Ecologia Numérica
Ítalo Monteiro
07/02/2022
Exercício 1: Introdução
A região a ser analisada encontra-se no semiário nordestino, corresponde ao bioma Caatinga em uma paisagem com áreas em processo de regeneração natural. Os organismos a serem analisados serão as comumidades de nematoides cuja composição taxonômica das famílias podem variar de acordo com o tempo de regeneração natural, temperatura, umidade e pH do solo assim como os indices pluviométricos da região, granulometria do solo e composição da vegetação. Para essa atividade cujos dados são ficticios, ultilisaremos para “brincar” com o R algumas ferramentas de analise de comunidades>
Iniciamos carregando os dados da tabelas com os valores de abundancia das famílias de nematoides em cada local de amostragem (que por algum motivo divino essa tabela não aparece aqui).
library(vegan)## Warning: package 'vegan' was built under R version 4.1.2## Warning: package 'permute' was built under R version 4.1.2tabela<- read.table(file = "eco_num_.txt", header = TRUE)library(vegan)
library(tidyverse)
library(forcats)
library(iNEXT)
comunidade<-read.table("eco_num_.txt", header=TRUE)
rownames(comunidade) <-paste0("Local", 1:nrow(comunidade))Brincando com alguns índices dessa comunidade atraves do pacote vegan.
Riqueza <-specnumber(comunidade)
Riqueza## Local1 Local2 Local3 Local4 Local5 Local6 Local7 Local8 Local9 Local10
## 6 6 6 6 6 6 6 6 6 6Riqueza_total<-specnumber(colSums(comunidade))
Riqueza_total## [1] 6Diversidade de espécies
indices de shannon e simpson de cada local, usando a função “diversity” do vegan. Usamos esses indices para medir a riqueza e abundancia das familias observadas em cada local. É visto que os locais 4,9 e 10 tem uma maior diversidade.
Shannon<-diversity(comunidade, index = "shannon")
Shannon## Local1 Local2 Local3 Local4 Local5 Local6 Local7 Local8
## 1.691434 1.715290 1.746197 1.774550 1.744471 1.712732 1.754105 1.736195
## Local9 Local10
## 1.768133 1.779842Observando Simpson. Através deles vvimos que os locais 4,9 e 10 tem uma maior riqueza.
Simpson<-diversity(comunidade, index = "simpson")
Simpson## Local1 Local2 Local3 Local4 Local5 Local6 Local7 Local8
## 0.8000000 0.8064206 0.8178980 0.8276644 0.8177393 0.8050000 0.8203125 0.8144044
## Local9 Local10
## 0.8253110 0.8293345Somando abundâncias para obter a diversidade total :
Shannon_total<-diversity(colSums(comunidade), index="shannon")
Shannon_total## [1] 1.766653Simpson_total<-diversity(colSums(comunidade), index = "simpson")
Simpson_total## [1] 0.8245323Aplicando a equitabilidade. Essa métrica vai nos dizer a abundância relativa das diferentes espécies que compõem a riqueza de uma área, é derivado do índice de diversidade de Shannon e permite representar a uniformidade da distribuição dos indivíduos entre as espécies existentes. O seu valor apresenta uma amplitude de 0 (uniformidade mínima) a 1 (uniformidade máxima) Calculando : Divide o indice de shannon pelo log da riqueza
J<-Shannon/log(Riqueza)
J## Local1 Local2 Local3 Local4 Local5 Local6 Local7 Local8
## 0.9440075 0.9573216 0.9745713 0.9903950 0.9736076 0.9558942 0.9789848 0.9689891
## Local9 Local10
## 0.9868140 0.9933486Equitabilidade total : Novamente as áreas 4,9 e 10 se sobressaem na uniformidade das cominidades
J_total<-Shannon_total/log(Riqueza_total)
J_total## [1] 0.9859877Série de Hill e perfil de diversidade: Tentei utilizar a série de Hill pois é uma métrica unificadora de Shannon e Simpson, mostra a riqueza de acordo com o peso das especies, quanto maiso próximo de zero maior a riqueza.
R<-renyi(comunidade, hill = TRUE)
R## 0 0.25 0.5 1 2 4 8 16
## Local1 6 5.843000 5.695094 5.427260 5.000000 4.485395 4.098911 3.910486
## Local2 6 5.886620 5.774835 5.558287 5.165839 4.583452 4.019293 3.688613
## Local3 6 5.931047 5.863455 5.732762 5.491429 5.095864 4.608788 4.238084
## Local4 6 5.973759 5.947939 5.897624 5.802632 5.636149 5.388292 5.111943
## Local5 6 5.927159 5.856610 5.722872 5.486647 5.135033 4.766203 4.524544
## Local6 6 5.884491 5.769548 5.544090 5.128205 4.508429 3.932947 3.611540
## Local7 6 5.944416 5.888803 5.778276 5.565217 5.200335 4.743976 4.381486
## Local8 6 5.916408 5.834282 5.675708 5.388060 4.949204 4.478783 4.145875
## Local9 6 5.964674 5.929524 5.859905 5.724458 5.476099 5.099254 4.728257
## Local10 6 5.982101 5.964284 5.928918 5.859416 5.726504 5.493135 5.181568
## 32 64 Inf
## Local1 3.826818 3.787604 3.750000
## Local2 3.536905 3.466691 3.400000
## Local3 4.048059 3.959217 3.875000
## Local4 4.900713 4.782140 4.666667
## Local5 4.407499 4.352565 4.300000
## Local6 3.465339 3.397648 3.333333
## Local7 4.182909 4.088994 4.000000
## Local8 3.967119 3.881383 3.800000
## Local9 4.507041 4.400718 4.300000
## Local10 4.940206 4.816883 4.700000Fazendo um gráfico de diversidade com os valores de Hill
g1 <- R %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
mutate(name = factor(name, name[1:length(R)])) %>%
ggplot(aes(x = name, y = value, group = rowname,
col = rowname)) +
geom_point(size = 2) +
geom_line(size = 1) +
xlab("Parâmetro de ordem de diversidade (q)") +
ylab("Diversidade") +
labs(col = "Locais") +
theme_bw() +
theme(text = element_text(size = 16))
g1
Por fim os gráficos mostram que as áreas 4,9 e 10 tem uma maior riqueza.
Exercício 2: Descrição de comunidades biológicas
Os dados ultilizados nesta atividade são os dados referente ao trabalho “Communities of molluscs, vascular plants and bryophytes on spring fens of West Carpathian” explloraremos de inícios a comunidade de moluscos.
library(vegan)
library(BiodiversityR)
data(moll)
moll <- read.delim ('https://raw.githubusercontent.com/zdealveindy/anadat-r/master/data/molluscs-fens.txt', row.names = 1)
env <- read.delim ('https://raw.githubusercontent.com/zdealveindy/anadat-r/master/data/env-fens.txt', row.names = 1)
dim(moll)## [1] 43 57specnumber(moll)## Baladi Bukovec Cudrak Dubcova Grigovci Grun HLplos HLrosn
## 19 9 10 14 13 20 3 3
## Hrncarky HrubBrod HuteDol ChmelLou ChmelStu Chmura Jasenka Javoruvk
## 23 13 25 8 16 16 20 27
## Kalabova Klubina Kralovec Krocil Lany Machala Machova Mechnac
## 26 9 24 12 9 12 23 13
## Milonov NizKel Obidova Pisojov PivoLou Pivovari Rajnoch Semetin
## 16 14 4 7 8 16 7 18
## Skanzen Solan Spanie StudVrch Tlsta Trubiska U Pavliku ValKlob
## 12 12 23 20 18 28 15 21
## Vlarsky VresStra VrchPre
## 20 4 5Foi observado então que nos 43 pontos de coleta (que eram coletados 12 litros de solo) foram encontradas 57 espécies de molluscos,é posivel observar também a quantidade de espécies por cada área de coleta, Trubiska e Javoruvk por exemplo apresentam um maior número de especies dentre os demais pontos.
rowSums(moll)## Baladi Bukovec Cudrak Dubcova Grigovci Grun HLplos HLrosn
## 1094 52 49 131 103 789 7 8
## Hrncarky HrubBrod HuteDol ChmelLou ChmelStu Chmura Jasenka Javoruvk
## 253 117 1556 45 133 216 914 251
## Kalabova Klubina Kralovec Krocil Lany Machala Machova Mechnac
## 450 54 229 258 67 337 454 250
## Milonov NizKel Obidova Pisojov PivoLou Pivovari Rajnoch Semetin
## 192 553 11 67 54 249 24 980
## Skanzen Solan Spanie StudVrch Tlsta Trubiska U Pavliku ValKlob
## 87 208 1138 597 328 1232 74 1490
## Vlarsky VresStra VrchPre
## 413 4 16Foi possível observar a quantidade de individuos coletados em cada plot, essa simple tabela pode mostrar por exemplo que algumas áreas apesar de muitas espécies diferentes podem ter poucos indiiduos em relação a áreas com mais individuos e poucas especies. Mas para uma melhor analise é possivel indicar ralmente quais possuem maior abundancia e riqueza através dos pacotes básicos do vegan.
colSums(moll)## PlaPol CarMin CarTri VerPyg VerSub VerAnt VerAng VerMou VerPus ColEde CocLub
## 29 3603 1681 984 577 904 1285 155 6 108 1041
## CocLul PupMus PupPra AcaAcu ValCos ValPul TruCyl PunPyg AliBip MacTum MacVen
## 58 3 8 46 121 387 10 715 14 56 39
## VesTur SucPut SucObl OxyEle AegPur AegMin PerHam OxyCel OxyGla VitCon VitCry
## 57 450 306 170 187 11 605 3 7 52 73
## VitDia CecAci SemSem VitPel EucFul EucAld ZonNit DauBre DauRuf AriSil AriSub
## 33 1 2 51 749 5 364 57 37 2 2
## DerAgr DerLae DerPra PliLub PetUni PerBid TriHis MonInc MonVic EuoStr AriArb
## 4 5 6 84 22 101 152 70 10 4 13
## CepHor CepVin
## 1 8Aqui podemos ver a abundancia de cada espécies, Carychium minimum (CarMin) e Carychium tridentatum (CarTri) são as espécies mais abundantes do estudo.
Agora observaremos curva de acumulação de especies para o grupo dos moluscos estudados, usando ainda o pacote do vegan. As curvas de acumulação ou curva do coletor é capaz de nos informar se a quantidades de dados coletados foram o suficiente para indicar o tamnha mínimo da comunidade numa determinada área.
sp1<- specaccum(moll, "random" )
sp1## Species Accumulation Curve
## Accumulation method: random, with 100 permutations
## Call: specaccum(comm = moll, method = "random")
##
##
## Sites 1.00000 2.00000 3.00000 4.00000 5.00000 6.00000 7.00000
## Richness 14.13000 20.99000 26.18000 29.17000 31.64000 34.42000 36.10000
## sd 6.69034 6.72624 6.43284 6.01355 5.59134 5.12309 5.20392
##
## Sites 8.00000 9.00000 10.00000 11.00000 12.00000 13.00000 14.00000
## Richness 38.02000 39.58000 41.06000 42.42000 43.75000 45.15000 46.01000
## sd 4.76303 4.60386 4.35964 4.40656 4.05113 3.88308 3.65009
##
## Sites 15.00000 16.000 17.00000 18.00000 19.00000 20.00000 21.00000 22.00000
## Richness 46.92000 47.630 48.27000 48.90000 49.41000 50.16000 50.52000 50.99000
## sd 3.40137 3.311 3.12001 2.84445 2.67459 2.46478 2.48828 2.38469
##
## Sites 23.00000 24.00000 25.00000 26.00000 27.00000 28.00000 29.00000
## Richness 51.53000 51.99000 52.52000 52.83000 53.15000 53.47000 53.79000
## sd 2.13889 2.12011 1.93574 1.90722 1.93519 1.83927 1.74249
##
## Sites 30.00000 31.00000 32.00000 33.00000 34.00000 35.00000 36.00000 37.0000
## Richness 54.21000 54.59000 54.90000 55.24000 55.40000 55.57000 55.75000 56.0200
## sd 1.67751 1.65813 1.63608 1.49828 1.42134 1.24928 1.15798 1.0539
##
## Sites 38.00000 39.00000 40.00000 41.00000 42.000 43
## Richness 56.19000 56.39000 56.50000 56.71000 56.870 57
## sd 0.91778 0.75069 0.65905 0.47768 0.338 0plot(sp1, ci.type="poly", col="blue", lwd=2, ci.lty=0, ci.col="lightgrey")
boxplot(sp1, col="white", add=TRUE, pch="+")
O grafico de curva do coletor por estar estabilizado de acordo com a quantidade de sites, nos diz que esses dados coletados foram o sufuciente para nos dizer que a comunidade de molusco se aproxima de um tamanho real. Agora para descrever melhor essa comunidade no quesito detalhamento de abunância utilizaremos curvas de rank-abundância, onde as espécies são ordenadas em sequência, da mais abundante para a mais rara, ao longo da abcissa, com abundâncias absolutas ou relativas log-transformadas na ordenada.
library(RADanalysis)## Warning: package 'RADanalysis' was built under R version 4.1.2setwd("C:/Users/italo/OneDrive/Documentos/eco_numerica")
mollusca <- read.table("abundanciamoll.txt")mod <- (rad.lognormal(mollusca))plot (mod)
Exercício 3: Medidas de diversidade biológica
Neste exerício aplicaremos os indices de diversidade de Shanon e Simpson em uma base de dados retirados do próprio R.
library(devtools)## Warning: package 'devtools' was built under R version 4.1.2## Carregando pacotes exigidos: usethis## Warning: package 'usethis' was built under R version 4.1.2##
## Attaching package: 'devtools'## The following object is masked from 'package:permute':
##
## checkdevtools::install_github("paternogbc/ecodados")## Skipping install of 'ecodados' from a github remote, the SHA1 (929ffae7) has not changed since last install.
## Use `force = TRUE` to force installationlibrary(ecodados)##
## Attaching package: 'ecodados'## The following object is masked _by_ '.GlobalEnv':
##
## envlibrary(vegan)
library(ggplot2)
library(BiodiversityR)#caregando os dados
composicao_especies <- ecodados::composicao_anuros_div_taxonomica
precipitacao <- ecodados::precipitacao_div_taxonomicaO conjunto de dados apresentados mostra a distribuição das esécies em 10 comunidades diferentes junto com os dados das precipitações de cda comunidade. # Utilizando as curvas de rank-abundância
rank_com2 <- rankabundance(composicao_especies[2, composicao_especies[2,] > 0])## Warning in qt(0.975, df = n - 1): NaNs produzidosrankabunplot(rank_com2, scale = "logabun", specnames = c(1),
pch = 19, col = "darkorange")
rank_com3 <- rankabundance(composicao_especies[3, composicao_especies[3,] > 0])## Warning in qt(0.975, df = n - 1): NaNs produzidosrankabunplot(rank_com3, scale = "logabun", specnames = c(1),
pch = 19, col = "darkgreen")
rank_com4 <- rankabundance(composicao_especies[4, composicao_especies[4,] > 0])## Warning in qt(0.975, df = n - 1): NaNs produzidosrankabunplot(rank_com4, scale = "logabun", specnames = c(1),
pch = 19, col = "darkblue")
rank_com5 <- rankabundance(composicao_especies[5, composicao_especies[5,] > 0])## Warning in qt(0.975, df = n - 1): NaNs produzidosrankabunplot(rank_com5, scale = "logabun", specnames = c(1),
pch = 19, col = "darkred")
#Aplicando os índices de diversidade nas comunidades
riqueza_dados <- specnumber(composicao_especies)
riqueza_dados## Com_1 Com_2 Com_3 Com_4 Com_5 Com_6 Com_7 Com_8 Com_9 Com_10
## 10 10 5 5 5 6 2 4 6 4Shannon_dados <- diversity(composicao_especies, index = "shannon", MARGIN = 1)
Shannon_dados## Com_1 Com_2 Com_3 Com_4 Com_5 Com_6 Com_7 Com_8
## 2.3025851 0.5002880 0.9580109 1.6068659 1.4861894 1.5607038 0.6931472 1.1058899
## Com_9 Com_10
## 1.7140875 1.2636544Simpson_dados <- diversity(composicao_especies, index = "simpson", MARGIN = 1)
Simpson_dados## Com_1 Com_2 Com_3 Com_4 Com_5 Com_6 Com_7 Com_8
## 0.9000000 0.1710000 0.4814815 0.7989636 0.7587500 0.7674858 0.5000000 0.5850000
## Com_9 Com_10
## 0.8088889 0.6942149Shannon_total <- diversity(colSums(composicao_especies), index = "shannon")
Shannon_total## [1] 2.164299Simpson_total <- diversity(colSums(composicao_especies), index = "simpson")
Simpson_total## [1] 0.8653032Aplicando a Equitabilidade pielou
O indice de equitabilidade de pielou é derivado do índice de diversidade de Shannon e permite representar a uniformidade da distribuição dos indivíduos entre as espécies existentes (PIELOU, 1966). Seu valor apresenta uma amplitude de 0 (uniformidade mínima) a 1 (uniformidade máxi-ma
equi <- Shannon / log(Riqueza)
equi## Local1 Local2 Local3 Local4 Local5 Local6 Local7 Local8
## 0.9440075 0.9573216 0.9745713 0.9903950 0.9736076 0.9558942 0.9789848 0.9689891
## Local9 Local10
## 0.9868140 0.9933486Analisando a relação da comunidade com a precipitação.
prec_dados <- data.frame(precipitacao$prec, riqueza_dados,Shannon_dados,
Simpson_dados,equi)
prec_dados## precipitacao.prec riqueza_dados Shannon_dados Simpson_dados equi
## Com_1 3200 10 2.3025851 0.9000000 0.9440075
## Com_2 3112 10 0.5002880 0.1710000 0.9573216
## Com_3 2800 5 0.9580109 0.4814815 0.9745713
## Com_4 1800 5 1.6068659 0.7989636 0.9903950
## Com_5 2906 5 1.4861894 0.7587500 0.9736076
## Com_6 3005 6 1.5607038 0.7674858 0.9558942
## Com_7 930 2 0.6931472 0.5000000 0.9789848
## Com_8 1000 4 1.1058899 0.5850000 0.9689891
## Com_9 1300 6 1.7140875 0.8088889 0.9868140
## Com_10 987 4 1.2636544 0.6942149 0.9933486colnames(prec_dados) <- c("Precipitacao", "Riqueza", "Shannon", "Simpson", "Equitabilidade")
prec_dados## Precipitacao Riqueza Shannon Simpson Equitabilidade
## Com_1 3200 10 2.3025851 0.9000000 0.9440075
## Com_2 3112 10 0.5002880 0.1710000 0.9573216
## Com_3 2800 5 0.9580109 0.4814815 0.9745713
## Com_4 1800 5 1.6068659 0.7989636 0.9903950
## Com_5 2906 5 1.4861894 0.7587500 0.9736076
## Com_6 3005 6 1.5607038 0.7674858 0.9558942
## Com_7 930 2 0.6931472 0.5000000 0.9789848
## Com_8 1000 4 1.1058899 0.5850000 0.9689891
## Com_9 1300 6 1.7140875 0.8088889 0.9868140
## Com_10 987 4 1.2636544 0.6942149 0.9933486anova_shan <- lm(Shannon ~ Precipitacao, data = prec_dados)
anova(anova_shan)## Analysis of Variance Table
##
## Response: Shannon
## Df Sum Sq Mean Sq F value Pr(>F)
## Precipitacao 1 0.10989 0.10989 0.3627 0.5637
## Residuals 8 2.42366 0.30296anova_simp <- lm(Simpson ~ Precipitacao, data = prec_dados)
anova (anova_simp)## Analysis of Variance Table
##
## Response: Simpson
## Df Sum Sq Mean Sq F value Pr(>F)
## Precipitacao 1 0.00132 0.001325 0.0252 0.8778
## Residuals 8 0.42064 0.052580anova_riq <- lm(Riqueza ~ Precipitacao, data = prec_dados)
anova(anova_riq)## Analysis of Variance Table
##
## Response: Riqueza
## Df Sum Sq Mean Sq F value Pr(>F)
## Precipitacao 1 30.622 30.6224 8.9156 0.01744 *
## Residuals 8 27.478 3.4347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova_equi <- lm(Equitabilidade ~ Precipitacao, data = prec_dados)
anova(anova_equi)## Analysis of Variance Table
##
## Response: Equitabilidade
## Df Sum Sq Mean Sq F value Pr(>F)
## Precipitacao 1 0.0012169 0.00121694 8.6878 0.0185 *
## Residuals 8 0.0011206 0.00014007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Exercício 4: Diversidade verdadeira
O índice de Hill é um cálculo cuja fórmula unifica outros índices de diversidade de Shannon e Simpson e fórmulas derivadas.
library(devtools)
library(ecodados)
library(vegan)
library(ggplot2)
library(BiodiversityR)
library(hillR)## Warning: package 'hillR' was built under R version 4.1.3composicao_especies <- ecodados::composicao_anuros_div_taxonomica
precipitacao <- ecodados::precipitacao_div_taxonomicahill_q_0 <- hillR::hill_taxa(moll, q = 0)
hill_q_0## Baladi Bukovec Cudrak Dubcova Grigovci Grun HLplos HLrosn
## 19 9 10 14 13 20 3 3
## Hrncarky HrubBrod HuteDol ChmelLou ChmelStu Chmura Jasenka Javoruvk
## 23 13 25 8 16 16 20 27
## Kalabova Klubina Kralovec Krocil Lany Machala Machova Mechnac
## 26 9 24 12 9 12 23 13
## Milonov NizKel Obidova Pisojov PivoLou Pivovari Rajnoch Semetin
## 16 14 4 7 8 16 7 18
## Skanzen Solan Spanie StudVrch Tlsta Trubiska U Pavliku ValKlob
## 12 12 23 20 18 28 15 21
## Vlarsky VresStra VrchPre
## 20 4 5hill_q_1 <- hillR::hill_taxa(moll, q = 1)
hill_q_1## Baladi Bukovec Cudrak Dubcova Grigovci Grun HLplos HLrosn
## 7.143260 5.940757 7.375416 8.663178 8.836557 11.818495 2.600490 2.649351
## Hrncarky HrubBrod HuteDol ChmelLou ChmelStu Chmura Jasenka Javoruvk
## 6.330010 8.204216 8.155648 6.039829 5.805370 7.865815 11.424821 14.361361
## Kalabova Klubina Kralovec Krocil Lany Machala Machova Mechnac
## 14.915726 6.123555 11.590345 8.183111 5.185746 7.580978 10.661884 7.014454
## Milonov NizKel Obidova Pisojov PivoLou Pivovari Rajnoch Semetin
## 5.158927 7.323815 3.827122 5.647906 5.077726 6.069975 4.689955 11.681803
## Skanzen Solan Spanie StudVrch Tlsta Trubiska U Pavliku ValKlob
## 7.215562 5.601488 11.950488 6.761065 10.316393 8.173482 10.674255 8.162188
## Vlarsky VresStra VrchPre
## 13.503723 4.000000 3.190527hill_q_2 <- hillR::hill_taxa(moll, q = 2)
hill_q_2## Baladi Bukovec Cudrak Dubcova Grigovci Grun HLplos HLrosn
## 5.135974 4.710801 6.335092 6.170802 6.848935 9.418294 2.333333 2.461538
## Hrncarky HrubBrod HuteDol ChmelLou ChmelStu Chmura Jasenka Javoruvk
## 3.161407 6.219446 4.801650 4.951100 3.766021 5.707854 9.121039 10.333115
## Kalabova Klubina Kralovec Krocil Lany Machala Machova Mechnac
## 11.765048 4.909091 7.656738 6.472579 3.487956 6.167871 7.879654 5.402835
## Milonov NizKel Obidova Pisojov PivoLou Pivovari Rajnoch Semetin
## 2.884959 5.439989 3.666667 4.993326 3.719388 3.662414 3.692308 9.722616
## Skanzen Solan Spanie StudVrch Tlsta Trubiska U Pavliku ValKlob
## 5.216402 4.032060 9.006746 4.668522 8.485881 5.085655 8.889610 4.639494
## Vlarsky VresStra VrchPre
## 11.643730 4.000000 2.327273res_hill <- data.frame(hill_q_0, hill_q_1, hill_q_2)
colnames(res_hill) <- c("q=0", "q=1", "q=2")
head(res_hill)## q=0 q=1 q=2
## Baladi 19 7.143260 5.135974
## Bukovec 9 5.940757 4.710801
## Cudrak 10 7.375416 6.335092
## Dubcova 14 8.663178 6.170802
## Grigovci 13 8.836557 6.848935
## Grun 20 11.818495 9.418294library(entropart)## Warning: package 'entropart' was built under R version 4.1.3mc<- MetaCommunity (moll)## Warning in FUN(newX[, i], ...): Zhang-Huang sample coverage cannot be estimated
## because one probability is over 1/2. Chao estimator is returned.## Warning in FUN(newX[, i], ...): Zhang-Huang sample coverage cannot be estimated
## because one probability is over 1/2. Chao estimator is returned.
## Warning in FUN(newX[, i], ...): Zhang-Huang sample coverage cannot be estimated
## because one probability is over 1/2. Chao estimator is returned.
## Warning in FUN(newX[, i], ...): Zhang-Huang sample coverage cannot be estimated
## because one probability is over 1/2. Chao estimator is returned.
## Warning in FUN(newX[, i], ...): Zhang-Huang sample coverage cannot be estimated
## because one probability is over 1/2. Chao estimator is returned.
## Warning in FUN(newX[, i], ...): Zhang-Huang sample coverage cannot be estimated
## because one probability is over 1/2. Chao estimator is returned.## Warning in FUN(newX[, i], ...): Sample coverage is 0, most bias corrections will
## return NaN.## Warning in FUN(newX[, i], ...): Zhang-Huang sample coverage cannot be estimated
## because one probability is over 1/2. Chao estimator is returned.## Warning in FUN(newX[, i], ...): Sample coverage is 0, most bias corrections will
## return NaN.## Warning in FUN(newX[, i], ...): Zhang-Huang sample coverage cannot be estimated
## because one probability is over 1/2. Chao estimator is returned.
## Warning in FUN(newX[, i], ...): Zhang-Huang sample coverage cannot be estimated
## because one probability is over 1/2. Chao estimator is returned.
## Warning in FUN(newX[, i], ...): Zhang-Huang sample coverage cannot be estimated
## because one probability is over 1/2. Chao estimator is returned.
## Warning in FUN(newX[, i], ...): Zhang-Huang sample coverage cannot be estimated
## because one probability is over 1/2. Chao estimator is returned.## Warning in FUN(newX[, i], ...): Sample coverage is 0, most bias corrections will
## return NaN.plot(mc)
summary(DivPart(q=0, mc), Correction="None")## HCDT diversity partitioning of order 0 of metaCommunity mc
##
## Alpha diversity of communities:
## PlaPol CarMin CarTri VerPyg VerSub VerAnt VerAng VerMou VerPus ColEde CocLub
## 7 35 31 32 33 32 15 6 3 13 36
## CocLul PupMus PupPra AcaAcu ValCos ValPul TruCyl PunPyg AliBip MacTum MacVen
## 4 2 1 6 13 32 4 30 3 3 2
## VesTur SucPut SucObl OxyEle AegPur AegMin PerHam OxyCel OxyGla VitCon VitCry
## 2 29 12 9 17 5 32 2 4 8 8
## VitDia CecAci SemSem VitPel EucFul EucAld ZonNit DauBre DauRuf AriSil AriSub
## 6 1 1 17 33 1 9 13 11 1 2
## DerAgr DerLae DerPra PliLub PetUni PerBid TriHis MonInc MonVic EuoStr AriArb
## 3 3 2 13 2 5 2 22 4 3 5
## CepHor CepVin
## 1 4
## Total alpha diversity of the communities:
## [1] 11.14035
## Beta diversity of the communities:
## None
## 3.859843
## Gamma diversity of the metacommunity:
## None
## 43summary(DivPart(q=1, mc), Correction="None")## HCDT diversity partitioning of order 1 of metaCommunity mc
##
## Alpha diversity of communities:
## PlaPol CarMin CarTri VerPyg VerSub VerAnt VerAng VerMou
## 5.279094 16.579841 12.631003 15.301876 14.624005 19.724971 9.334348 5.512841
## VerPus ColEde CocLub CocLul PupMus PupPra AcaAcu ValCos
## 2.749459 9.102995 20.639566 1.380795 1.889882 1.000000 4.121986 7.963352
## ValPul TruCyl PunPyg AliBip MacTum MacVen VesTur SucPut
## 20.577094 3.216463 16.827651 2.217347 1.944227 1.466620 1.948206 12.678030
## SucObl OxyEle AegPur AegMin PerHam OxyCel OxyGla VitCon
## 4.319488 6.231501 10.081696 3.922429 16.334704 1.889882 3.864313 5.058307
## VitCry VitDia CecAci SemSem VitPel EucFul EucAld ZonNit
## 3.921992 4.262229 1.000000 1.000000 12.686988 16.910298 1.000000 2.990222
## DauBre DauRuf AriSil AriSub DerAgr DerLae DerPra PliLub
## 9.557372 8.333814 1.000000 2.000000 2.828427 2.586409 2.000000 7.953035
## PetUni PerBid TriHis MonInc MonVic EuoStr AriArb CepHor
## 1.356131 2.674595 1.944164 15.582068 3.596115 2.828427 3.711482 1.000000
## CepVin
## 3.746748
## Total alpha diversity of the communities:
## [1] 4.38349
## Beta diversity of the communities:
## None
## 5.64667
## Gamma diversity of the metacommunity:
## None
## 24.75212summary(DivPart(q=2, mc), Correction="None")## HCDT diversity partitioning of order 2 of metaCommunity mc
##
## Alpha diversity of communities:
## PlaPol CarMin CarTri VerPyg VerSub VerAnt VerAng VerMou
## 4.357513 11.627463 7.631272 10.810048 10.181626 15.671691 7.120817 5.233065
## VerPus ColEde CocLub CocLul PupMus PupPra AcaAcu ValCos
## 2.571429 7.317440 15.829173 1.151266 1.800000 1.000000 3.550336 5.560577
## ValPul TruCyl PunPyg AliBip MacTum MacVen VesTur SucPut
## 15.583082 2.777778 13.009263 1.814815 1.640167 1.287892 1.901112 7.941799
## SucObl OxyEle AegPur AegMin PerHam OxyCel OxyGla VitCon
## 2.791605 5.104203 8.003891 3.270270 10.857732 1.800000 3.769231 3.830028
## VitCry VitDia CecAci SemSem VitPel EucFul EucAld ZonNit
## 2.949087 3.501608 1.000000 1.000000 10.042471 12.274927 1.000000 2.263960
## DauBre DauRuf AriSil AriSub DerAgr DerLae DerPra PliLub
## 7.754177 6.949239 1.000000 2.000000 2.666667 2.272727 2.000000 5.812191
## PetUni PerBid TriHis MonInc MonVic EuoStr AriArb CepHor
## 1.198020 1.959094 1.893770 11.238532 3.333333 2.666667 3.072727 1.000000
## CepVin
## 3.555556
## Total alpha diversity of the communities:
## [1] 2.65027
## Beta diversity of the communities:
## None
## 7.341057
## Gamma diversity of the metacommunity:
## None
## 19.45579autoplot(DivProfile(q.seq = seq(0, 2, 0.1),MC=mc, Correction = "None"))
alfa.est0<-DivEst(q = 0, mc, Simulations = 100, Correction = "None")plot(alfa.est0)
alfa.est1<-DivEst(q = 1, mc, Simulations = 100, Correction = "None")plot(alfa.est1)
alfa.est2<-DivEst(q = 2, mc, Simulations = 100, Correction = "None")plot(alfa.est2)
Exércicio 5: Diversidade Beta
séra usado os dados referente ao trabalho “Plant β-diversity in fragmented rain forests: testing floristic homogenization and differentiation hypotheses” o objetivo é analisar a diversidade Beta entre as áreas estudadas no trabalho.
dados<-read.csv("https://raw.githubusercontent.com/fplmelo/ecoa/main/content/en/courses/eco_num/betadiv/com_ltx_all.csv", row.names = "X")
dados<-as.data.frame(dados)
dim(dados)## [1] 179 36library(entropart)
mc1<- MetaCommunity (dados)Começamos calculando a divesidade alfa, gama e beta oara cada plot de cada tratamento utilizando o q=0, q=1 e q=2.
AlphaDiversity(mc1, q=0, Correction = "None")## $MetaCommunity
## [1] "mc1"
##
## $Method
## [1] "Neutral"
##
## $Type
## [1] "alpha"
##
## $Order
## [1] 0
##
## $Correction
## [1] "None"
##
## $Normalized
## [1] TRUE
##
## $Weights
## LDL1 LDL3 LDL4 LDL5 LDL8 LDL9 LDL10
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## LDL11 LDL12 LDL13 LDL14 LDL15 IDL1 IDL4
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## IDL5 IDL6 IDL7 IDL8 IDL9 IDL10 IDL11
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## IDL12 IDL13 IDL14 IDL15 HDL2 HDL3 HDL4
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## HDL5 HDL6 HDL10 HDL11 HDL12 HDL13 HDL14
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## HDL15
## 0.02777778
##
## $Communities
## LDL1 LDL3 LDL4 LDL5 LDL8 LDL9 LDL10 LDL11 LDL12 LDL13 LDL14 LDL15 IDL1
## 35 30 30 29 38 40 26 32 36 27 36 23 38
## IDL4 IDL5 IDL6 IDL7 IDL8 IDL9 IDL10 IDL11 IDL12 IDL13 IDL14 IDL15 HDL2
## 31 31 32 38 29 41 32 27 40 40 35 46 38
## HDL3 HDL4 HDL5 HDL6 HDL10 HDL11 HDL12 HDL13 HDL14 HDL15
## 40 25 26 25 27 15 18 21 39 29
##
## $Total
## [1] 31.80556
##
## attr(,"class")
## [1] "MCdiversity"AlphaDiversity(mc1, q=1, Correction = "None")## $MetaCommunity
## [1] "mc1"
##
## $Method
## [1] "Neutral"
##
## $Type
## [1] "alpha"
##
## $Order
## [1] 1
##
## $Correction
## [1] "None"
##
## $Normalized
## [1] TRUE
##
## $Weights
## LDL1 LDL3 LDL4 LDL5 LDL8 LDL9 LDL10
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## LDL11 LDL12 LDL13 LDL14 LDL15 IDL1 IDL4
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## IDL5 IDL6 IDL7 IDL8 IDL9 IDL10 IDL11
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## IDL12 IDL13 IDL14 IDL15 HDL2 HDL3 HDL4
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## HDL5 HDL6 HDL10 HDL11 HDL12 HDL13 HDL14
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## HDL15
## 0.02777778
##
## $Communities
## LDL1 LDL3 LDL4 LDL5 LDL8 LDL9 LDL10 LDL11
## 27.222539 25.629042 22.941212 24.387995 25.468065 29.897721 7.311644 21.888908
## LDL12 LDL13 LDL14 LDL15 IDL1 IDL4 IDL5 IDL6
## 29.349849 24.342215 25.964012 13.197723 30.755277 24.461617 19.699180 25.450513
## IDL7 IDL8 IDL9 IDL10 IDL11 IDL12 IDL13 IDL14
## 25.265590 23.159886 25.854673 27.138127 18.074630 30.816146 33.977934 21.894257
## IDL15 HDL2 HDL3 HDL4 HDL5 HDL6 HDL10 HDL11
## 35.997088 31.969923 30.800462 20.816537 17.365476 20.844865 20.562416 6.173823
## HDL12 HDL13 HDL14 HDL15
## 11.350338 17.717858 31.130084 21.344945
##
## $Total
## [1] 22.28671
##
## attr(,"class")
## [1] "MCdiversity"AlphaDiversity(mc1, q=2, Correction = "None")## $MetaCommunity
## [1] "mc1"
##
## $Method
## [1] "Neutral"
##
## $Type
## [1] "alpha"
##
## $Order
## [1] 2
##
## $Correction
## [1] "None"
##
## $Normalized
## [1] TRUE
##
## $Weights
## LDL1 LDL3 LDL4 LDL5 LDL8 LDL9 LDL10
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## LDL11 LDL12 LDL13 LDL14 LDL15 IDL1 IDL4
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## IDL5 IDL6 IDL7 IDL8 IDL9 IDL10 IDL11
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## IDL12 IDL13 IDL14 IDL15 HDL2 HDL3 HDL4
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## HDL5 HDL6 HDL10 HDL11 HDL12 HDL13 HDL14
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## HDL15
## 0.02777778
##
## $Communities
## LDL1 LDL3 LDL4 LDL5 LDL8 LDL9 LDL10 LDL11
## 20.353846 21.446602 17.147783 20.578231 17.963124 21.690323 3.275528 13.986877
## LDL12 LDL13 LDL14 LDL15 IDL1 IDL4 IDL5 IDL6
## 23.485207 22.153846 18.558185 8.125604 24.557604 20.374570 13.586621 20.433476
## IDL7 IDL8 IDL9 IDL10 IDL11 IDL12 IDL13 IDL14
## 17.016807 18.788820 16.346687 23.684444 12.686792 23.837838 28.285714 11.479245
## IDL15 HDL2 HDL3 HDL4 HDL5 HDL6 HDL10 HDL11
## 27.842105 26.560976 23.040134 17.899408 11.505618 17.344262 14.727273 3.835894
## HDL12 HDL13 HDL14 HDL15
## 8.294931 14.520000 25.137931 17.192837
##
## $Total
## [1] 14.11629
##
## attr(,"class")
## [1] "MCdiversity"BetaDiversity(mc1, q=0, Correction = "None")## $MetaCommunity
## [1] "mc1"
##
## $Method
## [1] "Neutral"
##
## $Type
## [1] "beta"
##
## $Order
## [1] 0
##
## $Correction
## [1] "None"
##
## $Normalized
## [1] TRUE
##
## $Weights
## LDL1 LDL3 LDL4 LDL5 LDL8 LDL9 LDL10
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## LDL11 LDL12 LDL13 LDL14 LDL15 IDL1 IDL4
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## IDL5 IDL6 IDL7 IDL8 IDL9 IDL10 IDL11
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## IDL12 IDL13 IDL14 IDL15 HDL2 HDL3 HDL4
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## HDL5 HDL6 HDL10 HDL11 HDL12 HDL13 HDL14
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## HDL15
## 0.02777778
##
## $Total
## [1] 5.627948
##
## attr(,"class")
## [1] "MCdiversity"BetaDiversity(mc1, q=1, Correction = "None")## $MetaCommunity
## [1] "mc1"
##
## $Method
## [1] "Neutral"
##
## $Type
## [1] "beta"
##
## $Order
## [1] 1
##
## $Correction
## [1] "None"
##
## $Normalized
## [1] TRUE
##
## $Weights
## LDL1 LDL3 LDL4 LDL5 LDL8 LDL9 LDL10
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## LDL11 LDL12 LDL13 LDL14 LDL15 IDL1 IDL4
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## IDL5 IDL6 IDL7 IDL8 IDL9 IDL10 IDL11
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## IDL12 IDL13 IDL14 IDL15 HDL2 HDL3 HDL4
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## HDL5 HDL6 HDL10 HDL11 HDL12 HDL13 HDL14
## 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778 0.02777778
## HDL15
## 0.02777778
##
## $Total
## [1] 4.25749
##
## attr(,"class")
## [1] "MCdiversity"BetaDiversity(mc1, q=2, Correction = "None")GammaDiversity(mc1, q=0, Correction = "None")## None
## 179GammaDiversity(mc1, q=1, Correction = "None")## None
## 94.88546GammaDiversity(mc1, q=2, Correction = "None")## None
## 62.38163Profile <- DivProfile(q.seq = seq(0, 2, 0.1), mc1, Biased = FALSE, Correction = "None")
plot(Profile)
Utilizando o pacote “betapart” analisamos a Beta diversidade de cada
tratamento estudados, onde são dados três valores, o “beta.SIM” que é a
dissimilaridade de Simpson o qual analisa apenas as mudanças
composicionais da “rotatividade” de espécies. O ’beta.SOR” correspondo
ao índice de Sorensen que vai explicar a variação totalda composição
entre as amostragens, o que inclui os padrões de totatividade e
aninhamento. E o “beta.SNE” representa a dissimilaridade
aninhada-resultante e é calculada como a diferença entre βsor e
βsim.
library(betapart)## Warning: package 'betapart' was built under R version 4.1.3dadosLDL<-dados[, 1:12]
dadosLDL<-ifelse(dadosLDL=="0",0,1)
beta.core<-betapart.core(dadosLDL)
beta.multi<-beta.multi(dadosLDL)
beta.multi## $beta.SIM
## [1] 0.9543153
##
## $beta.SNE
## [1] 0.03262383
##
## $beta.SOR
## [1] 0.9869392dadosIDL<-dados[, 13:25]
dadosIDL<-ifelse(dadosIDL=="0",0,1)
beta.core<-betapart.core(dadosIDL)
beta.multi<-beta.multi(dadosIDL)
beta.multi## $beta.SIM
## [1] 0.9315467
##
## $beta.SNE
## [1] 0.05502664
##
## $beta.SOR
## [1] 0.9865734dadosHDL<-dados[, 26:36]
dadosHDL<-ifelse(dadosHDL=="0",0,1)
beta.core<-betapart.core(dadosHDL)
beta.multi<-beta.multi(dadosHDL)
beta.multi## $beta.SIM
## [1] 0.9430244
##
## $beta.SNE
## [1] 0.04421616
##
## $beta.SOR
## [1] 0.9872406Exércicio 6: Diversidade Funcional
De acordo com a difinicção geral adotada por diversos autores, diversidade funcional nada mais é que o valor e a variedade das espécies (gama) e características do organismo que influenciam o funcionamento do ecossistema. Aqui utilizamos exemplo do capitulo oito do livro “Introdução ao R com aplicações em biodiversidade e conservação”.
library(FD)## Carregando pacotes exigidos: ade4## Carregando pacotes exigidos: ape## Carregando pacotes exigidos: geometrylibrary(ade4)
library(ecodados)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(ggplot2)
library(ggrepel)
library(tidyverse)
library(picante)## Carregando pacotes exigidos: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
##
## collapsecomun_fren_dat <- ecodados::fundiv_frenette2012a_comu
ambie_fren_dat <- ecodados::fundiv_frenette2012a_amb
trait_fren_dat <- ecodados::fundiv_frenette2012a_trait
trait_dat <- ecodados::fundiv_barbaro2009a_trait
comun_dat <- ecodados::fundiv_barbaro2009a_comu
ambie_dat <- ecodados::fundiv_barbaro2009a_ambApós carregar os dados fornecidos pelos autores, foi feito o índice de “dis(similaridade)” que nada mais é do que calcular o nível de semelhança entre as comunidades estudadas (similaridade) e calcular também a distancias entre esssas memas comunidades (dissimilaridade), ambas as medidas estão relacionadas a diversidade beta da área amostrada. Podemos observar que foi utilizada a distância euclidiana usada para expressar a distância entre duas amostras, baseadas atraves de um plano cartesiano.
trait_cont <- trait_fren_dat
trait_pad <- decostand(trait_cont, "standardize")
euclid_dis <- vegdist(trait_pad, "euclidean")
pcoa_traits_cont <- pcoa(euclid_dis, correction="cailliez")
eixos_cont <- as.data.frame(pcoa_traits_cont$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$vectors[,1:2]) # Selecionar os dois primeiros eixoslibrary(ggplot2)
library(tidyverse)
library(dplyr)
library(ggrepel)
eixos_cat %>%
ggplot(aes(x=Axis.1, y=Axis.2)) +
geom_point(pch=21, size=3, color = "black", fill="#4575b4") +
geom_text_repel(aes(Axis.1, Axis.2, label = rownames(eixos_cat))) +
xlab("PCO 1") + ylab("PCO 2") +
theme(axis.title.x = element_text(face="bold", size=14),
axis.text.x = element_text(vjust=0.5, size=12)) +
theme(axis.title.y = element_text(face="bold", size=14),
axis.text.y = element_text(vjust=0.5, size=12)) +
geom_hline(yintercept = 0, linetype=2) +
geom_vline(xintercept = 0, linetype=2)+
theme(legend.position = "top", legend.title=element_blank()) -> plot_trait_cat
plot_trait_cat
ggsave("trait_cat.pdf", plot_trait_cat, height = 14, width = 14, dpi = 600, units = "cm")
trait_dat %>%
dplyr::summarise_all(class) %>%
tidyr::gather(variable, class)## variable class
## 1 trend character
## 2 redlist character
## 3 regio integer
## 4 biog character
## 5 activ character
## 6 diet character
## 7 winter character
## 8 color character
## 9 breed character
## 10 body integer
## 11 wing character
## 12 period characterSão usados variaveis qualitativas do tipo categoricas e ordinais
trait_dat$regio <- as.ordered(trait_dat$regio)
trait_dat$body <- as.ordered(trait_dat$body)
# c\tegóricos
trait_categ <- cbind.data.frame(trend=trait_dat$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$period)
# Ordinais
trait_ord <- cbind.data.frame(regio=trait_dat$regio, body=trait_dat$body)
rownames(trait_categ) <- rownames(trait_dat)
rownames(trait_ord) <- rownames(trait_dat)
library(FD)
library(ade4)
library(ecodados)
library(gridExtra)
library(ggplot2)
library(ggrepel)
library(tidyverse)
library(picante)
ktab_list <- ktab.list.df(list(trait_categ, trait_ord))
dist_mist <- dist.ktab(ktab_list, type= c("N", "O"))
pcoa_traits_mist <- pcoa(dist_mist, correction="cailliez")
eixos_mist <- as.data.frame(pcoa_traits_mist$vectors[,1:2]) # exportar 2 eixos
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)
##Riqueza fincional (Fric) A partir disso é calculada a riqueza
funcional que é o número de espécies funcionais da comunidade presente
na área de estudo. Isso é feito através de uma matriz. Essa métrica vai
fornecer informações importantes a respeito da comunidade, uma baixa
riqueza funcional por exemplo, pode indicar que recursos do ambiente
necessarios para a comunidade não estão em uso, reduzindo a
produtividade local.
richness <- dbFD(dist_mist, comun_dat)$nbsp## FRic: Dimensionality reduction was required. The last 17 PCoA axes (out of 19 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.3243851
## 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## FRic: Dimensionality reduction was required. The last 17 PCoA axes (out of 19 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.3243851
## 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)$PDDivergencia Funcional
A divergência funcional vai fornecer informações a repeito da diferença entre as comunidade pelo ponto de vista funcional, por exemplo uma lta divergência funcional pode indicar um alto grau de diferenciação de nicho e, portanto, baixa concorrência de recursos. Assim, comunidades com alta divergência funcional podem ter aumentado a função ecossistêmica como resultado de um melhor uso dos recursos disponiveis.
library(FD)
library(tidyverse)
library(ecodados)
library(vegan)
library(SYNCSA)
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 0.7127595fdiv <- dbFD(dist_mist, comun_dat)$FDiv## FRic: Dimensionality reduction was required. The last 17 PCoA axes (out of 19 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.3243851
## CWM: When 'x' is a distance matrix, CWM cannot be calculated.fdis <- dbFD(dist_mist, comun_dat)$FDis## FRic: Dimensionality reduction was required. The last 17 PCoA axes (out of 19 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.3243851
## CWM: When 'x' is a distance matrix, CWM cannot be calculated.Regularidade Funcional
Regularidade ou uniformidade funcional vai verificar se há irregularidades ou discrepâncias no valores dos atributos distribuidos no espaço funcional. Um exemplo dessa métrica, a distribuição da biomassa de uma comunidade é distribuida no espaço do nicho para que seja ultilizada por toda comunidade de forma efetiva, se a distribuição dessa biomassa não é uniforme em alguma parte do nicho isso pode nos dizer que a área mesmo ocupada possui partes subutilizadas, isso tenderá a diminuir a produtividade e a confiabilidade, e aumentar a oportunidade para os invasores.
library(FD)
library(tidyverse)
library(ecodados)
library(vegan)
library(GGally)## 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## FRic: Dimensionality reduction was required. The last 17 PCoA axes (out of 19 in total) were removed.
## FRic: Quality of the reduced-space representation = 0.3243851
## 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 funcional (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 TRUEcolnames(comun_fren_pa)==rownames(trait)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] 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 0.170 448.661## Passo 1: calcular a distância funcional
trait_pad <- decostand(trait_fren_dat, "standardize")
euclid_dis <- vegdist(trait_pad, "euclidean")## Passo 2: calcular a Divergência funcional (FDis) e Regularidade Funcional (FEve)
fdis <- dbFD(euclid_dis, comun_fren_dat)$FDis# Fdis=0 em locais com somente uma espécie## FRic: No dimensionality reduction was required. All 5 PCoA axes were kept as 'traits'.
## CWM: When 'x' is a distance matrix, CWM cannot be calculated.feve <- dbFD(euclid_dis, comun_fren_dat)$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.## Passo 3: Utilizar um modelo linear para comparar o efeito da aridez sobre FDis (predição 1) e FEve (predição 2)
lm_dat <- data.frame(aridez = ambie_fren_dat$Aridity, fdis = fdis, feve = feve)
mod1 <- lm(fdis ~ aridez, data = lm_dat)
plot(mod1)
anova(mod1) # Conclusão: a aridez não tem efeito sobre a divergência funcional## 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 0.020080## Passo 4: gráfico para visualizar os dois resultados
lm_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)
### Exemplo 2: o pastejo influencia a diversidade beta funcional de 34 espécies de plantas (Frenette-Dussault et al. 2012)
# Dados
comun_fren_dat # matriz de espécies por localidade## 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 # matriz de variáveis ambientais por localidade## 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 # matriz de atributos contínuos por espécie## 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:
# Hipótese: o pastejo determina a ocorrência de espécies de plantas com diferentes atributos funcionais
# Predição 1: a composição funcional de plantas é diferente entre áreas com e sem pastejo
## Passo 1: calcular a distância funcional
cwm_dis <- vegdist(cwm_fren, "euclidean")
## Passo 2: calcular os valores de composição funcional (CWM)
cwm_fren <- functcomp(trait_pad, as.matrix(comun_fren_dat))
## Passo 3: testar se a composição funcional varia entre as áreas com uma PERMANOVA
perman_fren <- adonis(cwm_fren~Grazing, data = ambie_fren_dat)
## Passo 4: comparar a variação dentro de cada grupo com Betadisper
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 0.679
## Residuals 44 12.1763 0.276735plot(betad_fren)