vegetools: tools for reproducible vegetation analyses

Effectiveness landscapes: plotting the effectiveness landscape of plant-animal mutualisms

Incorporating evolution in ecological niche modelling

En este seminario se discutirán distintas aproximaciones metodológicas (Smith et al. 2018) para intentar mejorar nuestras estimaciones de nicho ecológico teniendo en cuenta la posible divergencia intraespecífica o adaptación local de las poblaciones, …

Computational Ecology & Data Science

Computational ecology, ecoinformatics, reproducible workflows, programming and data science.

aemet: R interface to AEMET API

aire: calidad del aire en Andalucía

New Ecoinformatics working group

Earlier this year a few colleagues (Ignasi Bartomeus, Sara Varela, Antonio J. Pérez-Luque, and myself) created a new working group on Ecoinformatics within the Spanish Terrestrial Ecology Association (AEET). Our main goals are to promote knowledge and training and exchange experiences on all aspects of ecoinformatics, including data management, statistical modelling, programming, etc.

Reproducible Science: What, Why, How

Reproducibility is a hot topic in science nowadays (e.g. see this Nature special). Some argue that we are in the middle of a ‘reproducibility crisis’, and thus scientists are being strongly encouraged to increase the reproducibility of their research.

Reproducible workflows

As a side product (or trailer) of our paper on reproducible science, we made a video promoting reproducible workflows. Particularly, showing how using Git and Rmarkdown make your research and scientific collaboration way much easier and better, compared to a typical (non-reproducible) workflow involving Excel, Word, some figure production software, and a lot of manual steps.

Writing papers in Rmarkdown

Rmarkdown is a great tool for reproducible science. You can combine text and code to produce dynamic reports that generate updated results with a single click, as in the example below.