Inferring a single variable from an assemblage with multiple controls: getting into deep water with cladoceran lake-depth transfer functions

Transfer functions have proved very useful for quantitative reconstruction of past environments. Inferring values of a single parameter based on changes in a community with multiple controls may result in unreliable inferences. To assess this unreliability cladoceran surface sediment assemblages from 53 lakes in Greenland, which have substantial variations in lake depth and fish abundance, both of which shape cladoceran communities, were analysed in this study. Redundancy analysis (RDA) revealed that maximum lake depth and either fish abundance or fish presence/absence exerted substantial and significant control on the cladoceran assemblage. Partial RDA showed that maximum lake depth and fish abundance uniquely explained 7.9 and 5.1%, respectively, with 5.3% variance being shared. A transfer function to infer lake depth from cladoceran sub-fossils was constructed and performed moderately well [coefficient of determination (r ²) = 0.65; root mean square error of prediction (RMSEP) = 0.32 log maximum depth] on the full dataset. When outliers, defined by a bootstrapped prediction error greater than 25% of the total depth gradient, were excluded, the model performed well (r ² = 0.74, RMSEP = 0.25 log maximum depth). The improved transfer function was then applied to sedimentary assemblage from a sediment core from Lake Bores?, in North-eastern Greenland, covering 9,000 years. A large increase in lake depth was inferred around 6250 bp. Whilst the climate was wetter at that time, the inferred changes in depth likely reflect the alteration of the food web, which resulted from the arrival of fish in the lake. This highlights the risks of using single-variable inference models for hindcasting change in lake physical and/or food web structure when there are other important co-variables.     

Adding depth to line artwork by digital stippling—a step-by-step guide to the method

Vector-based software has revolutionized scientific illustrating and is well established in taxonomy. However, simple line drawings lack depth information. Shading techniques, such as stippling—the application of dots to generate shade—are the methods of choice for simulating shade, structure, shape, and texture. In this paper, a step-by-step guide for digital stippling is presented. Manual stippling offers great flexibility to achieve highly realistic results. A round brush is applied to the line art by tapping. To drastically reduce time consumption and generate homogeneous tinges, a semiautomation was developed: the smallest units of symmetric stippling patterns are stored in a brush library. Using macroinstructions (macros), such stored raw patterns are converted into symmetric repetitive patterns. This way, stippling can be applied quickly and evenly across large areas of the underlying line drawing. These methods come with all the advantages of vector illustrations, such as high scalability, reproducibility and easy correction of strokes that have turned out imperfect.     

A stage‐wise approach for the analysis of multi‐environment trials

Plant breeders and variety testing agencies routinely test candidate genotypes (crop varieties, lines, test hybrids) in multiple environments. Such multi‐environment trials can be efficiently analysed by mixed models. A single‐stage analysis models the entire observed data at the level of individual plots. This kind of analysis is usually considered as the gold standard. In practice, however, it is more convenient to use a two‐stage approach, in which experiments are first analysed per environment, yielding adjusted means per genotype, which are then summarised across environments in the second stage. Stage‐wise approaches suggested so far are approximate in that they cannot fully reproduce a single‐stage analysis, except in very simple cases, because the variance–covariance matrix of adjusted means from individual environments needs to be approximated by a diagonal matrix. This paper proposes a fully efficient stage‐wise method, which carries forward the full variance–covariance matrix of adjusted means from the individual environments to the analysis across the series of trials. Provided the variance components are known, this method can fully reproduce the results of a single‐stage analysis. Computations are made efficient by a diagonalisation of the residual variance–covariance matrix, which necessitates a corresponding linear transformation of both the first‐stage estimates (e.g. adjusted means and regression slopes for plot covariates) and the corresponding design matrices for fixed and random effects. We also exemplify the extension of the general approach to a three‐stage analysis. The method is illustrated using two datasets, one real and the other simulated. The proposed approach has close connections with meta‐analysis, where environments correspond to centres and genotypes to medical treatments. We therefore compare our theoretical results with recently published results from a meta‐analysis.     

The CEPH consortium linkage map of human chromosome 2

This paper describes the Centre d'Etude du Polymorphisme Humain (CEPH) consortium linkage map of chromosome 2. The map contains 36 loci defined by genotyping generated from the CEPH family DNAs. A total of 73 different markers were typed by 14 contributing laboratories; of these, 36 loci are ordered on the map with likelihood support of at least 1000:1. Markers are placed along the length of the chromosome but no markers were available to anchor the map at either telomere or the centromere. Multilocus linkage analysis has produced male, female, and sex-averaged maps extending for 261, 430, and 328 cM, respectively. The sex-averaged map contains five intervals greater than 15 cM and the mean genetic distance between the 36 uniquely placed loci is 9.1 cM.     

Substrate recognition by three family 13 yeast alpha-glucosidases.

Important hydrogen bonding interactions between substrate OH-groups in yeast alpha-glucosidases and oligo-1,6-glucosidase from glycoside hydrolase family 13 have been identified by measuring the rates of hydrolysis of methyl alpha-isomaltoside and its seven monodeoxygenated analogs. The transition-state stabilization energy, DeltaDeltaG, contributed by the individual OH-groups was calculated from the activities for the parent and the deoxy analogs, respectively, according to DeltaDeltaG = -RT ln[(Vmax/Km)analog/(Vmax/Km)parent]. This analysis of the energetics gave DeltaDeltaG values for all three enzymes ranging from 16.1 to 24.0 kJ.mol-1 for OH-2', -3', -4', and -6', i.e. the OH-groups of the nonreducing sugar ring. These OH-groups interact with enzyme via charged hydrogen bonds. In contrast, OH-2 and -3 of the reducing sugar contribute to transition-state stabilization, by 5.8 and 4.1 kJ.mol-1, respectively, suggesting that these groups participate in neutral hydrogen bonds. The OH-4 group is found to be unimportant in this respect and very little or no contribution is indicated for all OH-groups of the reducing-end ring of the two alpha-glucosidases, probably reflecting their exposure to bulk solvent. The stereochemical course of hydrolysis by these three members of the retaining family 13 was confirmed by directly monitoring isomaltose hydrolysis using 1H NMR spectroscopy. Kinetic analysis of the hydrolysis of methyl 6-S-ethyl-alpha-isomaltoside and its 6-R-diastereoisomer indicates that alpha-glucosidase has 200-fold higher specificity for the S-isomer. Substrate molecular recognition by these alpha-glucosidases are compared to earlier findings for the inverting, exo-acting glucoamylase from Aspergillus niger and a retaining alpha-glucosidase of glycoside hydrolase family 31, respectively.