPP) in a deciduous temperate forest ecosystem. Using Bayesian melding, priors for the inputs of the process-based forest ecosystem PnET-II were propagated through the model, and likelihoods for the PnET-II output ANPP were calculated using the G-HAT predictions. Posterior distributions for ANPP and many PnET-II inputs obtained using the G-HAT predictions largely matched posteriors obtained using field data. Since empirical growth models are often more readily available than extensive field data sets, the method represents a potential gain in efficiency for reducing the uncertainty of process-based model predictions when reliable empirical models are available but high-quality data are not.     

Resampling from stochastic simulations

To model the uncertainty of an estimate of a global property, the estimation process is repeated on multiple simulated fields, with the same sampling strategy and estimation algorithm. As opposed to conventional bootstrap, this resampling scheme allows for spatially correlated data and the common situation of preferential and biased sampling. The practice of this technique is developed on a large data set where the reference sampling distributions are available. Comparison of the resampled distributions to that reference shows the probability intervals obtained by resampling to be reasonably accurate and conservative, provided the original and actual sample has been corrected for the major biases induced by preferential sampling.Andre G. Journel is a Professor of Petroleum Engineering at Stanford University with a joint appointment in the Department of Geological and Environmental Sciences. He is, also, Director of the Stanford Center for Reservoir Forecasting. Professor Journel has pioneered applications of geostatistical techniques in the mining/petroleum industry and extended his expertise to environmental applications and repository site characterization. Most notably, he developed the concept of non-parametric geostatistics and stochastic imaging with application to modeling uncertainty in reservoir/site characterization. Although the research described in this article has been supported by the United States Environmental Protection Agency under Cooperative Agreement CR819407, it has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

基于SPSS的苏北地区可持续发展能力空间分异研究

以区域可持续发展能力建设为切入点,结合苏北地区的实际,建立适合该地区的可持续发展能力评价指标体系,然后运用SPSS软件的因子分析和聚类分析的方法,对苏北地区可持续发展能力进行综合评价。结果表明,区内各县市可持续发展能力差异较大,但其空间分异呈现出某些特征:经济可持续发展能力呈现由地区东南部向中间递减的“一”字型空间格局;资源环境可持续发展能力呈现由地区中部分别向东、向西递减的“凸”字型空间格局;社会可持续发展能力呈“M”字型空间格局;综合可持续发展能力呈“H”字型空间格局;以中心城市为核心,可持续发展能力向周边呈辐射状递减趋势。根据这些空间分异特征,提出了一些促进苏北地区可持续发展能力建设的建议。     

基于放射源理论的区域生态系统评价方法

研究区域生态系统定量评价方法对区域生态系统的建设与保护具有十分重要的意义。从放射源理论出发,首次将生态影响距离、生态源及生态影响效应的概念引入分析评价中,提出了区域生态系统的定量评价方法,并建立了基于景观空间格局的区域生态系统生态影响的定量计算公式。甘垛镇新庄片和横泾镇沿荡片的应用实例表明,该方法原理简明、结果直观,为区域生态系统的科学评价提供了定量评价方法。