The goal of mappestRisk package is to facilitate the transition from
development data of pests obtained in lab-controlled conditions to
understandable forecasts assessing risk of pest occurrence in a given
region.
For that purpose, mappestRisk is 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 deductive, process-based approaches (i.e.,
those based on species performance variation across temperatures), as
suggested in Taylor et al. (2019) . For now, mappestRisk is built for
modelling developmental thermal performance curves, as this is the most
commonly measured life-history trait in experimental approaches and it
has been observed to have major contribution to fitness dependence on
temperature (Pawar et al. 2024).
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.multstartframework (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.
# install.packages("devtools")
# devtools::install_github("EcologyR/mappestRisk")
#or alternatively
# remotes::install_github("EcologyR/mappestRisk")
#and load the package
#library(mappestRisk)
devtools::load_all() #for now, provisionallyIf you want to clone or fork the repository or open and read some issues, you can find the code here.
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.
data("aphid")
fitted_tpcs_aphid <- fit_devmodels(temp = aphid$temperature,
dev_rate = aphid$rate_value,
model_name = "all")
#> fitting model beta
#> fitting model boatman
#> fitting model briere1
#> poorly informative start values for Brière-1 model
#> fitting model briere2
#> fitting model joehnk
#> fitting model kamykowski
#> fitting model lactin1
#> fitting model lactin2
#> fitting model mod_weibull
#> fitting model mod_polynomial
#> fitting model oneill
#> fitting model pawar
#> fitting model ratkowsky
#> fitting model schoolfield
#> fitting model regniere
#> generic starting values
#> fitting model thomas
#> fitting model wang
print(fitted_tpcs_aphid)
#> # A tibble: 68 × 8
#> param_name start_vals param_est param_se model_name model_AIC model_BIC
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 a 0.145 0.143 0.00630 beta -43.0 -43.3
#> 2 b 25 26.7 1.10 beta -43.0 -43.3
#> 3 c 25 202. 13602. beta -43.0 -43.3
#> 4 d 2 100 12049. beta -43.0 -43.3
#> 5 e 2 32.6 2283. beta -43.0 -43.3
#> 6 rmax 0.145 0.142 0.00648 boatman -42.4 -42.7
#> 7 tmin 15 0 104. boatman -42.4 -42.7
#> 8 tmax 32.5 43.7 66.3 boatman -42.4 -42.7
#> 9 a 1.1 1.42 1.03 boatman -42.4 -42.7
#> 10 b 0.4 2.43 19.9 boatman -42.4 -42.7
#> # ℹ 58 more rows
#> # ℹ 1 more variable: model_fit <list>To help select which model might be more appropriate, the function
plot_devmodels() draws the predicted TPCs for each
adequately-converged model. This step aims to improve model selection
based not only on statistical criteria (i.e. AIC and number of
parameters) but also on ecological realism, since curves can be
graphically checked to select realistic shapes and thermal traits
–vertical cuts with x-axis such as
plot_devmodels(temp = aphid$temperature,
dev_rate = aphid$rate_value,
fitted_parameters = fitted_tpcs_aphid,
species = "Brachycaudus schwartzi",
life_stage = "Nymphs")
After careful examination of fitted TPCs in plot_devmodels(), the
predictions of the selected models must be allocated in a data.frame
in order to continue the package workflow towards climatic projection
for pest risk analysis. 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 and by providing a number of
bootstrap sampling iterations in the argument n_boots_samples.
This function requires some time to compute bootstraps and predictions
of the multiple TPCs it generates. Hence, it is important to use only
one or few TPC models in the argument model_name_2boot based on visual
examination from plot_devmodels() with reasonable selection. We
discourage to bootstrap all TPC models for computational and time
consuming reasons.
In our example, we choose three models that performed similarly and,
unlike others with similar or even higher AICs, had a markedly higher
left-skewness, e.g., briere2, thomas and lactin2. After a few
minutes, we obtain the data.frame:
preds_boots_aphid <-predict_curves(temp = aphid$temperature,
dev_rate = aphid$rate_value,
fitted_parameters = fitted_tpcs_aphid,
model_name_2boot = c("briere2", "thomas", "lactin2"),
propagate_uncertainty = TRUE,
n_boots_samples = 100)
#>
#> ADVISE: the simulation of new bootstrapped curves takes some time. Await patiently or reduce your `n_boots_samples`
#>
#> Bootstrapping simulations completedThe bootstrapped uncertainty curves can be plotted easily with the
plot_uncertainties() function.
plot_uncertainties(bootstrap_uncertainties_tpcs = preds_boots_aphid,
temp = aphid$temperature,
dev_rate = aphid$rate_value,
species = "Brachycaudus schwartzi",
life_stage = "Nymphs")
Once a model have been selected under both ecological realism and
statistical criteria, the user can estimate the thermal boundaries
defining the suitable range of temperatures for the studied population.
The thermal_suitability_bounds() function calculate these values given
the tibble output from predict_curves() function and the selected
model name (note that only one model is allowed each time for this
function). Additionally, a value of suitability defining the
quantile-upper part of the curve can be provided by the user
(predict_curves() function, this function inherits the bootstrapped
TPCs to calculate thermal suitability boundaries for each bootstrapped
curved in addition to the estimated curve.
boundaries_aphid <- therm_suit_bounds(preds_tbl = preds_boots_aphid,
model_name = "lactin2",
suitability_threshold = 80)
print(boundaries_aphid)
#> # A tibble: 102 × 6
#> model_name suitability tval_left tval_right pred_suit iter
#> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 lactin2 80 % NA NA NA NA
#> 2 lactin2 80 % NA NA 0.188 1
#> 3 lactin2 80 % 21 37 0.115 2
#> 4 lactin2 80 % 21 36 0.112 3
#> 5 lactin2 80 % 22 36.5 0.113 4
#> 6 lactin2 80 % 21.5 36 0.115 5
#> 7 lactin2 80 % 21 35.5 0.117 6
#> 8 lactin2 80 % 21 36.5 0.115 7
#> 9 lactin2 80 % 21 36 0.115 8
#> 10 lactin2 80 % NA NA 0.204 9
#> # ℹ 92 more rowsUsing the thermal boundaries provided by the previous function and a set of raster maps of monthly temperatures for a given region (which can be provided by the user or downloaded by the function), two maps are plotted. The left panel (risk map) shows how many months a year (on average across simulations) thermal conditions are suitable for the development of the pest. The right panel (uncertainty map) shows the standard deviation of the risk index for each pixel.
Independently of whether plot is set to TRUE or FALSE, the
function returns a raster that can be later exported by the user.
# Downloading temperature data with geodata::wordlclim_global().
# May take time the first time you use the function on the same 'path'.
risk_rast <- map_risk(t_vals = boundaries_aphid,
path = "~/downloaded_maps", # directory to download data
region = "Morocco",
mask = TRUE,
plot = TRUE,
interactive = FALSE,
verbose = TRUE)
#>
#> (Down)loading countries map...
#>
#> (Down)loading temperature rasters...
#>
#> Cropping temperature rasters to region...
#>
#> Computing summary layers...
#>
#> Plotting map...
#>
#> Finished!
# We can also save the raster with:
# terra::writeRaster(risk_rast, filename = "~/output_maps/risk_rast.tif")
# Alternativele 1: if you already have a raster of monthly average temperatures for your region of interest, you can use that as input:
## load it (here Luxembourg data)
tavg_file <- system.file("extdata/tavg_lux.tif", package = "mappestRisk")
## import it with `terra`
tavg_rast <- terra::rast(tavg_file)
## and apply the function
risk_rast <- map_risk(t_vals = boundaries_aphid,
t_rast = tavg_rast,
mask = TRUE,
plot = TRUE,
interactive = FALSE,
verbose = TRUE)
#>
#> Computing summary layers...
#>
#> Plotting map...
#>
#> Finished!
# Alternative 2: if the region to project risk is given by the user as a spatial feature (sf) object.
andalucia_sf <- readRDS(system.file("extdata", "andalucia_sf.rds",
package = "mappestRisk"))
risk_rast <- map_risk(t_vals = boundaries_aphid,
path = "~/downloaded_maps", # directory to download data
region = andalucia_sf,
mask = TRUE,
plot = TRUE,
interactive = FALSE,
verbose = TRUE)
#>
#> (Down)loading temperature rasters...
#>
#> Cropping temperature rasters to region...
#>
#> Computing summary layers...
#>
#> Plotting map...
#>
#> Finished!
Additionally, the map_risk() can produce interactive maps based on
terra::plet() by setting the argument interactive to TRUE. These
maps are html objects that can be displayed in your browser, with one
clickable tab for the risk map and another one for the uncertainty map.
You can try yourself this option in your RStudio Viewer.
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: Create Maps Forecasting Risk
of Pest Occurrence
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},
}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).
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.
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
-
At least 4 unique temperatures are required. Fore more details, see documentation and vignettes. ↩