Compute Summary Statistics from Detection Histories

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Description

This function extracts various summary statistics from detection history data of various unmarkedFrame and unmarkedFit classes.

Usage

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detHist(object, ...)

## S3 method for class 'unmarkedFitColExt'
detHist(object, ...)

## S3 method for class 'unmarkedFitOccu'
detHist(object, ...)

## S3 method for class 'unmarkedFitOccuFP'
detHist(object, ...)

## S3 method for class 'unmarkedFitOccuRN'
detHist(object, ...)

## S3 method for class 'unmarkedFrameOccu'
detHist(object, ...)

## S3 method for class 'unmarkedFrameOccuFP'
detHist(object, ...)

## S3 method for class 'unmarkedMultFrame'
detHist(object, ...)

Arguments

object

an object of various unmarkedFrame or unmarkedFit classes containing detection history data.

...

additional arguments passed to the function.

Details

This function computes a number of summary statistics in data sets used for single-season occupancy models (MacKenzie et al. 2002), dynamic occupancy models (MacKenzie et al. 2003), Royle-Nichols models (Royle and Nichols 2003), and false-positive occupancy models (Royle and Link 2006, Miller et al. 2011).

detHist can take data frames of the unmarkedFrameOccu, unmarkedFrameOccuFP, and unmarkedMultFrame classes as input. For convenience, the function can also extract the raw data from model objects of classes unmarkedFitColExt, unmarkedFitOccu, unmarkedFitOccuFP, and detHist.unmarkedFitOccuRN. Note that different model objects using the same data set will have identical values.

Value

detHist returns a list with the following components:

hist.table.full

a table with the frequency of each observed detection history.

hist.table.seasons

a list of tables with the frequency of each season-specific detection history.

out.freqs

a matrix where the rows correspond to each sampling season and where columns consist of the number of sites sampled in season t (sampled) and the number of sites with at least one detection in season t (detected). For multiseason data, the matrix includes the number of sites sampled in season t - 1 with colonizations observed in season t (colonized), the number of sites sampled in season t - 1 with extinctions observed in season t (extinct), the number of sites sampled in season t - 1 without changes observed in season t (static), and the number of sites sampled in season t that were also sampled in season t - 1 (common).

out.props

a matrix where the rows correspond to each sampling season and where columns consist of the proportion of sites in season t with at least one detection (naive.occ). For multiseason data, the matrix includes the proportion of sites sampled in season t - 1 with colonizations observed in season t (naive.colonization), the proportion of sites sampled in season t - 1 with extinctions observed in season t (naive.extinction), and the proportion of sites sampled in season t - 1 with no changes observed in season t.

n.seasons

the number of seasons (primary periods) in the data set.

n.visits.season

the maximum number of visits per season in the data set.

Author(s)

Marc J. Mazerolle

References

MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Royle, J. A., Langtimm, C. A. (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255.

MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G., Franklin, A. B. (2003) Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84, 2200–2207.

Mazerolle, M. J. (2015) Estimating detectability and biological parameters of interest with the use of the R environment. Journal of Herpetology 49, 541–559.

Miller, D. A. W., Nichols, J. D., McClintock, B. T., Campbell Grant, E. H., Bailey, L. L. (2011) Improving occupancy estimation when two types of observational error occur: non-detection and species misidentification. Ecology 92, 1422–1428.

Royle, J. A., Link, W. A. (2006) Generalized site occupancy models allowing for false positive and false negative errors. Ecology 87, 835–841.

Royle, J. A., Nichols, J. D. (2003) Estimating abundance from repeated presence-absence data or point counts. Ecology 84, 777–790.

See Also

covDiag, countHist, countDist, mb.chisq, mb.gof.test,

Examples

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##data from Mazerolle (2015)
## Not run: 
data(bullfrog)

##detection data
detections <- bullfrog[, 3:9]

##load unmarked package
if(require(unmarked)){

##assemble in unmarkedFrameOccu
bfrog <- unmarkedFrameOccu(y = detections)

##compute descriptive stats from data object
detHist(bfrog)

##run model
fm <- occu(~ 1 ~ 1, data = bfrog)
##compute descriptive stats from model object
detHist(fm)
}

## End(Not run)

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