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.
Aquatic animal telemetry has revolutionized our understanding of the behaviour of aquatic animals. One of the important advantages of telemetry methods, including acoustic telemetry, is that they provide information at the individual level. This is very relevant because it enables investigating the natural variability in behaviour within populations (like here or here), but also because one can investigate what happens to each individual animal and relate it to its natural behaviour. Knowing “what happens to each individual” is normally referred to as “fate” and it can take many forms: some fish may end-up eaten by predators, other may be fished, some of them may disperse, etc. Knowing the fate of each individual fish is crucial as it links ecological processes at the individual level to evolutionary outcomes at the population level.
In this post, the authors discuss the background and key concepts of the article, and changes in the field that have happened since the paper was published.
Terrestrial laser scanning (TLS) calculates 3D locations by measuring the speed of light between a transmitted laser pulse and its return. Firing hundreds of thousands of pulses per second, these instruments can represent the surroundings in detailed 3D, displaying them as virtual environments made up of high density points. The main applications of commercial instruments in the early 2000s were engineering or mining, but their application in natural forested environments was in its infancy. Forest ecosystems are structurally complex; clear reference points used to register multiple scans are rare and trees move due to wind creating artefacts in the data.
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.
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.
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!
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!
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.
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.
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.