上海地区不同河道CDOM光谱吸收特征及其可能来源分析

于2013年8月对上海徐汇、青浦、宝山、嘉定、闵行和松江6个区14条河道水体中的有色可溶性有机物(CDOM)的光谱吸收特性、分布概况及其可能来源进行研究。结果表明,不同区域河道水体中CDOM吸收系数在紫外到可见光波段均呈指数递减趋势,波长350 nm处的吸收系数〔a(355)〕在2.34~4.33 m-1之间,不同区域差别较大,城镇河道水体CDOM吸收系数总体高于城郊农业区河道。相关性分析表明,a(355)分别与水体ρ(CODMn)和浊度呈显著相关,但与ρ(Chl-a)(平均为30.59μg·L-1)相关性不显著,说明河道水体CDOM主要来自于陆源,如城镇生活污水、工业废水和农业面源污染等,对250和365 nm波长处吸收系数的比值M和光谱吸收斜率S值的分析也得出相同结论。对上海河道水体CDOM光学特性的研究有助于了解上海不同区县河道水体水下光场的分布概况,为上海市河道水生植被的恢复提供理论基础。     

崇明岛前卫湖水中有色溶解性有机质的分布和来源

通过溶解性有机碳(DOC)、紫外-可见吸收光谱及三维荧光激发发射矩阵(3DEEM)分析,研究崇明岛前卫湖有色溶解性有机质(CDOM)的分布和来源.结果发现,表层水中DOC的空间分布比较均匀,变化范围为5.87～7.02mg*L-1,平均值为6.35mg*L-1.吸收光谱分析表明,校正吸收系数a355在养殖区最高,为4.58m-1,并呈向两边逐渐递减的趋势,暗示养殖区CDOM可能存在陆源之外的其他来源.进一步的荧光指数(f450/500)分析证实了水生生物源的存在,同时也表明前卫湖水体的CDOM总体上并没有明显偏向陆源或者水生生物源(f450/500为1.50～1.84),而a355与DOC含量的比值和荧光指数存在显著正相关(r=0.787,n=10,P<0.01),说明养殖区存在一个比较明显的生物信号,其可能来源为藻类.三维荧光的分析结果表明,类腐殖酸组分普遍存在于前卫湖CDOM中,且空间差异不大;而类蛋白组分的分布差异较大,可能是影响前卫湖CDOM空间分布的主要因素,这与上述生物源的分析结果相吻合.     

鄱阳湖枯水期CDOM光吸收特性与DOC浓度的定量关系研究

研究了2015年枯水期鄱阳湖水体(包括河口区、鄱阳湖主湖开阔区)有色可溶性有机物(CDOM)的光吸收特性,并讨论了CDOM光吸收特性与溶解性有机碳(DOC)浓度之间的关系。结果表明,整个鄱阳湖水体中CDOM在254、280、350和440 nm波长处的吸收系数分别为7.86~20.24、5.64~14.58、1.46~4.41和0.36~1.06 m-1;表征CDOM相对分子质量大小的M值(CDOM在波长250和365 nm处吸收系数比值)变化范围为5.27~7.66。枯水期湿地对鄱阳湖水体CDOM的影响较大,而5大入湖河流贡献不大。在各项参数中,光谱斜率比值(SR)更能体现枯水期鄱阳湖水体CDOM光吸收特征。枯水期鄱阳湖水体ρ(DOC)在1.61~4.86 mg·L~(-1)之间变化,其平均值为2.31 mg·L~(-1)。枯水期鄱阳湖水体CDOM光吸收系数(a254,x)与DOC浓度(y)呈显著正相关,回归方程为y=-0.53+0.27x(调整决定系数R2=0.76,P0.01),2016年1月的实测值验证显示利用枯水期鄱阳湖水体CDOM光吸收特性研究DOC浓度具有可行性。这意味着研究枯水期鄱阳湖水体CDOM光吸收特性对研究碳库具有指示作用。     

太原市不同类型夏季水体颗粒物与CDOM吸收特性研究

水体组分吸收特性是水下光场和水面光谱的主要影响因子,对其进行研究有助于提高对城市水体光学吸收特性的认识和利用遥感技术手段动态监测城市水环境质量。共采集太原市不同类型(晋阳湖和排洪渠、汾河太原城区段和汾河二库)夏季水体30个样本的实测数据,对水体总颗粒物、非色素颗粒物、色素颗粒物和CDOM的吸收特征进行分析。结果表明:(1)太原市不同类型夏季水体总颗粒物吸收特征的主导因素各异,排洪渠和汾河太原城区段为非色素颗粒物主导类型,晋阳湖和汾河二库为色素颗粒物主导类型;(2)太原市不同类型夏季水体绝大多数样本色素颗粒物吸收系数在440、675nm处呈现典型的Chla吸收峰,色素颗粒物吸收系数aph(440)、aph(675)与Chla浓度均存在显著线性正相关,但675nm处的相关性强于440 nm;(3)非色素颗粒物吸收系数ad(440)、ad(675)与TSM、ISM、OSM、TP和TN均存在显著的相关性,但与Chla没有相关性;(4)太原市不同类型夏季水体M值较小,CDOM中腐殖酸相对含量较高,相对分子质量较大,陆源性物质的输入占据主导地位;晋阳湖水体M值与太原市其他类型水体相比,M值较大,富里酸的相对含量较高,相对分子质量较小;(5)太原市不同类型水体非色素颗粒物、色素颗粒物和CDOM吸收贡献率在不同波段处差异显著,不同采样点水体吸收特性的主导影响因素各异。该研究对认识城市不同类型夏季水体固有光学特性和构建水质参数遥感反演模型提供重要参考。     

基于实测高光谱数据的太湖悬浮物浓度与透明度分区

通过实地测量太湖水体的反射光谱和表层水质,利用实测数据以及水-气界面辐射传输模型计算其遥感反射率,并根据太湖的实际水质状况将其划分为3个区域,分别分析太湖总体和分区的反射光谱曲线,建立太湖不同区域水体悬浮物浓度和透明度的反演模型。结果表明,就悬浮物浓度和透明度而言,太湖3个区域中以东太湖水质最佳,贡湖、梅梁湖和竺山湖次之,南部和西部最差;对太湖进行分区研究能够更好地反应太湖水质的差异性,拟合各区域的水质状况,分区模型精度较全湖区模型高,误差也普遍较小,能够提高研究结果的可信度和可行性。     

丹江口水库表层沉积物有色可溶性有机物空间分布特征及其来源分析

利用三维荧光光谱(Excitation-emission matrices,EEMs)与平行因子分析模型(Parallel factor analysis,PARAFAC)技术研究丹江口水库表层沉积物有色可溶性有机物(Chromophoric dissolved organic matter,CDOM)荧光组分、空间分布特征和来源,以及不同组分荧光强度与其它沉积物指标的相关性.结果表明,丹江口水库37个采样点CDOM由3类5个荧光组分组成,即类蛋白组分C1(285/345 nm)和C2(225,270/335 nm);陆源类腐殖质组分C3(260/450 nm)和C4(360/450 nm)及自生源类腐殖质组分C5(225,310/405 nm).其中代表类蛋白组分的C1和C2占较高比例,占总荧光强度的百分比的平均值为46.37%,C3与C4和C5占总组分的比例平均值分别为34.33%和19.31%.空间分布上3种荧光峰高值都集中在汉江的入湖河口处及丹江库区居民较多的城镇处,且呈现河流大于库区的趋势.荧光指数(FI)、腐殖化指数(HIX)和生物源指数(BIX)都表明丹江口水库沉积物CDOM的来源具有陆源及生物源的双重特征,陆源主要来源于汉江干流和周边城市居民的活动,以及部分区域农业畜禽养殖;生物源主要来自微生物及藻类的降解.相关性分析说明沉积物CDOM与N、P元素的迁移和转化有密切的关系.     

平衡渗析-多种光谱法研究太湖不同分子量溶解性有机质及其与铜的相互作用

采用平衡渗析技术将太湖沉积物溶解性有机质(DOM)按分子量分成不同的组分.利用光谱技术和离子选择电极(ISE)分析了不同分子量溶解性有机质的光谱特征及对Cu的结合,进一步探讨分子量对DOM特性和对金属结合能力的影响.结果表明,南太湖DOM分子量以高于10000 Da为主,然而北太湖分子量以低于3500 Da为主.三维荧光光谱(3DEEM)表明太湖沉积物DOM呈现出4种特征荧光峰.类富里酸荧光峰的荧光强度随着DOM分子量的增加而增强,然而类蛋白质荧光峰的荧光强度随着DOM分子量的增加而降低.南太湖DOM碳氮比(C/N)(20.30)显著高于北太湖(7.58),表明了DOM不同的来源.对于南北湖区,DOM-Cu浓度最大值都是在DOM分子量低于1000 Da组分,大约50%的DOM-Cu在分子量低于3500 Da的组分中.结合容量表明南太湖DOM分子量低于3500 Da的组分和北太湖分子量在1000—3500 Da的组分对于Cu结合起主要的作用,表明低分子量DOM对于结合重金属的重要性.     

富营养化湖泊藻型及草型区微生物群落对有色可溶有机物组成的影响

有色可溶有机物(CDOM)是水生生态系统溶解有机碳储库的主要组成,在湖泊生态系统结构、初级生产力以及物质交换等方面发挥着重要作用。研究秋季太湖藻型区—蓝藻死亡时期及草型区—水草腐烂时期CDOM的组成差异性特征及其影响因素。首先对两类典型湖区水体的理化性质进行了分析,然后利用紫外可见光谱、三维荧光光谱结合平行因子分析的技术确定CDOM组成,最后结合Illuminar高通量测序揭示了两个区域水体中不同的微生物群落结构对CDOM组成的影响。结果表明,藻型湖水的各理化性质指标,包括NH4~+-N、NO2~--N、NO3~--N、TN,TP,DOC的浓度都明显高于草型湖水,秋季藻源性CDOM的含量明显高于草源性CDOM的含量。虽然藻源性和草源性CDOM的荧光组分相同,即同为类腐殖、类色氨酸和类络氨酸荧光组分,然而,相比于草源性CDOM,荧光指数(FI)和腐殖化指数(HIX)均表明藻源性CDOM具有更高的分子量组成,更难被微生物利用,而造成此差异性的重要原因为两个区域微生物群落组成的差异性,藻型区水体含有更丰富的降解有机物的细菌种类,包括黄杆菌属(Flavobacterium),噬氢菌属(Hydrogenophaga)、生丝微菌属(Hyphomicrobium)等,可优先分解易于分解的有机物,残留更多难降解的有机物,具有更大的潜在的环境隐患。     

三江平原大豆田蒸散量模拟

基于涡度相关系统测量值和小气候观测资料,比较分析了Penman、FAO Penman-Monteith和Priestley-Taylor3个模型对三江平原大豆田蒸散量的模拟效果。结果表明:3个模型参数采用常数时模拟值均大于测量值,尤以Penman模型最为明显,平均高估174.6%。Penman和FAO Penman-Monteith模型模拟效率均小于0,表明其模拟效果较差,不能用于估算大豆田间蒸散量。Penman和FAO Penman-Monteith模型的作物系数K c值与叶面积指数之间呈显著正相关关系,与饱和水汽压差之间呈显著负相关关系;Priestley-Taylor模型α值与叶面积指数和风速之间均呈显著正相关关系,与饱和水汽压差之间呈显著负相关关系。依据多元线性回归方程修正K c或α值后,Penman、FAO Penman-Monteith和Priestley-Taylor模型估算精度均明显提高,平均偏差变化范围为-0.10~0.00 mm·d-1,均方根误差均为0.67 mm·d-1,模拟效率均为0.61。方差分析进一步表明3个模型模拟结果没有显著性差异,但相对而言,Priestley-Taylor模型的截距、斜率和平均偏差略优于其他2个模型,因此Priestley-Taylor模型修正式是估算三江平原大豆田实际蒸散量的最优模型。     

利用叶面积指数优化冬小麦高光谱水分预测模型

水分是影响冬小麦生长发育的重要指标,目前可以利用高光谱数据建模来对其进行预测诊断.但在冬小麦生育初期,这类预测模型精度较低,为解决这个问题,利用高光谱数据分析方法,结合叶面积指数,对基于高光谱的冬小麦水分状况预测模型进行优化.结果显示,冬小麦叶面积指数会随着灌水处理的不同产生显著差异;优化后,光谱与冬小麦水分状况相关的敏感波段范围在450-500 nm、620-690 nm和780 nm左右;相对于土壤含水率而言,优化模型对植株含水率有更好的预测精度;引入叶面积指数进行优化提高了冬小麦在生育前期的预测模型精度,使模型精度从0.2提升到0.4以上,并且还提高了基于原始光谱反射率模型的精度;最终获得的模型中,基于原始光谱反射率R780的植株含水率预测模型拟合精度最高,为0.862,基于光谱指数R(810, 460)的植株含水率诊断模型验证效果最好,均方根误差(RMSE)为4.341,平均绝对误差(MAE)为2.361;基于光谱指数VARI700的土壤含水率预测模型验证效果最好,均方根误差(RMSE)为4.506,平均绝对误差(MAE)为6.293.本研究表明利用叶面积指数优化模型可以很好地提高模型精度,在土壤含水率预测模型方面优化尤其显著,这为基于高光谱的水分状况预测模型构建与实际应用提供了新思路.(图6表5参31)     

Enchytraeid population dynamics: Resource limitation and size-dependent mortality

Enchytraeids are regarded as keystone soil organisms in forest ecosystems. Their abundance and biomass fluctuate widely. Predicting the consequences of anthropogenic disturbances requires an understanding of the mechanisms underlying enchytraeid population dynamics. Here I develop a simple model, which predicts that the type of dynamics is controlled by resource input rate. If fungal resource input is a discrete event once a year, an exponential growth phase is followed by starvation and sharp decline of enchytraeid abundance. Model simulations with three different forcing functions were compared to field data. Initial parameter values were obtained from various independent sources, and parameters were estimated by minimizing the residual sum of squares. The best fitting model with resource addition once a year explained 39% of the variation in enchytraeid biomass over an 8-year study period. Further, variation in rainfall explained 59% of the variation in R² of the exponential phase models, which is also an index of the stability of population size-structure. The results emphasize the importance of resource limitation for enchytraeid population dynamics and support the hypothesis that the mortality during the decline phase is size-dependent.     

Estimation methods for nonlinear state-space models in ecology

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.     

Modeling leaf dispersal in mixed hardwood forests using a ballistic approach

In mixed-species stands, modeling leaf litter dispersal is important to predict the physical and chemical characteristics of the forest floor, which plays a major role in nutrient cycling and in plant population dynamics. In this study, a spatially explicit model of leaf litterfall was developed and compared with two other models. These three models were calibrated for a mixed forest of oak and beech using litterfall data from mapped forest plots. All models assumed that an allometric equation described individual leaf litter production, but they strongly differed in the modeling of the probability density of leaf shedding with distance from source trees. Two models used a negative exponential function to account for leaf dispersal with distance, and this function was allowed to vary according to wind direction in one of them. In contrast, our approach was based on a simple ballistic equation considering release height, wind speed, wind direction, and leaf fall velocity; the distributions of wind speeds and wind directions were modeled according to a Weibull and a Von Mises distribution, respectively. Using an independent validation data set, all three models provided predictions well correlated to measurements (r > 0.83); however, the two models with a direction-dependent component were slightly more accurate. In addition, parameter estimates of the ballistic model were in close agreement with a foliar litter production equation derived from the literature for beech and with wind characteristics measured during leaf litterfall for both species. Because of its mechanistic background, such a spatially explicit model might be incorporated as a litterfall module in larger models (nutrient cycling, plant population dynamics) or used to determine the manner in which patch size in mixed-species stands influences litter mixture.     

Fitting complex population models by combining particle filters with Markov chain Monte Carlo

We show how a recent framework combining Markov chain Monte Carlo (MCMC) with particle filters (PFMCMC) may be used to estimate population state-space models. With the purpose of utilizing the strengths of each method, PFMCMC explores hidden states by particle filters, while process and observation parameters are estimated using an MCMC algorithm. PFMCMC is exemplified by analyzing time series data on a red kangaroo (Macropus rufus) population in New South Wales, Australia, using MCMC over model parameters based on an adaptive Metropolis-Hastings algorithm. We fit three population models to these data; a density-dependent logistic diffusion model with environmental variance, an unregulated stochastic exponential growth model, and a random-walk model. Bayes factors and posterior model probabilities show that there is little support for density dependence and that the random-walk model is the most parsimonious model. The particle filter Metropolis-Hastings algorithm is a brute-force method that may be used to fit a range of complex population models. Implementation is straightforward and less involved than standard MCMC for many models, and marginal densities for model selection can be obtained with little additional effort. The cost is mainly computational, resulting in long running times that may be improved by parallelizing the algorithm.     

3种毒性预测模型在镉对小麦根毒性预测中的应用及比较

一些基于重金属形态的生物毒性机理模型被广泛应用于水体和陆生生态系统,例如:自由离子活度模型(FIAM)、生物配体模型(BLM)和生物膜电势斯特恩双电层模型(GCSM),但不同模型的应用效果之间还缺乏系统比较。本研究选取小麦作为受试生物,采用水培实验进行了镉对小麦根伸长的毒性测试,通过数据分析软件对实验数据进行非线性拟合,分别建立了3种模型,并从根毒性、根表面吸附镉和根中富集镉3个方面对3个模型的预测能力进行了比较。除此之外,选取苹果酸和柠檬酸2种有机配体,研究了配体对镉生物毒性的影响。结果表明,Ca~(2+)和H~+对Cd的根毒性存在竞争效应,而Mg~(2+)、K~+和Na~+未发现竞争效应。由于BLM模型同时考虑了镉的自由离子态浓度和竞争离子的影响,在预测镉对杨麦13号根毒性和生物体内富集量时效果最好。而FIAM和GCSM模型由于计算中仅考虑了离子活度的影响,缺乏对竞争离子保护效应的考虑,因此预测效果相对较差;此外,Cd对小麦根毒性的主要受扩散过程控制,而非跨膜过程,这可能也是FIAM模型和GCSM模型预测不佳的原因之一。同时结果还发现有机配体存在时尽管降低了溶液中镉的离子活度,但未显著影响镉毒性,进一步证明了扩散过程对Cd毒性的影响。以上结果为评价和预测镉的陆生生态毒性提供了基础数据和模型依据。     

Predicting [corrected] extinction risk in spite of predator-prey oscillations.

Most population viability analyses (PVA) assume that the effects of species interactions are subsumed by population-level parameters. We examine how robust five commonly used PVA models are to violations of this assumption. We develop a stochastic, stage-structured predator-prey model and simulate prey population vital rates and abundance. We then use simulated data to parameterize and estimate risk for three demographic models (static projection matrix, stochastic projection matrix, stochastic vital rate matrix) and two time series models (diffusion approximation [DA], corrupted diffusion approximation [CDA]). Model bias is measured as the absolute deviation between estimated and observed quasi-extinction risk. Our results highlight three generalities about the application of single-species models to multi-species conservation problems. First, our collective model results suggest that most single-species PVA models overestimate extinction risk when species interactions cause periodic variation in abundance. Second, the DA model produces the most (conservatively) biased risk forecasts. Finally, the CDA model is the most robust PVA to population cycles caused by species interactions. CDA models produce virtually unbiased and relatively precise risk estimates even when populations cycle strongly. High performance of simple time series models like the CDA owes to their ability to effectively partition stochastic and deterministic sources of variation in population abundance.     

Biogeochemical marine ecosystem models II: the effect of physiological detail on model performance

The level of detail required to efficiently capture system dynamics in ecosystem models has not been well defined. To this end an ecosystem model of a generalised temperate bay, Bay Model 2 (BM2), was constructed. It is a trophically diverse biogeochemical model built using the functional groups from another ecosystem model, the Integrated Generic Bay Ecosystem Model (IGBEM) and the general framework from a model of Port Phillip Bay (PPB), Australia. BM2 captures the essential features of real marine systems, it is also capable of reproducing realistic levels of biomass and conforms with known ecological relationships. The model’s performance is not as good for some of the poorly known groups (like infauna) or when environmental conditions undergo extreme change. Despite this, the overall performance of BM2 indicated, it is as capable of representing systems as accurately as more physiologically detailed ecosystem models, such as IGBEM. This shows that physiological detail is not always required and that simpler formulations, such as those employed in BM2, are generally adequate for learning and general predictive purposes. This is important because, in comparison with IGBEM, BM2 uses substantially fewer parameters and has lower development, computation and maintenance costs.     

Modeling and simulation of landfill gas production from pretreated MSW landfill simulator

The cumulative landfill gas (LFG) production and its rate were simulated for pretreated municipal solid waste (MSW) landfill using four models namely first order exponential model, modified Gompertz model, single component combined growth and decay model and Gaussian function. Considering the behavior of the pretreated MSW landfill, a new multi component model was based on biochemical processes that occurring in landfilled pretreated MSW. The model was developed on the basis of single component combined growth and decay model using an anaerobic landfill simulator reactor which treats the pretreated MSW. It includes three components of the degradation i.e. quickly degradable, moderately degradable and slowly degradable. Moreover, the developed model was statistically analyzed for its goodness of fit. The results show that the multi components LFG production model is more suitable in comparison to the simulated models and can efficiently be used as a modeling tool for pretreated MSW landfills. The proposed model is likely to give assistance in sizing of LFG collection system, generates speedy results at lower cost, improves cost-benefit analysis and decreases LFG project risk. It also indicates the stabilization of the landfill and helps the managers in the reuse of the landfill space. The proposed model is limited to aerobically pretreated MSW landfill and also requires the values of delay times in LFG productions from moderately and slowly degradable fractions of pretreated MSW.     

Comparison of six correlative models in predictive vegetation mapping on a local scale

A variety of statistical techniques has been used in predictive vegetation modelling (PVM) that attempt to predict occurrence of a given community or species in respect to environmental conditions. We compared the performance of three profile models, BIOCLIM, GARP and MAXENT with three nonparametric models of group discrimination techniques, MARS, NPMR and LRT. The two latter models are relatively new statistical techniques that have just entered the field of PVM. We ran all models on a local scale for a given grassland community (Teucrio-Seslerietum) using the same input data to examine their performance. Model accuracy was evaluated both by Cohen’s kappa statistics (κ) and by area under receiver operating characteristics curve based both on resubstitution of training data and on an independent test data set. MAXENT of profile models and MARS of group discrimination techniques achieved the best prediction.     

Testing the Generality of Bird-Habitat Models

Bird-habitat models are frequently used as predictive modeling tools—for example, to predict how a species will respond to habitat modifications. We investigated the generality of the predictions from this type of model. Multivariate models were developed for Golden Eagle (Aquila chrysaetos), Raven (Corvus corax), and Buzzard (Buteo buteo) living in northwest Scotland. Data were obtained for all habitat and nest locations within an area of 2349 km². This assemblage of species is relatively static with respect to both occupancy and spatial positioning. The area was split into five geographic subregions: two on the mainland and three on the adjacent Island of Mull, which has one of United Kingdom's richest raptor fauna assemblages. Because data were collected for all nest locations and habitats, it was possible to build models that did not incorporate sampling error. A range of predictive models was developed using discriminant analysis and logistic regression. The models differed with respect to the geographical origin of the data used for model development. The predictive success of these models was then assessed by applying them to validation data. The models showed a wide range of predictive success, ranging from only 6% of nest sites correctly predicted to 100% correctly predicted. Model validation techniques were used to ensure that the models' predictions were not statistical artefacts. The variability in prediction success seemed to result from methodological and ecological processes, including the data recording scheme and interregional differences in nesting habitat. The results from this study suggest that conservation biologists must be very careful about making predictions from such studies because we may be working with systems that are inherently unpredictable.