Chapter 7 Diversity

load("data/metagenomics/data.Rdata")

7.1 Alpha diversity

# Calculate Hill numbers
richness <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 0) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(richness = 1) %>%
  rownames_to_column(var = "sample")

neutral <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(neutral = 1) %>%
  rownames_to_column(var = "sample")

phylogenetic <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1, tree = genome_tree) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(phylogenetic = 1) %>%
  rownames_to_column(var = "sample")

# Aggregate basal GIFT into elements
dist <- genome_gifts %>%
  to.elements(., GIFT_db) %>%
  traits2dist(., method = "gower")

#functional <- genome_counts_filt %>%
#  filter(genome %in% labels(dist)[[1]]) %>%
#  column_to_rownames(var = "genome") %>%
#  dplyr::select(where(~ !all(. == 0))) %>%
#  hilldiv(., q = 1, dist = dist) %>%
#  t() %>%
#  as.data.frame() %>%
#  dplyr::rename(functional = 1) %>%
#  rownames_to_column(var = "sample") %>%
#  mutate(functional = if_else(is.nan(functional), 1, functional))

# Merge all metrics
alpha_div_metagenomics <- richness %>%
  full_join(neutral, by = join_by(sample == sample)) %>%
  full_join(phylogenetic, by = join_by(sample == sample)) #%>%
  #full_join(functional, by = join_by(sample == sample))

alpha_div_metagenomics %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == sample)) %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = bat_species, group=bat_species, color=bat_species, fill=bat_species)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Species",
          breaks=c("Pipistrellus kuhlii","Eptesicus bottae","Hypsugo ariel"),
          labels=c("Pipistrellus kuhlii","Eptesicus bottae","Hypsugo ariel"),
          values=c("#e5bd5b", "#6b7398","#e2815a")) +
      scale_fill_manual(name="Species",
          breaks=c("Pipistrellus kuhlii","Eptesicus bottae","Hypsugo ariel"),
          labels=c("Pipistrellus kuhlii","Eptesicus bottae","Hypsugo ariel"),
          values=c("#e5bd5b50", "#6b739850","#e2815a50")) +
      facet_wrap(. ~ metric, scales = "free", ncol=4) +
      coord_cartesian(xlim = c(1, NA)) +
      theme_classic() +
      theme(
        strip.background = element_blank(),
        panel.grid.minor.x = element_line(size = .1, color = "grey"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.x = element_blank())

7.2 Beta diversity

beta_q0n <- genome_counts %>%
  select(where(~!all(. == 0))) %>% # remove empty samples
  column_to_rownames(., "genome") %>%
  hillpair(., q = 0)

beta_q1n <- genome_counts %>%
  select(where(~!all(. == 0))) %>% # remove empty samples
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1)

beta_q1p <- genome_counts %>%
  select(where(~!all(. == 0))) %>% # remove empty samples
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1, tree = genome_tree)

beta_q1f <- genome_counts %>%
  select(where(~!all(. == 0))) %>% # remove empty samples
  filter(genome %in% labels(dist)[[1]]) %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1, dist = dist)

7.2.1 Richness (q0n)

Homogeneity of variances

Df	Sum Sq	Mean Sq	F	N.Perm	Pr(>F)
2	0.002096317	0.0010481586	1.566315	999	0.227
48	0.032121016	0.0006691878	NA	NA	NA

Permanova

term	df	SumOfSqs	R2	statistic	p.value
bat_species	2	0.007009903	0.08603871	2.259318	0.013
Residual	48	0.074463917	0.91396129	NA	NA
Total	50	0.081473820	1.00000000	NA	NA

beta_q0n$C %>%
  vegan::metaMDS(., trymax = 500, k = 2, trace = 0) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
  group_by(bat_species) %>%
  mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
  mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = NMDS1, y = NMDS2, color = bat_species)) +
    scale_color_manual(values = c("#e5bd5b", "#6b7398","#e2815a")) +
    scale_shape_manual(values = 1:10) +
    geom_point(size = 4) +
    #   stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
    geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
    theme_classic() +
    theme(
      axis.text.x = element_text(size = 12),
      axis.text.y = element_text(size = 12),
      axis.title = element_text(size = 20, face = "bold"),
      axis.text = element_text(face = "bold", size = 18),
      panel.background = element_blank(),
      axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
      legend.text = element_text(size = 16),
      legend.title = element_text(size = 18),
      legend.position = "right", legend.box = "vertical"
    )

7.2.2 Neutral (q1n)

Homogeneity of variances

Df	Sum Sq	Mean Sq	F	N.Perm	Pr(>F)
2	0.2043596	0.10217979	4.995482	999	0.009
48	0.9818131	0.02045444	NA	NA	NA

Permanova

term	df	SumOfSqs	R2	statistic	p.value
bat_species	2	1.702184	0.0979211	2.605211	0.001
Residual	48	15.681038	0.9020789	NA	NA
Total	50	17.383222	1.0000000	NA	NA

beta_q1n$C %>%
  vegan::metaMDS(., trymax = 500, k = 2, trace = 0) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
  group_by(bat_species) %>%
  mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
  mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = NMDS1, y = NMDS2, color = bat_species)) +
    scale_color_manual(values = c("#e5bd5b", "#6b7398","#e2815a")) +
    scale_shape_manual(values = 1:10) +
    geom_point(size = 4) +
    #   stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
    geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
    theme_classic() +
    theme(
      axis.text.x = element_text(size = 12),
      axis.text.y = element_text(size = 12),
      axis.title = element_text(size = 20, face = "bold"),
      axis.text = element_text(face = "bold", size = 18),
      panel.background = element_blank(),
      axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
      legend.text = element_text(size = 16),
      legend.title = element_text(size = 18),
      legend.position = "right", legend.box = "vertical"
    )

7.2.3 Phylogenetic (q1p)

Homogeneity of variances

Df	Sum Sq	Mean Sq	F	N.Perm	Pr(>F)
2	0.1390146	0.06950729	3.719514	999	0.03
48	0.8969856	0.01868720	NA	NA	NA

Permanova

term	df	SumOfSqs	R2	statistic	p.value
bat_species	2	0.6452775	0.08607004	2.260218	0.012
Residual	48	6.8518437	0.91392996	NA	NA
Total	50	7.4971212	1.00000000	NA	NA

beta_q1p$C %>%
  vegan::metaMDS(., trymax = 500, k = 2, trace = 0) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
  group_by(bat_species) %>%
  mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
  mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = NMDS1, y = NMDS2, color = bat_species)) +
    scale_color_manual(values = c("#e5bd5b", "#6b7398","#e2815a")) +
    scale_shape_manual(values = 1:10) +
    geom_point(size = 4) +
    #   stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
    geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
    theme_classic() +
    theme(
      axis.text.x = element_text(size = 12),
      axis.text.y = element_text(size = 12),
      axis.title = element_text(size = 20, face = "bold"),
      axis.text = element_text(face = "bold", size = 18),
      panel.background = element_blank(),
      axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
      legend.text = element_text(size = 16),
      legend.title = element_text(size = 18),
      legend.position = "right", legend.box = "vertical"
    )

7.2.4 Functional (q1f)

Homogeneity of variances

Df	Sum Sq	Mean Sq	F	N.Perm	Pr(>F)
2	0.02809638	0.01404819	0.266268	999	0.782
48	2.53246042	0.05275959	NA	NA	NA

Permanova

term	df	SumOfSqs	R2	statistic	p.value
bat_species	2	1.062947	0.1351829	3.751532	0.015
Residual	48	6.800086	0.8648171	NA	NA
Total	50	7.863034	1.0000000	NA	NA

beta_q1f$C %>%
  vegan::metaMDS(., trymax = 500, k = 2, trace = 0) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
  group_by(bat_species) %>%
  mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
  mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = NMDS1, y = NMDS2, color = bat_species)) +
    scale_color_manual(values = c("#e5bd5b", "#6b7398","#e2815a")) +
    scale_shape_manual(values = 1:10) +
    geom_point(size = 4) +
    #   stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
    geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
    theme_classic() +
    theme(
      axis.text.x = element_text(size = 12),
      axis.text.y = element_text(size = 12),
      axis.title = element_text(size = 20, face = "bold"),
      axis.text = element_text(face = "bold", size = 18),
      panel.background = element_blank(),
      axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
      legend.text = element_text(size = 16),
      legend.title = element_text(size = 18),
      legend.position = "right", legend.box = "vertical"
    )