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Example angler interview data for aerial creel survey

Source: R/data.R
example_aerial_interviews.Rd

Angler interview data for an aerial creel survey at a Nebraska reservoir. Contains 48 interviews across 16 sampling days in June-July 2024, with 3 interviews per sampling day. Anglers target walleye and bass. All interviews are complete trips. Dates match example_aerial_counts.

Usage

example_aerial_interviews

Format

A data frame with 48 rows and 9 variables:

date

Interview date (Date class), June-July 2024.

day_type

Day type stratum: "weekday" or "weekend".

trip_status

Trip completion status: "complete" for all 48 interviews.

hours_fished

Numeric hours the party fished (range 1.0-5.0): the effort term of the catch rate in estimate_catch_rate. Aerial effort itself comes from the counts, not from these hours.

walleye_catch

Integer total walleye caught (kept + released).

walleye_kept

Integer walleye harvested; always <= walleye_catch.

bass_catch

Integer total bass caught (kept + released).

bass_kept

Integer bass harvested; always <= bass_catch.

n_anglers

Integer number of anglers in the party (1-4). Pass it to add_interviews() so the catch rate is per angler-hour, the unit of aerial effort.

Source

Simulated for package documentation.

See also

example_aerial_counts for matching count data, creel_design(), add_interviews(), estimate_catch_rate(), estimate_total_catch()

Other "Example Datasets": creel_counts_toy, creel_interviews_toy, example_aerial_counts, example_aerial_glmm_counts, example_ages, example_calendar, example_camera_counts, example_camera_interviews, example_camera_timestamps, example_catch, example_counts, example_ice_interviews, example_ice_sampling_frame, example_interviews, example_lengths, example_roving_interviews, example_season_calendar, example_season_counts, example_season_interviews, example_sections_calendar, example_sections_counts, example_sections_interviews

Examples

data(example_aerial_counts)
data(example_aerial_interviews)

# Build an aerial design and add interview data
aerial_cal <- data.frame(
  date = example_aerial_counts$date,
  day_type = example_aerial_counts$day_type,
  stringsAsFactors = FALSE
)
design <- creel_design(
  aerial_cal,
  date = date,
  strata = day_type,
  survey_type = "aerial",
  visibility_correction = "none",
  angler_ratio = 1,
  angler_ratio_se = 0,
  h_open = 14
)
design <- add_counts(design, example_aerial_counts)
design <- add_interviews(
  design,
  example_aerial_interviews,
  catch = walleye_catch,
  effort = hours_fished,
  trip_status = trip_status,
  n_anglers = n_anglers
)
#> ℹ 15 interviews have zero catch.
#>   Zero catch is usual; check it is not missing data recorded as 0.
#> ℹ Added 48 interviews: 48 complete (100%), 0 incomplete (0%)
estimate_catch_rate(design)
#> ℹ Using complete trips for CPUE estimation
#>   (n=48, 100% of 48 interviews) [default]
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Ratio-of-Means CPUE
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Unit: fish/angler-hour
#> 
#> # A tibble: 1 × 5
#>   estimate     se ci_lower ci_upper     n
#>      <dbl>  <dbl>    <dbl>    <dbl> <int>
#> 1    0.217 0.0339    0.151    0.284    48