Aerial creel surveys estimate total angler effort by conducting an
instantaneous count of anglers on the water from a low-flying aircraft.
Because the aircraft captures a snapshot of angler activity at a single
moment, the count must be expanded to total effort using the hours the
fishery is open (h_open) and the mean trip duration of
anglers on the water (L_bar). The basic estimator is:
\[\hat{E} = N_{obs} \times \frac{h_{open}}{v}\]
where \(N_{obs}\) is the observed (instantaneous) angler count, \(h_{open}\) is the number of hours the fishery is open per day, and \(v\) is the detection probability — the proportion of anglers present that are detected from the aircraft. The mean trip duration \(\bar{L}\) is estimated from ground interviews and enters the catch rate estimation step rather than the effort expansion step.
When not all anglers are visible from the air — for example, anglers
fishing under tree cover or in enclosed shelters — a visibility
correction adjusts the count upward. If observers detect only
85% of anglers present, the corrected effort estimate is scaled by \(1 / 0.85 \approx 1.18\), yielding a higher
and more accurate total. The visibility_correction argument
to creel_design() carries \(v\), and is required for an aerial design:
pass "none" to state explicitly that no correction
applies.
Field studies do not report \(v\) directly. The standard ground-truthing method reports the ratio
\[r = \frac{\text{ground count}}{\text{aerial count}}\]
which is greater than 1 whenever the aircraft
undercounts — Smucker et al. (2010) report \(r
= 2.69\) for shore anglers. Because
visibility_correction is a probability, convert before
supplying it:
\[v = 1 / r \qquad\text{so}\qquad r = 2.69 \;\longrightarrow\; v = 0.372\]
Passing \(r\) directly is rejected
by the (0, 1] check rather than silently accepted, because
the two parameterisations differ by a factor of \(r^2\) in the effort estimate.
\(v\) is estimated from paired
air–ground counts, and the standard field method reports its standard
error as routine output. Supply it as visibility_se — on
the same probability scale — and that uncertainty is propagated into the
effort SE. For an SE published on the ratio scale, convert with \(SE(v) = SE(r) / r^2\).
Because one estimate of \(v\)
divides every scaled count, it is a shared multiplier: its
contribution enters once at the total and does not shrink as more
flights are flown. Omitting visibility_se reports the
component as absent rather than as zero — a zero would be
indistinguishable from never having propagated it at all.
This vignette uses two built-in datasets representing a hypothetical summer walleye and bass fishery at a Nebraska reservoir in June-July 2024.
library(tidycreel)
data(example_aerial_counts)
data(example_aerial_interviews)
head(example_aerial_counts)
#> date day_type n_anglers
#> 1 2024-06-03 weekday 39
#> 2 2024-06-05 weekday 32
#> 3 2024-06-07 weekday 29
#> 4 2024-06-08 weekend 45
#> 5 2024-06-09 weekend 51
#> 6 2024-06-10 weekday 34
head(example_aerial_interviews)
#> date day_type trip_status hours_fished walleye_catch walleye_kept
#> 1 2024-06-03 weekday complete 3.4 3 2
#> 2 2024-06-03 weekday complete 3.2 0 0
#> 3 2024-06-03 weekday complete 2.5 0 0
#> 4 2024-06-05 weekday complete 4.9 1 0
#> 5 2024-06-05 weekday complete 2.2 1 0
#> 6 2024-06-05 weekday complete 2.3 1 0
#> bass_catch bass_kept
#> 1 0 0
#> 2 0 0
#> 3 0 0
#> 4 1 0
#> 5 0 0
#> 6 1 1example_aerial_counts contains 16 sampling days (one
overflight per day), each recording an instantaneous count of anglers on
the water. Weekday counts range from 15 to 40 anglers; weekend counts
range from 40 to 80. The example_aerial_interviews dataset
contains 48 angler interviews (3 per sampling day) with trip duration in
hours_fished and catch by species.
Build an aerial survey design with creel_design(). The
h_open argument is required for aerial surveys — it
specifies the number of hours the fishery is open each day, which sets
the expansion factor for the instantaneous count.
# Build the survey calendar from the unique count dates
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
)
print(design)
#>
#> ── Creel Survey Design ─────────────────────────────────────────────────────────
#> Type: "aerial"
#> Date column: date
#> Strata: day_type
#> Calendar: 16 days (2024-06-03 to 2024-07-06)
#> day_type: 2 levels
#> Counts: "none"
#> Interviews: "none"
#> Sections: "none"
#>
#> ── Aerial Survey Design ──
#>
#> Hours open (h_open): 14
#> Visibility correction: "none" (declared; SE is "NA")
#> Angler-to-people ratio: 1
#> Angler ratio SE: 0The printed design confirms the survey type, h_open, and
the number of sampling days in each stratum.
Attach the aerial count data with add_counts(). The
n_anglers column is auto-detected as the count
variable.
design <- add_counts(design, example_aerial_counts)
#> Warning in svydesign.default(ids = psu_formula, strata = strata_formula, : No
#> weights or probabilities supplied, assuming equal probabilityAerial effort estimation requires interview data to be attached
before calling estimate_effort(), because the estimator
uses the mean trip duration (\(\bar{L}\)) from ground interviews to
confirm the expansion factor. Attach the interview data with
add_interviews(), then estimate total effort.
design <- suppressWarnings(add_interviews(
design,
example_aerial_interviews,
catch = walleye_catch,
effort = hours_fished,
trip_status = trip_status
))
#> ℹ Added 48 interviews: 48 complete (100%), 0 incomplete (0%)effort <- suppressWarnings(estimate_effort(design))
print(effort)
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: aerial_total
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Effort target: sampled_days
#> Unit: angler-hours
#> Count-sampling SE: 658.3 (known, but se is `NA`)
#> within_day: 0 (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 8918 NA 658. 0 NA NA 16The estimate column is the projected total angler-hours
over the full survey period, and se_between quantifies
between-day variability in the instantaneous counts.
se, ci_lower and ci_upper are
NA, and that is the correct output for this design rather
than a gap in it. creel_design() above was given
visibility_correction = "none", which declares that no
detection study was done. The between-day component is known, but a
component of the total’s uncertainty is not, and a standard error that
silently omitted it would describe a survey more precise than this one.
NA says the uncertainty was never propagated;
0 would say it was measured and found to be nothing. The
next section supplies a correction and its standard error, and
the interval appears.
When aerial observers cannot detect all anglers on the water, the raw
count underestimates true effort. Supply a
visibility_correction to creel_design() to
account for this. A value of 0.85 means observers detected 85% of the
anglers actually present; the effort estimate is scaled up by \(1 / 0.85\).
Here the correction is accompanied by visibility_se, so
the reported effort SE includes the uncertainty in the correction itself
rather than treating 0.85 as exactly known.
design_corr <- creel_design(
aerial_cal,
date = date,
strata = day_type,
survey_type = "aerial",
h_open = 14,
visibility_correction = 0.85,
angler_ratio = 1,
angler_ratio_se = 0,
visibility_se = 0.04
)
design_corr <- add_counts(design_corr, example_aerial_counts)
#> Warning in svydesign.default(ids = psu_formula, strata = strata_formula, : No
#> weights or probabilities supplied, assuming equal probability
design_corr <- suppressWarnings(add_interviews(
design_corr,
example_aerial_interviews,
catch = walleye_catch,
effort = hours_fished,
trip_status = trip_status
))
#> ℹ Added 48 interviews: 48 complete (100%), 0 incomplete (0%)
effort_corr <- suppressWarnings(estimate_effort(design_corr))
print(effort_corr)
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: aerial_total
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Effort target: sampled_days
#> Unit: angler-hours
#> Count-sampling SE: 774.4 (included in se)
#> within_day: 0 (included in se)
#> visibility: 493.7 (included in se)
#> angler_ratio: 0 (included in se)
#>
#> # A tibble: 1 × 7
#> estimate se se_between se_within ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 10492. 918. 774. 0 8522. 12462. 16Comparing the two estimates: the corrected effort is higher than the uncorrected estimate because the visibility correction inflates the count to account for undetected anglers.
cat(
"Uncorrected effort:", round(effort$estimate[[1]], 0), "angler-hours\n",
"Corrected effort (v=0.85):", round(effort_corr$estimate[[1]], 0), "angler-hours\n"
)
#> Uncorrected effort: 8918 angler-hours
#> Corrected effort (v=0.85): 10492 angler-hoursAerial designs use the same interview workflow as other
tidycreel designs. The catch rate estimator computes CPUE
(walleye per angler-hour) from the complete-trip interviews already
attached to the design.
catch_rate <- suppressWarnings(estimate_catch_rate(design))
#> ℹ Using complete trips for CPUE estimation
#> (n=48, 100% of 48 interviews) [default]
print(catch_rate)
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Ratio-of-Means CPUE
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Unit: fish/party-hour
#>
#> # A tibble: 1 × 5
#> estimate se ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 0.413 0.0601 0.295 0.531 48estimate_total_catch() multiplies the CPUE estimate by
the total effort estimate to project total walleye catch over the survey
period.
total_catch <- suppressWarnings(estimate_total_catch(design))
print(total_catch)
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total Catch (Effort × CPUE)
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Effort target: sampled_days
#>
#> # A tibble: 1 × 5
#> estimate se ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 251. 45.1 160. 341. 48The delta-method standard error on total catch accounts for variance in both the effort estimate and the CPUE estimate.
The complete aerial survey workflow in tidycreel
consists of four steps:
creel_design(..., survey_type = "aerial", visibility_correction = v, h_open = N)
— define the survey with the required h_open expansion
factor and the required visibility_correction (a detection
probability, or "none" to declare that no correction
applies). Add visibility_se to propagate the correction’s
own uncertainty.add_counts(design, counts) — attach the instantaneous
angler count data from each overflight.add_interviews(design, interviews, catch = ..., effort = hours_fished, ...)
— attach ground interview data for catch rate estimation.estimate_effort(), estimate_catch_rate(),
estimate_total_catch() — run the estimators.All estimators return creel_estimates objects with point
estimates, standard errors, and 95% confidence intervals. Use
print() to display results.
Jones, C. M., & Pollock, K. H. (2012). Recreational survey methods: estimation of effort, harvest, and abundance. Chapter 19 in Fisheries Techniques (3rd ed.), pp. 883-919. American Fisheries Society.
Malvestuto, S. P. (1996). Sampling the recreational angler. Chapter 20 in Fisheries Techniques (2nd ed.), pp. 591-623. American Fisheries Society.
Pollock, K. H., Jones, C. M., & Brown, T. L. (1994). Angler Survey Methods and Their Applications in Fisheries Management. American Fisheries Society Special Publication 25. Chapter 12: Aerial counts.