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.     

印产毛喉鞘蕊花的引种栽培研究

本文报道印产毛喉鞘蕊花肉质根稳定性状的分离及栽培技术路线．肉质根株提纯到９９．５％;求得了露地最佳效益种植法,最优水平搭配是：３０ｄ、１５株／ｍ２、高垄、覆膜、底肥、整枝、去花穗,产鲜根８７００ｋｇ／ｈｍ２．     

Frequency-dependent interference by magnetic fields of nerve growth factor-induced neurite outgrowth in PC-12 cells

We have shown that 50 Hz sinusoidal magnetic fields within the 5-10 micro Tesla (μT) rms range cause an intensity-dependent reduction in nerve growth factor (NGF) stimulation of neurite outgrowth (NO) in PC-12 cells. Here we report on the frequency dependence of this response over the 15-70 Hz range at 5 Hz intervals. Primed PC-12 cells were plated in collagen-coated, 60 mm plastic petri dishes with or without 5 ng/ml NGF and were exposed to sinusoidal magnetic fields for 22 h in a CO₂ incubator at 37 °C. One 1,000-turn coil, 20 cm in diameter, generated vertically oriented magnetic fields. The dishes were stacked on the center axis of the coil to provide a range of intensities between 3.5 and 9.0 μT rms. The flux density of the ambient DC magnetic field was 37 μT vertical and 19 μT horizontal. The assay consisted of counting over 100 cells in the central portion (radius ≤0.3 cm) of each dish and scoring cells positive for NO. Sham exposure of cells treated identically with NGF demonstrated no difference in the percentage of cells with NO between exposed and magnetically shielded locations within the incubator. Analysis of variance demonstrated flux density-dependent reductions in NGF-stimulated NO over the 35-70 Hz frequency range, whereas frequencies between 15 Hz and 30 Hz produced no obvious reduction. The results also demonstrated a relative maximal sensitivity of cells at 40 Hz with a possible additional sensitivity region at or above 70 Hz. These findings suggest a biological influence of perpendicular AC/DC magnetic fields different from those identified by the ion parametric resonance model, which uses strictly parallel AC/DC fields. © 1995 Wiley-Liss, Inc.     

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.     

Optimal nonsingular control of fed-batch fermentation

Presented is a new simple method for multidimensional optimization of fed-batch fermentations based on the use of the orthogonal collocation technique. Considered is the problem of determination of optimal programs for fermentor temperature, substrate concentration in feed, feeding profile, and process duration. By reformulation of the state and control variables is obtained a nonsingular form of the optimization problem which has considerable advantage over the singular case since a complicated procedure for determination of switching times for feeding is avoided. The approximation of the state variables by Lagrange polynomials enables simple incorporation of split boundary conditions in the approximation, and the use of orthogonal collocations provides stability for integration of state and costate variables. The interpolation points are selected to obtain highest accuracy for approximation of the objective functional by the Radau-Lobatto formula. The control variables are determined by optimization of the Hamiltonian at the collocation points with the DFP method. Constraints are imposed on state and control variables.The method is applied for a homogeneous model of fermentation with volume, substrate, biomass, and product concentrations as the state variables. Computer study shows considerable simplicity of the method, its high accuracy for low order of approximation, and efficient convergence.