The distributions of parthenogenetic ascid mites (Acari: Parasitiformes) do not support the biotic uncertainty hypothesis

Fifteen out of 50 species of ascid mites (30%) that we collected from four synanthropic and seven natural habitats in North America and Australia existed as all-female populations. In contrast to the predictions of the biotic uncertainty hypothesis (i.e. that parthenogenetic species are rare, restricted in distribution and survive through dispersal ability), we found that parthenogentic ascid mites were present in ten out of 11 habitats sampled, but were not superior colonists. In a glasshouse experiment, pasteurized soil in pots was colonized first by bisexual species and only later by all-female species. Furthermore, a habitat requiring strong dispersal abilities (decaying fungal sporocarps) lacked parthenogenetic species and a review of literature and collections indicated that all-female ascid species rarely form the phoretic associations with insects necessary to exploit patchy and ephemeral resources. The assumptions that parthenogens are reproductively superior to but competitively inferior to sexual relatives were not supported by experiments comparing a bisexual and an all-female species of Lasioseius.     

An X-ray spectrum estimation method from transmission measurement combined with scatter correction

PurposeConventional x-ray spectrum estimation methods from transmission measurement often lead to inaccurate results when extensive x-ray scatter is present in the measured projection. This study aims to apply the weighted L1-norm scatter correction algorithm in spectrum estimation for reducing residual differences between the estimated and true spectrum.MethodThe scatter correction algorithm is based on a simple radiographic scattering model where the intensity of scattered x-ray is directly estimated from a transmission measurement. Then, the scatter-corrected measurement is used for the spectrum estimation method that consists of deciding the weights of predefined spectra and representing the spectrum as a linear combination of the predefined spectra with the weights. The performances of the estimation method combined with scatter correction are evaluated on both simulated and experimental data.ResultsThe results show that the estimated spectra using the scatter-corrected projection nearly match the true spectra. The normalized-root-mean-square-error and the mean energy difference between the estimated spectra and corresponding true spectra are reduced from 5.8% and 1.33 keV without the scatter correction to 3.2% and 0.73 keV with the scatter correction for both simulation and experimental data, respectively.ConclusionsThe proposed method is more accurate for the acquisition of x-ray spectrum than the estimation method without scatter correction and the spectrum can be successfully estimated even the materials of the filters and their thicknesses are unknown. The proposed method has the potential to be used in several diagnostic x-ray imaging applications.     

超短脉冲技术测量癌细胞荧光光谱与荧光寿命

研究用于癌症诊断与治疗的光敏剂血卟啉（hematoporphyrin derivative,HPD）的超快光动力学过程,采用超短脉冲激光光谱技术和皮秒时间相关单光子计数系统,测量经血卟啉培养的活体癌细胞与正常细胞的荧光光谱、荧光寿命特性及荧光峰值强度随时间变化曲线,并测量单一细胞内部不同位置的荧光寿命特性,观测到：癌细胞样品在645 nm处具有特有的光谱谱峰;癌细胞样品荧光寿命的快成分约150 ps慢成分约1200 ps,而正常细胞样品快成分约300 ps慢成分约2500 ps;癌细胞样品的荧光峰值强度经12小时衰减约10%,而正常细胞样品衰减约55%;在细胞内部荧光寿命300 ps的快成分十分显著,且中心部位血卟啉浓度最高.癌细胞与正常细胞的荧光光谱、荧光寿命特性及荧光峰值强度随时间变化曲线相差十分明显,反映了癌细胞与正常细胞对血卟啉亲和特性有显著的差异,测量结果确认了荧光光谱技术诊断与治疗癌症的可行性,并对发展超短脉冲激光光谱技术早期诊断与治疗癌症具有重要的指导意义和临床应用价值.     

区域气候模式与作物模型联接的影响评估模拟实验及不确定性分析

利用英国Hadley中心开发的区域气候模式RCMPRECIS(网格分辨率50km×50km),与经过田间试验资料和历史气候资料验证和校准过的CERES系列作物模式相结合,就区域气候模式与作物模式联接的影响评估方法及其不确定性进行了评估。结果表明,相对于大气环流模型来说,区域气候模式与作物模型的结合省去了随机天气发生器的中间环节,减小了不确定性产生的因素。在站点模拟上,该方法在平原地区的模拟效果较好,而山区的模拟效果较差,但如果能用实测天气数据对模拟的天气数据进行验证,模拟效果明显提高。在区域模拟上,该方法可以较好地体现出产量变化的空间分布规律,但由于空间数据的限制,模拟产量与实际产量的偏差较站点水平要大。     

利用数码相机和Photoshop软件非破坏性测定叶面积的简便方法

采用数码相机获取叶片的数字图像,用Photoshop图像处理软件计算叶面积,并与目前常用的剪纸法和叶面积仪测定法进行比较分析。结果表明,本方法和上述传统测定方法测定结果存在极显著线性相关;不同拍摄分辨率、单位叶面积存贮像素个数和拍摄角度对测定结果无显著影响。和其它方法相比,本方法具有准确、快速、成本低廉、适合非破坏性动态连续观测等优点,适用于植物生理生态学研究中叶面积的测定。     

Numerical Approaches to Life Cycle Interpretation - The case of the Ecoinvent’96 database (10 pp)

Goal, Scope and Background To strengthen the evaluative power of LCA, life cycle interpretation should be further developed. A previous contribution (Heijungs & Kleijn 2001) elaborated five examples of concrete methods within the subset of numerical approaches towards interpretation. These methods were: contribution analysis, perturbation analysis, uncertainty analysis, comparative analysis, and discernibility analysis. Developments in software have enabled the possibility to apply the five example methods to explore the much-used Ecoinvent”96 database.Discussion of Methods The numerical approaches implemented in this study include contribution analysis, perturbation analysis, uncertainty analysis, comparative analysis, discernibility analysis and the newly developed key issue analysis. The data used comes from a very large process database: Ecoinvent’96, containing 1163 processes, 1181 economic flows and 571 environmental flows. Conclusions Results are twofold: they serve as a benchmark to the usefulness and feasibility of these numerical approaches, and they shed light on the question of stability and structure in an often-used large system of interconnected processes. Most of the approaches perform quite well: computation time on a moderate PC is between a few seconds a few minutes. Only Monte Carlo analyses may require much longer, but even then it appears that most questions can be answered within a few hours. Moreover, analytical expressions for error propagation are much faster than Monte Carlo analyses, while giving almost identical results. Despite the fact that many processes are connected to each other, leading to the possibility of a very unstable system and very sensitive coefficients, the overall results show that most results are not extremely uncertain. There are, however, some exceptions to this positive message.     

An improved analysis of forest carbon dynamics using data assimilation

There are two broad approaches to quantifying landscape C dynamics – by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process‐based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques – which combine stock and flux observations with a dynamic model – improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3‐year period, and include eddy flux and soil CO₂ efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)=?251±197 g C m^?2 over the 3 years, compared with an estimate of ?419±29 g C m^?2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5 g C m^?2 day^?1, but the uncertainty on assimilated estimates averaged 0.47 g C m^?2 day^?1, and only exceeded 0.5 g C m^?2 day^?1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long‐running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote‐sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis – which might be provided indirectly by remotely sensed data – improves the analysis of NEE.     

Spectral Test Instrument for Color Vision Measurement

Common displays such as CRT or LCD screens have hmlted capabilities in displaying most color spectra correctly. The main disadvantage of these devices is that they work with three primaries and the colors displayed are the mixture of these three colours. Consequently these devices can be confusing in testing human color identification, because the spectral distribution of the colors displayed is the combined spectrum of the three primaries. We have developed a new instrument for spectrally correct color vision measurement. This instrument uses light emitting diodes (LEDs) and is capable of producing all spectra of perceivable colors, thus with appropriate test methods this instrument can be a reliable and useful tool in testing human color vision and in verifying color vision correction.     

Detection of ecological change using multiple organism groups: metrics and uncertainty

A number of biological approaches are commonly used to assess the ecological integrity of stream ecosystems. Recently, it is becoming increasingly common to use multiple organism groups in bioassessment. Advocates of the multiple organism approach argue that the use of different organism groups should strengthen inference-based models and ultimately result in lower assessment error, while opponents argue that organism groups often respond similarly to stress implying a high degree of redundancy. Using fish, macroinvertebrate, macrophyte and benthic diatom data, site-specific parameters (e.g., water chemistry and substratum) and catchment variables from European mountain (n = 77) and lowland (n = 85) streams we evaluated the discriminatory power and uncertainty associated with the use of a number of biological metrics commonly used in stream assessment. The primary environmental gradient for both streams types was land use and nutrient enrichment. Secondary and tertiary gradients were related to habitat quality or alterations in hydromorphology. Benthic diatom and macroinvertebrate metrics showed high discriminatory power (R² values often >0.50) and low error (<30%) with the primary (nutrient) gradient, while both fish and macrophyte metrics performed relatively poorly. Conversely, both fish and macrophyte metrics showed higher response (high coefficients of determination) than either benthic diatom or macroinvertebrate metrics to the second (e.g., alteration in habitat/hydromorphology) gradient. However, the discriminatory power and error associated with individual metrics varied markedly, indicating that caution should be exercised when selecting the ‘best’ organism group or metric to monitor stress.     

Measuring the Surface Roughness of Stream Stones

Measuring the fine-scale heterogeneity of stones and other substrates is a challenge for benthic ecologists. I describe a method for measuring the roughness of stones that is based on the ratio of two surface area measurements: one that follows substrate contours and one based on a similar-sized modified spheroid. This roughness index is easily measured, assesses the entire surface of stones, and enables the measurement of replicate stones. Roughness measurements of 14 rock types demonstrated that values obtained were consistent with perceived roughness and porosity. Application of the roughness index to a published data set produced a curvilinear relationship between stone roughness and the biomass of algae in roughness-associated crevices.