CTFS.recruitment {CTFS} | R Documentation |
Provides an overview for the analysis of recruitment rates for tree populations by a variety of categories including the functions that are available, supporting functions and ways for using the options of the main functions.
FUNCTIONS TO COMPUTE RECRUITMENT RATES
recruitment | Annual Recruitment Rates by Categories (User defined groups) |
recruitment.eachspp | Annual Recruitment Rates by Species |
FUNCTIONS FOR FORMATTING RESULTS
assemble.demography | Reformat the Output from Demographic Functions from List to Dataframe |
FUNCTIONS CALLED BY USER FUNCTIONS
find.climits | Calculates confidences limits for recruitment rates |
fill.dimension | Fills all the dimensions of a 2 dimensional array |
fill.1dimension | Fills all the dimensions of a 1 dimensional array |
COMPUTATION OF RECRUITMENT
The annual recruitment rate is calculated as
r = (logN1-logS) / mean(time1-time0)
where N1
is the number of live individuals at the second census,
where S
is the number of surviving individuals: trees that were
alive in the first census and alive in the second census,
where time1
and time0
are expressed in years and is the
length of the census interval as recorded for trees in the second census (which
includes all of the surviving trees.
The confidence limits are computed using find.climits
which
returns the number of survivors, S
, out of N
individuals for
each confidenc interval in turn. The beta distribution is used to
determine the number of S for the upper 95% and lower 5% (default
probability level). Confidence limits for mortality rate are computed as
from these CI for S
as:
rate.CI.upper = ( logN1 - log(S.lowerCI) ) / mean(time1 - time0)
rate.CI.lower = ( logN1 - log(S.upperCI) ) / mean(time1 - time0)
Note that S.lowerCI
is a lower value of S
which results in a higher
number of recruits and hence a higher recruitment rate. And S.upperCI
is a
higher value of S
which results in a lower number of deaths and hence a
lower recruitment rate.
Rick Condit and Pamela Hall