#import bird data
#libraries
library(dplyr)
library(tidyr)
library(ggplot2)
library(scales) 
library(ggpubr)


#plot understory vs. BA
ggplot(data=bird_HJAndrews, aes(x=Coniferous_BA, y=Under_per_cover)) +
  geom_point()+
  labs(x="Coniferous Basal Area", y = "Understory Percent Cover")+
  geom_smooth(method = "lm", se=FALSE, color="black", formula = y ~ x)

#correlation (r2) between coniferous basal area and understory percent cover
cor(bird_HJAndrews$Coniferous_BA, bird_HJAndrews$Under_per_cover)

#plot understory vs. Num of Warblers
ggplot(data=bird_HJAndrews, aes(x=Under_per_cover, y=NumIndiv, colour = Species)) +
  geom_point(aes(color = factor(Species)))+
  labs(x="Understory Percent Cover", y = "Number of Warblers (one year average)")+
  geom_smooth(method = "lm", se=FALSE, formula = y~x, linetype = "dashed")


#plot understory vs. Num of Warblers
ggplot(data=bird_HJAndrews, aes(x=Coniferous_BA, y=NumIndiv, color = Species)) +
  geom_point(aes(color = factor(Species)))+
  labs(x="Coniferous Basal Area", y = "Number of Warblers (one year average)")+
  geom_smooth(method = "lm", se=FALSE, formula = y~x, linetype = "dashed")

#HEWA data
HEWA = bird_HJAndrews %>%
  select(Species, NumIndiv, Under_per_cover, Coniferous_BA) %>%
  filter(Species == "HEWA")
HEWA 

HEWA_reg1 = lm(NumIndiv ~ Under_per_cover, data=HEWA)
summary(HEWA_reg1)

HEWA_reg2 = lm(NumIndiv ~ Coniferous_BA, data=HEWA)
summary(HEWA_reg2)


#WIWA data
WIWA = bird_HJAndrews %>%
  select(Species, NumIndiv, Under_per_cover, Coniferous_BA) %>%
  filter(Species == "WIWA")
WIWA 

WIWA_reg1 = lm(NumIndiv ~ Under_per_cover, data=WIWA)
summary(WIWA_reg1)

WIWA_reg2 = lm(NumIndiv ~ Coniferous_BA, data=WIWA)
summary(WIWA_reg2)