Create a set of capture or marking-and-resighting histories by simulated sampling of a 2-D population using an array of detectors.
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sim.capthist(traps, popn = list(D = 5, buffer = 100, Ndist = "poisson"), detectfn = 0, detectpar = list(), noccasions = 5, nsessions = 1, binomN = NULL, exactN = NULL, p.available = 1, renumber = TRUE, seed = NULL, maxperpoly = 100, chulltol = 0.001, userdist = NULL, savepopn = FALSE) sim.resight(traps, popn = list(D = 5, buffer = 100, Ndist = "poisson"), ..., pID = 1, unmarked = TRUE, nonID = TRUE, unsighted = TRUE, pmark = 0.5, Nmark = NULL, markingmask = NULL)
locations of individuals in the population to be sampled, either as
integer code or character string for shape of detection function 0 = halfnormal etc. – see detectfn
list of values for named parameters of detection function
number of occasions to simulate
number of sessions to simulate
integer code for distribution of counts (see Details)
integer number of telemetry fixes per occasion
vector of one or two probabilities (see Details)
logical for whether output rows should labeled sequentially (TRUE) or retain the numbering of the population from which they were drawn (FALSE)
an object specifying if and how the random number generator should be initialized (‘seeded’)
integer maximum number of detections of an individual in one polygon or transect on any occasion
numeric buffer (m) for polygon around telemetry locations
user-defined distance function or matrix (see details)
logical; if TRUE then the popn (input or simulated) is saved as an attribute
arguments to pass to
probability of individual identification for marked animals
logical, if TRUE unmarked individuals are not recorded during ‘sighting’
logical, if TRUE then unidentified marked individuals are not recorded during ‘sighting’
logical, if TRUE and sighting only then capthist includes all-zero histories
numeric probability that an individual is ‘pre-marked’ (see Details)
number of individuals to be ‘pre-marked’ (see Details)
popn is not of class ‘popn’ then a homogeneous Poisson
population with the desired density (animals/ha) is first simulated over
the rectangular area of the bounding box of
traps plus a buffer
of the requested width (metres). The detection algorithm depends on the
detector type of
traps. For ‘proximity’ detectors, the actual
detection probability of animal i at detector j is the
naive probability given by the detection function. For ‘single’ and
‘multi’ detectors the naive probability is modified by competition
between detectors and, in the case of ‘single’ detectors, between animals. See
Efford (2004) and other papers below for details.
Detection parameters in
detectpar are specific to the detection
function, which is indicated by a numeric code (
Parameters may vary with time - for this provide a vector of length
noccasions. The g0 parameter may vary both by time and detector
- for this provide a matrix with
noccasions rows and as many
columns as there are detectors. The default detection parameters are
list(g0 = 0.2, sigma = 25, z = 1).
The default is to simulate a single session. This may be overridden by
providing a list of populations to sample (argument
popn) or by
nsessions > 1 (if both then the number of sessions must
nsessions > 1 results in replicate samples of
populations with the same density etc. as specified directly in the
binomN determines the statistical distribution of the number of
detections of an individual at a particular ‘count’ detector or polygon
on a particular occasion. A Poisson distribution is indicated by
binomN = 0; see
secr.fit for more. The distribution
is always Bernoulli (binary) for ‘proximity’ and ‘signal’ detectors.
exactN is not specified or zero then the number of telemetry
fixes is a random variable determined by the other detection settings.
p.available specifies temporary non-availability for detection in
multi-session simulations. If a single probability is specified then
temporary non-availability is random (independent from session to
session). If two probabilities are given then non-availability is
Markovian (dependent on previous state) and the two values are for
animals available and not available at the preceding session. In the
Markovian case, availability in the first session is assigned at random
according to the equilibrium probability p2 / (1 - p1 + p2). Incomplete
availability is not implemented for sampling lists of populations.
detectpar may include a component ‘truncate’ for the distance
beyond which detection probability is set to zero. By default this value
is NULL (no specific limit).
detectpar may also include a component ‘recapfactor’ for a
general learned trap response. For ‘single’ and ‘multi’ detector types
the probability of detection changes by this factor for all occasions
after the occasion of first capture. Attempted use with other detector
types causes an error. If recapfactor x g(d) > 1.0, g(d) is truncated at
1.0. Other types of response (site-specific bk, Markovian B) are not
popn is specified by an object of class ‘popn’ then any
individual covariates will be passed on; the
of the output is otherwise set to NULL.
The random number seed is managed as in
chulltol is used only when simulating telemetry locations. By
default, a new 'traps' polygon is generated as the convex hull of the
simulated locations, with a slight (1 mm) added buffer to ensure
boundary points are within the polygon. Buffering is suppressed if
chulltol is NA or negative.
userdist cannot be set if ‘traps’ is any of polygon, polygonX,
transect or transectX.
sim.resight generates mark-resight data. The ‘markocc’ attribute
of ‘traps’ indicates the occasions which are for sighting-only (0) or
marking and recapture (1). The number
of occasions is determined by
sim.capthist is first
called with the arguments ‘traps’ and .... The detector type of ‘traps’
must be ‘proximity’ or ‘count’. At present the detector type is assumed to
be the same for marking and sighting occasions. If ... includes a non-null ‘seed’ the
random seed is reset in
sim.resight and not passed to
A special case arises when all occasions are sighting-only. Then it is assumed that individuals in the population are marked prior to the start of sampling with a known spatial distribution (i.e. marking then does not follow a spatial detection model). By default, animals are marked with the probability
Nmark is specified then a sample of size
Nmark will be selected for marking, overriding
The marked population may be restricted to a subset of the space spanned by
popn by specifying
markingmask, which may have a further covariate ‘marking’ to vary the intensity of marking.
sim.capthist, an object of class
capthist, a matrix or
3-dimensional array with additional attributes. Rows represent
individuals and columns represent occasions; the third dimension, used
when detector type = ‘proximity’, codes presence or absence at each
detector. For trap detectors (‘single’, ‘multi’) each entry in
capthist is either zero (no detection) or the sequence number of
The initial state of the R random number generator is stored in the ‘seed’ attribute.
sim.resight, an object of class
capthist, always a
3-dimensional array, with additional attributes markocc (marking occasions),
Tu (sightings of unmarked animals) and Tm (sightings of marked but not
sim.resight allows interspersed marking and sighting occasions, but data with interspersed occasions cannot yet be analysed with
** Mark-resight data formats and models are experimental in secr 2.10.0 and subject to change **
External code is called to speed the simulations. The present version
assumes a null model, i.e., naive detection probability is constant
except for effects of distance and possibly time (using vector-valued
detection parameters from 1.2.10). You can, however, use
rbind.capthist to combine detections of population
subclasses (e.g. males and females) simulated with different parameter
values. This is not valid for detector type "single" because it fails to
allow for competition for traps between subclasses. Future versions may
allow more complex models.
truncate has no effect (i) when using a uniform detection
function with radius (
truncate and (ii) with
signal strength detection (detectfn 10, 11). Note that truncated
detection functions are provided for de novo simulation, but are not
available when fitting models with in
secr.fit or simulating from
a fitted model with
maxperpoly limits the size of the array allocated for
detections in C code; an error results if the is number is exceeded.
Prior to 2.10.0
sim.resight interpreted length-2 vectors of detection parameters as referring to marking and sighting occasions; this feature has been discontinued.
Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture–recapture studies. Biometrics 64, 377–385.
Efford, M. G. (2004) Density estimation in live-trapping studies. Oikos 106, 598–610.
Efford, M. G., Borchers D. L. and Byrom, A. E. (2009) Density estimation by spatially explicit capture-recapture: likelihood-based methods. In: D. L. Thomson, E. G. Cooch and M. J. Conroy (eds) Modeling Demographic Processes in Marked Populations. Springer, New York. Pp. 255–269.
Efford, M. G., Dawson, D. K. and Borchers, D. L. (2009) Population density estimated from locations of individuals on a passive detector array. Ecology 90, 2676–2682.
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## simple example ## detector = "multi" (default) temptrap <- make.grid(nx = 6, ny = 6, spacing = 20) sim.capthist (temptrap, detectpar = list(g0 = 0.2, sigma = 20)) ## with detector = "proximity", there may be more than one ## detection per individual per occasion temptrap <- make.grid(nx = 6, ny = 6, spacing = 20, detector = "proximity") summary(sim.capthist (temptrap, detectpar = list(g0 = 0.2, sigma = 20))) ## marking on occasions 1, 3 only ## see Warning markocc(temptrap) <- c(1,0,1,0,0) summary(sim.resight (temptrap, detectpar = list(g0 = 0.2, sigma = 20))) ## multiple sessions grid4 <- make.grid(nx = 2, ny = 2) temp <- sim.capthist (grid4, popn = list(D = 1), nsessions = 20) summary(temp, terse = TRUE) ## unmarked or presence types grid <- make.grid(nx = 10, ny = 10, detector = "unmarked") CH <- sim.capthist (grid, noccasions = 5) CH ## "presence" and "unmarked" data are stored as "count" data ## behaviour is controlled by detector type, e.g. detector(traps(CH)) <- "presence" CH