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.

     

An Estimation Procedure for the Joint Distribution of Spatial Direction and Thickness of Flat Bodies Using Vertical Sections. Part II: An application in soil micromorphology

In soil micromorphology fissures are considered in vertical sections. To get information about the properties of the soil the joint distribution of spatial direction and width of these fissures is of interest. The fissures are mathematically generalized to flat bodies which are defined as stationary weighted surface processes with the weight “thickness”. In a typical point of the surface process suitable, joint parametric distributions of direction and thickness are assumed. The parameters have to be estimated from measurements on vertical sections which are taken from the soil. On these sections only a visible thickness and a visible angle can be observed. The joint distribution of these variables can be expressed by the joint distribution of spatial direction and thickness with the same parameters and in this indirect way the parameters can be estimated. The paper describes how to randomize the vertical section and how to measure the visible variables on the sections. The Chi-Square method is proposed for the parameter estimation. Further it is discussed how to derive good starting values for the numerical procedure. All this is demonstrated in a simulation study using the Bingham-Mardia distribution for the direction and the lognormal distribution for the thickness including a way to correlate the mean thickness and the direction. Finally an application in soil micromorphology is demonstrated for one soil horizon.     

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.     

Ecological optima and tolerances of Thelypteris limbosperma,Athyrium distentifolium,and Matteuccia struthiopteris along environmental gradients in Western Norway

The distribution and abundance of Thelypteris limbosperma, Athyrium distentifolium, and Matteuccia struthiopteris are modelled statistically in relation to 14 environmental variables along the major climatic, topographic, and edaphic gradients in western Norway. The data are from 624 stands from which measurements or estimates of mean January and mean July temperatures, humidity, altitude, aspect, and slope are available. From 182 of these stands eight soil variables have also been measured. The species responses are quantified by two numerical methods: Gaussian logit regression and weighted averaging (WA) regression. The estimated WA optima suggest that A. distentifolium has an ecological preference for low July and January temperatures, high altitudes, and soils of low-medium pH and base content. The species shows statistically significant Gaussian responses with summer temperature, humidity (= Martonnes humidity index), altitude, slope, aspect, pH, cation exchange capacity, and base saturation with optima of 8.7 °C, 188.9, 1220 m, 28°, 29°, 4.8, 13.77 mEq 100 g dry soil^-1, and 13.4%, respectively. These suggest that the occurrence and relative abundance of A. distentifolium are well predicted by summer temperature, topography, and soil pH and base status. T. limbosperma has WA optima that suggest that it favours moderately high winter and summer temperatures, high humidity, medium altitude, and soils of low pH and base content. It has significant Gaussian responses to summer temperature (optimum =12.6 °C), winter temperature (-1.8 °C), humidity (179.2), altitude (459.5 m), slope (22.5°), and Na (0.7 mg 100 g dry soil^-1). These suggest that climatic factors, altitude, and slope are significant predictors for its occurrence and abundance. M. struthiopteris has high WA optima for summer temperature, pH, Ca, Mg, K, Na, cation exchange capacity (CEC), and base saturation, and a low optima for humidity and winter temperature. Of these, summer temperature (16.0 °C), Ca (63.1 mg 100 g dry soil^-1), Mg (41.0 mg 100 g dry soil^-1), K (23.6 mg 100 g dry soil^-1), Na (5.0 mg 100 g dry soil^-1), CEC (60.7 mEq 100 g dry soil^-1), and base saturation (56.3%) have significant Gaussian logit responses, as do aspect (150.2°) and loss-on-ignition (9.4%). These results suggest that the occurrence and relative abundance of M. struthiopteris are well predicted by high soil base cations, a generally southern aspect, low organic content in the soil, and high July temperatures.     

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.