Francisco Rodríguez-Sánchez

Francisco Rodríguez-Sánchez


Universidad de Sevilla

I am a computational ecologist working at the intersection between ecology, biogeography, statistics and data science. My main research line focuses on better understanding and forecasting climate change effects on biodiversity, by coupling field observations with computational approaches based on big datasets, complex statistical models, and reproducible workflows.

My research spans multiple hierarchical scales: from ecological (years, decades) to geological (million years), from field plots to entire continents, and from individuals to populations, species and communities. I am particularly interested in developing new quantitative methods and software tools to facilitate reproducible research and investigate plant demography, community dynamics, and species range shifts and interactions under climate change.

I also teach several statistics and programming courses every year. I am fond of incorporating state-of-the-art data science techniques to deliver top-quality reproducible research. I am founding member and coordinator of the ‘R Users Group’ in Sevilla and the Ecoinformatics working group of the Spanish Terrestrial Ecology Association (AEET), which aims to foster good statistical and programming practice among ecologists.

I lead the Computational Ecology & Global Change group ( MAYBE_lab) at the Department of Plant Biology and Ecology at University of Sevilla (Spain). Please check our website to meet the team and learn more about our work and philosophy.


  • Global Change Ecology
  • Biogeography
  • Forest Dynamics
  • Plant-Animal Mutualisms
  • Open & Reproducible Science
  • Statistics & Data Science
  • R Programming
  • Teaching & Active Learning


  • PhD in Ecology, 2010

    Universidad de Sevilla

  • MSc in Ecology, 2005

    Universidad de Sevilla

  • BSc in Biology, 2001

    Universidad de Sevilla


Biogeography and evolution of plant biotic dispersal

Assembling a global, dynamic, open, reproducible database of fruit and seed traits

Computational Ecology & Data Science

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

Ecology and biogeography of laurel forests

Extinction, range dynamics and ecology of extant laurel forests in the Mediterranean and Macaronesia.

Ecology and conservation of southern Iberian forests

Investigating forest dynamics in the Strait of Gibraltar region.

Forest dynamics and climate change

Climate change, microclimates, refugia, and forest dynamics.

Modelling species range dynamics

New approaches to hindcast and forecast species range dynamics.

Recent Posts

Bayesian inference of bipartite networks structure

Inferring the structure of bipartite (e.g. pollination, frugivory, or herbivory) networks from field (observational) data is a challenging task. Interaction data are hard to collect and require typically large sampling efforts, particularly to characterize infrequent interactions.

Mapas de sombra

La sombra es un recurso escaso y muy necesario en la mayoría de ciudades y municipios. Este año -el más calido registrado hasta ahora- se estiman casi 5.000 muertes asociadas al calor en España.

Gracias / Thank you

I have just come back from a wonderful meeting of the Spanish Terrestrial Ecology Association ( AEET) where I received a prize for contributing towards a more open and reproducible science.

Using DHARMa to check Bayesian models fitted with brms

UPDATE 26 October 2022: There is now a DHARMa.helpers package that facilitates checking Bayesian brms models with DHARMa. Check it out! The R package DHARMa is incredibly useful to check many different kinds of statistical models.

Accessing data from large online rasters with Cloud-Optimized-Geotiff, GDAL, and terra R package

Quite often we want to extract climatic or environmental data from large global raster files. The traditional workflow requires downloading the rasters (quite often many GB) and then performing data extraction in your local computer.