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Drosophila suzukii multi-trait thermal-tolerance data

Source: R/data.R
dsuzukii.Rd

Per-individual thermal-tolerance assays for the spotted-wing fly (Drosophila suzukii), one row per fly, carrying three thermal-tolerance endpoints measured under static heat exposures at 34–38 degrees C: a lethal endpoint (dead), a sublethal knockdown time-to-event (t_coma), and a sublethal reproductive endpoint (prod). Aggregate dead to mortality/survival counts for Case 4. For the supported awake/coma arm of Case 4.2, aggregate is.na(t_coma) to an awake count at each temperature x level x sex cell. freqTLS does not fit the censored time-to-coma response or the hurdle productivity response; those analyses remain in bayesTLS. lvl indexes the exposure-duration grid as a percentage of the estimated median time-to-coma from the authors' initial TDT curves; time is the realised duration in minutes.

Usage

dsuzukii

Format

A data frame with 1407 rows and 9 variables:

id

Unique individual identifier (temp-lvl-sex-rep).

temp

Assay temperature (degrees C).

lvl

Exposure duration as a percentage of the estimated median time-to-coma from the authors' initial TDT curves.

time

Exposure duration (minutes).

sex

Sex, a factor with levels F, M.

rep

Replicate vial within a temperature x lvl x sex cell.

prod

Reproductive productivity (offspring per female per day).

dead

Mortality indicator: 1 = died, 0 = survived.

t_coma

Time to heat coma (minutes); NA where no coma was recorded for that individual.

Source

Ørsted M, Willot Q, Olsen AK, Kongsgaard V, Overgaard J (2024). Data for: Thermal limits of survival and reproduction depend on stress duration: a case study of Drosophila suzukii. Zenodo, doi:10.5281/zenodo.10602268 (distributed under CC BY 4.0). Associated article: doi:10.1111/ele.14421 . Raw file: system.file("extdata", "data_multitrait_TDT_drosophila_suzukii.csv", package = "freqTLS").

Examples

# Lethal endpoint: aggregate per-individual deaths to cell counts, then
# prepare the data for a beta-binomial 4PL.
cells <- stats::aggregate(
  cbind(n_total = rep.int(1L, nrow(dsuzukii)), n_dead = dead) ~
    temp + time + sex,
  data = dsuzukii,
  FUN = sum
)
std <- standardize_data(cells, temp = "temp", duration = "time",
                        n_total = "n_total", n_dead = "n_dead",
                        duration_unit = "minutes")