Camera Survey Analysis with tidycreel • tidycreel

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.

In both modes, use the camera_status column to identify failures. Unlike random missed observations (modeled via missing_sections), a camera failure leaves an informative gap: the number of anglers that passed during the outage is unknown and cannot be estimated from nearby observations. Remove these rows 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         0

The 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  weekday

Design 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_failure

The 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] 9

Effort Estimation

Camera effort is estimated by est_effort_camera(), not by the generic estimate_effort(). The distinction matters and is not cosmetic: a camera count is a daily total of arrivals, not an instantaneous count of anglers present, so summing it over days gives arrivals rather than effort. The generic estimator refuses a camera design for that reason:

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
estimate_effort(design_counter)
#> Error in `estimate_effort()`:
#> ! `estimate_effort()` does not estimate camera designs.
#> ✖ A camera count is a daily ingress total -- a count of arrivals -- not an
#>   instantaneous count of anglers present, so summing it over days gives
#>   arrivals rather than effort.
#> ℹ Use `est_effort_camera()`, which calibrates the counts against interview
#>   effort and propagates the calibration's uncertainty.
#> ℹ To expand the raw counts uncalibrated, pass `calibration = "none"` and
#>   `h_open` to that function. The reported standard error is `NA`, because the
#>   assumption of one angler-hour per count per hour open is unmeasured.

est_effort_camera() converts counts to effort by calibrating them against interview data: for each stratum it estimates rho, the hours of effort per camera count, from the days that carry both a count and interviews, then applies it to that stratum’s total counts. The counts are the first phase of a double sample and the days carrying interviews are the second, so the estimator is a double-sampling ratio estimator (Cochran 1977, Chapter 12); the ratio’s variance is Cochran’s eq. 2.46 with the finite-population correction omitted. Calibrating camera counts against paired creel observations is established practice – Hartill et al. (2016), van Poorten et al. (2015), Eckelbecker et al. (2022) – but each of those uses a different estimator, and this ratio-of-totals form is the package’s own application rather than a reproduction of any of them. See ?est_effort_camera for the full note.

example_camera_interviews records one row per angler, so n_anglers = 1 states that hours_fished is already an individual angler’s hours. Supplying it is what earns the result its angler-hours label — without it the ratio inherits whatever unit the effort column holds, which the package cannot identify, and the unit is reported as unknown.

effort_counter <- suppressWarnings(est_effort_camera(
  design_counter,
  interviews = example_camera_interviews,
  n_anglers  = 1
))
print(effort_counter)
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: camera_ratio
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Unit: angler-hours
#> Count-sampling SE: 4.277 (included in se)
#> Calibration SE: 11.71 (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     111.  12.5       12.5         0     81.4     140.     9

The estimate column is total angler-hours across the survey period. The standard error carries two named components, which se_components reports separately:

effort_counter$se_components
#> $count_sampling
#> [1] 4.277261
#> 
#> $calibration
#> [1] 11.70725

count_sampling is the sampling variance of the camera counts themselves; calibration is the variance of the estimated rho. Splitting them shows which half of the uncertainty dominates — here the calibration ratio does, so more interview days would buy more precision than more camera days would.

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              12.75

preprocess_camera_timestamps() sums all valid trip durations within each day and returns a data frame with date and daily_effort_hours. Rows where egress_time < ingress_time (negative durations) are dropped from that sum, and a warning reports how many.

Read that warning rather than dismissing it, because the returned data will not repeat it. A dropped pair does not make the day NA; it makes the day’s total smaller. On the fixture above, flipping a single ingress/egress pair takes one day from 11 hours to 7.75 — a 30% undercount that arrives as an ordinary number, indistinguishable in daily_effort_hours from a day that genuinely saw less fishing. The warning is the only place the exclusion is visible, so treat a non-zero count as something to resolve in the source data before estimating, not as routine noise.

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  weekend

Build 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

Ingress-egress counts are already in hours, so the calibration ratio here is close to dimensionless: it corrects camera-measured hours to interview-measured angler-hours rather than converting a count into a duration. The estimator is the same one.

ie_interviews <- example_camera_interviews[
  example_camera_interviews$date %in% daily_effort$date,
]

effort_ie <- suppressWarnings(est_effort_camera(
  design_ie,
  interviews = ie_interviews,
  n_anglers  = 1
))
print(effort_ie)
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: camera_ratio
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Unit: angler-hours
#> Count-sampling SE: 5.41 (known, but se is `NA`)
#> Calibration SE: NA (unknown, so 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     43.6    NA         NA         0       NA       NA     4

The standard error is NA here, and that is the estimator working rather than failing. These four sampling days give the weekend stratum only one day carrying both a count and interviews, and a single paired day gives the calibration ratio no measurable spread. Reporting 0 for that variance would present the most uncertain calibration as the most precise one, so the package carries it as NA and says which stratum is responsible. A second matched interview day in that stratum recovers a standard error.

Catch Estimation

Camera designs estimate effort only. estimate_catch_rate() still works — a catch rate comes from the interviews and does not involve the camera at all:

design_catch <- suppressMessages(add_interviews(
  design_counter,
  example_camera_interviews,
  catch       = walleye,
  effort      = hours_fished,
  trip_status = trip_status,
  n_anglers   = 1
))
#> Warning: 14 interviews have zero catch.
#> ℹ Zero catch may be valid (skunked) or indicate missing data.
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%
#> Unit: fish/angler-hour
#> 
#> # 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    40

A total catch is a different matter, and estimate_total_catch() refuses a camera design:

estimate_total_catch(design_catch)
#> Error in `estimate_total_catch()`:
#> ! `estimate_total_catch()` does not estimate camera designs.
#> ✖ A camera count is a daily ingress total -- a count of arrivals -- not an
#>   instantaneous count of anglers present, so summing it over days gives
#>   arrivals rather than effort.
#> ℹ Camera designs estimate effort only. `est_effort_camera()` returns
#>   angler-hours; there is no camera catch estimator to multiply them by.
#> ℹ A total from this function would multiply a rate per angler-hour by a count
#>   of arrivals and report the product as fish.

A total is the product of a rate and an effort, and this function builds its own effort by the generic route — the one that sums arrivals. It would therefore multiply a rate per angler-hour by a count of arrivals and report the product as fish. The number looked entirely plausible, which is why this is refused rather than warned about.

There is no camera catch estimator to reach for instead. est_effort_camera() gives calibrated angler-hours, and multiplying those by the catch rate above is arithmetic a reader can do deliberately — but the package will not do it silently, because the standard error of that product needs the calibration variance and the rate variance combined, and nothing here does that yet.

estimate_total_harvest() and estimate_total_release() refuse camera designs for the same reason.

Summary

The table below contrasts the two camera sub-modes:

Comparison of camera survey sub-modes in tidycreel
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 are estimated by the same function, est_effort_camera(), so no changes to downstream code are required when switching between them. Neither sub-mode goes through estimate_effort(), which refuses camera designs, nor through estimate_total_catch(), estimate_total_harvest() or estimate_total_release(), which refuse them for the same reason.

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.
Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. Section 2.11 (ratio estimator and its variance, eq. 2.46) and Chapter 12 (double sampling), Section 12.9, p. 343.
Hartill, B. W., Payne, G. W., Rush, N., & Bian, R. (2016). Bridging the temporal gap: continuous and cost-effective monitoring of dynamic recreational fisheries by web cameras and creel surveys. Fisheries Research, 183, 488-497.
van Poorten, B. T., Carruthers, T. R., Ward, H. G. M., & Varkey, D. A. (2015). Imputing recreational angling effort from time-lapse cameras using an hierarchical Bayesian model. Fisheries Research, 172, 265-273.
Eckelbecker, R. W., Coleman, T. S., & Catalano, M. J. (2022). Incorporating time-lapse digital cameras into creel surveys at three Alabama reservoirs. North American Journal of Fisheries Management, 42, 1349-1358.
Hartill, B. W., Taylor, S. M., Keller, K., & Weltersbach, M. S. (2020). Digital camera monitoring of recreational fishing effort: applications and challenges. Fish and Fisheries, 21, 204-215. A review of camera monitoring practice; it presents no estimator or variance.