| Title: | Forest Population Structure and Numeric Dynamics |
|---|---|
| Description: | Analysis of forest population structure and quantitative dynamics is the research and evaluation of the composition, distribution, age structure and changes in quantity over time of various populations in the forest. By deeply understanding these characteristics of forest populations, scientific basis can be provided for the management, protection and sustainable utilization of forest resources. This R package conducts a systematic analysis of forest population structure and quantitative dynamics through analyzing age structure, compiling life tables, population quantitative dynamic change indices and time series models, in order to provide support for forest population protection and sustainable management. References: Zhang Y, Wang J, Wang X, et al(2024)<doi:10.3390/plants13070946>. Yuan G, Guo Q, Xie N, et al(2023)<doi:10.1007/s11629-022-7429-z>. |
| Authors: | Zongzheng Chai [aut, cre]
|
| Maintainer: | Zongzheng Chai <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.0 |
| Built: | 2025-02-10 05:15:09 UTC |
| Source: | https://github.com/cran/forestPSD |
The analysis method of replacing age structure with diameter class structure.
dyn(ax)dyn(ax)
ax |
population number of within different age class. |
Quantitative method was employed to analyze the dynamics of the individual number between adjacent diameter classes for the populations
Result returns the results of population dynamics analysis.
Zongzheng Chai, [email protected]
Zhang Y, Wang J, Wang X, Wang L, Wang Y, Wei J, et al. 2024. Population structures and dynamics of Rhododendron communities with different stages of succession in northwest Guizhou, China. Plants-Basel 13.
data(Npop) dyn(ax=Npop$ax)data(Npop) dyn(ax=Npop$ax)
Model quality assessment.
goodness(model,data)goodness(model,data)
model |
A modle. |
data |
Dataset. |
Model quality index as follow: MSE: the mean-squared-error; RMSE: the root-mean-squared-error; Rsquare: the variance of the predictions divided by the variance of the response; adj.Rsquare: adjusted the variance of the predictions divided by the variance of the response; MAE: the mean absolute error; MAPE: the mean absolute percentage error; RASE: the relative sum of absolute errors; AIC: Akaike's An Information Criterion; BIC: Schwarz's Bayesian criterion.
Result returns the results model quality index.
Zongzheng Chai, [email protected]
mod <- lm(mpg ~ wt, data = mtcars) goodness(mod, mtcars)mod <- lm(mpg ~ wt, data = mtcars) goodness(mod, mtcars)
Static life tables were used to analyze the dynamic changes.
lifetable(ax)lifetable(ax)
ax |
Population number of within different age class. |
Generte the static life table to analyze the population dynamic changes.
Result returns the results of a static life table, which includes the following parameters, ax: existing individual number within age class x; lx: standardized survival number at the beginning of age class x (generally converted to 1000); lnlx: logarithmicstandardized survival number; dx: standardized death number within the interval from age class x to x + 1; qx: mortality rate; Lx: average survival number within the interval from ageclass x to x + 1; Tx: total survival number from age class x and beyond; ex: life expectancy of individuals entering age class x; Sx: survival rate; Kx: disappearance rate of the population.
Zongzheng Chai, [email protected]
Zhang Y, Wang J, Wang X, Wang L, Wang Y, Wei J, et al. 2024. Population structures and dynamics of Rhododendron communities with different stages of succession in northwest Guizhou, China. Plants-Basel 13.
data(Npop) lifetable(ax=Npop$ax)data(Npop) lifetable(ax=Npop$ax)
The renewal ability of populations was simulated and predicted using the moving average method.
Mpre(ax,n=c(2,4,6))Mpre(ax,n=c(2,4,6))
ax |
Population number of within different age class. |
n |
Number of periods to average over. Must be between 1 and nrow(x), inclusive. |
The renewal ability of populations was simulated and predicted using the moving average method.
Result returns the results of the simulated and predicted the population dynamic changes using the moving average method.
Zongzheng Chai, [email protected]
Zhang Y, Wang J, Wang X, Wang L, Wang Y, Wei J, et al. 2024. Population structures and dynamics of Rhododendron communities with different stages of succession in northwest Guizhou, China. Plants-Basel 13.
data(Npop) Mdata<-Mpre(ax=Npop$ax,n=c(2,3,5,6,8,10)) library(reshape2) Mdata.melt<-reshape2::melt(Mdata,id=c("rank","ageclass")) Mdata.melt$ageclass<-factor(Mdata.melt$ageclass,levels=unique(Mdata.melt$ageclass)) library(ggplot2) Mpre.p<-ggplot()+geom_line(aes(x=ageclass,y=value,color=variable,group=variable), linewidth=0.5,data=Mdata.melt)+ xlab("Age class")+ylab("Number of individuals")+labs(color=" ") Mpre.pdata(Npop) Mdata<-Mpre(ax=Npop$ax,n=c(2,3,5,6,8,10)) library(reshape2) Mdata.melt<-reshape2::melt(Mdata,id=c("rank","ageclass")) Mdata.melt$ageclass<-factor(Mdata.melt$ageclass,levels=unique(Mdata.melt$ageclass)) library(ggplot2) Mpre.p<-ggplot()+geom_line(aes(x=ageclass,y=value,color=variable,group=variable), linewidth=0.5,data=Mdata.melt)+ xlab("Age class")+ylab("Number of individuals")+labs(color=" ") Mpre.p
Forest population number of within different age class.
data("Npop")data("Npop")
A data frame with 11 observations on the following 3 variables from the forest population survey data
rankRank of age class of forest population.
ageclassAge class of forest population.
axForest population number of within different age class.
Population number of within different age class.
Zongzheng Chai, [email protected]
data(Npop) Npopdata(Npop) Npop
Organize the data into a data format suitable for population structure analysis.
Ntable(ax)Ntable(ax)
ax |
Population number of within different age class. |
Organize the data into a data format suitable for population structure analysis.
Result returns the data for forest population number of within different age class, the data format id the data.frame.
Zongzheng Chai, [email protected]
data(Npop) Npop ##Generate the Npop data## number=c(8283,5238,1921,1425,926,659,479,228,57,24,10) Ntable(ax=number)data(Npop) Npop ##Generate the Npop data## number=c(8283,5238,1921,1425,926,659,479,228,57,24,10) Ntable(ax=number)
Regression analysis for survival curves between number of individuals and age class.
psdfun(ax,a=100,b=6,index="Deevey2")psdfun(ax,a=100,b=6,index="Deevey2")
ax |
Population number of within different age class. |
a |
Initial values for model fitting. |
b |
Initial values for model fitting. |
index |
Forms of survival curves,which includes:Deevey1, Deevey2,Deevey3.Note:Deevey1 is the linear model; Deevey3 is the exponential model;Deevey2 is the power model. |
Regression analysis for survival curves between number of individuals and age class.
Result returns the results of regression analysis for survival curves.
Zongzheng Chai, [email protected]
Zhang Y, Wang J, Wang X, Wang L, Wang Y, Wei J, et al. 2024. Population structures and dynamics of Rhododendron communities with different stages of succession in northwest Guizhou, China. Plants-Basel 13.
data(Npop) psd_D1<-psdfun(ax=Npop$ax,index="Deevey1") psd_D1 psd_D2<-psdfun(ax=Npop$ax,index="Deevey2") psd_D2 psd_D3<-psdfun(ax=Npop$ax,index="Deevey3") psd_D3 library(ggplot2) psdnls.p<-ggplot()+geom_bar(aes(x=age,y=ax,group=ageclass),data=psd_D2$Data,stat = "identity")+ geom_line(aes(x=age,y=predict),color="blue",linewidth=1,data=psd_D2$Data)+ geom_text(aes(x=10,y=7700),label=expression(paste(italic(y),"=aexp(-b",italic(x),")")))+ geom_text(aes(x=10,y=7300),label=expression(paste(R^2,"=0.987")))+ scale_x_continuous(breaks=1:11)+ scale_x_discrete(limits=psd_D2$Data$ageclass)+ xlab("Age class")+ylab("Number of individuals") psdnls.pdata(Npop) psd_D1<-psdfun(ax=Npop$ax,index="Deevey1") psd_D1 psd_D2<-psdfun(ax=Npop$ax,index="Deevey2") psd_D2 psd_D3<-psdfun(ax=Npop$ax,index="Deevey3") psd_D3 library(ggplot2) psdnls.p<-ggplot()+geom_bar(aes(x=age,y=ax,group=ageclass),data=psd_D2$Data,stat = "identity")+ geom_line(aes(x=age,y=predict),color="blue",linewidth=1,data=psd_D2$Data)+ geom_text(aes(x=10,y=7700),label=expression(paste(italic(y),"=aexp(-b",italic(x),")")))+ geom_text(aes(x=10,y=7300),label=expression(paste(R^2,"=0.987")))+ scale_x_continuous(breaks=1:11)+ scale_x_discrete(limits=psd_D2$Data$ageclass)+ xlab("Age class")+ylab("Number of individuals") psdnls.p