For this example we’ll use a database of forestry inventories done in the amazon forest, and make a phytosociological analysis of the area.
library(forestmangr)
data(exfm20)
data_ex <- exfm20
data_ex
#> # A tibble: 12,295 x 18
#> cod transect tree common.name scientific.name family dbh canopy.pos
#> <fct> <fct> <int> <fct> <fct> <fct> <dbl> <fct>
#> 1 CAU_~ T01 2 macucu Licania guiane~ Chrys~ 10.3 S
#> 2 CAU_~ T01 5 casca seca Licania canesc~ Chrys~ 14.6 S
#> 3 CAU_~ T01 6 cajuacu Anacardium spr~ Anaca~ 78.8 E
#> 4 CAU_~ T01 7 breu branco Protium panicu~ Burse~ 14.7 S
#> 5 CAU_~ T01 9 breu branco Protium panicu~ Burse~ 10.6 E
#> 6 CAU_~ T01 10 caramuxi Pouteria hispi~ Sapot~ 27.1 C
#> # ... with 12,289 more rows, and 10 more variables: light <int>,
#> # dead <lgl>, Hcom <dbl>, Htot <dbl>, date <int>, utm.east <dbl>,
#> # utm.north <dbl>, vol <dbl>, plot.area <int>, total.area <int>First we’ll calculate the diversity indexes of the area, with the species_diversity function. It just needs the data and column name for species:
species_diversity(data_ex, "scientific.name")
#> # A tibble: 1 x 5
#> Shannon Simpson EqMaxima Pielou Jentsch
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 3.9 0.95 5.15 0.76 0.01We can evaluate similarity between plots by the Jaccard index, using the similarity_matrix function:
similarity_matrix(data_ex, "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 1.00We can also generate a dendrogram for this analysis:
similarity_matrix(exfm20, "scientific.name", "transect", index = "Jaccard", dendrogram = TRUE, n_groups = 3)
#> $Dendrogram
#>
#> $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 1.00To evaluate the level of aggregation among species in the area, we can use the species_aggreg function:
species_aggreg(data_ex, "scientific.name", "transect")
#> # A tibble: 172 x 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 aggreg~ 0 Rare
#> 2 Abarema jupunba 0.9 Regular 19 Not aggreg~ 0 Rare
#> 3 Abuta grandifolia 16 Aggregat~ 337. Aggregated 11.9 Aggregat~
#> 4 Aiouea sp. 9.5 Aggregat~ 200 Aggregated 10.4 Aggregat~
#> 5 Ambelania acida 10 Aggregat~ 210 Aggregated 22 Aggregat~
#> 6 Anacardium spruc~ 9.3 Aggregat~ 195. Aggregated 7.7 Aggregat~
#> # ... with 166 more rowsWe can also evaluate the horizontal structure of the forest. To do this, we can use the forest_structure function:
forest_structure(data_ex, "scientific.name", "dbh", "transect", 10000)
#> # A tibble: 172 x 9
#> especie AF RF AD DR ADo RDo IVC IVI
#> * <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <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 spruce~ 27.3 0.588 1.23 0.232 0.159 0.608 0.420 0.476
#> # ... with 166 more rowsIt’s also possible to calculate the vertical and internal structures:
forest_structure(data_ex, "scientific.name", "dbh", "transect", 10000, "canopy.pos", "light")
#> # A tibble: 172 x 19
#> especie AF RF AD DR ADo RDo IVC IVI VFC
#> * <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Abarem~ 18.2 0.392 0.182 0.0343 0.0359 0.137 0.0859 0.188 51.2
#> 2 Abarem~ 13.6 0.294 0.136 0.0258 0.0302 0.116 0.0707 0.145 25.6
#> 3 Abuta ~ 9.09 0.196 1.36 0.258 0.0162 0.0621 0.160 0.172 0
#> 4 Aiouea~ 9.09 0.196 0.909 0.172 0.0413 0.158 0.165 0.175 0
#> 5 Ambela~ 4.55 0.0979 0.454 0.0859 0.00379 0.0145 0.0502 0.0661 0
#> 6 Anacar~ 27.3 0.588 1.23 0.232 0.159 0.608 0.420 0.476 38.4
#> # ... with 166 more rows, and 9 more variables: VFE <dbl>, VFS <dbl>,
#> # PSA <dbl>, PSR <dbl>, QF1 <dbl>, QF2 <dbl>, QF3 <dbl>, QAF <dbl>,
#> # QRF <dbl>To check if the forest is regulated, we can use the BDq method, with the bdq_meyer function:
bdq_meyer(data_ex, "transect", "dbh", 1000,licourt_index = 2)
#> Class Class_Center NumIndv IndvHectare Meyer q MeyerBalan
#> 1 2 7.5 4730 2150.0 564 1.8 6198
#> 2 3 12.5 2700 1227.3 434 1.5 3099
#> 3 4 17.5 1840 836.4 335 2.0 1550
#> 4 5 22.5 930 422.7 258 1.4 775
#> 5 6 27.5 670 304.5 199 1.8 387
#> 6 7 32.5 369 167.7 153 1.3 194
#> 7 8 37.5 291 132.3 118 1.4 97
#> 8 9 42.5 208 94.5 91 1.2 48
#> 9 10 47.5 180 81.8 70 1.6 24
#> 10 11 52.5 116 52.7 54 1.9 12
#> 11 12 57.5 60 27.3 42 1.2 6
#> 12 13 62.5 49 22.3 32 1.2 3
#> 13 14 67.5 40 18.2 25 1.9 2
#> 14 15 72.5 21 9.5 19 0.7 1
#> 15 16 77.5 29 13.2 15 1.5 0
#> 16 17 82.5 20 9.1 11 1.5 0
#> 17 18 87.5 13 5.9 9 1.6 0
#> 18 19 92.5 8 3.6 7 2.6 0
#> 19 20 97.5 3 1.4 5 0.8 0
#> 20 21 102.5 4 1.8 4 1.3 0
#> 21 22 107.5 3 1.4 3 2.8 0
#> 22 23 112.5 1 0.5 2 0.4 0
#> 23 24 117.5 3 1.4 2 2.8 0
#> 24 25 122.5 1 0.5 1 1.0 0
#> 25 28 137.5 1 0.5 1 1.0 0
#> 26 29 142.5 1 0.5 0 0.6 0
#> 27 30 147.5 2 0.9 0 1.8 0
#> 28 32 157.5 1 0.5 0 1.0 0
#> 29 40 197.5 1 0.5 0 NA 0With the diameter_class function it’s possible to divide the data in diameter classes, and get the number of individuals per species in each class:
classified <- diameter_class(data_ex,"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 100.0000Another way of visualizing this table is to spread the center of class to columns. We can do this with the cc_to_column argument:
classified <- diameter_class(data_ex,"dbh", "transect", 10000, 10, 10,
"scientific.name", cc_to_column=TRUE)
head(classified)
#> scientific.name 15 25 35 45 55 65 75 85 95 105 115 125 135
#> 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
#> 155 165 195 Total
#> 1 0.2
#> 2 0.1
#> 3 1.4
#> 4 1
#> 5 0.5
#> 6 1.2