Version:	2.1.5
Date:	2024-11-23
Title:	Tools for Plant Ecophysiology & Modeling
Depends:	R (≥ 4.0.0), ggplot2 (≥ 3.4.0), minpack.lm (≥ 1.2-1), units (≥ 0.6.6)
Imports:	checkmate (≥ 2.0.0), crayon (≥ 1.3.4), dplyr (≥ 0.8.5), furrr (≥ 0.1.0), glue (≥ 1.4.0), graphics (≥ 4.0.0), grDevices (≥ 4.0.0), gunit (≥ 1.0.2), lifecycle (≥ 1.0.0), magrittr (≥ 1.5.0), methods (≥ 3.5.0), nlme (≥ 3.1-147), progress (≥ 1.2.0), purrr (≥ 0.3.3), readr (≥ 2.0.0), rlang (≥ 0.4.6), stats (≥ 4.0.0), stringr (≥ 1.4.0), tealeaves (≥ 1.0.5), utils (≥ 4.0.0)
Suggests:	brms, broom, future, knitr, rmarkdown, testthat, tibble, tidyr, tidyselect
Description:	Contains modeling and analytical tools for plant ecophysiology. MODELING: Simulate C3 photosynthesis using the Farquhar, von Caemmerer, Berry (1980) <doi:10.1007/BF00386231> model as described in Buckley and Diaz-Espejo (2015) <doi:10.1111/pce.12459>. It uses units to ensure that parameters are properly specified and transformed before calculations. Temperature response functions get automatically "baked" into all parameters based on leaf temperature following Bernacchi et al. (2002) <doi:10.1104/pp.008250>. The package includes boundary layer, cuticular, stomatal, and mesophyll conductances to CO2, which each can vary on the upper and lower portions of the leaf. Use straightforward functions to simulate photosynthesis over environmental gradients such as Photosynthetic Photon Flux Density (PPFD) and leaf temperature, or over trait gradients such as CO2 conductance or photochemistry. ANALYTICAL TOOLS: Fit ACi (Farquhar et al. (1980) <doi:10.1007/BF00386231>) and AQ curves (Marshall & Biscoe (1980) <doi:10.1093/jxb/31.1.29>), temperature responses (Heskel et al. (2016) <doi:10.1073/pnas.1520282113>; Kruse et al. (2008) <doi:10.1111/j.1365-3040.2008.01809.x>, Medlyn et al. (2002) <doi:10.1046/j.1365-3040.2002.00891.x>, Hobbs et al. (2013) <doi:10.1021/cb4005029>), respiration in the light (Kok (1956) <doi:10.1016/0006-3002(56)90003-8>, Walker & Ort (2015) <doi:10.1111/pce.12562>, Yin et al. (2009) <doi:10.1111/j.1365-3040.2009.01934.x>, Yin et al. (2011) <doi:10.1093/jxb/err038>), mesophyll conductance (Harley et al. (1992) <doi:10.1104/pp.98.4.1429>), pressure-volume curves (Koide et al. (2000) <doi:10.1007/978-94-009-2221-1_9>, Sack et al. (2003) <doi:10.1046/j.0016-8025.2003.01058.x>, Tyree et al. (1972) <doi:10.1093/jxb/23.1.267>), hydraulic vulnerability curves (Ogle et al. (2009) <doi:10.1111/j.1469-8137.2008.02760.x>, Pammenter et al. (1998) <doi:10.1093/treephys/18.8-9.589>), and tools for running sensitivity analyses particularly for variables with uncertainty (e.g. g_mc(), gamma_star(), R_d()).
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
URL:	https://github.com/cdmuir/photosynthesis
BugReports:	https://github.com/cdmuir/photosynthesis/issues
NeedsCompilation:	no
Packaged:	2024-11-24 19:18:24 UTC; cdmuir
Author:	Joseph Stinziano [aut], Cassaundra Roback [aut], Demi Sargent [aut], Bridget Murphy [aut], Patrick Hudson [aut, dtc], Chris Muir [aut, cre]
Maintainer:	Chris Muir <cdmuir@wisc.edu>
Repository:	CRAN
Date/Publication:	2024-11-24 19:40:02 UTC

photosynthesis package

Description

Tools for Plant Ecophysiology & Modeling

Details

See the README on GitHub

Author(s)

Maintainer: Chris Muir cdmuir@wisc.edu (ORCID)

Authors:

• Joseph Stinziano josephstinziano@gmail.com (ORCID)

• Cassaundra Roback croback@unm.edu

• Demi Sargent d.sargent@westernsydney.edu.au

• Bridget Murphy bridget.murphy@mail.utoronto.ca

• Patrick Hudson hudson.patrick.j@gmail.com [data contributor]

See Also

Useful links:

• https://github.com/cdmuir/photosynthesis

• Report bugs at https://github.com/cdmuir/photosynthesis/issues

CO2 supply and demand function (mol / m^2 s)

Description

This function is not intended to be called by users directly.

Usage

A_supply(C_chl, pars, unitless = FALSE, use_legacy_version = FALSE)

A_demand(C_chl, pars, unitless = FALSE)

Arguments

Details

Supply function:

A = g_\mathrm{tc} (C_\mathrm{air} - C_\mathrm{chl})

Demand function:

A = (1 - \Gamma* / C_\mathrm{chl}) \mathrm{min}(W_\mathrm{carbox}, W_\mathrm{regen}, W_\mathrm{tpu}) - R_\mathrm{d}

Value

Value in mol / (m^2 s) of class units

Examples

bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)
enviro_par = make_enviropar(use_tealeaves = FALSE)
leaf_par = make_leafpar(use_tealeaves = FALSE)
leaf_par = bake(leaf_par, enviro_par, bake_par, constants)
# Or bake with piping (need library(magrittr))
# leaf_par %<>% bake(enviro_par, bake_par, constants)
enviro_par$T_air = leaf_par$T_leaf

pars = c(leaf_par, enviro_par, constants)
C_chl = set_units(350, umol/mol)

A_supply(C_chl, pars)

A_demand(C_chl, pars)

Conductance to CO2 (mol / m^2 / s)

Description

Conductance to CO2 (mol / m^2 / s)

• g_tc: total conductance to CO2

• g_uc: cuticular conductance to CO2

• g_bc: boundary layer conductance to CO2

• g_mc: mesophyll conductance to CO2

• g_sc: stomatal conductance to CO2

Usage

.get_gtc(pars, unitless, use_legacy_version)

.get_guc(pars, surface, unitless)

.get_gbc(pars, surface, unitless, use_legacy_version)

.get_gmc(pars, surface, unitless)

.get_gsc(pars, surface, unitless)

Arguments

Details

Default conductance model

The conductance model described in this section is used by default unless additional anatomical parameters described in the next section are provided.

Total conductance to CO2 is the sum of parallel conductances on the lower (g_\mathrm{c,lower}) and upper (g_\mathrm{c,upper}) leaf portions:

g_\mathrm{c,total} = g_\mathrm{c,lower} + g_\mathrm{c,upper}

Each partial conductance consists of two parallel conductances, the cuticular conductance (g_\mathrm{u,c}) and the in-series conductances through mesophyll (g_\mathrm{m,c}), stomata (g_\mathrm{s,c}), and boundary layer (g_\mathrm{b,c}). To simplify the formula, I use substitute resistance where r_x = 1 / g_x. For surface i:

g_{\mathrm{c},i} = g_{\mathrm{u},i} + (1 / (r_{\mathrm{m},i} + r_{\mathrm{s},i} + r_{\mathrm{b},i}))

The cuticular, stomatal, and mesophyll conductances can be the same or different for upper and lower. The partitioning factors (k_x) divide the conductance between surfaces while keeping the total conductance constant:

g_{x,\mathrm{lower}} = g_x (1 / (1 + k_x))

g_{x,\mathrm{upper}} = g_x (k_x / (1 + k_x))

g_x = g_{x,\mathrm{lower}} + g_{x,\mathrm{upper}}

How the partitioning factors work:

The boundary layer conductances for each are calculated on the basis of mass and heat transfer (see .get_gbc()).

New conductance model

The conductance model described in this section is implemented in photosynthesis (>= 2.1.0) if parameters to calculate the internal airspace and liquid-phase conductances (A_mes_A, g_liqc) are provided. These parameters are 1) the effective path lengths through the lower and upper leaf internal airspaces (delta_ias_lower, delta_ias_upper) and 2) the mesophyll area per leaf area (A_mes_A) and liquid-phase conductance per mesophyll cell area (g_liqc).

Two parallel diffusion pathways, one from each leaf surface, converge to a single CO2 concentration at the mesophyll cell boundary. We use a single liquid-phase resistance to represent the combined cell wall, plasmalemma, and chloroplast resistances. The gas-phase resistance through boundary layer, cuticle/stomata, and internal airspace is r_\mathrm{gas,c}; the liquid-phase intracellular resistance is r_\mathrm{i,c}.

r_\mathrm{total,c} = r_\mathrm{gas,c} + r_\mathrm{i,c}

The gas-phase resistance occurs through two parallel pathways, which we refer to as the 'lower' and 'upper' pathways because horizontally oriented leaves often have different anatomical properties on each surface. The gas-phase resistance through pathway i \in \{\textrm{lower,upper\}} is:

r_{\mathrm{gas,c},i} = r_{\mathrm{b,c},i} + r_{\mathrm{u+s,c},i} + r_{\mathrm{ias,c},i}

The subscripts _\mathrm{b}, _\mathrm{u+s}, and _\mathrm{ias} denote boundary layer, cuticular + stomatal, and internal airspace, respectively. The subscript _\mathrm{c} indicates we are considering the conductance to CO2 rather than another molecular species.

Cuticular and stomatal conductances (1 / resistance) are parallel, so:

1 / r_{\mathrm{u+s,c},i} = g_{\mathrm{u+s,c},i} = g_{\mathrm{u,c},i} + g_{\mathrm{s,c},i}

Substituting the above expression into the equation for r_{\mathrm{gas,c},i}:

r_{\mathrm{gas,c},i} = r_{\mathrm{b,c},i} + 1 / (g_{\mathrm{u,c},i} = g_{\mathrm{s,c},i}) + r_{\mathrm{ias,c},i}

The total gas-phase resistance is the inverse of the sum of the parallel lower and upper conductances:

1 / r_{\mathrm{gas,c}} = g_\mathrm{gas,c,lower} + g_\mathrm{gas,c,upper}

The cuticular, stomatal, and mesophyll conductances can be the same or different for upper and lower. The partitioning factors k_u and k_s divide the total cuticular and stomatal conductances, respectively, between surfaces while keeping the total conductance constant:

g_{x,\mathrm{lower}} = g_x (1 / (1 + k_x))

g_{x,\mathrm{upper}} = g_x (k_x / (1 + k_x))

g_x = g_{x,\mathrm{lower}} + g_{x,\mathrm{upper}}

How the partitioning factors work:

The internal airspace conductance is the diffusivity of CO2 at a given temperature and pressure divided by the effective path length:

g_\mathrm{ias,c,lower} = D_\mathrm{c} / \delta_\mathrm{ias,lower}

g_\mathrm{ias,c,upper} = D_\mathrm{c} / \delta_\mathrm{ias,upper}

g_iasc_lower and g_iasc_upper are calculated in the bake function. See tealeaves::.get_Dx() for calculating D_c.

The liquid-phase intracellular resistance is given by:

1 / r_\mathrm{i,c} = g_\mathrm{i,c} = g_\mathrm{liq,c} A_\mathrm{mes} / A

g_\mathrm{liq,c} is temperature sensitive. See bake().

The boundary layer conductances for each are calculated on the basis of mass and heat transfer (see .get_gbc()).

Farquhar-von Caemmerer-Berry (FvCB) C3 photosynthesis model

Description

Farquhar-von Caemmerer-Berry (FvCB) C3 photosynthesis model

Rubisco-limited assimilation rate

RuBP regeneration-limited assimilation rate

TPU-limited assimilation rate

Usage

FvCB(C_chl, pars, unitless = FALSE)

W_carbox(C_chl, pars, unitless = FALSE)

W_regen(C_chl, pars, unitless = FALSE)

W_tpu(C_chl, pars, unitless = FALSE)

Arguments

Details

Equations following Buckley and Diaz-Espejo (2015):

Rubisco-limited assimilation rate:

W_\mathrm{carbox} = V_\mathrm{c,max} C_\mathrm{chl} / (C_\mathrm{chl} + K_\mathrm{m})

where:

K_\mathrm{m} = K_\mathrm{C} (1 + O / K_\mathrm{O})

RuBP regeneration-limited assimilation rate:

W_\mathrm{regen} = J C_\mathrm{chl} / (4 C_\mathrm{chl} + 8 \Gamma*)

where J is a function of PPFD, obtained by solving the equation:

0 = \theta_J J ^ 2 - J (J_\mathrm{max} + \phi_J PPFD) + J_\mathrm{max} \phi_J PPFD

TPU-limited assimilation rate:

W_\mathrm{tpu} = 3 V_\mathrm{tpu} C_\mathrm{chl} / (C_\mathrm{chl} - \Gamma*)

Value

A list of four values with units umol CO2 / (m^2 s) of class units:

• W_carbox: Rubisco-limited assimilation rate
  

• W_regen: RuBP regeneration-limited assimilation rate
  

• W_tpu: TPU-limited assimilation rate
  

• A: minimum of W_carbox, W_regen, and W_tpu

References

Buckley TN and Diaz-Espejo A. 2015. Partitioning changes in photosynthetic rate into contributions from different variables. Plant, Cell & Environment 38: 1200-11.

Farquhar GD, Caemmerer S, Berry JA. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149: 78–90.

Examples

bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)
enviro_par = make_enviropar(use_tealeaves = FALSE)
leaf_par = make_leafpar(use_tealeaves = FALSE)
leaf_par = bake(leaf_par, enviro_par, bake_par, constants)

pars = c(leaf_par, enviro_par, constants)
C_chl = set_units(246.0161, umol / mol)
FvCB(C_chl, pars)

J: Rate of electron transport (umol/m^2/s)

Description

Calculate the rate of electron transport as a function of photosynthetic photon flux density (PPFD).

Usage

J(pars, unitless = FALSE)

Arguments

Details

J as a function of PPFD is the solution to the quadratic expression:

0 = \theta_J J ^ 2 - J (J_\mathrm{max} + \phi_J PPFD) + J_\mathrm{max} \phi_J PPFD

Value

Value in \mumol/ (m^2 s) of class units

Examples

library(magrittr)
library(photosynthesis)

bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)
enviro_par = make_enviropar(use_tealeaves = FALSE)
leaf_par = make_leafpar(use_tealeaves = FALSE)
enviro_par$T_air = leaf_par$T_leaf
leaf_par %<>% bake(enviro_par, bake_par, constants)

pars = c(leaf_par, enviro_par, constants)
J(pars, FALSE)

Running 2-parameter sensitivity analyses

Description

Running 2-parameter sensitivity analyses

Usage

analyze_sensitivity(
  data,
  funct,
  test1 = NA,
  values1,
  test2 = NA,
  values2,
  element_out = 1,
  ...
)

Arguments

Value

analyze_sensitivity runs a 2-parameter sensitivity analysis. Note that any parameter value combinations that break the input function WILL break this function. For 1-parameter sensitivity analysis, use test1 only.

Examples

# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Define a grouping factor based on light intensity to split the ACi
# curves
data$Q_2 <- as.factor((round(data$Qin, digits = 0)))

# Convert leaf temperature to K
data$T_leaf <- data$Tleaf + 273.15

# Run a sensitivity analysis on gamma_star and mesophyll conductance
# at 25 Celsius for one individual curve
# pars <- analyze_sensitivity(
#   data = data[data$Q_2 == 1500, ],
#   funct = fit_aci_response,
#   varnames = list(
#     A_net = "A",
#     T_leaf = "T_leaf",
#     C_i = "Ci",
#     PPFD = "Qin"
#   ),
#   useg_mct = TRUE,
#   test1 = "gamma_star25",
#   element_out = 1,
#   test2 = "g_mc25",
#   fitTPU = TRUE,
#   Ea_gamma_star = 0,
#   Ea_g_mc = 0,
#   values1 = seq(
#     from = 20,
#     to = 40,
#     by = 2
#   ),
#   values2 = seq(
#     from = 0.5,
#     to = 2,
#     by = 0.1
#   )
# )

# Graph V_cmax
# ggplot(pars, aes(x = gamma_star25, y = g_mc25, z = V_cmax)) +
#   geom_tile(aes(fill = V_cmax)) +
#   labs(
#     x = expression(Gamma * "*"[25] ~ "(" * mu * mol ~ mol^
#       {
#         -1
#       } * ")"),
#     y = expression(g[m][25] ~ "(" * mu * mol ~ m^{
#       -2
#     } ~ s^{
#       -1
#     } ~ Pa^
#       {
#         -1
#       } * ")")
#   ) +
#   scale_fill_distiller(palette = "Greys") +
#   geom_contour(colour = "Black", size = 1) +
#   theme_bw()
# 

Non-rectangular hyperbolic model of light responses

Description

Please use marshall_biscoe_1980().

Usage

aq_response(k_sat, phi_J, Q_abs, theta_J)

Arguments

Value

aq_response is used to describe the response of a process to absorbed light intensity. Assumes that input is absorbed light. Note that if absorbed light is not used, then the meaning of phi_J becomes unclear. This function is designed to be used with fit_aq_response, however it could easily be fed into a different fitting approach (e.g. Bayesian approaches). Originally from Marshall et al. 1980.

References

Marshall B, Biscoe P. 1980. A model for C3 leaves describing the dependence of net photosynthesis on irradiance. J Ex Bot 31:29-39

Leaf parameter temperature responses

Description

'bake' leaf parameters using temperature response functions

Usage

bake(leaf_par, enviro_par, bake_par, constants, assert_units = TRUE)

temp_resp1(par25, E_a, R, T_leaf, T_ref, unitless)

temp_resp2(par25, D_s, E_a, E_d, R, T_leaf, T_ref, unitless)

Arguments

Details

Several leaf parameters (leaf_par()) are temperature sensitive. Temperature-sensitive parameters are input at a reference temperature of 25 °C. These parameters are provided as par_name25 and then "baked" using the appropriate temperature response function and parameters in bake_par(). The "baked" parameter will have the name without "25" appended (par_name). E.g. V_cmax25 becomes V_cmax.

Temperature response functions following Buckley and Diaz-Espejo (2015)

Temperature response function 1 (temp_response1):

\mathrm{par}(T_\mathrm{leaf}) = \mathrm{par25}~\mathrm{exp}(E_\mathrm{a} / (R T_\mathrm{ref}) (T_\mathrm{leaf} - 25) / (T_\mathrm{leaf} + 273.15))

T_\mathrm{ref} is the reference temperature in K
T_\mathrm{leaf} is the leaf temperature in °C

Temperature response function 2 (temp_response2) is the above equation multiplied by:

(1 + \mathrm{exp}((D_\mathrm{s} / R - E_\mathrm{d} / (R T_\mathrm{ref})))) / (1 + \mathrm{exp}((D_\mathrm{s} / R) - (E_\mathrm{d} / (R (T_\mathrm{leaf} + 273.15)))))

Function 1 increases exponentially with temperature; Function 2 peaks a particular temperature.

Value

Constructor function for baked class. This will also inherit class leaf_par() and list(). This function ensures that temperature is "baked in" to leaf parameter calculations T_leaf using temperature response functions detailed below.

References

Buckley TN, Diaz-Espejo A. 2015. Partitioning changes in photosynthetic rate into contributions from different variables. Plant, Cell and Environment 38: 1200-1211.

Examples

bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)
enviro_par = make_enviropar(use_tealeaves = FALSE)
leaf_par = make_leafpar(
  replace = list(T_leaf = set_units(293.15, K)),
  use_tealeaves = FALSE
)
baked_leafpar = bake(leaf_par, enviro_par, bake_par, constants)

baked_leafpar$V_cmax25
baked_leafpar$V_cmax

S3 class bake_par

Description

S3 class bake_par

Usage

bake_par(.x)

Arguments

Value

Constructor function for bake_par class. This function ensures that leaf temperature gets properly "baked" into leaf parameters.

S3 class baked

Description

See bake()

Inverse non-rectangular hyperbola for J_max calculation

Description

Inverse non-rectangular hyperbola for J_max calculation

Usage

calculate_jmax(PPFD, alpha, J, theta_J)

calculate_j(PPFD, alpha, J_max, theta_J)

Arguments

Value

calculate_jmax calculates J_max given PPFD and J. It is necessary for the electron transport component of the fit_aci_response function.

calculate_j provides a model of the light response of J. It is necessary for fitting the electron transport component of the photosynthetic CO2 response curves in fit_aci_response.

Get default functions for calculated parameters in photosynthesis

Description

Get default functions for calculated parameters in photosynthesis

Usage

get_f_parameter(.f_name)

Arguments

Compiling outputs from lists

Description

Compiling outputs from lists

Usage

compile_data(data, output_type = "list", list_element)

Arguments

Value

compile_data converts the outputs of fit_many into a form more readily usable for analysis. Can be used to create dataframe of all fitted parameters, a list of model outputs, a list of graphs for plotting. This function is NOT restricted to compiling outputs from plantecophystools but could be used to compile elements from ANY list of lists.

Examples

# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Define a grouping factor based on light intensity to split the ACi
# curves
data$Q_2 <- as.factor((round(data$Qin, digits = 0)))

# Convert leaf temperature to K
data$T_leaf <- data$Tleaf + 273.15

# Fit many curves
fits <- fit_many(
  data = data,
  varnames = list(
    A_net = "A",
    T_leaf = "T_leaf",
    C_i = "Ci",
    PPFD = "Qin"
  ),
  funct = fit_aci_response,
  group = "Q_2"
)

# Compile graphs into a list for plotting
fits_graphs <- compile_data(fits,
  list_element = 2
)

# Plot one graph from the compiled list
plot(fits_graphs[[1]])

Computing measures of sensitivity

Description

Computing measures of sensitivity

Usage

compute_sensitivity(
  data,
  varnames = list(Par = "Par", test1 = "test1", test2 = "test2"),
  test1_ref,
  test2_ref
)

Arguments

Value

compute_sensitivity calculates two sets of sensitivity measures: parameter effect (Bauerle et al., 2014), and control coefficient (Capaldo & Pandis, 1997). This function is useful in determining how much a given input (assumed or otherwise) can affect the model output and conclusions. Particularly useful if a given parameter is unknown during a fitting or modeling process.

References

Bauerle WL, Daniels AB, Barnard DM. 2014. Carbon and water flux responses to physiology by environment interactions: a sensitivity analysis of variation in climate on photosynthetic and stomatal parameters. Climate Dynamics 42: 2539-2554.

Capaldo KP, Pandis SN 1997. Dimethylsulfide chemistry in the remote marine atmosphere: evaluation and sensitivity analysis of available mechanisms. J Geophys Res 102:23251-23267

Examples

# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Define a grouping factor based on light intensity to split the ACi
# curves
data$Q_2 <- as.factor((round(data$Qin, digits = 0)))

# Convert leaf temperature to K
data$T_leaf <- data$Tleaf + 273.15

# Run a sensitivity analysis on gamma_star and mesophyll conductance
# at 25 Celsius for one individual curve
# pars <- analyze_sensitivity(
#   data = data[data$Q_2 == 1500, ],
#   funct = fit_aci_response,
#   varnames = list(
#     A_net = "A",
#     T_leaf = "T_leaf",
#     C_i = "Ci",
#     PPFD = "Qin"
#   ),
#   useg_mct = TRUE,
#   test1 = "gamma_star25",
#   element_out = 1,
#   test2 = "g_mc25",
#   fitTPU = TRUE,
#   Ea_gamma_star = 0,
#   Ea_g_mc = 0,
#   values1 = seq(
#     from = 20,
#     to = 60,
#     by = 2
#   ),
#   values2 = seq(
#     from = 0.2,
#     to = 2,
#     by = 0.1
#   )
# )
# Compute measures of sensitivity
# par2 <- compute_sensitivity(
#   data = pars,
#   varnames = list(
#     Par = "V_cmax",
#     test1 = "gamma_star25",
#     test2 = "g_mc25"
#   ),
#   test1_ref = 42,
#   test2_ref = 1
# )
# # Plot control coefficients
# ggplot(par2, aes(y = CE_gamma_star25, x = CE_g_mc25, colour = V_cmax)) +
#   geom_point() +
#   theme_bw()
# # Note that in this case a missing point appears due to an infinity

S3 class constants

Description

S3 class constants

Usage

constants(.x, use_tealeaves)

Arguments

Value

Constructor function for constants class. This function ensures that physical constant inputs are properly formatted.

S3 class enviro_par

Description

S3 class enviro_par

Usage

enviro_par(.x, use_tealeaves)

Arguments

Value

Constructor function for enviro_par class. This function ensures that environmental parameter inputs are properly formatted.

Fitting pressure-volume curves

Description

Fitting pressure-volume curves

Usage

fit_PV_curve(
  data,
  varnames = list(psi = "psi", mass = "mass", leaf_mass = "leaf_mass", bag_mass =
    "bag_mass", leaf_area = "leaf_area"),
  title = NULL
)

Arguments

Value

fit_PV_curve fits pressure-volume curve data to determine: SWC: saturated water content per leaf mass (g H2O g leaf dry mass ^ -1), PI_o: osmotic potential at full turgor (MPa), psi_TLP: leaf water potential at turgor loss point (TLP) (MPa), RWC_TLP: relative water content at TLP (%), eps: modulus of elasticity at full turgor (MPa), C_FT: relative capacitance at full turgor (MPa ^ -1), C_TLP: relative capacitance at TLP (MPa ^ -1), and C_FTStar: absolute capacitance per leaf area (g m ^ -2 MPa ^ -1). Element 1 of the output list contains the fitted parameters, element 2 contains the water-psi graph, and element 3 contains the 1/psi-100-RWC graph.

References

Koide RT, Robichaux RH, Morse SR, Smith CM. 2000. Plant water status, hydraulic resistance and capacitance. In: Plant Physiological Ecology: Field Methods and Instrumentation (eds RW Pearcy, JR Ehleringer, HA Mooney, PW Rundel), pp. 161-183. Kluwer, Dordrecht, the Netherlands

Sack L, Cowan PD, Jaikumar N, Holbrook NM. 2003. The 'hydrology' of leaves: co-ordination of structure and function in temperate woody species. Plant, Cell and Environment, 26, 1343-1356

Tyree MT, Hammel HT. 1972. Measurement of turgor pressure and water relations of plants by pressure bomb technique. Journal of Experimental Botany, 23, 267

Examples

# Read in data
data <- read.csv(system.file("extdata", "PV_curve.csv",
  package = "photosynthesis"
))

# Fit one PV curve
fit <- fit_PV_curve(data[data$ID == "L2", ],
  varnames = list(
    psi = "psi",
    mass = "mass",
    leaf_mass = "leaf_mass",
    bag_mass = "bag_mass",
    leaf_area = "leaf_area"
  )
)

# See fitted parameters
fit[[1]]

# Plot water mass graph
fit[[2]]

# Plot PV Curve
fit[[3]]

# Fit all PV curves in a file
fits <- fit_many(data,
  group = "ID",
  funct = fit_PV_curve,
  varnames = list(
    psi = "psi",
    mass = "mass",
    leaf_mass = "leaf_mass",
    bag_mass = "bag_mass",
    leaf_area = "leaf_area"
  )
)

# See parameters
fits[[1]][[1]]

# See water mass - water potential graph
fits[[1]][[2]]

# See PV curve
fits[[1]][[3]]

# Compile parameter outputs
pars <- compile_data(
  data = fits,
  output_type = "dataframe",
  list_element = 1
)

# Compile the water mass - water potential graphs
graphs1 <- compile_data(
  data = fits,
  output_type = "list",
  list_element = 2
)

# Compile the PV graphs
graphs2 <- compile_data(
  data = fits,
  output_type = "list",
  list_element = 3
)

Fitting ACi curves

Description

Fitting ACi curves

Usage

fit_aci_response(
  data,
  varnames = list(A_net = "A_net", T_leaf = "T_leaf", C_i = "C_i", PPFD = "PPFD", g_mc =
    "g_mc"),
  P = 100,
  fitTPU = TRUE,
  alpha_g = 0,
  R_d_meas = NULL,
  useR_d = FALSE,
  useg_mc = FALSE,
  useg_mct = FALSE,
  usegamma_star = FALSE,
  useK_M = FALSE,
  useK_C_K_O = FALSE,
  alpha = 0.24,
  theta_J = 0.85,
  gamma_star25 = 42.75,
  Ea_gamma_star = 37830,
  K_M25 = 718.4,
  Ea_K_M = 65508.28,
  g_mc25 = 0.08701,
  Ea_g_mc = 0,
  K_C25 = NULL,
  Ea_K_C = NULL,
  K_O25 = NULL,
  Ea_K_O = NULL,
  Oconc = 21,
  gamma_star_set = NULL,
  K_M_set = NULL,
  ...
)

Arguments

Value

fit_aci_response fits ACi curves using an approach similar to Gu et al. 2010. Iterates all possible C_i transition points and checks for inadmissible curve fits. If no curves are admissible (either due to poor data or poor assumed parameters), the output will include a dataframe of NA values. Default parameters are all from Bernacchi et al. 2001, 2002.

References

Bernacchi CJ, Singsaas EL, Pimentel C, Portis AR, Long SP. 2001. Improved temperature response functions for models of rubisco-limited photosynthesis. Plant Cell Environment 24:253-259.

Bernacchi CJ, Portis AR, Nakano H, von Caemmerer S, Long SP. 2002. Temperature response of mesophyll conductance. Implications for the determination of rubisco enzyme kinetics and for limitations to photosynthesis in vivo. Plant Physiology 130:1992-1998.

Gu L, Pallardy SG, Tu K, Law BE, Wullschleger SD. 2010. Reliable estimation of biochemical parameters from C3 leaf photosynthesis-intercellular carbon dioxide response curves. Plant Cell Environment 33:1852-1874.

von Caemmerer S. 2000. Biochemical models of leaf photosynthesis. CSIRO Publishing, Collingwood.

Examples

# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Define a grouping factor based on light intensity to split the ACi
# curves
data$Q_2 <- as.factor((round(data$Qin, digits = 0)))

# Convert leaf temperature to K
data$T_leaf <- data$Tleaf + 273.15

# Fit ACi curve. Note that we are subsetting the dataframe
# here to fit for a single value of Q_2
fit <- fit_aci_response(data[data$Q_2 == 1500, ],
  varnames = list(
    A_net = "A",
    T_leaf = "T_leaf",
    C_i = "Ci",
    PPFD = "Qin"
  )
)

# View fitted parameters
fit[[1]]

# View graph
fit[[2]]

# View data with modelled parameters attached
fit[[3]]

# Fit many curves
fits <- fit_many(
  data = data,
  varnames = list(
    A_net = "A",
    T_leaf = "T_leaf",
    C_i = "Ci",
    PPFD = "Qin"
  ),
  funct = fit_aci_response,
  group = "Q_2"
)

# Print the parameters
# First set of double parentheses selects an individual group value
# Second set selects an element of the sublist
fits[[3]][[1]]

# Print the graph
fits[[3]][[2]]

# Compile graphs into a list for plotting
fits_graphs <- compile_data(fits,
  list_element = 2
)

# Compile parameters into dataframe for analysis
fits_pars <- compile_data(fits,
  output_type = "dataframe",
  list_element = 1
)

Fitting light responses of net CO2 assimilation

Description

Please use fit_aq_response2().

Usage

fit_aq_response(
  data,
  varnames = list(A_net = "A_net", PPFD = "PPFD"),
  usealpha_Q = FALSE,
  alpha_Q = 0.84,
  title = NULL
)

Arguments

Value

fit_aq_response fits the light response of net CO2 assimilation. Output is a dataframe containing light saturated net CO2 assimilation, quantum yield of CO2 assimilation (phi_J), curvature of the light response (theta_J), respiration (Rd), light compensation point (LCP), and residual sum of squares (resid_SS). Note that Rd fitted in this way is essentially the same as the Kok method, and represents a respiration value in the light that may not be accurate. Rd output should thus be interpreted more as a residual parameter to ensure an accurate fit of the light response parameters. Model originally from Marshall & Biscoe 1980.

References

Marshall B, Biscoe P. 1980. A model for C3 leaves describing the dependence of net photosynthesis on irradiance. J Ex Bot 31:29-39

Examples

# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Fit many AQ curves
# Set your grouping variable
# Here we are grouping by CO2_s and individual
data$C_s = (round(data$CO2_s, digits = 0))

# For this example we need to round sequentially due to CO2_s setpoints
data$C_s = as.factor(round(data$C_s, digits = -1))

# To fit one AQ curve
fit = fit_aq_response(data[data$C_s == 600, ],
  varnames = list(
    A_net = "A",
    PPFD = "Qin"
  )
)

# Print model summary
summary(fit[[1]])

# Print fitted parameters
fit[[2]]

# Print graph
fit[[3]]

# Fit many curves
fits = fit_many(
  data = data,
  varnames = list(
    A_net = "A",
    PPFD = "Qin",
    group = "C_s"
  ),
  funct = fit_aq_response,
  group = "C_s"
)

# Look at model summary for a given fit
# First set of double parentheses selects an individual group value
# Second set selects an element of the sublist
summary(fits[[3]][[1]])

# Print the parameters
fits[[3]][[2]]

# Print the graph
fits[[3]][[3]]

# Compile graphs into a list for plotting
fits_graphs = compile_data(fits,
  list_element = 3
)

# Compile parameters into dataframe for analysis
fits_pars = compile_data(fits,
  output_type = "dataframe",
  list_element = 2
)

Fit photosynthetic light-response curves

Description

We recommend using fit_photosynthesis() with argument .photo_fun = "aq_response" rather than calling this function directly.

Usage

fit_aq_response2(
  .data,
  .model = "default",
  .method = "ls",
  usealpha_Q = FALSE,
  alpha_Q = 0.84,
  quiet = FALSE,
  brm_options = NULL
)

Arguments

Value

• If .method = 'ls': an stats::nls() object.

• If .method = 'brms': a brms::brmsfit() object.

Note

Rd fitted in this way is essentially the same as the Kok (1956) method, and represents a respiration value in the light that may not be accurate. Rd output should thus be interpreted more as a residual parameter to ensure an accurate fit of the light response parameters. Model originally from Marshall & Biscoe (1980).

References

Marshall B, Biscoe P. 1980. A model for C3 leaves describing the dependence of net photosynthesis on irradiance. J Ex Bot 31:29-39

Examples

library(broom)
library(dplyr)
library(photosynthesis)

# Read in your data
dat = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
  read.csv() |>
  # Set grouping variable
  mutate(group = round(CO2_s, digits = 0)) |>
  # For this example, round sequentially due to CO2_s set points
  mutate(group = as.factor(round(group, digits = -1)))

# Fit one light-response curve
fit = fit_photosynthesis(
  .data = filter(dat, group == 600),
  .photo_fun = "aq_response",
  .vars = list(.A = A, .Q = Qabs),
)

# The 'fit' object inherits class 'nls' and many methods can be used

## Model summary:
summary(fit)

## Estimated parameters:
coef(fit)

## 95% confidence intervals:
confint(fit)

## Tidy summary table using 'broom::tidy()'
tidy(fit, conf.int = TRUE, conf.level = 0.95)

# Fit multiple curves with **photosynthesis** and **purrr**

library(purrr)

fits = dat |>
  split(~ group) |>
  map(fit_photosynthesis, .photo_fun = "aq_response", .vars = list(.A = A, .Q = Qabs))

Fitting mesophyll conductance with the variable J method

Description

Fitting mesophyll conductance with the variable J method

Usage

fit_g_mc_variableJ(
  data,
  varnames = list(A_net = "A_net", J_etr = "J_etr", C_i = "C_i", PPFD = "PPFD", phi_PSII
    = "phi_PSII"),
  usealpha_Q = FALSE,
  alpha_Q = 0.84,
  beta_Q = 0.5,
  gamma_star,
  R_d,
  P = 100
)

Arguments

Value

fit_g_mc_variableJ fits mesophyll conductance according to Harley et al. 1992. It also tests the reliability of the calculation and calculates a mean with only reliable values. Note that the output is in units of umol m-2 s-1 Pa-1.

References

Harley PC, Loreto F, Di Marco G, Sharkey TD. 1992. Theoretical considerations when estimating mesophyll conductance to CO2 flux by analysis of the response of photosynthesis to CO2. Plant Physiol 98:1429 - 1436.

Examples

# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Note: there will be issues here if the alpha value used
# for calculating ETR is off, if gamma_star is incorrect,
# if R_d is incorrect.
data <- fit_g_mc_variableJ(data,
  varnames = list(
    A_net = "A",
    J_etr = "ETR",
    C_i = "Ci",
    PPFD = "Qin",
    phi_PSII = "PhiPS2"
  ),
  gamma_star = 46,
  R_d = 0.153,
  usealpha_Q = TRUE,
  alpha_Q = 0.84,
  beta_Q = 0.5,
  P = 84
)

# Note that many g_mc values from this method can be unreliable
ggplot(data, aes(x = CO2_s, y = g_mc, colour = reliable)) +
  labs(
    x = expression(CO[2] ~ "(" * mu * mol ~ mol^
      {
        -1
      } * ")"),
    y = expression(g[m] ~ "(mol" ~ m^{
      -2
    } ~ s^{
      -1
    } ~ Pa^
      {
        -1
      } * ")")
  ) +
  geom_point(size = 2) +
  theme_bw() +
  theme(legend.position = "bottom")

# Plot QAQC graph according to Harley et al. 1992
ggplot(data, aes(x = CO2_s, y = dCcdA, colour = reliable)) +
  labs(
    x = expression(CO[2] ~ "(" * mu * mol ~ mol^
      {
        -1
      } * ")"),
    y = expression(delta * C[chl] * "/" * delta * A)
  ) +
  geom_hline(yintercept = 10) +
  geom_point(size = 2) +
  theme_bw() +
  theme(legend.position = "bottom")

Fitting stomatal conductance models

Description

Fitting stomatal conductance models

Usage

fit_gs_model(
  data,
  varnames = list(A_net = "A_net", C_air = "C_air", g_sw = "g_sw", RH = "RH", VPD =
    "VPD"),
  model = c("BallBerry", "Leuning", "Medlyn_partial", "Medlyn_full"),
  D0 = 3,
  ...
)

Arguments

Value

fit_gs_model fits one or more stomatal conductance models to the data. The top level of the output list is named after the fitted model, while the second level contains the Model, Parameters, and Graph, in that order.

References

Ball JT, Woodrow IE, Berry JA. 1987. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions, in Progress in Photosynthesis Research, Proceedings of the VII International Congress on Photosynthesis, vol. 4, edited by I. Biggins, pp. 221–224, Martinus Nijhoff, Dordrecht, Netherlands.

Leuning R. 1995. A critical appraisal of a coupled stomatal- photosynthesis model for C3 plants. Plant Cell Environ 18:339-357

Medlyn BE, Duursma RA, Eamus D, Ellsworth DS, Prentice IC, Barton CVM, Crous KY, Angelis PD, Freeman M, Wingate L. 2011. Reconciling the optimal and empirical approaches to modeling stomatal conductance. Glob Chang Biol 17:2134-2144

Examples

# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Convert RH to a proportion
data$RH <- data$RHcham / 100

# Fit stomatal conductance models
# Can specify a single model, or all as below
fits <- fit_gs_model(
  data = data,
  varnames = list(
    A_net = "A",
    C_air = "Ca",
    g_sw = "gsw",
    RH = "RH",
    VPD = "VPDleaf"
  ),
  model = c(
    "BallBerry",
    "Leuning",
    "Medlyn_partial",
    "Medlyn_full"
  ),
  D0 = 3
)

# Look at BallBerry model summary:
summary(fits[["BallBerry"]][["Model"]])

# Look at BallBerry parameters
fits[["BallBerry"]][["Parameters"]]

# Look at BallBerry plot
fits[["BallBerry"]][["Graph"]]

# Fit many g_sw models
# Set your grouping variable
# Here we are grouping by Qin and individual
data$Q_2 <- as.factor((round(data$Qin, digits = 0)))

fits <- fit_many(data,
  varnames = list(
    A_net = "A",
    C_air = "Ca",
    g_sw = "gsw",
    RH = "RH",
    VPD = "VPDleaf"
  ),
  funct = fit_gs_model,
  group = "Q_2"
)

# Look at the Medlyn_partial outputs at 750 PAR
# Model summary
summary(fits[["750"]][["Medlyn_partial"]][["Model"]])

# Model parameters
fits[["750"]][["Medlyn_partial"]][["Parameters"]]

# Graph
fits[["750"]][["Medlyn_partial"]][["Graph"]]

# Compile parameter outputs for BallBerry model
# Note that it's the first element for each PAR value
# First compile list of BallBerry fits
bbmods <- compile_data(
  data = fits,
  output_type = "list",
  list_element = 1
)
# Now compile the parameters (2nd element) into a dataframe
bbpars <- compile_data(
  data = bbmods,
  output_type = "dataframe",
  list_element = 2
)

# Convert group variable back to numeric
bbpars$ID <- as.numeric(bbpars$ID)

# Take quick look at light response of intercept parameters
plot(g0 ~ ID, bbpars)

# Compile graphs
graphs <- compile_data(
  data = bbmods,
  output_type = "list",
  list_element = 3
)

# Look at 3rd graph
graphs[[3]]

Fitting hydraulic vulnerability curves

Description

Fitting hydraulic vulnerability curves

Usage

fit_hydra_vuln_curve(
  data,
  varnames = list(psi = "psi", PLC = "PLC"),
  start_weibull = list(a = 2, b = 2),
  title = NULL
)

Arguments

Value

fit_hydra_vuln_curve fits a sigmoidal function (Pammenter & Van der Willigen, 1998) linearized according to Ogle et al. (2009). Output is a list containing the sigmoidal model in element 1 and Weibull model in element 4, the fit parameters with 95% confidence interval for both models are in element 2, and hydraulic parameters in element 3 (including P25, P50, P88, P95, S50, Pe, Pmax, DSI). Px (25 to 95): water potential at which x% of conductivity is lost. S50: slope at 50% loss of conductivity. Pe: air entry point. Pmax: hydraulic failure threshold. DSI: drought stress interval. Element 5 is a graph showing the fit, P50, Pe, and Pmax.

References

Ogle K, Barber JJ, Willson C, Thompson B. 2009. Hierarchical statistical modeling of xylem vulnerability to cavitation. New Phytologist 182:541-554

Pammenter NW, Van der Willigen CV. 1998. A mathematical and statistical analysis of the curves illustrating vulnerability of xylem to cavitation. Tree Physiology 18:589-593

Examples

# Read in data
data <- read.csv(system.file("extdata", "hydraulic_vulnerability.csv",
  package = "photosynthesis"
))

# Fit hydraulic vulnerability curve
fit <- fit_hydra_vuln_curve(data[data$Tree == 4 & data$Plot == "Control", ],
  varnames = list(
    psi = "P",
    PLC = "PLC"
  ),
  title = "Control 4"
)

# Return Sigmoidal model summary
summary(fit[[1]])

# Return Weibull model summary
summary(fit[[4]])

# Return model parameters with 95\% confidence intervals
fit[[2]]

# Return hydraulic parameters
fit[[3]]

# Return graph
fit[[5]]

# Fit many curves
fits <- fit_many(
  data = data,
  varnames = list(
    psi = "P",
    PLC = "PLC"
  ),
  group = "Tree",
  funct = fit_hydra_vuln_curve
)

# To select individuals from the many fits
# Return model summary
summary(fits[[1]][[1]]) # Returns model summary

# Return sigmoidal model output
fits[[1]][[2]]

# Return hydraulic parameters
fits[[1]][[3]]

# Return graph
fits[[1]][[5]]

# Compile parameter outputs
pars <- compile_data(
  data = fits,
  output_type = "dataframe",
  list_element = 3
)

# Compile graphs
graphs <- compile_data(
  data = fits,
  output_type = "list",
  list_element = 5
)

Fitting many functions across groups

Description

We are no longer updating this function. Please use generic methods like map instead. See vignette("light-response") for an example.

Usage

fit_many(data, funct, group, progress = TRUE, ...)

Arguments

Value

fit_many fits a function across every instance of a grouping variable.

Examples

# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Define a grouping factor based on light intensity to split the ACi
# curves
data$Q_2 = as.factor((round(data$Qin, digits = 0)))

# Convert leaf temperature to K
data$T_leaf = data$Tleaf + 273.15

# Fit many curves
fits = fit_many(
  data = data,
  varnames = list(
    A_net = "A",
    T_leaf = "T_leaf",
    C_i = "Ci",
    PPFD = "Qin"
  ),
  funct = fit_aci_response,
  group = "Q_2"
)

# Print the parameters
# First set of double parentheses selects an individual group value
# Second set selects an element of the sublist
fits[[3]][[1]]

# Print the graph
fits[[3]][[2]]

# Compile graphs into a list for plotting
fits_graphs = compile_data(fits,
  list_element = 2
)


# Compile parameters into dataframe for analysis
fits_pars = compile_data(fits,
  output_type = "dataframe",
  list_element = 1
)

Fit photosynthetic models with gas-exchange data

Description

Fit photosynthetic models with gas-exchange data

Usage

fit_photosynthesis(
  .data,
  .photo_fun,
  .model = "default",
  .vars = NULL,
  .method = "ls",
  ...,
  quiet = FALSE,
  brm_options = NULL
)

Arguments

Value

A fitted model object

• class 'lm' or 'nls' if method = 'ls'

• class 'brmsfit' if method = 'brms'

Note

This function will fit models to data but several methods require post-processing to extract meaningful parameter estimates and confidence intervals. See vignettes for further explanation and examples.

• Light-response curves: vignette("light-response", package = "photosynthesis")

• Light respiration: vignette("light-respiration", package = "photosynthesis")

Fit models to estimate light respiration (R_\mathrm{d})

Description

We recommend using fit_photosynthesis() with argument .photo_fun = "r_light" rather than calling this function directly.

Usage

fit_r_light2(
  .data,
  .model = "default",
  .method = "ls",
  Q_lower = NA,
  Q_upper = NA,
  Q_levels = NULL,
  C_upper = NA,
  quiet = FALSE,
  brm_options = NULL
)

Arguments

Value

• If .method = 'ls': an stats::nls() or stats::lm() object.

• If .method = 'brms': a brms::brmsfit() object.

Note

Confusingly, R_\mathrm{d} typically denotes respiration in the light, but you might see R_\mathrm{day} or R_\mathrm{light}.

Models

Kok (1956)

The kok_1956 model estimates light respiration using the Kok method (Kok, 1956). The Kok method involves looking for a breakpoint in the light response of net CO2 assimilation at very low light intensities and extrapolating from data above the breakpoint to estimate light respiration as the y-intercept. Rd value should be negative, denoting an efflux of CO2.

Yin et al. (2011)

The yin_etal_2011 model estimates light respiration according to the Yin et al. (2009, 2011) modifications of the Kok method. The modification uses fluorescence data to get a better estimate of light respiration. Rd values should be negative here to denote an efflux of CO2.

Walker & Ort (2015)

The walker_ort_2015 model estimates light respiration and \Gamma* according to Walker & Ort (2015) using a slope- intercept regression method to find the intercept of multiple A-C curves run at multiple light intensities. The method estimates \Gamma* and R_\mathrm{d}. If estimated R_\mathrm{d} is positive this could indicate issues (i.e. leaks) in the gas exchange measurements. \Gamma* is in units of umol / mol and R_\mathrm{d} is in units of \mumol m^{-2} s^{-1} of respiratory flux. If using C_\mathrm{i}, the estimated value is technically C_\mathrm{i}*. You need to use C_\mathrm{c} to get \Gamma* Also note, however, that the convention in the field is to completely ignore this note.

References

Kok B. 1956. On the inhibition of photosynthesis by intense light. Biochimica et Biophysica Acta 21: 234–244

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 Environ 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 Environ 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

Examples

# Walker & Ort (2015) model

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)

## Estimated parameters:
coef(fit)

## 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)

## Tidy summary table using 'broom::tidy()'
tidy(fit, conf.int = TRUE, conf.level = 0.95)

## Calculate residual sum-of-squares
sum(resid(fit)^2)

# Yin et al. (2011) model

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)

## Estimated parameters:
coef(fit)

## 95% confidence intervals:
confint(fit)

## Tidy summary table using 'broom::tidy()'
tidy(fit, conf.int = TRUE, conf.level = 0.95)

## Calculate residual sum-of-squares
sum(resid(fit)^2)

# Kok (1956) model

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)

## Estimated parameters:
coef(fit)

## 95% confidence intervals:
confint(fit)

## Tidy summary table using 'broom::tidy()'
tidy(fit, conf.int = TRUE, conf.level = 0.95)

## Calculate residual sum-of-squares
sum(resid(fit)^2)

Estimating light respiration

Description

Please use fit_r_light2().

Usage

fit_r_light_kok(
  data,
  varnames = list(A_net = "A_net", PPFD = "PPFD"),
  PPFD_lower = 40,
  PPFD_upper = 100
)

fit_r_light_WalkerOrt(
  data,
  varnames = list(A_net = "A_net", C_i = "C_i", PPFD = "PPFD"),
  P = 100,
  C_i_threshold = 300
)

fit_r_light_yin(
  data,
  varnames = list(A_net = "A_net", PPFD = "PPFD", phi_PSII = "phi_PSII"),
  PPFD_lower = 40,
  PPFD_upper = 100
)

Arguments

Value

fit_r_light_kok estimates light respiration using the Kok method (Kok, 1956). The Kok method involves looking for a breakpoint in the light response of net CO2 assimilation at very low light intensities and extrapolating from data above the breakpoint to estimate light respiration as the y-intercept. r_light value should be negative, denoting an efflux of CO2.

fit_r_light_WalkerOrt estimates light respiration and GammaStar according to Walk & Ort (2015) using a slope- intercept regression method to find the intercept of multiple ACi curves run at multiple light intensities. Output GammaStar and respiration should be negative If output respiration is positive this could indicate issues (i.e. leaks) in the gas exchange measurements. GammaStar is output in umol mol-1, and respiration is output in umol m-2 s-1 of respiratory flux. Output is a list containing the slope intercept regression model, a graph of the fit, and estimates of the coefficients. NOTE: if using C_i, the output value is technically C_istar. You need to use Cc to get GammaStar. Also note, however, that the convention in the field is to completely ignore this note.

fit_r_light_yin estimates light respiration according to the Yin et al. (2009, 2011) modifications of the Kok method. The modification uses fluorescence data to get a better estimate of light respiration. Note that respiration output should be negative here to denote an efflux of CO2.

References

Kok B. 1956. On the inhibition of photosynthesis by intense light. Biochimica et Biophysica Acta 21: 234–244

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 Environ 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 Environ 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

Examples

# FITTING KOK METHOD
# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Fit light respiration with Kok method
r_light = fit_r_light_kok(
  data = data,
  varnames = list(
    A_net = "A",
    PPFD = "Qin"
  ),
  PPFD_lower = 20,
  PPFD_upper = 150
)
# Return r_light
r_light

# FITTING WALKER-ORT METHOD
# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Fit the Walker-Ort method for GammaStar and light respiration
walker_ort = fit_r_light_WalkerOrt(data,
  varnames = list(
    A_net = "A",
    C_i = "Ci",
    PPFD = "Qin"
  )
)
# Extract model
summary(walker_ort[[1]])

# View graph
walker_ort[[2]]

# View coefficients
walker_ort[[3]]

# FITTING THE YIN METHOD
# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Fit light respiration with Yin method
r_light = fit_r_light_yin(
  data = data,
  varnames = list(
    A_net = "A",
    PPFD = "Qin",
    phi_PSII = "PhiPS2"
  ),
  PPFD_lower = 20,
  PPFD_upper = 250
)

Fitting temperature responses

Description

Fitting temperature responses

Usage

fit_t_response(
  data,
  varnames = list(Par = "Par", T_leaf = "T_leaf"),
  model = c("Arrhenius", "Kruse", "Heskel", "Medlyn", "MMRT", "Quadratic", "Topt"),
  start = list(a = 1, b = 1, c = 1, dEa = 1, Ea_ref = 1, Par_ref = 1, Ea = 40000, Par25 =
    50, Hd = 2e+05, dS = 650, dCp = 1, dG = 1, dH = 1),
  setvar = "none",
  hdset = 2e+05,
  dSset = 650,
  title = NULL,
  ...
)

Arguments

Value

fit_t_response fits one or more temperature response models to a dataset, returning a list of lists. The parent list contains the models, while the child list for each model contains the fitted model in element 1, the coefficients in element 2, and a graph in element 3.

References

Arrhenius S. 1915. Quantitative laws in biological chemistry. Bell.

Heskel MA, O'Sullivan OS, Reich PB, Tjoelker MG, Weerasinghe LK, Penillard A, Egerton JJG, Creek D, Bloomfield KJ, Xiang J, Sinca F, Stangl ZR, la Torre AM, Griffin KL, Huntingford C, Hurry V, Meir P, Turnbull MH, Atkin OK. 2016. Convergence in the temperature response of leaf respiration across biomes and plant functional types. PNAS 113:3832-3837

Hobbs JK, Jiao W, Easter AD, Parker EJ, Schipper LA, Arcus VL. 2013. Change in heat capacity for enzyme catalysis determines temperature dependence of enzyme catalyzed rates. ACS Chemical Biology 8:2388-2393.

Kruse J, Adams MA. 2008. Three parameters comprehensively describe the temperature response of respiratory oxygen reduction. Plant Cell Environ 31:954-967

Liang LL, Arcus VL, Heskel MA, O'Sullivan OS, Weerasinghe LK, Creek D, Egerton JJG, Tjoelker MG, Atkin OK, Schipper LA. 2018. Macromolecular rate theory (MMRT) provides a thermodynamics rationale to underpin the convergent temperature response in plant leaf respiration. Glob Chang Biol 24:1538-1547

Medlyn BE, Dreyer E, Ellsworth D, Forstreuter M, Harley PC, Kirschbaum MUF, Le Roux X, Montpied P, Strassemeyer J, Walcroft A, Wang K, Loutstau D. 2002. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant Cell Environ 25:1167-1179

Examples

# Read in data
data <- read.csv(system.file("extdata", "A_Ci_T_data.csv",
  package = "photosynthesis"
),
stringsAsFactors = FALSE
)

library(tidyr)

# Round temperatures to group them appropriately
# Use sequential rounding
data$T2 <- round(data$Tleaf, 1)
data$T2 <- round(data$Tleaf, 0)

# Look at unique values to detect rounding issues
unique(data$T2)

# Some still did not round correctly,
# manually correct
for (i in 1:nrow(data)) {
  if (data$T2[i] == 18) {
    data$T2[i] <- 17
  }
  if (data$T2[i] == 23) {
    data$T2[i] <- 22
  }
  if (data$T2[i] == 28) {
    data$T2[i] <- 27
  }
  if (data$T2[i] == 33) {
    data$T2[i] <- 32
  }
  if (data$T2[i] == 38) {
    data$T2[i] <- 37
  }
}

# Make sure it is a character string for grouping
data$T2 <- as.character(data$T2)

# Create grouping variable by ID and measurement temperature
data <- unite(data,
  col = "ID2", c("ID", "T2"),
  sep = "_"
)

# Split by temperature group
data <- split(data, data$ID2)

# Obtain mean temperature for group so temperature
# response fitting is acceptable later, round to
# 2 decimal places
for (i in 1:length(data)) {
  data[[i]]$Curve_Tleaf <- round(mean(data[[i]]$Tleaf), 2)
}

# Convert from list back to dataframe
data <- do.call("rbind", data)

# Parse grouping variable by ID and measurement temperature
data <- separate(data,
  col = "ID2", into = c("ID", "T2"),
  sep = "_"
)

# Make sure number of values matches number of measurement
# temperatures. May vary slightly if plants had slightly
# different leaf temperatures during the measurements
unique(data$Curve_Tleaf)

# Create ID column to curve fit by ID and temperature
data <- unite(data,
  col = "ID2", c("ID", "Curve_Tleaf"),
  sep = "_"
)

# Convert leaf temperature to K
data$T_leaf <- data$Tleaf + 273.15

# Fit many CO2 response curves
fits2 <- fit_many(
  data = data,
  group = "ID2",
  varnames = list(
    A_net = "A",
    C_i = "Ci",
    T_leaf = "T_leaf",
    PPFD = "Qin",
    g_mc = "g_mc"
  ),
  funct = fit_aci_response,
  alphag = 0
)

# Extract ACi parameters
pars <- compile_data(fits2,
  output_type = "dataframe",
  list_element = 1
)

# Extract ACi graphs
graphs <- compile_data(fits2,
  output_type = "list",
  list_element = 2
)

# Parse the ID variable
pars <- separate(pars, col = "ID", into = c("ID", "Curve_Tleaf"), sep = "_")

# Make sure curve leaf temperature is numeric
pars$Curve_Tleaf <- as.numeric(pars$Curve_Tleaf)
pars$T_leaf <- pars$Curve_Tleaf + 273.15

# Fit all models, set Hd to constant in Medlyn model
out <- fit_t_response(
  data = pars[pars$ID == "S2", ],
  varnames = list(
    Par = "V_cmax",
    T_leaf = "T_leaf"
  ),
  setvar = "Hd",
  hdset = 200000
)

out[["Arrhenius"]][["Graph"]]
out[["Heskel"]][["Graph"]]
out[["Kruse"]][["Graph"]]
out[["Medlyn"]][["Graph"]]
out[["MMRT"]][["Graph"]]
out[["Quadratic"]][["Graph"]]
out[["Topt"]][["Graph"]]

Get default model

Description

Get the name of the default model used for different plant ecophysiological data analysis methods implemented in photosynthesis. Currently only used for fit_aq_response2() and fit_r_light2().

Light response models:

• marshall_biscoe_1980(): Non-rectangular hyperbolic model of light responses

• photoinhibition(): Non-rectangular hyperbolic model of light responses with photoinhibition of k_sat at increasing Q_abs

Usage

get_default_model(.photo_fun)

get_all_models(method)

marshall_biscoe_1980(Q_abs, k_sat, phi_J, theta_J)

photoinhibition(Q_abs, k_sat, phi_J, theta_J, b_inh)

Arguments

Value

A character string with name of model.

Examples

get_default_model("aq_response")
get_default_model("r_light")

Stomatal conductance models

Description

Stomatal conductance models

Usage

gs_mod_ballberry(A_net, C_air, RH)

gs_mod_leuning(A_net, C_air, D0, VPD)

gs_mod_opti(g0, g1, VPD, A_net, C_air)

gs_mod_optifull(g0, g1, gk, VPD, A_net, C_air)

Arguments

Value

gs_mod_ballberry is used for fitting the Ball et al. (1987) model of stomatal conductance

gs_mod_leuning is used for fitting the Leuning (1995) model of stomatal conductance

gs_mod_opti fits the optimal stomatal conductance model according to Medlyn et al. 2011

gs_mod_optifull fits the full optimal stomatal conductance model according to Medlyn et al. 2011

References

Ball JT, Woodrow IE, Berry JA. 1987. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions, in Progress in Photosynthesis Research, Proceedings of the VII International Congress on Photosynthesis, vol. 4, edited by I. Biggins, pp. 221–224, Martinus Nijhoff, Dordrecht, Netherlands.

Leuning R. 1995. A critical appraisal of a coupled stomatal- photosynthesis model for C3 plants. Plant Cell Environ 18:339-357

Medlyn BE, Duursma RA, Eamus D, Ellsworth DS, Prentice IC, Barton CVM, Crous KY, Angelis PD, Freeman M, Wingate L. 2011. Reconciling the optimal and empirical approaches to modeling stomatal conductance. Glob Chang Biol 17:2134-2144

S3 class leaf_par

Description

S3 class leaf_par

Usage

leaf_par(.x, use_tealeaves)

Arguments

Value

Constructor function for leaf_par class. This function ensures that leaf parameter inputs are properly formatted.

Make lists of parameters for photosynthesis

Description

Make lists of parameters for photosynthesis

make_leafpar

make_enviropar

make_bakepar

make_constants

Usage

make_leafpar(replace = NULL, use_tealeaves)

make_enviropar(replace = NULL, use_tealeaves)

make_bakepar(replace = NULL)

make_constants(replace = NULL, use_tealeaves)

Arguments

Details

Constants:

Baking (i.e. temperature response) parameters:

Environment parameters:

Leaf parameters:

If use_tealeaves = TRUE, additional parameters are:

Constants:

Baking (i.e. temperature response) parameters:

Environment parameters:

Leaf parameters:

Optional leaf parameters:

Value

make_leafpar: An object inheriting from class leaf_par()
make_enviropar: An object inheriting from class enviro_par()
make_bakepar: An object inheriting from class bake_par()
make_constants: An object inheriting from class constants()

References

Buckley TN and Diaz-Espejo A. 2015. Partitioning changes in photosynthetic rate into contributions from different variables. Plant, Cell & Environment 38: 1200-11.

Examples

bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)
enviro_par = make_enviropar(use_tealeaves = FALSE)
leaf_par = make_leafpar(use_tealeaves = FALSE)

leaf_par = make_leafpar(
  replace = list(
    g_sc = set_units(0.3, mol / m^2 / s),
    V_cmax25 = set_units(100, umol / m^2 / s)
  ), use_tealeaves = FALSE
)

Get vector of parameter names

Description

Get vector of parameter names

Usage

parameter_names(which, use_tealeaves)

Arguments

Value

A character vector with parameter names associated with each type, "leaf", "enviro", "bake", or "constants".

Examples

parameter_names("leaf", use_tealeaves = FALSE)

Input parameters to simulate C3 photosynthesis using photosynthesis()

Description

A table of input parameters used in photosynthesis()

Usage

photo_parameters

Format

photo_parameters

A data frame with 73 rows and 11 columns:

country

Country name

iso2, iso3

2 & 3 letter ISO country codes

year

Year

...

Source

https://www.who.int/teams/global-tuberculosis-programme/data

Simulate C3 photosynthesis

Description

photosynthesis: simulate C3 photosynthesis over multiple parameter sets

photo: simulate C3 photosynthesis over a single parameter set

Usage

photosynthesis(
  leaf_par,
  enviro_par,
  bake_par,
  constants,
  use_tealeaves,
  progress = TRUE,
  quiet = FALSE,
  assert_units = TRUE,
  check = TRUE,
  parallel = FALSE,
  use_legacy_version = FALSE
)

photo(
  leaf_par,
  enviro_par,
  bake_par,
  constants,
  use_tealeaves,
  quiet = FALSE,
  assert_units = TRUE,
  check = TRUE,
  prepare_for_tleaf = use_tealeaves,
  use_legacy_version = FALSE
)

Arguments

Details

photo: This function takes simulates photosynthetic rate using the Farquhar-von Caemmerer-Berry (FvCB()) model of C3 photosynthesis for single combined set of leaf parameters (leaf_par()), environmental parameters (enviro_par()), and physical constants (constants()). Leaf parameters are provided at reference temperature (25 °C) and then "baked" to the appropriate leaf temperature using temperature response functions (see bake()).

photosynthesis: This function uses photo to simulate photosynthesis over multiple parameter sets that are generated using cross_df().

Value

A data.frame with the following units columns

Inputs:

Baked Inputs:

Examples

# Single parameter set with 'photo'

bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)
enviro_par = make_enviropar(use_tealeaves = FALSE)
leaf_par = make_leafpar(use_tealeaves = FALSE)
photo(leaf_par, enviro_par, bake_par, constants,
  use_tealeaves = FALSE
)

# Multiple parameter sets with 'photosynthesis'

leaf_par = make_leafpar(
  replace = list(
    T_leaf = set_units(c(293.14, 298.15), "K")
  ), use_tealeaves = FALSE
)
photosynthesis(leaf_par, enviro_par, bake_par, constants,
  use_tealeaves = FALSE
)

Convert pressure from PPM to Pascals

Description

Convert pressure from PPM to Pascals

Usage

ppm2pa(ppm, P)

Arguments

Details

\mathrm{Press}(kPa) = \mathrm{Press}(ppm) P(kPa)

\mathrm{Press}(Pa) = 1000 \mathrm{Press}(kPa)

Value

Value in Pa of class units

Examples

ppm = set_units(400, "umol/mol")
P = set_units(101.325, "kPa")
ppm2pa(ppm, P)

Printing graphs to system

Description

Printing graphs to system

Usage

print_graphs(
  data,
  path,
  output_type = "jpeg",
  height = 5,
  width = 5,
  res = 600,
  units = "in",
  pdf_filename,
  ...
)

Arguments

Value

print_graphs creates graph files in current working directory from a list of graphs

Examples

# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Fit many AQ curves
# Set your grouping variable
# Here we are grouping by CO2_s and individual
data$C_s <- (round(data$CO2_s, digits = 0))

# For this example we need to round sequentially due to CO2_s setpoints
data$C_s <- as.factor(round(data$C_s, digits = -1))

# To fit one AQ curve
fit <- fit_aq_response(data[data$C_s == 600, ],
  varnames = list(
    A_net = "A",
    PPFD = "Qin"
  )
)

# Print model summary
summary(fit[[1]])

# Print fitted parameters
fit[[2]]

# Print graph
fit[[3]]

# Fit many curves
fits <- fit_many(
  data = data,
  varnames = list(
    A_net = "A",
    PPFD = "Qin",
    group = "C_s"
  ),
  funct = fit_aq_response,
  group = "C_s"
)

# Look at model summary for a given fit
# First set of double parentheses selects an individual group value
# Second set selects an element of the sublist
summary(fits[[3]][[1]])

# Print the parameters
fits[[3]][[2]]

# Print the graph
fits[[3]][[3]]

# Compile graphs into a list for plotting
fits_graphs <- compile_data(fits,
  list_element = 3
)

# Print graphs to pdf
# Uncomment to run
# print_graphs(data = fits_graphs,
#            output_type = "pdf",
#            path = tempdir(),
#            pdf_filename = "mygraphs.pdf")

Read a LI-COR file

Description

We are no longer updating this function. Please use read_licor instead.

Usage

read_li6800(x)

Arguments

Value

Returns a data.frame from raw LI-COR files. Current support for LI-COR LI-6800 files only.

Read a LI-COR file

Description

Reads a raw LI-COR LI6800 file, including remarks. This function was developed using output from Bluestem v.2.0.04 to v.2.1.08. We cannot guarantee backward compatibility with earlier versions of Bluestem. We will try to update code when new versions are released, but there maybe a time-lag, so inspect results carefully.

Usage

read_licor(
  file,
  bluestem_version = get_bluestem_version(file, n_max = 10L),
  ...
)

Arguments

Value

Returns a tibble from raw LI-COR LI6800 files.

Variables required for photosynthesis models

Description

Variables required for photosynthesis models

Usage

required_variables(.model, quiet)

Arguments

Simulate gas exchange data with measurement error

Description

Usage

simulate_error(
  ph_out,
  chamber_pars,
  n = 1L,
  use_tealeaves = ("T_air" %in% colnames(ph_out))
)

Arguments

Value

A data frame with n * nrow(ph_out) rows. It contains all the original output in ph_out as well as a column .rep indicating replicate number from 1 to n. Other new columns are assumed or measured chamber parameters and 'measured' values estimated from synthetic data with measurement error:

Note

The required parameters for the chamber_pars argument are:

• flow [\mumol / s]: chamber flow rate

• leaf_area [cm ^ 2]: leaf area in chamber

• sigma_CO2_s [\mumol / mol]: standard deviation of sample [CO_2] measurement error

• sigma_CO2_r [\mumol / mol]: standard deviation of reference [CO_2]

• sigma_H2O_s [mmol / mol]: standard deviation of sample [H_2O] measurement error

• sigma_H2O_r [mmol / mol]: standard deviation of sample [H_2O] measurement error

Units for flow and leaf_area should be provided; units are implied for sigma's but not necessary to specify because rnorm() drop units.

To evaluate the accuracy and precision of parameter estimation methods, it may be useful to simulate data with realistic measurement error. This function takes output from from photo() or photosynthesis() models, adds measurement error in CO_2 and H_2O concentrations, and calculates parameter estimates with synthetic data. Currently, the function assumes a simplified 1-dimensional CO_2 and H_2O conductance model: zero cuticular conductance, infinite boundary layer conductance, and infinite airspace conductance. Other assumptions include:

• chamber flow rate, leaf area, leaf temperature, and air pressure are known without error

• measurement error is normally distributed mean 0 and standard deviation specified in chamber_pars

This function was designed with the LI-COR LI6800 instrument in mind, but in principle applies to any open path gas exchange system.

[COeVJRVEZDCD9abtGBofcIo0mcCo2tpuCB-5-]: R:COeVJRVEZDCD9abtGBofcIo0mcCo2tpuCB-5-%5C

Examples

library(photosynthesis)

# Use photosynthesis() to simulate 'real' values
# `replace = ...` sets parameters to meet assumptions of `simulate_error()`
lp = make_leafpar(replace = list(
  g_sc = set_units(0.1, mol/m^2/s),
  g_uc = set_units(0, mol/m^2/s),
  k_mc = set_units(0, 1),
  k_sc = set_units(0, 1),
  k_uc = set_units(0, 1)
  ),
  use_tealeaves = FALSE)
   
 ep = make_enviropar(replace = list(
   wind = set_units(Inf, m/s)
 ), use_tealeaves = FALSE) 
 bp = make_bakepar()
 cs = make_constants(use_tealeaves = FALSE)
 
 chamber_pars = data.frame(
   flow = set_units(600, umol / s),
   leaf_area = set_units(6, cm ^ 2),
   sigma_CO2_s = 0.1,
   sigma_CO2_r = 0.1,
   sigma_H2O_s = 0.1,
   sigma_H2O_r = 0.1
 )
   
ph = photosynthesis(lp, ep, bp, cs, use_tealeaves = FALSE, quiet = TRUE) |>
  simulate_error(chamber_pars, n = 1L)
  

Temperature response functions

Description

Temperature response functions

Usage

t_response_arrhenius(T_leaf, Ea)

t_response_arrhenius_kruse(dEa, Ea_ref, Par_ref, T2)

t_response_arrhenius_medlyn(T_leaf, Ea, Hd, dS)

t_response_arrhenius_topt(T_leaf, Ea, Hd, Topt)

t_response_calc_dS(Ea, Hd, Topt)

t_response_calc_topt(Hd, dS, Ea)

t_response_heskel(T_leaf, a, b, c)

t_response_mmrt(dCp, dG, dH, T_leaf)

Arguments

Value

t_response_arrhenius calculates the rate of a process based on an Arrhenius-type curve

t_response_arrhenius_kruse fits a peaked Arrhenius response according to Kruse et al. 2008.

t_response_arrhenius_medlyn is a peaked Arrhenius response as found in Medlyn et al. 2002.

t_response_arrhenius_topt is a peaked Arrhenius temperature response function.

t_response_calc_dS calculates dS from the fitted Topt model.

t_response_calc_topt calculates Topt for a process from Arrhenius parameters.

t_response_heskel is a quadratic temperature response according to Heskel et al. 2016.

t_response_mmrt is a macromolecular rate theory temperature response according to Hobbs et al. 2013.

References

Arrhenius S. 1915. Quantitative laws in biological chemistry. Bell.

Heskel et al. 2016. Convergence in the temperature response of leaf respiration across biomes and plant functional types. PNAS 113:3832-3837

Hobbs et al. 2013. Change in heat capacity for enzyme catalysis determines temperature dependence of enzyme catalyzed rates. ACS Chemical Biology 8:2388-2393

Kruse J, Adams MA. 2008. Three parameters comprehensively describe the temperature response of respiratory oxygen reduction. Plant Cell Environ 31:954-967

Medlyn BE, Dreyer E, Ellsworth D, Forstreuter M, Harley PC, Kirschbaum MUF, Le Roux X, Montpied P, Strassemeyer J, Walcroft A, Wang K, Loutstau D. 2002. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant Cell Environ 25:1167-1179