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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MInOSSE: all you need to reconstruct past species geographic range is in the fossil record!

Post provided by Francesco Carotenuto

A very important ecological feature of a species is its geographic range, which can be described by its size, position and shape. Studying the geographic range can be useful to understand the ecological needs of a species and, thereby, to plan conservation strategies. In ecological studies, mathematical models are the new standard to reconstruct the distribution of living species on Earth because of their accuracy in predicting a species presence or absence at unsampled locations. These methods are able to reconstruct the climatic niche of a species and to project it onto a geographic domain in order to predict the species’ spatial distribution. To do this, besides the occurrences of a species, the models necessarily require the spatial maps of environmental variables, like temperature and precipitation, for all the study area.

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Biomechanically-Aware Behaviour Recognition using Accelerometers

Post provided by Pritish Chakravarty

 

Accelerometers, Ground Truthing, and Supervised Learning

Accelerometers are sensitive to movement and the lack of it. They are not sentient and must recognise animal behaviour based on a human observer’s cognition. Therefore, remote recognition of behaviour using accelerometers requires ground truth data which is based on human observation or knowledge. The need for validated behavioural information and for automating the analysis of the vast amounts of data collected today, have resulted in many studies opting for supervised machine learning approaches.

Ground-truthing. The acceleration data stream (recorded using the animal-borne data logger, bottom-left) is synchronised with simultaneously recorded video (near top right). Picture credit: Kamiar Aminian

In such approaches, the process of ground truthing involves time-synchronising acceleration signals with simultaneously recorded video, having an animal behaviour expert create an ethogram, and then annotate the video according to this ethogram. This links the recorded acceleration signal to the stream of observed animal behaviours that produced it. After this, acceleration signals are chopped up into finite sections of pre-set size (e.g. two seconds), called windows. From acceleration data within windows, quantities called ‘features’ are engineered with the aim of summarising characteristics of the acceleration signal. Typically, ~15-20 features are computed. Good features will have similar values for the same behaviour, and different values for different behaviours.

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An interview with the editors of “Population Ecology in Practice”: Part II

Post provided by Daniel Caetano

Today we bring the second part of an interview with Dennis Murray and Brett Sandercock about their brand new book in population ecology methods: “Population Ecology in Practice.” This time we talked about their experience as editors, including some useful advice for new editors.

If you missed the first part of the interview, check it out here.

Population Ecology in Practice introduces a synthesis of analytical and modelling approaches currently used in demographic, genetic, and spatial analyses. Chapters provide examples based on real datasets together with a companion website with study cases and exercises implemented in the R statistical programming language.

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Uma breve história sobre o pacote R ‘metan’

Post ESCRITO POR Tiago Olivoto

This post is also available in English

Em nosso recente artigo na Methods in Ecology and Evolution, Alessandro D. Lúcio e eu descrevemos um novo pacote R para análise de ensaios multi-ambientes chamado metan. Ensaios multi-ambientes são um tipo de ensaio em programas de melhoramento de plantas, onde vários genótipos são avaliados em um conjunto de ambientes. A análise desses dados requer a combinação de várias abordagens, incluindo manipulação, visualização e modelagem de dados. A versão estável mais recente do metan (v1.5.1) está disponível agora no repositório CRAN. Então, pensei em compartilhar a história da minha primeira incursão no uso do R criando um pacote e submetendo um artigo para uma revista que nunca havia submetido antes.

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A brief history about the R package ‘metan’

Post provided by Tiago Olivoto

Este post também pode ser lido em Português

In our recent paper in Methods in Ecology and Evolution, Alessandro Lúcio and I describe a new R package, metan, for multi-environment trial analysis. Multi-environment trials are a kind of trial in plant breeding programs where several genotypes are evaluated in a set of environments. Analyzing such data requires the combination of several approaches including data manipulation, visualization and modelling. The latest stable version of metan (v1.5.1) is now on CRAN. So, I want to share the history about my first foray into using R, creating an R package, and submitting a paper to a journal that I’ve never had submitted before.

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An interview with the editors of “Population Ecology in Practice”: Part I

Post provided by Daniel Caetano

Today we bring the first part of an interview with Dennis Murray and Brett Sandercock about their brand new book in population ecology methods: “Population Ecology in Practice.” The editors were kind enough to share some interesting backstage information with us.

Snowshoe hare in winter

Population Ecology in Practice introduces a synthesis of analytical and modelling approaches currently used in demographic, genetic, and spatial analyses. Chapters provide examples based on real datasets together with a companion website with study cases and exercises implemented in the R statistical programming language.

Stay tuned for the second part of this interview, where we talk about some of the challenges of editing a large book and the editors share essential advice for anyone looking into leading such a project!

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10th Anniversary Volume 1: The Art of Modelling Range-Shifting Species

Post provided by Jane Elith, Mike Kearney and Steven Phillips  

10th anniversary logo

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 1, we have selected ‘The art of modelling range-shifting species’ by Elith et al. (2010).  In this post, first author, Professor Jane Elith, discusses the background and key concepts of the article, and how things have changed since the paper was published.

Illustration of the idea that model settings affect prediction.

We started work on this manuscript around 2008, prompted by increasing use of species distribution models for climate change and invasive species problems. At that stage there was growing recognition of the problems in these applications (e.g. see a recent MEE review on transferability) but relatively few tools for dealing with them. In our view, if correlative models are to be used for such purposes, the data and models require special attention.

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Anacapa Toolkit: Automating the Cataloguing of Biodiversity

Post provided by Emily Curd

Imagine that you want to catalogue all of the biodiversity (all of the living organisms) from a particular location; how many trained experts would that require? How many person hours would it take to collect and identify all of the rare, well-disguised, and microscopic organisms? How many of these organisms would have to be removed from the environment and taken back to a lab for taxonomic analysis.

With eDNA, you can survey the presence of this gorgeous opalescent nudibranch without capturing or even touching it.
©Natural History Museum of Los Angeles County — Amanda Bemis & Brittany Cumming

Although there is no substitute for human expertise, we have begun using the traces of DNA that organisms leave behind (e.g. excretions, skin and hair cells) in the environment to catalogue biodiversity. These traces of DNA, referred to as environmental DNA, can persist in the environment for minutes or can persist for centuries depending on where they end up. This field of environmental DNA (eDNA) is rapidly becoming an effective tool to complement surveys of biodiversity, both past and present.

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