R resources for ecologists

Here is a summary and portfolio of rfrelat Github public repositories. The resources are organized in three sections: tutorial and workshops; dashboards/shiny apps; and companion scripts from published scientific articles.

All scripts were developed by R. Frelat and are under GNU General Public Licence v3.

Last update October 2024.

Tutorials

All tutorials below suppose basic knowledge of the statistical software R.

• Spatial analysis in R for marine scientists
  Full day workshop, divided in two session of approx. 2h. Guidelines and dataset can be download here (67Mb).

  • How to load, extract and analyse spatial data in R?
  • Hands-on multidimensional examples
    
    

• Introduction to food webs metrics
  2h tutorial introducing marine food web analyses and how to compute weighted and unweighted food web metrics.

• Fish morphometrics

  • Fish outline analysis with R
    2h tutorial introducing outline analysis on pictures of fish and how to interpret the output from Elliptical Fourier Transform.
  • Morphometric comparison with R
    Introduction and comparison of 3 morphometric methods: traditional morphometric, geometric morphometric and outline analysis.
    
    

• Applied multivariate analysis

  • Introduction to multivariate analysis : from 2D to 3D
    2h tutorial introducing classic Principal Component Analysis and its extensions in 3 dimensions called Tensor Decomposition. Guidelines and dataset can be download here
    
  • Methods to study traits-environment relationship
    Including advanced multivariate analysis (RLQ analysis), and Bayesian modeling (Hierarchical Modelling of Species Communities).
  • Introduction to principal component analysis for measuring gender context
    2h tutorial introducing Principal Component Analysis and its interpretation in the gender perspective
    
    

• Resilience Assessment
  R vignette to introduce the cuspra R-package for the quantification of resilience based on empirical data using the stochastic cusp model

Dashboards and Shiny Apps

• Farm household exploration dashboard: https://startistic.shinyapps.io/farmhousehold_explo/
  Interactive dashboard to help understand the diversity of farming systems. The dashboard is part of the farmhoushold R package that provides tools to analyse farm household data.

• CUSPRA Dashboard: https://rfrelat.shinyapps.io/CUSPRA/
  Interactive dashboard to quantify the resilience based on empirical data using the stochastic cusp model (Sguotti et al. 2024)

• GAIA Dashboard: https://startistic.shinyapps.io/GAIA_Dashboard/
  Interactive dashboard to Guiding Acid Soil Investments in sub-Saharan Africa DOI: 10.5281/zenodo.7242765

• COMITA: https://rfrelat.shinyapps.io/comita
  Comparative tools for Integrative Trend Analysis developed as an R-package for the ICES working group COMEDA. More explanations can be found in the report of the working group:
  ICES. 2019. Working Group on Comparative Analyses between European Atlantic and Mediterranean marine ecosystems to move towards an Ecosystem-based Approach to Fisheries (WGCOMEDA). ICES Scientific Reports. 1:49. 30 pp. DOI 10.17895/ices.pub.5542

• MetaBTS: https://rfrelat.shinyapps.io/metabts
  Interactive map of the inventory of bottom trawl surveys published in Global Change Biology by Maureaud A. et al. in 2020, Are we ready to track climate‐driven shifts in marine species across international boundaries? ‐ A global survey of scientific bottom trawl data DOI: 10.1111/gcb.15404

Open science

Below is a list of companion data and scripts from published articles, ordered chronologically:

• Sguotti, C., Vasilakopoulos, P., Tzanatos, E., & Frelat, R. (2024). “Resilience assessment in complex natural systems”. Proceedings of the Royal Society B, 291, 20240089. DOI 10.1098/rspb.2024.0089
  data+script: DOI 10.5281/zenodo.10912017
  R-package: https://github.com/rfrelat/cuspra
  tutorial: https://rfrelat.github.io/cuspra.html

• Frelat, R., Kortsch, S., Kröncke, I., Neumann, H., Nordström, M. C., Olivier, P. E., & Sell, A. F. (2022). “Food web structure and community composition: a comparison across space and time in the North Sea”. Ecography, 2: e05945. DOI 10.1111/ecog.05945
  data+script: https://github.com/rfrelat/NorthSeaFoodWeb

• Lopez, D. E., Frelat, R., & Badstue, L. B. (2022). “Towards gender-inclusive innovation: Assessing local conditions for agricultural targeting“. Plos one, 17(3), e0263771. DOI 10.1371/journal.pone.0263771
  data+script: https://github.com/rfrelat/GenderClimate
  tutorial: https://rfrelat.github.io/GenderClimate.html

• Quitzau, M., Frelat, R., Bonhomme, V., Möllmann, C., Nagelkerke, L., & Bejarano, S. (2022). “Traits, landmarks and outlines: Three congruent sides of a tale on coral reef fish morphology.” Ecology and Evolution, 12, e8787. DOI 10.1002/ece3.8787
  data+script: https://github.com/rfrelat/CoralFish
  tutorial: https://rfrelat.github.io/CoralFishes.html

• Emblemsvåg, M., Werner, K. M., Núñez-Riboni, I., Frelat, R., Torp Christensen, H., Fock, H. O., & Primicerio, R. (2022). “Deep demersal fish communities respond rapidly to warming in a frontal region between Arctic and Atlantic waters“. Global Change Biology, 28(9), 2979-2990. DOI 10.1111/gcb.16113
  data+script: https://github.com/rfrelat/GreenlandFish

• Kortsch, S., Frelat, R., Pecuchet, L., Olivier, P., Putnis, I., Bonsdorff, E., … & Nordström, M. C. (2021). Disentangling temporal food web dynamics facilitates understanding of ecosystem functioning. Journal of Animal Ecology. 90: 1205– 1216. DOI: 10.1111/1365-2656.13447
  raw data: DOI:10.5061/dryad.6t1g1jwwn
  data+script+tutorial: https://rfrelat.github.io/BalticFoodWeb.html

• Maureaud, A., Frelat, R., Pécuchet, L., Shackell, N., Mérigot, B., Pinsky, M. L., … & T Thorson, J. (2021). “Are we ready to track climate‐driven shifts in marine species across international boundaries?‐A global survey of scientific bottom trawl data”. Global change biology, 27(2), 220-236. DOI 10.1111/gcb.15404
  data+script+updated database: https://github.com/AquaAuma/TrawlSurveyMetadata
  Shiny app: https://rfrelat.shinyapps.io/metabts

• Beukhof E, Frelat R, Pécuchet L, Maureaud A, Dencker TS, Sólmundsson J, Punzon A, Primicerio R, Hidalgo M, Möllmann C and Lindegren M. “Marine fish traits follow fast-slow continuum along coastal-offshore gradient.”, Scientific Report, 9: 17878 DOI: 10.1038/s41598-019-53998-2
  data+script: Deposited in Dryad Digital Repository: (https://doi.org/10.5061/dryad.ttdz08kt8).

• Olivier, P., Frelat, R., Bonsdorff, E., Kortsch, S., Kröncke, I., Möllmann, C., … and Nordström, M. C. (2019). “Exploring the temporal variability of a food web using long‐term biomonitoring data”. Ecography, 42(12):1-19, DOI 10.1111/ecog.04461.
  data: Deposited in Dryad Digital Repository: (https://doi.org/10.5061/dryad.9tg3t75)

• Caillon F., Bonhomme V., Möllmann C. and Frelat R. (2018). “A morphometric dive into fish diversity”, Ecosphere, 9(5): e02220. DOI 10.1002/ecs2.2220
  data+script+tutorial: https://rfrelat.github.io/FishMorpho.html

• Frelat R, Orio A, Casini M, Lehmann A, Mérigot B, Otto SA, Sguotti C, Möllmann , (2018). “A three-dimensional view on biodiversity changes: spatial, temporal and functional perspectives on fish communities in the Baltic Sea”, ICES Journal of Marine Science, 75(7): 2463–2475. DOI 10.1093/icesjms/fsy027
  data+script: Deposited as Supplemetary material

• Frelat R, Lindegren M, Dencker TS, Floeter J, Fock HO, Sguotti C, Stäbler M, Otto SA and Möllmann C (2017). Community ecology in 3D: Tensor decomposition reveals spatio-temporal dynamics of large ecological communities. PLoS ONE, 12(11): e0188205. DOI 10.1371/journal.pone.0188205
  data+script+tutorial: https://rfrelat.github.io/Multivariate2D3D.html