New paper out! A common computational approach to predict organismal fitness and distributions in the field could vastly overpredict the potential range of thermal suitability for malaria transmission relative to performance characterized at constant temperatures: www.nature.com/articles/s41...
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)
Same fort me Paco, It's also been a pleasure for me :) Thanks a lot for your effort!
Two years ago we got together in a beautiful mountain to learn and develop #rstats packages for ecology
After {labeleR} (ecologyr.github.io/labeleR/) and {BlueCarbon} (ecologyr.github.io/BlueCarbon/), {mappestRisk} is now officially out! See thread by lead author @dario-ssm.bsky.social 👇
gracias :))
Gracias Julen :)
If you have any question or if you want to contribute and/or report possible bugs, please open an issue in the repo (github.com/EcologyR/map...). Thanks! :)
github.com/EcologyR/map...
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.
We also thanks authors of recent open-source software products, and particularly to
@padpadpadpad.bsky.social and @franwin.co.uk for the development of `rTPC` R package.
github.com/padpadpadpad...
This package has been developed by the collaboration of
@frodsan.bsky.social
@ambarbosa.bsky.social @ajpelu.bsky.social , and myself (@dario-ssm.bsky.social)
github.com/EcologyR/map...
You can install the package directly from CRAN by running in R `install.packages("mappestRisk")` or from its GitHub development version using `remotes::install_github("EcologyR/mappestRisk")`
cran.r-project.org/web/packages...
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().
We suggest following this workflow:
1. Fit thermal performance curves (TPCs) with multiple nonlinear models using fit_devmodels() (For now, 16 TPC model equations; we'll include soon new equations available in the development version of `rTPC` package
🗺️🐛 mappestRisk is now on CRAN! We developed this R package to help non-expert R users create pest-risk maps based on experimental data of the thermal biology of crop-pest species.
github.com/EcologyR/map...
We hope that this package contributes to facilitate ecological forecasts on pest risk for the scientific community and R users.
If you have any question or if you want to contribute and/or report possible bugs, please open an issue in the repo (github.com/EcologyR/map...).
Thanks! :)
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.
We also thanks authors of recent open-source software products, and particularly to @padpadpadpad.bsky.social and all the contributors to `rTPC` and `nls.multstart` R packages.
This package has been developed by the collaboration of @frodsan.bsky.social an.bsky.social, @ambarbosa.bsky.social osa.bsky.social, u.bsky.social and myself (@dario-ssm.bsky.social).
You can install the package directly from CRAN by running in R `install.packages("mappestRisk")` or from its GitHub development version using `remotes::install_github("EcologyR/mappestRisk")`
cran.r-project.org/web/packages...
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().
