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.
Understanding how aquatic animals move is becoming increasingly important for protecting them. Knowing where they migrate, how long they stay, and what they do when they travel through changing marine environments provides us with key information on movement corridors, habitat hotspots, and changing population distributions. This information can then be used to help manage and conserve many different aquatic species, from developing guidelines for recreational fishing practices to defining marine spatial planning measures.
Post provided by Paula Pappalardo (with contributions from Elizabeth Hamman, Jim Bence, Bruce Hungate & Craig Osenberg)
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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.
落红不是无情物，化作春泥更护花。 –龚自珍(清) “The fallen petals are not as cruel as they seem; they fertilize those in full bloom instead.” – Gong Zizhen (Qing Dynasty)
A decaying Douglas fir log
This picture shows a decomposing log of Douglas fir, Pseudotsuga menziesii (Mirb.) Franco, in Schovenhorst, The Netherlands, which is one of the deadwood incubation sites of the LOGLIFE “tree cemetery” project. 25 angiosperm and gymnosperm species covering a diverse range of functional traits were selected and incubated in the “common garden experiment”. This project was founded in 2012, aiming to disentangle the effects of different species’ wood traits and site-related environmental drivers on decomposition dynamics of wood, and its associated diversity of microbial and invertebrate communities.
Jolien Goossens tells us about the challenges of installing acoustic receivers on the seabed and the tripod they designed to overcome them.
Installing scientific instruments in the marine environment comes with many challenges. Equipment has to withstand the physical forces of tides, currents and storms. Researchers have to take into account the effects of biofouling, corrosion and human activities. Even access to the study site can pose its difficulties, as diving is limited by depth and weather conditions. Practical deployment mechanisms are therefore needed to sustain consistent data flows.
Acoustic telemetry enables the observation of animal movements in aquatic environments. Individual animals are fitted with a transmitter, relaying a signal that can be picked up by acoustic receivers. To facilitate a convenient installation of these instruments, we developed and tested a new design, mounting a receiver with an acoustic release on a tripod frame. This frame enables the recovery of all equipment and better yet, improves the quality of the data.
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.
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.