Analyze ForestGEO data

lifecycle Travis build status Coverage status

fgeo.analyze provides functions to analyze ForestGEO data.

Installation

Install the latest stable version of fgeo.analyze from CRAN with:

install.packages("fgeo.analyze", repos = these_repos)

Install the development version of fgeo.analyze with:

# install.packages("devtools")
devtools::install_github("forestgeo/fgeo.analyze")

Or install all fgeo packages in one step.

Example

library(fgeo.x)
library(fgeo.tool)
#> 
#> Attaching package: 'fgeo.tool'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library(fgeo.analyze)

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
count(vft)
#> # A tibble: 1 x 1
#>       n
#>   <int>
#> 1     4
summarize(vft, n = n())
#> Warning: Calling `n()` without importing or prefixing it is deprecated, use `dplyr::n()`.
#> This warning is displayed once per session.
#> # 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     4

But that result is likely not what you expect. Instead, you likely expect this:

summarize(vft, n = n_distinct(TreeID))
#> Warning: Calling `n_distinct()` without importing or prefixing it is deprecated, use `dplyr::n_distinct()`.
#> This warning is displayed once per session.
#> # A tibble: 1 x 1
#>       n
#>   <int>
#> 1     2

As 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 conveys your intention more clearly, 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     2

If you have data from multiple censuses, then you can compute by census (or any other group).

vft2 <- 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     1

Often 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     1

Basal area

If trees have buttresses, then you may need to pick the main stemid of each stem so you do not 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, then you need to pick the data first.

ten_to_twenty <- filter(by_census, DBH >= 10, DBH <= 20)
basal_area(ten_to_twenty)
#> # A tibble: 2 x 2
#> # Groups:   CensusID [2]
#>   CensusID basal_area
#>      <dbl>      <dbl>
#> 1        1       393.
#> 2        2       314.

Abundance and basal area aggregated by year

Example data.

vft <- tibble(
  PlotName = c("luq", "luq", "luq", "luq", "luq", "luq", "luq", "luq"),
  CensusID = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
  TreeID = c(1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L),
  StemID = c(1.1, 1.2, 2.1, 2.2, 1.1, 1.2, 2.1, 2.2),
  Status = c("alive", "dead", "alive", "alive", "alive", "gone",
    "dead", "dead"),
  DBH = c(10L, NA, 20L, 30L, 20L, NA, NA, NA),
  Genus = c("Gn", "Gn", "Gn", "Gn", "Gn", "Gn", "Gn", "Gn"),
  SpeciesName = c("spp", "spp", "spp", "spp", "spp", "spp", "spp", "spp"),
  ExactDate = c("2001-01-01", "2001-01-01", "2001-01-01", "2001-01-01",
    "2002-01-01", "2002-01-01", "2002-01-01",
    "2002-01-01"),
  PlotCensusNumber = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
  Family = c("f", "f", "f", "f", "f", "f", "f", "f"),
  Tag = c(1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L),
  HOM = c(130L, 130L, 130L, 130L, 130L, 130L, 130L, 130L)
)

vft
#> # A tibble: 8 x 13
#>   PlotName CensusID TreeID StemID Status   DBH Genus SpeciesName ExactDate
#>   <chr>       <int>  <int>  <dbl> <chr>  <int> <chr> <chr>       <chr>    
#> 1 luq             1      1    1.1 alive     10 Gn    spp         2001-01-…
#> 2 luq             1      1    1.2 dead      NA Gn    spp         2001-01-…
#> 3 luq             1      2    2.1 alive     20 Gn    spp         2001-01-…
#> 4 luq             1      2    2.2 alive     30 Gn    spp         2001-01-…
#> 5 luq             2      1    1.1 alive     20 Gn    spp         2002-01-…
#> 6 luq             2      1    1.2 gone      NA Gn    spp         2002-01-…
#> 7 luq             2      2    2.1 dead      NA Gn    spp         2002-01-…
#> 8 luq             2      2    2.2 dead      NA Gn    spp         2002-01-…
#> # … 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       1

Basal area by year.

basal_area_byyr(vft, DBH >= 10)
#> # A tibble: 1 x 4
#>   species family yr_2001 yr_2002
#>   <chr>   <chr>    <dbl>   <dbl>
#> 1 Gn spp  f        1100.    314.

Demography

census1 <- fgeo.x::tree5
census2 <- fgeo.x::tree6

Demography functions output a list that you can convert to a more convenient dataframe with as_tibble().

recruitment_ctfs(census1, census2)
#> Detected dbh ranges:
#>   * `census1` = 10.9-323.
#>   * `census2` = 10.5-347.
#> Using dbh `mindbh = 0` and above.
#> $N2
#> [1] 29
#> 
#> $R
#> [1] 3
#> 
#> $rate
#> [1] 0.02413113
#> 
#> $lower
#> [1] 0.0084585
#> 
#> $upper
#> [1] 0.06812388
#> 
#> $time
#> [1] 4.525246
#> 
#> $date1
#> [1] 18937.96
#> 
#> $date2
#> [1] 20600.72

as_tibble(
  recruitment_ctfs(census1, census2, quiet = TRUE)
)
#> # A tibble: 1 x 8
#>      N2     R   rate   lower  upper  time  date1  date2
#>   <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl>  <dbl>  <dbl>
#> 1    29     3 0.0241 0.00846 0.0681  4.53 18938. 20601.

Except if you use split2: This argument creates a complex data structure that as_tibble() cannot handle.

# Errs
as_tibble(
  recruitment_ctfs(
    census1, census2, 
    split1 = census1$sp, 
    split2 = census1$quadrat,  # `as_tibble()` can't handle this
    quiet = TRUE
  )
)
#> Warning: `split2` is deprecated.
#> * Bad: `split1 = x1, split2 = x2`
#> * Good: `split1 = interaction(x1, x2)`
#> This warning is displayed once per session.
#>   Can't deal with data created with `split2` (deprecated).
#>   * Bad: `split1 = x1, split2 = x2`
#>   * Good: `split1 = interaction(x1, x2)`

Instead, pass the multiple grouping variables to split via interaction(). This approach allows you to use any number of grouping variables and the output always works with as_tibble().

# Recommended
by_sp_and_quadrat <- interaction(census1$sp, census1$quadrat)

as_tibble(
  recruitment_ctfs(
    census1, census2, 
    split1 = by_sp_and_quadrat, 
    quiet = TRUE
  )
)
#> # A tibble: 540 x 9
#>    groups         N2     R  rate lower upper  time date1 date2
#>    <chr>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 MATDOM.1007     1     0     0     0 0.410  4.50 18891 20535
#>  2 CASSYL.1010     1     0     0     0 0.411  4.49 18914 20555
#>  3 SLOBER.110      1     0     0     0 0.409  4.51 18897 20543
#>  4 SLOBER.1106     1     0     0     0 0.404  4.56 18849 20516
#>  5 CECSCH.1114     1     0     0     0 0.413  4.47 18948 20580
#>  6 PSYBRA.1318     1     0     0     0 0.412  4.48 19011 20646
#>  7 HIRRUG.1403     1     0     0     0 0.403  4.58 18834 20506
#>  8 CASSYL.1411     1     0     0     0 0.414  4.45 18931 20558
#>  9 SLOBER.1414     1     0     0     0 0.403  4.57 18952 20622
#> 10 GUAGUI.1419     1     0     0     0 0.406  4.54 19012 20670
#> # … with 530 more rows

The same applies for other demography functions.

as_tibble(
  mortality_ctfs(
    census1, census2, 
    split1 = by_sp_and_quadrat, 
    quiet = TRUE
  )
)
#> # A tibble: 540 x 10
#>    groups          N     D  rate lower upper  time date1 date2 dbhmean
#>    <chr>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>
#>  1 MATDOM.1007     1     0     0     0 0.410  4.50 18891 20535   240  
#>  2 CASSYL.1010     1     0     0     0 0.411  4.49 18914 20555    67  
#>  3 SLOBER.110      1     0     0     0 0.409  4.51 18897 20543   150  
#>  4 SLOBER.1106     1     0     0     0 0.404  4.56 18849 20516    50  
#>  5 CECSCH.1114     1     0     0     0 0.413  4.47 18948 20580   228  
#>  6 PSYBRA.1318     1     0     0     0 0.412  4.48 19011 20646    14  
#>  7 HIRRUG.1403     1     0     0     0 0.403  4.58 18834 20506    12.9
#>  8 CASSYL.1411     1     0     0     0 0.414  4.45 18931 20558    13.1
#>  9 SLOBER.1414     1     0     0     0 0.403  4.57 18952 20622    16.6
#> 10 GUAGUI.1419     1     0     0     0 0.406  4.54 19012 20670   108  
#> # … with 530 more rows

A simple way to separate the grouping variables is with tidyr::separate().

growth <- growth_ctfs(
  census1, census2, 
  split1 = by_sp_and_quadrat, 
  quiet = TRUE
)
as_tibble(growth)
#> # A tibble: 540 x 8
#>    groups        rate     N  clim dbhmean  time date1 date2
#>    <chr>        <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl> <dbl>
#>  1 MATDOM.1007  0         1    NA   240    4.50 18891 20535
#>  2 CASSYL.1010  0.445     1    NA    67    4.49 18914 20555
#>  3 SLOBER.110   0.666     1    NA   150    4.51 18897 20543
#>  4 SLOBER.1106  0         1    NA    50    4.56 18849 20516
#>  5 CECSCH.1114  1.79      1    NA   228    4.47 18948 20580
#>  6 PSYBRA.1318  0.447     1    NA    14    4.48 19011 20646
#>  7 HIRRUG.1403  1.66      1    NA    12.9  4.58 18834 20506
#>  8 CASSYL.1411 NA         0    NA    NA   NA       NA    NA
#>  9 SLOBER.1414  1.40      1    NA    16.6  4.57 18952 20622
#> 10 GUAGUI.1419 NA         0    NA    NA   NA       NA    NA
#> # … with 530 more rows

as_tibble(growth) %>% 
  tidyr::separate(groups, into = c("species", "quadrats"))
#> # A tibble: 540 x 9
#>    species quadrats   rate     N  clim dbhmean  time date1 date2
#>    <chr>   <chr>     <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl> <dbl>
#>  1 MATDOM  1007      0         1    NA   240    4.50 18891 20535
#>  2 CASSYL  1010      0.445     1    NA    67    4.49 18914 20555
#>  3 SLOBER  110       0.666     1    NA   150    4.51 18897 20543
#>  4 SLOBER  1106      0         1    NA    50    4.56 18849 20516
#>  5 CECSCH  1114      1.79      1    NA   228    4.47 18948 20580
#>  6 PSYBRA  1318      0.447     1    NA    14    4.48 19011 20646
#>  7 HIRRUG  1403      1.66      1    NA    12.9  4.58 18834 20506
#>  8 CASSYL  1411     NA         0    NA    NA   NA       NA    NA
#>  9 SLOBER  1414      1.40      1    NA    16.6  4.57 18952 20622
#> 10 GUAGUI  1419     NA         0    NA    NA   NA       NA    NA
#> # … with 530 more rows

Species-habitat associations

# Pick alive trees, of 10 mm or more
tree <- download_data("luquillo_tree5_random")
census <- filter(tree, status == "A", dbh >= 10)
# Pick sufficiently abundant species
pick <- filter(add_count(census, sp), n > 50)

# Use your habitat data or create it from elevation data
elevation <- download_data("luquillo_elevation")
habitat <- fgeo_habitat(elevation, gridsize = 20, n = 4)

tt_test_result <- tt_test(pick, habitat)
#> Using `plotdim = c(320, 500)`. To change this value see `?tt_test()`.
#> Using `gridsize = 20`. To change this value see `?tt_test()`.
#> Warning: Is `census` a tree table (not a stem table)? See `?tt_test()`.

# A list or matrices
tt_test_result
#> [[1]]
#>        N.Hab.1 Gr.Hab.1 Ls.Hab.1 Eq.Hab.1 Rep.Agg.Neut.1 Obs.Quantile.1
#> CASARB      35     1313      282        5              0       0.820625
#>        N.Hab.2 Gr.Hab.2 Ls.Hab.2 Eq.Hab.2 Rep.Agg.Neut.2 Obs.Quantile.2
#> CASARB      24      394     1204        2              0        0.24625
#>        N.Hab.3 Gr.Hab.3 Ls.Hab.3 Eq.Hab.3 Rep.Agg.Neut.3 Obs.Quantile.3
#> CASARB      11      482     1114        4              0        0.30125
#>        N.Hab.4 Gr.Hab.4 Ls.Hab.4 Eq.Hab.4 Rep.Agg.Neut.4 Obs.Quantile.4
#> CASARB       8     1217      377        6              0       0.760625
#> 
#> [[2]]
#>        N.Hab.1 Gr.Hab.1 Ls.Hab.1 Eq.Hab.1 Rep.Agg.Neut.1 Obs.Quantile.1
#> PREMON      94     1005      594        1              0       0.628125
#>        N.Hab.2 Gr.Hab.2 Ls.Hab.2 Eq.Hab.2 Rep.Agg.Neut.2 Obs.Quantile.2
#> PREMON      97     1478      120        2              0        0.92375
#>        N.Hab.3 Gr.Hab.3 Ls.Hab.3 Eq.Hab.3 Rep.Agg.Neut.3 Obs.Quantile.3
#> PREMON      39      230     1367        3              0        0.14375
#>        N.Hab.4 Gr.Hab.4 Ls.Hab.4 Eq.Hab.4 Rep.Agg.Neut.4 Obs.Quantile.4
#> PREMON      15      130     1465        5              0        0.08125
#> 
#> [[3]]
#>        N.Hab.1 Gr.Hab.1 Ls.Hab.1 Eq.Hab.1 Rep.Agg.Neut.1 Obs.Quantile.1
#> SLOBER      21      270     1328        2              0        0.16875
#>        N.Hab.2 Gr.Hab.2 Ls.Hab.2 Eq.Hab.2 Rep.Agg.Neut.2 Obs.Quantile.2
#> SLOBER      25      516     1082        2              0         0.3225
#>        N.Hab.3 Gr.Hab.3 Ls.Hab.3 Eq.Hab.3 Rep.Agg.Neut.3 Obs.Quantile.3
#> SLOBER      21     1336      260        4              0          0.835
#>        N.Hab.4 Gr.Hab.4 Ls.Hab.4 Eq.Hab.4 Rep.Agg.Neut.4 Obs.Quantile.4
#> SLOBER       8     1193      396       11              0       0.745625

# A dataframe
as_tibble(tt_test_result)
#> # A tibble: 12 x 8
#>    habitat sp     N.Hab Gr.Hab Ls.Hab Eq.Hab Rep.Agg.Neut Obs.Quantile
#>  * <chr>   <chr>  <dbl>  <dbl>  <dbl>  <dbl>        <dbl>        <dbl>
#>  1 1       CASARB    35   1313    282      5            0       0.821 
#>  2 2       CASARB    24    394   1204      2            0       0.246 
#>  3 3       CASARB    11    482   1114      4            0       0.301 
#>  4 4       CASARB     8   1217    377      6            0       0.761 
#>  5 1       PREMON    94   1005    594      1            0       0.628 
#>  6 2       PREMON    97   1478    120      2            0       0.924 
#>  7 3       PREMON    39    230   1367      3            0       0.144 
#>  8 4       PREMON    15    130   1465      5            0       0.0812
#>  9 1       SLOBER    21    270   1328      2            0       0.169 
#> 10 2       SLOBER    25    516   1082      2            0       0.322 
#> 11 3       SLOBER    21   1336    260      4            0       0.835 
#> 12 4       SLOBER     8   1193    396     11            0       0.746

# A simple summary to help you interpret the results
summary(tt_test_result)
#> # A tibble: 12 x 3
#>    sp     habitat association
#>    <chr>  <chr>   <chr>      
#>  1 CASARB 1       neutral    
#>  2 CASARB 2       neutral    
#>  3 CASARB 3       neutral    
#>  4 CASARB 4       neutral    
#>  5 PREMON 1       neutral    
#>  6 PREMON 2       neutral    
#>  7 PREMON 3       neutral    
#>  8 PREMON 4       neutral    
#>  9 SLOBER 1       neutral    
#> 10 SLOBER 2       neutral    
#> 11 SLOBER 3       neutral    
#> 12 SLOBER 4       neutral

Get started with fgeo

Information