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 1.0000e-03 2.0000e-03 1.0000e-03 1.0000e-03 0.009
Brazil 8.1559e-05 1.0600e-01 8.0000e-02 9.0300e-01 0.125
CaboVerde 6.2922e-04 1.1254e-01 1.0000e-03 2.6000e-02 0.926
Spain 1.7363e-12 7.3505e-02 3.5940e-05 2.4000e-02 0.002
Denmark 1.3206e-07 9.0556e-01 2.9720e-02 2.3526e-02 0.053
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 0.002
Brazil 9.2383e-03 7.8200e-01 1.9800e-01 6.5900e-01 0.848
CaboVerde 7.7521e-04 7.7033e-01 2.9700e-01 8.7600e-01 0.898
Spain 1.6646e-05 2.2327e-01 3.0424e-01 3.6600e-01 0.264
Denmark 3.3254e-04 6.6322e-01 8.7642e-01 3.7291e-01 0.777
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 0.003 0.045 .
8 Brazil vs Malaysia 1 0.7313843 3.199010 0.09935119 0.001 0.015 .
9 Brazil vs Spain 1 0.4007935 1.948927 0.06297236 0.039 0.585
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 0.073 .
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 0.06300000 0.04900000 0.00100000 0.09000000 0.004
Brazil 0.06236518 0.86700000 0.33200000 0.63700000 0.676
CaboVerde 0.05279004 0.86912737 0.21000000 0.73100000 0.533
Spain 0.00060645 0.32391452 0.20693940 0.10700000 0.493
Denmark 0.09811567 0.63811338 0.73976492 0.10108967 0.325
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 0.001 0.015 .
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 0.017 0.255
8 Brazil vs Malaysia 1 0.19570506 2.5119802 0.07971509 0.031 0.465
9 Brazil vs Spain 1 0.06456161 0.9106384 0.03044530 0.456 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 0.002 0.030 .
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 0.018 0.270
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 2.0000e-03 2.0000e-03 0.511
Brazil 8.6955e-04 3.0000e-03 6.9400e-01 2.6100e-01 0.002
CaboVerde 5.8936e-01 9.7727e-04 2.0000e-03 1.0000e-03 0.888
Spain 1.5125e-03 6.8358e-01 1.5804e-03 4.0000e-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 0.012 0.180
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 0.016 0.240
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 0.939 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 0.974 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 0.627
Residuals 90 1.51634 0.0168483
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Domestic Feral
Domestic 0.625
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 | 0.023 |
| location | 5 | 6.4266125 | 0.23872335 | 5.514343 | 0.001 |
| origin:location | 5 | 1.4063756 | 0.05224132 | 1.206738 | 0.089 |
| 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$S))) %>% pull(location)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.4407951 | 0.0163738 | 1.498173 | 0.193 |
| Residual | 90 | 26.4799587 | 0.9836262 | NA | NA |
| Total | 91 | 26.9207538 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Domestic vs Feral 1 0.4407951 1.498173 0.0163738 0.078 0.078 beta_q0n$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 0.435
Residuals 90 1.2909 0.0143435
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Domestic Feral
Domestic 0.438
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 | 0.053 |
| location | 5 | 5.598811 | 0.21701130 | 4.886650 | 0.001 |
| origin:location | 5 | 1.482374 | 0.05745718 | 1.293818 | 0.050 |
| 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$S))) %>% pull(location)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.386668 | 0.01498735 | 1.369385 | 0.306 |
| Residual | 90 | 25.412962 | 0.98501265 | NA | NA |
| Total | 91 | 25.799630 | 1.00000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Domestic vs Feral 1 0.386668 1.369385 0.01498735 0.133 0.133 pdf("figures/beta_q1n_behaviour.pdf",width=9, height=5)
beta_q1n$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()RStudioGD
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 0.45
Residuals 90 2.03337 0.022593
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Domestic Feral
Domestic 0.454
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 | 0.029 |
| location | 5 | 2.6076315 | 0.26008838 | 6.247853 | 0.001 |
| origin:location | 5 | 0.5213738 | 0.05200247 | 1.249205 | 0.200 |
| 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$S))) %>% pull(location)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.2191073 | 0.02185403 | 2.010807 | 0.176 |
| Residual | 90 | 9.8068363 | 0.97814597 | NA | NA |
| Total | 91 | 10.0259435 | 1.00000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Domestic vs Feral 1 0.2191073 2.010807 0.02185403 0.09 0.09 beta_q1p$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 0.071 .
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 0.072
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 | 0.011 |
| location | 5 | 8.4723177 | 0.56173570 | 23.578769 | 0.001 |
| origin:location | 5 | 0.4792973 | 0.03177860 | 1.333902 | 0.239 |
| 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$S))) %>% pull(location)) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| origin | 1 | 0.2191073 | 0.02185403 | 2.010807 | 0.184 |
| Residual | 90 | 9.8068363 | 0.97814597 | NA | NA |
| Total | 91 | 10.0259435 | 1.00000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Domestic vs Feral 1 0.3816603 2.336579 0.02530502 0.105 0.105 beta_q1f$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"
) 