Section 4 Time of day analyses

In this script, we will assess if the time of day (1-hour segments) have an effect on the species richness.

4.1 Install necessary libraries

library(tidyverse)
library(dplyr)
library(stringr)
library(vegan)
library(ggplot2)
library(scico)
library(data.table)
library(extrafont)
library(ggstatsplot)

# Source any custom/other internal functions necessary for analysis
source("code/01_internal-functions.R")

4.2 Load dataframe containing point count and acoustic data

datSubset <- read.csv("results/datSubset.csv")

4.3 Load species trait data

trait <- read.csv("data/species-trait-dat.csv")

# add it to the subset data
datSubset <- left_join(datSubset,trait[,c(1,7,8)], by = "scientific_name")

4.4 What times of day have been sampled across sites?

Here we get a sense of what times of day have been visited/sampled most across sites for point count data and acoustic surveys

nSitesTime_pc <- datSubset%>% 
  filter(data_type == "point_count") %>%
  dplyr::select(site_id, time_of_day)%>%
  distinct() %>% arrange(time_of_day) %>% 
  count(time_of_day) %>%
  mutate(data_type = "point_count")

nSitesTime_aru <- datSubset%>% 
  filter(data_type == "acoustic_data") %>%
  dplyr::select(site_id, time_of_day)%>%
  distinct() %>% arrange(time_of_day) %>% 
  count(time_of_day) %>%
  mutate(data_type = "acoustic_data")

# This comparison lets us know that number of visits for the time_of_day - 9AM to 10AM is significantly different between acoustic data and point counts.

4.5 Estimate richness by time of day

Here we will estimate species richness for different 1-hour segments with the expectation that richness/detections would differ between point counts and acoustic surveys.

# point-count data
# estimate total abundance across all species for each site
abundance <- datSubset %>%
  filter(data_type == "point_count") %>%
  group_by(site_id, scientific_name,
           common_name, eBird_codes, time_of_day) %>% summarise(totAbundance = sum(number)) %>%
  #filter(time_of_day != "9AM to 10AM") %>%
  ungroup()

# estimate richness for point count data
pc_richness_time <- abundance %>%
  mutate(forRichness = case_when(totAbundance > 0 ~ 1)) %>%
  group_by(site_id, time_of_day) %>%
  summarise(richness = sum(forRichness)) %>%
  mutate(data_type = "point_count") %>%
  ungroup()

# estimate total number of detections across the acoustic data
# note: we cannot call this abundance as it refers to the total number of vocalizations across a 16-min period across all sites
detections <- datSubset %>%
  filter(data_type == "acoustic_data") %>%
  group_by(site_id, scientific_name,
           common_name, eBird_codes, time_of_day) %>% summarise(totDetections = sum(number)) %>%
  #filter(time_of_day != "9AM to 10AM") %>%
  ungroup()

# estimate richness for acoustic data
aru_richness_time <- detections %>%
  mutate(forRichness = case_when(totDetections > 0 ~ 1)) %>%
  group_by(site_id, time_of_day) %>%
  summarise(richness = sum(forRichness)) %>%
  mutate(data_type = "acoustic_data") %>%
  ungroup()

4.6 Visualize differences in richness between point count data and acoustic data

richness_time <- bind_rows(pc_richness_time, aru_richness_time)

# visualization for different times of day
fig_richness_by_time <- richness_time %>%
  grouped_ggbetweenstats(x = data_type,
                         y = richness,
                         grouping.var = time_of_day,
                         xlab = "Data type",
                         ylab = "Richness", 
                         pairwise.display = "significant",
                         package = "ggsci",
                         palette = "default_jco",
ggplot.component = list(theme(text = element_text(family = "Century Gothic", size = 15, face = "bold"),plot.title = element_text(family = "Century Gothic",
      size = 18, face = "bold"),
      plot.subtitle = element_text(family = "Century Gothic", 
      size = 15, face = "bold",color="#1b2838"),
      axis.title = element_text(family = "Century Gothic",
      size = 15, face = "bold"))))

ggsave(fig_richness_by_time, filename = "figs/fig_richness_by_time.png", width = 15, height = 10, device = png(), units = "in", dpi = 300)
dev.off()
Significant differences in richness for 6am to 7am, 8am to 9am, and 9am to 10am (which can be ignored as a function of more visits in acoustic data at that time) but no differences in richness for 7am to 8am between acoustic data and point count data
Significant differences in richness for 6am to 7am, 8am to 9am, and 9am to 10am (which can be ignored as a function of more visits in acoustic data at that time) but no differences in richness for 7am to 8am between acoustic data and point count data