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Creel surveys produce two types of summary products: unextrapolated summaries and extrapolated estimates. This vignette covers unextrapolated summaries — raw tabulations of interview records that describe the composition of interviewed parties without applying survey-design weighting.
When to use unextrapolated summaries:
All unextrapolated functions are interview-weighted, not
pressure-weighted. A day with many anglers and few interviews
has the same weight as a day with few anglers and many interviews. For
pressure-weighted, design-correct estimates, use
estimate_catch_rate(), estimate_total_catch(),
and related functions described in the “Interview-Based Catch
Estimation” vignette.
Start by assembling a complete design using all v0.5.0 data layers:
library(tidycreel)
# Load example datasets
data(example_calendar)
data(example_counts)
data(example_interviews)
data(example_catch)
data(example_lengths)
# Step 1: Define the survey calendar and stratification
design <- creel_design(example_calendar, date = date, strata = day_type)
# Step 2: Attach instantaneous count observations
design <- add_counts(design, example_counts)
#> Warning in svydesign.default(ids = psu_formula, strata = strata_formula, : No
#> weights or probabilities supplied, assuming equal probability
# Step 3: Attach interview records (all extended v0.5.0 fields)
design <- add_interviews(design, example_interviews,
catch = catch_total,
effort = hours_fished,
harvest = catch_kept,
trip_status = trip_status,
trip_duration = trip_duration,
angler_type = angler_type,
angler_method = angler_method,
species_sought = species_sought,
n_anglers = n_anglers,
refused = refused
)
# Step 4: Attach species-level catch data
design <- add_catch(design, example_catch,
catch_uid = interview_id,
interview_uid = interview_id,
species = species,
count = count,
catch_type = catch_type
)
# Step 5: Attach fish length data (individual harvest + binned release)
design <- add_lengths(design, example_lengths,
length_uid = interview_id,
interview_uid = interview_id,
species = species,
length = length,
length_type = length_type,
count = count,
release_format = "binned"
)
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
#> Angler type: angler_type
#> Angler method: angler_method
#> Species sought: species_sought
#> Party size: n_anglers
#> Refused: refused
#> Survey: <survey.design2> (constructed)
#> Catch Data: 42 rows, 3 species
#> caught: 8, harvested: 17, released: 17
#> Length Data: 20 rows, 3 species, length: 155–512 mm
#> harvest: 14 individual
#> release: 6 binned
#> Sections: "none"The print() output shows all five data layers: calendar,
counts, interviews, catch, and lengths.
Use summarize_refusals() to understand how many
potential interviewees declined to participate. High refusal rates can
bias estimates if refusers differ systematically from participants.
summarize_refusals(design)
#> month participation N percent
#> 1 June accepted 22 100All 22 interviews in example_interviews were accepted
(no refusals). In field surveys, a refusal rate above 20% warrants
investigation.
Seven tabulation functions describe the composition of the interview sample.
summarize_by_day_type(design)
#> month day_type N percent
#> 1 June weekday 13 59.1
#> 2 June weekend 9 40.9summarize_by_angler_type(design)
#> month angler_type N percent
#> 1 June bank 13 59.1
#> 2 June boat 9 40.9summarize_by_method(design)
#> month method N percent
#> 1 June artificial 7 31.8
#> 2 June bait 10 45.5
#> 3 June fly 5 22.7summarize_by_species_sought(design)
#> month species N percent
#> 1 June bass 6 27.3
#> 2 June panfish 5 22.7
#> 3 June walleye 11 50.0summarize_successful_parties() counts parties that
caught at least one fish of their target species (as recorded in catch
data), broken down by angler type and species sought.
summarize_successful_parties(design)
#> angler_type species_sought N_total N_successful percent
#> 1 bank bass 5 1 20.0
#> 2 bank panfish 3 1 33.3
#> 3 bank walleye 5 4 80.0
#> 4 boat bass 1 1 100.0
#> 5 boat panfish 2 0 0.0
#> 6 boat walleye 6 3 50.0summarize_by_trip_length(design)
#> trip_length_bin N percent
#> 1 [0,1) 2 9.1
#> 2 [1,2) 5 22.7
#> 3 [2,3) 8 36.4
#> 4 [3,4) 4 18.2
#> 5 [4,5) 3 13.6
#> 6 [5,6) 0 0.0
#> 7 [6,7) 0 0.0
#> 8 [7,8) 0 0.0
#> 9 [8,9) 0 0.0
#> 10 [9,10) 0 0.0
#> 11 10+ 0 0.0The trip-length distribution is useful for detecting outliers and understanding effort patterns.
CWS and HWS rates measure the proportion of interviews where anglers targeting a species actually caught (CWS) or kept (HWS) that species. These are useful quality indicators and input parameters for population models, but should not be treated as pressure-weighted catch rates for management use — they reflect the interview sample composition, not fishing pressure across the survey period.
summarize_cws_rates(design, by = species_sought)
#> species_sought N n_unknown_target n_unknown_effort n_nonpositive_effort
#> 1 bass 6 0 0 0
#> 2 panfish 5 0 0 0
#> 3 walleye 11 0 0 0
#> mean_rate se ci_lower ci_upper
#> 1 0.05208333 0.05208333 -0.08180114 0.1859678
#> 2 0.23333333 0.23333333 -0.41450386 0.8811705
#> 3 0.46158009 0.23247889 -0.05641517 0.9795753summarize_cws_rates(design, by = NULL)
#> N n_unknown_target n_unknown_effort n_nonpositive_effort mean_rate se
#> 1 22 0 0 0 0.2980249 0.1298807
#> ci_lower ci_upper
#> 1 0.02792314 0.5681266summarize_hws_rates(design, by = species_sought)
#> species_sought N n_unknown_target n_unknown_effort n_nonpositive_effort
#> 1 bass 6 0 0 0
#> 2 panfish 5 0 0 0
#> 3 walleye 11 0 0 0
#> mean_rate se ci_lower ci_upper
#> 1 0.0312500 0.0312500 -0.04908068 0.1115807
#> 2 0.1333333 0.1333333 -0.23685935 0.5035260
#> 3 0.3887987 0.1322997 0.09401668 0.6835807Interpretation guidance: CWS and HWS rates > 1.0 are possible when anglers catch multiple fish of the target species in a single interview. A CWS rate of 0.6 means 60% of interviews targeting that species resulted in at least one catch.
Length data attached via add_lengths() can be summarized
by catch type and species.
summarize_length_freq(design, type = "harvest", by = species, bin_width = 25)
#> species length_bin N percent cumulative_percent
#> 1 bass [225,250) 1 25.0 25.0
#> 2 bass [250,275) 1 25.0 50.0
#> 3 bass [275,300) 2 50.0 100.0
#> 4 panfish [150,175) 2 100.0 100.0
#> 5 walleye [375,400) 2 25.0 25.0
#> 6 walleye [400,425) 3 37.5 62.5
#> 7 walleye [475,500) 1 12.5 75.0
#> 8 walleye [500,525) 2 25.0 100.0Release lengths in example_lengths are stored in
pre-binned format. summarize_length_freq() handles this
automatically:
summarize_length_freq(design, type = "release", by = species)
#> species length_bin N percent cumulative_percent
#> 1 bass [275,276) 4 44.4 44.4
#> 2 bass [325,326) 5 55.6 100.0
#> 3 panfish [175,176) 6 66.7 66.7
#> 4 panfish [225,226) 3 33.3 100.0
#> 5 walleye [375,376) 2 40.0 40.0
#> 6 walleye [425,426) 3 60.0 100.0summarize_length_freq(design, type = "catch", by = species, bin_width = 25)
#> species length_bin N percent cumulative_percent
#> 1 bass [225,250) 1 7.7 7.7
#> 2 bass [250,275) 1 7.7 15.4
#> 3 bass [275,300) 6 46.2 61.6
#> 4 bass [325,350) 5 38.5 100.1
#> 5 panfish [150,175) 2 18.2 18.2
#> 6 panfish [175,200) 6 54.5 72.7
#> 7 panfish [225,250) 3 27.3 100.0
#> 8 walleye [375,400) 4 30.8 30.8
#> 9 walleye [400,425) 3 23.1 53.9
#> 10 walleye [425,450) 3 23.1 77.0
#> 11 walleye [475,500) 1 7.7 84.7
#> 12 walleye [500,525) 2 15.4 100.1For design-correct, pressure-weighted estimates broken down by species, use the extrapolated estimators. These combine effort estimates (from count data) with species-specific catch rates (from interview + catch data) using the ratio-of-means estimator.
estimate_catch_rate(design, by = species)
#> 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.
#> 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.
#> 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.
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: ratio-of-means-cpue-species
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Grouped by: species
#> Unit: fish/angler-hour
#>
#> # A tibble: 3 × 6
#> species estimate se ci_lower ci_upper n
#> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 bass 0.205 0.0743 0.0590 0.350 17
#> 2 panfish 0.0558 0.0311 -0.00518 0.117 17
#> 3 walleye 0.465 0.0957 0.278 0.653 17estimate_total_catch(design, by = species)
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total Catch (Effort × CPUE)
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Grouped by: species
#> Effort target: sampled_days
#>
#> # A tibble: 3 × 6
#> species estimate se ci_lower ci_upper n
#> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 bass 63.2 21.9 16.5 110. 17
#> 2 panfish 27.2 10.4 5.10 49.3 17
#> 3 walleye 190. 46.0 92.3 288. 17estimate_total_harvest(design, by = species)
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: Total Harvest (Effort × HPUE)
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Grouped by: species
#> Effort target: sampled_days
#>
#> # A tibble: 3 × 6
#> species estimate se ci_lower ci_upper n
#> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 bass 29.8 9.84 8.82 50.8 17
#> 2 panfish 18.1 6.92 3.40 32.9 17
#> 3 walleye 131. 31.4 64.4 198. 17estimate_release_rate(design, by = species)
#> 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.
#> 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.
#> 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.
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: ratio-of-means-rpue
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Grouped by: species
#> Unit: fish/angler-hour
#>
#> # A tibble: 3 × 6
#> species estimate se ci_lower ci_upper n
#> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 bass 0.102 0.0407 0.0225 0.182 17
#> 2 panfish 0.0186 0.0104 -0.00173 0.0389 17
#> 3 walleye 0.140 0.0385 0.0640 0.215 17
estimate_total_release(design, by = species)
#>
#> ── Creel Survey Estimates ──────────────────────────────────────────────────────
#> Method: product-total-release
#> Variance: Taylor linearization
#> Confidence level: 95%
#> Grouped by: species
#> Effort target: sampled_days
#>
#> # A tibble: 3 × 6
#> species estimate se ci_lower ci_upper n
#> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 bass 33.4 14.1 3.43 63.5 17
#> 2 panfish 9.07 3.46 1.70 16.4 17
#> 3 walleye 58.9 18.8 18.9 98.9 17For grouped estimates combining calendar strata with species, use
by = c(day_type, species). See
vignette("interview-estimation") for the complete
extrapolated estimation workflow.
| Function | Data required | Output |
|---|---|---|
summarize_refusals() |
refused field | month × participation × N × % |
summarize_by_day_type() |
strata | month × day_type × N × % |
summarize_by_angler_type() |
angler_type | month × angler_type × N × % |
summarize_by_method() |
angler_method | month × method × N × % |
summarize_by_species_sought() |
species_sought | month × species × N × % |
summarize_successful_parties() |
catch + angler_type + species_sought | angler_type × species × success rate |
summarize_by_trip_length() |
trip_duration | bin × N × % |
summarize_cws_rates() |
catch + species_sought | CWS rate ± SE by grouping |
summarize_hws_rates() |
catch + species_sought | HWS rate ± SE by grouping |
summarize_length_freq() |
lengths | bin × N × % × cumulative % |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.