10th Anniversary Volume 3: Phylogenetics Editor’s Choice

To celebrate our 10th Anniversary, we are highlighting a key article from each of our volumes. For Volume 3, we selected ‘paleotree: an R package for paleontological and phylogenetic analyses of evolution‘ by David W. Bapst (2012).

In this post, three of our Associate Editors with expertise in phylogenetics Simone Blomberg, Will Pearse and Michael Matschiner share their favourite MEE papers in the field of phylogenetics and beyond.

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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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October Issue out now!

Methods Issue 11.10 is now online!

We have a larger issue of 17 articles this month, featuring the ethics of wild animal research, an eco-acoustic monitoring network, a programmable optomotor and much more.

Senior Editor Rob Freckleton has selected six featured articles – find out about them below.

We also have three Applications, one Practical Tools and seven articles that are freely available to everyone – no subscription required!

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Sharing is Caring: Working With Other People’s Data

Post provided by Mariana García Criado, Isla Myers-Smith, Lander Baeten, Andrew Cunliffe, Gergana Daskalova, Elise Gallois and Jeffrey Kerby

 

The Team Shrub research group in 2017 on Qikiqtaruk – Herschel Island in the Canadian Arctic. Not only do Team Shrub work with other people’s data, we collect our own to share publicly following open science best practice. (Photo credit: Sandra Angers-Blondin, www.teamshrub.com).

Team Shrub (www.teamshrub.com), are ecologists working to understand how global change alters plant communities and ecosystem processes. In May 2020, Team Shrub held a lab meeting to discuss working with other people’s data. Inspired by the conversation, they decided to put a blog post together to explore the importance of careful data cleaning in open science, provide 10 best practice suggestions for working with other people’s data, and discuss ways forward towards more reproducible science. 

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Issue 11.9: Methods for individual bird recognition, zooplankton sampling and more!

The September issue of Methods is now online! 11.9 JPEG

We have a larger issue of 14 articles this month, featuring  methods for individual bird recognition, zooplankton sampling, coral health assessment and much more.

Senior Editor Lee Hsiang Liow has selected five featured articles – find out about them below. We also have three Applications, three Practical Tools articles and 11 articles that are freely available to everyone – no subscription required!

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Creating a package to infer species coexistence

Post provided by Ignasi Bartomeus, David García-Callejas, and Oscar Godoy

Ignasi Bartomeus and colleagues share the story behind their recent Methods article ‘cxr: A toolbox for modelling species coexistence in R’.

This post recalls the journey on how we ended up developing cxr (acronym for CoeXistence relationships in R), an R package for quantifying interactions among species and their coexistence relationships. In other words, it provides tools for telling apart the situations in which different species can persist together in a community from the cases in which one species completely overcomes another.

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Navigating the data-rich world of aquatic acoustic telemetry

Post provided by Kim Whoriskey

Early Career Researcher Kim Whoriskey takes us behind the Methods paper ‘Current and emerging statistical techniques for aquatic telemetry data: A guide to analysing spatially discrete animal detections’ which led to her being shortlisted for our Robert May Prize in 2019.

Understanding how aquatic animals move is becoming increasingly important for protecting them. Knowing where they migrate, how long they stay, and what they do when they travel through changing marine environments provides us with key information on movement corridors, habitat hotspots, and changing population distributions. This information can then be used to help manage and conserve many different aquatic species, from developing guidelines for recreational fishing practices to defining marine spatial planning measures.

sharks

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How to Sample Nectar of Flowers at Height

Post provided by Daniela Scaccabarozzi, Tristan Campbell and Kenneth Dods.

Daniela Scaccabarozzi, Tristan Campbell and Kenneth Dods tell us about the logistical challenges of sampling flowers at height and their new ground-based method for overcoming these problems.

Operational maneuvers while using the practical ground-based tool for nectar collection, prior to placing the organza bag over the inflorescence. Picture credit: Tristan Campbell.

Sampling flower nectar from forest canopies is logistically challenging, as it requires physical access to the canopy at a height greater than can be achieved by hand. The most common solutions comprise the use of cherry pickers, cranes or tree climbers, however these techniques are generally expensive, complex to organise, and often involve additional safety risk assessment and specialised technicians.

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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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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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