Description Usage Arguments Details Value See Also Examples
Finds abundance, basal area, or agb in two censuses and the rate of change between them. Accepts two dataframes, each an R Analytical Table for one census, the earlier census first.
1 2 |
census1 |
The R Analytical Table for a single census, either tree or stem. |
census2 |
The matching R Analytical Table for a later census. |
type |
Either 'abund' (default) for abundance, 'ba' for basal area, or 'agb'. |
dbhunit |
'cm' or 'mm', only used for basal area |
alivecode |
character, codes of the variable |
mindbh |
The minimum diameter above which the counts are done. Trees
smaller than |
split1 |
a vector of categories, one per individual |
split2 |
another vector of categories, one per individual |
Do not use this function with diameter categories as a split variable! The
results won't make sense. The categories need to be permanent attributes,
such as species, genus, quadrat. To find population change of dbh categories
use instead pop.change.dbh()
Mean census date for a species is not the mean census date for all living individuals in that census, but the mean census date for all individuals alive in either census. Plants recruited between the two censuses get a first census date equal to the date on which the quadrat they later appear in was censused in the first census. Plants dead in the second census get a census date equal to the date on which their quadrat was censused
The return value is a list of 6 components:
N.1 (or BA.1 or AGB.1) an array of abundance (or basal area or agb) in the first census; one dimension of the array for split1, the second for split2
N.2 (or BA.2 or AGB.2) abundance (or basal area or agb) in the second census in a matching array
date1 mean date of first census in a matching array
date2 mean date of second census in a matching array
interval the time interval in years in a matching array
little.r the rate of population change in a matching array,
(log(N2) - log(N1))/time
This list can be submitted to assemble.demography()
(topic utilitiesCTFS)
to convert into a convenient table.
abundance()
, assemble.demography()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## Not run:
bcichange = pop.change(
bciex::bci12t5mini,
bciex::bci12t6mini,
type = 'abund',
split1 = bciex::bci12t5mini$sp,
mindbh = 10
)
str(bcichange)
head(bcichange$N.1)
change.table = assemble.demography(bcichange, type = 'a')
head(change.table)
## End(Not run)
|
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