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!
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!
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.
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.
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.
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.
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.
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.
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.
Today, science extends beyond the research bench or the fieldsite more often than ever before. Scientists are continuously interacting with educators and the general public, and people are reciprocating the interest with a drive to be involved.
With this integration of science and the public, citizen-science efforts to crowdsource information have become increasingly popular (check out Zooniverse, SciStarter, NASA Citizen Science Projects, Project FeederWatch, and Foldit to get involved!). In the birding community, enthusiasts have been observing and recording birds for decades, but now there are methods for immediate data sharing among the community (eBird).
Hackathons have become a regular feature in the data-science world. Get a group of people with a shared interest together, give them data, food, and a limited amount of time and see what they can produce (often with prizes to be won). Translated into the world of academia as research hackathons, these events are a fantastic way to foster collaboration, interdisciplinary working and skills sharing.
The Quantitative Ecology hackathon was an intense day of coding resulting in creative and innovative research ideas using social and ecological data. Teams worked through the day to develop their ideas with support from experts in R, open science and statistics. We ended up with five projects addressing questions from, ‘Who has the least access to nature?’ to ‘Where should citizen scientists go to collect new data?’.