Setup.
library(tidyverse)
library(fgeo)To create tables of abundance and basal area first pick the data you want. Your code should look something like this:
pick1 <- pick_plotname(VIEWFULLTABLE, "PLOTNAME")
pick2 <- pick_dbh_min(pick1, MINIMUM_DBH)Then you can create the tables with:
abundance <- abundance_byyr(pick2)
basal_area <- basal_area_byyr(pick2)To standardize the table of basal area use something like this:
years <- setdiff(names(basal_area), c("species", "Family"))
in_sq_m <- convert_unit_at(basal_area, .at = years, from = "mm2", to = "m2")
basal_area_in_he <- standardize_at(in_sq_m, .at = years, denominator = denominator_HECTARES)Next, I do this for three plots: Sherman, Cocoli and BCI. To avoid duplication I first write some disposable utility-funcitons.
# Pick specific data from a ViewFullTable.
pick_vft <- function(vft, plot_nm) {
pick <- pick_plotname(vft, plot_nm)
pick_dbh_min(pick, 10)
}
# Standardize basal area by total plot-hectares.
standardize_ba <- function(ba, denominator) {
years <- setdiff(names(ba), c("species", "Family"))
in_he <- convert_unit_at(ba, .at = years, from = "mm2", to = "m2")
standardize_at(in_he, years, denominator)
}
path_iss59 <- function(path) {
here::here("inst/issues/59_abund_tables", path)
}
write_tables <- function(vft, plot_nm, denominator) {
pick <- pick_vft(vft, plot_nm)
abun <- abundance_byyr(pick)
readr::write_csv(abun, path_iss59(paste0("tbl/", plot_nm, "_abundance.csv")))
ba <- standardize_ba(basal_area_byyr(pick), denominator)
readr::write_csv(ba, path_iss59(paste0("tbl/", plot_nm, "_basal_area.csv")))
}Now I’m ready to read the data and crete the tables – which I save locally:
# BCI
path_bci <- here::here("inst/issues/59_abund_tables/vft_bci.csv")
vft_bci <- readr::read_csv(path_bci)
# Sherman and Cocoli
path_sc <- here::here("inst/issues/59_abund_tables/vft_sc.csv")
vft_sc <- readr::read_csv(path_sc)write_tables(vft_sc, "sherman", denominator = 5.96)
write_tables(vft_sc, "cocoli", denominator = 4)
write_tables(vft_bci, "bci", denominator = 50)Les’t visualize the output.
folder <- here::here("inst/issues/59_abund_tables/tbl")
dfs <- csv_to_dfs(folder)
dfs
#> $bci_abundance.csv
#> # A tibble: 325 x 10
#> species Family `1982` `1985` `1990` `1995` `2000` `2005` `2010` `2015`
#> <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 Abarema~ Fabac~ 10 10 11 12 12 16 32 44
#> 2 Acacia ~ Fabac~ 5 7 11 12 10 21 48 44
#> 3 Acalyph~ Eupho~ 1562 1273 1072 685 576 850 1142 1605
#> 4 Acalyph~ Eupho~ 79 68 48 42 43 52 53 72
#> 5 Adelia ~ Eupho~ 346 321 290 227 167 145 146 133
#> 6 Aegiphi~ Lamia~ 136 129 99 80 62 46 40 26
#> 7 Alchorn~ Eupho~ 385 315 271 230 231 230 320 274
#> 8 Alchorn~ Eupho~ 2 2 3 2 2 2 1 0
#> 9 Alibert~ Rubia~ 304 345 393 399 378 394 450 479
#> 10 Allophy~ Sapin~ 175 176 164 131 114 105 116 104
#> # ... with 315 more rows
#>
#> $bci_basal_area.csv
#> # A tibble: 325 x 10
#> species Family `1982` `1985` `1990` `1995` `2000` `2005` `2010`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Abarema~ Fabac~ 2.51e-3 4.08e-3 6.26e-3 8.44e-3 1.08e-2 1.29e-2 1.53e-2
#> 2 Acacia ~ Fabac~ 9.22e-4 6.41e-4 8.20e-4 1.22e-3 6.10e-4 8.67e-4 1.88e-3
#> 3 Acalyph~ Eupho~ 2.06e-2 2.15e-2 1.83e-2 1.18e-2 1.04e-2 1.23e-2 1.78e-2
#> 4 Acalyph~ Eupho~ 3.93e-3 2.89e-3 2.67e-3 1.75e-3 1.91e-3 2.38e-3 2.50e-3
#> 5 Adelia ~ Eupho~ 6.92e-2 6.94e-2 7.01e-2 6.16e-2 5.88e-2 5.03e-2 5.04e-2
#> 6 Aegiphi~ Lamia~ 1.08e-2 1.04e-2 1.04e-2 1.01e-2 9.41e-3 7.58e-3 6.11e-3
#> 7 Alchorn~ Eupho~ 2.63e-1 2.75e-1 3.48e-1 2.73e-1 2.32e-1 2.47e-1 2.36e-1
#> 8 Alchorn~ Eupho~ 8.81e-4 9.39e-4 9.95e-4 4.71e-4 6.73e-4 8.02e-4 9.66e-4
#> 9 Alibert~ Rubia~ 4.37e-3 4.75e-3 6.36e-3 6.23e-3 6.09e-3 6.22e-3 6.44e-3
#> 10 Allophy~ Sapin~ 1.73e-2 1.82e-2 1.71e-2 1.70e-2 1.55e-2 1.78e-2 1.91e-2
#> # ... with 315 more rows, and 1 more variable: `2015` <dbl>
#>
#> $cocoli_abundance.csv
#> # A tibble: 175 x 5
#> species Family `1994` `1997` `1998`
#> <chr> <chr> <int> <int> <int>
#> 1 Acacia melanoceras Fabaceae-mimosoideae 23 31 31
#> 2 Acalypha diversifolia Euphorbiaceae 14 18 23
#> 3 Acalypha macrostachya Euphorbiaceae 1 2 2
#> 4 Adelia triloba Euphorbiaceae 5 6 6
#> 5 Aegiphila panamensis Lamiaceae 2 1 1
#> 6 Albizia adinocephala Fabaceae-mimosoideae 55 57 61
#> 7 Albizia procera Fabaceae-mimosoideae 2 2 2
#> 8 Alchornea costaricensis Euphorbiaceae 2 1 1
#> 9 Alibertia edulis Rubiaceae 253 236 230
#> 10 Alseis blackiana Rubiaceae 4 4 4
#> # ... with 165 more rows
#>
#> $cocoli_basal_area.csv
#> # A tibble: 175 x 5
#> species Family `1994` `1997` `1998`
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Acacia melanoceras Fabaceae-mimosoideae 0.00200 1.25e-3 1.29e-3
#> 2 Acalypha diversifolia Euphorbiaceae 0.00352 4.36e-3 4.57e-3
#> 3 Acalypha macrostachya Euphorbiaceae 0.0000283 7.64e-5 9.60e-5
#> 4 Adelia triloba Euphorbiaceae 0.00764 8.36e-3 8.62e-3
#> 5 Aegiphila panamensis Lamiaceae 0.000671 3.85e-5 3.85e-5
#> 6 Albizia adinocephala Fabaceae-mimosoideae 0.0857 6.84e-2 6.43e-2
#> 7 Albizia procera Fabaceae-mimosoideae 0.000228 4.48e-4 5.22e-4
#> 8 Alchornea costaricensis Euphorbiaceae 0.00241 2.83e-3 2.87e-3
#> 9 Alibertia edulis Rubiaceae 0.0499 4.80e-2 4.73e-2
#> 10 Alseis blackiana Rubiaceae 0.0234 2.49e-2 2.56e-2
#> # ... with 165 more rows
#>
#> $sherman_abundance.csv
#> # A tibble: 274 x 7
#> species Family `1996` `1997` `1999` `2009` `2016`
#> <chr> <chr> <int> <int> <int> <int> <int>
#> 1 Abarema barbouriana Fabaceae-mi~ 10 12 12 10 10
#> 2 Acacia melanoceras Fabaceae-mi~ 1 1 1 1 0
#> 3 Acalypha diversifolia Euphorbiace~ 9 7 7 20 15
#> 4 Aegiphila panamensis Lamiaceae 1 0 0 0 0
#> 5 Alchornea costaricensis Euphorbiace~ 1 1 1 1 1
#> 6 Alchornea latifolia Euphorbiace~ 92 91 94 98 84
#> 7 Alibertia edulis Rubiaceae 2 2 2 2 1
#> 8 Amaioua corymbosa Rubiaceae 89 88 89 85 90
#> 9 Andira inermis Fabaceae-pa~ 41 39 39 38 33
#> 10 Annona spraguei Annonaceae 15 9 7 12 13
#> # ... with 264 more rows
#>
#> $sherman_basal_area.csv
#> # A tibble: 274 x 7
#> species Family `1996` `1997` `1999` `2009` `2016`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Abarema barbouriana Fabace~ 2.06e-2 2.25e-2 2.47e-2 0.0515 0.0748
#> 2 Acacia melanoceras Fabace~ 4.27e-5 5.27e-5 0. 0 0
#> 3 Acalypha diversifolia Euphor~ 1.10e-3 4.51e-4 4.34e-4 0.00177 0.00208
#> 4 Aegiphila panamensis Lamiac~ 1.90e-4 0. 0. 0 0
#> 5 Alchornea costaricensis Euphor~ 4.91e-3 5.01e-3 4.96e-3 0.00543 0.00543
#> 6 Alchornea latifolia Euphor~ 6.35e-2 6.03e-2 6.12e-2 0.0600 0.0537
#> 7 Alibertia edulis Rubiac~ 1.39e-4 5.71e-5 5.71e-5 0 0
#> 8 Amaioua corymbosa Rubiac~ 3.27e-2 3.30e-2 3.33e-2 0.0315 0.0344
#> 9 Andira inermis Fabace~ 6.05e-2 6.26e-2 6.39e-2 0.0530 0.0327
#> 10 Annona spraguei Annona~ 1.78e-2 1.61e-2 4.59e-3 0.00171 0.00195
#> # ... with 264 more rowsFor comparison see:
Compare with:
Saplings and trees: https://forestgeo.si.edu/bci-abundance-all-tree-species-50-ha-plot-1982-2005-saplings-and-trees
Trees: https://forestgeo.si.edu/bci-abundance-all-tree-species-50-ha-plot-1982-2005-trees
Also:
folder <- here::here("inst/issues/59_abund_tables/condit_et_al")
paths <- fs::path(folder, dir(folder))
dfs <- map(paths, read_delim, delim = "\t")
set_names(dfs, basename(paths))
#> $bciAbundTable.tsv
#> # A tibble: 328 x 11
#> Latin Family Authority `1982` `1985` `1990` `1995` `2000` `2005` `2010`
#> <chr> <chr> <chr> <int> <int> <int> <int> <int> <int> <int>
#> 1 Vach~ Fabac~ (Beurl.)~ 5 6 11 12 10 21 47
#> 2 Acal~ Eupho~ Jacq. 1562 1200 817 523 488 741 1019
#> 3 Acal~ Eupho~ Jacq. 79 66 44 42 43 52 52
#> 4 Adel~ Eupho~ (Müll.Ar~ 346 315 280 219 163 143 143
#> 5 Aegi~ Lamia~ Moldenke 136 126 92 77 62 44 40
#> 6 Alch~ Eupho~ Pax & K.~ 385 314 266 228 229 229 319
#> 7 Alch~ Eupho~ Sw. 2 2 3 2 1 1 1
#> 8 Alib~ Rubia~ (Rich.) ~ 304 342 378 379 357 372 417
#> 9 Allo~ Sapin~ Radlk. 175 171 153 123 112 103 112
#> 10 Alse~ Rubia~ Hemsl. 7598 8050 8412 8175 7869 7752 7928
#> # ... with 318 more rows, and 1 more variable: `2015` <int>
#>
#> $cocoliAbundTable.tsv
#> # A tibble: 176 x 6
#> Latin Family Authority `1994` `1997` `1998`
#> <chr> <chr> <chr> <int> <int> <int>
#> 1 Acacia melanoceras Fabaceae Beurl. 23 19 19
#> 2 Acalypha diversifolia Euphorbiaceae Jacq. 14 18 22
#> 3 Acalypha macrostachya Euphorbiaceae Jacq. 1 2 2
#> 4 Adelia triloba Euphorbiaceae (Müll.Arg.)~ 5 6 6
#> 5 Aegiphila panamensis Lamiaceae Moldenke 2 1 1
#> 6 Albizia adinocephala Fabaceae (Donn. Sm.)~ 55 53 57
#> 7 Albizia procera Fabaceae (Roxb.) Ben~ 2 2 2
#> 8 Alchornea costaricensis Euphorbiaceae Pax & K. Ho~ 2 1 1
#> 9 Alibertia edulis Rubiaceae (Rich.) A. ~ 253 228 224
#> 10 Alseis blackiana Rubiaceae Hemsl. 4 4 4
#> # ... with 166 more rows
#>
#> $shermanAbundTable.tsv
#> # A tibble: 270 x 7
#> Latin Family Authority `1996` `1997` `1999` `2009`
#> <chr> <chr> <chr> <int> <int> <int> <int>
#> 1 Acacia sp.1 Fabaceae <NA> 0 0 0 4
#> 2 Acacia melanoceras Fabaceae Beurl. 1 1 0 0
#> 3 Acalypha diversifolia Euphorbi~ Jacq. 9 7 5 20
#> 4 Aegiphila panamensis Lamiaceae Moldenke 1 0 0 0
#> 5 Alchornea sp.3 Euphorbi~ <NA> 33 24 22 12
#> 6 Alchornea latifolia Euphorbi~ Sw. 93 90 94 96
#> 7 Alibertia edulis Rubiaceae (Rich.) A.~ 2 2 2 0
#> 8 Amaioua corymbosa Rubiaceae Kunth 89 88 89 83
#> 9 Andira inermis Fabaceae (W. Wright~ 41 38 38 37
#> 10 Annona spraguei Annonace~ Saff. 15 9 7 12
#> # ... with 260 more rows