Editor Recommendation: Quantitative Evolutionary Patterns in Bipartite Networks

Post provided by ROB FRECKLETON

The study of interactions and their impacts on communities is a fundamental part of ecology. Much work has been done on measuring the interactions between species and their impacts on relative abundances of species. Progress has been made in understanding of the interactions at the ecological level, but we know that co-evolution is important in shaping the structure of communities in terms of the species that live there and their characteristics. Continue reading

2018 Robert May Prize Winner: Laura Russo

The Robert May Prize is awarded annually for the best paper published in Methods in Ecology and Evolution by an Early Career Researcher. We’re delighted to announce that the 2018 winner is Laura Russo, for her article ‘Quantitative evolutionary patterns in bipartite networks: Vicariance, phylogenetic tracking or diffuse co‐evolution?‘.

Plant-pollinator interactions are often considered to be the textbook example of co-evolution. But specialised interactions between plants and pollinators are the exception, not the rule. Plants tend to be visited by many different putative pollinator species, and pollinating insects tend to visit many plant hosts. This means that diffuse co-evolution is a much more likely driver of speciation in these communities. So, the standard phylogenetic methods for evaluating co-evolution aren’t applicable in most plant-pollinator interactions. Also, many plant-pollinator communities involve insect species for which we do not yet have fully resolved phylogenies. Continue reading

Introducing fishtree and fishtreeoflife.org

This post was originally published on Jonathan Chang’s blog.

In our recent publication (Rabosky et al. 2018) we assembled a huge phylogeny of ray-finned fishes: the most comprehensive to date! While all of our data are accessible via Dryad, we felt like we could go the extra mile to make it easy to repurpose and reuse our work. I’m pleased to report that this effort has resulted in two resources for the community: the Fish Tree of Life website, and the fishtree R package. The package is available on CRAN now, and you can install it with:

install.packages("fishtree")

The source is on GitHub in the repository jonchang/fishtree. The manuscript describing these resources has been published in Methods in Ecology and Evolution (Chang et al. 2019).

Continue reading

Phylogenetic Tip Rates: How Well Can We Estimate Diversification?

Post provided by Pascal Title and Dan Rabosky

Analyzing diversification rate heterogeneity across phylogenies allows us to explore all manner of questions, including why Australia has such an incredible diversity of lizards and snakes.

Analyzing diversification rate heterogeneity across phylogenies allows us to explore all manner of questions, including why Australia has such an incredible diversity of lizards and snakes.

Within the tree of life there are differences in speciation and extinction rates over time and across lineages. Biologists have long been interested in how speciation rates change as a function of ecological opportunity or whether key innovations lead to increases in the rate of speciation. Exploring this rate variation and examining how clades differ in terms of their diversification dynamics can help us to understand why species diversity varies so dramatically in time and space. Learning more about the relationship between traits and diversification rates is especially important because it has the potential to reveal the causes of pervasive variation in species richness among clades and across geographic regions.

Several different classes of methods are available for studying the effects of species traits on lineage diversification rates. These include state-dependent diversification models (e.g., BiSSE, QuaSSE, HiSSE) and several non-model-based approaches. In our article – ‘Tip rates, phylogenies and diversification: What are we estimating, and how good are the estimates?’ – we assessed the accuracy of a number of model-free metrics (the DR statistic, node density metric, inverse of terminal branch lengths) and model-based approaches (Bayesian Analysis of Macroevolutionary Mixtures, BAMM) to determine how they perform under a variety of different types of rate heterogeneity. The “tip rates” using these approaches have become widely used for a few reasons, including ease of computation and how easy it is to pair them with other types of data. Continue reading

What the Past Can Tell Us About the Future: Notes from Crossing the Palaeontological – Ecological Gap

Post provided by Karen Bacon

I had the pleasure of delivering one of the plenary talks at the first (hopefully of many) Crossing the Palaeontological – Ecological Gap meeting held in the University of Leeds on August 30th and 31st. I’m a geologist and a botanist, so this is a topic that’s close to my heart and my professional interests.

How Palaeoecology Can Help Us Today

©Gail Hampshire

©Gail Hampshire

As we move into an ecologically uncertain future with pressures of climate change, land-use change and resource limitations, the fossil record offers the only truly long-term record of how Earth’s ecosystems respond to major environmental upheaval driven by climate change events. The fossil record is, of course, not without its problems – there are gaps, not everything fossilises in the same way or numbers, and comparisons to today’s ecology are extremely difficult.  It’s these difficulties (and other challenges) that make the uniting of palaeontology and ecology essential to fully address how plants, animals and other organisms have responded to major changes in the past. Perhaps uniting them could give us an idea of what to expect in our near-term future, as carbon dioxide levels return to those not previously experienced on Earth since the Pliocene, over 2 million years ago. Continue reading

Integrating Evolution and Ecology

©H. Zell

©H. Zell

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

Editor Recommendation: Accounting for Genetic Differences among Unknown Parents in Microevolutionary Studies

Post provided by Laura Graham

Song sparrows show substantial genetic variation in multiple life-history traits. Application of ‘genetic group animal models’ show that this is partly due to genetic effects of immigrants © Jane Reid

Song sparrows show genetic variation in multiple life-history traits. Application of ‘genetic group animal models’ show this is partly due to genetic effects of immigrants ©Jane Reid

Understanding how wild populations respond and adapt to environmental change is a key question in evolutionary biology. To understand this, we need to be able to separate genetic and environmental effects on phenotypic variation. Statistical ‘animal models’, which can do just this, have revolutionised the field of quantitative genetics. A lack of full knowledge of individual pedigrees can lead to severe bias in quantitative genetic parameter estimates though – particularly when genetic values for focal traits vary non-randomly in unknown parents.

In the Journal of Animal Ecology ‘How To…’ paper “Accounting for genetic differences among unknown parents in microevolutionary studies: how to include genetic groups in quantitative genetic animal models”, the extent of this bias is highlighted. Matthew Wolak and Quantitative Ecology 2018 keynote speaker Jane Reid show how genetic group methods – a technique developed in agricultural science – can be employed to minimise it. Continue reading

How Strong is Natural Selection? Stitching Together Linear and Nonlinear Selection on a Single Scale

Post provided by Robert May Prize Winner Jonathan Henshaw

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.

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.

Continue reading

A New Method for Computing Evolutionary Rates and Rate Shifts

Post provided by Pasquale Raia

Phylogenetic Effects

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