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 7.3

Issue 7.3 is now online!

The March 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.

METAGEAR: A comprehensive, multifunctional toolbox with capabilities aimed to cover much of the research synthesis taxonomy: from applying a systematic review approach to objectively assemble and screen the literature, to extracting data from studies, and to finally summarize and analyse these data with the statistics of meta-analysis.

Universal FQA Calculator: A free, open-source web-based Floristic Quality Assessment (FQA) Calculator. The calculator offers 30 FQA data bases (with more being added regularly) from across the United States and Canada and has been used to calculate thousands of assessments. Its growing repository for site inventory and transect data is accessible via a REST API and represents a valuable resource for data on the occurrence and abundance of plant species. Continue reading

A new tool based on microbial interactions to analyze bipartite networks

Below is a press release about the Methods paper ‘BiMat: a MATLAB package to facilitate the analysis of bipartite networks‘ taken from the Pompeu Fabra University.

The Georgia Institute of Technology has created, together with the Pompeu Fabra University and the University of Canterbury, a new open-access and open-source tool for the study of bipartite networks

The team led by Joshua S. Weitz, Associate Professor at the School of Biology from the Georgia Institute of Technology, has developed BiMat: an open source MATLAB® package for the study of the structure of bipartite ecological networks inspired by real problems in microbiology and with broader applications. Cesar O. Flores, researcher at the School of Physics of the same institute, describes this new tool in an article published in the journal Methods in Ecology and Evolution. Sergi Valverde, Visiting Professor at the Complex Systems Lab from the Pompeu Fabra University, and Timothée Poisot, from the School of Biological Sciences of the University of Canterbury, are involved in the project. Continue reading

Detecting effects of predators on prey: the method matters

In a paper published online today in Methods in Ecology and Evolution, Malcolm Nicoll and Ken Norris look at a controversial issue, that of detecting effects of predators on bird populations. This is controversial because some predators, especially raptors, were formerly rather scarce, but have become more abundant in recent years – in the case of raptors because organochloride chemicals are not used any more. At the same time mammal predators have also increased in numbers. This has led to suggestions that increases in predators may be a contributory factor to declines in some groups of birds, such as farmland birds, and there has been a great deal of discussion and debate over the issue.

Unfortunately this is a hypothesis that is not very easily addressed, unsurprising given the spatial and temporal scales that may be involved. Experimental approachs would be the ‘gold standard’, but these are difficult. Gradients of predator abundance have to be created by means of barriers or removal, which is expensive, logistically challenging and potentially expensive.

More usually opportunistic, observational evidence has to be relied upon. For example, this might take the form of statistically comparing populations in birds in areas with high numbers of predators with those with low numbers of predators. Whilst more practicable, such studies can suffer from the possibility of confounding: if a third ‘hidden’ variable also varies between sites, then this could generate problems for the interpretation.

The previous literature has presented mixed results: some studies have demonstrated effects of predators on prey populations, others have not. Whether this variation has an ecological basis is not clear.

In this new study, Nicoll & Norris re-evaluate previous observational studies by means of meta-analysis. They look at the effects of the quality of data and the number of predator species studied on the outcome of analyses. Importantly they find that the probability of detecting an effect depends on both the quality of data and the number of predators studied.

There are several implications of this work. Studies with poor quality data and that include small numbers of predators cannot reliably tell whether there are effects of predators or not, they are simply inconclusive. Nicoll & Norris go as far as to say that one should be skeptical about any short-term observational study that reports no effects of predators. They also suggest that the combined effects of predators are likely to be more important than that of any single predator, and that future studies should account for this.

Finally this study highlights the importance of methods in ecology: one cannot interpret evidence unless the method used is shown to be reliable and fit for purpose. Nicoll & Norris’ study is a good example of how re-evaluation of methods can help improve ecological understanding.

Phylogenetic comparative methods

Phylogenetic comparative methods are always an area of hot discussion and lots of methodological development. So I thought it would be useful to highlight some recent papers that have developed new methods in the past year. Please email me or leave a comment if there is anything I have omitted or if something new comes out.

Thomas Hansen and colleagues have introduced a new method for studying adaptation using comparative methods. Their approach is a generalisation of the Ornstein-Uhlenbeck model that allows for adaptive constraints and phylogenetic intertia. They have an R-package SLOUCH which can be used to fit the model.

In Evolution Liam Revell has developed a new approach for data reduction and size correction using phylogenetic approaches – this is often done wrongly as the transformation is commonly applied before phylogenetic analysis, however it should be correctly done at the same time.

A new method in Functional Ecology allows one to test for phylogenetic dependence in complex multivariate data that also incorporate measurement error.

What will prove, I think, to be a very popular method is a new approach for testing for phylogenetic signal and analysing correlates of binary traits, basically a phylogenetic logistic regression by Anthony Ives and Ted Garland. The approach will allow linear modelling of correlates of a binary traits, which has been difficult before.

In a related area, another very important development is in the analysis of speciation and extinction rates when these are affected by a binary trait. FitzJohn et al. have shown how the BiSSE model, developed to do this, can be applied when phylogenies are incompletely resolved.

Likely to be of interest to many using comparative methods is a paper by Richard Smith on the use and misuse of Reduced Major Axis line fitting. He discusses the assumptions of this method, which are not widely appreciated.

In the American Naturalist Marc Lajeunesse has developed methods linking comparative analysis and meta-analysis, basically allowing meta-analysis to be corrected for phylogenetic non-independence.

In Proc B a new method for integrating spatial and phylogenetic dependence has been presented, and in JEB there has been a review of the ‘deadly sins of comparative analysis’ (apologies for self-promotion!).

Just to end with here is another method for explaining adaptation.