Ecology and evolution of multigenomic arbuscular mycorrhizal fungi.

Arbuscular mycorrhizal fungi (AMF) form extremely important mutualistic symbioses with most plants. Their role in nutrient acquisition, plant community structure, plant diversity, and ecosystem productivity and function has been demonstrated in recent years. New findings on the genetics and biology of AMF also give us a new picture of how these fungi exist in ecosystems. In this article, I bring together some recent findings that indicate that AMF have evolved to contain multiple genomes, that they connect plants together by a hyphal network, and that these different genomes may potentially move around in this network. These findings show the need for more intensive studies on AMF population biology and genetics in order to understand how they have evolved with plants, to better understand their ecological role, and for applying AMF in environmental management programs and in agriculture. A number of key features of AMF population biology have been identified for future studies and most of these concern the need to understand drift, selection, and genetic exchange in multigenomic organisms, a task that has not previously presented itself to evolutionary biologists.

Measuring intangibles' productivity. Empirical evidence from Spanish firms

As companies and shareholders begin to note the potential repercussions of intangible assets uponbusiness results, the inability of the traditional financial statement model to reflect these new waysof creating business value has become evident. Companies have widely adopted newmanagement tools, covering in this way the inability of the traditional financial statement model toreflect these new ways of creating business value.However, there are few prior studies measuring on a quantifiable manner the level of productivityunexplained in the financial statements. In this study, we measure the effect of intangible assets onproductivity using data from Spanish firms selected randomly by size and sector over a ten-yearperiod, from 1995 to 2004. Through a sample of more than 10,000 Spanish firms we analyse towhat extent labour productivity can be explained by physical capital deepening, by quantifiedintangible capital deepening and by firm s economic efficiency (or total factor productivity PTF).Our results confirm the hypothesis that PTF weigh has increased during the period studied,especially on those firms that have experienced a significant raise in quantified intangible capital,evidencing that there are some important complementary effects between capital investment andintangible resources in the explanation of productivity growth. These results have significantdifferences considering economic sector and firm s dimension.

Using composite estimators to improve both domain and total area estimation

In this article we propose using small area estimators to improve the estimatesof both the small and large area parameters. When the objective is to estimateparameters at both levels accurately, optimality is achieved by a mixed sampledesign of fixed and proportional allocations. In the mixed sample design, oncea sample size has been determined, one fraction of it is distributedproportionally among the different small areas while the rest is evenlydistributed among them. We use Monte Carlo simulations to assess theperformance of the direct estimator and two composite covariant-freesmall area estimators, for different sample sizes and different sampledistributions. Performance is measured in terms of Mean Squared Errors(MSE) of both small and large area parameters. It is found that the adoptionof small area composite estimators open the possibility of 1) reducingsample size when precision is given, or 2) improving precision for a givensample size.

How well do individuals predict the selling prices of their homes?

Self-reported home values are widely used as a measure of housing wealth by researchers employing a variety of data sets and studying a number of different individual and household level decisions. The accuracy of this measure is an open empirical question, and requires some type of market assessment of the values reported. In this research, we study the predictive power of self-reported housing wealth when estimating sales prices utilizing the Health and Retirement Study. We find that homeowners, on average, overestimate the value of their properties by between 5% and 10%. More importantly, we are the first to document a strong correlation between accuracy and the economic conditions at the time of the purchase of the property (measured by the prevalent interest rate, the growth of household income, and the growth of median housing prices). While most individuals overestimate the value of their properties, those who bought during more difficult economic times tend to be more accurate, and in some cases even underestimate the value of their house. These results establish a surprisingly strong, likely permanent, and in many cases long-lived, effect of the initial conditions surrounding the purchases of properties, on how individuals value them. This cyclicality of the overestimation of house prices can provide some explanations for the difficulties currently faced by many homeowners, who were expecting large appreciations in home value to rescue them in case of increases in interest rates which could jeopardize their ability to live up to their financial commitments.

International capital flows and credit market imperfections: A tale of two frictions

The financial crisis of 2007-08 has underscored the importance of adverse selection in financialmarkets. This friction has been mostly neglected by macroeconomic models of financialimperfections, however, which have focused almost exclusively on the effects of limited pledgeability.In this paper, we fill this gap by developing a standard growth model with adverseselection. Our main results are that, by fostering unproductive investment, adverse selection:(i) leads to an increase in the economy s equilibrium interest rate, and; (ii) it generates a negativewedge between the marginal return to investment and the equilibrium interest rate. Underfinancial integration, we show how this translates into excessive capital inflows and endogenouscycles. We also extend our model to the more general case in which adverse selection and limitedpledgeability coexist. We conclude that both frictions complement one another and show thatlimited pledgeability exacerbates the effects of adverse selection.

Inequalities for a new data-based method for selecting nonparametric density estimates

We continue the development of a method for the selection of a bandwidth or a number of design parameters in density estimation. We provideexplicit non-asymptotic density-free inequalities that relate the $L_1$ error of the selected estimate with that of the best possible estimate,and study in particular the connection between the richness of the classof density estimates and the performance bound. For example, our methodallows one to pick the bandwidth and kernel order in the kernel estimatesimultaneously and still assure that for {\it all densities}, the $L_1$error of the corresponding kernel estimate is not larger than aboutthree times the error of the estimate with the optimal smoothing factor and kernel plus a constant times $\sqrt{\log n/n}$, where $n$ is the sample size, and the constant only depends on the complexity of the family of kernels used in the estimate. Further applications include multivariate kernel estimates, transformed kernel estimates, and variablekernel estimates.

Scaling of semivariograms and the kriging estimation of field-measured properties

Two methods were evaluated for scaling a set of semivariograms into a unified function for kriging estimation of field-measured properties. Scaling is performed using sample variances and sills of individual semivariograms as scale factors. Theoretical developments show that kriging weights are independent of the scaling factor which appears simply as a constant multiplying both sides of the kriging equations. The scaling techniques were applied to four sets of semivariograms representing spatial scales of 30 x 30 m to 600 x 900 km. Experimental semivariograms in each set successfully coalesced into a single curve by variances and sills of individual semivariograms. To evaluate the scaling techniques, kriged estimates derived from scaled semivariogram models were compared with those derived from unscaled models. Differences in kriged estimates of the order of 5% were found for the cases in which the scaling technique was not successful in coalescing the individual semivariograms, which also means that the spatial variability of these properties is different. The proposed scaling techniques enhance interpretation of semivariograms when a variety of measurements are made at the same location. They also reduce computational times for kriging estimations because kriging weights only need to be calculated for one variable. Weights remain unchanged for all other variables in the data set whose semivariograms are scaled.

Informing the judgments of fingerprint analysts using quality metric and statistical assessment tools

The aim of this research was to evaluate how fingerprint analysts would incorporate information from newly developed tools into their decision making processes. Specifically, we assessed effects using the following: (1) a quality tool to aid in the assessment of the clarity of the friction ridge details, (2) a statistical tool to provide likelihood ratios representing the strength of the corresponding features between compared fingerprints, and (3) consensus information from a group of trained fingerprint experts. The measured variables for the effect on examiner performance were the accuracy and reproducibility of the conclusions against the ground truth (including the impact on error rates) and the analyst accuracy and variation for feature selection and comparison.¦The results showed that participants using the consensus information from other fingerprint experts demonstrated more consistency and accuracy in minutiae selection. They also demonstrated higher accuracy, sensitivity, and specificity in the decisions reported. The quality tool also affected minutiae selection (which, in turn, had limited influence on the reported decisions); the statistical tool did not appear to influence the reported decisions.

Nets: Network estimation for time series

This work proposes novel network analysis techniques for multivariate time series.We define the network of a multivariate time series as a graph where verticesdenote the components of the process and edges denote non zero long run partialcorrelations. We then introduce a two step LASSO procedure, called NETS, toestimate high dimensional sparse Long Run Partial Correlation networks. This approachis based on a VAR approximation of the process and allows to decomposethe long run linkages into the contribution of the dynamic and contemporaneousdependence relations of the system. The large sample properties of the estimatorare analysed and we establish conditions for consistent selection and estimation ofthe non zero long run partial correlations. The methodology is illustrated with anapplication to a panel of U.S. bluechips.

Chemical characterization and infrared spectroscopy of soil organic matter from two southern brazilian soils

Soil organic matter from the surface horizon of two Brazilian soils (a Latosol and a Chernosol), in bulk samples (in situ SOM) and in HF-treated samples (SOM), was characterized by elemental analyses, diffuse reflectance (DRIFT) and transmission Fourier transform infrared spectroscopy (T-FTIR). Humic acids (HA), fulvic acids (FA) and humin (HU) isolated from the SOM were characterized additionally by ultraviolet-visible spectroscopy (UV-VIS). After sample oxidation and alkaline treatment, the DRIFT technique proved to be more informative for the detection of "in situ SOM" and of residual organic matter than T-FTIR. The higher hydrophobicity index (HI) and H/C ratio obtained in the Chernosol samples indicate a stronger aliphatic character of the organic matter in this soil than the Latosol. In the latter, a pronounced HI decrease was observed after the removal of humic substances (HS). The weaker aliphatic character, the higher O/C ratio, and the T-FTIR spectrum obtained for the HU fraction in the Latosol suggest the occurrence of surface coordination of carboxylate ions. The Chernosol HU fraction was also oxygenated to a relatively high extent, but presented a stronger hydrophobic character in comparison with the Latosol HU. These differences in the chemical and functional group composition suggest a higher organic matter protection in the Latosol. After the HF treatment, decreases in the FA proportion and the A350/A550 ratio were observed. A possible loss of FA and condensation of organic molecules due to the highly acid medium should not be neglected.

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data.

The stationary distribution of a continuously varying strategy in a class-structured population under mutation-selection-drift balance.

Many traits and/or strategies expressed by organisms are quantitative phenotypes. Because populations are of finite size and genomes are subject to mutations, these continuously varying phenotypes are under the joint pressure of mutation, natural selection and random genetic drift. This article derives the stationary distribution for such a phenotype under a mutation-selection-drift balance in a class-structured population allowing for demographically varying class sizes and/or changing environmental conditions. The salient feature of the stationary distribution is that it can be entirely characterized in terms of the average size of the gene pool and Hamilton's inclusive fitness effect. The exploration of the phenotypic space varies exponentially with the cumulative inclusive fitness effect over state space, which determines an adaptive landscape. The peaks of the landscapes are those phenotypes that are candidate evolutionary stable strategies and can be determined by standard phenotypic selection gradient methods (e.g. evolutionary game theory, kin selection theory, adaptive dynamics). The curvature of the stationary distribution provides a measure of the stability by convergence of candidate evolutionary stable strategies, and it is evaluated explicitly for two biological scenarios: first, a coordination game, which illustrates that, for a multipeaked adaptive landscape, stochastically stable strategies can be singled out by letting the size of the gene pool grow large; second, a sex-allocation game for diploids and haplo-diploids, which suggests that the equilibrium sex ratio follows a Beta distribution with parameters depending on the features of the genetic system.

The role of population origin and microenvironment in seedling emergence and early survival in Mediterranean maritime pine (Pinus pinaster Aiton).

Understanding tree recruitment is needed to forecast future forest distribution. Many studies have reported the relevant ecological factors that affect recruitment success in trees, but the potential for genetic-based differences in recruitment has often been neglected. In this study, we established a semi-natural reciprocal sowing experiment to test for local adaptation and microenvironment effects (evaluated here by canopy cover) in the emergence and early survival of maritime pine (Pinus pinaster Aiton), an emblematic Mediterranean forest tree. A novel application of molecular markers was also developed to test for family selection and, thus, for potential genetic change over generations. Overall, we did not find evidence to support local adaptation at the recruitment stage in our semi-natural experiment. Moreover, only weak family selection (if any) was found, suggesting that in stressful environments with low survival, stochastic processes and among-year climate variability may drive recruitment. Nevertheless, our study revealed that, at early stages of recruitment, microenvironments may favor the population with the best adapted life strategy, irrespectively of its (local or non-local) origin. We also found that emergence time is a key factor for seedling survival in stressful Mediterranean environments. Our study highlights the complexity of the factors influencing the early stages of establishment of maritime pine and provides insights into possible management actions aimed at environmental change impact mitigation. In particular, we found that the high stochasticity of the recruitment process in stressful environments and the differences in population-specific adaptive strategies may difficult assisted migration schemes.

The weaker points of fish acute toxicity tests and how tests on embryos can solve some issues.

Fish acute toxicity tests play an important role in environmental risk assessment and hazard classification because they allow for first estimates of the relative toxicity of various chemicals in various species. However, such tests need to be carefully interpreted. Here we shortly summarize the main issues which are linked to the genetics and the condition of the test animals, the standardized test situations, the uncertainty about whether a given test species can be seen as representative to a given fish fauna, the often missing knowledge about possible interaction effects, especially with micropathogens, and statistical problems like small sample sizes and, in some cases, pseudoreplication. We suggest that multi-factorial embryo tests on ecologically relevant species solve many of these issues, and we shortly explain how such tests could be done to avoid the weaker points of fish acute toxicity tests.

Gene loss and evolutionary rates following whole-genome duplication in teleost fishes.

Teleost fishes provide the first unambiguous support for ancient whole-genome duplication in an animal lineage. Studies in yeast or plants have shown that the effects of such duplications can be mediated by a complex pattern of gene retention and changes in evolutionary pressure. To explore such patterns in fishes, we have determined by phylogenetic analysis the evolutionary origin of 675 Tetraodon duplicated genes assigned to chromosomes, using additional data from other species of actinopterygian fishes. The subset of genes, which was retained in double after the genome duplication, is enriched in development, signaling, behavior, and regulation functional categories. The evolutionary rate of duplicate fish genes appears to be determined by 3 forces: 1) fish proteins evolve faster than mammalian orthologs; 2) the genes kept in double after genome duplication represent the subset under strongest purifying selection; and 3) following duplication, there is an asymmetric acceleration of evolutionary rate in one of the paralogs. These results show that similar mechanisms are at work in fishes as in yeast or plants and provide a framework for future investigation of the consequences of duplication in fishes and other animals.