Modules Ecologia Numérica Beta-diversidade
Beta-diversidade
Beta diversidade é medir diferença entre comunidades
Exemplo
Vamos usar os dados de um artigo em que participei para medir a diverisdade beta dentro de paisagens com diferentes graus de desmatamento.
Plant β-diversity in fragmented rain forests: testing floristic homogenization and differentiation hypotheses
https://doi.org/10.1111/1365-2745.12153
Nessa figura vemos as paisagens estudadas. São três, LDL, IDL e HDL. Elas se diferenciam pelo grau de desmatamento que cada uma apresenta. LDL = LOW deforestain level, IDL = INTERMEDIATE deforestation level e HDL = HIGH deforestaion level.
Portanto, é um gradiente ambiental de desmatamento sobre o qual testamos se essa variável causava homogenização ou dierenciaçãp florística na paisagem. Portanto estávemos interessados sobre como a diversidade beta variava em função desse gradiente.
Mas, para os fins desse exercício, vamos apenas brincar com a beta-diversidade.
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) # note que em vez de 45 plots, 15 para cada paisagem, temos apenas 36 plots porque alugns forma excluídos por baixa cobertura.
## [1] 179 36
Passo 1) Calcular a Alfa uando o “entropart”
mc<-MetaCommunity(dados)
# head(mc) descomente essa somente se quiser ver o output
AlphaDiversity(mc, q=0, Correction = "None") # use sempre correction "None" para não gerar números diferentes
## $MetaCommunity
## [1] "mc"
##
## $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(mc, q=1, Correction = "None")
## $MetaCommunity
## [1] "mc"
##
## $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(mc, q=2, Correction = "None")
## $MetaCommunity
## [1] "mc"
##
## $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"
Passo 2) Calcular a Beta uando o “entropart”
BetaDiversity(mc, q=0, Correction = "None") # use sempre correction "None" para não gerar números diferentes
## $MetaCommunity
## [1] "mc"
##
## $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(mc, q=1, Correction = "None")
## $MetaCommunity
## [1] "mc"
##
## $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(mc, q=2, Correction = "None")
## $MetaCommunity
## [1] "mc"
##
## $Method
## [1] "Neutral"
##
## $Type
## [1] "beta"
##
## $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
##
## $Total
## [1] 4.419122
##
## attr(,"class")
## [1] "MCdiversity"
Passo 3) Calcular a Gama uando o “entropart”
GammaDiversity(mc, q=0, Correction = "None") # use sempre correction "None" para não gerar números diferentes
## None
## 179
GammaDiversity(mc, q=1, Correction = "None")
## None
## 94.88546
GammaDiversity(mc, q=2, Correction = "None")
## None
## 62.38163
Passo 4) Gerar um perfil de diversidade
Profile <- DivProfile(q.seq = seq(0, 2, 0.1), mc, Biased = FALSE, Correction = "None")
plot(Profile)
summary(Profile)
## Diversity profile of MetaCommunity mc
## with correction: None
## Diversity against its order:
## Order Alpha Diversity Beta Diversity Gamma Diversity
## None 0.0 31.80556 5.627948 179.00000
## None 0.1 30.85554 5.417085 167.14707
## None 0.2 29.89974 5.221463 156.12037
## None 0.3 28.93997 5.041821 145.91015
## None 0.4 27.97820 4.878732 136.49813
## None 0.5 27.01649 4.732591 127.85800
## None 0.6 26.05700 4.603608 119.95622
## None 0.7 25.10198 4.491810 112.75332
## None 0.8 24.15370 4.397063 106.20534
## None 0.9 23.21450 4.319087 100.26545
## None 1.0 22.28671 4.257490 94.88546
## None 1.1 21.37266 4.211794 90.01725
## None 1.2 20.47465 4.181460 85.61391
## None 1.3 19.59491 4.165909 81.63063
## None 1.4 18.73564 4.164541 78.02535
## None 1.5 17.89891 4.176742 74.75913
## None 1.6 17.08668 4.201890 71.79636
## None 1.7 16.30077 4.239354 69.10475
## None 1.8 15.54282 4.288491 66.65526
## None 1.9 14.81427 4.348641 64.42193
## None 2.0 14.11629 4.419122 62.38163
Passo 5) Agora é com vocês e o pacote betapart. A intenção aqui é entender a contribuição dos componentes de “aninhamento” e “substituição de espécies” de cada paisagem. Vou fazer abaixo pra uma delas, LDL
dadosLDL<-dados[, 1:12]
dadosLDL<-ifelse(dadosLDL=="0",0,1) # Tranformei em 0 e 1
beta.core<-betapart.core(dadosLDL)
beta.multi<-beta.multi(dadosLDL)
beta.multi # Aquei stão os valores... interprete
## $beta.SIM
## [1] 0.9543153
##
## $beta.SNE
## [1] 0.03262383
##
## $beta.SOR
## [1] 0.9869392
Passo 6) Faça o mesmo para as outras dus paisagens, interprete e grafique como quiser. Escreva sua interpretação no seu Rmarkdown e coloque no exercício.