Example multi-flight aerial count data for GLMM effort estimation
Source:R/data.R
example_aerial_glmm_counts.RdSimulated instantaneous angler counts from aerial overflights of a Nebraska reservoir, designed to demonstrate GLMM-based effort estimation following Askey (2018). Contains 48 rows: 12 survey days with 4 overflights per day at fixed hours (07:00, 10:00, 13:00, 16:00). Counts follow a diurnal curve (low at dawn, peak mid-morning, lower in afternoon) with day-level Poisson variability and a day random intercept.
Format
A data frame with 48 rows and 4 columns:
- date
Survey date (Date class), 12 days spaced 3 days apart starting 2024-06-03.
- day_type
Day type stratum:
"weekday"or"weekend", derived from the calendar date.- n_anglers
Instantaneous angler count from one aerial overflight (integer). Follows a diurnal curve with day-level random effects.
- time_of_flight
Hour of the aerial overflight (numeric). One of
7.0,10.0,13.0, or16.0.
References
Askey, P.J., Ward, H., Godin, T., Boucher, M., and Northrup, S. (2018). Angler effort estimates from instantaneous aerial counts: use of high-frequency time-lapse camera data to inform model-based estimators. North American Journal of Fisheries Management, 38, 194-209. doi:10.1002/nafm.10010
See also
example_aerial_counts for the simple single-flight dataset,
estimate_effort_aerial_glmm() for the GLMM-based estimator,
creel_design(), add_counts()
Other "Example Datasets":
creel_counts_toy,
creel_interviews_toy,
example_aerial_counts,
example_aerial_interviews,
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_sections_calendar,
example_sections_counts,
example_sections_interviews
Examples
data(example_aerial_glmm_counts)
head(example_aerial_glmm_counts)
#> date day_type n_anglers time_of_flight
#> 1 2024-06-03 weekday 3 7
#> 2 2024-06-03 weekday 30 10
#> 3 2024-06-03 weekday 65 13
#> 4 2024-06-03 weekday 50 16
#> 5 2024-06-06 weekday 5 7
#> 6 2024-06-06 weekday 15 10
# The workflow below fits a GLMM, so it needs lme4 (a Suggests).
if (rlang::is_installed("lme4")) {
# Build an aerial design and estimate effort with GLMM correction
aerial_cal <- data.frame(
date = unique(example_aerial_glmm_counts$date),
day_type = unique(example_aerial_glmm_counts[, c("date", "day_type")])[["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_glmm_counts, count_col = n_anglers)
result <- estimate_effort_aerial_glmm(design, time_col = time_of_flight)
print(result)
}
#> Warning: `counts` has 36 repeated sampling units, with no count time to tell them apart.
#> ℹ The repeated rows are keyed on date and day_type.
#> ℹ Estimators that sum these rows refuse them; supply `count_time_col` if they
#> are repeat counts, or `unit_cols` if they are distinct units.
#> Warning: No weights or probabilities supplied, assuming equal probability
#> Warning: iteration limit reached
#> ℹ Integration window start derived from data: 6.5 h (earliest flight - 0.5 h).
#> Specify `open_start` in `creel_design()` for a fixed fishery opening time.
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: aerial_glmm_total
#> Variance: delta
#> Confidence level: 95%
#> Effort target: sampled_days
#> model: 412 (known, but se is `NA`)
#> visibility: NA (unknown, so se is `NA`)
#> angler_ratio: 0 (known, but se is `NA`)
#>
#> # A tibble: 1 × 7
#> estimate se se_between se_within ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 4729. NA NA NA NA NA 48