Ex - livro
Emmilly Mel
03/04/2022
Reproduzindo as análises do capítulo 8 do livro de diversidade funcional.
Definindo a dis (similaridade) entre espécies
library(ade4)## Warning: package 'ade4' was built under R version 4.1.3library(FD)## Warning: package 'FD' was built under R version 4.1.3## Carregando pacotes exigidos: ape## Carregando pacotes exigidos: geometry## Carregando pacotes exigidos: vegan## Carregando pacotes exigidos: permute## Carregando pacotes exigidos: lattice## This is vegan 2.5-7library(ecodados)
library(gridExtra)
library(ggplot2)
library(ggrepel)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## v purrr 0.3.4## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::combine() masks gridExtra::combine()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(picante)## Carregando pacotes exigidos: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
##
## collapsecomun_fren_dat <- ecodados::fundiv_frenette2012a_comu
ambie_fren_dat <- ecodados::fundiv_frenette2012a_amb
trait_fren_dat <- ecodados::fundiv_frenette2012a_trait
trait_dat <- ecodados::fundiv_barbaro2009a_trait
comun_dat <- ecodados::fundiv_barbaro2009a_comu
ambie_dat <- ecodados::fundiv_barbaro2009a_ambtrait_cont <- trait_fren_dat
trait_pad <- decostand(trait_cont, "standardize")
euclid_dis <- vegdist(trait_pad, "euclidean")
pcoa_traits_cont <- pcoa(euclid_dis, correction="cailliez")
eixos_cont <- as.data.frame(pcoa_traits_cont$vectors[,1:2])eixos_cont %>%
ggplot(aes(x=Axis.1, y=Axis.2)) +
geom_point(pch=21, size=3, color = "black", fill="#4575b4") +
geom_text_repel(aes(Axis.1, Axis.2, label = rownames(eixos_cont))) +
xlab("PCO 1") + ylab("PCO 2") +
theme(axis.title.x = element_text(face="bold", size=14),
axis.text.x = element_text(vjust=0.5, size=12)) +
theme(axis.title.y = element_text(face="bold", size=14),
axis.text.y = element_text(vjust=0.5, size=12)) +
geom_hline(yintercept = 0, linetype=2) +
geom_vline(xintercept = 0, linetype=2)+
theme(legend.position = "top", legend.title=element_blank()) -> plot_trait_cont
plot_trait_cont
ggsave("trait_cont.pdf", plot_trait_cont, height = 14, width = 14, dpi = 600, units = "cm")
trait_dat %>%
dplyr::select_if(is.character) -> trait_cat
dist_categ <- gowdis(trait_cat)
pcoa_traits_cat <- pcoa(dist_categ, correction="cailliez")
eixos_cat <- as.data.frame(pcoa_traits_cat$vectors[,1:2])eixos_cat %>%
ggplot(aes(x=Axis.1, y=Axis.2)) +
geom_point(pch=21, size=3, color = "black", fill="#4575b4") +
geom_text_repel(aes(Axis.1, Axis.2, label = rownames(eixos_cat))) +
xlab("PCO 1") + ylab("PCO 2") +
theme(axis.title.x = element_text(face="bold", size=14),
axis.text.x = element_text(vjust=0.5, size=12)) +
theme(axis.title.y = element_text(face="bold", size=14),
axis.text.y = element_text(vjust=0.5, size=12)) +
geom_hline(yintercept = 0, linetype=2) +
geom_vline(xintercept = 0, linetype=2)+
theme(legend.position = "top", legend.title=element_blank()) -> plot_trait_cat
plot_trait_cat
ggsave("trait_cat.pdf", plot_trait_cat, height = 14, width = 14, dpi = 600, units = "cm")
trait_dat %>%
dplyr::summarise_all(class) %>%
tidyr::gather(variable, class)## variable class
## 1 trend character
## 2 redlist character
## 3 regio integer
## 4 biog character
## 5 activ character
## 6 diet character
## 7 winter character
## 8 color character
## 9 breed character
## 10 body integer
## 11 wing character
## 12 period charactertrait_dat$regio <- as.ordered(trait_dat$regio)
trait_dat$body <- as.ordered(trait_dat$body)
trait_categ <- cbind.data.frame(trend=trait_dat$trend, redlist=trait_dat$redlist, biog=trait_dat$biog, activ=trait_dat$activ, diet=trait_dat$diet, winter=trait_dat$winter,color=trait_dat$color, breed=trait_dat$breed,wing=trait_dat$wing, period=trait_dat$period)
trait_ord <- cbind.data.frame(regio=trait_dat$regio, body=trait_dat$body)
rownames(trait_categ) <- rownames(trait_dat)
rownames(trait_ord) <- rownames(trait_dat)
ktab_list <- ktab.list.df(list(trait_categ, trait_ord))
dist_mist <- dist.ktab(ktab_list, type= c("N", "O"))
pcoa_traits_mist <- pcoa(dist_mist, correction="cailliez")
eixos_mist <- as.data.frame(pcoa_traits_mist$vectors[,1:2])eixos_mist %>%
ggplot(aes(x=Axis.1, y=Axis.2)) +
geom_point(pch=21, size=3, color = "black", fill="#d73027") +
geom_text_repel(aes(Axis.1, Axis.2, label = rownames(eixos_mist)))+
xlab("PCO 1") + ylab("PCO 2") +
theme(axis.title.x = element_text(face="bold", size=14),
axis.text.x = element_text(vjust=0.5, size=12)) +
theme(axis.title.y = element_text(face="bold", size=14),
axis.text.y = element_text(vjust=0.5, size=12)) +
geom_hline(yintercept = 0, linetype=2) +
geom_vline(xintercept = 0, linetype=2)+
theme(legend.position = "top", legend.title=element_blank()) -> plot_trait_mist
plot_trait_mist
ggsave("trait_mist.pdf", plot_trait_mist, height = 14, width = 14, dpi = 600, units = "cm")
grid.arrange(plot_trait_cat, plot_trait_mist, ncol=2)
#### Métricas de diversidade funcional (alpha)
richness <- dbFD(dist_mist, comun_dat)$nbsp ## Warning in is.euclid(x.dist): Zero distance(s)
## Warning in is.euclid(x.dist): Zero distance(s)## FRic: Dimensionality reduction was required. The last 17 PCoA axes (out of 19 in total) were removed.## Warning in is.euclid(x.dist): Zero distance(s)## FRic: Quality of the reduced-space representation = 0.3243851## Warning in is.euclid(x.dist): Zero distance(s)
## Warning in is.euclid(x.dist): Zero distance(s)## CWM: When 'x' is a distance matrix, CWM cannot be calculated.dist_mist## sp1 sp2 sp3 sp4 sp5 sp6 sp7
## sp2 0.5000000
## sp3 0.7107801 0.6808389
## sp4 0.7771900 0.8538292 0.7179711
## sp5 0.6107116 0.7345988 0.7381353 0.5106682
## sp6 0.5041691 0.6487320 0.6527339 0.6522593 0.5177440
## sp7 0.5041691 0.5809646 0.7137190 0.7132850 0.5177440 0.4082483
## sp8 0.9574271 0.9128709 0.8447140 0.8106696 0.7689990 0.8466582 0.8466582
## sp9 0.8322170 0.7743619 0.8705685 0.7862568 0.6712758 0.8618286 0.7590006
## sp10 0.8196798 0.8447140 0.8164966 0.7259317 0.6556685 0.7452327 0.8497285
## sp11 0.7381353 0.7658396 0.6755013 0.7674028 0.7671647 0.5971604 0.5971604
## sp12 0.8322170 0.8264198 0.7689102 0.8859268 0.8370252 0.7019605 0.7019605
## sp13 0.6454972 0.7637626 0.8196798 0.7845499 0.6516846 0.6192712 0.6192712
## sp14 0.9128709 0.7637626 0.7395100 0.8605338 0.9172565 0.7959251 0.8466582
## sp15 0.8684391 0.7100609 0.7423352 0.8468228 0.8274814 0.7637626 0.8164966
## sp16 0.8660254 0.8660254 0.6808389 0.6290397 0.7892837 0.7664984 0.8190563
## sp17 0.2886751 0.5773503 0.7671647 0.7771900 0.6107116 0.5809646 0.5809646
## sp18 0.6652707 0.5160455 0.7689102 0.8859268 0.7307151 0.7019605 0.6398556
## sp19 0.7892837 0.6755013 0.8184398 0.7432689 0.8164966 0.7754948 0.7197631
## sp20 0.6149610 0.6479535 0.5381778 0.7674028 0.7671647 0.5971604 0.7233721
## sp21 0.6755013 0.6755013 0.7658396 0.7432689 0.8164966 0.7197631 0.6593372
## sp22 0.6107116 0.6107116 0.7658396 0.8480067 0.7637626 0.5927834 0.5927834
## sp23 0.6573806 0.5906064 0.6921158 0.7395100 0.8199027 0.7096247 0.7096247
## sp24 0.5292494 0.5948301 0.7155949 0.8234825 0.7375481 0.5252793 0.5252793
## sp25 0.8404178 0.7345988 0.7093497 0.7432689 0.6454972 0.7754948 0.7197631
## sp26 0.8886143 0.7892837 0.8184398 0.7973594 0.7071068 0.8274814 0.7754948
## sp27 0.8347284 0.8289488 0.7716277 0.7712263 0.7920294 0.6652707 0.6652707
## sp28 0.6454972 0.7637626 0.7671647 0.8290703 0.7345988 0.5809646 0.5809646
## sp29 0.7137190 0.6839065 0.7100609 0.7660943 0.5971604 0.7107801 0.5818433
## sp30 0.8347284 0.8289488 0.7155949 0.6543114 0.7920294 0.6652707 0.6652707
## sp31 0.8259233 0.6573806 0.8035489 0.8447140 0.8199027 0.7096247 0.7660943
## sp32 0.9213121 0.8259233 0.9013088 0.7938566 0.9159187 0.8680825 0.8186781
## sp33 0.8414675 0.7842952 0.8794158 0.8705685 0.8355590 0.7276576 0.7276576
## sp34 0.7520331 0.7456127 0.6813161 0.6558429 0.7252023 0.6787075 0.5421967
## sp35 0.8322170 0.8264198 0.7689102 0.8859268 0.8370252 0.7019605 0.7019605
## sp36 0.6944209 0.7995447 0.7942658 0.6558429 0.6652707 0.6142561 0.7375481
## sp8 sp9 sp10 sp11 sp12 sp13 sp14
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9 0.5160455
## sp10 0.5448624 0.4177992
## sp11 0.6867080 0.7182727 0.7127595
## sp12 0.5160455 0.7071068 0.7126637 0.4272966
## sp13 0.7071068 0.6652707 0.6495191 0.5887001 0.6652707
## sp14 0.7637626 0.8264198 0.7938566 0.7449169 0.5913006 0.8660254
## sp15 0.6192712 0.6950778 0.6560273 0.8051504 0.6950778 0.8945186 0.5478718
## sp16 0.7637626 0.8753873 0.8447140 0.8184398 0.7185423 1.0000000 0.6454972
## sp17 0.9574271 0.8322170 0.8196798 0.7925762 0.8808623 0.6454972 0.9574271
## sp18 0.7743619 0.5773503 0.6515798 0.6577099 0.7071068 0.6652707 0.7185423
## sp19 0.8213969 0.7241058 0.7988958 0.7395100 0.7795271 0.6516846 0.7689990
## sp20 0.8971628 0.8261854 0.7127595 0.6454972 0.7182727 0.7164039 0.6230847
## sp21 0.8213969 0.7795271 0.8494515 0.6808389 0.7241058 0.5842598 0.7689990
## sp22 0.7127595 0.7241058 0.7449169 0.5448624 0.5980489 0.4179627 0.7689990
## sp23 0.8814629 0.8425832 0.8605338 0.7432689 0.7915890 0.6660657 0.7259317
## sp24 0.7526397 0.7417301 0.7470295 0.5253079 0.6192712 0.3992684 0.8061016
## sp25 0.7127595 0.6640752 0.7449169 0.7395100 0.7241058 0.8706470 0.7127595
## sp26 0.5842598 0.6640752 0.7449169 0.8447140 0.7241058 0.9172565 0.7127595
## sp27 0.6950778 0.8466582 0.8513047 0.5253079 0.5478718 0.7590006 0.6950778
## sp28 0.8164966 0.7805458 0.7671647 0.6793455 0.7805458 0.5773503 0.8660254
## sp29 0.8248465 0.6256088 0.7417301 0.7197631 0.8008660 0.7452327 0.8248465
## sp30 0.7526397 0.7959251 0.8008660 0.5994011 0.7417301 0.7590006 0.7526397
## sp31 0.7259317 0.6781935 0.6381106 0.7432689 0.7370752 0.7812235 0.6002584
## sp32 0.7259317 0.7370752 0.8106696 0.8480067 0.7370752 0.8814629 0.6002584
## sp33 0.6136882 0.6660657 0.6719621 0.7165635 0.6002584 0.7435692 0.6136882
## sp34 0.8312616 0.7689990 0.8751661 0.5078283 0.6516846 0.7856703 0.6640752
## sp35 0.5160455 0.7071068 0.7126637 0.4272966 0.0000000 0.6652707 0.5913006
## sp36 0.7241058 0.8213969 0.7182727 0.7126637 0.7127595 0.7307151 0.7241058
## sp15 sp16 sp17 sp18 sp19 sp20 sp21
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9
## sp10
## sp11
## sp12
## sp13
## sp14
## sp15
## sp16 0.6487320
## sp17 0.9151611 0.9128709
## sp18 0.6950778 0.9217572 0.7252023
## sp19 0.8763897 0.8404178 0.7345988 0.6640752
## sp20 0.8051504 0.7658396 0.6793455 0.6577099 0.6808389
## sp21 0.8763897 0.7892837 0.7345988 0.6640752 0.4082483 0.6808389
## sp22 0.7754948 0.8886143 0.6755013 0.5980489 0.5773503 0.6808389 0.4082483
## sp23 0.8186781 0.7738320 0.7179711 0.6136882 0.5055430 0.6849200 0.2981729
## sp24 0.7743619 0.8777753 0.6028584 0.6192712 0.6063788 0.6652932 0.4483621
## sp25 0.6593372 0.6755013 0.7892837 0.7241058 0.8164966 0.8447140 0.8660254
## sp26 0.5177440 0.6755013 0.8404178 0.7241058 0.8660254 0.9381942 0.9128709
## sp27 0.7185423 0.7214496 0.7832230 0.8466582 0.7859359 0.7805650 0.8372745
## sp28 0.7664984 0.9128709 0.5773503 0.7252023 0.7345988 0.7381353 0.7345988
## sp29 0.7395100 0.7964891 0.6527339 0.6889991 0.6939744 0.7197631 0.8051504
## sp30 0.7743619 0.7770604 0.7832230 0.7959251 0.6715866 0.7252230 0.7310006
## sp31 0.5035545 0.8259233 0.7738320 0.6136882 0.7110371 0.7432689 0.8199027
## sp32 0.6482545 0.6573806 0.8749186 0.7915890 0.5821572 0.7973594 0.7110371
## sp33 0.5194903 0.7842952 0.7904013 0.7259317 0.7224104 0.7725261 0.8297852
## sp34 0.8372745 0.6874627 0.8055353 0.7127595 0.7185423 0.6515798 0.6580043
## sp35 0.6950778 0.7185423 0.8808623 0.7071068 0.7795271 0.7182727 0.7241058
## sp36 0.6063788 0.6874627 0.7520331 0.8213969 0.8753873 0.7689102 0.7743619
## sp22 sp23 sp24 sp25 sp26 sp27 sp28
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9
## sp10
## sp11
## sp12
## sp13
## sp14
## sp15
## sp16
## sp17
## sp18
## sp19
## sp20
## sp21
## sp22
## sp23 0.5055430
## sp24 0.1853695 0.5194903
## sp25 0.8164966 0.8199027 0.8372745
## sp26 0.8660254 0.8692374 0.8856421 0.4082483
## sp27 0.7310006 0.8774225 0.7071068 0.6715866 0.6715866
## sp28 0.6107116 0.7738320 0.5292494 0.7892837 0.7892837 0.6028584
## sp29 0.7516208 0.8468228 0.7126637 0.5611897 0.6939744 0.6515798 0.5854299
## sp30 0.7310006 0.7766618 0.7071068 0.7310006 0.7310006 0.5000000 0.5292494
## sp31 0.7110371 0.7637626 0.7210202 0.5821572 0.5821572 0.6607094 0.5906064
## sp32 0.8199027 0.7637626 0.8285752 0.7674028 0.6498003 0.6607094 0.7179711
## sp33 0.7224104 0.8753873 0.7096247 0.7224104 0.5959950 0.5035545 0.5398157
## sp34 0.7185423 0.7224104 0.7197631 0.6580043 0.7743619 0.5927834 0.7520331
## sp35 0.5980489 0.7915890 0.6192712 0.7241058 0.7241058 0.5478718 0.7805458
## sp36 0.6580043 0.7779525 0.6593372 0.7743619 0.7185423 0.6593372 0.7520331
## sp29 sp30 sp31 sp32 sp33 sp34 sp35
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9
## sp10
## sp11
## sp12
## sp13
## sp14
## sp15
## sp16
## sp17
## sp18
## sp19
## sp20
## sp21
## sp22
## sp23
## sp24
## sp25
## sp26
## sp27
## sp28
## sp29
## sp30 0.7126637
## sp31 0.6834536 0.5943093
## sp32 0.7419179 0.5943093 0.5773503
## sp33 0.6543114 0.5804313 0.4277495 0.4277495
## sp34 0.6652932 0.5177440 0.7779525 0.7224104 0.7674028
## sp35 0.8008660 0.7417301 0.7370752 0.7370752 0.6002584 0.6516846
## sp36 0.7805650 0.7754948 0.7779525 0.8297852 0.7110371 0.7637626 0.7127595fric <- dbFD(dist_mist, comun_dat)$FRic## Warning in is.euclid(x.dist): Zero distance(s)
## Warning in is.euclid(x.dist): Zero distance(s)## FRic: Dimensionality reduction was required. The last 17 PCoA axes (out of 19 in total) were removed.## Warning in is.euclid(x.dist): Zero distance(s)## FRic: Quality of the reduced-space representation = 0.3243851## Warning in is.euclid(x.dist): Zero distance(s)
## Warning in is.euclid(x.dist): Zero distance(s)## CWM: When 'x' is a distance matrix, CWM cannot be calculated.dend <- hclust(dist_mist, "average")
tree_dend <-as.phylo(dend)
FD <- pd(comun_dat, tree_dend)$PDDivergência Funcional - Análise
library(FD)
library(tidyverse)
library(ecodados)
library(vegan)
library(SYNCSA)## Warning: package 'SYNCSA' was built under R version 4.1.3dist_mist## sp1 sp2 sp3 sp4 sp5 sp6 sp7
## sp2 0.5000000
## sp3 0.7107801 0.6808389
## sp4 0.7771900 0.8538292 0.7179711
## sp5 0.6107116 0.7345988 0.7381353 0.5106682
## sp6 0.5041691 0.6487320 0.6527339 0.6522593 0.5177440
## sp7 0.5041691 0.5809646 0.7137190 0.7132850 0.5177440 0.4082483
## sp8 0.9574271 0.9128709 0.8447140 0.8106696 0.7689990 0.8466582 0.8466582
## sp9 0.8322170 0.7743619 0.8705685 0.7862568 0.6712758 0.8618286 0.7590006
## sp10 0.8196798 0.8447140 0.8164966 0.7259317 0.6556685 0.7452327 0.8497285
## sp11 0.7381353 0.7658396 0.6755013 0.7674028 0.7671647 0.5971604 0.5971604
## sp12 0.8322170 0.8264198 0.7689102 0.8859268 0.8370252 0.7019605 0.7019605
## sp13 0.6454972 0.7637626 0.8196798 0.7845499 0.6516846 0.6192712 0.6192712
## sp14 0.9128709 0.7637626 0.7395100 0.8605338 0.9172565 0.7959251 0.8466582
## sp15 0.8684391 0.7100609 0.7423352 0.8468228 0.8274814 0.7637626 0.8164966
## sp16 0.8660254 0.8660254 0.6808389 0.6290397 0.7892837 0.7664984 0.8190563
## sp17 0.2886751 0.5773503 0.7671647 0.7771900 0.6107116 0.5809646 0.5809646
## sp18 0.6652707 0.5160455 0.7689102 0.8859268 0.7307151 0.7019605 0.6398556
## sp19 0.7892837 0.6755013 0.8184398 0.7432689 0.8164966 0.7754948 0.7197631
## sp20 0.6149610 0.6479535 0.5381778 0.7674028 0.7671647 0.5971604 0.7233721
## sp21 0.6755013 0.6755013 0.7658396 0.7432689 0.8164966 0.7197631 0.6593372
## sp22 0.6107116 0.6107116 0.7658396 0.8480067 0.7637626 0.5927834 0.5927834
## sp23 0.6573806 0.5906064 0.6921158 0.7395100 0.8199027 0.7096247 0.7096247
## sp24 0.5292494 0.5948301 0.7155949 0.8234825 0.7375481 0.5252793 0.5252793
## sp25 0.8404178 0.7345988 0.7093497 0.7432689 0.6454972 0.7754948 0.7197631
## sp26 0.8886143 0.7892837 0.8184398 0.7973594 0.7071068 0.8274814 0.7754948
## sp27 0.8347284 0.8289488 0.7716277 0.7712263 0.7920294 0.6652707 0.6652707
## sp28 0.6454972 0.7637626 0.7671647 0.8290703 0.7345988 0.5809646 0.5809646
## sp29 0.7137190 0.6839065 0.7100609 0.7660943 0.5971604 0.7107801 0.5818433
## sp30 0.8347284 0.8289488 0.7155949 0.6543114 0.7920294 0.6652707 0.6652707
## sp31 0.8259233 0.6573806 0.8035489 0.8447140 0.8199027 0.7096247 0.7660943
## sp32 0.9213121 0.8259233 0.9013088 0.7938566 0.9159187 0.8680825 0.8186781
## sp33 0.8414675 0.7842952 0.8794158 0.8705685 0.8355590 0.7276576 0.7276576
## sp34 0.7520331 0.7456127 0.6813161 0.6558429 0.7252023 0.6787075 0.5421967
## sp35 0.8322170 0.8264198 0.7689102 0.8859268 0.8370252 0.7019605 0.7019605
## sp36 0.6944209 0.7995447 0.7942658 0.6558429 0.6652707 0.6142561 0.7375481
## sp8 sp9 sp10 sp11 sp12 sp13 sp14
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9 0.5160455
## sp10 0.5448624 0.4177992
## sp11 0.6867080 0.7182727 0.7127595
## sp12 0.5160455 0.7071068 0.7126637 0.4272966
## sp13 0.7071068 0.6652707 0.6495191 0.5887001 0.6652707
## sp14 0.7637626 0.8264198 0.7938566 0.7449169 0.5913006 0.8660254
## sp15 0.6192712 0.6950778 0.6560273 0.8051504 0.6950778 0.8945186 0.5478718
## sp16 0.7637626 0.8753873 0.8447140 0.8184398 0.7185423 1.0000000 0.6454972
## sp17 0.9574271 0.8322170 0.8196798 0.7925762 0.8808623 0.6454972 0.9574271
## sp18 0.7743619 0.5773503 0.6515798 0.6577099 0.7071068 0.6652707 0.7185423
## sp19 0.8213969 0.7241058 0.7988958 0.7395100 0.7795271 0.6516846 0.7689990
## sp20 0.8971628 0.8261854 0.7127595 0.6454972 0.7182727 0.7164039 0.6230847
## sp21 0.8213969 0.7795271 0.8494515 0.6808389 0.7241058 0.5842598 0.7689990
## sp22 0.7127595 0.7241058 0.7449169 0.5448624 0.5980489 0.4179627 0.7689990
## sp23 0.8814629 0.8425832 0.8605338 0.7432689 0.7915890 0.6660657 0.7259317
## sp24 0.7526397 0.7417301 0.7470295 0.5253079 0.6192712 0.3992684 0.8061016
## sp25 0.7127595 0.6640752 0.7449169 0.7395100 0.7241058 0.8706470 0.7127595
## sp26 0.5842598 0.6640752 0.7449169 0.8447140 0.7241058 0.9172565 0.7127595
## sp27 0.6950778 0.8466582 0.8513047 0.5253079 0.5478718 0.7590006 0.6950778
## sp28 0.8164966 0.7805458 0.7671647 0.6793455 0.7805458 0.5773503 0.8660254
## sp29 0.8248465 0.6256088 0.7417301 0.7197631 0.8008660 0.7452327 0.8248465
## sp30 0.7526397 0.7959251 0.8008660 0.5994011 0.7417301 0.7590006 0.7526397
## sp31 0.7259317 0.6781935 0.6381106 0.7432689 0.7370752 0.7812235 0.6002584
## sp32 0.7259317 0.7370752 0.8106696 0.8480067 0.7370752 0.8814629 0.6002584
## sp33 0.6136882 0.6660657 0.6719621 0.7165635 0.6002584 0.7435692 0.6136882
## sp34 0.8312616 0.7689990 0.8751661 0.5078283 0.6516846 0.7856703 0.6640752
## sp35 0.5160455 0.7071068 0.7126637 0.4272966 0.0000000 0.6652707 0.5913006
## sp36 0.7241058 0.8213969 0.7182727 0.7126637 0.7127595 0.7307151 0.7241058
## sp15 sp16 sp17 sp18 sp19 sp20 sp21
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9
## sp10
## sp11
## sp12
## sp13
## sp14
## sp15
## sp16 0.6487320
## sp17 0.9151611 0.9128709
## sp18 0.6950778 0.9217572 0.7252023
## sp19 0.8763897 0.8404178 0.7345988 0.6640752
## sp20 0.8051504 0.7658396 0.6793455 0.6577099 0.6808389
## sp21 0.8763897 0.7892837 0.7345988 0.6640752 0.4082483 0.6808389
## sp22 0.7754948 0.8886143 0.6755013 0.5980489 0.5773503 0.6808389 0.4082483
## sp23 0.8186781 0.7738320 0.7179711 0.6136882 0.5055430 0.6849200 0.2981729
## sp24 0.7743619 0.8777753 0.6028584 0.6192712 0.6063788 0.6652932 0.4483621
## sp25 0.6593372 0.6755013 0.7892837 0.7241058 0.8164966 0.8447140 0.8660254
## sp26 0.5177440 0.6755013 0.8404178 0.7241058 0.8660254 0.9381942 0.9128709
## sp27 0.7185423 0.7214496 0.7832230 0.8466582 0.7859359 0.7805650 0.8372745
## sp28 0.7664984 0.9128709 0.5773503 0.7252023 0.7345988 0.7381353 0.7345988
## sp29 0.7395100 0.7964891 0.6527339 0.6889991 0.6939744 0.7197631 0.8051504
## sp30 0.7743619 0.7770604 0.7832230 0.7959251 0.6715866 0.7252230 0.7310006
## sp31 0.5035545 0.8259233 0.7738320 0.6136882 0.7110371 0.7432689 0.8199027
## sp32 0.6482545 0.6573806 0.8749186 0.7915890 0.5821572 0.7973594 0.7110371
## sp33 0.5194903 0.7842952 0.7904013 0.7259317 0.7224104 0.7725261 0.8297852
## sp34 0.8372745 0.6874627 0.8055353 0.7127595 0.7185423 0.6515798 0.6580043
## sp35 0.6950778 0.7185423 0.8808623 0.7071068 0.7795271 0.7182727 0.7241058
## sp36 0.6063788 0.6874627 0.7520331 0.8213969 0.8753873 0.7689102 0.7743619
## sp22 sp23 sp24 sp25 sp26 sp27 sp28
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9
## sp10
## sp11
## sp12
## sp13
## sp14
## sp15
## sp16
## sp17
## sp18
## sp19
## sp20
## sp21
## sp22
## sp23 0.5055430
## sp24 0.1853695 0.5194903
## sp25 0.8164966 0.8199027 0.8372745
## sp26 0.8660254 0.8692374 0.8856421 0.4082483
## sp27 0.7310006 0.8774225 0.7071068 0.6715866 0.6715866
## sp28 0.6107116 0.7738320 0.5292494 0.7892837 0.7892837 0.6028584
## sp29 0.7516208 0.8468228 0.7126637 0.5611897 0.6939744 0.6515798 0.5854299
## sp30 0.7310006 0.7766618 0.7071068 0.7310006 0.7310006 0.5000000 0.5292494
## sp31 0.7110371 0.7637626 0.7210202 0.5821572 0.5821572 0.6607094 0.5906064
## sp32 0.8199027 0.7637626 0.8285752 0.7674028 0.6498003 0.6607094 0.7179711
## sp33 0.7224104 0.8753873 0.7096247 0.7224104 0.5959950 0.5035545 0.5398157
## sp34 0.7185423 0.7224104 0.7197631 0.6580043 0.7743619 0.5927834 0.7520331
## sp35 0.5980489 0.7915890 0.6192712 0.7241058 0.7241058 0.5478718 0.7805458
## sp36 0.6580043 0.7779525 0.6593372 0.7743619 0.7185423 0.6593372 0.7520331
## sp29 sp30 sp31 sp32 sp33 sp34 sp35
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9
## sp10
## sp11
## sp12
## sp13
## sp14
## sp15
## sp16
## sp17
## sp18
## sp19
## sp20
## sp21
## sp22
## sp23
## sp24
## sp25
## sp26
## sp27
## sp28
## sp29
## sp30 0.7126637
## sp31 0.6834536 0.5943093
## sp32 0.7419179 0.5943093 0.5773503
## sp33 0.6543114 0.5804313 0.4277495 0.4277495
## sp34 0.6652932 0.5177440 0.7779525 0.7224104 0.7674028
## sp35 0.8008660 0.7417301 0.7370752 0.7370752 0.6002584 0.6516846
## sp36 0.7805650 0.7754948 0.7779525 0.8297852 0.7110371 0.7637626 0.7127595fdiv <- dbFD(dist_mist, comun_dat)$FDiv## Warning in is.euclid(x.dist): Zero distance(s)
## Warning in is.euclid(x.dist): Zero distance(s)## FRic: Dimensionality reduction was required. The last 17 PCoA axes (out of 19 in total) were removed.## Warning in is.euclid(x.dist): Zero distance(s)## FRic: Quality of the reduced-space representation = 0.3243851## Warning in is.euclid(x.dist): Zero distance(s)
## Warning in is.euclid(x.dist): Zero distance(s)## CWM: When 'x' is a distance matrix, CWM cannot be calculated.fdis <- dbFD(dist_mist, comun_dat)$FDis## Warning in is.euclid(x.dist): Zero distance(s)
## Warning in is.euclid(x.dist): Zero distance(s)## FRic: Dimensionality reduction was required. The last 17 PCoA axes (out of 19 in total) were removed.## Warning in is.euclid(x.dist): Zero distance(s)## FRic: Quality of the reduced-space representation = 0.3243851## Warning in is.euclid(x.dist): Zero distance(s)
## Warning in is.euclid(x.dist): Zero distance(s)## CWM: When 'x' is a distance matrix, CWM cannot be calculated.Regularidade funcional - Análise
library(FD)
library(tidyverse)
library(ecodados)
library(vegan)
library(GGally)## Warning: package 'GGally' was built under R version 4.1.3## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2dist_mist## sp1 sp2 sp3 sp4 sp5 sp6 sp7
## sp2 0.5000000
## sp3 0.7107801 0.6808389
## sp4 0.7771900 0.8538292 0.7179711
## sp5 0.6107116 0.7345988 0.7381353 0.5106682
## sp6 0.5041691 0.6487320 0.6527339 0.6522593 0.5177440
## sp7 0.5041691 0.5809646 0.7137190 0.7132850 0.5177440 0.4082483
## sp8 0.9574271 0.9128709 0.8447140 0.8106696 0.7689990 0.8466582 0.8466582
## sp9 0.8322170 0.7743619 0.8705685 0.7862568 0.6712758 0.8618286 0.7590006
## sp10 0.8196798 0.8447140 0.8164966 0.7259317 0.6556685 0.7452327 0.8497285
## sp11 0.7381353 0.7658396 0.6755013 0.7674028 0.7671647 0.5971604 0.5971604
## sp12 0.8322170 0.8264198 0.7689102 0.8859268 0.8370252 0.7019605 0.7019605
## sp13 0.6454972 0.7637626 0.8196798 0.7845499 0.6516846 0.6192712 0.6192712
## sp14 0.9128709 0.7637626 0.7395100 0.8605338 0.9172565 0.7959251 0.8466582
## sp15 0.8684391 0.7100609 0.7423352 0.8468228 0.8274814 0.7637626 0.8164966
## sp16 0.8660254 0.8660254 0.6808389 0.6290397 0.7892837 0.7664984 0.8190563
## sp17 0.2886751 0.5773503 0.7671647 0.7771900 0.6107116 0.5809646 0.5809646
## sp18 0.6652707 0.5160455 0.7689102 0.8859268 0.7307151 0.7019605 0.6398556
## sp19 0.7892837 0.6755013 0.8184398 0.7432689 0.8164966 0.7754948 0.7197631
## sp20 0.6149610 0.6479535 0.5381778 0.7674028 0.7671647 0.5971604 0.7233721
## sp21 0.6755013 0.6755013 0.7658396 0.7432689 0.8164966 0.7197631 0.6593372
## sp22 0.6107116 0.6107116 0.7658396 0.8480067 0.7637626 0.5927834 0.5927834
## sp23 0.6573806 0.5906064 0.6921158 0.7395100 0.8199027 0.7096247 0.7096247
## sp24 0.5292494 0.5948301 0.7155949 0.8234825 0.7375481 0.5252793 0.5252793
## sp25 0.8404178 0.7345988 0.7093497 0.7432689 0.6454972 0.7754948 0.7197631
## sp26 0.8886143 0.7892837 0.8184398 0.7973594 0.7071068 0.8274814 0.7754948
## sp27 0.8347284 0.8289488 0.7716277 0.7712263 0.7920294 0.6652707 0.6652707
## sp28 0.6454972 0.7637626 0.7671647 0.8290703 0.7345988 0.5809646 0.5809646
## sp29 0.7137190 0.6839065 0.7100609 0.7660943 0.5971604 0.7107801 0.5818433
## sp30 0.8347284 0.8289488 0.7155949 0.6543114 0.7920294 0.6652707 0.6652707
## sp31 0.8259233 0.6573806 0.8035489 0.8447140 0.8199027 0.7096247 0.7660943
## sp32 0.9213121 0.8259233 0.9013088 0.7938566 0.9159187 0.8680825 0.8186781
## sp33 0.8414675 0.7842952 0.8794158 0.8705685 0.8355590 0.7276576 0.7276576
## sp34 0.7520331 0.7456127 0.6813161 0.6558429 0.7252023 0.6787075 0.5421967
## sp35 0.8322170 0.8264198 0.7689102 0.8859268 0.8370252 0.7019605 0.7019605
## sp36 0.6944209 0.7995447 0.7942658 0.6558429 0.6652707 0.6142561 0.7375481
## sp8 sp9 sp10 sp11 sp12 sp13 sp14
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9 0.5160455
## sp10 0.5448624 0.4177992
## sp11 0.6867080 0.7182727 0.7127595
## sp12 0.5160455 0.7071068 0.7126637 0.4272966
## sp13 0.7071068 0.6652707 0.6495191 0.5887001 0.6652707
## sp14 0.7637626 0.8264198 0.7938566 0.7449169 0.5913006 0.8660254
## sp15 0.6192712 0.6950778 0.6560273 0.8051504 0.6950778 0.8945186 0.5478718
## sp16 0.7637626 0.8753873 0.8447140 0.8184398 0.7185423 1.0000000 0.6454972
## sp17 0.9574271 0.8322170 0.8196798 0.7925762 0.8808623 0.6454972 0.9574271
## sp18 0.7743619 0.5773503 0.6515798 0.6577099 0.7071068 0.6652707 0.7185423
## sp19 0.8213969 0.7241058 0.7988958 0.7395100 0.7795271 0.6516846 0.7689990
## sp20 0.8971628 0.8261854 0.7127595 0.6454972 0.7182727 0.7164039 0.6230847
## sp21 0.8213969 0.7795271 0.8494515 0.6808389 0.7241058 0.5842598 0.7689990
## sp22 0.7127595 0.7241058 0.7449169 0.5448624 0.5980489 0.4179627 0.7689990
## sp23 0.8814629 0.8425832 0.8605338 0.7432689 0.7915890 0.6660657 0.7259317
## sp24 0.7526397 0.7417301 0.7470295 0.5253079 0.6192712 0.3992684 0.8061016
## sp25 0.7127595 0.6640752 0.7449169 0.7395100 0.7241058 0.8706470 0.7127595
## sp26 0.5842598 0.6640752 0.7449169 0.8447140 0.7241058 0.9172565 0.7127595
## sp27 0.6950778 0.8466582 0.8513047 0.5253079 0.5478718 0.7590006 0.6950778
## sp28 0.8164966 0.7805458 0.7671647 0.6793455 0.7805458 0.5773503 0.8660254
## sp29 0.8248465 0.6256088 0.7417301 0.7197631 0.8008660 0.7452327 0.8248465
## sp30 0.7526397 0.7959251 0.8008660 0.5994011 0.7417301 0.7590006 0.7526397
## sp31 0.7259317 0.6781935 0.6381106 0.7432689 0.7370752 0.7812235 0.6002584
## sp32 0.7259317 0.7370752 0.8106696 0.8480067 0.7370752 0.8814629 0.6002584
## sp33 0.6136882 0.6660657 0.6719621 0.7165635 0.6002584 0.7435692 0.6136882
## sp34 0.8312616 0.7689990 0.8751661 0.5078283 0.6516846 0.7856703 0.6640752
## sp35 0.5160455 0.7071068 0.7126637 0.4272966 0.0000000 0.6652707 0.5913006
## sp36 0.7241058 0.8213969 0.7182727 0.7126637 0.7127595 0.7307151 0.7241058
## sp15 sp16 sp17 sp18 sp19 sp20 sp21
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9
## sp10
## sp11
## sp12
## sp13
## sp14
## sp15
## sp16 0.6487320
## sp17 0.9151611 0.9128709
## sp18 0.6950778 0.9217572 0.7252023
## sp19 0.8763897 0.8404178 0.7345988 0.6640752
## sp20 0.8051504 0.7658396 0.6793455 0.6577099 0.6808389
## sp21 0.8763897 0.7892837 0.7345988 0.6640752 0.4082483 0.6808389
## sp22 0.7754948 0.8886143 0.6755013 0.5980489 0.5773503 0.6808389 0.4082483
## sp23 0.8186781 0.7738320 0.7179711 0.6136882 0.5055430 0.6849200 0.2981729
## sp24 0.7743619 0.8777753 0.6028584 0.6192712 0.6063788 0.6652932 0.4483621
## sp25 0.6593372 0.6755013 0.7892837 0.7241058 0.8164966 0.8447140 0.8660254
## sp26 0.5177440 0.6755013 0.8404178 0.7241058 0.8660254 0.9381942 0.9128709
## sp27 0.7185423 0.7214496 0.7832230 0.8466582 0.7859359 0.7805650 0.8372745
## sp28 0.7664984 0.9128709 0.5773503 0.7252023 0.7345988 0.7381353 0.7345988
## sp29 0.7395100 0.7964891 0.6527339 0.6889991 0.6939744 0.7197631 0.8051504
## sp30 0.7743619 0.7770604 0.7832230 0.7959251 0.6715866 0.7252230 0.7310006
## sp31 0.5035545 0.8259233 0.7738320 0.6136882 0.7110371 0.7432689 0.8199027
## sp32 0.6482545 0.6573806 0.8749186 0.7915890 0.5821572 0.7973594 0.7110371
## sp33 0.5194903 0.7842952 0.7904013 0.7259317 0.7224104 0.7725261 0.8297852
## sp34 0.8372745 0.6874627 0.8055353 0.7127595 0.7185423 0.6515798 0.6580043
## sp35 0.6950778 0.7185423 0.8808623 0.7071068 0.7795271 0.7182727 0.7241058
## sp36 0.6063788 0.6874627 0.7520331 0.8213969 0.8753873 0.7689102 0.7743619
## sp22 sp23 sp24 sp25 sp26 sp27 sp28
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9
## sp10
## sp11
## sp12
## sp13
## sp14
## sp15
## sp16
## sp17
## sp18
## sp19
## sp20
## sp21
## sp22
## sp23 0.5055430
## sp24 0.1853695 0.5194903
## sp25 0.8164966 0.8199027 0.8372745
## sp26 0.8660254 0.8692374 0.8856421 0.4082483
## sp27 0.7310006 0.8774225 0.7071068 0.6715866 0.6715866
## sp28 0.6107116 0.7738320 0.5292494 0.7892837 0.7892837 0.6028584
## sp29 0.7516208 0.8468228 0.7126637 0.5611897 0.6939744 0.6515798 0.5854299
## sp30 0.7310006 0.7766618 0.7071068 0.7310006 0.7310006 0.5000000 0.5292494
## sp31 0.7110371 0.7637626 0.7210202 0.5821572 0.5821572 0.6607094 0.5906064
## sp32 0.8199027 0.7637626 0.8285752 0.7674028 0.6498003 0.6607094 0.7179711
## sp33 0.7224104 0.8753873 0.7096247 0.7224104 0.5959950 0.5035545 0.5398157
## sp34 0.7185423 0.7224104 0.7197631 0.6580043 0.7743619 0.5927834 0.7520331
## sp35 0.5980489 0.7915890 0.6192712 0.7241058 0.7241058 0.5478718 0.7805458
## sp36 0.6580043 0.7779525 0.6593372 0.7743619 0.7185423 0.6593372 0.7520331
## sp29 sp30 sp31 sp32 sp33 sp34 sp35
## sp2
## sp3
## sp4
## sp5
## sp6
## sp7
## sp8
## sp9
## sp10
## sp11
## sp12
## sp13
## sp14
## sp15
## sp16
## sp17
## sp18
## sp19
## sp20
## sp21
## sp22
## sp23
## sp24
## sp25
## sp26
## sp27
## sp28
## sp29
## sp30 0.7126637
## sp31 0.6834536 0.5943093
## sp32 0.7419179 0.5943093 0.5773503
## sp33 0.6543114 0.5804313 0.4277495 0.4277495
## sp34 0.6652932 0.5177440 0.7779525 0.7224104 0.7674028
## sp35 0.8008660 0.7417301 0.7370752 0.7370752 0.6002584 0.6516846
## sp36 0.7805650 0.7754948 0.7779525 0.8297852 0.7110371 0.7637626 0.7127595feve <- dbFD(dist_mist, comun_dat)$FEve## Warning in is.euclid(x.dist): Zero distance(s)
## Warning in is.euclid(x.dist): Zero distance(s)## FRic: Dimensionality reduction was required. The last 17 PCoA axes (out of 19 in total) were removed.## Warning in is.euclid(x.dist): Zero distance(s)## FRic: Quality of the reduced-space representation = 0.3243851## Warning in is.euclid(x.dist): Zero distance(s)
## Warning in is.euclid(x.dist): Zero distance(s)## CWM: When 'x' is a distance matrix, CWM cannot be calculated.locais <- rownames(comun_dat)
metricas <- data.frame(richness=richness,
FD_gp = FD,
fric = fric,
fdiv = fdiv,
fdis = fdis,
feve = feve)
ggpairs(metricas)
Métricas de diversidade funcionak (beta)
library(FD)
library(tidyverse)
library(ecodados)
library(vegan)
library(GGally)
library(betapart)
library(vegan)comun_fren_pa <- as.matrix(decostand(comun_fren_dat, "pa"))
trait <- as.matrix(trait_fren_dat)
rowSums(comun_fren_pa)>ncol(trait)## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17
## TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## 18 19 20 21 22 23 24 25 26 27 28 29 31 32 33 34
## TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## 41 42 43 44 45 46 47 48 49 50 51 53 54 55
## TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEcwm_fren <- functcomp(trait_pad, as.matrix(comun_fren_dat))
cwm_fren## LA SLA LDMC LN15 LCC
## 1 -0.24117001 -0.34855154 0.197452005 0.187400303 -0.53673676
## 2 -0.39773712 0.23266219 -0.090932698 -0.285977720 0.16431901
## 3 -0.18571342 0.20107562 -0.398772647 -0.125064251 -0.43046175
## 4 -0.22840640 0.16041005 0.804963068 -0.370425340 0.71938526
## 5 -0.16647900 0.34869559 0.022322127 -0.204193092 0.20513913
## 6 -0.32588213 0.36645830 0.049968287 -0.335257153 0.47130894
## 7 -0.43197058 -0.25619948 0.882004760 0.096681649 0.42065181
## 8 -0.33857863 1.12929965 0.278478893 -0.339493573 0.35571124
## 9 -0.40637654 0.66868021 0.377931295 -0.759687717 0.67746099
## 10 -0.33465801 0.26219601 0.683907823 -0.531993528 0.48456065
## 11 -0.35362891 0.44031044 -0.098078566 -0.447057493 0.25490829
## 12 -0.17542712 0.46363786 -0.252119368 -0.375615215 0.03807885
## 13 -0.23670889 0.64165706 -0.243427434 -0.510792769 0.16037083
## 14 -0.17552077 0.38780017 -0.221048163 -0.169623873 -0.11858724
## 15 -0.13274039 0.36304974 -0.049173336 -0.254725433 -0.03772986
## 17 0.01771351 0.87327677 -0.145441057 -0.489096566 0.08335821
## 18 1.03239586 0.50046791 -0.426467881 -0.416310755 0.02468425
## 19 0.37208764 1.45649761 -0.804115966 0.388010050 -0.20704973
## 20 1.13432791 0.75003603 -0.294438976 -0.499286730 -0.06243781
## 21 1.35442421 -0.70515495 2.082205988 -1.038022002 0.81956154
## 22 -0.36252857 -0.11915470 0.470138892 -0.349437843 0.55666135
## 23 -0.29349787 0.19703824 0.679820129 -0.474449656 0.71142400
## 24 -0.33836216 0.08304760 0.099230530 -0.121483271 0.35394645
## 25 0.31915111 -0.25335758 1.057707296 -0.542238715 0.63662030
## 26 -0.24534578 -0.21751810 0.695937334 -0.007139066 0.47627258
## 27 -0.20960264 -0.58180437 0.394392498 -0.518763303 0.37889078
## 28 -0.26982346 -0.42908491 0.450840875 -0.202161091 0.02367337
## 29 -0.32058272 -0.29318472 0.453475403 -0.212532176 0.10144194
## 31 -0.38641680 -0.38582263 0.554819836 0.173638980 0.67867808
## 32 -0.31712244 -0.11381172 0.791643497 0.096268499 0.32899087
## 33 -0.32213376 -0.48103699 1.038751044 0.205328512 0.30745238
## 34 -0.33013501 -0.65581527 1.293037498 0.231480875 0.26741920
## 41 -0.21326809 -0.50951184 0.308211438 0.621624073 -0.02416227
## 42 -0.26935232 0.21916126 0.572210470 -0.361020644 0.59665773
## 43 -0.22062318 0.11645607 0.340636401 0.151210561 0.27108509
## 44 -0.26028393 -0.26173176 0.172017848 0.244822665 0.23292430
## 45 -0.23379351 -0.25943724 0.278658873 0.250480329 0.21130657
## 46 -0.33192013 0.74464867 0.130344698 -0.929207679 0.45776144
## 47 0.34630198 0.63422133 -0.042007884 -0.730381834 0.17446721
## 48 0.39131426 1.01748971 -0.358288180 0.112660259 -0.12592045
## 49 0.42699578 0.72427942 0.001705993 -0.893869698 0.29221358
## 50 -0.13939830 0.90998495 -0.158000274 -0.706916062 0.41680064
## 51 -0.27746336 -0.02797998 0.838975452 -0.400596491 0.68387800
## 53 0.42752854 -0.40185894 0.806015665 -0.411297259 0.40868170
## 54 0.03358955 -0.54014447 0.465576291 -0.403175303 0.33532396
## 55 0.40337693 -0.42511472 1.072441309 -0.737290886 0.79404728comun_fren_dat## sp1 sp2 sp3 sp5 sp6 sp7 sp8 sp9 sp10 sp11 sp12 sp13 sp14 sp15 sp17 sp18 sp19
## 1 0 74 0 5 5 0 0 0 0 47 3 2 0 0 14 0 0
## 2 0 10 0 6 0 0 0 0 2 4 2 24 0 0 47 0 0
## 3 0 1 0 0 0 0 0 0 0 0 0 0 1 0 24 0 0
## 4 0 3 0 0 0 0 0 0 0 0 0 2 0 0 3 0 0
## 5 0 0 0 15 0 0 0 0 0 27 0 2 0 0 0 0 0
## 6 23 0 22 2 0 0 7 0 4 0 0 0 0 0 0 0 0
## 7 2 0 0 3 0 0 0 0 0 0 0 2 0 2 0 0 0
## 8 7 0 0 7 0 0 10 0 43 0 0 0 0 2 0 0 0
## 9 0 0 5 8 0 0 8 0 1 0 0 0 0 0 0 0 0
## 10 1 0 0 4 0 0 16 0 0 0 0 0 0 1 0 0 5
## 11 0 0 0 2 0 0 0 0 0 0 1 0 0 0 21 0 0
## 12 0 5 0 1 0 0 0 0 0 0 0 0 0 0 35 0 0
## 13 0 7 0 3 0 0 0 0 0 0 0 0 0 0 29 0 0
## 14 0 1 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0
## 15 0 8 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 34 0 0 29 0 8 0 0 0 6 0 0 3 0
## 18 0 0 0 3 0 0 0 59 0 0 0 9 9 0 0 0 30
## 19 0 0 0 21 3 0 128 0 1 0 0 3 1 0 0 0 0
## 20 0 0 0 8 0 0 0 38 6 0 0 7 1 0 0 1 0
## 21 0 0 0 1 1 0 0 0 0 0 0 17 0 0 0 138 0
## 22 0 0 0 0 1 0 0 0 0 0 4 17 0 0 6 0 14
## 23 0 0 0 0 4 0 0 0 0 0 0 12 0 0 1 0 0
## 24 0 0 0 1 1 0 0 0 3 0 2 1 0 0 50 0 5
## 25 0 0 0 0 4 0 0 0 0 0 0 8 0 0 22 36 0
## 26 0 0 0 0 0 0 0 0 0 47 25 0 0 0 6 0 0
## 27 0 11 0 1 0 29 0 0 0 0 221 0 0 0 9 0 0
## 28 0 47 0 1 24 0 0 0 0 47 78 0 0 0 28 0 0
## 29 0 18 0 1 0 0 0 0 0 0 66 0 0 0 23 0 0
## 31 15 0 134 11 0 0 0 0 0 0 0 0 0 0 0 0 0
## 32 0 0 1 82 0 0 5 0 0 0 0 0 0 46 0 0 0
## 33 0 0 0 53 0 0 1 0 0 0 0 0 0 57 0 0 7
## 34 0 0 0 15 0 0 2 0 3 0 0 0 0 26 0 0 0
## 41 0 0 0 8 3 0 0 0 0 171 15 1 0 0 0 0 0
## 42 0 0 0 19 0 0 0 0 2 32 0 0 0 0 0 0 0
## 43 0 0 2 3 1 0 0 0 12 62 2 1 4 0 0 0 0
## 44 0 0 11 4 12 0 0 0 2 66 12 31 0 0 0 0 19
## 45 0 0 0 10 2 0 0 0 0 84 6 0 3 0 0 0 0
## 46 0 0 0 4 20 0 16 0 10 0 0 1 3 0 0 0 0
## 47 0 0 0 12 13 0 0 45 14 0 0 0 11 0 0 0 0
## 48 0 0 0 7 0 0 133 0 1 0 0 13 3 0 0 18 0
## 49 0 0 0 1 5 0 0 50 0 0 0 1 0 0 0 0 0
## 50 0 0 0 10 22 0 75 0 4 0 0 0 45 0 0 0 0
## 51 0 0 0 1 2 0 0 0 0 3 0 44 0 0 0 0 0
## 53 0 0 0 0 13 0 0 1 0 0 0 109 0 0 0 66 0
## 54 0 0 1 0 12 0 0 0 0 0 113 78 0 0 41 45 0
## 55 0 0 16 0 55 0 0 0 0 0 0 11 0 0 23 94 36
## sp20 sp21 sp22 sp23 sp24 sp25 sp27 sp28 sp29 sp30 sp31 sp32 sp33 sp34 sp35
## 1 1 0 0 0 21 5 0 0 1 4 0 113 0 0 4
## 2 0 0 0 0 5 21 0 0 0 63 0 29 0 0 0
## 3 0 9 1 0 64 12 0 1 30 6 0 21 0 0 0
## 4 1 5 0 0 8 1 0 0 0 0 0 298 0 0 0
## 5 6 14 1 0 42 6 0 5 0 15 0 21 0 0 1
## 6 0 0 14 13 0 0 0 0 0 42 0 0 2 4 0
## 7 0 0 1 2 0 0 41 0 0 71 0 0 80 19 0
## 8 0 0 3 0 0 0 0 0 0 59 0 0 13 12 0
## 9 0 0 1 2 0 0 14 0 0 139 0 0 9 3 0
## 10 0 0 0 17 0 0 0 0 0 135 0 0 75 16 0
## 11 12 7 3 0 16 10 0 9 0 48 1 5 0 0 0
## 12 6 5 11 0 103 0 0 0 0 27 0 17 0 0 0
## 13 1 22 14 0 70 0 0 0 0 63 0 6 0 0 0
## 14 48 5 2 0 63 1 0 12 0 3 0 7 0 0 0
## 15 9 11 1 0 57 0 0 12 0 0 1 31 0 0 0
## 17 0 0 0 68 0 0 0 0 0 28 0 0 0 0 0
## 18 0 0 0 52 0 0 0 0 0 31 5 0 0 0 0
## 19 0 0 0 6 0 1 0 0 0 41 0 0 0 0 0
## 20 0 0 0 35 0 1 0 0 0 24 1 0 0 0 0
## 21 0 1 6 0 0 0 0 0 0 1 0 80 0 0 2
## 22 0 0 7 1 0 1 0 0 0 7 0 46 0 2 19
## 23 0 0 2 0 0 0 0 0 0 20 2 112 0 0 0
## 24 0 0 2 2 0 1 0 0 0 8 0 73 0 0 0
## 25 0 0 2 1 0 0 0 0 0 2 0 97 0 1 1
## 26 0 0 0 0 0 2 0 3 0 2 0 142 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0 0 13 0 0 1
## 28 0 1 2 0 34 1 0 1 0 0 0 124 0 9 38
## 29 2 1 2 0 3 0 0 0 12 0 0 110 0 20 0
## 31 0 0 1 0 0 0 0 0 0 8 0 0 67 65 0
## 32 0 0 0 0 0 0 0 0 0 2 0 0 123 10 0
## 33 0 0 0 0 0 0 8 0 0 5 0 0 68 213 0
## 34 0 0 0 0 0 0 0 0 0 1 0 0 106 140 0
## 41 2 7 1 0 4 24 0 8 0 28 0 16 0 3 0
## 42 12 3 0 0 3 25 0 0 0 46 0 194 0 0 0
## 43 26 16 0 0 0 7 0 0 0 1 3 107 0 0 0
## 44 3 12 0 11 0 3 0 1 0 2 1 34 0 12 0
## 45 1 2 1 0 0 64 0 0 0 2 0 57 0 1 0
## 46 0 0 0 17 0 7 0 0 0 162 24 0 0 1 0
## 47 0 0 2 113 0 18 0 0 0 100 3 0 0 0 0
## 48 0 0 2 59 0 1 0 0 0 61 0 0 0 0 0
## 49 0 0 0 33 0 2 0 0 0 162 20 0 0 0 0
## 50 0 0 0 21 0 3 0 0 0 182 11 0 0 0 0
## 51 0 0 0 3 0 2 0 0 0 1 0 315 0 0 49
## 53 0 1 0 0 0 0 0 10 0 6 3 36 0 0 0
## 54 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0
## 55 0 0 2 0 0 0 0 0 0 0 0 165 0 0 0ambie_fren_dat## Grazing Aridity Exclosure Bare.ground.2009 Altitude
## 1 Grazed 5.003 0 77.6 928
## 2 Grazed 5.003 0 84.9 976
## 3 Grazed 5.109 0 85.7 936
## 4 Grazed 5.109 0 59.2 937
## 5 Grazed 5.003 0 85.0 937
## 6 Grazed 2.625 0 78.8 1628
## 7 Grazed 2.625 0 75.2 1599
## 8 Grazed 2.985 0 73.7 1611
## 9 Grazed 2.625 0 71.8 1598
## 10 Grazed 2.985 0 72.5 1593
## 11 Grazed 5.109 0 81.5 917
## 12 Grazed 5.109 0 81.3 929
## 13 Grazed 5.109 0 75.2 941
## 14 Grazed 5.109 0 84.9 945
## 15 Grazed 5.109 0 92.1 927
## 17 Grazed 4.254 0 75.3 1286
## 18 Grazed 4.088 0 76.7 1314
## 19 Grazed 4.425 0 62.8 1302
## 20 Grazed 4.425 0 74.8 1302
## 21 Grazed 4.536 0 82.6 978
## 22 Grazed 4.533 0 90.1 919
## 23 Grazed 4.533 0 85.7 921
## 24 Grazed 4.397 0 90.3 911
## 25 Grazed 3.903 0 84.4 938
## 26 Ungrazed 5.003 13 75.7 950
## 27 Ungrazed 5.003 13 75.3 946
## 28 Ungrazed 5.003 13 75.3 947
## 29 Ungrazed 5.003 13 59.8 955
## 31 Ungrazed 2.625 5 76.5 1628
## 32 Ungrazed 2.625 5 43.5 1617
## 33 Ungrazed 2.625 5 57.0 1611
## 34 Ungrazed 2.625 5 68.5 1611
## 41 Ungrazed 4.965 50 84.8 1050
## 42 Ungrazed 4.965 50 45.8 1048
## 43 Ungrazed 4.965 50 68.3 1063
## 44 Ungrazed 4.965 50 83.9 1064
## 45 Ungrazed 4.965 50 74.0 1068
## 46 Ungrazed 4.254 1 70.0 1291
## 47 Ungrazed 4.254 1 65.5 1297
## 48 Ungrazed 4.254 1 55.5 1293
## 49 Ungrazed 4.254 1 60.3 1302
## 50 Ungrazed 4.425 1 52.3 1302
## 51 Ungrazed 4.536 11 52.5 988
## 53 Ungrazed 3.906 11 80.5 1034
## 54 Ungrazed 3.906 11 79.3 1034
## 55 Ungrazed 3.906 11 59.3 1034trait_fren_dat ## LA SLA LDMC LN15 LCC
## sp1 22.201 5.393 257.969 3.210 432.680
## sp2 27.803 3.642 194.628 6.257 281.276
## sp3 14.960 9.524 236.038 5.051 468.355
## sp5 30.828 15.949 244.372 -0.421 450.266
## sp6 50.863 4.505 315.679 -1.904 463.876
## sp7 286.552 8.619 201.724 3.193 450.805
## sp8 182.924 20.532 119.861 7.623 389.473
## sp9 629.033 15.377 143.187 3.340 393.802
## sp10 17.820 26.600 247.820 4.549 419.162
## sp11 55.044 3.751 301.926 8.052 404.560
## sp12 14.048 5.933 325.822 1.669 443.315
## sp13 3.643 9.843 209.086 4.929 420.098
## sp14 35.037 13.796 178.776 -0.400 462.883
## sp15 37.975 8.975 306.213 10.228 419.355
## sp17 10.163 8.379 154.654 5.182 403.914
## sp18 430.583 1.906 598.311 -1.167 462.227
## sp19 9.744 8.334 210.496 3.841 458.277
## sp20 25.373 11.740 231.553 4.023 405.811
## sp21 76.946 20.456 139.723 3.147 433.548
## sp22 4.296 12.278 227.964 3.833 426.892
## sp23 71.477 11.130 270.938 2.336 408.617
## sp24 78.794 13.624 235.067 1.997 420.955
## sp25 25.448 10.468 234.652 3.026 433.149
## sp27 2.255 5.392 279.853 10.324 425.468
## sp28 3.851 3.513 301.856 6.186 401.162
## sp29 23.231 4.510 158.147 5.936 294.205
## sp30 6.978 14.850 311.852 -0.157 457.671
## sp31 9.564 10.946 159.904 3.021 369.946
## sp32 40.091 10.578 358.145 2.499 455.474
## sp33 18.449 4.445 432.189 4.480 432.073
## sp34 29.501 4.860 410.760 3.689 433.906
## sp35 21.275 3.577 441.302 0.170 448.661trait_pad <- decostand(trait_fren_dat, "standardize")
euclid_dis <- vegdist(trait_pad, "euclidean")fdis <- dbFD(euclid_dis, comun_fren_dat)$FDis## FRic: No dimensionality reduction was required. All 5 PCoA axes were kept as 'traits'.
## CWM: When 'x' is a distance matrix, CWM cannot be calculated.feve <- dbFD(euclid_dis, comun_fren_dat)$FEve## FRic: No dimensionality reduction was required. All 5 PCoA axes were kept as 'traits'.
## CWM: When 'x' is a distance matrix, CWM cannot be calculated.lm_dat <- data.frame(aridez = ambie_fren_dat$Aridity, fdis = fdis, feve = feve)
mod1 <- lm(fdis ~ aridez, data = lm_dat)
plot(mod1)
anova(mod1) ## Analysis of Variance Table
##
## Response: fdis
## Df Sum Sq Mean Sq F value Pr(>F)
## aridez 1 0.2083 0.20834 0.9945 0.3241
## Residuals 44 9.2179 0.20950mod2 <- lm(feve ~ aridez, data = lm_dat)
plot(mod2)
anova(mod2)## Analysis of Variance Table
##
## Response: feve
## Df Sum Sq Mean Sq F value Pr(>F)
## aridez 1 0.02098 0.020979 1.0447 0.3123
## Residuals 44 0.88353 0.020080lm_dat %>%
ggplot(aes(x=aridez, y=fdis)) +
geom_point(pch=21, size=3, color = "black", fill="royalblue") +
xlab("Aridez") + ylab("Divergência Funcional (FDis)") +
theme(axis.title.x = element_text(face="bold", size=14),
axis.text.x = element_text(vjust=0.5, size=12)) +
theme(axis.title.y = element_text(face="bold", size=14),
axis.text.y = element_text(vjust=0.5, size=12)) +
theme(legend.position = "top", legend.title=element_blank()) -> plot_pred1
plot_pred1
lm_dat %>%
ggplot(aes(x=aridez, y=feve)) +
geom_point(pch=21, size=3, color = "black", fill="#d73027") +
xlab("Aridez") + ylab("Regularidade Funcional (FEve)") +
theme(axis.title.x = element_text(face="bold", size=14),
axis.text.x = element_text(vjust=0.5, size=12)) +
theme(axis.title.y = element_text(face="bold", size=14),
axis.text.y = element_text(vjust=0.5, size=12)) +
theme(legend.position = "top", legend.title=element_blank()) -> plot_pred2
plot_pred2
grid.arrange(plot_pred1, plot_pred2, ncol=2)
ggsave("plot_pred1.pdf", plot_pred1, height = 14, width = 14, dpi = 600, units = "cm")
ggsave("plot_pred2.pdf", plot_pred2, height = 14, width = 14, dpi = 600, units = "cm")comun_fren_dat## sp1 sp2 sp3 sp5 sp6 sp7 sp8 sp9 sp10 sp11 sp12 sp13 sp14 sp15 sp17 sp18 sp19
## 1 0 74 0 5 5 0 0 0 0 47 3 2 0 0 14 0 0
## 2 0 10 0 6 0 0 0 0 2 4 2 24 0 0 47 0 0
## 3 0 1 0 0 0 0 0 0 0 0 0 0 1 0 24 0 0
## 4 0 3 0 0 0 0 0 0 0 0 0 2 0 0 3 0 0
## 5 0 0 0 15 0 0 0 0 0 27 0 2 0 0 0 0 0
## 6 23 0 22 2 0 0 7 0 4 0 0 0 0 0 0 0 0
## 7 2 0 0 3 0 0 0 0 0 0 0 2 0 2 0 0 0
## 8 7 0 0 7 0 0 10 0 43 0 0 0 0 2 0 0 0
## 9 0 0 5 8 0 0 8 0 1 0 0 0 0 0 0 0 0
## 10 1 0 0 4 0 0 16 0 0 0 0 0 0 1 0 0 5
## 11 0 0 0 2 0 0 0 0 0 0 1 0 0 0 21 0 0
## 12 0 5 0 1 0 0 0 0 0 0 0 0 0 0 35 0 0
## 13 0 7 0 3 0 0 0 0 0 0 0 0 0 0 29 0 0
## 14 0 1 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0
## 15 0 8 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 34 0 0 29 0 8 0 0 0 6 0 0 3 0
## 18 0 0 0 3 0 0 0 59 0 0 0 9 9 0 0 0 30
## 19 0 0 0 21 3 0 128 0 1 0 0 3 1 0 0 0 0
## 20 0 0 0 8 0 0 0 38 6 0 0 7 1 0 0 1 0
## 21 0 0 0 1 1 0 0 0 0 0 0 17 0 0 0 138 0
## 22 0 0 0 0 1 0 0 0 0 0 4 17 0 0 6 0 14
## 23 0 0 0 0 4 0 0 0 0 0 0 12 0 0 1 0 0
## 24 0 0 0 1 1 0 0 0 3 0 2 1 0 0 50 0 5
## 25 0 0 0 0 4 0 0 0 0 0 0 8 0 0 22 36 0
## 26 0 0 0 0 0 0 0 0 0 47 25 0 0 0 6 0 0
## 27 0 11 0 1 0 29 0 0 0 0 221 0 0 0 9 0 0
## 28 0 47 0 1 24 0 0 0 0 47 78 0 0 0 28 0 0
## 29 0 18 0 1 0 0 0 0 0 0 66 0 0 0 23 0 0
## 31 15 0 134 11 0 0 0 0 0 0 0 0 0 0 0 0 0
## 32 0 0 1 82 0 0 5 0 0 0 0 0 0 46 0 0 0
## 33 0 0 0 53 0 0 1 0 0 0 0 0 0 57 0 0 7
## 34 0 0 0 15 0 0 2 0 3 0 0 0 0 26 0 0 0
## 41 0 0 0 8 3 0 0 0 0 171 15 1 0 0 0 0 0
## 42 0 0 0 19 0 0 0 0 2 32 0 0 0 0 0 0 0
## 43 0 0 2 3 1 0 0 0 12 62 2 1 4 0 0 0 0
## 44 0 0 11 4 12 0 0 0 2 66 12 31 0 0 0 0 19
## 45 0 0 0 10 2 0 0 0 0 84 6 0 3 0 0 0 0
## 46 0 0 0 4 20 0 16 0 10 0 0 1 3 0 0 0 0
## 47 0 0 0 12 13 0 0 45 14 0 0 0 11 0 0 0 0
## 48 0 0 0 7 0 0 133 0 1 0 0 13 3 0 0 18 0
## 49 0 0 0 1 5 0 0 50 0 0 0 1 0 0 0 0 0
## 50 0 0 0 10 22 0 75 0 4 0 0 0 45 0 0 0 0
## 51 0 0 0 1 2 0 0 0 0 3 0 44 0 0 0 0 0
## 53 0 0 0 0 13 0 0 1 0 0 0 109 0 0 0 66 0
## 54 0 0 1 0 12 0 0 0 0 0 113 78 0 0 41 45 0
## 55 0 0 16 0 55 0 0 0 0 0 0 11 0 0 23 94 36
## sp20 sp21 sp22 sp23 sp24 sp25 sp27 sp28 sp29 sp30 sp31 sp32 sp33 sp34 sp35
## 1 1 0 0 0 21 5 0 0 1 4 0 113 0 0 4
## 2 0 0 0 0 5 21 0 0 0 63 0 29 0 0 0
## 3 0 9 1 0 64 12 0 1 30 6 0 21 0 0 0
## 4 1 5 0 0 8 1 0 0 0 0 0 298 0 0 0
## 5 6 14 1 0 42 6 0 5 0 15 0 21 0 0 1
## 6 0 0 14 13 0 0 0 0 0 42 0 0 2 4 0
## 7 0 0 1 2 0 0 41 0 0 71 0 0 80 19 0
## 8 0 0 3 0 0 0 0 0 0 59 0 0 13 12 0
## 9 0 0 1 2 0 0 14 0 0 139 0 0 9 3 0
## 10 0 0 0 17 0 0 0 0 0 135 0 0 75 16 0
## 11 12 7 3 0 16 10 0 9 0 48 1 5 0 0 0
## 12 6 5 11 0 103 0 0 0 0 27 0 17 0 0 0
## 13 1 22 14 0 70 0 0 0 0 63 0 6 0 0 0
## 14 48 5 2 0 63 1 0 12 0 3 0 7 0 0 0
## 15 9 11 1 0 57 0 0 12 0 0 1 31 0 0 0
## 17 0 0 0 68 0 0 0 0 0 28 0 0 0 0 0
## 18 0 0 0 52 0 0 0 0 0 31 5 0 0 0 0
## 19 0 0 0 6 0 1 0 0 0 41 0 0 0 0 0
## 20 0 0 0 35 0 1 0 0 0 24 1 0 0 0 0
## 21 0 1 6 0 0 0 0 0 0 1 0 80 0 0 2
## 22 0 0 7 1 0 1 0 0 0 7 0 46 0 2 19
## 23 0 0 2 0 0 0 0 0 0 20 2 112 0 0 0
## 24 0 0 2 2 0 1 0 0 0 8 0 73 0 0 0
## 25 0 0 2 1 0 0 0 0 0 2 0 97 0 1 1
## 26 0 0 0 0 0 2 0 3 0 2 0 142 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0 0 13 0 0 1
## 28 0 1 2 0 34 1 0 1 0 0 0 124 0 9 38
## 29 2 1 2 0 3 0 0 0 12 0 0 110 0 20 0
## 31 0 0 1 0 0 0 0 0 0 8 0 0 67 65 0
## 32 0 0 0 0 0 0 0 0 0 2 0 0 123 10 0
## 33 0 0 0 0 0 0 8 0 0 5 0 0 68 213 0
## 34 0 0 0 0 0 0 0 0 0 1 0 0 106 140 0
## 41 2 7 1 0 4 24 0 8 0 28 0 16 0 3 0
## 42 12 3 0 0 3 25 0 0 0 46 0 194 0 0 0
## 43 26 16 0 0 0 7 0 0 0 1 3 107 0 0 0
## 44 3 12 0 11 0 3 0 1 0 2 1 34 0 12 0
## 45 1 2 1 0 0 64 0 0 0 2 0 57 0 1 0
## 46 0 0 0 17 0 7 0 0 0 162 24 0 0 1 0
## 47 0 0 2 113 0 18 0 0 0 100 3 0 0 0 0
## 48 0 0 2 59 0 1 0 0 0 61 0 0 0 0 0
## 49 0 0 0 33 0 2 0 0 0 162 20 0 0 0 0
## 50 0 0 0 21 0 3 0 0 0 182 11 0 0 0 0
## 51 0 0 0 3 0 2 0 0 0 1 0 315 0 0 49
## 53 0 1 0 0 0 0 0 10 0 6 3 36 0 0 0
## 54 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0
## 55 0 0 2 0 0 0 0 0 0 0 0 165 0 0 0ambie_fren_dat## Grazing Aridity Exclosure Bare.ground.2009 Altitude
## 1 Grazed 5.003 0 77.6 928
## 2 Grazed 5.003 0 84.9 976
## 3 Grazed 5.109 0 85.7 936
## 4 Grazed 5.109 0 59.2 937
## 5 Grazed 5.003 0 85.0 937
## 6 Grazed 2.625 0 78.8 1628
## 7 Grazed 2.625 0 75.2 1599
## 8 Grazed 2.985 0 73.7 1611
## 9 Grazed 2.625 0 71.8 1598
## 10 Grazed 2.985 0 72.5 1593
## 11 Grazed 5.109 0 81.5 917
## 12 Grazed 5.109 0 81.3 929
## 13 Grazed 5.109 0 75.2 941
## 14 Grazed 5.109 0 84.9 945
## 15 Grazed 5.109 0 92.1 927
## 17 Grazed 4.254 0 75.3 1286
## 18 Grazed 4.088 0 76.7 1314
## 19 Grazed 4.425 0 62.8 1302
## 20 Grazed 4.425 0 74.8 1302
## 21 Grazed 4.536 0 82.6 978
## 22 Grazed 4.533 0 90.1 919
## 23 Grazed 4.533 0 85.7 921
## 24 Grazed 4.397 0 90.3 911
## 25 Grazed 3.903 0 84.4 938
## 26 Ungrazed 5.003 13 75.7 950
## 27 Ungrazed 5.003 13 75.3 946
## 28 Ungrazed 5.003 13 75.3 947
## 29 Ungrazed 5.003 13 59.8 955
## 31 Ungrazed 2.625 5 76.5 1628
## 32 Ungrazed 2.625 5 43.5 1617
## 33 Ungrazed 2.625 5 57.0 1611
## 34 Ungrazed 2.625 5 68.5 1611
## 41 Ungrazed 4.965 50 84.8 1050
## 42 Ungrazed 4.965 50 45.8 1048
## 43 Ungrazed 4.965 50 68.3 1063
## 44 Ungrazed 4.965 50 83.9 1064
## 45 Ungrazed 4.965 50 74.0 1068
## 46 Ungrazed 4.254 1 70.0 1291
## 47 Ungrazed 4.254 1 65.5 1297
## 48 Ungrazed 4.254 1 55.5 1293
## 49 Ungrazed 4.254 1 60.3 1302
## 50 Ungrazed 4.425 1 52.3 1302
## 51 Ungrazed 4.536 11 52.5 988
## 53 Ungrazed 3.906 11 80.5 1034
## 54 Ungrazed 3.906 11 79.3 1034
## 55 Ungrazed 3.906 11 59.3 1034trait_fren_dat## LA SLA LDMC LN15 LCC
## sp1 22.201 5.393 257.969 3.210 432.680
## sp2 27.803 3.642 194.628 6.257 281.276
## sp3 14.960 9.524 236.038 5.051 468.355
## sp5 30.828 15.949 244.372 -0.421 450.266
## sp6 50.863 4.505 315.679 -1.904 463.876
## sp7 286.552 8.619 201.724 3.193 450.805
## sp8 182.924 20.532 119.861 7.623 389.473
## sp9 629.033 15.377 143.187 3.340 393.802
## sp10 17.820 26.600 247.820 4.549 419.162
## sp11 55.044 3.751 301.926 8.052 404.560
## sp12 14.048 5.933 325.822 1.669 443.315
## sp13 3.643 9.843 209.086 4.929 420.098
## sp14 35.037 13.796 178.776 -0.400 462.883
## sp15 37.975 8.975 306.213 10.228 419.355
## sp17 10.163 8.379 154.654 5.182 403.914
## sp18 430.583 1.906 598.311 -1.167 462.227
## sp19 9.744 8.334 210.496 3.841 458.277
## sp20 25.373 11.740 231.553 4.023 405.811
## sp21 76.946 20.456 139.723 3.147 433.548
## sp22 4.296 12.278 227.964 3.833 426.892
## sp23 71.477 11.130 270.938 2.336 408.617
## sp24 78.794 13.624 235.067 1.997 420.955
## sp25 25.448 10.468 234.652 3.026 433.149
## sp27 2.255 5.392 279.853 10.324 425.468
## sp28 3.851 3.513 301.856 6.186 401.162
## sp29 23.231 4.510 158.147 5.936 294.205
## sp30 6.978 14.850 311.852 -0.157 457.671
## sp31 9.564 10.946 159.904 3.021 369.946
## sp32 40.091 10.578 358.145 2.499 455.474
## sp33 18.449 4.445 432.189 4.480 432.073
## sp34 29.501 4.860 410.760 3.689 433.906
## sp35 21.275 3.577 441.302 0.170 448.661#### Hipótese e predições
cwm_dis <- vegdist(cwm_fren, "euclidean")
cwm_fren <- functcomp(trait_pad, as.matrix(comun_fren_dat))
perman_fren <- adonis(cwm_fren~Grazing, data = ambie_fren_dat)## Warning in vegdist(lhs, method = method, ...): results may be meaningless
## because data have negative entries in method "bray"betad_fren <- betadisper(cwm_dis, ambie_fren_dat$Grazing)
permutest(betad_fren)##
## 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 0.0539 0.053858 0.1946 999 0.687
## Residuals 44 12.1763 0.276735plot(betad_fren)