Conformist learning in nine-spined sticklebacks' foraging decisions

Theoretical analyses have reported that in most circumstances where natural selection favours reliance on social learning, conformity (positive frequency-dependent social learning) is also favoured. These findings suggest that much animal social learning should involve a copy-the-majority strategy, yet there is currently surprisingly little evidence for conformist learning among animals. Here, we investigate this possibility in the nine-spined stickleback (Pungitius pungitius) by manipulating the number of demonstrator fish at two feeders, one rich and one poor, during a demonstration phase and evaluating how this affects the likelihood that the focal fish copy the demonstrators'' apparent choices. As predicted, we observed a significantly increased level of copying with increasing numbers of demonstrators at the richer of the two feeders, with copying increasing disproportionately, rather than linearly, with the proportion of demonstrators at the rich foraging patch. Control conditions with non-feeding demonstrators showed that this was not simply the result of a preference for shoaling with larger groups, implying that nine-spined sticklebacks copy in a conformist manner.     

Information Sets and Excess Zeros in Random Effects Modeling of Longitudinal Data

Marginal regression via generalized estimating equations is widely used in biostatistics to model longitudinal data from subjects whose outcomes and covariates are observed at several time points. In this paper we consider two issues that have been raised in the literature concerning the marginal regression approach. The first is that even though the past history may be predictive of outcome, the marginal approach does not use this history. Although marginal regression has the flexibility of allowing between-subject variations in the observation times, it may lose substantial prediction power in comparison with the transitional modeling approach that relates the responses to the covariate and outcome histories. We address this issue by using the concept of “information sets” for prediction to generalize the “partly conditional mean” approach of Pepe and Couper (J. Am. Stat. Assoc. 92:991–998, 1997). This modeling approach strikes a balance between the flexibility of the marginal approach and the predictive power of transitional modeling. Another issue is the problem of excess zeros in the outcomes over what the underlying model for marginal regression implies. We show how our predictive modeling approach based on information sets can be readily modified to handle the excess zeros in the longitudinal time series. By synthesizing the marginal, transitional, and mixed effects modeling approaches in a predictive framework, we also discuss how their respective advantages can be retained while their limitations can be circumvented for modeling longitudinal data.     

Growth of acetotrophic,methane-producing bacteria in a pH auxostat

Three acetotrophicMethanosarcina species, which included marine, nonmarine, and thermophilic strains, were grown on acetate in a 10-liter pH auxostat. Specific growth rates and molar growth yields were constant throughout growth. Cell yields were up to 18-fold greater than previously reported. These properties of the pH auxostat indicate that it is a preferred culture method for the biochemical study of methanogenesis from acetate.     

The influence of temporal variation on relationships between ecosystem services

A growing literature aims to identify areas of congruence in the provision of multiple ecosystem goods and services. However, little attention has been paid to the effect that temporal variation in the provision of such services may have on understanding of these relationships. Due to a lack of temporally and spatially replicated monitoring surveys, such relationships are often assessed using data from disparate time periods. Utilising temporally replicated data for indices of freshwater quality and agricultural production we demonstrate that through time the biophysical values of ecosystem services may vary in a spatially non-uniform way. This can lead to differing conclusions being reached about the strength of relationships between services, which in turn has implications for the prioritisation of areas for management of multiple services. We present this first analysis to illustrate the effect that the use of such temporally disparate datasets may have, and to highlight the need for further research to assess under what circumstances temporal variation of this sort will have the greatest impact.     

BICAR: A New Algorithm for Multiresolution Spatiotemporal Data Fusion

We introduce a method for spatiotemporal data fusion and demonstrate its performance on three constructed data sets: one entirely simulated, one with temporal speech signals and simulated spatial images, and another with recorded music time series and astronomical images defining the spatial patterns. Each case study is constructed to present specific challenges to test the method and demonstrate its capabilities. Our algorithm, BICAR (Bidirectional Independent Component Averaged Representation), is based on independent component analysis (ICA) and extracts pairs of temporal and spatial sources from two data matrices with arbitrarily different spatiotemporal resolution. We pair the temporal and spatial sources using a physical transfer function that connects the dynamics of the two. BICAR produces a hierarchy of sources ranked according to reproducibility; we show that sources which are more reproducible are more similar to true (known) sources. BICAR is robust to added noise, even in a “worst case” scenario where all physical sources are equally noisy. BICAR is also relatively robust to misspecification of the transfer function. BICAR holds promise as a useful data-driven assimilation method in neuroscience, earth science, astronomy, and other signal processing domains.     

Application of nuclear magnetic resonance spectroscopy to analysis of ethanol fermentation kinetics in yeasts and bacteria

Nuclear Magnetic Resonance (NMR) spectroscopy is proving to be a very valuable technique for characterizing the metabolic status of a range of microbial fermentations. This non-invasive method allows us not only to determine the presence of particular metabolites, but also to monitor reaction rates, enzyme activities and transport mechanisms in vivo. Despite the low levels of the carbon-13 isotope (1.1%), natural-abundance ¹³C-NMR studies have proven useful in monitoring the progress of various fermentation processes. Furthermore, ³¹P-NMR can provide noninvasive information relating to cellular metabolism, and on the energy status of the cells. This results from the facility with NMR to identify various nucleotide phosphates and other energy-rich compounds in the cell, as well as to characterize changes in the intracellular pH from the chemical shifts of internal phosphate and other phosphorylated intermediates. In this review, we will summarize the use of NMR as an analytical tool in biotechnology and also discuss examples that illustrate how NMR can be used to obtain significant information on the characteristics of ethanol fermentations in both yeasts and bacteria.