mappestRisk

Aims of the package

The goal of mappestRisk package is to facilitate the transition from development data of arthropod crop-pests’ thermal biology obtained in lab-controlled conditions to understandable forecasts assessing risk of pest occurrence in a given region. Closing this gap usually involves two key steps: (1) to fit and select nonlinear regression models and derived thermal traits under ecological criteria, and (2) to project these traits onto interest regions for pest risk assessment by extracting climate data. However, most data producers from physiology labs have limited opportunities and time to develop their R programming skills, so address these two steps may not be straightforward.

For this purpose, mappestRisk intends to facilitate this workflow for any researcher with minimal, basic R programming skills. This package has been built upon previous efforts such as devRate (François Rebaudo, Struelens, and Dangles 2018), rTPC and nls.multstart packages (Padfield, O’Sullivan, and Pawar 2021) and a methodology for predicting climatic suitability based on fundamental thermal niche as estimated by thermal performance curves, as suggested in Taylor et al. (2019) . Up to date, mappestRisk is built for modelling developmental thermal performance curves, since this is the most commonly measured life-history trait in experimental approaches and it has major contributions to fitness dependence on temperature (Pawar et al. 2024) and it also allows to predict phenologies (Schmalensee et al. 2021).

Therefore, mappestRisk has three different modules: (1) model fitting & selection using a set of the most commonly used equations describing developmental responses to temperature under the nls.multstart framework (Padfield and Matheson 2020) using equation helpers from rTPC(Padfield and O’Sullivan 2023) and devRate (Francois Rebaudo and Regnier 2024), with visualization of model fitting to help model selection by the user; (2) calculation of suitability thermal limits, which consist on a temperature interval delimiting the optimal performance zone or suitability; and (3) climatic data extraction & visualization with either exportable rasters or static or interactive map figures.

Installation

mappestRisk package can be installed from the GitHub repository:

#remotes::install_github("EcologyR/mappestRisk")  
#library(mappestRisk) 
devtools::load_all() #for now, provisionally 

If you want to clone or fork the repository or open and read some issues, you can find the code here.

Example: mappestRisk workflow

1. Fit thermal performance curves (TPC) to your data and plot results and parameter uncertainty:

In this example, we’ll show how to fit one to several thermal performance curves to a data set of development rate variation across temperatures¹. The following code provides an example as given in fit_devmodels() function documentation, with a data table showing the output of fitted models, and how to visualize them for selecting curves using plot_devmodels().

data("aphid") 
fitted_tpcs_aphid <- fit_devmodels(temp = aphid$temperature,  
                                   dev_rate = aphid$rate_value, 
                                   model_name = c("briere2", "lactin2", "flextpc"))

plot_devmodels(temp = aphid$temperature,
               dev_rate = aphid$rate_value,
               fitted_parameters = fitted_tpcs_aphid,
               species = "Brachycaudus schwartzi",
               life_stage = "Nymphs")

For a more complete explanation and example of model fitting and visualization, see TPCs model fitting article.

Additionally, we recommend here to propagate uncertainty in parameter estimation of the fitted and selected TPC models using bootstrap procedures with residual resampling, following vignettes of rTPC package (Padfield, O’Sullivan, and Pawar 2021). This can be done with the function predict_curves() by setting the argument propagate_uncertainty to be TRUE. Resulting predictions can be plotted using plot_uncertainties(). A detailed explanation is given in the TPCs model fitting article.

preds_boots_aphid <-predict_curves(temp = aphid$temperature,          
                                   dev_rate = aphid$rate_value,
                                   fitted_parameters = fitted_tpcs_aphid,
                                   model_name_2boot = c("briere2", "lactin2", "flextpc"),
                                   propagate_uncertainty = TRUE,
                                   n_boots_samples = 100)
#> Loading required namespace: boot
#> 
#> ADVISE: the simulation of new bootstrapped curves takes some time. Await patiently or reduce your `n_boots_samples`
#> 
#>  Bootstrapping simulations completed

plot_uncertainties(bootstrap_uncertainties_tpcs = preds_boots_aphid,
                   temp = aphid$temperature,
                   dev_rate = aphid$rate_value,
                   species = "Brachycaudus schwartzi",
                   life_stage = "Nymphs")

2. Calculate thermal suitability bounds:

After the previous steps, the user can calculate the thermal boundaries of the optimal zone of the TPC –i.e., those temperature values yielding the Y-th quantile of the development rate (default to $\mathrm{Q}{75}$) at both sides of the curve peak or $R\max$. Once a model have been selected under both ecological and statistical criteria, the the thermal_suitability_bounds() function calculates these values:

boundaries_aphid <- therm_suit_bounds(preds_tbl = preds_boots_aphid,       
                                      model_name = "lactin2",        
                                      suitability_threshold = 80) 

3. Climatic data extraction and projection

These optimal thermal boundaries are used for spatial projection of pest risk. The map_risk() function automatically downloads temperature data into a SpatRaster format from WorldClim masked into an user-defined region or area, and then calculates the number of months per year with highly suitable temperatures for pest development.

risk_rast <- map_risk(t_vals = boundaries_aphid, 
                      path = "~/downloaded_maps", # directory to download data 
                      region = "Réunion",    
                      mask = TRUE,
                      plot = TRUE,
                      interactive = FALSE,
                      verbose = TRUE)
#> 
#> (Down)loading countries map...
#> 
#> (Down)loading temperature rasters...
#> 
#> Cropping temperature rasters to region...
#> 
#> Masking temperature rasters with region...
#> 
#> Computing summary layers...
#> 
#> Plotting map...

Citation

If using this package, please cite it:

citation("mappestRisk") 
To cite mappestRisk in publications use:

  San Segundo Molina, D., Barbosa, A.M., Pérez-Luque, A.J. &
  Rodríguez-Sánchez, F. 2024. mappestRisk: An R package for modelling
  and mapping risk of pest development based on known thermal limits
  https://ecologyr.github.io/templateRpackage/mappestRisk

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {mappestRisk},
    author = {Darío {San-Segundo  Molina} and A. Márcia Barbosa and Antonio Jesús Pérez-Luque and Francisco Rodríguez-Sánchez},
    year = {2024},
    url = {https://ecologyr.github.io/templateRpackage/mappestRisk},
  }

Funding

The development of this software has been funded by Fondo Europeo de Desarrollo Regional (FEDER) and Consejería de Transformación Económica, Industria, Conocimiento y Universidades of Junta de Andalucía (proyecto US-1381388 led by Francisco Rodríguez Sánchez, Universidad de Sevilla).

References:

Padfield, Daniel, and Granville Matheson. 2020. “Nls.multstart: Robust Non-Linear Regression Using AIC Scores.” https://CRAN.R-project.org/package=nls.multstart.

Padfield, Daniel, and Hannah O’Sullivan. 2023. “rTPC: Fitting and Analysing Thermal Performance Curves.” https://CRAN.R-project.org/package=rTPC.

Padfield, Daniel, Hannah O’Sullivan, and Samraat Pawar. 2021. “rTPC and Nls.multstart: A New Pipeline to Fit Thermal Performance Curves in r.” Methods in Ecology and Evolution 12 (6): 1138–43. https://doi.org/10.1111/2041-210X.13585.

Pawar, Samraat, Paul J. Huxley, Thomas R. C. Smallwood, Miles L. Nesbit, Alex H. H. Chan, Marta S. Shocket, Leah R. Johnson, Dimitrios-Georgios Kontopoulos, and Lauren J. Cator. 2024. “Variation in Temperature of Peak Trait Performance Constrains Adaptation of Arthropod Populations to Climatic Warming.” Nature Ecology & Evolution, January, 1–11. https://doi.org/10.1038/s41559-023-02301-8.

Rebaudo, Francois, and Baptiste Regnier. 2024. “devRate: Quantify the Relationship Between Development Rate and Temperature in Ectotherms.” https://CRAN.R-project.org/package=devRate.

Rebaudo, François, Quentin Struelens, and Olivier Dangles. 2018. “Modelling Temperature-Dependent Development Rate and Phenology in Arthropods: The devRate Package for r.” Methods in Ecology and Evolution 9 (4): 1144–50. https://doi.org/https://doi.org/10.1111/2041-210X.12935.

Schmalensee, Loke von, Katrín Hulda Gunnarsdóttir, Joacim Näslund, Karl Gotthard, and Philipp Lehmann. 2021. “Thermal Performance Under Constant Temperatures Can Accurately Predict Insect Development Times Across Naturally Variable Microclimates.” Ecology Letters 24 (8): 1633–45. https://doi.org/10.1111/ele.13779.

Taylor, Rachel A., Sadie J. Ryan, Catherine A. Lippi, David G. Hall, Hossein A. Narouei-Khandan, Jason R. Rohr, and Leah R. Johnson. 2019. “Predicting the Fundamental Thermal Niche of Crop Pests and Diseases in a Changing World: A Case Study on Citrus Greening.” Journal of Applied Ecology 56 (8): 2057–68. https://doi.org/10.1111/1365-2664.13455.

Footnotes

1. At least 4 unique temperatures are required. Fore more details, see documentation and vignettes. ↩