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.

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2017 Robert May Prize Winner: Jonathan Henshaw

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 2017 winner is Jonathan Henshaw, for his article ‘A unified measure of linear and nonlinear selection on quantitative traits.

The standard approach to quantifying natural selection, developed by Lande and Arnold, does not allow for comparable metrics between linear (i.e. selection on the mean phenotype) and nonlinear (i.e. selection on all other aspects of the phenotypic distribution, including variance and the number of modes) selection gradients. Jonathan Henshaw’s winning submission provides the first integrated measure of the strength of selection that applies across qualitatively different selection regimes (e.g. directional, stabilizing or disruptive selection). Continue reading

Issue 9.2

Issue 9.2 is now online!

The February issue of Methods is now online!

This double-size issue contains six Applications articles (one of which is Open Access) and two Open Access research articles. These eight papers are freely available to everyone, no subscription required.

 Temperature Manipulation: Welshofer et al. present a modified International Tundra Experiment (ITEX) chamber design for year-round outdoor use in warming taller-stature plant communities up to 1.5 m tall.This design is a valuable tool for examining the effects of in situ warming on understudied taller-stature plant communities

 ZoonThe disjointed nature of the current species distribution modelling (SDM) research environment hinders evaluation of new methods, synthesis of current knowledge and the dissemination of new methods to SDM users. The zoon R package aims to overcome these problems by providing a modular framework for constructing reproducible SDM workflows.

 BEIN R Package: The Botanical Information and Ecology Network (BIEN) database comprises an unprecedented wealth of cleaned and standardised botanical data. The bien r package allows users to access the multiple types of data in the BIEN database. This represents a significant achievement in biological data integration, cleaning and standardisation.

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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

Exploring Coevolutionary History: Do Entire Communities Shape the Evolution of Individual Species?

Post provided by Laura Russo, Katriona Shea, and Adam Miller

Diffuse Coevolution

Interactions between plants and pollinators tend to be highly generalized.

Interactions between plants and pollinators tend to be highly generalized.

In 1980, Janzen published an article titled “When is it coevolution?” where he explained the concept of diffuse coevolution: the idea that evolution of interacting species is shaped by entire communities, rather than simple paired interactions. This idea, though compelling, remains poorly understood, and strong evidence of diffuse coevolution acting on a community is lacking. Perhaps this is because there’s a lack of consensus on what would constitute evidence in support of the concept of diffuse coevolution, or, indeed, coevolution in general (Nuismer et al 2010). Continue reading

Issue 9.1: Qualitative Methods for Eliciting Judgements for Decision Making

Issue 9.1 is now online!

Our first issue of 2018, which includes our latest Special Feature – “Qualitative methods for eliciting judgements for decision making” – is now online!

This new Special Feature is a collection of five articles (plus an Editorial from Guest Editors Bill Sutherland, Lynn Dicks, Mark Everard and Davide Geneletti) brings together authors from a range of disciplines (including ecology, human geography, political science, land economy and management) to examine a set of qualitative techniques used in conservation research. They highlight a worrying extent of poor justification and inadequate reporting of qualitative methods in the conservation literature.

As stated by the Guest Editors in their Editorial “these articles constitute a useful resource to facilitate selection and use of some common qualitative methods in conservation science. They provide a guide for inter-disciplinary researchers to gauge the suitability of each technique to their research questions, and serve as a series of checklists for journal editors and reviewers to determine appropriate reporting.”

All of the articles in the ‘Qualitative methods for eliciting judgements for decision making‘  Special Feature are all freely available.
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Making YOUR Code Reproducible: Tips and Tricks

When we were putting together the British Ecological Society’s Guide to Reproducible Code we asked the community to send us their advice on how to make code reproducible. We got a lot of excellent responses and we tried to fit as many as we could into the Guide. Unfortunately, we ran out of space and there were a few that we couldn’t include.

Luckily, we have a blog where we can post all of those tips and tricks so that you don’t miss out. A massive thanks to everyone who contributed their tips and tricks for making code reproducible – we really appreciate it. Without further ado, here’s the advice that we were sent about making code reproducible that we couldn’t squeeze into the Guide:

Organising Code

©Leejiah Dorward

“Don’t overwrite data files. If data files change, create a new file. At the top of an analysis file define paths to all data files (even if they are not read in until later in the script).” – Tim Lucas, University of Oxford

“Keep one copy of all code files, and keep this copy under revision management.” – April Wright, Iowa State University

“Learn how to write simple functions – they save your ctrl c & v keys from getting worn out.” – Bob O’Hara, NTNU

For complex figures, it can make sense to pre-compute the items to be plotted as its own intermediate output data structure. The code to do the calculation then only needs to be adjusted if an analysis changes, while the things to be plotted can be reused any number of times while you tweak how the figure looks.” – Hao Ye, UC San Diego Continue reading

A Guide to Reproducible Code in Ecology and Evolution

Post provided by Natalie Cooper and Pen-Yuan Hsing

Cover image by David J. Bird

The way we do science is changing — data are getting bigger, analyses are getting more complex, and governments, funding agencies and the scientific method itself demand more transparency and accountability in research. One way to deal with these changes is to make our research more reproducible, especially our code.

Although most of us now write code to perform our analyses, it’s often not very reproducible. We’ve all come back to a piece of work we haven’t looked at for a while and had no idea what our code was doing or which of the many “final_analysis” scripts truly was the final analysis! Unfortunately, the number of tools for reproducibility and all the jargon can leave new users feeling overwhelmed, with no idea how to start making their code more reproducible. So, we’ve put together the Guide to Reproducible Code in Ecology and Evolution to help. Continue reading

How Can We Quantify the Strength of Migratory Connectivity?

Technological advancements in the past 20 years or so have spurred rapid growth in the study of migratory connectivity (the linkage of individuals and populations between seasons of the annual cycle). A new article in Methods in Ecology and Evolution provides methods to help make quantitative comparisons of migratory connectivity across studies, data types, and taxa to better understand the causes and consequences of the seasonal distributions of populations.

In a new video, Emily Cohen, Jeffrey Hostetler and Michael Hallworth explain what migratory connectivity is and how the methods in their new article – ‘Quantifying the strength of migratory connectivity‘ – can help you to study it. They also introduce and give a quick tutorial on their new R package MigConnectivity.

This video is based on the article ‘Quantifying the strength of migratory connectivity by Cohen et al.

Sticking Together or Drifting Apart? Quantifying the Strength of Migratory Connectivity

Post provided by Emily Cohen

Red Knot migratory connectivity is studied with tracking technologies and color band resighting. © Tim Romano

Red Knot migratory connectivity is studied with tracking technologies and colour band resighting. © Tim Romano

The seasonal long-distance migration of all kinds of animals – from whales to dragonflies to amphibians to birds – is as astonishing a feat as it is mysterious and this is an especially exciting time to study migratory animals. In the past 20 years, rapidly advancing technologies  – from tracking devices, to stable isotopes in tissues, to genomics and analytical techniques for the analysis of ring re-encounter databases – mean that it’s now possible to follow many animals throughout the year and solve many of the mysteries of migration.

What is Migratory Connectivity?

One of the many important things we’re now able to measure is migratory connectivity, the connections of migratory individuals and populations between seasons. There are really two components of migratory connectivity:

  1. Linking the geography of where individuals and populations occur between seasons.
  2. The extent, or strength, of co-occurrence of individuals and populations between seasons.

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