In hgriesbauer/pliDrought2019: Data and functions to analyze Pli drought mortality data

knitr::opts_chunk$set(echo = TRUE)

# Correlation matrix to look at Pli data

library(tidyverse)
library(dplR)
library(corrplot)

source("http://www.sthda.com/upload/rquery_cormat.r")

load("~/Documents/Git/pliDrought2019/data/pliDat.RData")

Description

Basically I am using correlation analysis to identify patterns between cores in terms of annual ring widths.

x<-
  pliDat$ringData

# Correlation matrix
x<-
  pliDat$ringData %>% 
  filter(Type=="lw") %>% 
  filter(Tree.Num==2) %>% # filter for live trees
  mutate(coreID=paste(Opening,coreID,sep="-")) %>% 
  dplyr::select(coreID,Year,Value) %>% 
  pivot_wider(names_from=coreID,values_from=Value) %>% 
  filter(Year>=1990) %>% 
  column_to_rownames("Year") 
  # detrend(method="Spline")

x.cor<-
  rquery.cormat(x,type="flatten", graph=FALSE) %>% 
  pluck(1) %>% 
  arrange(desc(cor))

x.list<-
  c("264-10-2","264-2-2","CarmiB-3-2","264-4-2","CarmiA-1-2",
    "CarmiB-15-2","48Y-8-2")

x %>% 
  dplyr::select(x.list) %>% 
  rownames_to_column("Year") %>% 
  pivot_longer(-Year,names_to="coreID",values_to="value") %>% 
  ggplot()+
  aes(x=Year,y=value,group=coreID,color=coreID)+
  geom_point()+
  ylab("Relative ring width")+
  geom_line()+
    theme(axis.text.x = element_text(angle=270,vjust=0.4))

Site level analysis

site="48Y"

x %>% 
  dplyr::select(contains(site)) %>% 
  rownames_to_column("Year") %>%
  filter(Year>=2010) %>% 
  pivot_longer(-Year,names_to="coreID",values_to="value") %>% 
  ggplot()+
  aes(x=Year,y=value,group=coreID,color=coreID)+
  geom_point()+
  ylab("Relative ring width")+
  geom_line()+
    theme(axis.text.x = element_text(angle=270,vjust=0.4))

Short answer is that at the stand-levell, we see almost no correlation between live trees.

Let's look at trees adjacent to each other:

plotAdjTrees<-function(site,ringVar="rw") {

  # come up with list to remove trees that lack a pair
  plotList<-
    pliDat$ringData %>% 
    filter(Opening==site) %>% 
    filter(Type==ringVar) %>% 

    group_by(Plot) %>% 
    distinct(Tree.Num) %>% 
    group_by(Plot) %>% 
    summarise(Num.Trees=n()) %>% 
    filter(Num.Trees==2) %>% 
    pull(Plot)



    pliDat$ringData %>% 
    filter(Opening==site) %>% 
    filter(Type=="lw") %>% 
    filter(Plot %in% plotList) %>% 

    mutate(Plot=paste("Plot",":",Plot,sep="")) %>% 
    mutate(Tree.Num=fct_recode(Tree.Num,Dead="1",Live="2")) %>% 
      rename(Tree=Tree.Num) %>% 

    ggplot()+
    aes(x=Year,y=Value,color=Tree,group=Tree)+
    geom_point()+
    geom_line()+
    facet_wrap(~Plot)+
      theme(legend.position="bottom",
            axis.text.x = element_text(angle = 270, vjust = 0.5, hjust=1))

}

Opening: 48Y

plotAdjTrees(site="264",ringVar="mxd")+
  ylab("Maximum ring density (g/cm3)")

hgriesbauer/pliDrought2019 documentation built on Jan. 29, 2021, 6:51 p.m.