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library(dplyr)
library(tidyverse)
library(sf) #for reading in shapefiles
Shinagh_Habitats = st_read("C:/Users/Administrator/Documents/Shinagh/Data/Scripts/Shinagh_Habitats.shp")
Shinagh_Habitats$Shape_Area <- st_area(Shinagh_Habitats$geometry)
Shinagh_Habitats$Shape_Area <- as.numeric(Shinagh_Habitats$Shape_Area)

Shinagh_Farm = st_read("C:/Users/Administrator/Documents/Shinagh/Data/Scripts/Shinagh_Farm.shp")
Shinagh_Habitats$Name <- as.factor(Shinagh_Habitats$Name) 

Shinagh_Farm_proj = st_read("C:/Users/Administrator/Documents/Shinagh/Data/Scripts/Shinagh_Farm_Project.shp")

Shinagh_Habitats_proj = st_read("C:/Users/Administrator/Documents/Shinagh/Data/Scripts/Shinagh_Habitats_Project.shp") %>% 
  select(Name, PopupInfo) %>% 
  mutate(Area =  st_area(geometry)) %>% 
  st_transform("+proj=longlat +datum=WGS84 +no_defs")

Shinagh_Habitats_proj <- Shinagh_Habitats_proj[105:140,]
Shinagh_Habitats_proj$Area <- as.numeric(Shinagh_Habitats_proj$Area)
st_crs(Shinagh_Habitats_proj)

Shinagh_Habitats_proj[Shinagh_Habitats_proj$Name == "Hedgerow",]$PopupInfo <- c(1:15)
Shinagh_Habitats_proj[Shinagh_Habitats_proj$Name == "Ditch",]$PopupInfo <- c(1:5)
Shinagh_Habitats_proj[Shinagh_Habitats_proj$Name == "Grassy Bank",]$PopupInfo <- c(1:3)
Shinagh_Habitats_proj[Shinagh_Habitats_proj$Name == "Field Margin",]$PopupInfo <- c(1:4)
Shinagh_Habitats_proj[Shinagh_Habitats_proj$Name == "Woodland",]$PopupInfo <- c(1:9)


Bird_Surveys <- Shinagh_Farm_proj[Shinagh_Farm_proj$Name == "Bird_Proj",]

#st_write(Bird_Surveys, "C:/Users/Administrator/Documents/Shinagh/Data/Scripts/Bird_Surveys.shp")

Bird_Survey_Habitat <-  st_read("C:/Users/Administrator/Documents/Shinagh/Data/Scripts/Bird_Surveys_Intersect_Project.shp") %>% 
  select(Name_1, PopupInfo_, AltMode) %>% 
  mutate(Area =  st_area(geometry)) %>% 
  st_transform("+proj=longlat +datum=WGS84 +no_defs")

Bird_Survey_Habitat$Area <- as.numeric(Bird_Survey_Habitat$Area)

st_crs(Bird_Survey_Habitat)
st_crs(Shinagh_Farm)

saving map as PNG

Interactive Map for web

Shinagh_Habitats %>% 
  group_by(Name) %>% 
  summarise(Area = sum(Shape_Area)) %>% 
  st_drop_geometry() -> Habitat_Area

`summarise()` ungrouping output (override with `.groups` argument)

farm_area <- Shinagh_Farm$Shape_Area

Habitat_Area$Percent_Covering <- round(Habitat_Area$Area/farm_area[1],4)*100
Habitat_Area$Transect_Distance <- (Habitat_Area$Percent_Covering/100)*1000

Habitat_on_Farm <- Habitat_Area %>% 
  filter(Name != "BL3",
         Name != "ED2",
         Name != "GA1",
         Name != "GA2",
         Name != "WD5")

Natural_Habitat_Cover <- sum(Habitat_on_Farm$Percent_Covering)
Hedgerow_Cover <- sum(Habitat_on_Farm$Percent_Covering[Habitat_on_Farm$Name == "WL1"], Habitat_on_Farm$Percent_Covering[Habitat_on_Farm$Name == "WL2"])
Woodland_Cover <- sum(
  Habitat_on_Farm$Percent_Covering[Habitat_on_Farm$Name == "WD1"],
  Habitat_on_Farm$Percent_Covering[Habitat_on_Farm$Name == "WD3"],
  Habitat_on_Farm$Percent_Covering[Habitat_on_Farm$Name == "WN2"],
  Habitat_on_Farm$Percent_Covering[Habitat_on_Farm$Name == "WN5"])

Natural_Habitat_Cover - (Woodland_Cover + Hedgerow_Cover)

[1] 1.26

(Natural_Habitat_Cover/farm_area)*100

 [1] 0.0009547527 1.2922859165          Inf          Inf          Inf          Inf          Inf          Inf
 [9]          Inf          Inf          Inf          Inf          Inf          Inf          Inf          Inf
[17]          Inf          Inf          Inf          Inf          Inf          Inf          Inf          Inf
[25]          Inf          Inf          Inf          Inf          Inf          Inf          Inf          Inf
[33]          Inf          Inf

sum(Habitat_on_Farm$Area)/10000

[1] 8.720607

g2 <- ggplot(Habitat_Area, aes(x=Name, y = Percent_Covering)) +
  geom_col(aes(fill=Name))
g2

Bird_Data <- readxl::read_xlsx("C:/Users/Administrator/Documents/Shinagh/Data/Scripts/Bird_Survey.xlsx")
Bird_Data$Point <- as.factor(Bird_Data$Point)
Bird_Data$Habitat <- as.factor(Bird_Data$Habitat)

colnames(Bird_Survey_Habitat) <- c("Habitat", "PopupInfo", "Point", "geometry", "Area")
Bird_Survey_Habitat$Point <- as.factor(Bird_Survey_Habitat$Point)

Birds <- left_join(Bird_Survey_Habitat, Bird_Data)

Joining, by = c("Habitat", "Point")

Birds$Abundance <- as.numeric(Birds$Abundance)

NAs introduced by coercion

Birds$Species[Birds$Species == "NA"] <- NA
unique(Birds$Species)

 [1] "Magpie"           "Hooded Crow"      "Swallow"          NA                 "Wren"            
 [6] "Song Thrush"      "Blue Tit"         "Swift"            "Long Tailed Tits" "Stonechat"       
[11] "Blackbird"        "Robin"            "Goldfinch"        "Goldcrest"        "Jackdaw"         
[16] "Woodpigeon"       "Great Tit"        "Chiffchaff"       "Blackcap"         "Rook"            
[21] "Starling"         "Buzzard"          "Chaffinch"        "Bullfinch"        "Pied Wagtail"    
[26] "House Martin"    

length(na.omit(unique(Birds$Species)))

[1] 25

Birds$Species <- as.factor(Birds$Species)

Birds %>% 
  group_by(Habitat) %>% 
  summarise(Abundance = sum(Abundance, na.rm = TRUE),
            Area= round(sum(Area),1)) %>% 
  mutate(bird_per_m2 = Abundance/Area) %>% 
  st_drop_geometry() -> Abundance

`summarise()` ungrouping output (override with `.groups` argument)

g3 <- ggplot(Abundance, aes(x=Habitat, y = Abundance)) +
  geom_col(aes(fill=Habitat))
g4 <- ggplot(Abundance, aes(x=Habitat, y = bird_per_m2)) +
  geom_col(aes(fill=Habitat))


Birds %>% 
  group_by(Habitat) %>% 
  summarise(Richness = length(na.omit(unique(Species))),
            Area= round(sum(Area),1)) %>% 
  mutate(species_per_m2 = Richness/Area) %>% 
  st_drop_geometry() -> Richness

`summarise()` ungrouping output (override with `.groups` argument)

g5 <- ggplot(Richness, aes(x=Habitat, y = Richness)) +
  geom_col(aes(fill=Habitat))
g6 <- ggplot(Richness, aes(x=Habitat, y = species_per_m2)) +
  geom_col(aes(fill=Habitat))

g1

g2

g3

g4

g5

g6

Bird_Data$Abundance <- as.numeric(Bird_Data$Abundance)

NAs introduced by coercion

Bird_Data %>% 
  group_by(Species, Habitat) %>% 
  summarise(Abundance = sum(Abundance, na.rm = TRUE)) %>% 
  pivot_wider(names_from = Species, values_from = Abundance) -> Species_Habitat

`summarise()` regrouping output by 'Species' (override with `.groups` argument)

order <- as.character(Species_Habitat$Habitat)

Species_Habitat[is.na(Species_Habitat)] <- 0
Species_Habitat[,2:26]

library(vegan)

Loading required package: permute
Loading required package: lattice
This is vegan 2.5-6

div <- vegan::contribdiv(Species_Habitat[,2:26], index = "simpson",
                  relative = TRUE, scaled = TRUE, drop.zero = FALSE)
## S3 method for class 'contribdiv'
#plot(div)

div$habitat <- order

g7 <- ggplot(div, aes(x=habitat)) +
  geom_col(aes(y = alpha, fill=habitat))
g8 <- ggplot(div, aes(x=habitat)) +
  geom_col(aes(y = beta, fill=habitat))
g9 <- ggplot(div, aes(x=habitat)) +
  geom_col(aes(y = gamma, fill=habitat))
g7

g8

g9

plot(contribdiv(Species_Habitat[,2:26], "richness"), main = "Absolute")


opar <- par(mfrow=c(2,2))

plot(contribdiv(Species_Habitat[,2:26], "richness"), main = "Absolute")
plot(contribdiv(Species_Habitat[,2:26], "richness", relative = TRUE), main = "Relative")

plot(contribdiv(Species_Habitat[,2:26], "simpson"))
plot(contribdiv(Species_Habitat[,2:26], "simpson", relative = TRUE))

par(opar)