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Determine Thermal Boundaries for Optimal Performance Level

Source: R/therm_suit_bounds.R
therm_suit_bounds.Rd

Calculate thermal boundaries that define the suitable region of a Thermal Performance Curve (TPC) corresponding to a user-defined optimal performance level.

Usage

therm_suit_bounds(
  preds_tbl = NULL,
  model_name = NULL,
  suitability_threshold = NULL
)

Source

The dataset used in the example was originally published in Satar & Yokomi (2022) under the CC-BY-NC license

Arguments

preds_tbl

a tibble object as produced by predict_curves().

model_name

character. Name of one or several of the TPC models fitted first in fit_devmodels() and predicted next in predict_curves(). If using model_name = "all" all models contained in preds_tbl will be used.

suitability_threshold

A numeric value from 50 to 100 representing the quantile of the curve that provides the user-defined optimal performance. For instance, setting suitability_threshold to 80 identifies the top 20% (or quantile 80) of the maximum values of the development rate predicted by the chosen TPC model. If suitability_threshold equals 100, the function returns the optimum temperature for development rate.

Value

A tibble with six columns:

  • model_name: A string indicating the selected TPC model used for projections.

  • suitability: A string indicating the suitability threshold in percentage (see suitability_threshold).

  • tval_left: A number representing the lower thermal boundary delimiting the suitable region of the TPC.

  • tval_right: A number representing the upper thermal boundary delimiting the suitable region of the TPC.

  • pred_suit: A number corresponding to the predicted development rate value determining the chosen quantile threshold of the maximum rate (i.e., suitability percentage of maximum rate).

  • iter: A string determining the TPC identity from the bootstrapping procedure in predict_curves() function, or estimate when it represents the estimated TPC fitted in fit_devmodels().

References

Angilletta, M.J., (2006). Estimating and comparing thermal performance curves. J. Therm. Biol. 31: 541-545. (for model selection in TPC framework)

Padfield, D., O'Sullivan, H. and Pawar, S. (2021). rTPC and nls.multstart: A new pipeline to fit thermal performance curves in R. Methods Ecol Evol. 12: 1138-1143.

Rebaudo, F., Struelens, Q. and Dangles, O. (2018). Modelling temperature-dependent development rate and phenology in arthropods: The devRate package for R. Methods Ecol Evol. 9: 1144-1150.

Satar, S. and Yokomi, R. (2002). Effect of temperature and host on development of Brachycaudus schwartzi (Homoptera: Aphididae). Ann. Entomol. Soc. Am. 95: 597-602.

See also

browseVignettes("rTPC") for model names, start values searching workflows, and bootstrapping procedures using both rTPC::get_start_vals() and nls.multstart::nls_multstart()

fit_devmodels() for fitting Thermal Performance Curves to development rate data, which is in turn based on nls.multstart::nls_multstart(). predict_curves() for bootstrapping procedure based on the above-mentioned rTPC vignettes.

Examples

if (FALSE) { # interactive()
data("aphid")

fitted_tpcs <- fit_devmodels(temp = aphid$temperature,
                             dev_rate = aphid$rate_value,
                             model_name = "all")

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

# Obtain prediction TPCs with bootstraps for propagating uncertainty:
boot_tpcs <- predict_curves(temp = aphid$temperature,
                            dev_rate = aphid$rate_value,
                            fitted_parameters = fitted_tpcs,
                            model_name_2boot = c("lactin2", "briere2", "beta"),
                            propagate_uncertainty = TRUE,
                            n_boots_samples = 10)

print(boot_tpcs)

# Plot bootstrapped curves:

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

# Calculate Q80 thermal bounds

boundaries <- therm_suit_bounds(preds_tbl = boot_tpcs,
                                model_name = "lactin2",
                                suitability_threshold = 80)
head(boundaries)
}