Estimate the expected and realised populations in a region, using a fitted spatially explicit capture--recapture model. Density is assumed to follow an inhomogeneous Poisson process in two dimensions. Expected \(N\) is the volume under a fitted density surface; realised \(N\) is the number of individuals within the region for the current realisation of the process (cf Johnson et al. 2010; see Note).
region.N(object, ...)# S3 method for secr
region.N(object, region = NULL, spacing = NULL, session = NULL,
group = NULL, se.N = TRUE, alpha = 0.05, loginterval = TRUE,
keep.region = FALSE, nlowerbound = TRUE, RN.method = "poisson",
pooled.RN = FALSE, ncores = NULL, ...)
# S3 method for secrlist
region.N(object, region = NULL, spacing = NULL, session = NULL,
group = NULL, se.N = TRUE, alpha = 0.05, loginterval = TRUE,
keep.region = FALSE, nlowerbound = TRUE, RN.method = "poisson",
pooled.RN = FALSE, ncores = NULL, ...)
secr object output from secr.fit
mask object defining the possibly non-contiguous region for which population size is required, or vector polygon(s) (see Details)
spacing between grid points (metres) if region mask is constructed on the fly
character session
group -- for future use
logical for whether to estimate SE(\(\hat{N}\)) and confidence interval
alpha level for confidence intervals
logical for whether to base interval on log(N)
logical for whether to save the raster region
logical for whether to use n as lower bound when computing log interval for realised N
character string for method used to calculate realised N (RN) and its sampling variance. `poisson' or `MSPE'.
logical; if TRUE the estimate of realised N for a multi-session model is computed as if for combined sampling with all detectors (see Details)
integer number of threads to be used for parallel processing
other arguments (not used)
If se.N = FALSE, the numeric value of expected population size,
otherwise, a dataframe with rows `E.N' and `R.N', and columns as
below.
| estimate | estimate of N (expected or realised, depending on row) |
| SE.estimate | standard error of estimated N |
| lcl | lower 100(1--alpha)% confidence limit |
| ucl | upper 100(1--alpha)% confidence limit |
| n | total number of individuals detected |
For multiple sessions, the value is a list with one component per session, each component as above.
If keep.region = TRUE then the mask object for the region is
saved as the attribute `region' (see Examples).
The area in hectares of the region is saved as attribute `regionarea'.
If the density surface of the fitted model is flat
(i.e. object$model$D == ~1 or object$CL == TRUE) then
\(E(N)\) is simply the density multiplied by the area of region,
and the standard error is also a simple product. In the conditional
likelihood case, the density and standard error are obtained by first
calling derived.
If, on the other hand, the density has been modelled then the density
surface is predicted at each point in region and \(E(N)\) is
obtained by discrete summation. Pixel size may have a minor effect on
the result - check by varying spacing. Sampling variance is
determined by the delta method, using a numerical approximation to the
gradient of \(E(N)\) with respect to each beta parameter.
The region may be defined as a mask object (if omitted, the mask
component of object will be used). Alternatively, region
may be a SpatialPolygonsDataFrame object (see package sp), and a
raster mask will be constructed on the fly using the specified
spacing. See make.mask for an example importing a
shapefile to a SpatialPolygonsDataFrame.
Note: The option of specifying a polygon rather than a mask for
region does not work if the density model in object uses
spatial covariates: these must be passed in a mask.
Group-specific N has yet to be implemented.
Population size is adjusted automatically for the number of clusters
in `mashed' models (see mash). However, the population
size reported is that associated with a single cluster unless
regionmask is specified.
pooled.RN = TRUE handles the special case of a multi-session
model in which the region of interest spans several patches (i.e.,
sampling in each session is localised within region. This is not
yet fully implemented.
Setting ncores = NULL uses the existing value from the environment variable
RCPP_PARALLEL_NUM_THREADS (see setNumThreads).
Use par.region.N to apply region.N in parallel to
several models.
Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture--recapture studies. Biometrics 64, 377--385.
Efford, M. G. and Fewster, R. M. (2013) Estimating population size by spatially explicit capture--recapture. Oikos 122, 918--928.
Johnson, D. S., Laake, J. L. and Ver Hoef, J. M. (2010) A model-based approach for making ecological inference from distance sampling data. Biometrics 66, 310--318.
# NOT RUN {
region.N(secrdemo.0)
# }
# NOT RUN {
## a couple more routine examples
region.N(secrdemo.CL)
region.N(ovenbird.model.D)
## region defined as vector polygon
## retain and plot region mask
temp <- region.N(possum.model.0, possumarea, spacing = 40,
keep.region = TRUE)
temp
plot (attr(temp, "region"), type = "l")
# }
# NOT RUN {
# }
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