Split survey lines at breaks in effort

Assigns new_trackno, an identifier for each stretch of continuous effort. These stretches, not the designated survey lines, are what get chopped into segments.

Usage

split_tracks(dat)

Arguments

dat: A data frame with LEGNO3 (see make_leg_id()), OnOff.Effort (see flag_effort()), and DATE, in survey order.

Value

dat with a character new_trackno column added.

The rule

Walking through a survey day's records in order, a new track begins when either

the line occupation changes (LEGNO3 differs from the previous record), or
effort breaks — this record and the next are both off effort.

Otherwise the record continues the current track. A single off-effort record between two on-effort ones is not treated as a break, so a momentary excursion logged as one point does not fragment the track, while a sustained break — circling for photographs, transiting around fog — does. This is the rule from the original create_new_track_nos().

A sustained break yields three tracks, not two: the effort before it, the break itself, and the effort after. The middle one carries no effort and is dropped by track_effort(). The original incremented the track number on every record of a break, so a five-record circling excursion produced four spurious tracks and left the first two off-effort records attached to the track that resumed afterwards. Grouping the break into one track leaves each on-effort track containing only on-effort records.

One consequence is worth knowing: a circling excursion logged as a single record (as in handbook Figure 2, event 13) does not split the track, because the record after it is back on effort. Computer-logged surveys record many positions while circling, so in modern data the break is seen and the track does split.

Track numbers restart at 1 on each survey date.

References

Becker, E.A., Forney, K.A., Ferguson, M.C., Foley, D.G., Smith, R.C., Barlow, J. and Redfern, J.V. (2010) Comparing California Current cetacean-habitat models developed using in situ and remotely sensed sea surface temperature data. Marine Ecology Progress Series 413:163-183. doi:10.3354/meps08696

Examples

path <- system.file("extdata", "narwc-example.csv", package = "distsamp")
dat <- split_tracks(flag_effort(make_leg_id(read_narwc(path))))
#> `read_narwc()` renamed 2 columns:
#>   LAT_DD  -> LATITUDE
#>   LONG_DD -> LONGITUDE
#> All matched an exact entry in the alias table; `narwc_column_mapping()` returns this, and `quiet = TRUE` silences it.
table(dat$DATE, dat$new_trackno)
#>             
#>               1  2  3  4  5  6
#>   2024-04-01  3 26  2 11  5  9
#>   2024-04-02 34  4  2  8  9  0