CTFS.densdepend {CTFS} | R Documentation |
In order to identify the presence of density dependence for
a focal species, attributes of the neighborhood surrounding the individuals
of that species need to be determined. In the CTFS package, this is done
using neighbordens.1sp
, described in greater detail
below. The result of this function is a dataframe containing measures of
neighborhood attributes for each defined neighborhood for each tree of a
focal species. This file can be used to analyse the effect of
neighborhood conditions on the size, growth, mortality and recruitment of
individuals for each species by combining it with the data for each tree
of the focal species.
PREPARATIONS
In order to run these functions, a dataset structured by quadrate needs
to be created. It must be a list containing dataframes
for each quadrate which, in turn, have the data for a single census for
all the trees in each quadrate. This list is created from a
CTFS census file. Use sep.quadinfo
first to create a
vector containing the quadrate number for each tree. Then
sep.quadinfo
, using this vector, will create an
appropriately structured list. The example data sets are: tst.bci90.quad
and
tst.bci95.quad
.
FUNCTIONS FOR COMPUTING NEIGHBORHOOD ATTRIBUTES
Cover function that returns a dataframe with neighborhood attributes for individuals of a single species (focal species). This function can be called from other functions to produce dataframes for many species.
Called by neighborhdens.1sp
for each quadrate in the plot.
Calls findborderquads
to locate surrounding quadrates that
constitute the area of the neighborhoods. This limits the searching for
neighbors to only surrounding quadrates within the radius of the
neighborhood. Then it calls countdens.1quad
to compute
neighborhood attributes for individuals of the focal species in a
single quad.
Called by neighbordens.1quad
for each quadrate using the subset of
the data that contains the trees in the neighboring quadrates and the
trees of the focal species. Using borderdist
, the neighborhood
attributes are set to NA for trees that are sufficiently close to a
border of the plot such that for a given neighborhood distance, that
neighborhood is not entirely within the plot.
This function actually computes the number and basal area of conspecifics and heterospecifics in a given neighborhood for a given individual tree of the focal species.
LIST OF FUNCTIONS CALLED BY USER FUNCTIONS
findborderquads
identified bordering quadrates
xydistvect
computes xy distance between points for vectors
of points
fill.distclasses
fills a matrix of values, structured for
density dependent functions
borderdist
computes distance to the plot border for a given
coordinate pair
NEIGHBORHOOD ATTRIBUTES COMPUTED AND HOW TO COMPUTE OTHER ATTRIBUTES
Number of conspecifics, Ncon
and sum of the basal area of all
conspecifics, BAcon
Number of heterospecifics, Nhet
and sum of the basal area of all
heterospecifics, BAhet
If the user wishes to compute other attributes of neighbors or select
neighbors on other basis than being alive (or alive and broken), then
the user should write new versions of countdens.1tree
and/or
countdens.1tree.1quad
. The tree data for the focal species is
contained in the variable focaldata
and the data for the trees in the
neighboring quadrates that could be valid neighbors is in the variable
neighdata
. These are both dataframes.
Changing the types of attributes computed from all living neighbors
involves rewriting countdens.1tree
. At this time
countdens.1tree
is passed only the dbh
of a neighbor tree. If
the desired attribute can be computed from that, then only
countdens.1tree
needs to be rewritten. If other information about the
neighbor tree is needed, then the user needs to rewrite countdens.1tree
and countdens.1tree.1quad
so that the calling statements provide the
needed variables for the user defined neighbor attribute.
If the user wishes to select neighbors based on other attributes than
species name and status of alive (or alive but broken), it is suggested
that the user rewrite countdens.1tree.1quad
as this is where the
subsetting of the focal species tree data and the neighbor tree data take
place. The logical vector inc
defines which neighbor trees to
use. Redefining this vector would, most likely, be the simpliest
adjustment. There is a comment in the code at the location of the
definition of inc
.
Rick Condit and Pamela Hall
CTFS.mortality
, CTFS.growth
,
CTFS.recruitment
, |link{CTFS-internal}
## Not run: data(tst.bci90.quad) data(tst.bci90.spp) socrex90.neigh <- neighbordens.1sp("socrex",tst.bci90.quad, tst.bci90.spp) str(socrex90.neigh) plot(socrex90.neigh$Ncon.0.5,tst.bci90.spp$socrex$dbh) ## End(Not run)