Visualising by site type

Canopy cover

library(readxl)
library(ggplot2)
canopy <- read_excel("Field data single park transects.xlsx", sheet = "Canopy cover")

With grass transects

ggplot(canopy, aes(x = Site_type, y = canopy_cover)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "% Canopy Cover") +
  theme_minimal()

**Aggregated park sites for canopy cover* *

canopyagg <- read_excel("Field data single park transects.xlsx", sheet = "Canopy agg")
ggplot(canopyagg, aes(x = Site_type, y = canopy_cover)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "% Canopy Cover") +
  theme_minimal()

Understorey density

Original

understorey <- read_excel("Field data single park transects.xlsx", sheet = "Understorey density")
ggplot(understorey, aes(x = Site_type, y = Understorey_density)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "Understorey density (0-5)") +
  theme_minimal()

Aggregated understorey density

under.agg <- read_excel("Field data single park transects.xlsx", sheet = "Under agg")
ggplot(under.agg, aes(x = Site_type, y = Understorey_density)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "Understorey density (0-5)") +
  theme_minimal()

Shrub density

Original

shrub <- read_excel("Field data single park transects.xlsx", sheet = "Shrub density")
ggplot(shrub, aes(x = Site_type, y = Shrub_density)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "Shrub density (0-5)") +
  theme_minimal()

Aggregated shrub density

shrub.agg <- read_excel("Field data single park transects.xlsx", sheet = "Shrub agg")
ggplot(shrub.agg, aes(x = Site_type, y = Shrub_density)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "Shrub density (0-5)") +
  theme_minimal()

Ground cover

Aggregated

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)
library(ggplot2)

ground <- read_excel("Field data single park transects.xlsx", sheet = "Ground agg")

# Reshape data to long format
ground_agg_long <-  ground %>%
  pivot_longer(cols = c(`Vegetation`, `Leaf litter`, `Bare soil`), 
               names_to = "Ground_Cover_Type", 
               values_to = "Percentage")

ggplot(ground_agg_long, aes(x = Site_type, y = Percentage, fill = Ground_Cover_Type)) + 
  geom_boxplot(position = position_dodge(width = 0.75)) + 
  labs(x = "Site type", y = "Ground cover (%)", title = "Ground Cover Types by Site Type") +
  theme_minimal()

Strata height

Strata aggregated with maximum values

strata_max <- read_excel("Field data single park transects.xlsx", sheet = "Strata agg max")

# Reshape data to long format
strata_max_long <-  strata_max %>%
  pivot_longer(cols = c(`Canopy`, `Understorey`, `Shrub`, `Ground`), 
               names_to = "Strata_heights", 
               values_to = "Centimetres")

ggplot(strata_max_long, aes(x = Site_type, y = Centimetres, fill = Strata_heights)) + 
  geom_boxplot(position = position_dodge(width = 0.75)) + 
  labs(x = "Site type", y = "Strata height (cm)", title = "Strata heights (max) by site type") +
  theme_minimal()

Strata aggregated with averages

strata_avg <- read_excel("Field data single park transects.xlsx", sheet = "Strata agg avg")

# Reshape data to long format
strata_avg_long <-  strata_avg %>%
   pivot_longer(cols = c(`Canopy`, `Understorey`, `Shrub`, `Ground`), 
               names_to = "Strata_heights", 
               values_to = "Centimetres")

ggplot(strata_avg_long, aes(x = Site_type, y = Centimetres, fill = Strata_heights)) + 
  geom_boxplot(position = position_dodge(width = 0.75)) + 
  labs(x = "Site type", y = "Centimetres", title = "Strata heights (avg) by site type") +
  theme_minimal()

Logs

Aggregated

logs <- read_excel("Field data single park transects.xlsx", sheet = "Logs")
ggplot(logs, aes(x = Site_type, y = Log_count)) + 
  geom_boxplot() +
  labs(x = "Site type", y = "Number of logs") +
  theme_minimal()

Hollows

## Hollows
hollows <- read_excel("Field data single park transects.xlsx", sheet = "Hollows")
ggplot(hollows, aes(x = Site_type, y = Hollow_count)) + 
  geom_boxplot() +
  labs(x = "Site type", y = "Number of hollows") +
  theme_minimal()

Overall species richness and abundance per site type

Species richness

overall <- read_excel("Spp per site and strata.xlsx", sheet = "Sprich")

overall_sprich <- overall %>%
group_by(Site, Site_type) %>%
  summarise(richness = n_distinct(`Species`))

## `summarise()` has grouped output by 'Site'. You can override using the
## `.groups` argument.

ggplot(overall_sprich, aes(x = Site_type, y = richness)) + 
  geom_boxplot() +
  labs(x = "Site type", y = "Species richness") +
  theme_minimal()

Species abundance

ggplot(overall, aes(x = Site_type, y = Abundance)) + 
  geom_boxplot() +
  labs(x = "Site type", y = "Species abundance") +
  theme_minimal()

Overall species richness and abundance per strata type

Richness per strata

strata_sprich <- overall %>%
group_by(Strata, Site, Site_type) %>%
  summarise(richness = n_distinct(`Species`))

## `summarise()` has grouped output by 'Strata', 'Site'. You can override using
## the `.groups` argument.

ggplot(strata_sprich, aes(x = Site_type, y = richness, fill = Strata)) + 
  geom_boxplot(position = position_dodge(width = 0.75)) + 
  labs(x = "Site type", y = "Richness", title = "Richness per strata") +
  theme_minimal()

Abundance per strata

ggplot(overall, aes(x = Site_type, y = Abundance, fill = Strata)) + 
  geom_boxplot(position = position_dodge(width = 0.75)) + 
  labs(x = "Site type", y = "Abundance", title = "Abundance per strata") +
  theme_minimal()

Biostatus per site type

biostatus <- read_excel("Spp per site and strata.xlsx", sheet = "Sprich")

biostatus$Biostatus <- as.factor(biostatus$Biostatus)

ggplot(biostatus, aes(x = Site_type, y = Abundance, fill = Biostatus)) + 
  geom_boxplot(position = position_dodge(width = 0.75)) + 
  labs(x = "Site type", y = "Richness", title = "Abundance per biostatus") +
  theme_minimal()

ggplot(biostatus, aes(x = Site_type, y = Abundance, fill = Biostatus)) +
  geom_bar(stat = "identity", position = "dodge") 

  facet_wrap(~ Biostatus)

## <ggproto object: Class FacetWrap, Facet, gg>
##     compute_layout: function
##     draw_back: function
##     draw_front: function
##     draw_labels: function
##     draw_panels: function
##     finish_data: function
##     init_scales: function
##     map_data: function
##     params: list
##     setup_data: function
##     setup_params: function
##     shrink: TRUE
##     train_scales: function
##     vars: function
##     super:  <ggproto object: Class FacetWrap, Facet, gg>

  labs(x = "Site Type", y = "Abundance", fill = "Biostatus") +
  theme_minimal()

## NULL

Richness per biostatus

bio_sprich <- biostatus %>%
group_by(Site, Site_type, Biostatus) %>%
  summarise(richness = n_distinct(`Species`))

## `summarise()` has grouped output by 'Site', 'Site_type'. You can override using
## the `.groups` argument.

# Remove entries with NA as the biostatus
bio_sprich_filtered <- bio_sprich %>%
  filter(!is.na(Biostatus))

ggplot(bio_sprich_filtered, aes(x = Site_type, y = richness, fill = Biostatus)) + 
  geom_boxplot(position = position_dodge(width = 0.75)) + 
  labs(x = "Site type", y = "Richness", title = "Richness by biostatus") +
  theme_minimal()

Trees

DBH

DBH <- read_excel("Field data single park transects.xlsx", sheet = "Tree DBH")
ggplot(DBH, aes(x = Site_type, y = DBH)) + 
  geom_boxplot() +
  labs(x = "Site type", y = "DBH") +
  theme_minimal()

Tree abundance by site type

library(dplyr)
trees <- read_excel("Field data single park transects.xlsx", sheet = "Tree agg")

tree_abund <- trees %>%
  group_by(Site, Site_type) %>%
  summarise(abundance = n())

## `summarise()` has grouped output by 'Site'. You can override using the
## `.groups` argument.

ggplot(tree_abund, aes(x = Site_type, y = abundance)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "Tree abundance") +
  theme_minimal()

Tree richness by site type

tree_sprich <- trees %>%
group_by(Site, Site_type) %>%
  summarise(richness = n_distinct(`Spp ID`))

## `summarise()` has grouped output by 'Site'. You can override using the
## `.groups` argument.

ggplot(tree_sprich, aes(x = Site_type, y = richness)) + 
  geom_boxplot() +
  labs(x = "Site type", y = "Species richness") +
  theme_minimal()

Diversity

Saplings

Sapling abundance by site type

sapling <- read_excel("Field data single park transects.xlsx", sheet = "Saplings agg")
ggplot(sapling, aes(x = Site_type, y = Abundance)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "Sapling abundance") +
  theme_minimal()

Sapling richness by site type

sapling_sprich <- sapling %>%
group_by(Site, Site_type) %>%
  summarise(richness = n_distinct(`Species`))

## `summarise()` has grouped output by 'Site'. You can override using the
## `.groups` argument.

ggplot(sapling_sprich, aes(x = Site_type, y = richness)) + 
  geom_boxplot() +
  labs(x = "Site type", y = "Species richness") +
  theme_minimal()

Diversity

Shrub species

Shrub species abundance by site type

shrub <- read_excel("Field data single park transects.xlsx", sheet = "Shrub spp agg")

ggplot(shrub, aes(x = Site_type, y = Abundance)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "Sapling abundance") +
  theme_minimal()

Shrub species richness by site type

shrub_sprich <- shrub %>%
group_by(Site, Site_type) %>%
  summarise(richness = n_distinct(`Spp ID`))

## `summarise()` has grouped output by 'Site'. You can override using the
## `.groups` argument.

ggplot(shrub_sprich, aes(x = Site_type, y = richness)) + 
  geom_boxplot() +
  labs(x = "Site type", y = "Species richness") +
  theme_minimal()

## Shrub spp diversity

Floor species

Floor spp abundance by site type

floor <- read_excel("Field data single park transects.xlsx", sheet = "Floor spp agg")

ggplot(floor, aes(x = Site_type, y = Abundance)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "Floor species abundance") +
  theme_minimal()

Floor spp richness by site type

floor_sprich <- floor %>%
group_by(Site, Site_type) %>%
  summarise(richness = n_distinct(`Spp ID`))

## `summarise()` has grouped output by 'Site'. You can override using the
## `.groups` argument.

ggplot(floor_sprich, aes(x = Site_type, y = richness)) + 
  geom_boxplot() +
  labs(x = "Site type", y = "Species richness") +
  theme_minimal()

## Diversity

Other spp

Other spp abundance by site type

other <- read_excel("Field data single park transects.xlsx", sheet = "Other spp agg")

ggplot(other, aes(x = Site_type, y = Abundance)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "Species abundance") +
  theme_minimal()

Other spp richness by site type

other_sprich <- other %>%
group_by(Site, Site_type) %>%
  summarise(richness = n_distinct(`Spp ID`))

## `summarise()` has grouped output by 'Site'. You can override using the
## `.groups` argument.

ggplot(other_sprich, aes(x = Site_type, y = richness)) + 
  geom_boxplot() +
  labs(x = "Site type", y = "Species richness") +
  theme_minimal()

# Floral resources

By number of inflorescences

floral <- read_excel("Field data single park transects.xlsx", sheet = "Floral agg")

ggplot(floral, aes(x = Site_type, y = No_floral_units)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "Species abundance") +
  theme_minimal()

By number of flowers

ggplot(floral, aes(x = Site_type, y = No_flowers)) + 
  geom_boxplot() +
  labs(x = "Site Type", y = "Species abundance") +
  theme_minimal()