Overview
Remote cameras mounted at boat launches or trail heads record angler
arrivals with no observer present. This enables 24-hour coverage of
access points that would otherwise require continuous staffing. The
tidycreel package supports two camera-based sub-modes:
- Counter mode — the camera records a single daily total of incoming anglers (e.g., a passive infrared counter). The data arrive as one row per sampling day with a numeric ingress count.
-
Ingress-egress mode — the camera records individual
arrival and departure timestamps for each angler or party. The raw data
are paired POSIXct timestamps that are preprocessed with
preprocess_camera_timestamps()before entering the standard effort estimation workflow.
Both modes share a key concept: the camera_status
column. Unlike random missed observations (modeled via
missing_sections), a camera failure is an
informative gap — the number of anglers that passed
during the outage is unknown and cannot be estimated from the
surrounding data. These rows must be removed before calling
add_counts().
Example Data
This vignette uses three built-in datasets representing a hypothetical summer creel survey at a Nebraska reservoir boat launch in June 2024.
library(tidycreel)
data(example_camera_counts)
data(example_camera_timestamps)
data(example_camera_interviews)
head(example_camera_counts)
#> date day_type ingress_count camera_status
#> 1 2024-06-03 weekday 48 operational
#> 2 2024-06-04 weekday 55 operational
#> 3 2024-06-05 weekday 43 operational
#> 4 2024-06-07 weekend 91 operational
#> 5 2024-06-08 weekend 85 operational
#> 6 2024-06-10 weekday 50 operational
head(example_camera_timestamps)
#> date day_type ingress_time egress_time
#> 1 2024-06-03 weekday 2024-06-03 06:30:00 2024-06-03 09:45:00
#> 2 2024-06-03 weekday 2024-06-03 07:15:00 2024-06-03 12:15:00
#> 3 2024-06-03 weekday 2024-06-03 08:00:00 2024-06-03 10:45:00
#> 4 2024-06-04 weekday 2024-06-04 05:45:00 2024-06-04 10:00:00
#> 5 2024-06-04 weekday 2024-06-04 06:30:00 2024-06-04 10:00:00
#> 6 2024-06-04 weekday 2024-06-04 07:00:00 2024-06-04 12:30:00
head(example_camera_interviews)
#> date day_type trip_status hours_fished walleye walleye_kept bass
#> 1 2024-06-03 weekday complete 3.7 0 0 3
#> 2 2024-06-03 weekday complete 2.5 0 0 0
#> 3 2024-06-03 weekday complete 1.4 1 0 1
#> 4 2024-06-03 weekday complete 4.0 1 1 2
#> 5 2024-06-03 weekday complete 2.8 0 0 1
#> 6 2024-06-04 weekday complete 0.7 1 0 1
#> bass_kept
#> 1 2
#> 2 0
#> 3 0
#> 4 2
#> 5 0
#> 6 0The example_camera_counts dataset contains 10 rows: nine
operational days plus one battery failure gap. The
example_camera_timestamps dataset has 14 raw
arrival/departure pairs across four sampling days. The
example_camera_interviews dataset has 40 complete angler
interviews targeting walleye and bass across eight sampling days.
Counter Mode
Build a Survey Calendar
Counter-mode surveys require a calendar that covers the sampling frame. Here we build one from the unique dates in the counts and interview data.
# Collect all unique sampling dates across datasets
all_dates <- sort(unique(c(
example_camera_counts$date,
example_camera_interviews$date
)))
# Assign day type for each date (weekday = Mon-Fri, weekend = Sat-Sun)
cam_calendar <- data.frame(
date = all_dates,
day_type = ifelse(
weekdays(all_dates) %in% c("Saturday", "Sunday"),
"weekend", "weekday"
),
stringsAsFactors = FALSE
)
head(cam_calendar)
#> date day_type
#> 1 2024-06-03 weekday
#> 2 2024-06-04 weekday
#> 3 2024-06-05 weekday
#> 4 2024-06-07 weekday
#> 5 2024-06-08 weekend
#> 6 2024-06-10 weekdayDesign Construction
Build a camera design using creel_design() with
survey_type = "camera". The camera_mode
argument is required. Omitting it produces an informative error:
creel_design(
cam_calendar,
date = date, strata = day_type,
survey_type = "camera"
)
#> Error in `creel_design()`:
#> ! `camera_mode` is required for "camera" survey designs.
#> ✖ No `camera_mode` supplied.
#> ℹ Valid values: "counter" and "ingress_egress".Build the correct design with
camera_mode = "counter":
design_counter <- creel_design(
cam_calendar,
date = date,
strata = day_type,
survey_type = "camera",
camera_mode = "counter"
)
print(design_counter)
#>
#> ── Creel Survey Design ─────────────────────────────────────────────────────────
#> Type: "camera"
#> Date column: date
#> Strata: day_type
#> Calendar: 10 days (2024-06-03 to 2024-06-15)
#> day_type: 2 levels
#> Counts: "none"
#> Interviews: "none"
#> Sections: "none"
#>
#> ── Camera Survey Design ──
#>
#> Camera mode: "counter"Handling Informative Gaps
The battery failure row on 2024-06-11 has
ingress_count = NA. This is not a random unsampled day —
the camera was physically unable to record. Including it in
add_counts() would silently propagate a missing value into
the Horvitz-Thompson estimator.
# The gap row
subset(example_camera_counts, camera_status != "operational")
#> date day_type ingress_count camera_status
#> 7 2024-06-11 weekday NA battery_failureThe correct approach is to filter to operational
rows before calling add_counts(). This is
fundamentally different from missing_sections, which models
probabilistic non-coverage within a sampled period. A camera failure
means no data exist — the effort during that period is unknown.
# Keep only days when the camera was working
counts_clean <- subset(example_camera_counts, camera_status == "operational")
nrow(counts_clean) # 9 operational rows
#> [1] 9Effort Estimation
design_counter <- add_counts(design_counter, counts_clean)
#> Warning in svydesign.default(ids = psu_formula, strata = strata_formula, : No
#> weights or probabilities supplied, assuming equal probability
effort_counter <- suppressWarnings(estimate_effort(design_counter))
print(effort_counter)
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Effort target: sampled_days
#>
#> # A tibble: 1 × 7
#> estimate se se_between se_within ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 613 20.9 20.9 0 564. 662. 9The estimate column is the Horvitz-Thompson estimate of
total ingress angler visits across the full survey period, with a 95%
confidence interval.
Ingress-Egress Mode
When the camera records individual arrival and departure timestamps,
use preprocess_camera_timestamps() to aggregate the raw
pairs into daily effort hours before calling
add_counts().
Preprocess Timestamps
daily_effort <- preprocess_camera_timestamps(
example_camera_timestamps,
date_col = date,
ingress_col = ingress_time,
egress_col = egress_time
)
print(daily_effort)
#> date daily_effort_hours
#> 1 2024-06-03 11.00
#> 2 2024-06-04 14.75
#> 3 2024-06-08 19.50
#> 4 2024-06-09 12.75preprocess_camera_timestamps() sums all valid trip
durations within each day and returns a data frame with
date and daily_effort_hours. A warning is
issued for any rows where egress_time < ingress_time
(negative durations); those rows are set to NA and excluded
from the daily sum.
Because add_counts() requires all design strata columns,
merge the day type back in from the raw timestamps:
day_type_key <- unique(example_camera_timestamps[, c("date", "day_type")])
daily_effort <- merge(daily_effort, day_type_key, by = "date")
print(daily_effort)
#> date daily_effort_hours day_type
#> 1 2024-06-03 11.00 weekday
#> 2 2024-06-04 14.75 weekday
#> 3 2024-06-08 19.50 weekend
#> 4 2024-06-09 12.75 weekendBuild the Design and Estimate Effort
ie_calendar <- data.frame(
date = daily_effort$date,
day_type = daily_effort$day_type,
stringsAsFactors = FALSE
)
design_ie <- creel_design(
ie_calendar,
date = date,
strata = day_type,
survey_type = "camera",
camera_mode = "ingress_egress"
)
design_ie <- add_counts(design_ie, daily_effort)
#> Warning in svydesign.default(ids = psu_formula, strata = strata_formula, : No
#> weights or probabilities supplied, assuming equal probability
effort_ie <- suppressWarnings(estimate_effort(design_ie))
print(effort_ie)
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Effort target: sampled_days
#>
#> # A tibble: 1 × 7
#> estimate se se_between se_within ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 58 7.72 7.72 0 24.8 91.2 4Interview-Based Catch Estimation
Camera designs support the same interview workflow as standard
instantaneous designs. Use the counter-mode design (which spans all
eight interview dates) and attach the interview data with
add_interviews().
design_catch <- add_interviews(
design_counter,
example_camera_interviews,
catch = walleye,
effort = hours_fished,
trip_status = trip_status
)
#> ℹ No `n_anglers` provided — assuming 1 angler per interview.
#> ℹ Pass `n_anglers = <column>` to use actual party sizes for angler-hour
#> normalization.
#> Warning: 14 interviews have zero catch.
#> ℹ Zero catch may be valid (skunked) or indicate missing data.
#> ℹ Added 40 interviews: 40 complete (100%), 0 incomplete (0%)Catch Rate
estimate_catch_rate() computes the ratio-of-means CPUE
(walleye per angler-hour) using only complete trips.
catch_rate <- suppressWarnings(estimate_catch_rate(design_catch))
#> ℹ Using complete trips for CPUE estimation
#> (n=40, 100% of 40 interviews) [default]
print(catch_rate)
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Ratio-of-Means CPUE
#> Variance: Taylor linearization
#> Confidence level: 95%
#>
#> # A tibble: 1 × 5
#> estimate se ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 0.453 0.0755 0.305 0.601 40Total Catch
estimate_total_catch() multiplies the CPUE estimate by
the total effort estimate to produce a projected total walleye catch
over the survey period.
total_catch <- suppressWarnings(estimate_total_catch(design_catch))
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>
#> 1 283. 46.0 192. 373. 40Summary
The table below contrasts the two camera sub-modes:
| Feature | Counter mode | Ingress-egress mode |
|---|---|---|
| Input data | One count per day | POSIXct arrival/departure pairs |
| Preprocessing step | None (counts used directly) | preprocess_camera_timestamps() |
| Effort unit | Daily ingress count | Daily effort-hours |
| Gap handling | Exclude camera_status != ‘operational’ rows | Negative durations warned and excluded |
| camera_mode value | “counter” | “ingress_egress” |
Both sub-modes feed into the same estimate_effort(),
add_interviews(), estimate_catch_rate(), and
estimate_total_catch() pipeline — no changes to downstream
code are required when switching modes.
References
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.