taylorsexton_1972: Taylor-Sexton model for fitting thermal performance curves

In padpadpadpad/rTPC: Fitting and Analysing Thermal Performance Curves

Taylor-Sexton model for fitting thermal performance curves

Description

Taylor-Sexton model for fitting thermal performance curves

Usage

taylorsexton_1972(temp, rmax, tmin, topt)

Arguments

temp	temperature in degrees centigrade
rmax	maximum performance/value of the trait
tmin	low temperature (ºC) at which rates become negative
topt	optimum temperature (ºC)

Details

Equation:

rate = R_{\text{max}} \cdot \frac{-(T-T_{\text{min}})^4 + 2 \cdot (T - T_{\text{min}})^2 \cdot (T_{\text{opt}}-T_{\text{min}})^2}{(T_{\text{opt}}-T_{\text{min}})^4}

Start values in get_start_vals are derived from the data or sensible values from the literature.

Limits in get_lower_lims and get_upper_lims are based on extreme values that are unlikely to occur in ecological settings.

Value

a numeric vector of rate values based on the temperatures and parameter values provided to the function

Note

Generally we found this model easy to fit.

Author(s)

Francis Windram

References

Taylor, S. E. & Sexton, O. J. Some implications of leaf tearing in Musaceae. Ecology 53, 143–149 (1972).

Examples

# load in ggplot
library(ggplot2)

# subset for the first TPC curve
data('chlorella_tpc')
d <- subset(chlorella_tpc, curve_id == 1)

# get start values and fit model
start_vals <- get_start_vals(d$temp, d$rate, model_name = 'taylorsexton_1972')
# fit model
mod <- nls.multstart::nls_multstart(rate~taylorsexton_1972(temp = temp, rmax, tmin, topt),
data = d,
iter = 200,
start_lower = start_vals - 10,
start_upper = start_vals + 10,
lower = get_lower_lims(d$temp, d$rate, model_name = 'taylorsexton_1972'),
upper = get_upper_lims(d$temp, d$rate, model_name = 'taylorsexton_1972'),
supp_errors = 'Y',
convergence_count = FALSE)

# look at model fit
summary(mod)

# get predictions
preds <- data.frame(temp = seq(min(d$temp), max(d$temp), length.out = 100))
preds <- broom::augment(mod, newdata = preds)

# plot
ggplot(preds) +
geom_point(aes(temp, rate), d) +
geom_line(aes(temp, .fitted), col = 'blue') +
theme_bw()

padpadpadpad/rTPC documentation built on Feb. 21, 2025, 5:30 a.m.