The latest Methods in Ecology and Evolution Virtual Issue – ‘Integrating Evolution and Ecology‘ – is in honour of the late Isabelle Olivieri (1957-2016): an international, interdisciplinary and ground-breaking biologist. It was edited by Louise Johnson and James Bullock and features papers on topics she researched, and in many cases pioneered. But it might perhaps have been more difficult to find 15 Methods papers on areas outside of Isabelle’s research interests!
Isabelle was the first Professor of Population Genetics at Montpellier, a past President of the European Society for Evolutionary Biology (2007-2009), and a member of the European Molecular Biology Organization. She spanned subject boundaries as easily as she collaborated across geographical borders. Her publications range through metapopulation and dispersal ecology, host-parasite coevolution, life history, invasive species and conservation ecology. In keeping with this breadth of interests, she also combined theory easily with experiment, and worked with a wide range of study systems from mites to Medicago. Continue reading →
Some individuals survive and reproduce better than others. Traits that help them do so may be passed on to the next generation, leading to evolutionary change. Because of this, evolutionary biologists are interested in what differentiates the winners from the losers – how do their traits differ, and by how much? These differences are known as natural selection.
Linear and Nonlinear Selection
Traditionally, natural selection is separated into linear selection (differences in average trait values) and nonlinear selection (any other differences in trait distributions between winners and the rest). For example, successful individuals might be unusually close to average: this is known as stabilizing selection. Alternatively, winners might split into two camps, some with unusually high trait values, and others with unusually low trait values. This is disruptive selection (famously thought to explain the ur-origin of sperm and eggs). Stabilizing and disruptive selection are important types of nonlinear selection. In general, though, the trait distribution of successful individuals can differ from the general population in arbitrarily complicated ways.
When individuals with larger trait values have higher fitness on average (left panel), the trait distribution of successful individuals is shifted towards the right (right panel, orange curve). The difference in mean trait values between the winners and the general population is called linear selection.
Happy New Year! We hope that you all had a wonderful Winter Break and that you’re ready to start 2018. We’re beginning the year with a look back at some of our highlights of 2017. Here’s how last year looked at Methods in Ecology and Evolution.
We published some amazing articles in 2017, too many to mention them all here. However, we would like to take a moment to thank all of the Authors, Reviewers and Editors who contributed to the journal last year. Your time and effort make the journal what it is and we are incredibly grateful. THANK YOU for all of your hard work!
Technological Advances at the Interface between Ecology and Statistics
Our first Special Feature of the year came in the April issue of the journal. The idea forTechnological Advances at the Interface between Ecology and Statistics came from the 2015 Eco-Stats Symposium at the University of New South Wales and the feature was guest edited by Associate Editor David Warton. It consists of five articles based on talks from that conference and shows how interdisciplinary collaboration help to solve problems around estimating biodiversity and how it changes over space and time.
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 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) aspectsof understanding the nuts and bolts of evolutionary change.Continue reading →
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.”
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:
Characterize phylogenetic trees by plotting their spectral density profiles
Compare trees and cluster them according to their similarities
Identify and plot distinct branching patterns within trees
Compare the fit of alternative diversification models to phylogenetic trees
Estimate rates of speciation and extinction
Estimate and plot how these rates have varied with time and environmental variables
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.