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Vamos utilizar dados de inventário da amazônia, e fazer uma análise fitossociológica da área.
library(forestmangr)
data(exfm20)
dados <- exfm20
dados
#> # A tibble: 12,295 × 18
#> cod transect tree common.name scientific.name family dbh canopy.pos light
#> <fct> <fct> <int> <fct> <fct> <fct> <dbl> <fct> <int>
#> 1 CAU_… T01 2 macucu Licania guiane… Chrys… 10.3 S 2
#> 2 CAU_… T01 5 casca seca Licania canesc… Chrys… 14.6 S 2
#> 3 CAU_… T01 6 cajuacu Anacardium spr… Anaca… 78.8 E 1
#> 4 CAU_… T01 7 breu branco Protium panicu… Burse… 14.7 S 2
#> 5 CAU_… T01 9 breu branco Protium panicu… Burse… 10.6 E 3
#> 6 CAU_… T01 10 caramuxi Pouteria hispi… Sapot… 27.1 C 2
#> # ℹ 12,289 more rows
#> # ℹ 9 more variables: dead <lgl>, Hcom <dbl>, Htot <dbl>, date <int>,
#> # utm.east <dbl>, utm.north <dbl>, vol <dbl>, plot.area <int>,
#> # total.area <int>Primeiro, vamos calcular os índices de diversidade da área, com a
função species_diversity. Basta fornecer o nome da coluna
referente às espécies:
species_diversity(dados, "scientific.name")
#> # A tibble: 1 × 5
#> Shannon Simpson EqMaxima Pielou Jentsch
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 3.9 0.95 5.15 0.76 0.01Podemos verificar a similaridade entre parcelas pelo índice de
Jaccard, utlizando a função similarity_matrix:
similarity_matrix(dados, "scientific.name", "transect", index = "Jaccard")
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 1.00 0.38 0.44 0.42 0.36 0.29 0.44 0.39 0.37 0.38 0.39 0.34 0.35
#> [2,] 0.38 1.00 0.30 0.36 0.30 0.20 0.49 0.34 0.34 0.30 0.37 0.18 0.35
#> [3,] 0.44 0.30 1.00 0.34 0.33 0.30 0.35 0.36 0.38 0.35 0.37 0.28 0.34
#> [4,] 0.42 0.36 0.34 1.00 0.29 0.28 0.40 0.36 0.40 0.37 0.35 0.26 0.31
#> [5,] 0.36 0.30 0.33 0.29 1.00 0.34 0.33 0.44 0.40 0.39 0.32 0.32 0.31
#> [6,] 0.29 0.20 0.30 0.28 0.34 1.00 0.26 0.44 0.42 0.41 0.33 0.29 0.28
#> [7,] 0.44 0.49 0.35 0.40 0.33 0.26 1.00 0.36 0.34 0.35 0.34 0.30 0.33
#> [8,] 0.39 0.34 0.36 0.36 0.44 0.44 0.36 1.00 0.48 0.34 0.37 0.29 0.32
#> [9,] 0.37 0.34 0.38 0.40 0.40 0.42 0.34 0.48 1.00 0.42 0.43 0.29 0.34
#> [10,] 0.38 0.30 0.35 0.37 0.39 0.41 0.35 0.34 0.42 1.00 0.36 0.26 0.31
#> [11,] 0.39 0.37 0.37 0.35 0.32 0.33 0.34 0.37 0.43 0.36 1.00 0.23 0.33
#> [12,] 0.34 0.18 0.28 0.26 0.32 0.29 0.30 0.29 0.29 0.26 0.23 1.00 0.35
#> [13,] 0.35 0.35 0.34 0.31 0.31 0.28 0.33 0.32 0.34 0.31 0.33 0.35 1.00
#> [14,] 0.32 0.23 0.33 0.31 0.32 0.41 0.24 0.41 0.33 0.32 0.30 0.39 0.35
#> [15,] 0.42 0.31 0.38 0.41 0.35 0.34 0.33 0.36 0.40 0.35 0.42 0.31 0.38
#> [16,] 0.22 0.28 0.31 0.29 0.35 0.34 0.33 0.40 0.30 0.31 0.30 0.26 0.31
#> [17,] 0.24 0.26 0.30 0.22 0.29 0.28 0.28 0.30 0.30 0.29 0.27 0.25 0.28
#> [18,] 0.28 0.25 0.32 0.29 0.29 0.34 0.27 0.36 0.36 0.33 0.25 0.31 0.29
#> [19,] 0.29 0.31 0.26 0.28 0.36 0.29 0.31 0.31 0.33 0.29 0.31 0.29 0.36
#> [20,] 0.26 0.24 0.29 0.29 0.30 0.31 0.23 0.37 0.35 0.26 0.39 0.24 0.38
#> [21,] 0.36 0.33 0.26 0.30 0.31 0.28 0.39 0.38 0.34 0.27 0.42 0.33 0.30
#> [22,] 0.28 0.31 0.27 0.32 0.31 0.32 0.33 0.40 0.38 0.35 0.28 0.29 0.27
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22]
#> [1,] 0.32 0.42 0.22 0.24 0.28 0.29 0.26 0.36 0.28
#> [2,] 0.23 0.31 0.28 0.26 0.25 0.31 0.24 0.33 0.31
#> [3,] 0.33 0.38 0.31 0.30 0.32 0.26 0.29 0.26 0.27
#> [4,] 0.31 0.41 0.29 0.22 0.29 0.28 0.29 0.30 0.32
#> [5,] 0.32 0.35 0.35 0.29 0.29 0.36 0.30 0.31 0.31
#> [6,] 0.41 0.34 0.34 0.28 0.34 0.29 0.31 0.28 0.32
#> [7,] 0.24 0.33 0.33 0.28 0.27 0.31 0.23 0.39 0.33
#> [8,] 0.41 0.36 0.40 0.30 0.36 0.31 0.37 0.38 0.40
#> [9,] 0.33 0.40 0.30 0.30 0.36 0.33 0.35 0.34 0.38
#> [10,] 0.32 0.35 0.31 0.29 0.33 0.29 0.26 0.27 0.35
#> [11,] 0.30 0.42 0.30 0.27 0.25 0.31 0.39 0.42 0.28
#> [12,] 0.39 0.31 0.26 0.25 0.31 0.29 0.24 0.33 0.29
#> [13,] 0.35 0.38 0.31 0.28 0.29 0.36 0.38 0.30 0.27
#> [14,] 1.00 0.40 0.39 0.27 0.37 0.37 0.41 0.40 0.35
#> [15,] 0.40 1.00 0.32 0.32 0.36 0.34 0.40 0.37 0.27
#> [16,] 0.39 0.32 1.00 0.38 0.27 0.28 0.40 0.30 0.36
#> [17,] 0.27 0.32 0.38 1.00 0.30 0.26 0.31 0.26 0.28
#> [18,] 0.37 0.36 0.27 0.30 1.00 0.32 0.33 0.30 0.36
#> [19,] 0.37 0.34 0.28 0.26 0.32 1.00 0.35 0.41 0.34
#> [20,] 0.41 0.40 0.40 0.31 0.33 0.35 1.00 0.33 0.33
#> [21,] 0.40 0.37 0.30 0.26 0.30 0.41 0.33 1.00 0.28
#> [22,] 0.35 0.27 0.36 0.28 0.36 0.34 0.33 0.28 1.00Podemos também gerar um dendrograma desta análise:
similarity_matrix(exfm20, "scientific.name", "transect", index = "Jaccard", dendrogram = TRUE, n_groups = 3)
#> $Dendrogram
#> $Dendrogram[[1]]
#>
#>
#> $Matrix
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 1.00 0.38 0.44 0.42 0.36 0.29 0.44 0.39 0.37 0.38 0.39 0.34 0.35
#> [2,] 0.38 1.00 0.30 0.36 0.30 0.20 0.49 0.34 0.34 0.30 0.37 0.18 0.35
#> [3,] 0.44 0.30 1.00 0.34 0.33 0.30 0.35 0.36 0.38 0.35 0.37 0.28 0.34
#> [4,] 0.42 0.36 0.34 1.00 0.29 0.28 0.40 0.36 0.40 0.37 0.35 0.26 0.31
#> [5,] 0.36 0.30 0.33 0.29 1.00 0.34 0.33 0.44 0.40 0.39 0.32 0.32 0.31
#> [6,] 0.29 0.20 0.30 0.28 0.34 1.00 0.26 0.44 0.42 0.41 0.33 0.29 0.28
#> [7,] 0.44 0.49 0.35 0.40 0.33 0.26 1.00 0.36 0.34 0.35 0.34 0.30 0.33
#> [8,] 0.39 0.34 0.36 0.36 0.44 0.44 0.36 1.00 0.48 0.34 0.37 0.29 0.32
#> [9,] 0.37 0.34 0.38 0.40 0.40 0.42 0.34 0.48 1.00 0.42 0.43 0.29 0.34
#> [10,] 0.38 0.30 0.35 0.37 0.39 0.41 0.35 0.34 0.42 1.00 0.36 0.26 0.31
#> [11,] 0.39 0.37 0.37 0.35 0.32 0.33 0.34 0.37 0.43 0.36 1.00 0.23 0.33
#> [12,] 0.34 0.18 0.28 0.26 0.32 0.29 0.30 0.29 0.29 0.26 0.23 1.00 0.35
#> [13,] 0.35 0.35 0.34 0.31 0.31 0.28 0.33 0.32 0.34 0.31 0.33 0.35 1.00
#> [14,] 0.32 0.23 0.33 0.31 0.32 0.41 0.24 0.41 0.33 0.32 0.30 0.39 0.35
#> [15,] 0.42 0.31 0.38 0.41 0.35 0.34 0.33 0.36 0.40 0.35 0.42 0.31 0.38
#> [16,] 0.22 0.28 0.31 0.29 0.35 0.34 0.33 0.40 0.30 0.31 0.30 0.26 0.31
#> [17,] 0.24 0.26 0.30 0.22 0.29 0.28 0.28 0.30 0.30 0.29 0.27 0.25 0.28
#> [18,] 0.28 0.25 0.32 0.29 0.29 0.34 0.27 0.36 0.36 0.33 0.25 0.31 0.29
#> [19,] 0.29 0.31 0.26 0.28 0.36 0.29 0.31 0.31 0.33 0.29 0.31 0.29 0.36
#> [20,] 0.26 0.24 0.29 0.29 0.30 0.31 0.23 0.37 0.35 0.26 0.39 0.24 0.38
#> [21,] 0.36 0.33 0.26 0.30 0.31 0.28 0.39 0.38 0.34 0.27 0.42 0.33 0.30
#> [22,] 0.28 0.31 0.27 0.32 0.31 0.32 0.33 0.40 0.38 0.35 0.28 0.29 0.27
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22]
#> [1,] 0.32 0.42 0.22 0.24 0.28 0.29 0.26 0.36 0.28
#> [2,] 0.23 0.31 0.28 0.26 0.25 0.31 0.24 0.33 0.31
#> [3,] 0.33 0.38 0.31 0.30 0.32 0.26 0.29 0.26 0.27
#> [4,] 0.31 0.41 0.29 0.22 0.29 0.28 0.29 0.30 0.32
#> [5,] 0.32 0.35 0.35 0.29 0.29 0.36 0.30 0.31 0.31
#> [6,] 0.41 0.34 0.34 0.28 0.34 0.29 0.31 0.28 0.32
#> [7,] 0.24 0.33 0.33 0.28 0.27 0.31 0.23 0.39 0.33
#> [8,] 0.41 0.36 0.40 0.30 0.36 0.31 0.37 0.38 0.40
#> [9,] 0.33 0.40 0.30 0.30 0.36 0.33 0.35 0.34 0.38
#> [10,] 0.32 0.35 0.31 0.29 0.33 0.29 0.26 0.27 0.35
#> [11,] 0.30 0.42 0.30 0.27 0.25 0.31 0.39 0.42 0.28
#> [12,] 0.39 0.31 0.26 0.25 0.31 0.29 0.24 0.33 0.29
#> [13,] 0.35 0.38 0.31 0.28 0.29 0.36 0.38 0.30 0.27
#> [14,] 1.00 0.40 0.39 0.27 0.37 0.37 0.41 0.40 0.35
#> [15,] 0.40 1.00 0.32 0.32 0.36 0.34 0.40 0.37 0.27
#> [16,] 0.39 0.32 1.00 0.38 0.27 0.28 0.40 0.30 0.36
#> [17,] 0.27 0.32 0.38 1.00 0.30 0.26 0.31 0.26 0.28
#> [18,] 0.37 0.36 0.27 0.30 1.00 0.32 0.33 0.30 0.36
#> [19,] 0.37 0.34 0.28 0.26 0.32 1.00 0.35 0.41 0.34
#> [20,] 0.41 0.40 0.40 0.31 0.33 0.35 1.00 0.33 0.33
#> [21,] 0.40 0.37 0.30 0.26 0.30 0.41 0.33 1.00 0.28
#> [22,] 0.35 0.27 0.36 0.28 0.36 0.34 0.33 0.28 1.00Para avaliar o nível de agregação das espécies área, podemos utilizar
a função species_aggreg:
species_aggreg(dados, "scientific.name", "transect")
#> # A tibble: 172 × 7
#> especie Payandeh Pay.res Hazen Haz.res Morisita Mor.res
#> <fct> <dbl> <chr> <dbl> <chr> <dbl> <chr>
#> 1 Abarema cochleata 0.9 Regular 18 Not aggregat… 0 Rare
#> 2 Abarema jupunba 0.9 Regular 19 Not aggregat… 0 Rare
#> 3 Abuta grandifolia 16 Aggregated 337. Aggregated 11.9 Aggreg…
#> 4 Aiouea sp. 9.5 Aggregated 200 Aggregated 10.4 Aggreg…
#> 5 Ambelania acida 10 Aggregated 210 Aggregated 22 Aggreg…
#> 6 Anacardium spruceanum 9.3 Aggregated 195. Aggregated 7.7 Aggreg…
#> # ℹ 166 more rowsPodemos também avaliar a estrutura horizontal da floresta. Para isso,
utilizamos a função forest_structure:
forest_structure(dados, "scientific.name", "dbh", "transect", 10000)
#> # A tibble: 172 × 9
#> especie AF RF AD DR ADo RDo IVC IVI
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl[1d]> <dbl[> <dbl> <dbl>
#> 1 Abarema cochleata 18.2 0.392 0.182 0.0343 0.0359 0.137 0.0859 0.188
#> 2 Abarema jupunba 13.6 0.294 0.136 0.0258 0.0302 0.116 0.0707 0.145
#> 3 Abuta grandifolia 9.09 0.196 1.36 0.258 0.0162 0.0621 0.160 0.172
#> 4 Aiouea sp. 9.09 0.196 0.909 0.172 0.0413 0.158 0.165 0.175
#> 5 Ambelania acida 4.55 0.0979 0.454 0.0859 0.00379 0.0145 0.0502 0.0661
#> 6 Anacardium spruceanum 27.3 0.588 1.23 0.232 0.159 0.608 0.420 0.476
#> # ℹ 166 more rowsTambém é possível calcular a estrutura vertical e interna:
forest_structure(dados, "scientific.name", "dbh", "transect", 10000, "canopy.pos", "light")
#> # A tibble: 172 × 19
#> especie AF RF AD DR ADo RDo IVC IVI VFC VFE
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl[1> <dbl[> <dbl> <dbl> <dbl> <dbl>
#> 1 Abarema co… 18.2 0.392 0.182 0.0343 0.0359 0.137 0.0859 0.188 51.2 0
#> 2 Abarema ju… 13.6 0.294 0.136 0.0258 0.0302 0.116 0.0707 0.145 25.6 0
#> 3 Abuta gran… 9.09 0.196 1.36 0.258 0.0162 0.0621 0.160 0.172 0 0
#> 4 Aiouea sp. 9.09 0.196 0.909 0.172 0.0413 0.158 0.165 0.175 0 0
#> 5 Ambelania … 4.55 0.0979 0.454 0.0859 0.00379 0.0145 0.0502 0.0661 0 0
#> 6 Anacardium… 27.3 0.588 1.23 0.232 0.159 0.608 0.420 0.476 38.4 13.4
#> # ℹ 166 more rows
#> # ℹ 8 more variables: VFS <dbl>, PSA <dbl>, PSR <dbl>, QF1 <dbl>, QF2 <dbl>,
#> # QF3 <dbl>, QAF <dbl>, QRF <dbl>É possível também verificar se a floresta está regulada, pelo método
BDq, utilizando a função bdq_meyer:
bdq_meyer(dados, "transect", "dbh", 1000,licourt_index = 2)
#> Class_Center NumIndv IndvHectare Meyer q MeyerBalan
#> 1 12.5 4730 2150.0 564 1.8 5101
#> 2 17.5 2700 1227.3 434 1.5 2550
#> 3 22.5 1840 836.4 335 2.0 1275
#> 4 27.5 930 422.7 258 1.4 638
#> 5 32.5 670 304.5 199 1.8 319
#> 6 37.5 369 167.7 153 1.3 159
#> 7 42.5 291 132.3 118 1.4 80
#> 8 47.5 208 94.5 91 1.2 40
#> 9 52.5 180 81.8 70 1.6 20
#> 10 57.5 116 52.7 54 1.9 10
#> 11 62.5 60 27.3 42 1.2 5
#> 12 67.5 49 22.3 32 1.2 2
#> 13 72.5 40 18.2 25 1.9 1
#> 14 77.5 21 9.5 19 0.7 1
#> 15 82.5 29 13.2 15 1.5 0
#> 16 87.5 20 9.1 11 1.5 0
#> 17 92.5 13 5.9 9 1.6 0
#> 18 97.5 8 3.6 7 2.6 0
#> 19 102.5 3 1.4 5 0.8 0
#> 20 107.5 4 1.8 4 1.3 0
#> 21 112.5 3 1.4 3 2.8 0
#> 22 117.5 1 0.5 2 0.4 0
#> 23 122.5 3 1.4 2 2.8 0
#> 24 127.5 1 0.5 1 1.0 0
#> 25 142.5 1 0.5 1 1.0 0
#> 26 147.5 1 0.5 0 0.6 0
#> 27 152.5 2 0.9 0 1.8 0
#> 28 162.5 1 0.5 0 1.0 0
#> 29 202.5 1 0.5 0 NA 0Com a função diameter_class é possível dividir os dados
em classes de diâmetro, e verificar o número de indivíduos por espécie
em cada classe:
classified <- diameter_class(dados,"dbh", "transect", 10000, 10, 10, "scientific.name")
head(classified)
#> scientific.name CC NumIndv IndvHA G G_ha RD
#> 1 Abuta grandifolia 15 30 1.4 0.35676712 0.016216687 100.0000
#> 2 Aiouea sp. 15 10 0.5 0.09331316 0.004241507 50.0000
#> 3 Ambelania acida 15 10 0.5 0.08332289 0.003787404 100.0000
#> 4 Anacardium spruceanum 15 10 0.5 0.27171635 0.012350743 37.0370
#> 5 Aniba canelilla 15 10 0.5 0.08332289 0.003787404 90.9091
#> 6 Aniba parviflora 15 10 0.5 0.10568318 0.004803781 100.0000Uma outra forma de visualizar esta tabela é com o centro de classe na
coluna. Podemos fazer isso com o argumento
cc_to_column:
classified <- diameter_class(dados,"dbh", "transect", 10000, 10, 10,
"scientific.name", cc_to_column=TRUE)
head(classified)
#> scientific.name 15 25 35 45 55 65 75 85 95 105 115 125 145 155
#> 1 Abarema cochleata 0.1 0.1
#> 2 Abarema jupunba 0.1
#> 3 Abuta grandifolia 1.4
#> 4 Aiouea sp. 0.5 0.5
#> 5 Ambelania acida 0.5
#> 6 Anacardium spruceanum 0.5 0.5 0.1 0.1
#> 165 205 Total
#> 1 0.2
#> 2 0.1
#> 3 1.4
#> 4 1
#> 5 0.5
#> 6 1.2These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.