10th Anniversary Volume 10: R packages

To celebrate our 10th Anniversary, we are highlighting a key article from each of our volumes. For Volume 10 we selected ‘An automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks’ by Grames et al. (2019), an Application article introducing the R package litsearchr.

In this post Thomas White and Laura Graham, two of our Associate Editors with expertise in Application articles, highlight their favourite of our papers about R packages.

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10th Anniversary Volume 10: Evidence synthesis technology and automating systematic reviews

Post provided by Eliza M. grames

To celebrate the 10th Anniversary of the launch of Methods in Ecology and Evolution, we are highlighting an article from each volume to feature on the Methods.blog. For Volume 10, we have selected ‘An automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks’ by Grames et al. (2019).

In this post, Eliza Grames shares the motivation behind the litsearchr search approach, and developments since the article’s publication.

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Individual History and the Matrix Projection Model

Post provided by Rich Shefferson

A single time-step projection of a historical matrix projection model (hMPM), for a 7 life stage life history model of Cypripedium parviflorum, the small yellow lady’s slipper. In this case, the vector of biologically plausible stage pairs in time 2 is equal to the full projection matrix multiplied by the vector of biologically plausible stage pairs in time 1.

Matrix projection modeling is a mainstay of population ecology. Ecologists working in natural area management and conservation, as well as in theoretical and academic realms such as the study of life history evolution, develop and use these models routinely. Matrix projection models (MPMs) have advanced dramatically in complexity over the years, originating from age-based and stage-based matrix models parameterized directly from the data, to complex matrices developed from statistical models of vital rates such as integral projection models (IPMs) and age-by-stage models. We consider IPMs to be a class of function-based MPM, while age-by-stage MPMs may be raw or function-based, but are typically the latter due to a better ability to handle smaller dataset. The rapid development of these methods can leave many feeling bewildered if they need to use these methods but lack sufficient understanding of scientific programming and of the background theory to analyze them properly.

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10th Anniversary Volume 8: Phylogenetics Editor’s Choice

To celebrate our 10th Anniversary, we are highlighting a key article from each of our volumes. For Volume 8, we selected ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data by Yu et al. (2016).

In this post, our Associate Editors Samantha Price and Francisco Balao share their favourite MEE papers in the field of phylogenetics.

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10th Anniversary Volume 8: Phylogenetic tree visualization with multivariate data

Post provided by Guangchuang Yu and Tommy Tsan-Yuk Lam

To celebrate the 10th Anniversary of the launch of Methods in Ecology and Evolution, we are highlighting an article from each volume to feature in the Methods.blog. For Volume 8, we have selected ‘ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data‘ by Yu et al. (2016).

In this post, the authors share their inspiration behind the ggtree package for R and present new resources of ggtree and a series of other related packages.

The team publishing the ggtree paper is working in the field of emerging infectious diseases. Particularly the corresponding author Tommy Lam (TL) has been advocating the integration of different biological and epidemiological information in the studies of fast-evolving viral pathogens. The lead author Guangchuang Yu (GY) joined The University of Hong Kong to pursue his doctorate degree under the supervision of TL and Yi Guan (co-author in the paper), as he was very curious about the application of genomics and phylogenetics in the study of emerging infectious diseases.

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Announcing our new Associate Editors 2020

Following an open call for applicants in July, we are pleased to welcome 30 new Associate Editors to the Methods in Ecology and Evolution Editorial Board. The researchers joining us span 16 different countries, including our first editors working in Iran, Italy and Portugal. Find out more about them below.

We are really delighted to have further expanded the expertise on our board so that we can continue to promote the development of new methods in ecology and evolution.

Welcome to the team!

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November Issue Out Now!

The latest issue of Methods in Ecology and Evolution is now online!

Senior Editor Aaron Ellison has selected six Featured Articles this month. You can find out about all of them below. We also have eight Applications articles and seven Practical Tools articles in the November issue that are freely available to everyone – no subscription required!

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10th Anniversary Volume 3: paleotree: A Retrospective

Post provided by David bapst

To celebrate the 10th Anniversary of the launch of Methods in Ecology and Evolution, we are highlighting an article from each volume to feature on the Methods.blog. For Volume 3, we have selected ‘paleotree: an R package for paleontological and phylogenetic analyses of evolution‘ by David W. Bapst (2012). In this post, David discusses the background to the Application he wrote as a graduate student, and how the field has changed since.

I was a fourth year graduate student when I first had the idea to make an R package. Quite a few people thought it was a bit silly, or a bit of a time-waste, but I thought it was the right thing to do at the time, and I think it has proven to be the right decision in hindsight.

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Stop, think, and beware of default options

Post provided by Paula Pappalardo (with contributions from Elizabeth Hamman, Jim Bence, Bruce Hungate & Craig Osenberg)

Esta publicación también está disponible en español.

You spent months carefully collecting data from articles addressing your favorite scientific question, you have dozens of articles neatly arranged on a spreadsheet, you found software or code to analyze the data, and then daydream about how your publication will be the most cited in your field while making cool plots. If that sounds familiar, you have probably done a meta-analysis. Meta-analysis uses statistical models to combine data from different publications to answer a specific question.

What you may not have realized when going down the meta-analysis rabbit hole, is that small, seemingly inconsequential, choices can greatly affect your results. If you want to know about one of them lurking behind the scenes… read on!

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Para, piensa, y ten cuidado con las configuraciones por defecto

Post escrito por Paula Pappalardo (con aportes de Elizabeth Hamman, Jim Bence, Bruce Hungate & Craig Osenberg)

This post is also available in English.

Pasaste meses laboriosamente colectando datos de artículos científicos acerca de tu pregunta favorita, tienes decenas de artículos perfectamente organizados en una base de datos, ya encontraste el programa o código para analizar los datos, y entonces imaginas como tu publicación va a ser la más citada en tu campo de investigación mientras haces unos gráficos lindísimos. Si esto te suena familiar, seguramente has hecho un meta-análisis. Un meta-análisis usa modelos estadísticos para combinar datos de distintas publicaciones para responder a una pregunta específica.

Lo que quizás no te diste cuenta mientras navegabas los pasos del meta-análisis, es que pequeñas decisiones (a veces pareciendo de muy poca importancia) pueden tener grandes efectos en los resultados. Si quieres saber más acerca de una de estas decisiones en particular… ¡sigue leyendo!

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