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.     

Weighted averaging partial least squares regression (WA-PLS): an improved method for reconstructing environmental variables from species assemblages

Weighted averaging regression and calibration form a simple, yet powerful method for reconstructing environmental variables from species assemblages. Based on the concepts of niche-space partitioning and ecological optima of species (indicator values), it performs well with noisy, species-rich data that cover a long ecological gradient (>3 SD units). Partial least squares regression is a linear method for multivariate calibration that is popular in chemometrics as a robust alternative to principal component regression. It successively selects linear components so as to maximize predictive power. In this paper the ideas of the two methods are combined. It is shown that the weighted averaging method is a form of partial least squares regression applied to transformed data that uses the first PLS-component only. The new combined method, ast squares, consists of using further components, namely as many as are useful in terms of predictive power. The further components utilize the residual structure in the species data to improve the species parameters (optima) in the final weighted averaging predictor. Simulations show that the new method can give 70% reduction in prediction error in data sets with low noise, but only a small reduction in noisy data sets. In three real data sets of diatom assemblages collected for the reconstruction of acidity and salinity, the reduction in prediction error was zero, 19% and 32%.     

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.     

基于GIS的滨海盐渍化农田土壤空间变异及其分区管理

针对滨海盐渍化农田盐碱瘠薄、土壤属性空间变异大、粗放管理效益低的现实,研究管理分区精准划分方法,采取差异化措施,提升盐渍化土地利用水平。该文以无棣县农田为研究区,采用网格法结合土地利用现状定点野外采样、室内化验分析获取土壤属性数据,运用ArcGIS 10.2地统计方法分析土壤属性的空间变异特征;在MATLAB R2016a中采用模糊c-均值聚类法（FCM）计算各样点的模糊隶属度,通过插值预测模糊隶属度的空间分布,基于最大隶属度原则进行分区;通过变异性分析和最小极差法（LSR）差异显著性检验,对分区结果进行精度验证。结果表明：无棣县农田土壤总体呈轻中度盐渍化,有效氮含量偏低,有机质、有效磷含量中等,速效钾含量较高;有机质、有效氮、有效磷、速效钾和含盐量呈中等变异性（变异系数25.0%-52.3%）,空间变异性较大,应分区调控;速效钾、含盐量和pH的块金效应值小于25%,主要受土壤质地、地下水矿化度等结构因素影响,有机质、有效氮和有效磷的块金效应值在50%-75%之间,受耕作方式、施肥等人为因素影响较大。将全县农田划分为3类管理区,估算面积分别为2.56万hm²、1.76万hm²、3.24万hm²;各分区土壤养分变异系数分别为23.9%-51.5%、15.9%-50.3%、14.7%-33.0%,检验结果表明各分区间差异显著,而各分区内部变异性明显低于未分区。管理分区与土壤属性空间分布特征具有较高的拟合度,分区结果可以作为差异化管理的作业单元。研究结果为各分区内部统一、不同分区间差异化管理提供了依据,研究有助于推进滨海盐渍化农田精准化管理水平的提高。     

Reliable Method for Fabricating Tissue‐Mimicking Materials With Designated Relative Permittivity and Conductivity at 128 MHz

Artificial materials that can simultaneously mimic the relative permittivity and conductivity of various human tissues are usually used in medical applications. However, the method of precisely designing these materials with designated values of both relative permittivity and conductivity at 3 T MRI resonance frequency is lacking. In this study, a reliable method is established to determine the compositions of artificial dielectric materials with designated relative permittivity and conductivity at 128 MHz. Sixty dielectric materials were produced using oil, sodium chloride, gelatin, and deionized water as the main raw materials. The dielectric properties of these dielectric materials were measured using the open‐ended coaxial line method at 128 MHz. Nonlinear least‐squares Marquardt–Levenberg algorithm was used to obtain the formula, establishing the relationship between the compositions of the dielectric materials and their dielectric properties at 128 MHz. The dielectric properties of the blood, gall bladder, muscle, skin, lung, and bone at 128 MHz were selected to verify the reliability of the obtained formula. For the obtained formula, the coefficient of determination and the expanded uncertainties with a coverage factor of k = 2 were 0.991% and 4.9% for relative permittivity and 0.992% and 6.4% for conductivity. For the obtained artificial materials measured using the open‐ended coaxial line method, the maximal difference of relative permittivity and conductivity were 1.0 and 0.02 S/m, respectively, with respect to the designated values. In conclusion, the compositions of tissue‐mimicking material can be quickly determined after the establishment of the formulas with the expanded uncertainties of less than 10%. Bioelectromagnetics. 2021;42:86–94. © 2020 Bioelectromagnetics Society.     

Repeat proliferation and partial endoreplication jointly shape the patterns of genome size evolution in orchids

Although the evolutionary drivers of genome size change are known, the general patterns and mechanisms of plant genome size evolution are yet to be established. Here we aim to assess the relative importance of proliferation of repetitive DNA, chromosomal variation (including polyploidy), and the type of endoreplication for genome size evolution of the Pleurothallidinae, the most species-rich orchid lineage. Phylogenetic relationships between 341 Pleurothallidinae representatives were refined using a target enrichment hybrid capture combined with high-throughput sequencing approach. Genome size and the type of endoreplication were assessed using flow cytometry supplemented with karyological analysis and low-coverage Illumina sequencing for repeatome analysis on a subset of samples. Data were analyzed using phylogeny-based models. Genome size diversity (0.2–5.1 Gbp) was mostly independent of profound chromosome count variation (2n = 12–90) but tightly linked with the overall content of repetitive DNA elements. Species with partial endoreplication (PE) had significantly greater genome sizes, and genomic repeat content was tightly correlated with the size of the non-endoreplicated part of the genome. In PE species, repetitive DNA is preferentially accumulated in the non-endoreplicated parts of their genomes. Our results demonstrate that proliferation of repetitive DNA elements and PE together shape the patterns of genome size diversity in orchids.