IntensiveACiFit

The goal of IntensiveACiFit is to fit intensive data mentioned in Zhou et al (2019). Most of the codes are from C4-Parameter-Estimation, and revised for use with LI-6800 RACiRor ACi data measured by LI-6800(use the method advised by Zhou et al (2019)). If you use this package for your data, please cite Zhou et al (2019) first:

Zhou, Haoran, Erol Akçay, 和 Brent R. Helliker. 2019. Estimating C4 photosynthesis parameters by fitting intensive A/Ci curves. Photosynthesis Research 141 (2): 181–94. https://doi.org/10.1007/s11120-019-00619-8.

Installation

You can install the development version of IntensiveACiFit like so:

remotes::install_github('zhujiedong/IntensiveACiFit')
# or
remotes::install_git(
        'https://gitee.com/zhu_jie_dong/IntensiveACiFit.git')

Example code

Please note that this is the very beginning of this package. I plan to add plot method to give a quick way to plot the fitted data, but it is still in plan. The following show some codes that may be helpful to give a quick view of the results.

In the extdata folder of the repo, I included a simulated RACiR data generated by plantecophys::ACiC4 (as currently I do not have RACiR data measured in C4 plant).

The format of the data should be LI-6800 measurement without changing the header, something like the following table:

elapsed	E	Emm	A
0	0.0018594	1.859397	5.589868
2	0.0018582	1.858232	7.963561
4	0.0018556	1.855645	10.675401
6	0.0018550	1.854993	13.590991
8	0.0018522	1.852195	15.891795

You can also refer to the example data.

Please refer to Zhou et al (2019) for the details of the function parameters.

library(IntensiveACiFit)

aci = read.csv('data/sim-data-utf8.csv')

#-------------- C4EstimateWithCA -------------------#

# 直接使用相关的数据进行拟合即可
# aci 是刚刚导入的 LI-6800 数据
# 其他参数请参考帮助文档或者文献
# startp 由推荐值，但可能需要根据具体情况微调，
# 例如我这里进行了调整，是拟合的初值
# 一般 CaBreakL 和 CaBreakH 在 20 和 60 之间
# 意义同 plantecophys 的 ci transition 
# alpha1 和 x 一般是这个默认值
# 除非文献中看到更适合自己测量植物的值或
# 者有实际测量计算值才能修改
ca_c4estimate = C4EstimateWithCA(aci, alpha1 = 0.15, x=0.4, 
    CaBreakL = 10, CaBreakH = 45, 
    startp = c(45, 300, 3, 20, 120, 100))
# 此处主要是展示数据的结构
str(ca_c4estimate)

A.obs = aci$A
Ci.obs = aci$Pci

# 调用拟合的各阶段的数据
RcPc = unlist(ca_c4estimate$limitation_stage[1], 
    use.names=FALSE)
RcPr = unlist(ca_c4estimate$limitation_stage[2], 
    use.names=FALSE)
RrPc = unlist(ca_c4estimate$limitation_stage[3], 
    use.names=FALSE)
RrPr = unlist(ca_c4estimate$limitation_stage[4], 
    use.names=FALSE)

# 设置个喜欢的颜色，设置相关作图用到的参数和图例
cols = palette('Set 3')[3:7]
xrange<-max(Ci.obs)
yrange<-max(A.obs) + max(A.obs) * 0.1
leg.text<-c("Obs A", "Cal RcPc", "Cal RcPr",
    "Cal RrPc","Cal RrPr")

plot_c4_fit = function(){
    plot(Ci.obs, A.obs, pch =19, col = cols[1],
        xlim=range(0,xrange),ylim=range(0,yrange), 
        xlab="Ci(Pa)",ylab="A")
    lines(Ci.obs[order(Ci.obs)], RcPc[order(Ci.obs)], 
        col=cols[2], lwd=4.2, pch = 20)
    lines(Ci.obs[order(Ci.obs)], RcPr[order(Ci.obs)], 
        col=cols[3], lwd=4.2, pch = 20)
    lines(Ci.obs[order(Ci.obs)], RrPc[order(Ci.obs)], 
        col=cols[4], lwd=4.2, pch = 20)
    lines(Ci.obs[order(Ci.obs)], RrPr[order(Ci.obs)], 
        col=cols[5], lwd=4.2, pch = 20)
    legend("bottomright",leg.text,col=cols,
            pch=c(20,NA,NA,NA,NA),lty=c(0,1,1,1,1),
            cex=0.5,lwd=c(0,2,2,2,2))
}
plot_c4_fit()

#-------------- C4EstimateWithCAT -------------------#
cat_c4estimate = C4EstimateWithCAT(aci, CaBreakL = 10, CaBreakH = 35)
#str(cat_c4estimate)

RcPc = unlist(cat_c4estimate$limitation_stage[1], 
    use.names=FALSE)
RcPr = unlist(cat_c4estimate$limitation_stage[2], 
    use.names=FALSE)
RrPc = unlist(cat_c4estimate$limitation_stage[3], 
    use.names=FALSE)
RrPr = unlist(cat_c4estimate$limitation_stage[4], 
    use.names=FALSE)

plot_c4_fit()

#-------------- C4EstimateWithoutCA -------------------#
noca_c4estimate = C4EstimateWithoutCA(aci, CaBreakL = 10, 
    CaBreakH = 35)
#str(noca_c4estimate)


RcPc = unlist(noca_c4estimate$limitation_stage[1], 
    use.names=FALSE)
RcPr = unlist(noca_c4estimate$limitation_stage[2], 
    use.names=FALSE)
RrPc = unlist(noca_c4estimate$limitation_stage[3], 
    use.names=FALSE)
RrPr = unlist(noca_c4estimate$limitation_stage[4], 
    use.names=FALSE)

plot_c4_fit()


#-------------- C4EstimateWithoutCAT -------------------#
nocat_c4estimate = C4EstimateWithoutCAT(aci, 
    CaBreakL = 10, CaBreakH = 25)
# str(nocat_c4estimate)

RcPc = unlist(nocat_c4estimate$limitation_stage[1], 
    use.names=FALSE)
RcPr = unlist(nocat_c4estimate$limitation_stage[2], 
    use.names=FALSE)
RrPc = unlist(nocat_c4estimate$limitation_stage[3], 
    use.names=FALSE)
RrPr = unlist(nocat_c4estimate$limitation_stage[4], 
    use.names=FALSE)

plot_c4_fit()


#-------------- C4EstimateWithoutCAYin -------------------#
noca_c4estimate_yin = C4EstimateWithoutCAYin(aci, CaBreakL = 10, 
    CaBreakH = 45)
# str(noca_c4estimate_yin)

RcPc = unlist(noca_c4estimate_yin$limitation_stage[1], 
    use.names=FALSE)
RcPr = unlist(noca_c4estimate_yin$limitation_stage[2], 
    use.names=FALSE)
RrPc = unlist(noca_c4estimate_yin$limitation_stage[3], 
    use.names=FALSE)
RrPr = unlist(noca_c4estimate_yin$limitation_stage[4], 
    use.names=FALSE)

plot_c4_fit()

#-------------- C4EstimateWithoutCAYin -------------------#
nocat_c4estimate_yin = C4EstimateWithoutCAYinT(aci, CaBreakL = 10, CaBreakH = 45)
#str(nocat_c4estimate_yin)

RcPc = unlist(nocat_c4estimate_yin$limitation_stage[1], use.names=FALSE)
RcPr = unlist(nocat_c4estimate_yin$limitation_stage[2], use.names=FALSE)
RrPc = unlist(nocat_c4estimate_yin$limitation_stage[3], use.names=FALSE)
RrPr = unlist(nocat_c4estimate_yin$limitation_stage[4], use.names=FALSE)

plot_c4_fit()

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