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.
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.
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!
Há alguns dias, me deparei com um interessante vídeo sobre os chamados “fósseis vivos”. O vídeo focou mais nos problemas de usá-los como argumentos contra a teoria da evolução, e aproveitei a oportunidade para falar mais sobre essas linhagens longevas.
‘Fóssil vivo‘ é um termo usado para descrever linhagens que acredita-se terem se originado há muito tempo e que mantêm características que se assemelham a seus parentes fósseis. Alguns exemplos bem conhecidos dessas linhagens são os Tuatara da Nova Zelândia (Sphenodon punctatus) e as árvores Gingkos (Gingko biloba).
A couple of days ago I came across a nice video (in Portuguese only, sorry) about so-called “living fossils”. The video focused on the problems of using them as arguments against evolution. But I’d like to take the opportunity to talk more about these long-lived lineages.
‘Living fossil’ is a term used to describe lineages that are thought to have been around for a very long time and retain characteristics that resemble of their fossil relatives. A couple of well-known examples of these lineages are the Tuatara of New Zealand (Sphenodon punctatus) and the Gingko tree (Gingko biloba).
Artificial intelligence (or AI) is an enormously hot topic, regularly hitting the news with the latest milestone where computers matching or exceeding the capacity of humans at a particular task. For ecologists, one of the most exciting and promising uses of artificial intelligence is the automatic identification of species. If this could be reliably cracked, the streams of real-time species distribution data that could be unlocked worldwide would be phenomenal.
Despite the hype and rapid improvements, we’re not quite there yet. Although AI naturalists have had some successes, they can also often make basic mistakes. But we shouldn’t be too harsh on the computers, since identifying the correct species just from a picture can be really hard. Ask an experienced naturalist and they’ll often need to know where and when the photo was taken. This information can be crucial for ruling out alternatives. There’s a reason why field guides include range maps!
Currently, most AI identification tools only use an image. So, we set out to see if a computer can be taught to think more like a human, and make use of this extra information. Continue reading →