09_occurrence-exploratory-analyses.Rmd

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editor_options: 
  chunk_output_type: console
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# Exploratory analysis of occurrence data

This script carries out exploratory analysis of historical and modern occurrence data from the Nilgiri hills.   

## Load necessary libraries
```{r}
library(dplyr)
library(stringr)
library(tidyverse)
library(scico)
library(RColorBrewer)
library(extrafont)
```

## Load the historical occurrence data and explore patterns

The historical occurrence data is a combination of data from museum specimens and those observations made in published literature/journals (titled journal data). We apply the following key filters on this dataset prior to carrying out further exploratory analyses: a) we subset historical data to only include specimen information until the year 1950 and b) we excluded historical sampling locations that had very few records (total specimen data < 10).
```{r}
hist_occ <- read.csv("data/historical-occurrence-data.csv")

# after exploring the data, we will apply the following filters on the dataset

# a: include only data until 1950
hist_occ <- hist_occ %>%
  filter(year <= 1950)

# b: a second filter includes only those species that had a minimum count of atleast three specimens and occurred across atleast two unique historical survey locations

# total species count across historical data
spp_count <- hist_occ %>%
  group_by(scientific_name, common_name) %>%
  count() # total of 179 species

sppMinThree <- spp_count %>%
  filter(n >= 3) # total of 92 species
# please note, we include ~four species that were collected from a single historical location Apus melba, Apus melba, Brachypodius priocephalus, Phylloscopus tytleri, Picus chlorolophus

# filter historical data
hist_occ <- hist_occ %>%
  filter(scientific_name %in% sppMinThree$scientific_name)

# count of data by year
occ_year <- hist_occ %>%
  group_by(year) %>%
  count()

# The above count suggests that majority of the bird specimens were recorded in 1881, followed by some data from 1876.

collector <- hist_occ %>%
  group_by(collectedBy) %>%
  count()

# 37 unique collectors/ornithologists who collected museum specimens in this time period, with the maximum number of records by William Davison, followed by R G Wardlaw-Ramsay. 
```

## Examining historical data by locality
```{r}
# Twenty unique historical site localities across which species were recorded
occ_locality <- hist_occ %>%
  group_by(historical_site_code) %>%
  count()

## OTCM had the highest number of specimens (~674) while RNMD had the lowest (1 specimen)

# Examining species by locality
spp_loc <- hist_occ %>%
  group_by(common_name, historical_site_code) %>%
  count()

# how many unique sites
prop_count <- spp_loc %>%
  group_by(common_name) %>%
  summarise(uniqueSites = length(unique(historical_site_code)))
```

## Visualizations of historical data
```{r}
fig_loc_timePeriod <- ggplot(occ_locality, aes(x = reorder(historical_site_code,-n), y = n, fill = historical_site_code)) +
  geom_bar(stat = "identity", position = position_dodge(), fill = "#883107", alpha = 0.9) +
  geom_text(aes(label = n, hjust = "middle", vjust = -0.5),
    position = position_dodge(), angle = 0, size = 5
  ) +
  theme_bw() +
  labs(
    x = "\nLocality",
    y = "Count\n"
  ) +
  theme(
    axis.title = element_text(
      family = "Century Gothic",
      size = 14, face = "bold"
    ),
    axis.text = element_text(family = "Century Gothic", size = 14),
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
    legend.position = "none"
  )

ggsave(fig_loc_timePeriod, filename = "figs/fig_histCount_locality.png", width = 12, height = 7, device = png(), units = "in", dpi = 300)
dev.off()
```

![A count of historical occurrence data across resurvey locations](figs/fig_histCount_locality.png)

## Examining species specific counts by locality
```{r}
fig_spp <- ggplot(spp_count, aes(x = reorder(common_name, -n), y = n, fill = scientific_name)) +
  geom_bar(stat = "identity", position = position_dodge(), fill = "#883107", alpha = 0.9) +
  geom_text(aes(label = n, hjust = "middle", vjust = -0.5),
    position = position_dodge(), angle = 0, size = 5
  ) +
  theme_bw() +
  labs(
    x = "\nSpecies",
    y = "Count\n"
  ) +
  theme(
    axis.title = element_text(
      family = "Century Gothic",
      size = 14, face = "bold"
    ),
    axis.text = element_text(family = "Century Gothic", size = 14),
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
    legend.position = "none"
  )

ggsave(fig_spp, filename = "figs/fig_histSpp.png", width = 50, height = 20, device = png(), units = "in", dpi = 300, limitsize = F)
dev.off()
```

![Species-specific counts of historical data across resurvey locations](figs/fig_histSpp.png)

## Load modern occurrence data and explore patterns
```{r}
# note, we are only including those species that were reported in the historical data
# this is mainly done to avoid artificial increases in abundance in the modern time period

mod_occ <- read.csv("data/modern-occurrence-data.csv") %>%
  filter(!is.na(historical_site_code)) # 2301 observations of 103 bird species

# ensure the data is comparable with historical data
mod_occ <- mod_occ %>%
  filter(!is.na(species_code)) %>% 
  filter(common_name %in% hist_occ$common_name) %>%
  filter(historical_site_code %in% hist_occ$historical_site_code) 

# total observations of species in the modern surveys
modOcc_spp <- mod_occ %>%
  group_by(common_name) %>%
  count() # 63 bird species that were common to modern & historical

# plot figure of species by locality
mod_occ_loc <- mod_occ %>%
  group_by(common_name, historical_site_code) %>%
  count()

# figure of species by locality
fig_modSpp_loc <- ggplot(mod_occ_loc, aes(x = reorder(historical_site_code,-n), y = n, fill = historical_site_code)) +
  geom_bar(stat = "identity", position = position_dodge(), fill = "#883107", alpha = 0.9) +
  geom_text(aes(label = n, hjust = "middle", vjust = -0.5),
    position = position_dodge(), angle = 0, size = 5
  ) +
  facet_wrap(~common_name) +
  theme_bw() +
  labs(
    x = "\nSpecies and Locality",
    y = "Count\n"
  ) +
  theme(
    axis.title = element_text(
      family = "Century Gothic",
      size = 14, face = "bold"
    ),
    axis.text = element_text(family = "Century Gothic", size = 12),
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
    legend.position = "none"
  )

ggsave(fig_modSpp_loc, filename = "figs/fig_modSpp_loc.png", width = 40, height = 17, device = png(), units = "in", dpi = 300)
dev.off()

## Some interesting patterns of certain species being more abundant/detected in certain sites over others.
```

![Modern occurrence data of species across resurvey locations](figs/fig_modSpp_loc.png)