closedN {secr}

Closed population estimates

Description

Estimate N, the size of a closed population, by several conventional non-spatial capture–recapture methods.

Usage

closedN(object, estimator = NULL, level = 0.95, maxN = 1e+07,
    dmax = 10 )

Arguments

object

capthist object

estimator

character; name of estimator (see Details)

level

confidence level (1 – alpha)

maxN

upper bound for population size

dmax

numeric, the maximum AIC difference for inclusion in confidence set

Details

Data are provided as spatial capture histories, but the spatial information (trapping locations) is ignored.

AIC-based model selection is available for the maximum-likelihood estimators null, zippin, darroch, h2, and betabinomial.

Model weights are calculated as

w_i = \frac{\exp(-\Delta_i / 2)}{ \sum{\exp(-\Delta_i / 2)}}

Models for which dAICc > dmax are given a weight of zero and are excluded from the summation, as are non-likelihood models.

Computation of null, zippin and darroch estimates differs slightly from Otis et al. (1978) in that the likelihood is maximized over real values of N between Mt1 and maxN, whereas Otis et al. considered only integer values.

Asymmetric confidence intervals are obtained in the same way for all estimators, using a log transformation of \hat{N}-Mt1 following Burnham et al. (1987), Chao (1987) and Rexstad and Burnham (1991).

The available estimators are

Name	Model	Description	Reference
null	M0	null	Otis et al. 1978 p.105
zippin	Mb	removal	Otis et al. 1978 p.108
darroch	Mt	Darroch	Otis et al. 1978 p.106-7
h2	Mh	2-part finite mixture	Pledger 2000
betabinomial	Mh	Beta-binomial continuous mixture	Dorazio and Royle 2003
jackknife	Mh	jackknife	Burnham and Overton 1978
chao	Mh	Chao's Mh estimator	Chao 1987
chaomod	Mh	Chao's modified Mh estimator	Chao 1987
chao.th1	Mth	sample coverage estimator 1	Lee and Chao 1994
chao.th2	Mth	sample coverage estimator 2	Lee and Chao 1994

Value

A dataframe with one row per estimator and columns

model

model in the sense of Otis et al. 1978

npar

number of parameters estimated

loglik

maximized log likelihood

AIC

Akaike's information criterion

AICc

AIC with small-sample adjustment of Hurvich & Tsai (1989)

dAICc

difference between AICc of this model and the one with smallest AICc

Mt1

number of distinct individuals caught

Nhat

estimate of population size

seNhat

estimated standard error of Nhat

lclNhat

lower 100 x level % confidence limit

uclNhat

upper 100 x level % confidence limit

Warning

If your data are from spatial sampling (e.g. grid trapping) it is recommended that you do not use these methods to estimate population size (see Efford and Fewster 2013). Instead, fit a spatial model and estimate population size with region.N.

Note

Prof. Anne Chao generously allowed me to adapt her code for the variance of the ‘chao.th1’ and ‘chao.th2’ estimators.

Chao's estimators have been subject to various improvements not included here (e.g., Chao et al. 2016).

References

Burnham, K. P. and Overton, W. S. (1978) Estimating the size of a closed population when capture probabilities vary among animals. Biometrika 65, 625–633.

Chao, A. (1987) Estimating the population size for capture–recapture data with unequal catchability. Biometrics 43, 783–791.

Chao, A., Ma, K. H., Hsieh, T. C. and Chiu, Chun-Huo (2016) SpadeR: Species-Richness Prediction and Diversity Estimation with R. R package version 0.1.1. https://CRAN.R-project.org/package=SpadeR

Dorazio, R. M. and Royle, J. A. (2003) Mixture models for estimating the size of a closed population when capture rates vary among individuals. Biometrics 59, 351–364.

Efford, M. G. and Fewster, R. M. (2013) Estimating population size by spatially explicit capture–recapture. Oikos 122, 918–928.

Hurvich, C. M. and Tsai, C. L. (1989) Regression and time series model selection in small samples. Biometrika 76, 297–307.

Lee, S.-M. and Chao, A. (1994) Estimating population size via sample coverage for closed capture-recapture models. Biometrics 50, 88–97.

Otis, D. L., Burnham, K. P., White, G. C. and Anderson, D. R. (1978) Statistical inference from capture data on closed animal populations. Wildlife Monographs 62, 1–135.

Pledger, S. (2000) Unified maximum likelihood estimates for closed capture-recapture models using mixtures. Biometrics 56, 434–442.

Rexstad, E. and Burnham, K. (1991) User's guide for interactive program CAPTURE. Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, Colorado, USA.

See Also

capthist, closure.test, region.N

Examples

closedN(deermouse.ESG)