Category Archives: Code and Applications

10th Anniversary Volume 3: paleotree: A Retrospective

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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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Creating a package to infer species coexistence

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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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Stop, think, and beware of default options

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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

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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!

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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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A brief history about the R package ‘metan’

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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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Uma breve história sobre o pacote R ‘metan’

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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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Gaining Genetic and Epigenetic Data from a Single Established Next-Generation Sequencing Approach

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Post provided by Marco Crotti

How organisms adapt to the environment they live in is a key question in evolutionary biology. Genetic variation, i.e. how individuals within populations differ from each other in terms of their DNA, is an essential element in the process of adaptation. It can arise through different mechanisms, including DNA mutations, genetic drift, and recombination.

Example of how genetic drift can occur over generations via random sampling (i.e. random mating) in a population. (Picture credit: Gringer).

Differences in DNA sequences between individuals can results in differences in the expression of genes. This can therefore determine the organism’s capacity to grow, develop, and react to environmental stimuli. However, a growing body of literature reveals that there are other ways organisms can change the way they interact with the world without mutations in the DNA sequence.

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Anacapa Toolkit: Automating the Cataloguing of Biodiversity

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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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Hyperoverlap: Detecting Overlap in n-Dimensional Space

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Post provided by Matilda Brown, Barbara Holland, and Greg Jordan

Overlap can help us to learn why Microcachrys
is now only found in the mountains of
Tasmania. ©Greg Jordan

There are many reasons that we might be interested in whether individuals, species or populations overlap in multidimensional space.  In ecology and evolution, we might be interested in climatic overlap, morphological overlap, phenological or biochemical overlap. We can use analyses of overlap to study resource partitioning, evolutionary histories and palaeoenvironmental conditions, or to inform conservation management and taxonomy. Even these represent only a subset of the possible cases in which we might want to investigate overlap between entities. Databases such as GBIF, TRY and WorldClim make vast amounts of data publicly available for these investigations. However, these studies require complex multivariate data and distilling such data into meaningful conclusions is no walk in the park.

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