Instantaneous-Count Surveys: Access and Roving Interviews

library(tidycreel)

What this design is

The most common creel survey has two parts, collected separately:

Instantaneous counts give effort. A clerk counts the anglers fishing at a moment, and that count stands for a period of time.
Interviews give catch rates. The clerk asks parties how long they have fished and what they have caught.

A total is effort times rate. The interviews come in two kinds, and the only difference in tidycreel is one argument, interview_type:

	Access interviews	Roving interviews
Where the clerk is	At the access point, as parties leave	Moving through the fishery
Trips	Mostly complete	All incomplete: the party is still fishing
Catch rate	Ratio of means, complete trips	Mean of ratios, all trips but the shortest
`interview_type`	`"access"` (the default)	`"roving"`

The counts, and so the effort, are the same either way. This page runs one season’s counts with each kind of interview. vignette("tidycreel") walks through the access half step by step; this page is the short version, plus roving.

Design and counts

design <- creel_design(example_season_calendar, date = date, strata = day_type)
design <- add_counts(design, example_season_counts,
  count_col = total_anglers,
  period_length_col = daylight_hours
)

One count was made on each of the 36 sampled days, and it stands for the 14-hour fishing day, so daylight_hours is the period length. Effort for a day is the count times that length, in angler-hours. Without period_length_col the unit of effort is unknown and estimate_effort() warns.

Effort

estimate_effort(design, target = "period_total")
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Effort target: period_total
#> Unit: angler-hours
#> 
#> # A tibble: 1 × 7
#>   estimate    se se_between se_within ci_lower ci_upper     n
#>      <dbl> <dbl>      <dbl>     <dbl>    <dbl>    <dbl> <int>
#> 1    9994. 1286.      1286.         0    7380.   12609.    36

About 9,994 angler-hours over the 120-day season. target = "period_total" asks for the season; the default, "sampled_days", is the effort on the 36 days counted. Both kinds of interview below use this effort.

Access interviews

access <- add_interviews(design, example_season_interviews,
  catch = catch_total,
  harvest = catch_kept,
  effort = hours_fished,
  trip_status = trip_status,
  n_anglers = n_anglers
)
#> ℹ 153 interviews have zero catch.
#>   Zero catch is usual; check it is not missing data recorded as 0.
#> ℹ Added 231 interviews: 172 complete (74%), 59 incomplete (26%)

estimate_catch_rate(access)
#> ℹ Using complete trips for CPUE estimation
#>   (n=172, 74.5% of 231 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    0.221 0.0415    0.139    0.302   172
access_catch <- estimate_total_catch(access, target = "period_total")
access_catch
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total Catch (Effort × CPUE)
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Effort target: period_total
#> Unit: fish
#> 
#> # A tibble: 1 × 5
#>   estimate    se ci_lower ci_upper     n
#>      <dbl> <dbl>    <dbl>    <dbl> <int>
#> 1    2230.  506.    1231.    3230.   172

interview_type is left at "access". The catch rate is a ratio of means over the 172 complete trips: total catch divided by total angler-hours, about 0.22 fish per angler-hour. The 59 incomplete trips are left out, because a party met while still fishing is more likely to be met the longer it stays. Times about 9,994 angler-hours, that is about 2,230 fish.

Roving interviews

head(example_roving_interviews)
#>         date day_type interview_id trip_status hours_fished n_anglers
#> 1 2024-05-06  weekday            1  incomplete         1.85         1
#> 2 2024-05-06  weekday            2  incomplete         1.74         2
#> 3 2024-05-06  weekday            3  incomplete         1.74         1
#> 4 2024-05-06  weekday            4  incomplete         0.56         2
#> 5 2024-05-06  weekday            5  incomplete         2.90         3
#> 6 2024-05-07  weekday            6  incomplete         2.51         1
#>   catch_total catch_kept
#> 1           2          0
#> 2           0          0
#> 3           0          0
#> 4           0          0
#> 5           0          0
#> 6           2          1

roving <- add_interviews(design, example_roving_interviews,
  catch = catch_total,
  harvest = catch_kept,
  effort = hours_fished,
  trip_status = trip_status,
  trip_duration = hours_fished,
  n_anglers = n_anglers,
  interview_type = "roving"
)
#> ℹ 133 interviews have zero catch.
#>   Zero catch is usual; check it is not missing data recorded as 0.
#> ℹ Added 212 interviews: 0 complete (0%), 212 incomplete (100%)

Every party in example_roving_interviews was still fishing, so catch_total and hours_fished are the catch and the hours so far. trip_duration is the time the party had fished when interviewed, which here is hours_fished. tidycreel uses it to drop very short trips, below.

estimate_catch_rate(roving)
#> ℹ Roving design: using all 212 interviews for CPUE via MOR [auto]
#>   (0 complete, 212 incomplete)
#>   Override with `use_trips = 'complete'` for access-point estimation.
#> Warning: ! MOR truncation: 29 trips excluded (13.7%)
#> ℹ Trips < 0.5 hours excluded to prevent unstable variance
#> ! High truncation rate may indicate data quality issues
#> ℹ Consider reviewing trip duration data for errors
#> ℹ Repeats on this design are recorded, not printed: see `get_warnings()`.
#> ── MOR Estimator (All Trips) ───────────────────────────────────────────────────
#> Averages per-trip CPUE ratios over 183 interviews (183 incomplete).
#> Truncation: 29 trips excluded (< 0.5 hours)
#> 
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Mean-of-Ratios 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    0.191 0.0193    0.154    0.229   183

With interview_type = "roving", tidycreel uses the mean-of-ratios estimator of Hoenig et al. (1997) over all trips: each party’s catch per angler-hour so far, averaged with each party counted once per angler in it, because meeting a party meets all its anglers. A ratio of means would be wrong here. A roving clerk meets long trips more often than short ones, and the ratio of means gives those trips more weight again. Averaging each party’s own rate does not.

The warning is expected, and is the subject of this paragraph. A party interviewed a few minutes into its trip has a catch rate that is mostly noise: one fish in ten minutes is six fish an hour. By default trips that have fished less than half an hour (truncate_at = 0.5, following Hoenig et al. 1997) are left out of the rate, and tidycreel reports how many. Here it is 29 of 212 parties. The warning also suggests this many short trips may be a data problem. It is not here: a clerk who meets parties at random points in their trips meets some of them early. Check your own data before deciding the same.

The rate is about 0.19 fish per angler-hour from the 183 parties that had fished at least half an hour. The parties were simulated with a true catch rate of 0.22 fish per angler-hour, inside the 95% confidence interval of 0.15 to 0.23.

roving_catch <- estimate_total_catch(roving, target = "period_total")
roving_catch
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total Catch (Effort × CPUE)
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Effort target: period_total
#> Unit: fish
#> 
#> # A tibble: 1 × 5
#>   estimate    se ci_lower ci_upper     n
#>      <dbl> <dbl>    <dbl>    <dbl> <int>
#> 1    1900.  313.    1283.    2516.   183
estimate_total_harvest(roving, target = "period_total")
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total Harvest (Effort × HPUE)
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Effort target: period_total
#> Unit: fish
#> 
#> # A tibble: 1 × 5
#>   estimate    se ci_lower ci_upper     n
#>      <dbl> <dbl>    <dbl>    <dbl> <int>
#> 1     580.  126.     331.     830.   183

The totals multiply the same season effort by the roving rates: about 1,900 fish caught and 580 kept. Each estimate built on a roving rate drops the short trips again. The warning printed once for this design; each estimate still records it, and get_warnings(roving_catch) lists it.

Results as data

as_tibble() turns an estimate into a table with one row per estimate and the metadata in columns. Binding the two catch totals shows them side by side:

totals <- rbind(
  data.frame(
    interviews = "access",
    tibble::as_tibble(access_catch)
  ),
  data.frame(
    interviews = "roving",
    tibble::as_tibble(roving_catch)
  )
)
totals[, c("interviews", "estimate", "se", "estimator", "unit")]
#>   interviews estimate       se      estimator unit
#> 1     access 2230.484 506.2577 ratio-of-means fish
#> 2     roving 1899.513 312.5565            mor fish

The two are different sets of interviews from the same days, so their totals need not agree; they should be close when both kinds of interview describe the same fishery.

Plot

ggplot2::autoplot(
  estimate_total_catch(roving, by = day_type, target = "period_total")
)

Total catch on weekdays and weekends from the roving interviews, with 95 percent confidence intervals.

References

Hoenig, J. M., Jones, C. M., Pollock, K. H., Robson, D. S., and Wade, D. L. (1997). Calculation of catch rate and total catch in roving surveys of anglers. Biometrics 53:306–317.

Pollock, K. H., Jones, C. M., and Brown, T. L. (1994). Angler Survey Methods and Their Applications in Fisheries Management. American Fisheries Society Special Publication 25.