Section 9 Phylogenetic Generalized Least Squares (PGLS) regressions
In this script, we examine which predictor/hypothesis (light availability, peak frequency, foraging guild, territoriality, and sociality) best explains the variation in vocal activity between dawn and dusk. We use linear regresssions while controlling for potential phylogenetic signals/relationships.
9.1 Loading necessary libraries
library(ape)
library(nlme)
library(geiger)
library(phytools)
library(phangorn)
library(dplyr)
library(tidyverse)
library(tidyr)
library(ggplot2)
library(stringr)
library(vegan)
library(scico)
library(data.table)
library(extrafont)
library(suncalc)
library(lutz)
library(ggpmisc)
library(ggpubr)
library(hms)
library(phyr)
library(sjPlot)
library(emmeans)9.2 Loading necessary data
acoustic_data <- read.csv("results/acoustic_data.csv")
species_codes <- read.csv("data/species-annotation-codes.csv")
trait <- read.csv("data/species-trait-dat.csv")
territoriality <- read.csv("data/territoriality-data.csv")
sociality <- read.csv("data/sociality-data.csv")
freq <- read.csv("data/frequency-data.csv")
sites <- read.csv("data/list-of-sites.csv")
tree <- read.nexus("data/birdtree.nex") # obtained 100 trees using Hackett all species9.4 Filtering acoustic data to ensure sampling periods are even across dawn and dusk
Since our recording schedule was uneven (6am to 10am in the morning and 4pm to 7pm in the evening), we filter acoustic data to retain recordings between 6am and 830am and recordings made between 4pm and 630pm so that the two sampling windows capture a similar amount of time right after dawn and right before dusk.
9.5 Vocal Activity
We use normalized percent detections (percent detections which account for sampling effort) as a response variable in our PGLS analysis.
# sampling effort by time_of_day
effort <- acoustic_data %>%
dplyr::select(site_id, date, time_of_day) %>%
distinct() %>%
arrange(time_of_day) %>%
count(time_of_day) %>%
rename(., nVisits = n)
# Above, we note that we had sampled ~145 site-date combinations at dawn, while ~230 site-date combinations were sampled at dusk
# total number of acoustic detections summarized across every 10-s audio file
# here, we estimate % detections at dawn and dusk, while accounting for sampling effort
vocal_act <- acoustic_data %>%
group_by(time_of_day, eBird_codes) %>%
summarise(detections = sum(number)) %>%
left_join(., species_codes[, c(1, 2, 5)],
by = "eBird_codes"
) %>%
group_by(eBird_codes) %>%
mutate(total_detections = sum(detections)) %>%
mutate(percent_detections = (detections / total_detections) * 100) %>%
ungroup()
## accouting for sampling effort and normalizing data
vocal_act <- vocal_act %>%
left_join(., effort, by = "time_of_day") %>%
mutate(normalized_detections = detections / nVisits) %>%
group_by(eBird_codes) %>%
mutate(total_normalized_detections = sum(normalized_detections)) %>%
mutate(percent_normalized_detections = (normalized_detections / total_normalized_detections) * 100) %>%
ungroup()9.6 Light availability
Here, we prepare a predictor for light availability or time to darkness for the PGLS analysis
# add longitude and latitude to acoustic_data
acoustic_data <- left_join(acoustic_data, sites[, c(2, 4, 5)],
by = "site_id"
)
acoustic_data$date <- lubridate::ymd(acoustic_data$date)
names(acoustic_data)[c(10, 11)] <- c("lon", "lat")
# find out what time zone needs to be provided for the sunlight calculations
acoustic_data$tz <- tz_lookup_coords(
lat = acoustic_data$lat,
lon = acoustic_data$lon,
method = "accurate",
warn = FALSE
)
# extract nauticalDawn, nauticalDusk, sunrise and sunset times
light_data <- getSunlightTimes(
data = acoustic_data,
keep = c(
"sunrise", "sunset",
"nauticalDawn", "nauticalDusk"
),
tz = "Asia/Kolkata"
) %>% distinct(.)
# strip dates from new columms and keep only time
light_data$sunrise <- as_hms(light_data$sunrise)
light_data$sunset <- as_hms(light_data$sunset)
light_data$nauticalDawn <- as_hms(light_data$nauticalDawn)
light_data$nauticalDusk <- as_hms(light_data$nauticalDusk)
# summarize detections of species for every 15-min window
acoustic_data <- acoustic_data %>%
group_by(
site_id, date, start_time, time_of_day, eBird_codes,
lon, lat, hour_of_day
) %>%
summarise(detections = sum(number)) %>%
ungroup()
# join the two datasets
acoustic_data <- left_join(acoustic_data, light_data,
by = c("date", "lon", "lat")
)
# format the start_time column in the acoustic data to keep it as the same format as light_data
acoustic_data <- acoustic_data %>%
mutate(across(start_time, str_pad, width = 6, pad = "0"))
acoustic_data$start_time <- format(strptime(acoustic_data$start_time,
format = "%H%M%S"
), format = "%H:%M:%S")
acoustic_data$start_time <- as_hms(acoustic_data$start_time)
# add end_times for each recording
acoustic_data <- acoustic_data %>%
mutate(end_time = start_time + dminutes(20))
acoustic_data$end_time <- as_hms(acoustic_data$end_time)
# subtract times from sunrise, sunset, nauticalDawn and nauticalDusk from start_time of acoustic detections
acoustic_data <- acoustic_data %>%
mutate(time_from_dawn = as.numeric((start_time - nauticalDawn),
units = "hours"
)) %>%
mutate(time_from_sunrise = as.numeric((start_time - sunrise),
units = "hours"
)) %>%
mutate(time_to_dusk = as.numeric((nauticalDusk - end_time),
units = "hours"
)) %>%
mutate(time_to_sunset = as.numeric((sunset - end_time),
units = "hours"
))
# add species scientific name to this data
acoustic_data <- acoustic_data %>%
left_join(., species_codes[, c(1, 5)],
by = "eBird_codes"
)
# binding dawn and dusk times together to get at median time/light availability at dawn
dawn <- acoustic_data %>%
group_by(eBird_codes) %>%
filter(time_of_day == "dawn") %>%
dplyr::select(time_from_dawn, time_of_day) %>%
rename(., time_from_startTime = time_from_dawn) %>%
summarise(median_startTime = median(time_from_startTime)) %>%
mutate(time_of_day = "dawn")
dusk <- acoustic_data %>%
group_by(eBird_codes) %>%
filter(time_of_day == "dusk") %>%
dplyr::select(time_to_dusk, time_of_day) %>%
rename(., time_from_startTime = time_to_dusk) %>%
summarise(median_startTime = median(time_from_startTime)) %>%
mutate(time_of_day = "dusk")
light <- bind_rows(dawn, dusk) # this dataframe gives us the median time from dawn and time to dusk for each species
vocal_act <- vocal_act %>%
left_join(light, by = c("eBird_codes", "time_of_day")) # adding median time from dawn and time to dusk to vocal activity data9.7 Territoriality
We prepare territoriality as a predictor for the PGLS analysis
territoriality <- territoriality %>% dplyr::select(Species, Territory)
colnames(territoriality) <- c("scientific_name", "territory")
# Updating the scientific names of following species as per our dataset:
# Flame-throated bulbul- Rubigula gularis (earlier Pycnonotus gularis)
# Black eagle- Ictinaetus malaiensis (earlier Ictinaetus malayensis)
# Brown-capped pygmy woodpecker- Yungipicus nanus (earlier Dendrocopos nanus)
# Malabar barbet- Psilopogon malabaricus (earlier Megalaima malabarica)
# Dark-fronted babbler- Dumetia atriceps (earlier Rhopocichla atriceps)
# Greater flameback- Chrysocolaptes guttacristatus (earlier Chrysocolaptes lucidus)
# Indian blue robin- Larvivora brunnea (earlier Luscinia brunnea)
# Indian yellow tit- Machlolophus aplonotus (earlier Parus aplonotus)
# Jungle babbler- Argya striata (earlier Turdoides striata)
# Orange-headed thrush- Geokichla citrina (earlier Zoothera citrina)
# Rufous babbler- Argya subrufa (earlier Turdoides subrufa)
# Rufous woodpecker- Micropternus brachyurus (earlier Celeus brachyurus)
# Rusty-tailed flycatcher- Ficedula ruficauda (earlier Muscicapa ruficauda
# Spot-bellied eagle owl- Ketupa nipalensis (earlier Bubo nipalensis)
# Spotted dove- Spilopelia chinensis (earlier Stigmatopelia chinensis)
# Thick-billed warbler- Arundinax aedon (earlier Acrocephalus aedon)
# White-bellied flycatcher- Cyornis pallidipes (earlier Cyornis pallipes)
# White-cheeked barbet- Psilopogon viridis (earlier Megalaima viridis)
# Wayanad laughingthrush- Pterorhinus delesserti (earlier Garrulax delesserti)
# Yellow-browed bulbul- Iole indica (earlier Acritillas indica)
territoriality <- territoriality %>% mutate(scientific_name = recode(scientific_name, "Pycnonotus gularis" = "Rubigula gularis", "Ictinaetus malayensis" = "Ictinaetus malaiensis", "Dendrocopos nanus" = "Yungipicus nanus", "Megalaima malabarica" = "Psilopogon malabaricus", "Rhopocichla atriceps" = "Dumetia atriceps", "Chrysocolaptes lucidus" = "Chrysocolaptes guttacristatus", "Luscinia brunnea" = "Larvivora brunnea", "Parus aplonotus" = "Machlolophus aplonotus", "Turdoides striata" = "Argya striata", "Zoothera citrina" = "Geokichla citrina", "Turdoides subrufa" = "Argya subrufa", "Celeus brachyurus" = "Micropternus brachyurus", "Muscicapa ruficauda" = "Ficedula ruficauda", "Bubo nipalensis" = "Ketupa nipalensis", "Stigmatopelia chinensis" = "Spilopelia chinensis", "Acrocephalus aedon" = "Arundinax aedon", "Cyornis pallipes" = "Cyornis pallidipes", "Megalaima viridis" = "Psilopogon viridis", "Garrulax delesserti" = "Pterorhinus delesserti", "Acritillas indica" = "Iole indica"))
vocal_act <- vocal_act %>%
left_join(territoriality, by = "scientific_name")
vocal_act <- vocal_act %>% mutate(territory = case_when(territory %in% "1" ~ "Non-territorial", territory %in% "2" ~ "Weakly territorial", territory %in% "3" ~ "Highly territorial"))9.9 Peak frequency
We prepare peak frequency as a predictor for the PGLS analysis
# add standardized eBird codes to the frequency data
freq <- left_join(freq, species_codes[c(3, 5)],
by = "species_annotation_codes"
)
# Only a total of 99 species are left after filtering species with very few templates
nTemplates_5 <- freq %>%
group_by(eBird_codes) %>%
count() %>%
filter(n >= 5) %>%
drop_na()
# left-join to remove species with less than 10 templates in the frequency dataset
freq_5 <- left_join(nTemplates_5[, 1], freq)
# calculate median peak frequency after grouping by time of day and species
median_pf <- separate(freq_5, col = filename, into = c("site_id", "date", "time", "splits"), sep = "_") %>%
mutate(
time_of_day =
case_when(
time >= "060000" & time <= "100000" ~ "dawn",
time >= "160000" & time <= "190000" ~ "dusk"
)
) %>%
group_by(eBird_codes, time_of_day) %>%
summarise(median_peak_freq = median(peak_freq_in_Hz))
## join the frequency data to the vocal activity data
vocal_act <- vocal_act %>%
left_join(median_pf, by = c("eBird_codes", "time_of_day")) %>%
drop_na()
# A total of 66 species were included and three species were excluded from final analysis.9.10 Foraging guild
We prepare foraging guild categories for the PGLS analysis
vocal_act <- vocal_act %>%
left_join(trait[, c(1, 2, 29)], by = c(
"scientific_name",
"common_name"
))
## remove species that are poorly represented by a particular trophic niche
## we get rid of nectarivore species, aquatic predator and granivore
vocal_act <- vocal_act %>%
filter(trophic_niche != "Aquatic predator") %>%
filter(trophic_niche != "Granivore") %>%
filter(trophic_niche != "Nectarivore") %>%
filter(trophic_niche != "Vertivore")
## Only 62 species remain at this stage.9.11 Data cleaning
We clean the objects prior to running PGLS regressions
# converting the scientific names of vocal_act as per tip.labels of the tree
vocal_act <- vocal_act %>%
mutate(across(scientific_name, str_replace, " ", "_"))
# converting the scientific names of vocal_act as per the birdtree data so that both are matching
vocal_act <- vocal_act %>%
mutate(
scientific_name =
case_when(scientific_name == "Dumetia_atriceps" ~ "Rhopocichla_atriceps", scientific_name == "Chrysocolaptes_guttacristatus" ~ "Chrysocolaptes_lucidus",
scientific_name == "Turdus_simillimus" ~ "Turdus_merula",
scientific_name == "Larvivora_brunnea" ~ "Luscinia_brunnea",
scientific_name == "Machlolophus_aplonotus" ~ "Parus_xanthogenys",
scientific_name == "Psilopogon_malabaricus" ~ "Megalaima_rubricapillus",
scientific_name == "Tephrodornis_sylvicola" ~ "Tephrodornis_gularis",
scientific_name == "Geokichla_citrina" ~ "Zoothera_citrina",
scientific_name == "Cinnyris_asiaticus" ~ "Nectarinia_asiatica",
scientific_name == "Leptocoma_minima" ~ "Nectarinia_minima",
scientific_name == "Spilopelia_chinensis" ~ "Stigmatopelia_chinensis",
scientific_name == "Argya_subrufa" ~ "Turdoides_subrufa",
scientific_name == "Ficedula_nigrorufa" ~ "Muscicapa_ruficauda",
scientific_name == "Gracula_indica" ~ "Gracula_religiosa",
scientific_name == "Ketupa_nipalensis" ~ "Bubo_nipalensis",
scientific_name == "Hypsipetes_ganeesa" ~ "Hypsipetes_leucocephalus",
scientific_name == "Cyornis_pallidipes" ~ "Cyornis_pallipes",
scientific_name == "Psilopogon_viridis" ~ "Megalaima_viridis",
.default = scientific_name
)
) # final dataframe with variables
# check same species in vocal_act and phylogenetic data, and pruning species from the tree which are not present in the vocal_act data
diff <- setdiff(tree$tip.label, vocal_act$scientific_name)
pruned.tree <- drop.tip(tree, diff) # final phylogenetic dataframe
setdiff(vocal_act$scientific_name, pruned.tree$tip.label) # checking if vocal_act and pruned.tree have the same species: result- 'character(0)'
## testing for correlations
library(psych)
for_corr <- vocal_act %>%
filter(time_of_day == "dawn") %>%
mutate(median_startTime_z = scale(median_startTime)) %>%
mutate(median_peak_freq_z = scale(median_peak_freq))
pairs.panels(for_corr[, c(13:16)])
# Territoriality and sociality are correlated to one another (r = 0.66), so we remove sociality from the multivariate model (see below). 9.12 PGLS analysis
Here we test if the higher rates of acoustic detections at dawn can be explained by environmental and/or social factors
## Using normalized detections for dawn only
# Univariate models:
# percent_normalized_detections ~ median_startTime only
model1 <- vocal_act %>%
filter(time_of_day == "dawn") %>%
gls(percent_normalized_detections ~ median_startTime,
data = .,
correlation = corBrownian(1, pruned.tree, form = ~scientific_name)
)
summary(model1) # AIC = 497.72
# percent_normalized_detections ~ territoriality only
model2 <- vocal_act %>%
filter(time_of_day == "dawn") %>%
gls(percent_normalized_detections ~ relevel(factor(territory), ref = "Non-territorial"), data = ., correlation = corBrownian(1, pruned.tree, form = ~scientific_name))
summary(model2) # AIC = 485.27
# percent_normalized_detections ~ median_peak_freq only
model3 <- vocal_act %>%
filter(time_of_day == "dawn") %>%
gls(percent_normalized_detections ~ scale(median_peak_freq), data = ., correlation = corBrownian(1, pruned.tree, form = ~scientific_name))
summary(model3) # AIC = 497.78
# percent_normalized_detections ~ trophic_niche only
model4 <- vocal_act %>%
filter(time_of_day == "dawn") %>%
gls(percent_normalized_detections ~ relevel(factor(trophic_niche), ref = "Frugivore"),
data = ., correlation = corBrownian(1, pruned.tree, form = ~scientific_name)
)
summary(model4) # AIC = 488.58
# Multi-variate model with all predictor variables:
data <- vocal_act %>%
filter(time_of_day == "dawn")
# adding a reference category for categorical predictors
data$territory <- relevel(factor(data$territory), ref = "Non-territorial")
data$sociality <-relevel(factor(data$sociality), ref = "Non-communal signallers")
data$trophic_niche <- relevel(factor(data$trophic_niche), ref = "Frugivore")
# scaling and centering continuous predictors
data$scaled_median_startTime <- scale(data$median_startTime)
data$scaled_median_peak_freq <- scale(data$median_peak_freq)
model5 <- gls(
percent_normalized_detections ~ scaled_median_startTime +
territory +
scaled_median_peak_freq +
trophic_niche,
data = data,
correlation = corBrownian(1, pruned.tree, form = ~scientific_name)) #with sociality removed
summary(model5) # AIC = 469.81
anova(model5)
# calculation of R2
rr2::R2_lik(model5) #R2 measures how well the model predicts the outcome of the observed data
# calculating marginal means
terrEff <- emmeans(model5, ~"territory", data = data)
pairs(terrEff)
eff_size(terrEff, sigma = sigma(model5), edf = 39)
plot(terrEff, comparisons = TRUE)
pwpp(terrEff)
nicheEff <- emmeans(model5, ~"trophic_niche", data = data)
pairs(nicheEff)
eff_size(nicheEff, sigma = sigma(model5), edf = 39)
plot(nicheEff, comparisons = TRUE)
pwpp(nicheEff)
## visualization
pgls_plot <- plot_model(model5, show.values = TRUE, value.offset = 0.3, show.p = TRUE, p.threshold = c(0.07, 0.01, 0.001), order.terms = c(6, 5, 4, 3, 2, 1), axis.labels = c("Ambient Light (Median time to darkness)", "Territoriality (Highly territorial)", "Territoriality (Weakly territorial)", "Median Peak frequency", "Foraging guild (Invertivore)", "Foraging guild (Omnivore)"), title = "", dot.size = 3, line.size = 1, value.size = 6, type = "std") +
theme_bw() +
theme(axis.title = element_text(size = 20, family = "Century Gothic", face = "bold"), axis.text = element_text(size = 16, family = "Century Gothic", colour = "black")) +
scale_color_manual(values = c("#d95f02", "#1b9e77"))
## write the standardized coefficient plots to file
write.csv(data.frame(pgls_plot[["data"]]), "results/pgls-standardized-estimates.csv")
ggsave(pgls_plot, filename = "figs/fig_pgls.svg", width = 15, height = 9, device = svg(), units = "in", dpi = 300)
dev.off()
## Territoriality (Highly territorial compared to Non-territorial) show a significant positive association with percent_normalized_detections. Trophic guild (Omnivores compared to Frugivores) show a marginally significant positive association with percent_normalized_detections.
## Here we will run a model using dusk data anyway
model5 <- vocal_act %>%
filter(time_of_day == "dusk") %>%
gls(
percent_normalized_detections ~ scale(median_startTime) +
relevel(factor(territory), ref = "Non-territorial") +
relevel(factor(sociality), ref = "Non-communal signallers") +
scale(median_peak_freq) +
relevel(factor(trophic_niche), ref = "Frugivore"),
data = .,
correlation = corBrownian(1, pruned.tree, form = ~scientific_name)
)
summary(model6)
plot_model(model6, type = "std")
## No significant association was detected between dusk data and any environmental/social factors (when controlled for phylogeny).
9.8 Sociality
We prepare sociality as a predictor for the PGLS analysis