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NMDS_PCA.R

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+library(vegan)
+library(tidyverse)
+
+setwd("~/lifewatch_network_analysis/cpod/")
+df_day <- read_csv("csv/df_day.csv") #output from cpod_analysis.R
+
+#-----------------------PCA
+df.pca <- df_day %>% filter(species == "NBHF") %>% group_by(station,Season) %>% summarise(sum(dpm),sum(lost_minutes), sum(noise), sum(hrs)) 
+
+colnames(df.pca)[3] <- "DPM"
+colnames(df.pca)[4] <- "Lost minutes"
+colnames(df.pca)[5] <- "Noise"
+colnames(df.pca)[6] <- "Hours of activity"
+
+library(ggbiplot)
+pca <- prcomp(df.pca[,c(3:6)], center = TRUE,scale. = TRUE)
+summary(pca)
+
+#plot PCA
+ggbiplot(pca,labels=df.pca$station, groups=df.pca$Season)+theme_linedraw()
+ggsave("plots/pca_nbhf.png", width = 11, height = 6, dpi=300)
+
+
+#----------------------NMDS
+df_nmds = df_day %>%  filter(species == "Dolphins") %>% group_by(station, deployment_fk, Season) %>% summarise(dpm= sum(dpm), lost_minutes=sum(lost_minutes), noise= sum(noise), hrs=sum(hrs))%>% 
+                mutate(station_type = case_when(station == "Birkenfels" | station=="Gardencity" | station=="Belwindreefballs"| station=="Westhinder" ~ "offshore",
+                                                station == "Cpowerreefballs"|station=="Buitenratel" | station=="Faulbaums"|station=="Grafton"~"midshore",
+                                               station == "Nauticaena"~"nearshore"))
+
+mat_nmds = df_nmds[,c("dpm", "lost_minutes","noise", "hrs")] %>%  as.matrix()
+
+set.seed(123)
+nmds = metaMDS(mat_nmds, distance = "bray")
+
+plot(nmds)
+
+#extract NMDS scores (x and y coordinates) for sites from newer versions of vegan package
+data.scores = as.data.frame(scores(nmds)$sites)
+
+#add columns to data frame 
+data.scores$Season = df_nmds$Season
+data.scores$station_type = df_nmds$station_type
+
+head(data.scores)
+
+plot.nmds = ggplot(data.scores, aes(x = NMDS1, y = NMDS2)) + 
+  geom_point(size = 4, aes( shape = station_type, colour = Season))+ 
+  theme(axis.text.y = element_text(colour = "black", size = 12, face = "bold"), 
+        axis.text.x = element_text(colour = "black", face = "bold", size = 12), 
+        legend.text = element_text(size = 12, face ="bold", colour ="black"), 
+        legend.position = "right", axis.title.y = element_text(face = "bold", size = 14), 
+        axis.title.x = element_text(face = "bold", size = 14, colour = "black"), 
+        legend.title = element_text(size = 14, colour = "black", face = "bold"), 
+        panel.background = element_blank(), panel.border = element_rect(colour = "black", fill = NA, linewidth = 1.2),
+        legend.key=element_blank()) + 
+  labs(x = "NMDS1", colour = "Season", y = "NMDS2", shape = "Station type") 
+
+plot.nmds
+
+#add factors
+env <- df_nmds[,c("dpm", "lost_minutes","noise", "hrs")]
+en = envfit(nmds, env, permutations = 999, na.rm = TRUE)
+en_coord_cont = as.data.frame(scores(en, "vectors")) * ordiArrowMul(en)
+
+plot.nmds2 = ggplot(data = data.scores, aes(x = NMDS1, y = NMDS2)) + 
+  geom_point(data = data.scores, aes(colour = Season, shape = station_type), size = 3, alpha = 0.5) + 
+  scale_colour_manual(values = c("orange", "steelblue","violet","green"))  + 
+  geom_segment(aes(x = 0, y = 0, xend = NMDS1, yend = NMDS2), 
+               data = en_coord_cont, linewidth =1, alpha = 0.5, colour = "grey30") +
+  geom_text(data = en_coord_cont, aes(x = NMDS1, y = NMDS2), colour = "grey30", 
+            fontface = "bold", label = row.names(en_coord_cont)) + 
+  theme(axis.title = element_text(size = 10, face = "bold", colour = "grey30"), 
+        panel.background = element_blank(), panel.border = element_rect(fill = NA, colour = "grey30"), 
+        legend.title = element_text(size = 10, face = "bold", colour = "gr ey30"), 
+        legend.text = element_text(size = 9, colour = "grey30")) + 
+  labs(colour = "Season", shape ="Station type")
+plot.nmds2
+
+ggsave("plots/nmds_dol.png", width = 11, height = 6, dpi=300)
+
+#------------------ANOSIM
+ano = anosim(mat_nmds, df_nmds$station_type, distance = "bray", permutations = 9999)
+ano #no statistical diff among the 3 types of stations
+
+ano = anosim(mat_nmds, df_nmds$Season, distance = "bray", permutations = 9999)
+ano #no statistical diff among the 4 seasons
+
+#------------------PERMANOVA
+df.dist <- vegdist(mat_nmds, method="bray")
+adonis2(df.dist ~ station_type*Season, data = df_nmds, permutations = 999, method="bray") #no statistical diff
+
+dune.div <- adonis2(dune ~ Management*A1, data = dune.env, permutations = 999, method="bray")
+
+

README.md

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+# Belgian passive acoustic network (BPAN) analysis (C-POD network)
+Analysis of data from C-POD stations:
+1. Data exploration & statistical tests: cpod_analysis.R
+2. Correlation of different variables (e.g. lost minutes, detection positive minutes (DPM), days of activity): correlation.R
+3. Heatmap to visualize data per hour per day per year: heatmap.R
+4. NMDS & PERMANOVA: NMDS_PCA.R
+5. Cross correlation function for time series of tide and noise: cross_correlation_function.R
+
+# Passive Belgian acoustic receiver network (PBARN) analysis
+Analysis of data from acoustic receivers:
+1. Data exploration: explore_data.R
+2. Calculation of receiver efficiency index (REI): REI_compute.R
+3. Interpolation of calculated REI: interpolation.py
+4. Assessment of station performance based on how much time a receiver was lost/broken/replaced and data availability: station_performance.R
+
+# Reference
+Ellis, R. D., Flaherty-Walia, K. E., Collins, A. B., Bickford, J. W., Boucek, R., Walters Burnsed, S. L., & Lowerre-Barbieri, S. K. (2019). Acoustic telemetry array evolution: From species- and project-specific designs to large-scale, multispecies, cooperative networks. Fisheries Research, 209(April 2018), 186–195. https://doi.org/10.1016/j.fishres.2018.09.015
+