Getting Serious About Transposable Elements

Post Provided by: Gabriel Rech and José Luis Villanueva-Cañas

So Simple yet so Complex

A long standing research topic in evolutionary biology is the genetic basis of adaptation. In other words, how does a novel trait appear (or spread) in response to an environmental change? Despite the rapid advances in sequencing over the last two decades, we have only been able to fully characterize a few adaptations.

As stated by Richard Dawkins in Climbing Mount Improbable, while natural selection is a very simple process, modeling natural selection and determining its causes, effects and consequences is an extremely difficult task. Also, most of our efforts so far have been focused on just one type of genetic variation: single nucleotide polymorphisms (SNPs). Other types of variations such as transposable element (TE) insertions have received much less attention. Paradoxically, some great examples of the role of TEs in adaptation have been right under our noses the whole time, in basic biology textbooks. Continue reading

Evolutionary Quantitative Genetics: Virtual Issue

Post provided by Michael Morrissey

©Dr. Jane Ogilvie, Rocky Mountain Biological Laboratory

Evolutionary quantitative genetics provides formal theoretical frameworks for quantitatively linking natural selection, genetic variation, and the rate and direction of adaptive evolution. This strong theoretical foundation has been key to guiding empirical work for a long time. For example, rather than generally understanding selection to be merely an association of traits and fitness in some general way, theory tells us that specific quantities, such as the change in mean phenotype within generations (the selection differential; Lush 1937), or the partial regressions of relative fitness on traits (direct selection gradients; Lande 1979, Lande and Arnold 1983) will relate to genetic variation and evolution in specific, informative ways.

These specific examples highlight the importance of the theoretical foundation of evolutionary quantitative genetics for informing the study of natural selection. However, this foundation also supports the study other critical (quantification of genetic variation and evolution) and complimentary (e.g., interpretation when environments, change, the role of plasticity and genetic variation in plasticity) aspects of understanding the nuts and bolts of evolutionary change. Continue reading

Issue 8.6: How to Measure Natural Selection

Issue 8.6 is now online!

The April issue of Methods, which includes our latest Special Feature – ‘How to Measure Natural Selection – is now online!

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.

Three of the ‘How to Measure Natural Selection’ papers – Franklin and Morrissey, Thomson and Hadfield, and Hadfield and Thomson – clarify unresolved aspects of the literature in meaningful and important ways. Following on from this Hermisson and Pennings; Lotterhos et al.; and Villanueva‐Cañas et al. tackle the genomic results of evolution by natural selection: namely, how we can detect natural selection from genomic data? Finally, Wadgymar et al. address the issue of how much we know about the underlying loci or agents of selection.

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.”

All of the articles in the ‘How to Measure Natural Selection‘ Special Feature will be freely available for a limited time.
Continue reading

Issue 7.5

Issue 7.5 is now online!

The May issue of Methods is now online!

This month’s issue contains two Applications articles and two Open Access articles, all of which are freely available.

piecewiseSEM: A practical implementation of confirmatory path analysis for the R programming language. This package extends the method to all current (generalized) linear, (phylogenetic) least-square, and mixed effects models, relying on familiar R syntax. The article also includes two worked examples.

 RPANDA: An R package that implements model-free and model-based phylogenetic comparative methods for macroevolutionary analyses. It can be used to:

  1. Characterize phylogenetic trees by plotting their spectral density profiles
  2. Compare trees and cluster them according to their similarities
  3. Identify and plot distinct branching patterns within trees
  4. Compare the fit of alternative diversification models to phylogenetic trees
  5. Estimate rates of speciation and extinction
  6. Estimate and plot how these rates have varied with time and environmental variables
  7. Deduce and plot estimates of species richness through geological time. Continue reading

How Did We Get Here From There? A Brief History of Evolving Integral Projection Models

Post provided by MARK REES and Steve Ellner

The Early Days: Illyrian Thistle and IBMs

Illyrian Thistle

Illyrian Thistle

Back in 1997 MR was awarded a travel grant from CSIRO to visit Andy Sheppard in Canberra. CSIRO had been collecting detailed long-term demographic data on several plant species and Andy was keen to develop data-driven models for management.

Andy decided Illyrian thistle (Onopordum Illyricum) would be a good place to start, as this was the most complicated in terms of its demography. The field study provided information on size, age and seed production. The initial goal was to quantify the impact of seed feeders on plant abundance, but after a few weeks of data analysis it became apparent that the annual seed production per quadrat was huge (in the 1000s) but there were always ~20 or so recruits. This meant that effects of seed feeders (if any) occurred outside the range of the data, which wasn’t ideal for quantitative prediction.

So the project developed in a different direction. Onopordum is a monocarpic perennial (it lives for several years then flowers and dies) and Tom de Jong and Peter Klinkhamer had recently developed models to predict at what size or age monocarps should flower, so it seemed reasonable to see if this would work. Continue reading