Effects of temperature variation on TSD in turtle (C. picta) populations

We formulate a two-sex model of temperature-dependent sex determination (TSD) for a freshwater turtle (C. picta) population. The aim is to understand how environmental temperature variations and nest heat conduction properties affect the long term dynamics of the population. This is a key to understanding how global temperature changes may affect their survival. With stochastic inputs of ambient temperature and solar radiation, the model uses the heat equation to determine the temperature in the egg layer in the nest; in turn, this determines the sex ratio in the egg clutch using a variable degree-day model. Finally, a nonlinear Leslie type, stage-based, two-sex model, is used to determine the long term male and female populations. A two-sex model is required because of different development rates for males and females. The model is flexible enough to enable other researchers to examine the effects of temperature variation variations on other species with TSD, e.g., crocodilians, reptilians, as well as other turtle species. It can be adapted to study effects of nest location, soil type, rain events, different incubation periods, and density effects, for example, the dependence of the mating function on the ratio of males to females and each’s contribution to the sex of hatchlings. Modifications can be easily made to fit a specific life history traits. The model is a beginning step in understanding the long term, high fitness shown by many reptile species with TSD, and it may suggest to experimentalists what data may be relevant to these issues; it can also be useful to wildlife managers in developing strategies for intervention if needed. Among the principal findings are that temperature variability and detailed nest heat conduction properties may buffer projected negative effects on a population.     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

Multiple structural modifications to dendritic ecological networks produce simple responses

Structural modifications to landscapes affect the ability of organisms to access different habitat patches. There exist, however, very few general methods by which to relate modifications to expectations of effects, and even fewer that enable understanding of how multiple modifications may interact. In the absence of any guiding principles, ecologists have assumed that interactions will result in complex landscape-scale effects. One way of understanding such effects is through rendering a landscape as a graph or network, among the simplest of which are dendritic networks typified by stream systems. Yet even for stream networks, there are no known general principles concerning the nature of interactions between multiple modifications. We developed a model to describe the ability of fish to access and use different habitat patches within dendritic networks. We used mathematical and numerical analyses of the model to investigate how the habitat value of a network is affected by changes in connectivity and habitat quality, and then to examine interactions between multiple modifications. Rather than showing complex interactions, our analytic and simulation-based results show that the combined effect of multiple modifications approximately equals the sum of individually predicted effects. Dendritic networks thus appear to respond far more simply to multiple modifications than has previously been assumed. These results have implications for stream management planning, and offer a firm foundation from which to better understand population processes within dendritic networks.     

Biogeochemical model (BGC-ES) and its basin-level application for evaluating ecosystem services under forest management practices

It is important for humans to live in harmony with ecosystems. Evaluation of ecosystem services (ES) may be helpful in achieving this objective. In Japan, forest ecosystems need to be re-evaluated to prevent their degradation due to lack of forest management.In order to evaluate the effects of forest management on forest ES, we developed a process-based biogeochemical model to estimate water, carbon, and nitrogen cycles in forest ecosystems (BGC-ES). This model consists of four submodels: biomass, water cycle, carbon-nitrogen (CN) cycles, and forest management. The biomass submodel can calculate growth of forest biomass under forest managements.Several parameters of the model were calibrated using data from observations of evapotranspiration flux and quality of stream flow in forests. The model results were compared with observations of runoff water from a dam catchment site and with carbon flux observations.Our model was coupled with a basin-level GIS database of forests. Evaluations under various forest management scenarios were carried out for forests in a basin contained in the Ise Bay basin (Chubu region, Japan), where plantations (artificial forests) seemed to have degraded from poor forest management.Comparing our simulation results with those of forests without management in the basin, we found that the amounts of absorbed carbon and runoff were larger in managed forests. In addition, the volume of harvested timber was larger and its quality (diameter) was better in managed forests. Changes of ES within the various scenarios were estimated for their economic value and were compared with the cost of forest management.     

铁磁性纳米材料的制备及对17β-雌二醇的选择性富集——基于分子印迹技术

将铁磁性分离与分子印迹技术结合,以四氧化三铁纳米颗粒为核,以17β-雌二醇(E2)为模板分子,3-氨基丙基三乙氧基硅烷(APTES)为功能单体,利用表面聚合法制备了E2分子印迹纳米材料(MIPs)和不含E2的非印迹纳米材料(NIPs).通过傅里叶变换红外光谱(FT-IR)、X射线衍射(XRD)、X射线光电子能谱(XPS...     

千河下游水体-沉积物重金属空间分布、风险及影响因素

为了研究千河下游水体-沉积物重金属污染特征、风险程度及其影响因素,采集千河下游表层沉积物19份及水样20份,使用电感耦合等离子体质谱仪（ICP-MS）测定8种重金属（As、Cd、Cr、Cu、Mn、Ni、Pb和Zn）含量,分析了水体中重金属含量和健康风险,对沉积物重金属空间分布及污染特征进行探析,以DEM、气温和降水等11种因子为自变量,结合地理探测器和地理加权回归模型对沉积物重金属污染影响因素进行空间分异探究.结果表明,千河下游8种水体重金属浓度均未超过地表水环境质量标准Ⅱ类水体标准,其中Pb元素变异系数为3.11,浓度高值区主要集中于东岭冶炼公司和凤翔火车站周围.水体成人致癌物R^c均值为7.72E-06,呈较轻风险程度,儿童致癌物R^c均值为1.17E-04,呈强风险程度,成人与儿童非致癌物风险均为可承受风险程度,儿童R_总的高值主要集中在凤翔火车站周边,呈较强风险程度;千河下游沉积物重金属除As和Mn外,其余6种元素均超过陕西省土壤背景值,其中Cd元素含量均值为1.12 mg ·kg^-1,是陕西省土壤背景值的12倍,Cd、Zn和Pb污染较严重,主要分布在长青村、南湾村地区、牛家滩村、高庄和东岭冶炼公司周围;研究区沉积物重金属PLI_zone为1.71,属于轻度污染;DEM、温度和降水是沉积物重金属PLI空间格局的主要自然影响因素,其交互作用均为非线性增强,对沉积物重金属空间污染扩散起到一定驱动作用.     

预处理后生物成因施氏矿物的矿物学特征及对As(Ⅲ)吸附的影响

生物成因施氏矿物虽然在吸附As(Ⅲ)能力上表现出较其它吸附剂更为优越的吸附性能,但其制备过程中细菌分泌的多糖类物质使得矿物颗粒间存在明显的团聚现象.本研究通过比较不同预处理方法对施氏矿物矿物学特征及吸附性能的影响,寻求一种最佳的矿物预处理方法,以期进一步改善矿物颗粒团聚现象和提高对As(Ⅲ)吸附能力.采用NaOH、NaCl、200℃灼烧及乙醇-超声方法对施氏矿物进行预处理,结果表明,处理后矿物的矿相均未发生变化,但其理化性质随处理方式的不同差异较大,矿物比表面积、Fe/S摩尔比、SEM图谱、As(Ⅲ)吸附性能与原始矿物相比均发生了明显的变化.经pH 12NaOH处理后矿物的吸附性能最佳,在室温下最大吸附量从原来的101.9 mg.g-1增加到143.3 mg.g-1,比表面积从原来的45.63 m2.g-1增加到325.18 m2.g-1,矿物干燥时团聚现象明显减弱.     

水文变异条件下潦河生态流量计算研究

气候变化和高强度人类活动改变了流域的自然水文循环过程,导致水文序列出现变异,严重影响了流域水文生态系统的稳定性.目前,基于水文变异条件下的河道内生态流量计算已成为当前变化环境下生态水文学研究的热点问题之一.以江西鄱阳湖西北部的潦河为例,采用水文时间序列变异检验方法分析径流变异性及成因,应用水文模型对水文变异后的径流进行还原,以Kolmogorov-Smirnov（K-S）、Anderson Darling（A-D）和概率点据相关系数（PPCC）3种检验法确定月径流的最优概率分布函数,进而提出水文变异条件下最适宜的潦河生态流量计算方法.结果表明:①潦河万家埠站径流在1972年发生水文变异,突变点后流域年径流增加了12%,降水量的增加和蒸发量的减少是其主要驱动要素.②采用分布式时变增益模型（DTVGM）对径流进行还原,率定期和验证期的相关系数和Nash-Sutchliffe效率系数均大于0.78,径流模拟值和实测值拟合程度较高,表明基于DTVGM进行径流还原是可行的.③基于还原后的径流,对5种概率分布函数进行拟合优度综合检验,确定逐月最优分布函数,并估算月河道生态流量.与Tennant法、最枯月平均流量法、7Q10法等方法比较,基于最优分布函数的生态流量结果更具确定性与合理性.在气候变化和人类活动引起径流变异的背景下,考虑水文变异的河道内生态需水计算方法能够更科学地体现水文变异对生态需水过程的影响,研究结果可为潦河流域水生态保护和水资源管理提供数据支撑,也可为变化环境下水资源规划和配置提供科学依据.      

硫脲改性猪粪生物质炭对模拟农田径流中镉和草甘膦吸附特征

为同时去除农田地表径流中的重金属和农药,利用猪粪制备未改性猪粪生物质炭（简称"未改性生物质炭"）和硫脲改性猪粪生物质炭（简称"改性生物质炭"）,分析比较硫脲改性对生物质炭的pH、元素组成、表面含氧官能团和巯基含量等理化性质的影响,并系统地研究了单一和复合污染体系中初始浓度对两种生物质炭吸附水溶液中镉（Cd）和草甘膦效率的影响.结果表明:①与未改性生物质炭相比,改性生物质炭的pH、O/C（原子比）和H/C（原子比）降低,比表面积增大,含氧官能团和巯基含量增加.②与未改性生物质炭相比,改性生物质炭对Cd和草甘膦的吸附能力增强,最大表观吸附量（Q_max）增加了近3倍;随着Cd和草甘膦初始浓度的增加,未改性和改性生物质炭对Cd和草甘膦的吸附量逐渐增加,增加量最高分别达18.52%和7.60%.③单一污染体系中两种生物质炭对Cd或草甘膦的吸附更符合Langmuir等温吸附模型,说明其对Cd或草甘膦的吸附机理是单分子层的吸附起主导作用.④复合污染体系中,未改性和改性生物质炭对Cd的吸附能力分别增加了25.28%和21.26%,未改性生物质炭对Cd的最大表观吸附量增加了29.34%,但改性生物质炭对Cd的最大表观吸附量降低了47.28%;未改性和改性生物质炭对草甘膦的吸附能力减弱,但最大表观吸附量分别增加了2.63和3.45倍.研究显示,硫脲改性猪粪生物质炭作为一项有前景的新技术,为解决实际环境中的复合污染问题提供了经济环保的技术手段.      

汽油车技术发展对尾气排放影响研究进展

近年来,汽油车尾气排放已成为城市大气污染的主要来源之一.为减少油耗、温室气体和大气污染物的排放,汽油直喷技术(GDI)、醇类燃料替代以及混合动力系统等新兴技术被应用到汽车产品中,该研究对GDI发动机汽车、醇类燃料车和混合动力车的颗粒物(PM)、氮氧化物(NO_x)、总碳氢化合物(THC)的排放研究进行梳理和总结,综合评估先进动力技术和醇类燃料的环境影响.结果表明:GDI汽油车的PM排放因子为进气道喷射(PFI)汽油车的1.2~5倍,加装汽油颗粒物捕集器(GPF)后GDI汽油车的PM排放大幅下降,同时具备催化能力的GPF可减少NO_x和THC排放.与汽油车相比,乙醇燃料车PM排放量减少了35%~56%,尾气THC排放减少了10%~44%,但挥发性有机物(VOCs)蒸发排放增加了20%~41%,其主要来自于日呼吸损失.各类型车辆的NO_x排放差异较小,比较结果存在一定的不确定性.混合动力车相比传统内燃机汽车污染物减排优势明显,可积极推广其在公共交通和私家车队中的应用.建议今后研究应着重关注以下几个方面:①GDI和混合动力车在实际条件下排放污染物的环境影响;②醇类燃料车VOCs蒸发排放控制技术及相关法规标准的完善;③新兴技术汽油车排放污染物的生成机理及其影响因素.