new methods in evolutionary research
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DESCRIPTIONNew Methods in Evolutionary Research
New Methods in Evolutionary Research Edited by Rob Freckleton and Bob OHara When we launched Methods in Ecology and Evolution we were keen to encompass a range of methodologies, and to give authors the chance to publish as wide a variety of methods as possible, as well as maximise the audience of ecologists and evolutionary biologists that had access to this work. In this Virtual Issue, we highlight the variety of papers and evolutionary methods that we have published in the first 2.5 years of MEE. The breadth of subjects is remarkable - these range from the analysis of barcodes and DNA sequences, to citizen science approaches for collecting data. Population genetics and macroevolution have been well represented, while statistical methods papers have been popular with a healthy number of submissions, as well as downloads by our readers. Of course, as a journal that aims to link evolution and ecology, this is only one part of the papers we publish and in some ways the division between ecology and evolution is slightly artificial as many methods are cross-cutting. However here we wish to take the opportunity to showcase the excellent papers that have a largely evolutionary content.
Biodiversity soup: metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring Douglas W. Yu, Yinqiu Ji, Brent C. Emerson, Xiaoyang Wang, Chengxi Ye, Chunyan Yang, Zhaoli Ding
1. Traditional biodiversity assessment is costly in time, money and taxonomic expertise. Moreover, data are frequently collected in ways (e.g. visual bird lists) that are unsuitable for auditing by neutral parties, which is necessary for dispute resolution.
2. We present protocols for the extraction of ecological, taxonomic and phylogenetic information from bulk samples of arthropods. The protocols combine mass trapping of arthropods, mass-PCR amplification of the COI barcode gene, pyrosequencing and bioinformatic analysis, which together we call metabarcoding.
3. We construct seven communities of arthropods (mostly insects) and show that it is possible to recover a substantial proportion of the original taxonomic information. We further demonstrate, for the first time, that metabarcoding allows for the precise estimation of pairwise community dissimilarity (beta diversity) and within-community phylogenetic diversity (alpha diversity), despite the inevitable loss of taxonomic information inherent to metabarcoding.
4. Alpha and beta diversity metrics are the raw materials of ecology and the environmental sciences, facilitating assessment of the state of the environment with a broad and efficient measure of biodiversity.
Barcoding's next top model: an evaluation of nucleotide substitution models for specimen identification Rupert A. Collins, Laura M. Boykin, Robert H. Cruickshank, Karen F. Armstrong Summary 1. DNA barcoding studies use Kimura's two-parameter substitution model (K2P) as the de facto standard for constructing genetic distance matrices. Distances generated under this model then provide the basis for most downstream analyses, but uncertainty in model choice is rarely explored and could potentially affect how reliably DNA barcodes discriminate species.
2. Using information-theoretic approaches for a data set comprising 14 472 DNA barcodes from 14 published studies, we tested whether the K2P model was a good fit at the species level and whether applying a better fitting model biased error rates or changed overall identification success.
3. We report that the K2P was a poorly fitting model at the species level; it was never selected as the best model and very rarely selected as a credible alternative model. Despite the lack of support for the K2P model, differences in distance between best model and K2P model estimates were usually minimal, and importantly, identification success rates were largely unaffected by model choice even when interspecific threshold values were reassessed.
4. Although these conclusions may justify using the K2P model for specimen identification purposes, we found simpler metrics such as p distance performed equally well, perhaps obviating the requirement for model correction in DNA barcoding. Conversely, when incorporating genetic distance data into taxonomic studies, we advocate a more thorough examination of model uncertainty.
nadiv: an R package to create relatedness matrices for estimating non-additive genetic variances in animal models Matthew E. Wolak Summary
1. The Non-Additive InVerses (nadiv) R software package contains functions to create and use non-additive genetic relationship matrices in the animal model of quantitative genetics.
2. This study discusses the concepts relevant to non-additive genetic effects and introduces the package.
3. nadiv includes functions to create the inverse of the dominance and epistatic relatedness matrices from a pedigree, which are required for estimating these genetic variances in an animal model. The study focuses on three widely used software programs in ecology and in evolutionary biology (ASReml, MCMCglmm and WOMBAT) and how nadiv can be used in conjunction with each. Simple tutorials are provided in the Supporting Information.
jPopGen Suite: population genetic analysis of DNA polymorphism from nucleotide sequences with errors Xiaoming Liu Summary
1. Next-generation sequencing (NGS) is being increasingly used in ecological and evolutionary studies. Though promising, NGS is known to be error-prone. Sequencing error can cause significant bias for population genetic analysis of a sequence sample.
2. We present jPopGen Suite, an integrated tool for population genetic analysis of DNA polymorphisms from nucleotide sequences. It is specially designed for data with a non-negligible error rate, although it serves well for error-free data. It implements several methods for estimating the population mutation rate, population growth rate and conducting neutrality tests.
3. jPopGen Suite facilitates the population genetic analysis of NGS data in various applications and is freely available for non-commercial users at http://sites.google.com/site/jpopgen/.
TempNet: a method to display statistical parsimony networks for heterochronous DNA sequence data Stefan Prost, Christian N. K. Anderson Summary
1. Heterochronous data have been used to study demographic changes in epidemiology and ancient DNA studies, revolutionizing our understanding of complex evolutionary processes such as invasions, migrations and responses to drugs or climate change. While there are sophisticated applications based on Markov-Chain Monte Carlo or Approximate Bayesian Computation to study these processes through time, summarizing the raw genetic data in an intuitively meaningful graphic can be challenging, most notably if identical haplotypes are present at different points in time.
2. We present temporal networks, an attractive way to display and summarize relationships within the heterochronous data so commonly used in ancient DNA or epidemiological research. TempNet is a user-friendly R script that creates journal-quality figures from genetic data in standard formats (FASTA, CLUSTAL, etc.). These figures are customizable and interactive within the R graphics window. Using three examples, we demonstrate that TempNet can deal with standard-sized datasets, as well as datasets of hundreds of sequences from fast-evolving organisms.
3. Temporal networks are flexible ways to illustrate genetic relationships through time. Furthermore, this approach is not limited to time-stamped data, but can also be used for different data partitioning strategies, such as spatial or phenotypic groupings. The R script presented here will be useful in illustrating complex genetic relationships between groups.
Accounting for uncertainty in species delineation during the analysis of environmental DNA sequence data Jeff R. Powell Summary
1. Defining species boundaries represents a significant challenge in biodiversity studies, especially as these studies increasingly rely on high-throughput DNA sequencing technologies. A promising approach for delineating species in environmental sequence data combines phylogenetics and coalescence theory to estimate species boundaries from distributions of lineage birth rates within multispecies coalescent trees.
2. Existing methods for interpreting these models utilize hypothetico-deductive reasoning to identify thresholds associated with a mixed speciation-coalescent model that fits the data better than a null model. Here, I describe an alternative approach that ranks and assigns weights to models based on their fit to the data using information criteria and then uses model averaging to estimate parameters and species probabilities.
3. This approach is applied to data from two independent studies that address (i) patterns of cospeciation in an aphidbacterial symbiosis and (ii) diversity of bacterial communities associated with the human gut. In both of these cases, accounting for uncertainty during model selection allowed greater flexibility to detect variable (with respect to time) speciation-coalescent thresholds among lineages.
4. The precision of the predicted species boundaries varied among the studies, and the variance-to-mean ratio for richness estimates ranged from 0.023 to 0.079. Sample-based estimates of gut bacteria richness revealed that accounting for uncertainty during species delineation increased the variance in the estimates of population means (by individual from which the samples were taken or by sex of the individuals) by up to 7.5%.
5. In ecological and evolutionary studies, conclusions are highly dependent on the classification system that is adopted; gi