Install the pre-release version of fgeo.abundance:
# install.packages("devtools")
devtools::install_github("forestgeo/fgeo.abundance@pre-release")Or install the development version of fgeo.abundance:
# install.packages("devtools")
devtools::install_github("forestgeo/fgeo.abundance")Or install all fgeo packages in one step.
For details on how to install packages from GitHub, see this article.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(fgeo.tool)
#>
#> Attaching package: 'fgeo.tool'
#> The following object is masked from 'package:stats':
#>
#> filter
library(fgeo.abundance)Your data may have multiple stems per treeid and even multiple measures per stemid (if trees have buttresses).
# Trees with buttresses may have multiple measurements of a single stem.
# Main stems have highest `HOM`, then largest `DBH`.
vft <- tribble(
~CensusID, ~TreeID, ~StemID, ~DBH, ~HOM,
1, "1", "1.1", 88, 130,
1, "1", "1.1", 10, 160, # Main stem
1, "2", "2.1", 20, 130,
1, "2", "2.2", 30, 130, # Main stem
)Fundamentally, abundance() counts rows. All of these results are the
same:
nrow(vft)
#> [1] 4
dplyr::count(vft)
#> # A tibble: 1 x 1
#> n
#> <int>
#> 1 4
dplyr::summarize(vft, n = n())
#> # A tibble: 1 x 1
#> n
#> <int>
#> 1 4
abundance(vft)
#> Warning: `treeid`: Duplicated values were detected. Do you need to pick
#> main stems?
#> # A tibble: 1 x 1
#> n
#> <int>
#> 1 4But that result is likely not what you expect. Instead, you likely expect this:
summarize(vft, n = n_distinct(TreeID))
#> # A tibble: 1 x 1
#> n
#> <int>
#> 1 2As shown above, you can get a correct result by combining summarize()
and n_distinct() (from the dplyr package). But abundance()
includes some useful additional features (see ?abundance()). This code
more clearly conveys your intention, i.e. to calculate tree abundance by
counting the number of main stems:
(main_stems <- pick_main_stem(vft))
#> # A tibble: 2 x 5
#> CensusID TreeID StemID DBH HOM
#> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 1 1 1.1 10 160
#> 2 1 2 2.2 30 130
abundance(main_stems)
#> # A tibble: 1 x 1
#> n
#> <int>
#> 1 2If you have data from multiple censuses, you can compute by census (or any other group).
vft2 <- tibble::tribble(
~CensusID, ~TreeID, ~StemID, ~DBH, ~HOM,
1, "1", "1.1", 10, 130,
1, "1", "1.2", 20, 130, # Main stem
2, "1", "1.1", 12, 130,
2, "1", "1.2", 22, 130 # Main stem
)
by_census <- group_by(vft2, CensusID)
(main_stems_by_census <- pick_main_stem(by_census))
#> # A tibble: 2 x 5
#> # Groups: CensusID [2]
#> CensusID TreeID StemID DBH HOM
#> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 1 1 1.2 20 130
#> 2 2 1 1.2 22 130
abundance(main_stems_by_census)
#> # A tibble: 2 x 2
#> # Groups: CensusID [2]
#> CensusID n
#> <dbl> <int>
#> 1 1 1
#> 2 2 1Often you will need to first subset data (e.g. by status or DBH) and
then count.
over20 <- filter(main_stems_by_census, DBH > 20)
abundance(over20)
#> # A tibble: 1 x 2
#> # Groups: CensusID [1]
#> CensusID n
#> <dbl> <int>
#> 1 2 1If trees have buttresses, you may need to pick the main stemid of each stem so you don’t count the same stem more than once.
vft3 <- tribble(
~CensusID, ~TreeID, ~StemID, ~DBH, ~HOM,
1, "1", "1.1", 88, 130,
1, "1", "1.1", 10, 160, # Main stem
1, "2", "2.1", 20, 130,
1, "2", "2.2", 30, 130, # Main stem
2, "1", "1.1", 98, 130,
2, "1", "1.1", 20, 160, # Main stem
2, "2", "2.1", 30, 130,
2, "2", "2.2", 40, 130, # Main stem
)
(main_stemids <- pick_main_stemid(vft3))
#> # A tibble: 6 x 5
#> CensusID TreeID StemID DBH HOM
#> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 1 1 1.1 10 160
#> 2 1 2 2.1 20 130
#> 3 1 2 2.2 30 130
#> 4 2 1 1.1 20 160
#> 5 2 2 2.1 30 130
#> 6 2 2 2.2 40 130
main_stemids
#> # A tibble: 6 x 5
#> CensusID TreeID StemID DBH HOM
#> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 1 1 1.1 10 160
#> 2 1 2 2.1 20 130
#> 3 1 2 2.2 30 130
#> 4 2 1 1.1 20 160
#> 5 2 2 2.1 30 130
#> 6 2 2 2.2 40 130
basal_area(main_stemids)
#> Warning: `stemid`: Duplicated values were detected. Do you need to pick
#> largest `hom` values?
#> Warning: `censusid`: Multiple values were detected. Do you need to group by
#> censusid?
#> # A tibble: 1 x 1
#> basal_area
#> <dbl>
#> 1 3377.basal_area() also allows you to compute by groups.
by_census <- group_by(main_stemids, CensusID)
basal_area(by_census)
#> # A tibble: 2 x 2
#> # Groups: CensusID [2]
#> CensusID basal_area
#> <dbl> <dbl>
#> 1 1 1100.
#> 2 2 2278.But if you want to compute on a subset of data, you need to first pick the data.
ten_to_twenty <- filter(by_census, DBH >= 10, DBH <= 20)
ba <- basal_area(ten_to_twenty)
ba
#> # A tibble: 2 x 2
#> # Groups: CensusID [2]
#> CensusID basal_area
#> <dbl> <dbl>
#> 1 1 393.
#> 2 2 314.And if you need to convert units, you can do so after the fact with
fgeo.tool::convert_unit_at().
convert_unit_at(ba, .at = "basal_area", from = "mm2", to = "hectare")
#> # A tibble: 2 x 2
#> # Groups: CensusID [2]
#> CensusID basal_area
#> <dbl> <dbl>
#> 1 1 0.0000000393
#> 2 2 0.0000000314Example data.
vft <- example_byyr
vft
#> # A tibble: 8 x 13
#> PlotName CensusID TreeID StemID Status DBH Genus SpeciesName ExactDate
#> <chr> <int> <int> <dbl> <chr> <int> <chr> <chr> <date>
#> 1 luq 1 1 1.1 alive 10 Gn spp 2001-01-01
#> 2 luq 1 1 1.2 dead NA Gn spp 2001-01-01
#> 3 luq 1 2 2.1 alive 20 Gn spp 2001-01-01
#> 4 luq 1 2 2.2 alive 30 Gn spp 2001-01-01
#> 5 luq 2 1 1.1 alive 20 Gn spp 2002-01-01
#> 6 luq 2 1 1.2 gone NA Gn spp 2002-01-01
#> 7 luq 2 2 2.1 dead NA Gn spp 2002-01-01
#> 8 luq 2 2 2.2 dead NA Gn spp 2002-01-01
#> # ... with 4 more variables: PlotCensusNumber <int>, Family <chr>,
#> # Tag <int>, HOM <int>Abundance by year.
abundance_byyr(vft, DBH >= 10, DBH < 20)
#> # A tibble: 1 x 3
#> species family yr_2001
#> <chr> <chr> <dbl>
#> 1 Gn spp f 1
abundance_byyr(vft, DBH >= 10)
#> # A tibble: 1 x 4
#> species family yr_2001 yr_2002
#> <chr> <chr> <dbl> <dbl>
#> 1 Gn spp f 2 1Basal area by year.
basal <- basal_area_byyr(vft, DBH >= 10)
basal
#> # A tibble: 1 x 4
#> species family yr_2001 yr_2002
#> <chr> <chr> <dbl> <dbl>
#> 1 Gn spp f 1100. 314.
# Convert units and standardize by plot size in hectares
years <- c("yr_2001", "yr_2002")
in_he <- convert_unit_at(basal, .at = years, from = "mm2", to = "hectare")
standardize_at(in_he, .at = years, denominator = 50)
#> # A tibble: 1 x 4
#> species family yr_2001 yr_2002
#> <chr> <chr> <dbl> <dbl>
#> 1 Gn spp f 0.00000000220 6.28e-10Thanks to all partners of ForestGEO, for sharing their ideas and code.