Hoy en el wrapped me ha salido @rufustf.bsky.social como mi principal acompañante.

En medio del caos, algunas cosas buenas que lo valen todo.
(Foto de una pradera al norte de Madrid en una mañana de domingo de paseo con mi madre)

This workshop has been one of the best moments in these PhD years, as it was celebrated in an inspiring place near Grazalema, in Southern Spain

mappestRisk has been possible thanks to the Workshop on R packages for ecology (ecologyr.github.io/workshop/), a great initiative by
@frodsan.bsky.social
). The R packages BlueCarbon (github.com/EcologyR/Blu...) and labeler (github.com/EcologyR/lab...) are also results of this workshop.

4. The function map_risk() automatically downloads climatic data from WorldClim (worldclim.org/data/worldcl...) for the user-defined target region and calculate how many months per year would have optimal temperatures for pest development. The output is a raster map with the potential pest risk.

4. Calculate the range of temperatures that result optimal for pest development with therm_suit_bounds().

3. Simulate multiple curves with bootstrap to propagate uncertainty in the parameters of the fitted TPCs and draw ribbons with predict_curves() and plot_uncertainties()

2. Visualize and compare fitted curves easily with plot_devmodels().

which was celebrated in an inspiring place near Grazalema, in Southern Spain.

…and would not have been possible without the R packages for ecology workshop (ecologyr.github.io/workshop/), a great initiative by @frodsan.bsky.social).
The R packages BlueCarbon (github.com/EcologyR/Blu...) and labeler (github.com/EcologyR/lab...) are also results of this workshop.

4. The function map_risk() automatically downloads climatic data from WorldClim (worldclim.org/data/worldcl...) for the user-defined target region and calculate how many months per year would have optimal temperatures for pest development. The output is a raster map with the potential pest risk.

3. Calculate the range of temperatures that result optimal for pest development with therm_suit_bounds().

3. Simulate multiple curves with bootstrap to propagate uncertainty in the parameters of the fitted TPCs and draw ribbons with predict_curves() and plot_uncertainties()

2. Visualize and compare fitted curves easily with plot_devmodels().

Estas sí son más útiles

A veces la figura no sirve de mucho pero queda bonita