A spatially stratified creel survey divides the lake into discrete geographic sections — for example, North, Central, and South — each managed as a separate reporting unit. Each section has its own observed count data and its own set of angler interviews, so effort levels and catch rates can differ materially between sections.
add_sections() is the right tool when the survey design
explicitly stratifies by section: when different field crews
patrol different areas, when section-level estimates are required in the
final report, or when the lake is large enough that assuming uniform
effort across the full water body would be misleading. If you are simply
curious about spatial patterns but did not design the survey with
sections in mind, use the standard creel_design() workflow
without add_sections().
library(tidycreel)
data(example_sections_calendar)
data(example_sections_counts)
data(example_sections_interviews)
sections_df <- data.frame(
section = c("North", "Central", "South"),
stringsAsFactors = FALSE
)
design <- creel_design(example_sections_calendar, date = date, strata = day_type)
design <- add_sections(design, sections_df, section_col = section)
design <- add_counts(design, example_sections_counts)
design <- add_interviews(
design, example_sections_interviews,
catch = catch_total,
effort = hours_fished,
harvest = catch_kept,
trip_status = trip_status,
trip_duration = trip_duration
)
#> ℹ Added 27 interviews: 27 complete (100%), 0 incomplete (0%)
print(design)
#>
#> ── Creel Survey Design ─────────────────────────────────────────────────────────
#> Type: "instantaneous"
#> Date column: date
#> Strata: day_type
#> Calendar: 12 days (2024-06-03 to 2024-06-21)
#> day_type: 2 levels
#> Counts: 36 observations
#> PSU column: date
#> Count column: effort_hours
#> Party-size term: not carried
#> Count type: "instantaneous"
#> Survey: <survey.design2> (constructed)
#> Interviews: 27 observations
#> Type: "access"
#> Catch: catch_total
#> Effort: hours_fished
#> Harvest: catch_kept
#> Trip status: 27 complete, 0 incomplete
#> Survey: <survey.design2> (constructed)
#> Sections: 3 registered
#> North
#> Central
#> SouthThe design now shows three registered sections (North, Central, South). All downstream estimators automatically detect sections and return per-section output.
estimate_effort() detects the registered sections and
returns one row per section plus a .lake_total row that
aggregates across sections. The lake-total standard error accounts for
cross-section covariance (described in the Variance Aggregation section
below).
effort_est <- estimate_effort(design)
print(effort_est$estimates)
#> # A tibble: 4 × 11
#> section estimate se se_between se_within ci_lower ci_upper n
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 North 269 12.3 12.3 0 242. 296. 12
#> 2 Central 472 19.0 19.0 0 430. 514. 12
#> 3 South 105 9.18 9.18 0 84.6 125. 12
#> 4 .lake_total 846 39.4 39.4 0 758. 934. 36
#> # ℹ 3 more variables: prop_of_lake_total <dbl>, se_prop_of_lake_total <dbl>,
#> # data_available <lgl>Central has the highest fishing effort among the three sections,
while South has the lowest. The prop_of_lake_total column
shows each section’s share of total angler-hours for the survey period,
and se_prop_of_lake_total its standard error, so shares can
be compared with the same regard for precision as the totals themselves.
Both come from one survey::svyratio() call, which accounts
for the correlation between a section’s total and the lake-wide total
that contains it. On the .lake_total row the share is
exactly 1 with a standard error of 0 — that row’s share of itself is
structural, not estimated.
Catch rate (CPUE, fish per angler-hour) is a ratio estimator. Ratios
are not additive across sections: you cannot average North’s rate and
South’s rate to produce a valid lake-wide CPUE. For this reason,
estimate_catch_rate() on a sectioned design returns one row
per section with no .lake_total row.
cpue_est <- estimate_catch_rate(design)
#> ℹ Using complete trips for CPUE estimation
#> (n=27, 100% of 27 interviews) [default]
print(cpue_est$estimates)
#> # A tibble: 3 × 7
#> section estimate se ci_lower ci_upper n data_available
#> <chr> <dbl> <dbl> <dbl> <dbl> <int> <lgl>
#> 1 North 1.06 0.0710 0.922 1.20 9 TRUE
#> 2 Central 1.51 0.0179 1.47 1.54 9 TRUE
#> 3 South 2.45 0.0287 2.39 2.51 9 TRUENotice there is no .lake_total row.
Catch rate is a ratio estimator and ratios are not additive — South’s
high catch rate cannot simply be averaged with North’s low rate to
produce a valid lake-wide CPUE. To estimate the lake-wide catch rate,
call estimate_catch_rate() on a design without section
registration.
# Lake-wide catch rate: build an unsectioned design using the same data
design_nosections <- creel_design(example_sections_calendar, date = date, strata = day_type)
design_nosections <- add_counts(design_nosections, example_sections_counts)
design_nosections <- add_interviews(
design_nosections, example_sections_interviews,
catch = catch_total, effort = hours_fished,
harvest = catch_kept, trip_status = trip_status, trip_duration = trip_duration
)
#> ℹ Added 27 interviews: 27 complete (100%), 0 incomplete (0%)
estimate_catch_rate(design_nosections)$estimates
#> ℹ Using complete trips for CPUE estimation
#> (n=27, 100% of 27 interviews) [default]
#> # A tibble: 1 × 5
#> estimate se ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1.77 0.118 1.54 2.00 27Total catch combines effort and catch rate within each section:
TC_i = E_i × CPUE_i. The lake-wide total is
sum(TC_i) — not
E_total × CPUE_pooled. With
aggregate_sections = TRUE (the default), a
.lake_total row is appended to the output.
catch_est <- estimate_total_catch(design, aggregate_sections = TRUE)
print(catch_est$estimates)
#> # A tibble: 4 × 9
#> section estimate se ci_lower ci_upper n prop_of_lake_total
#> <chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 North 285. 23.1 232. 339. 9 0.228
#> 2 Central 711. 29.9 642. 780. 9 0.567
#> 3 South 257. 22.7 205. 310. 9 0.205
#> 4 .lake_total 1254. 44.1 1163. 1345. 3 1
#> # ℹ 2 more variables: se_prop_of_lake_total <dbl>, data_available <lgl>South has a high catch rate but the lowest effort, which limits its
contribution to the lake total. Central has a moderate catch rate
combined with the highest effort, making it the largest contributor. The
prop_of_lake_total column shows each section’s share of the
lake-wide total catch, and se_prop_of_lake_total its
standard error, so the shares can be compared against their uncertainty
rather than read as exact. The error is derived from the same section
variances the .lake_total row’s own standard error is built
from; the .lake_total row reports 0, because
its share of itself is 1 by construction rather than estimated.
harvest_est <- estimate_total_harvest(design, aggregate_sections = TRUE)
print(harvest_est$estimates)
#> # A tibble: 4 × 9
#> section estimate se ci_lower ci_upper n prop_of_lake_total
#> <chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 North 165. 15.8 128. 201. 9 0.204
#> 2 Central 466. 23.8 411. 520. 9 0.576
#> 3 South 178. 15.9 142. 215. 9 0.221
#> 4 .lake_total 809. 32.7 741. 876. 3 1
#> # ℹ 2 more variables: se_prop_of_lake_total <dbl>, data_available <lgl>example_sections_interviews carries no party-size
column, so both calls above raise a warning that the rate they multiply
is per party-hour while the count-derived effort is
angler-hours. It is correct for this dataset and is hidden here only by
the chunks’ warning = FALSE, which predates it. With one
angler per party the totals are right as printed; with larger parties
they are understated. Supply n_anglers from your own
interview data, or state a constant party size
(n_anglers = 1) when every interview really is a single
angler, to remove the ambiguity.
If a registered section has no count data on any survey day,
estimate_effort() produces an NA row with
data_available = FALSE and emits a warning. This prevents
silent omission of sections that should have been observed.
north_central_counts <- example_sections_counts[
example_sections_counts$section != "South",
]
design_missing <- creel_design(example_sections_calendar, date = date, strata = day_type)
design_missing <- add_sections(design_missing, sections_df, section_col = section)
design_missing <- suppressWarnings(add_counts(design_missing, north_central_counts))
estimate_effort(design_missing, missing_sections = "warn")$estimates
#> Warning: 1 missing section(s) in count data.
#> ! Section(s) not found: "South"
#> ℹ Inserting NA row(s) with data_available = FALSE.
#> # A tibble: 4 × 11
#> section estimate se se_between se_within ci_lower ci_upper n
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 North 269 12.3 12.3 0 242. 296. 12
#> 2 Central 472 19.0 19.0 0 430. 514. 12
#> 3 South NA NA NA NA NA NA 0
#> 4 .lake_total 741 31.1 31.1 0 672. 810. 24
#> # ℹ 3 more variables: prop_of_lake_total <dbl>, se_prop_of_lake_total <dbl>,
#> # data_available <lgl>To turn warnings into errors (for automated pipelines), use
missing_sections = "error".