Descrição de comunidade biológia em Barro Colorado
Giovanna Pessoa
2023-01-20
Atividade 2 - Descrição de comunidade biológica na estação de Barro Colorado
Nessa atividade, iremos descrever e analisar uma comunidade na estação biológica de Barro Colorado.
library(devtools)## Carregando pacotes exigidos: usethislibrary(vegan)## Carregando pacotes exigidos: permute##
## Attaching package: 'permute'## The following object is masked from 'package:devtools':
##
## check## Carregando pacotes exigidos: lattice## This is vegan 2.6-4library(BiodiversityR)## Carregando pacotes exigidos: tcltk## BiodiversityR 2.15-1: Use command BiodiversityRGUI() to launch the Graphical User Interface;
## to see changes use BiodiversityRGUI(changeLog=TRUE, backward.compatibility.messages=TRUE)library(ggplot2)
library(ggrepel)data (BCI)
BCI.env <- read.delim ('https://raw.githubusercontent.com/zdealveindy/anadat-r/master/data/bci.env.txt', row.names = 1)
BCI.soil <- read.delim ('https://raw.githubusercontent.com/zdealveindy/anadat-r/master/data/bci.soil.txt')Tabela de 50 linhas e 225 colunas
dim(BCI)## [1] 50 225Agora podemos fazer algumas perguntas para nossa base de dados:
Quantas espécies há em cada amostra?
specnumber(BCI)## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 93 84 90 94 101 85 82 88 90 94 87 84 93 98 93 93 93 89 109 100
## 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
## 99 91 99 95 105 91 99 85 86 97 77 88 86 92 83 92 88 82 84 80
## 41 42 43 44 45 46 47 48 49 50
## 102 87 86 81 81 86 102 91 91 93Quantos indivíduos há em cada amostra?
rowSums(BCI)## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 448 435 463 508 505 412 416 431 409 483 401 366 409 438 462 437 381 347 433 429
## 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
## 408 418 340 392 442 407 417 387 364 475 421 459 436 447 601 430 435 447 424 489
## 41 42 43 44 45 46 47 48 49 50
## 402 414 407 409 444 430 425 415 427 432Qual a abundância de cada espécie?
head(colSums(BCI))## Abarema.macradenia Vachellia.melanoceras Acalypha.diversifolia
## 1 3 2
## Acalypha.macrostachya Adelia.triloba Aegiphila.panamensis
## 1 92 23Podemos elaborar e analisar a curva de acumulação das espécies nessa comunidade:
sp1<-specaccum(BCI, "random")
sp1## Species Accumulation Curve
## Accumulation method: random, with 100 permutations
## Call: specaccum(comm = BCI, method = "random")
##
##
## Sites 1.00000 2.00000 3.00000 4.00000 5.00000 6.00000 7.00000
## Richness 90.51000 122.35000 139.69000 151.01000 159.42000 166.00000 171.43000
## sd 5.91436 7.00126 6.09138 6.60654 6.24771 5.95098 5.84523
##
## Sites 8.00000 9.00000 10.0000 11.00000 12.00000 13.00000 14.00000
## Richness 176.12000 180.01000 183.1100 186.18000 188.78000 190.89000 193.05000
## sd 5.47405 5.68268 5.4121 5.30557 5.21958 4.87396 4.82706
##
## Sites 15.00000 16.00000 17.00000 18.00000 19.00000 20.00000 21.0000
## Richness 194.96000 196.67000 198.32000 199.78000 201.05000 202.42000 203.6200
## sd 4.56584 4.26651 4.22804 4.12085 4.17877 3.95985 3.8423
##
## Sites 22.00000 23.00000 24.00000 25.00000 26.00000 27.00000 28.00000
## Richness 204.86000 206.06000 207.09000 208.14000 209.25000 210.16000 211.11000
## sd 3.69553 3.51855 3.61002 3.66259 3.57142 3.51539 3.34813
##
## Sites 29.00000 30.00000 31.00000 32.00000 33.00000 34.00000 35.00000
## Richness 211.88000 212.88000 213.71000 214.72000 215.52000 216.21000 216.83000
## sd 3.42091 3.42091 3.34028 3.17528 2.92837 2.68289 2.70449
##
## Sites 36.00000 37.00000 38.00000 39.00000 40.00000 41.00000 42.00000
## Richness 217.55000 218.24000 218.86000 219.52000 220.07000 220.68000 221.26000
## sd 2.46747 2.46232 2.36566 2.33757 2.26638 2.13144 1.98286
##
## Sites 43.00000 44.00000 45.00000 46.0000 47.00000 48.00000 49.00000
## Richness 221.61000 222.05000 222.56000 223.2600 223.70000 224.18000 224.53000
## sd 1.91694 1.83883 1.71341 1.4538 1.25931 0.90319 0.70288
##
## Sites 50
## Richness 225
## sd 0Vamos plotar um gráfico dos dados acima para obtermos uma melhor análise visual:
plot(sp1, ci.type="poly", col="green", lwd=2, ci.lty=0, ci.col="lightblue")
boxplot(sp1, col="yellow", add=TRUE, pch="+")
Agora vamos elaborar uma curva de abundância das espécies:
mod <- rad.lognormal(BCI[5,])
mod##
## RAD model: Log-Normal
## Family: poisson
## No. of species: 101
## Total abundance: 505
##
## log.mu log.sigma Deviance AIC BIC
## 0.951926 1.165929 17.077549 317.656487 322.886728plot(mod)
Ao analisar essa curva, podemos perceber que a abundância das espécies diminui ao longo das amostras analisadas.
mod2 <- radfit(BCI[1,])
mod2##
## RAD models, family poisson
## No. of species 93, total abundance 448
##
## par1 par2 par3 Deviance AIC BIC
## Null 39.5261 315.4362 315.4362
## Preemption 0.042797 21.8939 299.8041 302.3367
## Lognormal 1.0687 1.0186 25.1528 305.0629 310.1281
## Zipf 0.11033 -0.74705 61.0465 340.9567 346.0219
## Mandelbrot 100.52 -2.312 24.084 4.2271 286.1372 293.7350plot(mod2)
data(dune)
data("dune.env")
bio<-rankabuncomp(dune, dune.env, factor='Management', return.data=TRUE, specnames=c(1:2), legend=FALSE)
bio## Grouping species labelit rank abundance proportion plower pupper accumfreq
## 1 BF Lolipere TRUE 1 18 15.4 3.6 27.2 15.4
## 2 BF Trifrepe TRUE 2 14 12.0 6.6 17.3 27.4
## 3 BF Scorautu FALSE 3 13 11.1 0.7 21.5 38.5
## 4 BF Poaprat FALSE 4 12 10.3 6.3 14.2 48.7
## 5 BF Poatriv FALSE 5 11 9.4 -9.9 28.7 58.1
## 6 BF Bromhord FALSE 6 8 6.8 -5.2 18.9 65.0
## 7 BF Achimill FALSE 7 7 6.0 -5.0 17.0 70.9
## 8 BF Planlanc FALSE 8 6 5.1 -6.9 17.1 76.1
## 9 BF Bracruta FALSE 9 6 5.1 -9.3 19.5 81.2
## 10 BF Bellpere FALSE 10 5 4.3 -4.0 12.5 85.5
## 11 BF Anthodor FALSE 11 4 3.4 -10.6 17.4 88.9
## 12 BF Elymrepe FALSE 12 4 3.4 -10.8 17.6 92.3
## 13 BF Vicilath FALSE 13 3 2.6 -4.6 9.8 94.9
## 14 BF Alopgeni FALSE 14 2 1.7 -5.4 8.8 96.6
## 15 BF Hyporadi FALSE 15 2 1.7 -6.3 9.7 98.3
## 16 BF Sagiproc FALSE 16 2 1.7 -6.3 9.7 100.0
## 17 HF Poatriv TRUE 1 24 11.3 8.4 14.1 11.3
## 18 HF Lolipere TRUE 2 20 9.4 3.7 15.1 20.7
## 19 HF Poaprat FALSE 3 17 8.0 4.9 11.1 28.6
## 20 HF Rumeacet FALSE 4 16 7.5 1.1 13.9 36.2
## 21 HF Planlanc FALSE 5 15 7.0 -0.7 14.7 43.2
## 22 HF Scorautu FALSE 6 14 6.6 5.2 8.0 49.8
## 23 HF Trifrepe FALSE 7 14 6.6 3.3 9.8 56.3
## 24 HF Bracruta FALSE 8 14 6.6 1.9 11.2 62.9
## 25 HF Elymrepe FALSE 9 10 4.7 -3.6 13.0 67.6
## 26 HF Anthodor FALSE 10 9 4.2 -0.8 9.2 71.8
## 27 HF Trifprat FALSE 11 9 4.2 -1.4 9.9 76.1
## 28 HF Alopgeni FALSE 12 8 3.8 -3.1 10.6 79.8
## 29 HF Juncarti FALSE 13 8 3.8 -2.8 10.3 83.6
## 30 HF Agrostol FALSE 14 7 3.3 -2.6 9.1 86.9
## 31 HF Achimill FALSE 15 6 2.8 -0.3 5.9 89.7
## 32 HF Juncbufo FALSE 16 6 2.8 -2.5 8.1 92.5
## 33 HF Eleopalu FALSE 17 4 1.9 -3.4 7.2 94.4
## 34 HF Sagiproc FALSE 18 4 1.9 -1.4 5.2 96.2
## 35 HF Bromhord FALSE 19 4 1.9 -1.4 5.1 98.1
## 36 HF Bellpere FALSE 20 2 0.9 -1.7 3.5 99.1
## 37 HF Ranuflam FALSE 21 2 0.9 -1.7 3.6 100.0
## 38 NM Scorautu TRUE 1 19 12.6 6.0 19.2 12.6
## 39 NM Bracruta TRUE 2 17 11.3 2.6 19.9 23.8
## 40 NM Agrostol FALSE 3 13 8.6 -1.2 18.4 32.5
## 41 NM Eleopalu FALSE 4 13 8.6 -1.4 18.6 41.1
## 42 NM Salirepe FALSE 5 11 7.3 -0.2 14.8 48.3
## 43 NM Trifrepe FALSE 6 11 7.3 -2.0 16.6 55.6
## 44 NM Anthodor FALSE 7 8 5.3 -3.8 14.4 60.9
## 45 NM Ranuflam FALSE 8 8 5.3 -1.2 11.8 66.2
## 46 NM Hyporadi FALSE 9 7 4.6 -3.8 13.1 70.9
## 47 NM Juncarti FALSE 10 7 4.6 -2.7 12.0 75.5
## 48 NM Callcusp FALSE 11 7 4.6 -2.8 12.1 80.1
## 49 NM Airaprae FALSE 12 5 3.3 -2.4 9.0 83.4
## 50 NM Planlanc FALSE 13 5 3.3 -2.6 9.2 86.8
## 51 NM Poaprat FALSE 14 4 2.6 -2.5 7.8 89.4
## 52 NM Comapalu FALSE 15 4 2.6 -1.8 7.1 92.1
## 53 NM Sagiproc FALSE 16 3 2.0 -2.9 6.9 94.0
## 54 NM Achimill FALSE 17 2 1.3 -2.4 5.0 95.4
## 55 NM Bellpere FALSE 18 2 1.3 -2.0 4.7 96.7
## 56 NM Empenigr FALSE 19 2 1.3 -1.9 4.6 98.0
## 57 NM Lolipere FALSE 20 2 1.3 -2.0 4.7 99.3
## 58 NM Vicilath FALSE 21 1 0.7 -1.0 2.3 100.0
## 59 SF Agrostol TRUE 1 28 13.7 7.8 19.7 13.7
## 60 SF Poatriv TRUE 2 28 13.7 6.3 21.1 27.5
## 61 SF Alopgeni FALSE 3 26 12.7 4.5 21.0 40.2
## 62 SF Lolipere FALSE 4 18 8.8 -2.1 19.8 49.0
## 63 SF Poaprat FALSE 5 15 7.4 0.5 14.2 56.4
## 64 SF Elymrepe FALSE 6 12 5.9 -1.0 12.8 62.3
## 65 SF Bracruta FALSE 7 12 5.9 0.8 10.9 68.1
## 66 SF Sagiproc FALSE 8 11 5.4 -0.8 11.5 73.5
## 67 SF Eleopalu FALSE 9 8 3.9 -6.3 14.1 77.5
## 68 SF Scorautu FALSE 10 8 3.9 1.4 6.4 81.4
## 69 SF Trifrepe FALSE 11 8 3.9 0.5 7.3 85.3
## 70 SF Juncbufo FALSE 12 7 3.4 -2.3 9.2 88.7
## 71 SF Bellpere FALSE 13 4 2.0 -0.9 4.8 90.7
## 72 SF Ranuflam FALSE 14 4 2.0 -1.3 5.2 92.6
## 73 SF Juncarti FALSE 15 3 1.5 -2.4 5.3 94.1
## 74 SF Callcusp FALSE 16 3 1.5 -2.4 5.3 95.6
## 75 SF Bromhord FALSE 17 3 1.5 -2.1 5.0 97.1
## 76 SF Rumeacet FALSE 18 2 1.0 -1.5 3.5 98.0
## 77 SF Cirsarve FALSE 19 2 1.0 -1.4 3.3 99.0
## 78 SF Achimill FALSE 20 1 0.5 -0.9 1.9 99.5
## 79 SF Chenalbu FALSE 21 1 0.5 -0.8 1.8 100.0
## logabun rankfreq
## 1 1.3 6.2
## 2 1.1 12.5
## 3 1.1 18.8
## 4 1.1 25.0
## 5 1.0 31.2
## 6 0.9 37.5
## 7 0.8 43.8
## 8 0.8 50.0
## 9 0.8 56.2
## 10 0.7 62.5
## 11 0.6 68.8
## 12 0.6 75.0
## 13 0.5 81.2
## 14 0.3 87.5
## 15 0.3 93.8
## 16 0.3 100.0
## 17 1.4 4.8
## 18 1.3 9.5
## 19 1.2 14.3
## 20 1.2 19.0
## 21 1.2 23.8
## 22 1.1 28.6
## 23 1.1 33.3
## 24 1.1 38.1
## 25 1.0 42.9
## 26 1.0 47.6
## 27 1.0 52.4
## 28 0.9 57.1
## 29 0.9 61.9
## 30 0.8 66.7
## 31 0.8 71.4
## 32 0.8 76.2
## 33 0.6 81.0
## 34 0.6 85.7
## 35 0.6 90.5
## 36 0.3 95.2
## 37 0.3 100.0
## 38 1.3 4.8
## 39 1.2 9.5
## 40 1.1 14.3
## 41 1.1 19.0
## 42 1.0 23.8
## 43 1.0 28.6
## 44 0.9 33.3
## 45 0.9 38.1
## 46 0.8 42.9
## 47 0.8 47.6
## 48 0.8 52.4
## 49 0.7 57.1
## 50 0.7 61.9
## 51 0.6 66.7
## 52 0.6 71.4
## 53 0.5 76.2
## 54 0.3 81.0
## 55 0.3 85.7
## 56 0.3 90.5
## 57 0.3 95.2
## 58 0.0 100.0
## 59 1.4 4.8
## 60 1.4 9.5
## 61 1.4 14.3
## 62 1.3 19.0
## 63 1.2 23.8
## 64 1.1 28.6
## 65 1.1 33.3
## 66 1.0 38.1
## 67 0.9 42.9
## 68 0.9 47.6
## 69 0.9 52.4
## 70 0.8 57.1
## 71 0.6 61.9
## 72 0.6 66.7
## 73 0.5 71.4
## 74 0.5 76.2
## 75 0.5 81.0
## 76 0.3 85.7
## 77 0.3 90.5
## 78 0.0 95.2
## 79 0.0 100.0plotgg1 <- ggplot(data=bio, aes(x = rank, y = abundance)) +
scale_x_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_line(aes(colour=Grouping), linewidth=1) +
geom_point(aes(colour=Grouping, shape=Grouping), size=5, alpha=0.7) +
geom_text_repel(data=subset(bio, labelit == TRUE),
aes(colour=Grouping, label=species),
angle=45, nudge_x=1, nudge_y=1, show.legend=FALSE) +
scale_color_brewer(palette = "Set1") +
labs(x = "rank", y = "abundance", colour = "Management", shape = "Management")plotgg1## Warning: ggrepel: Repulsion works correctly only for rotation angles multiple
## of 90 degrees
RA.data <- rankabuncomp(dune, y=dune.env, factor='Management',
return.data=TRUE, specnames=c(1:10), legend=FALSE)
plotgg2 <- ggplot(data=RA.data, aes(x = rank, y = abundance)) +
scale_x_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_line(aes(colour=Grouping), linewidth=1) +
geom_point(aes(colour=Grouping), linewidth=5, alpha=0.7) +
geom_text_repel(data=subset(RA.data, labelit == TRUE),
aes(label=species),
angle=45, nudge_x=1, nudge_y=1, show.legend=FALSE) +
scale_color_brewer(palette = "Set1") +
facet_wrap(~ Grouping) +
labs(x = "rank", y = "abundance", colour = "Management")## Warning in geom_point(aes(colour = Grouping), linewidth = 5, alpha = 0.7):
## Ignoring unknown parameters: `linewidth`plotgg2## Warning: ggrepel: Repulsion works correctly only for rotation angles multiple
## of 90 degrees## Warning: ggrepel: Repulsion works correctly only for rotation angles multiple of 90 degrees
## ggrepel: Repulsion works correctly only for rotation angles multiple of 90 degrees
## ggrepel: Repulsion works correctly only for rotation angles multiple of 90 degrees