Recruitment, mortality, and growth.

Source: R/demography_ctfs.R

These functions are adapted from the CTFS-R package. Compared to the original functions, these ones have a similar interface but use more conservative defaults and allow suppressing messages. These functions also feature formal tests, bug fixes, additional assertions, and improved messages.

recruitment_ctfs(
  census1,
  census2,
  mindbh = NULL,
  alivecode = NULL,
  split1 = NULL,
  split2 = NULL,
  quiet = FALSE
)

mortality_ctfs(
  census1,
  census2,
  alivecode = NULL,
  split1 = NULL,
  split2 = NULL,
  quiet = FALSE
)

growth_ctfs(
  census1,
  census2,
  rounddown = FALSE,
  method = "I",
  stdev = FALSE,
  dbhunit = "mm",
  mindbh = NULL,
  growthcol = "dbh",
  err.limit = 1000,
  maxgrow = 1000,
  split1 = NULL,
  split2 = NULL,
  quiet = FALSE
)

Arguments

census1, census2

Two census tables, each being a ForestGEO-like tree table (dataframe). A stem table won't fail, but you should use a tree table because demography analyses make more sense at the scale of trees than at the scale of stems.
mindbh

The minimum diameter above which the counts are done. Trees smaller than mindbh are excluded. By default all living trees of any size are included.
alivecode

Character; valid values of status indicating that a tree is alive. The default, 'A', is the standard CTFS designation for living trees or stems.
split1, split2

Optional vector(s) to aggregate results by. Each vector should be a column of either census1 or census2. The default aggregates the result across the entire census datasets.
quiet

Use TRUE to suppress messages.
rounddown

If TRUE, all dbh < 55 are rounded down to the nearest multiple of 5.
method

Either "I" or "E":

  • Use "I" to calculate annual dbh increment as (dbh2 - dbh1)/time

  • Use "E" to calculate the relative growth rate as (log(dbh2) - log(dbh1)) / time
stdev

Logical:

  • FALSE returns confidence limits.

  • TRUE returns the SD in growth rate per group.
dbhunit

"cm" or "mm".
growthcol

Either "dbh" or "agb" to define how growth is measured.
err.limit, maxgrow

A number. Numbers such as 10000 are high and will return all measures.

Value

Metrics of recruitment: Similar to metrics of mortality.

Metrics of mortality:

  • N: the number of individuals alive in the census 1 per category selected.

  • D: the number of individuals no longer alive in census 2.

  • rate: the mean annualized mortality rate constant per category selected, calculated as (log(N)-log(S))/time.

  • upper: upper confidence limit of mean rate.

  • lower: lower confidence limit of mean rate.

  • time: mean time interval in years.

  • date1: mean date included individuals were measured in census 1, as julian object (R displays as date, but treats as integer).

  • date2: mean date in census 2.

  • dbhmean: mean dbh in census 1 of individuals included.

Metrics of growth:

  • rate, the mean annualized growth rate per category selected, either dbh increment, or relative growth.

  • N, the number of individuals included in the mean (not counting any excluded).

  • clim (or sd with stdev = TRUE), width of confidence interval; add this number to the mean rate to get upper confidence limit, substract to get lower.

  • dbhmean, mean dbh in census 1 of individuals included.

  • time, mean time interval in years.

  • date1, mean date included individuals were measured in census 1, as julian object (R displays as date, but treats as integer).

  • date2, mean date in census 2.

Details

Survivors are all individuals alive in both censuses, with status == A in the first census, and a diameter greater than mindbh in the first census. The total population in the second census includes all those alive plus any other survivors. Individuals whose status is NA in either census are deleted from all calculations.

Author

Richard Condit, Suzanne Lao.

Examples

assert_is_installed("fgeo.x")

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

as_tibble(
  recruitment_ctfs(census1, census2)
)

#> Detected dbh ranges:
#>   * `census1` = 10.9-323.
#>   * `census2` = 10.5-347.
#> Using dbh `mindbh = 0` and above.
#> # 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.

# Use `interaction(...)` to aggregate by any number of grouping variables
sp_quadrat <- interaction(census1$sp, census1$quadrat)

recruitment <- recruitment_ctfs(
  census1, census2,
  split1 = sp_quadrat,
  quiet = TRUE
)
as_tibble(recruitment)

#> # 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

mortality <- mortality_ctfs(
  census1, census2,
  split1 = sp_quadrat, quiet = TRUE
)
as_tibble(mortality)

#> # 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

growth <- growth_ctfs(census1, census2, split1 = sp_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

# Easy way to separate grouping variables
tidyr_is_installed <- requireNamespace("tidyr", quietly = TRUE)
if (tidyr_is_installed) {
  library(tidyr)

  as_tibble(growth) %>%
    separate(groups, into = c("sp", "quadrat"))
}

#> # A tibble: 540 x 9
#>    sp     quadrat   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