The package currently supports fitting light respiration according to the slope-intercept regression extension of the Laisk method (Laisk 1977; Walker & Ort 2015), the Kok method (Kok 1956), and the Yin modification of the Kok method (Yin et al. 2009, 2011).
library(broom)
library(dplyr)
library(photosynthesis)
acq_data = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
read.csv()
fit = fit_photosynthesis(
.data = acq_data,
.photo_fun = "r_light",
.model = "walker_ort_2015",
.vars = list(.A = A, .Q = Qin, .C = Ci),
C_upper = 300,
# Irradiance levels used in experiment
Q_levels = c(1500, 750, 375, 125, 100, 75, 50, 25),
)
# The 'fit' object inherits class 'lm' and many methods can be used
## Model summary:
summary(fit)##
## Call:
## lm(formula = `(Intercept)` ~ gamma_star, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.218920 -0.136553 0.007685 0.117016 0.249953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1949 0.1002 -1.945 0.0998 .
## gamma_star 44.3113 3.2518 13.627 9.7e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1738 on 6 degrees of freedom
## Multiple R-squared: 0.9687, Adjusted R-squared: 0.9635
## F-statistic: 185.7 on 1 and 6 DF, p-value: 9.698e-06## (Intercept) gamma_star
## -0.19485 44.31131## 95% confidence intervals:
## n.b. these confidence intervals are not correct because the regression is fit
## sequentially. It ignores the underlying data and uncertainty in estimates of
## slopes and intercepts with each A-C curve. Use '.method = "brms"' to properly
## calculate uncertainty.
confint(fit)## 2.5 % 97.5 %
## (Intercept) -0.4400221 0.05032205
## gamma_star 36.3544139 52.26820542## # A tibble: 2 × 7
## term estimate std.error statistic p.value conf.low conf.high
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -0.195 0.100 -1.94 0.0998 -0.440 0.0503
## 2 gamma_star 44.3 3.25 13.6 0.00000970 36.4 52.3## [1] 0.1812764library(broom)
library(dplyr)
library(photosynthesis)
acq_data = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
read.csv()
fit = fit_photosynthesis(
.data = acq_data,
.photo_fun = "r_light",
.model = "yin_etal_2011",
.vars = list(.A = A, .phiPSII = PhiPS2, .Q = Qin),
Q_lower = 20,
Q_upper = 250
)
# The 'fit' object inherits class 'lm' and many methods can be used
## Model summary:
summary(fit)##
## Call:
## lm(formula = .A ~ x_var, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8587 -0.4493 0.2041 0.7420 2.2232
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.77214 0.39419 -1.959 0.0549 .
## x_var 0.22661 0.02739 8.274 2.13e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.253 on 58 degrees of freedom
## Multiple R-squared: 0.5414, Adjusted R-squared: 0.5335
## F-statistic: 68.46 on 1 and 58 DF, p-value: 2.129e-11## (Intercept) x_var
## -0.7721399 0.2266078## 2.5 % 97.5 %
## (Intercept) -1.5611923 0.01691245
## x_var 0.1717862 0.28142935## # A tibble: 2 × 7
## term estimate std.error statistic p.value conf.low conf.high
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -0.772 0.394 -1.96 5.49e- 2 -1.56 0.0169
## 2 x_var 0.227 0.0274 8.27 2.13e-11 0.172 0.281## [1] 91.04856library(broom)
library(dplyr)
library(photosynthesis)
acq_data = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
read.csv()
fit = fit_photosynthesis(
.data = acq_data,
.photo_fun = "r_light",
.model = "kok_1956",
.vars = list(.A = A, .Q = Qin),
Q_lower = 20,
Q_upper = 150
)
# The 'fit' object inherits class 'lm' and many methods can be used
## Model summary:
summary(fit)##
## Call:
## lm(formula = .A ~ .Q, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1759 -0.4492 0.2077 0.6613 2.3544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.523701 0.396419 -1.321 0.192
## .Q 0.036347 0.004781 7.602 2.85e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.309 on 58 degrees of freedom
## Multiple R-squared: 0.4991, Adjusted R-squared: 0.4905
## F-statistic: 57.79 on 1 and 58 DF, p-value: 2.851e-10## (Intercept) .Q
## -0.52370105 0.03634651## 2.5 % 97.5 %
## (Intercept) -1.3172199 0.26981784
## .Q 0.0267761 0.04591691## # A tibble: 2 × 7
## term estimate std.error statistic p.value conf.low conf.high
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -0.524 0.396 -1.32 1.92e- 1 -1.32 0.270
## 2 .Q 0.0363 0.00478 7.60 2.85e-10 0.0268 0.0459## [1] 99.4385We added an option to estimate light respiration (Rd) and the CO2 compensation point (Ci* or Γ*) using the amazing brms package which fits models in Stan. This method has the advantage in that it properly estimates confidence intervals, but requires some post-processing. The example below shows how to do that.
The code below is not run, but copy-and-paste into your console to see output.
library(dplyr)
library(photosynthesis)
library(tidyr)
acq_data = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
read.csv()
fit = fit_photosynthesis(
.data = acq_data,
.photo_fun = "r_light",
.model = "walker_ort_2015",
.method = "brms",
.vars = list(.A = A, .Q = Qin, .C = Ci),
C_upper = 300,
# Irradiance levels used in experiment
Q_levels = c(1500, 750, 375, 125, 100, 75, 50, 25),
brm_options = list(chains = 1, seed = 20221118)
)
# The 'fit' object inherits class 'brmsfit' and many methods can be used
# The following code will extract parameter estimates and 95% CIs from the
# posterior distribution
ests = fit |>
as.data.frame() |>
dplyr::select(
r_xy = `cor_.Q_level__Intercept__.C`,
sd_x = `sd_.Q_level__Intercept`,
sd_y = `sd_.Q_level__.C`,
mu_x = b_Intercept,
mu_y = b_.C
) |>
dplyr::mutate(
gamma_star = -r_xy * sd_x / sd_y,
Rd = mu_x + gamma_star * mu_y
) |>
dplyr::summarise(dplyr::across(gamma_star:Rd, list(
estimate = median,
lower = ~quantile(., probs = 0.25),
upper = ~quantile(., probs = 0.975)
))) |>
pivot_longer(
everything(),
names_to = c("parameter", ".value"),
names_pattern = "(gamma_star|Rd)_(estimate|lower|upper)"
)The code below is not run, but copy-and-paste into your console to see output.
library(photosynthesis)
acq_data = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
read.csv()
fit = fit_photosynthesis(
.data = acq_data,
.photo_fun = "r_light",
.model = "yin_etal_2011",
.method = "brms",
.vars = list(.A = A, .phiPSII = PhiPS2, .Q = Qin),
Q_lower = 20,
Q_upper = 250,
brm_options = list(chains = 1)
)
# The 'fit' object inherits class 'brmsfit' and many methods can be used
summary(fit)The code below is not run, but copy-and-paste into your console to see output.
library(photosynthesis)
acq_data = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
read.csv()
fit = fit_photosynthesis(
.data = acq_data,
.photo_fun = "r_light",
.model = "kok_1956",
.method = "brms",
.vars = list(.A = A, .Q = Qin),
Q_lower = 20,
Q_upper = 150,
brm_options = list(chains = 1)
)
# The 'fit' object inherits class 'brmsfit' and many methods can be used
summary(fit)The functions to estimate light respiration in the original version
(fit_r_light_kok(), fit_r_light_yin(),
fit_r_light_WalkerOrt()) will no longer be updated and may
phase it out of future releases. Use one of:
fit_photosynthesisi(..., .photo_fun = 'r_light', .model = 'kok_1956')fit_photosynthesisi(..., .photo_fun = 'r_light', .model = 'walker_ort_1956')fit_photosynthesisi(..., .photo_fun = 'r_light', .model = 'yin_etal_2011')library(dplyr)
library(photosynthesis)
round_to_nearest = function(x, values) {
sapply(x, function(y, values) {
values[which.min(abs(y - values))]
}, values = values)
}
# Read in your data
dat = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
read.csv() |>
mutate(group = photosynthesis:::round_to_nearest(CO2_s, values = c(50, 100, 200, 300, 410, 600, 820, 1200, 1600))) |>
rename(A_net = A, PPFD = Qin, phi_PSII = PhiPS2, C_i = Ci)
# Fit light respiration with Yin method
r_light = fit_r_light_yin(data = dat, PPFD_lower = 20, PPFD_upper = 250)
# Fit light respiration with Kok method
r_light = fit_r_light_kok(data = dat, PPFD_lower = 20, PPFD_upper = 150)
# Fit light respiration across groups with Yin method
r_lights = fit_many(
data = dat,
funct = fit_r_light_yin,
group = "group",
PPFD_lower = 20, PPFD_upper = 250,
progress = FALSE
)
#Fit the Walker-Ort method for GammaStar and light respiration
walker_ort = fit_r_light_WalkerOrt(dat)
# View model output
summary(walker_ort[[1]])##
## Call:
## lm(formula = Intercept ~ Slope, data = coefs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.36114 -0.10958 -0.05553 0.08166 0.62016
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1526 0.1176 -1.297 0.224
## Slope -4.6004 0.4069 -11.307 5.1e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2513 on 10 degrees of freedom
## Multiple R-squared: 0.9275, Adjusted R-squared: 0.9202
## F-statistic: 127.8 on 1 and 10 DF, p-value: 5.103e-07## GammaStar r_light
## Slope 46.00427 -0.152643Kok B. 1956. On the inhibition of photosynthesis by intense light. Biochimica et Biophysica Acta 21: 234–244
Laisk A. 1977. Kinetics of photosynthesis and photorespiration in C3 plants. Nauka, Moscow.
Walker BJ, Ort DR. 2015. Improved method for measuring the apparent CO2 photocompensation point resolves the impact of multiple internal conductances to CO2 to net gas exchange. Plant, Cell & Environment 38:2462- 2474
Yin X, Struik PC, Romero P, Harbinson J, Evers JB, van der Putten PEL, Vos J. 2009. Using combined measurements of gas exchange and chlorophyll fluorescence to estimate parameters of a biochemical C3 photosynthesis model: a critical appraisal and a new integrated approach applied to leaves in a wheat (Triticum aestivum) canopy. Plant, Cell & Environment 32:448-464
Yin X, Sun Z, Struik PC, Gu J. 2011. Evaluating a new method to estimate the rate of leaf respiration in the light by analysis of combined gas exchange and chlorophyll fluorescence measurements. Journal of Experimental Botany 62: 3489–3499