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Atkin model for fitting thermal performance curves

Usage

atkin_2005(temp, r0, a, b)

Arguments

temp

temperature in degrees centigrade

r0

scaling parameter, the minimum trait value

a

arbitrary scaling parameter

b

arbitrary scaling parameter

Value

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

Details

Equation: $$rate = B_0 \cdot (a - b \cdot T)^{\frac{T}{10}}$$

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 derived from the data or based extreme values that are unlikely to occur in ecological settings.

Note

Generally we found this model easy to fit.

References

Atkin, OK, Bruhn D, Tjoelker MG. Response of Plant Respiration to Changes in Temperature: Mechanisms and Consequences of Variations in Q10 Values and Acclimation. In Plant Respiration. 2005.

Author

Francis Windram

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 = 'atkin_2005')
# fit model
mod <- nls.multstart::nls_multstart(rate~atkin_2005(temp = temp, r0, a, b),
data = d,
iter = 200,
start_lower = start_vals - 10,
start_upper = start_vals + 10,
lower = get_lower_lims(d$temp, d$rate, model_name = 'atkin_2005'),
upper = get_upper_lims(d$temp, d$rate, model_name = 'atkin_2005'),
supp_errors = 'Y',
convergence_count = FALSE)

# look at model fit
summary(mod)
#> 
#> Formula: rate ~ atkin_2005(temp = temp, r0, a, b)
#> 
#> Parameters:
#>     Estimate Std. Error t value Pr(>|t|)  
#> r0 7.332e-04  9.598e-04   0.764   0.4644  
#> a  2.352e+01  1.086e+01   2.166   0.0585 .
#> b  4.212e-01  2.152e-01   1.957   0.0820 .
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 0.2769 on 9 degrees of freedom
#> 
#> Number of iterations to convergence: 20 
#> Achieved convergence tolerance: 1.49e-08
#> 

# 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()