Apply Arrhenius scaling to 25 degC for temperature-dependent traits

Rows whose measurement temperature covariate is missing are dropped with a warning rather than silently assigned a default temperature. If no temperature covariate is recorded for any observation the function returns an empty data frame (zero rows, same columns as data).

Usage

arrhenius.scaling.traits(
  data,
  covariates,
  temp.covariates,
  new.temp = 25,
  missing.temp = 25
)

Arguments

data

data frame of data to scale, as returned by query.data()

covariates

data frame of covariates, as returned by query.covariates().

temp.covariates

names of covariates used to adjust for temperature; if length > 1, order matters (first will be used preferentially)

new.temp

the reference temperature for the scaled traits. Currently 25 degC

missing.temp

no longer used; kept for backward compatibility only

Author

Carl Davidson, David LeBauer, Ryan Kelly