Today is the first day of the Crossing the Palaeontological-Ecological Gap (CPEG) conference. The aim of the conference is to open a dialogue between palaeontologists and ecologists who work on similar questions but across vastly different timescales. This splitting of temporal scales tends to make communication, data integration and synthesis in ecology harder. A lot of this comes from the fact that palaeontologists and ecologists tend to publish in different journals and attend different meetings.
‘babette‘ is an R package that works with the popular phylogenetic tool BEAST2. BEAST2 uses one or more alignments and a model setup to create a Bayesian posterior of jointly estimated model parameters and phylogenies.
babette lets you call BEAST2 from an R script. This makes it easier to explore models and/or alignments than using the graphical user interface programs that BEAST2 provides. It will also help you to improve the reproducibility of your work with BEAST2.
babette Tutorial Videos
If you’re new to phylogentic analyses, the video ‘babette demo‘ demonstrates the package. It has all of the information that you need to be able to start using the package
Today, everyone knows about the importance of accounting for phylogenetic effects when it comes to understanding trait evolution. How to account for phylogenetic effects is another matter though.
A couple of years ago, I was having a discussion on the R-sig-phylo blog and dared to define the Brownian Motion (BM) as kind of a null hypothesis that more realistic scenarios should be compared to. Maybe I crossed a line or made too simplistic a statement (see Adams and Collyer’s article in Systematic Biology for an explanation of why this matter is far trickier and more complicated than my reply suggested). The point is, my comment was hotly contested and a colleague ‘put the onus on me’ to do something better than the almighty (emphasis mine) BM.
The RRphylo method was my attempt to do just that. It may not be better than BM, but it is different. Often, that can be exactly what you need. Continue reading →
Today we are welcoming another two Associate Editors to the Methods in Ecology and Evolution. Just like the seven AEs who joined last week, Michael Matschiner (of the University of Basel, Switzerland) and Tiago Bosisio Quental (of the University of São Paulo, Brazil) were both invited to work with the journal following our open call earlier this year. You can find out more about both of them below.
“I am an evolutionary biologist interested in the processes that drive speciation and generate biodiversity. To learn about these processes, I use phylogenetic divergence-time estimation based on genome sequences and the fossil record. Since both of these data sources do not usually conform to expectations in standard phylogenetic workflows (no recombination, no hybridization, no sampling bias), much of my work involves method development to assess the impact of model violations, and to account for them in phylogenetic reconstruction.”
Tiago Bosisio Quental
“I am interested on understanding spatial and temporal patterns of biodiversity and the mechanisms involved in generating species diversity. I have a particular interest in mammals, but my research interests are not limited to a specific taxonomic group but are instead motivated by a range of questions and structured around them. At the moment, I am particularly interested in understanding the role of biotic interactions on biodiversity changes in deep time. The main tools used to approach those questions are molecular phylogenies, fossil record, ecological data and numerical simulation.”
We are thrilled to welcome Michael and Tiago to the Associate Editor Board and we look forward to working with them over the coming years.
The comparative methods we use to study the evolution of traits are mainly based on the idea that since species share a common evolutionary history, the traits observed on these lineages will share this same history. In the light of phylogenetics, we can always make a good bet about how a species will look if we know how closely related it is to another species or group. Comparative models aim to quantify the likelihood of our bet being right and use the same principle to estimate how fast evolutionary changes accumulate over time. Continue reading →
This double-sized issue contains three Applications articles and two Open Access articles. These five papers are freely available to everyone, no subscription required.
–Phylogenetic Trees: The fields of phylogenetic tree and network inference have advanced independently, with only a few attempts to bridge them. Schliep et al. provide a framework, implemented in R, to transfer information between trees and networks.
–Emon: Studies, surveys and monitoring are often costly, so small investments in preliminary data collection and systematic planning of these activities can help to make best use of resources. To meet recognised needs for accessible tools to plan some aspects of studies, surveys and monitoring, Barry et al. developed the R package emon, which includes routines for study design through power analysis and feature detection.
–Haplostrips: A tool to visualise polymorphisms of a given region of the genome in the form of independently clustered and sorted haplotypes. Haplostrips is a command-line tool written in Python and R, that uses variant call format files as input and generates a heatmap view.
This issue contains two Applications articles and three Open Access articles. These five papers are freely available to everyone, no subscription required.
–qfasar: A new R package for diet estimation using quantitative fatty acid signature analysis methods. It also provides functionality to evaluate and potentially improve the performance of a library of prey signature data, compute goodness-of-fit diagnostics, and support simulation-based research.
–biomass: An r package designed to compute both AGB/AGC estimate and its associated uncertainty from forest plot datasets, using a Bayesian inference procedure. The package builds upon previous work on pantropical and regional biomass allometric equations and published datasets by default, but it can also integrate unpublished or complementary datasets in many steps.
This issue contains two Applications articles and two Open Access articles. These four papers are freely available to everyone, no subscription required.
–Paco: An R package that assesses the phylogenetic congruence, or evolutionary dependence, of two groups of interacting species using both ecological interaction networks and their phylogenetic history.
–Open MEE: Open Meta-analyst for Ecology and Evolution (Open MEE) addresses the need for advanced, easy-to-use software for meta-analysis and meta-regression.It offers a suite of advanced meta-analysis and meta-regression methods for synthesizing continuous and categorical data, including meta-regression with multiple covariates and their interactions, phylogenetic analyses, and simple missing data imputation.
Understanding how and why some individuals survive and reproduce better than others, the traits that allow them to do so, the genetic basis of those traits, and the signatures of past and present selection in patterns of variation in the genome remain at the top of the research agenda for evolutionary biology. This Special Feature – Guest Edited by Jeff Conner, John Stinchcombe and Joanna Kelley – draws together a collection of seven papers that highlight new methodological and conceptual approaches to meeting this agenda.
To use the Editors’ own words, the articles in this issue “deal with how we can detect selection in a way that can be used to predict evolutionary responses, how selection affects the genome, and how selection and genetics underlie adaptive differentiation.”
Time flies… in the blink of an eye! And even more so in science. The molecular lab work we were used to two decades ago seems like ancient history to today’s PhD students. The speed of change in sequencing technology is so overwhelming that imagination usually fails to foresee how our daily work will be in 10 years’ time. But in the field of biodiversity assessment, we have very good clues. Next Generation Sequencing is quickly becoming our workhorse for ambitious projects of species and genetic inventories.
One by One Approach to Studying Biodiversity
For decades, most initiatives measured biodiversity in the same way: collect a sample of many individuals in the field, sort the specimens, identify them to a Linnaean species one at a time (if there was a good taxonomist in the group which, unfortunately, it is kind of lucky these days!), and count them. Or, if identification was based on molecular data, the specimen was subject to DNA extraction, to sequence one (or several) short DNA markers. This involved countless hours of work that could be saved if, instead of inventorying biodiversity specimen-by-specimen, we followed a sample-by-sample approach. To do this now, we just have to make a “biodiversity soup”.
Biodiversity assessment based on morphological identification and/or Sanger sequencing (“The one-by-one approach”)