oneill_1972: O'Neill model for fitting thermal performance curves

In padpadpadpad/rTPC: Fitting and Analysing Thermal Performance Curves

O'Neill model for fitting thermal performance curves

Description

O'Neill model for fitting thermal performance curves

Usage

oneill_1972(temp, rmax, ctmax, topt, q10)

Arguments

temp	temperature in degrees centigrade
rmax	maximum rate at optimum temperature
ctmax	high temperature (ºC) at which rates become negative
topt	optimum temperature (ºC)
q10	defines the fold change in performance as a result of increasing the temperature by 10 ºC

Details

Equation:

rate = r_{max} \cdot \bigg(\frac{ct_{max} - temp}{ct_{max} - t_{opt}}\bigg)^{x} \cdot exp^{x \cdot \frac{temp - t_{opt}}{ct_{max} - t_{opt}}}

where: x = \frac{w^{2}}{400}\cdot\bigg(1 + \sqrt{1 + \frac{40}{w}}\bigg)^{2}

and:\ w = (q_{10} - 1)\cdot (ct_{max} - t_{opt})

Start values in get_start_vals are derived from the data and previous values in 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.

References

O’Neill, R.V., Goldstein, R.A., Shugart, H.H., Mankin, J.B. Terrestrial Ecosystem Energy Model. Eastern Deciduous Forest Biome Memo Report Oak Ridge. The Environmental Sciences Division of the Oak Ridge National Laboratory. (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 = 'oneill_1972')
# fit model
mod <- nls.multstart::nls_multstart(rate~oneill_1972(temp = temp, rmax, ctmax, topt, q10),
data = d,
iter = c(4,4,4,4),
start_lower = start_vals - 10,
start_upper = start_vals + 10,
lower = get_lower_lims(d$temp, d$rate, model_name = 'oneill_1972'),
upper = get_upper_lims(d$temp, d$rate, model_name = 'oneill_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 Jan. 17, 2024, 5:33 a.m.