Chapter 6 Beta diversity
6.1 Hill numbers
beta_q0n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
dplyr::select_if(~!all(. == 0)) %>%
hillpair(., q = 0)
beta_q1n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
dplyr::select_if(~!all(. == 0)) %>%
hillpair(., q = 1)
beta_q1p <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
dplyr::select_if(~!all(. == 0)) %>%
hillpair(., q = 1, tree = genome_tree)
beta_q1f <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
dplyr::select_if(~!all(. == 0)) %>%
hillpair(., q = 1, dist = dist)6.2 By location
6.2.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 5 0.51463 0.102926 11.076 999 0.001 ***
Residuals 86 0.79917 0.009293
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Aruba Brazil CaboVerde Spain Denmark Malaysia
Aruba 2.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 0.008
Brazil 8.1559e-05 9.6000e-02 5.9000e-02 8.9900e-01 0.138
CaboVerde 6.2922e-04 1.1254e-01 1.0000e-03 2.4000e-02 0.930
Spain 1.7363e-12 7.3505e-02 3.5940e-05 1.3000e-02 0.002
Denmark 1.3206e-07 9.0556e-01 2.9720e-02 2.3526e-02 0.069
Malaysia 9.6128e-03 1.3730e-01 9.2726e-01 4.6570e-04 5.9273e-02 adonis2(beta_q0n$S ~ location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q0n$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 5 | 6.57607 | 0.2442751 | 5.559605 | 0.001 |
| Residual | 86 | 20.34468 | 0.7557249 | NA | NA |
| Total | 91 | 26.92075 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Aruba vs Brazil 1 2.0520609 7.280171 0.19528265 0.001 0.015 .
2 Aruba vs CaboVerde 1 1.0026264 3.213494 0.09975614 0.001 0.015 .
3 Aruba vs Denmark 1 1.9513181 7.018855 0.19486614 0.001 0.015 .
4 Aruba vs Malaysia 1 1.1650328 3.678697 0.11257172 0.003 0.045 .
5 Aruba vs Spain 1 2.0433431 8.167535 0.21974917 0.001 0.015 .
6 Brazil vs CaboVerde 1 2.1090004 9.164611 0.24013374 0.001 0.015 .
7 Brazil vs Denmark 1 0.5703370 2.907951 0.09113563 0.001 0.015 .
8 Brazil vs Malaysia 1 0.9283278 3.953419 0.11996993 0.001 0.015 .
9 Brazil vs Spain 1 0.6343957 3.769479 0.11503016 0.001 0.015 .
10 CaboVerde vs Denmark 1 1.8099512 8.070070 0.22373314 0.001 0.015 .
11 CaboVerde vs Malaysia 1 1.2143821 4.593887 0.14094322 0.001 0.015 .
12 CaboVerde vs Spain 1 1.8141430 9.281722 0.24896172 0.001 0.015 .
13 Denmark vs Malaysia 1 1.0124763 4.418610 0.13629855 0.001 0.015 .
14 Denmark vs Spain 1 0.5305346 3.310771 0.10573905 0.001 0.015 .
15 Malaysia vs Spain 1 0.8450695 4.218747 0.13094076 0.001 0.015 .#pdf("figures/beta_q0_loca.pdf",width=9, height=5)
beta_q0n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = "sample") %>%
group_by(location) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = location, fill = location)) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values = location_colors)+
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"
) 
6.2.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 5 0.2728 0.054560 3.7079 999 0.005 **
Residuals 86 1.2654 0.014715
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Aruba Brazil CaboVerde Spain Denmark Malaysia
Aruba 1.2000e-02 1.0000e-03 1.0000e-03 1.0000e-03 0.001
Brazil 9.2383e-03 7.4000e-01 2.2100e-01 6.8500e-01 0.869
CaboVerde 7.7521e-04 7.7033e-01 3.0200e-01 8.7000e-01 0.909
Spain 1.6646e-05 2.2327e-01 3.0424e-01 3.9200e-01 0.241
Denmark 3.3254e-04 6.6322e-01 8.7642e-01 3.7291e-01 0.783
Malaysia 1.1422e-03 8.5989e-01 8.9763e-01 2.4754e-01 7.7435e-01 adonis2(beta_q1n$S ~ location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 5 | 5.709562 | 0.221304 | 4.88821 | 0.001 |
| Residual | 86 | 20.090068 | 0.778696 | NA | NA |
| Total | 91 | 25.799630 | 1.000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Aruba vs Brazil 1 1.3739907 4.789154 0.13766227 0.001 0.015 .
2 Aruba vs CaboVerde 1 1.0636589 3.806728 0.11603497 0.001 0.015 .
3 Aruba vs Denmark 1 1.6037782 5.813780 0.16699651 0.001 0.015 .
4 Aruba vs Malaysia 1 1.2584980 4.466640 0.13346545 0.001 0.015 .
5 Aruba vs Spain 1 1.4382789 5.558030 0.16083181 0.001 0.015 .
6 Brazil vs CaboVerde 1 1.5901673 7.027154 0.19505161 0.001 0.015 .
7 Brazil vs Denmark 1 0.6954159 3.122217 0.09719804 0.001 0.015 .
8 Brazil vs Malaysia 1 0.7313843 3.199010 0.09935119 0.002 0.030 .
9 Brazil vs Spain 1 0.4007935 1.948927 0.06297236 0.043 0.645
10 CaboVerde vs Denmark 1 1.8198218 8.556140 0.23405480 0.001 0.015 .
11 CaboVerde vs Malaysia 1 1.3171418 6.019861 0.17695136 0.001 0.015 .
12 CaboVerde vs Spain 1 1.5415454 7.905417 0.22017337 0.001 0.015 .
13 Denmark vs Malaysia 1 1.0691578 4.970172 0.15074753 0.001 0.015 .
14 Denmark vs Spain 1 0.5766622 3.014217 0.09718823 0.002 0.030 .
15 Malaysia vs Spain 1 0.6446972 3.265586 0.10444666 0.001 0.015 .#pdf("figures/beta_q1n_loca.pdf",width=9, height=5)
beta_q1n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = "sample") %>%
group_by(location) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = location, fill = location)) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values = location_colors)+
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"
) 
6.2.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 5 0.2152 0.04304 2.1047 999 0.07 .
Residuals 86 1.7587 0.02045
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Aruba Brazil CaboVerde Spain Denmark Malaysia
Aruba 0.05900000 0.04500000 0.00100000 0.08500000 0.003
Brazil 0.06236518 0.86000000 0.33200000 0.65800000 0.666
CaboVerde 0.05279004 0.86912737 0.20200000 0.74900000 0.565
Spain 0.00060645 0.32391452 0.20693940 0.10000000 0.491
Denmark 0.09811567 0.63811338 0.73976492 0.10108967 0.330
Malaysia 0.00519311 0.67845753 0.52055144 0.50696000 0.31212601 adonis2(beta_q1p$S ~ location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1p$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 5 | 2.690791 | 0.2683828 | 6.309563 | 0.001 |
| Residual | 86 | 7.335152 | 0.7316172 | NA | NA |
| Total | 91 | 10.025944 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Aruba vs Brazil 1 0.64714035 6.1133065 0.16928127 0.001 0.015 .
2 Aruba vs CaboVerde 1 0.56229778 5.3504131 0.15575979 0.002 0.030 .
3 Aruba vs Denmark 1 1.03758657 9.5591181 0.24790811 0.001 0.015 .
4 Aruba vs Malaysia 1 0.48047063 5.0352414 0.14794199 0.002 0.030 .
5 Aruba vs Spain 1 0.60047832 6.7919744 0.18976250 0.001 0.015 .
6 Brazil vs CaboVerde 1 0.70961443 8.1023431 0.21837821 0.001 0.015 .
7 Brazil vs Denmark 1 0.32742702 3.5968626 0.11034383 0.028 0.420
8 Brazil vs Malaysia 1 0.19570506 2.5119802 0.07971509 0.020 0.300
9 Brazil vs Spain 1 0.06456161 0.9106384 0.03044530 0.440 1.000
10 CaboVerde vs Denmark 1 1.07120011 11.9405505 0.29895808 0.001 0.015 .
11 CaboVerde vs Malaysia 1 0.50032095 6.5728056 0.19011490 0.001 0.015 .
12 CaboVerde vs Spain 1 0.82104026 11.9236999 0.29866220 0.001 0.015 .
13 Denmark vs Malaysia 1 0.54258755 6.8084757 0.19559821 0.001 0.015 .
14 Denmark vs Spain 1 0.24626102 3.3999417 0.10827860 0.027 0.405
15 Malaysia vs Spain 1 0.26932060 4.5771932 0.14050299 0.001 0.015 .#pdf("figures/beta_q1p_loca.pdf",width=9, height=5)
beta_q1p$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = "sample") %>%
group_by(location) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = location, fill = location)) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values = location_colors)+
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"
) 
6.2.4 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 5 1.5016 0.300325 9.1732 999 0.001 ***
Residuals 86 2.8156 0.032739
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Aruba Brazil CaboVerde Spain Denmark Malaysia
Aruba 2.0000e-03 5.8700e-01 4.0000e-03 1.0000e-03 0.511
Brazil 8.6955e-04 2.0000e-03 6.8600e-01 2.4300e-01 0.002
CaboVerde 5.8936e-01 9.7727e-04 3.0000e-03 1.0000e-03 0.885
Spain 1.5125e-03 6.8358e-01 1.5804e-03 3.5000e-02 0.001
Denmark 1.7539e-04 2.3957e-01 9.2505e-05 3.3658e-02 0.001
Malaysia 4.9615e-01 1.0071e-03 8.8292e-01 1.5959e-03 7.4748e-05 adonis2(beta_q1f$S ~ location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1f$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| Model | 5 | 8.815599 | 0.5844961 | 24.19552 | 0.001 |
| Residual | 86 | 6.266793 | 0.4155039 | NA | NA |
| Total | 91 | 15.082391 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Aruba vs Brazil 1 3.77109355 40.6261637 0.575228238 0.001 0.015 .
2 Aruba vs CaboVerde 1 0.81599223 5.5801053 0.161367505 0.022 0.330
3 Aruba vs Denmark 1 4.54372380 50.8948430 0.637022880 0.001 0.015 .
4 Aruba vs Malaysia 1 0.87672195 6.0397240 0.172367910 0.013 0.195
5 Aruba vs Spain 1 3.49955224 36.9984243 0.560595570 0.001 0.015 .
6 Brazil vs CaboVerde 1 1.79573937 27.7696018 0.489163230 0.001 0.015 .
7 Brazil vs Denmark 1 0.27026868 35.0545409 0.547260825 0.001 0.015 .
8 Brazil vs Malaysia 1 1.69391038 26.6369446 0.478763612 0.001 0.015 .
9 Brazil vs Spain 1 -0.01258568 -0.9666596 -0.034482498 0.944 1.000
10 CaboVerde vs Denmark 1 2.85656348 47.6207158 0.629731090 0.001 0.015 .
11 CaboVerde vs Malaysia 1 -0.02164970 -0.1836835 -0.006603446 0.981 1.000
12 CaboVerde vs Spain 1 1.56212224 23.8545897 0.460028512 0.001 0.015 .
13 Denmark vs Malaysia 1 2.82610262 48.0023359 0.631590271 0.001 0.015 .
14 Denmark vs Spain 1 0.38029019 58.5480612 0.676480332 0.001 0.015 .
15 Malaysia vs Spain 1 1.47580731 22.9256154 0.450178466 0.001 0.015 .#pdf("figures/beta_q1f_loca.pdf",width=9, height=5)
beta_q1f$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = "sample") %>%
group_by(location) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = location, fill = location)) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values = location_colors)+
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"
) 
6.3 By behaviour
6.3.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00411 0.0041147 0.2442 999 0.656
Residuals 90 1.51634 0.0168483
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Domestic Feral
Domestic 0.655
Feral 0.62238 adonis2(beta_q0n$S ~ origin*location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q0n$S))),
permutations = 999,
by = "terms") %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.4407951 | 0.01637380 | 1.891117 | 0.034 |
| location | 5 | 6.4266125 | 0.23872335 | 5.514343 | 0.001 |
| origin:location | 5 | 1.4063756 | 0.05224132 | 1.206738 | 0.093 |
| Residual | 80 | 18.6469706 | 0.69266153 | NA | NA |
| Total | 91 | 26.9207538 | 1.00000000 | NA | NA |
adonis2(beta_q0n$S ~ origin,
data = sample_metadata %>% arrange(match(sample,labels(beta_q0n$S))),
permutations = 999,
by = "terms",
strata = sample_metadata %>% arrange(match(sample,labels(beta_q0n$S))) %>% pull(location)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.4407951 | 0.0163738 | 1.498173 | 0.187 |
| Residual | 90 | 26.4799587 | 0.9836262 | NA | NA |
| Total | 91 | 26.9207538 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Domestic vs Feral 1 0.4407951 1.498173 0.0163738 0.082 0.082 beta_q0n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(origin) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = origin, fill = origin)) +
geom_point(size = 4) +
scale_color_manual(values = origin_colors) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
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"
) 
6.3.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.0080 0.0080048 0.5581 999 0.483
Residuals 90 1.2909 0.0143435
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Domestic Feral
Domestic 0.482
Feral 0.45698 adonis2(beta_q1n$S ~ origin*location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$S))),
permutations = 999,
by = "terms") %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.386668 | 0.01498735 | 1.687422 | 0.058 |
| location | 5 | 5.598811 | 0.21701130 | 4.886650 | 0.001 |
| origin:location | 5 | 1.482374 | 0.05745718 | 1.293818 | 0.057 |
| Residual | 80 | 18.331777 | 0.71054418 | NA | NA |
| Total | 91 | 25.799630 | 1.00000000 | NA | NA |
adonis2(beta_q1n$S ~ origin,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$S))),
permutations = 999,
by = "terms",
strata = sample_metadata %>% arrange(match(sample,labels(beta_q1n$S))) %>% pull(location)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.386668 | 0.01498735 | 1.369385 | 0.291 |
| Residual | 90 | 25.412962 | 0.98501265 | NA | NA |
| Total | 91 | 25.799630 | 1.00000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Domestic vs Feral 1 0.386668 1.369385 0.01498735 0.157 0.157 pdf("figures/beta_q1n_behaviour.pdf",width=9, height=5)
beta_q1n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(origin) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = origin, fill = origin)) +
geom_point(size = 4) +
scale_color_manual(values = origin_colors) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
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"
)
dev.off()quartz_off_screen
2 6.3.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01229 0.012295 0.5442 999 0.427
Residuals 90 2.03337 0.022593
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Domestic Feral
Domestic 0.433
Feral 0.46262 adonis2(beta_q1p$S ~ origin*location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1p$S))),
permutations = 999,
by = "terms") %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.2191073 | 0.02185403 | 2.624892 | 0.031 |
| location | 5 | 2.6076315 | 0.26008838 | 6.247853 | 0.001 |
| origin:location | 5 | 0.5213738 | 0.05200247 | 1.249205 | 0.190 |
| Residual | 80 | 6.6778310 | 0.66605512 | NA | NA |
| Total | 91 | 10.0259435 | 1.00000000 | NA | NA |
adonis2(beta_q1p$S ~ origin,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1p$S))),
permutations = 999,
by = "terms",
strata = sample_metadata %>% arrange(match(sample,labels(beta_q1p$S))) %>% pull(location)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.2191073 | 0.02185403 | 2.010807 | 0.171 |
| Residual | 90 | 9.8068363 | 0.97814597 | NA | NA |
| Total | 91 | 10.0259435 | 1.00000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Domestic vs Feral 1 0.2191073 2.010807 0.02185403 0.072 0.072 beta_q1p$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(origin) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = origin, fill = origin)) +
geom_point(size = 4) +
scale_color_manual(values = origin_colors) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
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"
) 
6.3.4 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.2486 0.248604 3.3502 999 0.062 .
Residuals 90 6.6786 0.074206
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Domestic Feral
Domestic 0.065
Feral 0.070508 adonis2(beta_q1f$S ~ origin*location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1f$S))),
permutations = 999,
by = "terms") %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.3816603 | 0.02530502 | 5.310872 | 0.015 |
| location | 5 | 8.4723177 | 0.56173570 | 23.578769 | 0.001 |
| origin:location | 5 | 0.4792973 | 0.03177860 | 1.333902 | 0.242 |
| Residual | 80 | 5.7491162 | 0.38118068 | NA | NA |
| Total | 91 | 15.0823914 | 1.00000000 | NA | NA |
adonis2(beta_q1p$S ~ origin,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1f$S))),
permutations = 999,
by = "terms",
strata = sample_metadata %>% arrange(match(sample,labels(beta_q1f$S))) %>% pull(location)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.2191073 | 0.02185403 | 2.010807 | 0.172 |
| Residual | 90 | 9.8068363 | 0.97814597 | NA | NA |
| Total | 91 | 10.0259435 | 1.00000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Domestic vs Feral 1 0.3816603 2.336579 0.02530502 0.099 0.099 beta_q1f$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(origin) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = origin, fill = origin)) +
geom_point(size = 4) +
scale_color_manual(values = origin_colors) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
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"
) 