A consumer's guide to superorganisms

The organism, like the molecule, the cell, and the species, is one of the fundamental levels in our hierarchical classification of life and its components. The units ranked at these levels, being concrete, particular things, are individuals in the broadest philosophical sense. But in a much narrower and more familiar sense, individual means an individual organism. Like species, the term individual is hard to define, but in most biological discourse it has meant the unit of philosophical autonomy. Some authors have attempted to revise this terminology, restricting individual to organisms, and redefining organism to include families and other units. Such semantic surgery is unnecessary if the goal is merely to justify selection at more than one level. Analogies between levels may be interesting, but many of them do not deserve to be taken seriously.     

Tetrazolium salts: a consumer's guide

Synopsis The purities of seven tetrazolium salts, obtained from various commercial sources, have been assessed by thin layer chromatography, relative extinction coefficients, and melting points. MTT and INT were largely homogeneous on thin layer chromatography, although significant variations occurred in the melting point behaviour. All the samples of TT examined were contaminated to a small extent with non-tetrazolium u.v.-absorbing material. TNBT and NBT were contaminated with small amounts of mono-tetrazolium salts, although one sample of each was heavily contaminated with another di-tetrazolium compound. Four samples of TNBT contained high melting point contaminants. BT was also contaminated with mono-tetrazolium salts, and some samples also contained di-tetrazolium salt contaminants. NT was the most heavily contaminated of all, most samples containing no less than five separate tetrazolium compounds. Prices varied widely, and in general were not related to purity. Some catalogue entries were very easy to find; others were more difficult. Few specifications were given; of these, most were arbitrary (for example, pure, grade I, and ... probably the finest INT offered anywhere).     

A protocol to compare nestedness among submatrices

Searching for nestedness has become a popular exercise in community ecology. Significance of a nestedness index is usually evaluated using z values, and finding that a matrix is nested is typically a common result. However, nestedness is not likely to be spread uniformly within a matrix of species presence/absence per site. Selected parts of the matrix may show a degree of nestedness significantly higher (or lower) than expected from the overall pattern. Here we describe a procedure to assess if a particular submatrix (i.e., a peculiar combination of rows and columns extracted from the complete matrix) is more or less nested than expected for an assortment of sites and species taken at random from the same overall matrix. The idea is to obtain several submatrices of different sizes from the same overall matrix and to calculate their z values. A regression is then performed between z values of submatrices and their sizes. A nestedness index independent of matrix size is suggested as the deviation of the z value of a particular submatrix from that expected according to the regression line. We applied our protocol to 55 matrices with different nestedness indices under various null-models and, for purpose of demonstration, we discussed in detail a single case study regarding various animal groups of the Aegean Islands (Greece). The obtained results strongly encourage further research to focus not only on the question whether a matrix is nested or not, but also on where and why nestedness is confined.     

A concise guide to cDNA microarray analysis

Microarray expression analysis has become one of the most widely used functional genomics tools. Efficient application of this technique requires the development of robust and reproducible protocols. We have optimized all aspects of the process, including PCR amplification of target cDNA clones, microarray printing, probe labeling and hybridization, and have developed strategies for data normalization and analysis.     

A practical guide to methods of parentage analysis

The use of molecular techniques for parentage analysis has been a booming science for over a decade. The most important technological breakthrough was the introduction of microsatellite markers to molecular ecology, an advance that was accompanied by a proliferation and refinement of statistical techniques for the analysis of parentage data. Over the last several years, we have seen steady progress in a number of areas related to parentage analysis, and the prospects for successful studies continue to improve. Here, we provide an updated guide for scientists interested in embarking on parentage analysis in natural or artificial populations of organisms, with a particular focus on computer software packages that implement various methods of analysis. Our survey of the literature shows that there are a few established methods that perform extremely well in the analysis of most types of parentage studies. However, particular experimental designs or study systems can benefit from some of the less well-known computer packages available. Overall, we find that parentage analysis is feasible and satisfying in most systems, and we try to provide a simple roadmap to help other scientists navigate the confusing topography of statistical techniques.     

A guide to analysis of mouse energy metabolism

We present a consolidated view of the complexity and challenges of designing studies for measurement of energy metabolism in mouse models, including a practical guide to the assessment of energy expenditure, energy intake and body composition and statistical analysis thereof. We hope this guide will facilitate comparisons across studies and minimize spurious interpretations of data. We recommend that division of energy expenditure data by either body weight or lean body weight and that presentation of group effects as histograms should be replaced by plotting individual data and analyzing both group and body-composition effects using analysis of covariance (ANCOVA).     

A comparative evaluation of pairwise nestedness measures

This paper deals with nestedness measures that are based on pairwise comparisons of sites, evaluates their performance and suggests improvements and generalizations. There are several conceptual and technical criteria to judge their ecological applicability. It is of primary concern whether the measures 1) have a clear mathematical definition, 2) are influenced by the ordering of the data matrix, 3) incorporate similarity alone or similarity together with a dissimilarity component, 4) consider site pairs with identical species number negatively or positively, 5) show sensitivity to small changes in the data, and 6) are not vulnerable to type I and type II error rates. We performed a detailed comparison of the nestedness metric based on overlap and decreasing fill (NODF), the percentage relativized nestedness and the percentage relativized strict nestedness functions (PRN and PRSN, respectively), based on analytical results as well as on artificial and actual examples. We show that NODF is in fact the average Simpson similarity of sites with different species totals, and that its value depends on how the matrix is actually ordered. NODF is modified to always produce the maximum possible result (NODF_max), independently of the order of columns and rows. Being based on similarities, NODF and NODF_max overemphasize the overlap component of nestedness and underrate richness difference which is also an important constituent of nested pattern in meta‐community data. This latter feature is reflected adequately by PRN and PRSN. However, PRSN is similar to NODF and NODF_max in sharing the disadvantages that 1) complete agreement and segregation in species composition are not distinguished, 2) a random matrix can have a higher value than truly nested patterns, and 3) they are ill‐conditioned statistically. These problems are rooted mostly in that site pairs with tied totals affect the result negatively. We emphasize that PRN is free from these difficulties. PRN, PRSN, and NODF_max, together with mean Simpson similarity exhibit highly similar statistical performance: they are resistant to type I and type II errors for the less constrained null models, although there are subtle differences depending on matrix fill and algorithm of randomization. The most constrained null model, with all marginal totals fixed, makes all statistics more sensitive to type I errors, although vulnerability depends greatly on matrix fill.     

A practical guide to environmental association analysis in landscape genomics

Landscape genomics is an emerging research field that aims to identify the environmental factors that shape adaptive genetic variation and the gene variants that drive local adaptation. Its development has been facilitated by next‐generation sequencing, which allows for screening thousands to millions of single nucleotide polymorphisms in many individuals and populations at reasonable costs. In parallel, data sets describing environmental factors have greatly improved and increasingly become publicly accessible. Accordingly, numerous analytical methods for environmental association studies have been developed. Environmental association analysis identifies genetic variants associated with particular environmental factors and has the potential to uncover adaptive patterns that are not discovered by traditional tests for the detection of outlier loci based on population genetic differentiation. We review methods for conducting environmental association analysis including categorical tests, logistic regressions, matrix correlations, general linear models and mixed effects models. We discuss the advantages and disadvantages of different approaches, provide a list of dedicated software packages and their specific properties, and stress the importance of incorporating neutral genetic structure in the analysis. We also touch on additional important aspects such as sampling design, environmental data preparation, pooled and reduced‐representation sequencing, candidate‐gene approaches, linearity of allele–environment associations and the combination of environmental association analyses with traditional outlier detection tests. We conclude by summarizing expected future directions in the field, such as the extension of statistical approaches, environmental association analysis for ecological gene annotation, and the need for replication and post hoc validation studies.     

A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement

Nestedness has been widely reported for both metacommunities and networks of interacting species. Even though the concept of this ecological pattern has been well-defined, there are several metrics by which it can be quantified. We noted that current metrics do not correctly quantify two major properties of nestedness: (1) whether marginal totals (i.e. fills) differ among columns and/or among rows, and (2) whether the presences (1's) in less-filled columns and rows coincide, respectively, with those found in the more-filled columns and rows. We propose a new metric directly based on these properties and compare its behavior with that of the most used metrics, using a set of model matrices ranging from highly-nested to alternative structures in which no nestedness should be detected. We also used an empirical dataset to explore possible biases generated by the metrics as well as to evaluate correlations between metrics. We found that nestedness has been quantified by metrics that inappropriately detect this pattern, even for matrices in which there is no nestedness. In addition, the most used metrics are prone to type I statistical errors while our new metric has better statistical properties and consistently rejects a nested pattern for different types of random matrices. The analysis of the empirical data showed that two nestedness metrics, matrix temperature and the discrepancy measure, tend to overestimate the degrees of nestedness in metacommunities. We emphasize and discuss some implications of these biases for the theoretical understanding of the processes shaping species interaction networks and metacommunity structure.     

A new algorithm to calculate the nestedness temperature of presence–absence matrices

Aim The nestedness temperature of presence–absence matrices is currently calculated with the nestedness temperature calculator (NTC). In the algorithm implemented by the NTC: (1) the line of perfect order is not uniquely defined, (2) rows and columns are reordered in such a way that the packed matrix is not the one with the lowest temperature, and (3) the null model used to determine the probabilities of finding random matrices with the same or lower temperature is not adequate for most applications. We develop a new algorithm, BINMATNEST (binary matrix nestedness temperature calculator), that overcomes these difficulties. Methods BINMATNEST implements a line of perfect order that is uniquely defined, uses genetic algorithms to determine the reordering of rows and columns that leads to minimum matrix temperature, and provides three alternative null models to calculate the statistical significance of matrix temperature. Results The NTC performs poorly when the input matrix has checkerboard patterns. The more efficient packing of BINMATNEST translates into matrix temperatures that are lower than those computed with the NTC. The null model implemented in the NTC is associated with a large frequency of type I error, while the other null models implemented in BINMATNEST (null models 2 and 3) are conservative. Overall, null model 3 provides the best performance. The nestedness temperature of a matrix is affected by its size and fill, but the probability that such a temperature is obtained by chance is not. BINMATNEST reorders the input matrix in such a way that, if fragment size/isolation plays a role in determining community structure, there will be a significant rank correlation between the size/isolation of the fragments and the way that they are ordered in the packed matrix. Main conclusions The nestedness temperature of presence–absence matrices should not be calculated with the NTC. The algorithm implemented by BINMATNEST is more robust, allowing for across‐study comparisons of the extent to which the nestedness of communities departs from randomness. The sequence in which BINMATNEST reorders habitat fragments provides information about the causal role of immigration and extinction in shaping the community under study.     

A practical guide to amplicon and metagenomic analysis of microbiome data

Advances in high-throughput sequencing(HTS)have fostered rapid developments in the field of microbiome research,and massive microbiome datasets are now being generated.However,the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field.Here,we systematically summarize the advantages and limitations of micro-biome methods.Then,we recommend specific pipelines for amplicon and metagenomic analyses,and describe commonly-used software and databases,to help researchers select the appropriate tools.Furthermore,we introduce statistical and visualization methods suit-able for microbiome analysis,including alpha-and beta-diversity,taxonomic composition,difference compar-isons,correlation,networks,machine learning,evolu-tion,source tracing,and common visualization styles to help researchers make informed choices.Finally,a step-by-step reproducible analysis guide is introduced.We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the bio-logical significance behind the data.