考虑雾的微物理特征条件下的水雾扩散数值模拟研究

由于雾滴谱的不易获得，传统的水雾扩散研究一般不考虑其滴谱特征，而将其雾滴粒径作为常数处理。为此，引入实测的雾滴谱资料，考虑水雾在流专程 的扩散，蒸发及相变潜热对流场的反馈机制，利用三维非线性PLB方程组，采用工程上实用的湍流[E-ε]闭合方案，建立了一个高分辨率的PBL模式来模拟不同层结下，水电站泄洪产生的水务的扩散及共对环境湿度的影响，模拟结果表明，引入雾滴谱后的水雾扩散范围和相对湿度改变范围与不考虑雾滴谱条件下的相比，与日俱增接近实测，符合甚好。     

长期施肥对农牧交错带旱地土壤微生物多样性及群落结构的影响

为探讨长期不同施肥模式对农牧交错带旱地土壤微生物多样性和群落结构的影响,以农业农村部内蒙古耕地保育科学观测试验站的长期定位施肥试验为研究对象,选取不施肥(CK)、单施氮肥(NF)、单施化肥(CF)和有机肥+化肥配施(CFM)这4个处理,于试验开展16 a后(2019年)的马铃薯成熟期分取各处理0～10 cm和10～20 cm土层土壤,利用高通量测序技术对土壤细菌和真菌群落进行测定,以期从微生物学的角度探究农田不同施肥措施对土壤质量的影响,并通过偏最小二乘路径模型(PLS-PM)揭示了施肥模式转变中旱地土壤微生物群落演替和作物产量提升的关键环境驱动因子.结果表明：(1)CF和CFM处理均改善了土壤肥力,但后者效果明显优于前者,CFM处理土壤碱解氮、有效磷和速效钾分别提高131.9%～174.7%、 216.9%～283.3%和103.3%～109.3%,有机质和全氮也显著增加;CF处理仍保持了较高的土壤pH,而NF处理土壤pH显著降低,对土壤肥力的提升效果微弱.(2)与CK相比,NF处理显著降低了土壤细菌的Chao1和Shannon指数,CFM处理显著增加了细菌的物种丰富度、 Chao1...     

Simulated photo-degradation of dissolved organic matter in lakes revealed by three-dimensional excitation-emission matrix with regional integration and parallel factor analysis

Simulated photo-degradation of fluorescent dissolved organic matter(FDOM) in Lake Baihua(BH) and Lake Hongfeng(HF) was investigated with three-dimensional excitationemission matrix(3 DEEM) fluorescence combined with the fluorescence regional integration(FRI),parallel factor(PARAFAC) analysis,and multi-order kinetic models.In the FRI analysis,fulvic-like and humic-like materials were the main constituents for both BH-FDOM and HF-FDOM.Four individual components were identified by use of PARAFAC analysis as humic-like components(C1),fulvic-like components(C2),protein-like components(C3) and unidentified components(C4).The maximum 3 DEEM fluorescence intensity of PARAFAC components C1-C3 decreased by about 60%,70% and 90%,respectively after photo-degradation.The multi-order kinetic model was acceptable to represent the photo-degradation of FDOM with correlation coefficient(R_(adj)~2)(0.963-0.998).The photo-degradation rate constants(k_n) showed differences of three orders of magnitude,from 1.09 x 10~(-6) to 4.02 x 10~(-4) min-1,and half-life of multi-order model(T_(1/2)~n)ranged from 5.26 to 64.01 min.The decreased values of fluorescence index(FI) and biogenic index(BI),the fact that of percent fluorescence response parameter of Region I(P_(Ⅰ,n)) showed the greatest change ratio,followed by percent fluorescence response parameter of Region II(P_(Ⅱ,n),while the largest decrease ratio was found for C3 components,and the lowest T_(1/2)~n was observed for C3,indicated preferential degradation of protein-like materials/components derived from biological sources during photodegradation.This research on the degradation of FDOM by 3 DEEM/FRI-PARAFAC would be beneficial to understanding the photo-degradation of FD OM in natural environments and accurately predicting the environmental behaviors of contaminants in the presence of FDOM.     

东北红豆杉植物地理学研究

总结了东北红豆杉的地理分布概况和分布区特点，绘制了分布图，以在我国境内分布区域的主导气象因子构成指示指标组建立了东北红豆杉自然地理分布模型，并通过实例证明了该模型的有效性。     

基于GAMs模型分析成都市气象因子交互作用对O3浓度变化的影响

为探究成都市大气环境中气象因子交互作用对臭氧(8h浓度平均最大值,统一用O₃表示)浓度变化的影响特征,利用成都市2014～2019年逐日大气污染物资料以及同期的气象资料,采用广义相加模型(generalized additive models,GAMs)分析气象因子对O₃浓度变化的影响效应.结果表明,单影响因素的GAMs模型中,O₃浓度与最高气温、日照时数、相对湿度、风速、降水量、最大混合层厚度(maximum mixed depth,MMD)和通风系数(ventilation coefficient,VC)间均呈非线性关系,无论全年还是夏季,最高气温、日照时数、MMD和相对湿度对O₃浓度影响均较大,值得注意的是,夏季相对湿度和降水量对O₃浓度变化的影响较全年更加显著.在构建O₃浓度变化的多气象因子GAMs模型中,除平均风速以外的其他气象因子共同作用对O₃浓度变化有显著影响,就全年而言,构建的GAMs模型判定系数(R²     

基于数字图像处理的人员运动参数提取方法研究

针对人员疏散研究中提取疏散场景行人运动参数的需要,采用数字图像处理技术对记录行人运动的视频进行了处理.首先对背景进行混合高斯建模并提取前景,然后采用mean-shift算法对视频中彼此相连的多个行人目标进行有效地聚类分割并准确地跟踪每个行人,得到了包含有行人运动特性时空信息的运动轨迹,利用轨迹得到了行人在行进过程中的速度变化特征.结果表明该文所提出方法可以方便、快速、准确地提取人员运动特性参数,可望对提取人员疏散实验以及真实场景中行人运动的基础数据,如速度、密度、流量等提供有效的技术手段.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas

As human activities expand beyond national jurisdictions to the high seas, there is an increasing need to consider anthropogenic impacts to species inhabiting these waters. The current scarcity of scientific observations of cetaceans in the high seas impedes the assessment of population‐level impacts of these activities. We developed plausible density estimates to facilitate a quantitative assessment of anthropogenic impacts on cetacean populations in these waters. Our study region extended from a well‐surveyed region within the U.S. Exclusive Economic Zone into a large region of the western North Atlantic sparsely surveyed for cetaceans. We modeled densities of 15 cetacean taxa with available line transect survey data and habitat covariates and extrapolated predictions to sparsely surveyed regions. We formulated models to reduce the extent of extrapolation beyond covariate ranges, and constrained them to model simple and generalizable relationships. To evaluate confidence in the predictions, we mapped where predictions were made outside sampled covariate ranges, examined alternate models, and compared predicted densities with maps of sightings from sources that could not be integrated into our models. Confidence levels in model results depended on the taxon and geographic area and highlighted the need for additional surveying in environmentally distinct areas. With application of necessary caution, our density estimates can inform management needs in the high seas, such as the quantification of potential cetacean interactions with military training exercises, shipping, fisheries, and deep‐sea mining and be used to delineate areas of special biological significance in international waters. Our approach is generally applicable to other marine taxa and geographic regions for which management will be implemented but data are sparse.     

Personality and economic choices

There is substantial variation in individual preferences for public goods, yet much of that variation remains poorly understood. However, simple measures of personality can help to explain economic values and choices in a systematic way. In this paper, we examine the effects of personality on individual economic choices over public environmental goods. Based on three datasets from three separate stated preference studies, we use a hybrid choice econometric framework to examine the effects of personality on preferences for the status quo, changes in environmental quality, and costs of investing in environmental improvements. We find effects that are consistent across all datasets. Personality, a stable feature of an individual's character that is simple to measure, enriches explanations of why the demand for environmental goods varies across people, provides an indication of how different people are likely to react to the introduction of environmental policies, and explains substantial differences in Willingness to Pay.     

How decisions about fitting species distribution models affect conservation outcomes

Species distribution models (SDMs) are increasingly used in conservation and land-use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10–20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model-based uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most.