Variation in demographic and physiological attributes of life history is thought to fall on one single axis, a phenomenon termed the Pace-of-Life. A slow Pace-of-Life is characterized by low annual reproduction, long life span and low metabolic rate, a fast Pace-of-Life by the opposite characteristics. The existence of a single axis has been attributed to constraints among physiological mechanisms that are thought to restrict evolutionary potential. In that case, physiological traits should covary in the same fashion at the levels of individual organisms and species. We examined covariation at the levels of individual and subspecies in three physiological systems (metabolic, endocrine and immune) using four stonechat subspecies with distinct life-history strategies in a common-garden set-up. We measured basal metabolic rate, corticosterone as endocrine measure and six measures of constitutive immunity. Metabolic rate covaried with two indices of immunity at the individual level, and with corticosterone concentrations and one index of immunity at the subspecies level, but not with other measures. The different patterns of covariation among individuals and among subspecies demonstrate that links among physiological traits are loose and suggest that these traits can evolve independent of each other.

Till date, morphology in general was characterized qualitatively to support the traditional classification system. This study for the first time uses an integrated approach to explore the appropriateness of traditional classification by superposing quantitative characters on qualitative classification using advanced mathematical techniques. Here, a quantitative method was applied that calculates changes in body shape by digitizing the inferred ecological niche and functional attributes of relevant morphological traits. Subsequently, absolute values were assigned to structural–functional traits of extinct atrypids to determine their taxonomy at a higher resolution. Investigating phenotypic diversity in these once abundant Paleozoic brachiopods from deep time is important for predicting future marine biodiversity of their closest living relatives and in conserving the marine ecosystem at large. Results show taxonomic over-splitting, a possible consequence of qualitative taxonomy. This study highlights the necessity of revisiting prior taxonomy by incorporating quantified traits and elicits the hazards of proposing classification based on qualitative traits alone. Perhaps, this study can be a starting point to improve the biological classification system in places where it must be based on morphology alone.

This paper revisits the proposal for the classification of meditation methods which we introduced in our initial 2013 publication, "Toward a Universal Taxonomy and Definition of Meditation". At that time, we advanced the thesis that meditation methods could be effectively segregated into three orthogonal categories by integrating the taxonomic principle of functional essentialism and the paradigm of Affect and Cognition; and we presented relevant research findings which supported that assertion. This iteration expands upon those theoretical and methodological elements by articulating a more comprehensive Three Tier Classification System which accounts for the full range of meditation methods; and demonstrates how recent neuroscience research continues to validate and support our thesis. This paper also introduces a novel criterion-based protocol for formulating classification systems of meditation methods, and demonstrates how this model can be used to compare and evaluate various other taxonomy proposals that have been published over the past 15 years.

The vast majority of microbes are unculturable and thus cannot be sequenced by means of traditional methods. High-throughput sequencing techniques like 454 or Solexa-Illumina make it possible to explore those microbes by studying whole natural microbial communities and analysing their biological diversity as well as the underlying metabolic pathways. Over the past few years, different methods have been developed for the taxonomic and functional characterization of metagenomic shotgun sequences. However, the taxonomic classification of metagenomic sequences from novel species without close homologue in the biological sequence databases poses a challenge due to the high number of wrong taxonomic predictions on lower taxonomic ranks. Here we present CARMA3, a new method for the taxonomic classification of assembled and unassembled metagenomic sequences that has been adapted to work with both BLAST and HMMER3 homology searches. We show that our method makes fewer wrong taxonomic predictions (at the same sensitivity) than other BLAST-based methods. CARMA3 is freely accessible via the web application WebCARMA from http://webcarma.cebitec.uni-bielefeld.de.

Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing classification accuracy with computational efficiency presents a fundamental challenge.Results: A method is presented to shift computational costs to an off-line computation by creating a taxonomy/genome index that supports scalable metagenomic classification. Scalable performance is demonstrated on real and simulated data to show accurate classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample.Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmatContact: allen99@llnl.govSupplementary information: Supplementary data are available at Bioinformatics online.

Taxonomy of prokaryotes has remained a controversial discipline due to the extreme plasticity of microorganisms, causing inconsistencies between phenotypic and genotypic classifications. The genomics era has enhanced taxonomy but also opened new debates about the best practices for incorporating genomic data into polyphasic taxonomy protocols, which are fairly biased towards the identification of bacterial species. Here we use an extensive dataset of Archaea and Bacteria to prove that metabolic signatures coded in their genomes are informative traits that allow to accurately classify organisms coherently to higher taxonomic ranks, and to associate functional features with the definition of taxa. Our results support the ecological coherence of higher taxonomic ranks and reconciles taxonomy with traditional chemotaxonomic traits inferred from genomes. KARL, a simple and free tool useful for assisting polyphasic taxonomy or to perform functional prospections is also presented (https://github.com/giraola/KARL).

There is a long tradition of river monitoring using taxonomy-based metrics to assess environmental quality in Europe via benthic macroinvertebrate communities. A promising alternative is the use of their species life-history traits. Both methods (taxonomy-based and trait-based), however, have relied on the time-consuming identification of taxa. River biotopes, (i.e. 1–100m2 ‘habitats’ with associated species assemblages), have long been seen as a useful and meaningful way of linking the ecology of macroinvertebrates and river hydro-morphology and can be used to assess hydro-morphological degradation in rivers. However, between-river taxonomic differences, especially at large spatial scale, had prevented a general test of this concept until now. The species trait approach may overcome this obstacle across broad geographical areas, using biotopes as the hydro-morphological units which have characteristic species trait assemblages. We collected macroinvertebrate data from discrete 512 patches, comprising 13 river biotopes, from seven rivers in England and Wales. The aim was to test whether river biotopes were better predictors of macroinvertebrate trait profiles than taxonomic composition (genera, families, orders) in rivers, independently of the phylogenetic effects and catchment scale characteristics (i.e. hydrology, geography and land cover). We also tested whether species richness and diversity were better related to biotopes than to rivers. River biotopes explained 40% of the variance in macroinvertebrate trait profiles across the rivers, largely independently of catchment characteristics. There was a strong phylogenetic signature, however. River biotopes were better at predicting macroinvertebrate trait profiles than taxonomic composition across rivers, whatever the taxonomic resolution. River biotopes were better than river identity at explaining the variability in taxonomic richness and diversity (40% and ≤10%, respectively). Detailed trait-biotope associations agreed with independent a priori predictions relating trait categories to near river bed flows. Hence, species traits provided a much needed mechanistic understanding and predictive ability across a broad geographical area. We show that integration of the multiple biological trait approach with river biotopes at the interface between ecology and hydro-morphology provides a wealth of new information and potential applications for river science and management.

Different studies have shown a strong correlation between the rumen microbiome and a range of production traits (e.g., feed efficiency, milk yield and components) in dairy cows. Underlying dynamics concerning cause and effect are, however, still widely unknown and warrant further investigation. The aim of the current study was to describe possible functional interrelations and pathways using a large set of variables describing the production, the metabolic and immunological state, as well as the rumen microbiome and fermentation characteristics of dairy cows in early lactation (n = 36, 56 ± 3 d in milk). It was further hypothesized that the feed intake-associated behavior may influence the ruminal fermentation pattern, and a set of variables describing these individual animal attributes was included. Principal component analysis as well as Spearman's rank correlations were conducted including a total of 265 variables. The attained plots describe several well-known associations between metabolic, immunological, and production traits. Main drivers of variance within the data set included milk production and efficiency as well as rumen fermentation and microbiome diversity attributes, whereas behavioral, metabolic, and immunological variables did not exhibit any strong interrelations with the other variables. The previously well-documented strong correlation of production traits with distinct prokaryote groups was confirmed. This mainly included a negative correlation of operational taxonomic units ascribed to the Prevotella genus with milk and fat yield and feed efficiency. A central role of the animals' feed intake behavior in this context could not be affirmed. Furthermore, different methodological and interpretability aspects concerning the microbiome analysis by 16S rRNA gene sequencing, such as the discrepancy between taxonomic classification and functional communality, as well as the comparability with other studies, are discussed. We concluded that, to further investigate the driving force that causes the difference between efficient and inefficient animals, studies including more sophisticated methods to describe phenotypical traits of the host (e.g., rumen physiology, metabolic and genetic aspects) as well as the rumen microbiome (e.g., metagenome, metatranscriptome, metaproteome, and metabolome analysis) are needed.

Genome sequencing aids in understanding the nature, characteristics, habitat and evolutionary history of all living organisms. Apart from sequencing, the more important task is to correctly place the sequenced genome in the taxonomy. Generally, the taxonomic classification of the living organisms is done by observing their morphological, behavioral, genetic and biochemical characteristics. Among them, taxonomic classification using genetic observation is more accurate scientifically as the Genome sequence analysis exploits the complete characteristics of the organism. In this paper, we developed a novel Frequency based Feature Extraction Technique (FFET) which extracts 120 features and helps to analyze the genome sequence of the organism and to classify them in the taxonomy accordingly. We performed a kingdom level taxonomic classification using the proposed FFET. The proposed FFET extracts features based on storage, frequency of nucleotide bases, pattern arrangement and amino acid composition of genome sequences. The feature extraction technique is applied to 150 samples of genome sequences of various organisms which were downloaded from National Centre for Biotechnology and Information (NCBI) database. The extracted features are classified using various Machine learning and Deep learning classifiers. From the results, it is evident that FFET performs well for classification with Convolutional Neural Network (CNN) classifier with an accuracy of 96.73 %.

This study was conducted with the objective to investigate the diversity of wild and cultivated yams based on morphological characters and to assess its correspondence with folk taxonomy. The local classification system in South-west Ethiopia was studied by recording attributes of each landrace used in the folk taxonomy. Farmers differentiate various named plants based on variations in morphological, physiological, plant cycle and tuber quality attributes. A total of 75 accessions representing 30 differently named landraces were assessed using 37 qualitative and 13 quantitative characters. Principal component analysis showed that all the traits used were useful for capturing the variability among accessions. Traits such as leaf position, twining direction, type of tuber, petiole colour on young leaves, the entire wing traits, flowering and size of leaves were useful for capturing the variability among species. All the other traits were useful for capturing the variability among accessions of the same and different species. The cluster study separated the 75 accessions into four and five major clusters based on qualitative and quantitative traits, respectively. The study indicates that the local classification corresponds well with the morphological variability, but farmers somewhat underestimate the diversity of yams at lower level taxa. Our study shows the existence of high phenotypic polymorphism among accessions, which could be exploited, if improvement need arises. Yet, regarding the validity of member species in the D. cayenensis complex, many questions remain confusing, and will need to be solved with DNA-based studies.