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.
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, ...)
Returns an object of class wiqid
, see wiqid-class for details.
a 1/0/NA matrix (or data frame) of detection histories, sites x occasions. Rows with all NAs are silently removed.
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.
a list of formulae symbolically defining a linear predictor for each parameter in terms of covariates. The default corresponds to an intercept-only model.
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
.
the confidence interval to use.
if TRUE, the data provided will be checked.
other arguments passed to nlm
.
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.
Mike Meredith
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.
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.
data(GrandSkinks)
DH <- GrandSkinks[, 1:15]
occMS0(DH, 3)
# \donttest{
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)
# }
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