Multi-season occupancy estimation

Description

Functions to estimate occupancy from detection/non-detection data for multiple seasons. occMS is the general purpose function; it allows for site-, season- and survey-level covariates, but it is slow. occMScovSite excludes survey-level covariates, but is fast. occMStime and occMS0 are simpler and faster.

Usage

1
2
3
4
5
6
7
occMS0(DH, occsPerSeason, ci=0.95, verify=TRUE)

occMStime(DH, occsPerSeason, model=NULL, data=NULL, ci=0.95, verify=TRUE)

occMS(DH, occsPerSeason, model=NULL, data=NULL, ci=0.95, verify=TRUE)
             
occMScovSite(DH, occsPerSeason, model=NULL, data=NULL, ci=0.95, verify=TRUE)

Arguments

DH

a 1/0/NA matrix (or data frame) of detection histories, sites x occasions. Rows with all NAs are silently removed.

occsPerSeason

the number of survey occasions per season; either a scalar if the number of surveys is constant, or a vector with one element for each season.

model

a list of formulae symbolically defining a linear predictor for each parameter in terms of covariates. The default corresponds to an intercept-only model.

data

a data frame containing the variables in the model: one row for each season or between-season period for occMStime and one for each site for occMScovSite. Each survey covariate has one column for each occasion, and the column name must end with the occasion number (without leading zeros); eg, Cov1, Cov2, ..., Cov15.

ci

the confidence interval to use.

verify

if TRUE, the data provided will be checked.

Details

occMS0 implements a simple multi-season model with one parameter each for initial occupancy, colonisation, local extinction, and probability of detection, ie. a psi1(.) gamma(.) epsilon(.) p(.) model.

occMStime allows for between-season differences in colonisation, local extinction, and probability of detection, either with covariates given in data or the in-built covariates .interval (for colonisation or extinction, or .season (for detection).

occMScovSite allows for between-season differences in colonisation, local extinction, and probability of detection with the in-built covariate .season and for between-site differences with covariates defined in data.

occMS allows for survey-level covariates in addition to the above, and separate covariates for between-season colonisation and local extinction.

Value

Returns an object of class wiqid, which is a list with the following elements:

call

The call used to produce the results

beta

Values of the coefficients of the terms in the linear predictors, with standard errors and confidence intervals.

beta.vcv

The variance-covariance matrix for the beta estimates.

real

Estimates of occupancy and probability of detection on the real scale, with confidence intervals; occSSrn gives occupancy, lambda and r.

logLik

a vector with elements for log(likelihood), number of parameters, and effective sample size. If parameters and their SEs cannot be estimated, the first element should be NA.

There are print, logLik, and nobs methods for class wiqid.

Benchmarks

Output has been checked against output from PRESENCE (Hines 2006) v.5.5 for the GrandSkinks data set. Real values are mostly the same to 4 decimal places, though there is occasionally a discrepancy of 0.0001. AICs are the same.

Author(s)

Mike Meredith

References

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

MacKenzie, D I; J D Nichols; A J Royle; K H Pollock; L L Bailey; J E Hines 2006. Occupancy estimation and modeling : inferring patterns and dynamics of species occurrence. Elsevier Publishing.

Hines, J. E. (2006). PRESENCE - Software to estimate patch occupancy and related parameters. SGS-PWRC. http://www.mbr-pwrc.usgs.gov/software/presence.html.

MacKenzie, D I; J D Nichols; J E Hines; et al 2003. Estimating site occupancy, colonization, and local extinction when a species is imperfectly detected. Ecology 84, 2200-2207.

Examples

1
2
3
4
5
6
7
8
9
data(GrandSkinks)
DH <- GrandSkinks[, 1:15]

occMS0(DH, 3)

occMStime(DH, 3, model=list(gamma ~ .interval, epsilon~1, p~.season))
occMScovSite(DH, 3,
   model=list(psi1~habitat, gamma ~ .interval, epsilon~habitat, p~.season),
   data=GrandSkinks)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.