Richness diversity
betadisper(beta_q0n$S, sample_metadata$location) %>% permutest(., pairwise=TRUE)
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 0.010
Brazil 8.1559e-05 1.0700e-01 7.5000e-02 9.0300e-01 0.150
CaboVerde 6.2922e-04 1.1254e-01 1.0000e-03 2.8000e-02 0.930
Spain 1.7363e-12 7.3505e-02 3.5940e-05 1.8000e-02 0.001
Denmark 1.3206e-07 9.0556e-01 2.9720e-02 2.3526e-02 0.067
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()
tinytable_eoiajtn3cbxy8uu1jw0l
term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
location |
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 |
pairwise.adonis(beta_q0n$S, sample_metadata$location, perm = 999)
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 0.002 0.030 .
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"
)
Neutral diversity
betadisper(beta_q1n$S, sample_metadata$location) %>% permutest(., pairwise=TRUE)
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 0.008 **
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 1.0000e-02 1.0000e-03 1.0000e-03 2.0000e-03 0.003
Brazil 9.2383e-03 7.6700e-01 2.3300e-01 6.4700e-01 0.853
CaboVerde 7.7521e-04 7.7033e-01 2.9700e-01 8.7500e-01 0.905
Spain 1.6646e-05 2.2327e-01 3.0424e-01 3.8300e-01 0.245
Denmark 3.3254e-04 6.6322e-01 8.7642e-01 3.7291e-01 0.751
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()
tinytable_61uqj14e5vyalb0alq0u
term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
location |
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 |
pairwise.adonis(beta_q1n$S, sample_metadata$location, perm = 999)
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 0.034 0.510
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 0.003 0.045 .
15 Malaysia vs Spain 1 0.6446972 3.265586 0.10444666 0.002 0.030 .
#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"
)
Phylogenetic diversity
betadisper(beta_q1p$S, sample_metadata$location) %>% permutest(., pairwise=TRUE)
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 0.075 .
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 0.06800000 0.05200000 0.00100000 0.10400000 0.007
Brazil 0.06236518 0.87300000 0.36100000 0.65200000 0.679
CaboVerde 0.05279004 0.86912737 0.19700000 0.74800000 0.529
Spain 0.00060645 0.32391452 0.20693940 0.11100000 0.514
Denmark 0.09811567 0.63811338 0.73976492 0.10108967 0.321
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()
tinytable_h1cjg81jnjeceegchvxw
term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
location |
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 |
pairwise.adonis(beta_q1p$S, sample_metadata$location, perm = 999)
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 0.003 0.045 .
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 0.001 0.015 .
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 0.015 0.225
8 Brazil vs Malaysia 1 0.19570506 2.5119802 0.07971509 0.030 0.450
9 Brazil vs Spain 1 0.06456161 0.9106384 0.03044530 0.457 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 0.009 0.135
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"
)
Functional diversity
betadisper(beta_q1f$S, sample_metadata$location) %>% permutest(., pairwise=TRUE)
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 1.0000e-03 5.7800e-01 1.0000e-03 2.0000e-03 0.497
Brazil 8.6955e-04 1.0000e-03 6.9500e-01 2.2300e-01 0.003
CaboVerde 5.8936e-01 9.7727e-04 3.0000e-03 1.0000e-03 0.868
Spain 1.5125e-03 6.8358e-01 1.5804e-03 4.6000e-02 0.003
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()
tinytable_16zo9u8iiabvor6i0hdm
term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
location |
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 |
pairwise.adonis(beta_q1f$S, sample_metadata$location, perm = 999)
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 0.015 0.225
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 0.018 0.270
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 0.951 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 0.982 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 0.002 0.030 .
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"
)