Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series

1. The growing pace of environmental change has increased the need for large‐scale monitoring of biodiversity. Declining intraspecific genetic variation is likely a critical factor in biodiversity loss, but is especially difficult to monitor: assessments of genetic variation are commonly based on measuring allele pools, which requires sampling of individuals and extensive sample processing, limiting spatial coverage. Alternatively, imaging spectroscopy data from remote platforms may hold the potential to reveal genetic structure of populations. In this study, we investigated how differences detected in an airborne imaging spectroscopy time series correspond to genetic variation within a population of Fagus sylvatica under natural conditions.
2. We used multi‐annual APEX (Airborne Prism Experiment) imaging spectrometer data from a temperate forest located in the Swiss midlands (Laegern, 47°28'N, 8°21'E), along with microsatellite data from F. sylvatica individuals collected at the site. We identified variation in foliar reflectance independent of annual and seasonal changes which we hypothesize is more likely to correspond to stable genetic differences. We established a direct connection between the spectroscopy and genetics data by using partial least squares (PLS) regression to predict the probability of belonging to a genetic cluster from spectral data.
3. We achieved the best genetic structure prediction by using derivatives of reflectance and a subset of wavebands rather than full‐analyzed spectra. Our model indicates that spectral regions related to leaf water content, phenols, pigments, and wax composition contribute most to the ability of this approach to predict genetic structure of F. sylvatica population in natural conditions.
4. This study advances the use of airborne imaging spectroscopy to assess tree genetic diversity at canopy level under natural conditions, which could overcome current spatiotemporal limitations on monitoring, understanding, and preventing genetic biodiversity loss imposed by requirements for extensive in situ sampling.

     

Estimation of Individual Growth Curves by Empirically Weighted Least Squares

The simultaneous estimation of individual growth curves and a mean growth curve is accomplished by weighted least squares. A polynomial curve is fitted for each individual and the polynomial parameters are linear functions of parameters corresponding to covariates. A simple, computationally efficient variance-covariance estimator is derived. The resultant estimate is used in the weighted least squares estimation. The results are compared to empirical Bayes estimation.     

pathmatrix: a geographical information system tool to compute effective distances among samples

pathmatrix is a tool used to compute matrices of effective geographical distances among samples using a least‐cost path algorithm. This program is dedicated to the study of the role of the environment on the spatial genetic structure of populations. Punctual locations (e.g. individuals) or zones encompassing sample data points (e.g. demes) are used in conjunction with a species‐specific friction map representing the cost of movement through the landscape. Matrices of effective distances can then be exported to population genetic software to test, for example, for isolation by distance. pathmatrix is an extension to the geographical information system (GIS) software arcview 3.x.     

The catchment and climate regulation of pCO2 in boreal lakes

The regulation of surface water pCO₂ was studied in a set of 33 unproductive boreal lakes of different humic content, situated along a latitudinal gradient (57°N to 64°N) in Sweden. The lakes were sampled four times during one year, and analyzed on a wide variety of water chemistry parameters. With only one exception, all lakes were supersaturated with CO₂ with respect to the atmosphere at all sampling occasions. pCO₂ was closely related to the DOC concentration in lakes, which in turn was mainly regulated by catchment characteristics. This pattern was similar along the latitudinal gradient and at different seasons of the year, indicating that it is valid for a variety of climatic conditions within the boreal forest zone. We suggest that landscape characteristics determine the accumulation and subsequent supply of allochthonous organic matter from boreal catchments to lakes, which in turn results in boreal lakes becoming net sources of atmospheric CO₂.     

Evaluation of several methods for estimating phylogenetic trees when substitution rates differ over nucleotide sites

Several maximum likelihood and distance matrix methods for estimating phylogenetic trees from homologous DNA sequences were compared when substitution rates at sites were assumed to follow a gamma distribution. Computer simulations were performed to estimate the probabilities that various tree estimation methods recover the true tree topology. The case of four species was considered, and a few combinations of parameters were examined. Attention was applied to discriminating among different sources of error in tree reconstruction, i.e., the inconsistency of the tree estimation method, the sampling error in the estimated tree due to limited sequence length, and the sampling error in the estimated probability due to the number of simulations being limited. Compared to the least squares method based on pairwise distance estimates, the joint likelihood analysis is found to be more robust when rate variation over sites is present but ignored and an assumption is thus violated. With limited data, the likelihood method has a much higher probability of recovering the true tree and is therefore more efficient than the least squares method. The concept of statistical consistency of a tree estimation method and its implications were explored, and it is suggested that, while the efficiency (or sampling error) of a tree estimation method is a very important property, statistical consistency of the method over a wide range of, if not all, parameter values is prerequisite.     

A comparison of neural networks and partial least squares for deconvoluting fluorescence spectra

This article compares backpropagation neural networks (BNN) with partial least squares (PLS) techniques in terms of their ability to deconvolute fluorescence spectra. Both actual experimental and simulated spectral data are studied for 2 binary systems. These systems consist of mixtures of tryptophan and tyrosine, and NADH and tryptophan over a total concentration range of 10(-7) to 10(-4) M. It is shown that BNN is superior to PLS for both systems.     

奇异数据筛选法在玉米籽粒蛋白质近红外光谱检测中的应用

相对于传统生化测定方法,基于近红外光谱(Near infrared spectroscopy,NIRS)玉米籽粒蛋白质含量检测是一种快速、非破坏、且适用于多组分同时检测的新方法。但在建模过程中,由于奇异数据(异常值)的存在会影响近红外光谱模型的预测精度和稳定性,我们采用奇异数据筛选法剔除了玉米籽粒近红外光谱中的奇异数据并建立了玉米籽粒蛋白质含量的偏最小二乘支持向量机(Least squares support vector machine,LS-SVM)模型。本文分别采用杠杆值法(Leverage)、半数重采样法(Resampling by Half-Mean,RHM)和蒙特卡洛采样法(Monte-Carlo Sampling,MCS)剔除了玉米籽粒蛋白质光谱数据中的奇异数据并对模型结果进行比较。在剔除奇异数据的基础上,采用偏最小二乘回归法(Partial least squares regression,PLSR)提取主成分,并基于小生境蚁群算法(Niche ant colony algorithm,NACA)优化偏最小二乘支持向量机(LS-SVM)模型参数(γ和σ2),建立基于LS-SVM的玉米籽粒蛋白质定量分析模型。结果表明,采用3种奇异数据筛选法剔除奇异数据后所建LS-SVM模型的预测结果都优于采用原光谱数据所建模型,相比较而言,蒙特卡洛采样法为基于近红外光谱检测玉米籽粒蛋白质的最佳奇异数据筛选法。     

PLS-ANN判别分析自体荧光光谱识别胃癌

本文对58例胃癌病人离体标本的癌浆膜和正常浆膜进行以308nm为激发光的自体荧光光谱检测,采用多因素分析法进行光谱信息提取,以识别胃癌。研究表明偏最小二乘法结合神经网络法（简称PLS—ANN）进行判别分析,诊断胃癌的灵敏度为86％,特异度为100％,准确率为93％,有望成为手术中快速识别胃癌在胃壁的浸润范围的有效方法。     

整合电路理论的生态廊道及其重要性识别

景观连接度被认为是影响诸多生态过程的一个重要因素.基于最小累积阻力模型的最小成本路径识别方法可以有效识别异质性景观中的功能连接,已被广泛应用到景观的功能连接评价与生态廊道模拟的研究中.基于电路理论的连接度模型用电阻代替了图论中的边、用电阻距离代替成本距离,来衡量异质性景观的功能连接.本文以SIMMAP 2.0软件生成的模拟景观为对象,借助于Linkage Mapper工具和Circuitscape软件,探讨如何将最小累积阻力模型与基于电路理论的连接度模型相结合来识别生态廊道及其景观要素的相对重要性.结果表明: 两种模型在应用中各有优势,互为补充.最小成本路径方法可以有效识别栖息地之间的最小成本廊道,基于电路理论的连接度模型通过电流密度的计算可以有效识别对景观连接性有重要影响的景观要素和“夹点”地区,并且“夹点”的位置不受廊道宽度的影响,在廊道重要性识别研究中具有明显优势.该方法可为区域生态保护规划和生态廊道设计提供科学依据.     

南丰蜜桔可溶性固形物近红外检测的多元线性回归变量筛选

文章采用反向区间偏最小二乘法结合连续投影算法,筛选南丰蜜桔近红外检测的多元线性回归变量。对南丰蜜桔近红外光谱进行多元散射校正后,利用反向间隔偏最小二乘法,从500～1750 nm中初选出7个光谱区间,用于多元线性回归变量筛选。利用通过遗传算法和连续投影算法筛选出的变量建立了多元线性回归模型。经比较发现,利用反向区间偏最小二乘法结合连续投影算法筛选出的变量建立的多元线性回归模型,预测结果最优,模型预测相关系数为0.937,模型预测均方根误差为0.613 oBrix。结果表明,反向区间偏最小二乘法结合连续投影算法,可以有效地筛选近红外光谱的多元线性回归变量,提高南丰蜜桔可溶性固形物模型的预测精度。