Estimate abundance from an Rdistance fitted model.
This function is called internally by abundEstim. Most users will call
abundEstim to estimate abundance unless they are running simulations or
bootstrapping.
estimateN(object, area = NULL, propUnitSurveyed = 1)A list containing the following components:
Estimated density in the surveyed area.
Estimated abundance on the study area. Equals density if area is not specified.
The number of detected groups (not individuals, unless all group sizes = 1).
The number of individuals (sum of group sizes).
Total area of inference. Study area size
Number of surveyed sites. This is total transect length for line-transects or number of points for point-transects. This total transect length does not include transects with missing lengths.
Proportion of the standard survey unit that was observed
Average group size on non-NA transects
Strip width.
Probability of detection.
For line-transects that do not involve covariates, object$density is object$n.seen / (2 * propUnitSurveyed * object$w * object$pDetection * object$surveyedUnits)
An Rdistance model frame or fitted distance function,
normally produced by a call to dfuncEstim.
A scalar containing the total area of inference. Usually, this is
study area size. If area is NULL (the default),
area will be set to 1 square unit of the output units and density estimates
will be produced.
If area is not NULL, it must have measurement units
assigned by the units package.
The units on area must be convertible
to squared output units. Units
on area must be two-dimensional.
For example, if output units are "foo",
units on area must be convertible to "foo^2" by the units
package. Units of "km^2", "cm^2", "ha", "m^2", "acre", "mi^2", and several
others are acceptable.
A scalar or vector of real numbers between 0 and 1.
The proportion of the default sampling unit that
was surveyed. If both sides of line transects were observed,
propUnitSurveyed
= 1. If only a single side of line transects were observed, set
propUnitSurveyed = 0.5. For point transects, this should be set to
the proportion of each circle that was observed. Length must either be
1 or the total number of transects in x.
The abundance estimate for line-transect surveys (if no covariates
are included in the detection function and both sides of the transect
are observed) is
$$N =\frac{n(A)}{2(ESW)(L)}$$
where n is total number of sighted individuals
(i.e., sum(groupSizes(dfunc))), L is the total length of
surveyed transect (i.e., sum(effort(dfunc))),
and ESW is effective strip width
computed from the estimated distance function (i.e., ESW(dfunc)).
If only one side of transects were observed, the "2" in the denominator
is not present (or, replaced with a "1").
The abundance estimate for point transect surveys (if no covariates are
included) is
$$N =\frac{n(A)}{\pi(ESR^2)(P)}$$
where n is total number of sighted individuals (i.e., sum(groupSizes(dfunc))),
P is the total number of surveyed points (i.e., sum(effort(dfunc))),
and ESR is effective search radius
computed from the estimated distance function (i.e., ESR(dfunc)).
Setting plot.bs=FALSE and showProgress=FALSE
suppresses all intermediate output.
Estimation of site-specific density (e.g., on every transect) is accomplished by
predict(x, type = "density"), which returns a
tibble containing density and abundance on the area surveyed by every
transect.
dfuncEstim, abundEstim