Interview-Based Catch Estimation • tidycreel

Introduction

This vignette extends the effort estimation workflow covered in “Getting Started with tidycreel” to interview-based catch and harvest estimation. The complete workflow involves five steps:

Design: Define your survey calendar and stratification
Counts: Attach instantaneous count observations
Interviews: Attach complete trip interview data
CPUE: Estimate catch per unit effort
Total Catch: Combine effort and CPUE estimates

The package uses ratio-of-means estimation for catch per unit effort (CPUE), which is appropriate for access point surveys with complete trip interviews. This estimator accounts for the correlation between catch and effort within each interview.

Survey Design and Count Data

We start with the same design and count data workflow from the “Getting Started” vignette:

library(tidycreel)

# Load example calendar and counts
data(example_calendar)
data(example_counts)

# Create design with counts
design <- creel_design(example_calendar, date = date, strata = day_type)
design <- add_counts(design, example_counts)
#> Warning in svydesign.default(ids = psu_formula, strata = strata_formula, : No
#> weights or probabilities supplied, assuming equal probability

# Estimate total effort
effort_est <- estimate_effort(design)
#> Warning: Instantaneous counts were expanded without a period length.
#> ℹ No `period_length_col` was supplied to `add_counts()`, so the estimate is the
#>   count column summed over days.
#> ! If that column holds an instantaneous angler count, the result is in
#>   angler-days, not angler-hours.
#> ℹ Supply the period each count was randomised within: `add_counts(design,
#>   counts, period_length_col = <col>)`.
#> This warning is displayed once per session.
print(effort_est)
#> 
#> ── 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     372.  13.2       13.2         0     344.     401.    14

The effort estimate provides the total angler-hours for the survey period, which we’ll combine with CPUE to estimate total catch.

Adding Interview Data

Next, we attach interview data containing catch and effort for complete trips:

# Load example interview data
data(example_interviews)
head(example_interviews)
#>         date hours_fished catch_total catch_kept trip_status trip_duration
#> 1 2024-06-01          2.0           5          2    complete           2.0
#> 2 2024-06-01          3.5           8          5    complete           3.5
#> 3 2024-06-02          1.5           2          1    complete           1.5
#> 4 2024-06-02          2.0           3          2  incomplete           1.0
#> 5 2024-06-03          2.5           6          3    complete           2.5
#> 6 2024-06-03          4.0          12          8    complete           4.0
#>   interview_id angler_type angler_method species_sought n_anglers refused
#> 1            1        bank          bait        walleye         2   FALSE
#> 2            2        boat    artificial        walleye         1   FALSE
#> 3            3        bank          bait           bass         3   FALSE
#> 4            4        bank           fly        panfish         2   FALSE
#> 5            5        boat    artificial        walleye         1   FALSE
#> 6            6        boat          bait           bass         4   FALSE

# Attach interviews to the design
design <- add_interviews(design, example_interviews,
  catch = catch_total,
  effort = hours_fished,
  n_anglers = n_anglers,
  harvest = catch_kept,
  trip_status = trip_status,
  trip_duration = trip_duration
)
#> ℹ Added 22 interviews: 17 complete (77%), 5 incomplete (23%)
print(design)
#> 
#> ── Creel Survey Design ─────────────────────────────────────────────────────────
#> Type: "instantaneous"
#> Date column: date
#> Strata: day_type
#> Calendar: 14 days (2024-06-01 to 2024-06-14)
#> day_type: 2 levels
#> Counts: 14 observations
#> PSU column: date
#> Count column: effort_hours
#> Party-size term: not carried
#> Count type: "instantaneous"
#> Survey: <survey.design2> (constructed)
#> Interviews: 22 observations
#> Type: "access"
#> Catch: catch_total
#> Effort: hours_fished
#> Harvest: catch_kept
#> Trip status: 17 complete, 5 incomplete
#> Party size: n_anglers
#> Survey: <survey.design2> (constructed)
#> Sections: "none"

The add_interviews() function maps the interview data columns to the design structure. The design now shows both count and interview data attached. Interview data is treated as a parallel data stream to count data—the two datasets do not need to align on specific dates.

Estimating CPUE

With interview data attached, we can estimate catch per unit effort:

# Estimate CPUE
cpue_est <- estimate_catch_rate(design)
#> ℹ Using complete trips for CPUE estimation
#>   (n=17, 77.3% of 22 interviews) [default]
#> Warning: Small sample size for CPUE estimation.
#> ! Sample size is 17. Ratio estimates are more stable with n >= 30.
#> ℹ Variance estimates may be unstable with n < 30.
print(cpue_est)
#> 
#> ── 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    0.967 0.110    0.752     1.18    17

The CPUE estimate uses a ratio-of-means estimator: total catch divided by total effort across all interviews. This estimator is appropriate for access point surveys because it accounts for the correlation between catch and effort within each trip. The variance is computed using the delta method, accounting for the covariance between numerator and denominator.

For details on the ratio-of-means formula and variance calculation, see ?estimate_catch_rate.

Estimating Total Catch

We can combine the effort and CPUE estimates to compute total catch:

# Estimate total catch
total_catch_est <- estimate_total_catch(design)
#> Warning: `estimate_total_catch()` is pooling over domains the counts do not classify:
#> angler_method and species_sought.
#> ! The rate differs across their levels in these interviews (angler_method:
#>   artificial 1.121, bait 0.8, fly 1.31 and species_sought: bass 1.018, panfish
#>   1.2, walleye 0.933), so the total depends on the interview sample's mix over
#>   those domains.
#> ℹ Without the domain in the counts the total is `E_total * rate_pooled`,
#>   weighted by the interview mix rather than the effort mix. Interview selection
#>   is not proportional to effort by construction (Malvestuto 1996).
#> ℹ This is a risk, not an error: the counts carry no composition to check
#>   against, so it cannot be verified from the data.
#> ℹ Classifying angler_method and species_sought in the count data removes the
#>   assumption -- the total becomes `sum(E_h * rate_h)`.
#> This warning is displayed once per session.
print(total_catch_est)
#> 
#> ── 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     364.  47.9     262.     466.    17

The total catch estimate multiplies the effort and CPUE estimates. The variance is computed using the delta method with the formula:

Var(E × C) = E² Var(C) + C² Var(E)

This formula assumes independence between the count and interview data streams, which is appropriate since they are collected through separate sampling processes. See ?estimate_total_catch for more details on the variance propagation.

Estimating Harvest

The package distinguishes between total catch (all fish caught) and harvest (fish kept). We can estimate harvest per unit effort (HPUE) and total harvest:

# Estimate HPUE
hpue_est <- estimate_harvest_rate(design)
#> ℹ Filtering to complete trips for HPUE estimation
#>   (n=17, 77.3% of 22 interviews) [default]
#> Warning: Small sample size for harvest estimation.
#> ! Sample size is 17. Ratio estimates are more stable with n >= 30.
#> ℹ Variance estimates may be unstable with n < 30.
print(hpue_est)
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Ratio-of-Means HPUE
#> 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    0.595 0.0616    0.475    0.716    17

# Estimate total harvest
total_harvest_est <- estimate_total_harvest(design)
#> Warning: `estimate_total_harvest()` is pooling over a domain the counts do not classify:
#> angler_method.
#> ! The rate differs across its levels in these interviews (angler_method:
#>   artificial 0.667, bait 0.483, fly 0.897), so the total depends on the
#>   interview sample's mix over that domain.
#> ℹ Without the domain in the counts the total is `E_total * rate_pooled`,
#>   weighted by the interview mix rather than the effort mix. Interview selection
#>   is not proportional to effort by construction (Malvestuto 1996).
#> ℹ This is a risk, not an error: the counts carry no composition to check
#>   against, so it cannot be verified from the data.
#> ℹ Classifying angler_method in the count data removes the assumption -- the
#>   total becomes `sum(E_h * rate_h)`.
#> This warning is displayed once per session.
print(total_harvest_est)
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total Harvest (Effort × HPUE)
#> 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     225.  28.1     165.     284.    17

Harvest estimation uses the same ratio-of-means approach as CPUE, but with the harvest column (catch_kept) as the numerator. As expected, total harvest is lower than total catch since not all caught fish are kept.

Grouped Estimation

Like effort estimation, CPUE and total catch functions support grouped estimation using the by parameter:

# Estimate CPUE by day_type (not evaluated - example data too small)
cpue_by_day <- estimate_catch_rate(design, by = day_type)

# Estimate total catch by day_type
total_catch_by_day <- estimate_total_catch(design, by = day_type)

Note: The example data has insufficient interview sample sizes for grouped estimation (weekend n=9, weekday n=13). Ratio estimation requires at least 10 observations per group for stability. In real surveys, aim for at least 30 interviews per stratum for reliable grouped estimates.

Variance Methods

Like effort estimation, CPUE estimation supports multiple variance methods. The default is Taylor linearization, but bootstrap and jackknife are also available:

# Bootstrap variance estimation
set.seed(42) # For reproducibility
cpue_boot <- estimate_catch_rate(design, variance = "bootstrap")
#> ℹ Using complete trips for CPUE estimation
#>   (n=17, 77.3% of 22 interviews) [default]
#> Warning: Small sample size for CPUE estimation.
#> ! Sample size is 17. Ratio estimates are more stable with n >= 30.
#> ℹ Variance estimates may be unstable with n < 30.

# Jackknife variance estimation
cpue_jk <- estimate_catch_rate(design, variance = "jackknife")
#> ℹ Using complete trips for CPUE estimation
#>   (n=17, 77.3% of 22 interviews) [default]
#> Warning: Small sample size for CPUE estimation.
#> ! Sample size is 17. Ratio estimates are more stable with n >= 30.
#> ℹ Variance estimates may be unstable with n < 30.

All three methods produce similar results for these data. Use bootstrap or jackknife when you want to verify Taylor linearization assumptions or when working with complex grouped estimates.

When to use each method:

Taylor linearization (default): Computationally efficient and appropriate for most smooth statistics. This is the recommended default.
Bootstrap: Use when working with non-smooth statistics or when you want to verify Taylor linearization assumptions. More computationally intensive.
Jackknife: Alternative resampling method that is deterministic (unlike bootstrap). Useful for verification or when bootstrap is too slow.

Age Structure Estimation

Age data collected during interviews can be used to estimate a pressure-weighted age distribution and design-weighted mean age for the catch or harvest. Attach age records with add_ages(), then call est_age_distribution() followed by est_mean_age().

data(example_ages)
data(example_catch)
head(example_ages)
#>   interview_id species age age_type
#> 1            1 walleye   4  harvest
#> 2            1 walleye   3  harvest
#> 3            1 walleye   5  harvest
#> 4            2    bass   2  harvest
#> 5            2    bass   3  harvest
#> 6            6 walleye   6  harvest

# Ages are read from a subsample of the catch, so the age-class totals are
# scaled onto the reported catch. Grouping by species needs this table, which
# is the only record of catch per species.
design <- add_catch(
  design,
  example_catch,
  catch_uid     = interview_id,
  interview_uid = interview_id,
  species       = species,
  count         = count,
  catch_type    = catch_type
)

# Attach age data — one row per aged fish
design <- add_ages(
  design,
  example_ages,
  age_uid      = interview_id,
  interview_uid = interview_id,
  species      = species,
  age          = age,
  age_type     = age_type
)

# Estimate weighted age frequency by species
ad <- est_age_distribution(design, by = species)
#> Warning: ! Age totals were rescaled onto the reported catch.
#> ℹ Measured fish (weighted): 18; reported: 93 -- a factor of 5.17.
#> ℹ estimate, se and the confidence bounds describe the REPORTED catch, estimated
#>   from the measured subsample. Shares (percent) are unaffected.
print(ad)
#>   species age estimate       se   ci_lower  ci_upper percent cumulative_percent
#> 1    bass   2 10.00000 4.233202  1.7030763 18.296924    40.0               40.0
#> 2    bass   3 15.00000 6.524569  2.2120798 27.787920    60.0              100.0
#> 3 panfish   0  3.25000 1.899150 -0.4722657  6.972266    25.0               25.0
#> 4 panfish   1  6.50000 4.023369 -1.3856588 14.385659    50.0               75.0
#> 5 panfish   2  3.25000 3.991201 -4.5726107 11.072611    25.0              100.0
#> 6 walleye   3 12.22222 4.069220  4.2466972 20.197747    22.2               22.2
#> 7 walleye   4 18.33333 6.136191  6.3066208 30.360046    33.3               55.6
#> 8 walleye   5 12.22222 4.069220  4.2466972 20.197747    22.2               77.8
#> 9 walleye   6 12.22222 8.491868 -4.4215332 28.865978    22.2              100.0
#>   n
#> 1 3
#> 2 3
#> 3 2
#> 4 2
#> 5 2
#> 6 3
#> 7 3
#> 8 3
#> 9 3

Ages come from a subsample of the catch, so est_age_distribution() reports a two-phase estimate: the age composition among aged fish, scaled onto the design-estimated reported catch. The call warns when it rescales, naming the factor. Shares (percent) are unaffected by how many fish were aged.

Each row is one integer age class. The estimate column is the survey-design-weighted count of fish at that age, percent is the within-species proportion, and cumulative_percent accumulates from the youngest age class up. The same variance, type, and conf_level arguments available in est_length_distribution() are accepted here.

# Compute weighted mean age from the distribution object
est_mean_age(ad)
#>   species mean_age mean_age_se mean_age_ci_lower mean_age_ci_upper
#> 1    bass 2.600000   0.1456703         2.3144914          2.885509
#> 2 panfish 1.000000   0.3400005         0.3336113          1.666389
#> 3 walleye 4.444444   0.2706522         3.9139759          4.974913

est_mean_age() applies the ratio estimator (Σ a N̂_a / N̂) to the weighted age counts returned by est_age_distribution() and propagates variance via the delta method.

Complete Workflow Example

The full pipeline in one workflow is:

# Load data
data(example_calendar)
data(example_counts)
data(example_interviews)

# Build design and attach data
complete_design <- creel_design(example_calendar, date = date, strata = day_type) |>
  add_counts(example_counts) |>
  add_interviews(example_interviews,
    catch = catch_total,
    effort = hours_fished,
    n_anglers = n_anglers,
    harvest = catch_kept,
    trip_status = trip_status,
    trip_duration = trip_duration
  )
#> Warning in svydesign.default(ids = psu_formula, strata = strata_formula, : No
#> weights or probabilities supplied, assuming equal probability
#> ℹ Added 22 interviews: 17 complete (77%), 5 incomplete (23%)

# Compute all estimates
effort <- estimate_effort(complete_design)
cpue <- estimate_catch_rate(complete_design)
#> ℹ Using complete trips for CPUE estimation
#>   (n=17, 77.3% of 22 interviews) [default]
#> Warning: Small sample size for CPUE estimation.
#> ! Sample size is 17. Ratio estimates are more stable with n >= 30.
#> ℹ Variance estimates may be unstable with n < 30.
hpue <- estimate_harvest_rate(complete_design)
#> ℹ Filtering to complete trips for HPUE estimation
#>   (n=17, 77.3% of 22 interviews) [default]
#> Warning: Small sample size for harvest estimation.
#> ! Sample size is 17. Ratio estimates are more stable with n >= 30.
#> ℹ Variance estimates may be unstable with n < 30.
total_catch <- estimate_total_catch(complete_design)
total_harvest <- estimate_total_harvest(complete_design)

# Print key results
print(effort)
#> 
#> ── 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     372.  13.2       13.2         0     344.     401.    14
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     364.  47.9     262.     466.    17
print(total_harvest)
#> 
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total Harvest (Effort × HPUE)
#> 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     225.  28.1     165.     284.    17

This demonstrates the complete v0.2.0 workflow from survey design through total catch and harvest estimation.

Working with Incomplete Trips

This vignette demonstrates the default workflow using complete trips, which is the recommended approach following Pollock et al. (1994) roving-access design principles.

For situations with incomplete-trip interviews where you want to examine using them for estimation, see the Incomplete Trip Estimation vignette (vignette("incomplete-trips", package = "tidycreel")). That vignette covers:

When incomplete trip estimation is scientifically valid
How to validate incomplete trip estimates using TOST equivalence testing
Step-by-step workflow with validate_incomplete_trips()
Examples of passing and failing validation scenarios
Why you should NEVER pool complete and incomplete trips

Important: The package defaults to complete trips only. Incomplete trip estimation requires explicit opt-in via the use_trips parameter in estimate_catch_rate() and should only be used after validation with validate_incomplete_trips().

Next Steps

For more details on interview-based estimation functions, see:

?add_interviews - Attach interview data to a design
?estimate_catch_rate - Estimate catch per unit effort
?estimate_harvest_rate - Estimate harvest per unit effort
?estimate_total_catch - Estimate total catch
?estimate_total_harvest - Estimate total harvest
?add_ages - Attach age data to a design
?est_age_distribution - Estimate weighted age frequency
?est_mean_age - Compute design-weighted mean age
?example_interviews - Example interview dataset
?example_ages - Example age dataset

For the effort estimation workflow, see the “Getting Started with tidycreel” vignette.