There may be situations where a tag that was expelled or from a dead animal may appear to move among stations or locations. This apparent movement could be due to the tag drifting with currents or tides. If a tag is located within range of two overlapping receivers, simultaneous or near-simultaneous detections at the two receivers may be assigned to different residence events. Left unaddressed, these residence events at different stations would be considered by mort to be station changes.
Specifying drift stations
Drift stations must be specified by the user. These can be determined by common sense (e.g., Station 5 is upstream of Station 4, so drift can occur from Station 5 to Station 4). Potential drift stations can also be determined by plotting and exploring residence events (see “Plotting” vignette). A series of long residences at one station that are followed by a series of long residences at another station may indicate drift from unmapped currents in the study area. Brief residence events with numerous station changes between two stations may indicate stations that overlap under certain environmental conditions.
To apply drift, the user must specify the drift stations in a
dataframe. The dataframe must have two columns: the station where the
tag is drifting from, and the station where the tag is drifting to. The
station names must match those in the station argument in
all mort functions. Each row will specify one possible drift movement.
The movement is directional, so if two stations overlap, both directions
must be specified. The drift dataframe is later called with the argument
ddd - “ddd” for “dead drift direction”, as a reminder that
drift is applied directionally.
The table below is a subset from the sample drift dataframe. Note that drift between Stations 2 and 3 can be bidirectional, so both drift from Station 2 to Station 3 and drift from Station 3 to Station 2 are included.
| From | To |
|---|---|
| 19 | 18 |
| 2 | 3 |
| 3 | 2 |
| 9 | 17 |
| 8 | 14 |
| 14 | 8 |
When drift is applied within morts() or
infrequent(), residence events that could be due to drift
are combined to form drift residence events. The station name of each
drift residence event is a list of the stations involved in the drift
event, in the same order that the tag was detected. In the example
below, tag A drifted from station 14 to station 8 to station 2. Tag C
was likely within range of both receiver 14 and receiver 8, and was
detected by both receivers.
| ResidenceStart | Station.Name | ID | ResidenceEnd | ResidenceLength.days |
|---|---|---|---|---|
| 2004-06-24 17:30:42 | 14, 8 , 2 | A | 2004-06-25 06:47:40 | 0.5534491 days |
| 2004-02-16 17:28:37 | 14, 8 , 14, 8 , 14, 8 | C | 2004-02-17 16:52:39 | 0.9750231 days |
Residence events before applying drift:
Residence events, including drift residence events:
Note that, when using the mortsplot() function, the
legend entry for the station name of a drift residence event begins with
“Drift”, followed by an alphabetical list of all station names involved
in the residence event.
Applying drift
Drift is applied in morts() and
infrequent() by including five arguments:
ddd- the drift dataframe (see above for preparing a drift dataframe)from.station- a character string of the column name indddwith the station names where a tag could be drifting fromto.station- a character string of the column name indddwith the station names where a tag could be drifting todrift.cutoff- the maximum allowable time difference between detections to be considered a single residence event. This is usually the same cutoff that was used to gernerate the residence events from detection data.drift.units- the units ofdrift.cutoff
drift_ex<-infrequent(data=events,type="mort",ID="ID",station="Station.Name",
ddd=ddd,from.station="From",to.station="To",
drift.cutoff=1,drift.units="days")For morts(), there is a sixth argument
drift that specifies when drift should be applied. Drift
can be applied to identifying mortalities only
drift="morts", identifying thresholds only
drift="threshold", or both identifying mortalities and
thresholds drift="both".
Applying drift to thresholds drift="threshold" will
increase the threshold, and fewer tags may be flagged as mortalities.
There are few cases where this will be desirable, but it may be useful
for arrays with two or more overlapping receivers. A single drift
residence event would be generated for a tag located within range of two
receivers, and this drift residence event would be included in
identifying thresholds.
The most conservative option is drift="morts". This is
because drift residences are removed from identifying thresholds, and
thresholds will likely be shorter than if drift residences were
included. Including drift residences in identifying mortalities will
likely lengthen residence events, making it easier for an event to
exceed the threshold. See below for an example of identifying
mortalities with and without applying drift.
no_drift<-morts(data=events,type="mort",ID="ID",station="Station.Name",method="all")
drift_ex<-morts(data=events,type="mort",ID="ID",station="Station.Name",method="all",
drift="morts",ddd=ddd,from.station="From",to.station="To",
drift.cutoff=1,drift.units="days")
drift() function
If you are interested in exploring how drift residences are applied,
the drift() function that is called by morts()
and infrequent() is also available as a standalone
function. The arguments for drift() are the same as those
for the other functions.
drift.data<-drift(data=events,type="mort",ID="ID",station="Station.Name",
ddd=drift.dataframe,from.station="From",to.station="To",
cutoff=1,cutoff.units="days")