Two data.frame
s must be provided to dsm
. They are referred to as
observation.data
and segment.data
.
Details
The segment.data
table has the sample identifiers which define the
segments, the corresponding effort (line length) expended and the
environmental covariates that will be used to model abundance/density.
observation.data
provides a link table between the observations used in
the detection function and the samples (segments), so that we can aggregate
the observations to the segments (i.e., observation.data
is a "look-up
table" between the observations and the segments).
observation.data
- the observation data.frame
must have (at least) the
following columns:
object
unique object identifierSample.Label
the identifier for the segment where observation occurredsize
the size of each observed group (e.g., 1 if all animals occurred individually)distance
distance to observation
One can often also use observation.data
to fit a detection function (so
additional columns for detection function covariates are allowed in this
table).
segment.data
: the segment data.frame
must have (at least) the following
columns:
Effort
the effort (in terms of length of the segment)Sample.Label
identifier for the segment (unique!)??? environmental covariates, for example location (projected latitude and longitude), and other relevant covariates (sea surface temperature, foliage type, altitude, bathymetry etc).
Multiple detection functions
If multiple detection functions are to be used, then a column named ddfobj
must be included in observation.data
and segment.data
. This lets the
model know which detection function each observation is from. These are
numeric and ordered as the ddf.obj
argument to dsm
, e.g.,
ddf.obj=list(ship_ddf, aerial_ddf)
means ship detections have ddfobj=1
and aerial detections have ddfobj=2
in the observation data.
Mark-recapture distance sampling models
When using mrds
models that include mark-recapture components (currently
independent observer and trial modes are supported) then the format of the
observation data needs to be checked to ensure that observations are not
duplicated. The observer
column is also required in the
observation.data
.
Independent observer mode only unique observations (unique object IDs) are required.
Trial mode only observations made by observer 1 are required.