--- title: "Glossary of Creel Survey Terms" output: rmarkdown::html_vignette: highlight: null vignette: > %\VignetteIndexEntry{Glossary of Creel Survey Terms} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` This glossary collects the core terms that appear throughout tidycreel's functions, vignettes, and printed outputs. It is meant to be a quick reference: plain-language definitions first, then pointers to where each concept appears in the package workflow. ```{r load} library(tidycreel) ``` ## Survey design terms ### Creel survey A **creel survey** is a survey of anglers, angling effort, and fish catch. In practice this means some combination of calendars, count observations, interviews, and species-level catch or length data collected over a defined season. ### Design A **design** is the survey structure that tells tidycreel what was sampled, when it was sampled, and how observations should be grouped for estimation. In this package, the design starts with `creel_design()` and is then enriched with additional data layers such as counts, interviews, catch, or lengths. ### Calendar The **calendar** is the table of dates and strata used to define the survey frame. It answers the question: *what days were in scope for the survey?* ### Stratum / strata A **stratum** is a grouping variable used to divide the survey into more homogeneous sampling units — for example `weekday` versus `weekend`. **Strata** help estimation and planning by allowing different parts of the survey to have different effort levels, variances, or sample sizes. ### PSU (Primary Sampling Unit) A **PSU** is the primary unit that is sampled for design-based inference. In tidycreel count designs the PSU is the **day**: expanded effort counts the days in each stratum on the calendar, so a PSU must identify a day. Sites or effort types counted within a day are units inside the PSU (`unit_cols` in `add_counts()`), summed into the day's total. Bus-route designs carry their own site frame with site inclusion probabilities. Variance estimators depend on having enough PSUs per stratum. ### Survey type The **survey type** identifies the data-collection design. tidycreel currently supports types such as `instantaneous`, `bus_route`, `ice`, `camera`, and `aerial`. The survey type determines which estimation path is used under the hood. ## Data layer terms ### Count data **Count data** are effort-related observations attached with `add_counts()`. For instantaneous surveys each row is the number of anglers seen at a randomly chosen moment. A count becomes a day's effort only when it is multiplied by the length of the period the moment was drawn from, so give that period with `period_length_col`; the effort is then in angler-hours. Without it the estimate is a sum of counts, its unit is `NA`, and a warning says so. Effort already totalled per day (like the bundled `example_counts`) goes through `prep_counts_daily_effort()` instead (`vignette("unit-rules")`). ### Interview data **Interview data** are party-level observations attached with `add_interviews()`. These records typically include catch, effort, trip status, and optional metadata such as angler type, method, or species sought. ### Catch data **Catch data** are species-level rows attached with `add_catch()`. They expand interview totals into a long format so species-specific rates and totals can be estimated. ### Length data **Length data** are fish-size observations attached with `add_lengths()`. These may be stored as individual fish lengths or as pre-binned release lengths, depending on the field workflow. ### Section A **section** is a spatial subdivision of the fishery, registered with `add_sections()`. Section-aware estimators let you estimate effort or catch for each part of a lake or river separately. ## Effort and catch terms ### Effort **Effort** is the amount of fishing activity. In tidycreel this is often measured in **angler-hours**. Effort can be observed directly in count data, reported in interviews, or estimated over an entire season. ### Angler-hours **Angler-hours** are hours fished multiplied by the number of anglers. This is a standard effort unit for creel work because it combines trip duration and party size into one comparable measure. ### Party-hours **Party-hours** are hours fished by a party, whatever its size. An interview records a party, so without `n_anglers` its effort is in party-hours, and a rate from it is per party-hour. A party-hour rate times angler-hour effort is not a catch; tidycreel warns when the two meet. `vignette("unit-rules")` covers how each estimate's `unit` is derived. ### Catch rate A **catch rate** is catch per unit of effort. In tidycreel this surface is exposed through `estimate_catch_rate()`. ### Harvest rate A **harvest rate** is harvested fish per unit of effort. In tidycreel this is estimated with `estimate_harvest_rate()`. ### Release rate A **release rate** is released fish per unit of effort. In tidycreel this is estimated with `estimate_release_rate()`. ### Ratio of means / mean of ratios The two catch-rate estimators. The **ratio of means** divides total catch by total effort across interviews, and is used for access (complete-trip) interviews. The **mean of ratios** averages each interview's own rate, and is used for roving (incomplete-trip) interviews, where a clerk meets anglers in proportion to how long they fish; each party is weighted by its size when `n_anglers` is given. `vignette("estimator-choice")` says which runs when. ### CPUE / HPUE **CPUE** means **catch per unit effort**. **HPUE** means **harvest per unit effort**. These are common fisheries abbreviations, but tidycreel's exported function names use the more explicit `estimate_catch_rate()` and `estimate_harvest_rate()`. ### Total catch / total harvest / total release These are totals that combine estimated effort with estimated rates, stratum by stratum. tidycreel provides `estimate_total_catch()`, `estimate_total_harvest()`, and `estimate_total_release()` for these products. What they total over is set by `target` (next entry). ### Target: sampled days, stratum total, period total `target` says what an effort or total covers. The default, `"sampled_days"`, is the total over the days actually sampled. `"period_total"` expands each stratum to all its calendar days, which is the season total in most reports. `"stratum_total"` runs the same expansion on standard count designs; the returned `effort_target` records which was asked for. Expanded targets are not yet available for bus-route or ice designs. ## Trip-status and interview terms ### Complete trip A **complete trip** is an interview where the angler has finished fishing. These records provide a full accounting of effort and catch for the trip. ### Incomplete trip An **incomplete trip** is an interview conducted before the angler has finished fishing. These interviews can still be useful, but they require more care when estimating rates because the trip outcome is only partially observed. ### Refusal A **refusal** is a sampled angler or party who declines to be interviewed. Refusals matter because high refusal rates can bias summaries and estimates if participants differ systematically from non-participants. ### Species sought **Species sought** is the primary species an angler reports targeting. This is used in summaries such as caught-while-sought and harvested-while-sought rates. ## Estimation terms ### Design-based inference **Design-based inference** means uncertainty is computed from the sampling design rather than from a fully specified population model. tidycreel relies on the `survey` package for this work. ### Weighted estimate A **weighted estimate** adjusts observed data according to the survey design so that sampled observations represent the broader fishery correctly. In this package, estimators such as `estimate_effort()` or `est_length_distribution()` use the internal survey design rather than simple raw tabulations. ### Unextrapolated summary An **unextrapolated summary** describes the sample as observed, without survey-design weighting. Examples include `summarize_by_method()` and `summarize_length_freq()`. ### Extrapolated estimate An **extrapolated estimate** projects from the sample to the broader survey period or population using the survey design. Examples include `estimate_effort()` and `estimate_total_catch()` with `target = "period_total"`. A rate from `estimate_catch_rate()` is design-based too, but it is a rate, not a projection. ### Variance **Variance** is the sampling variability of an estimate. In practical terms it measures how much the estimate would vary across repeated samples under the same design. ### Standard error (SE) The **standard error** is the square root of the variance. It is the most common uncertainty value shown next to an estimate. ### SE components: absent, NA and zero Some estimates list the parts of their standard error in `se_components`. A part that is **absent** was not propagated: it does not apply, or its quantity was supplied without a standard error and is treated as fixed. **`NA`** means it applies and is unknown, which makes `se` `NA` too. **Zero** means it is known to be zero. `vignette("refusals-and-na")` shows all three. One exception: on the bootstrap path of `estimate_effort_aerial_glmm()` the visibility and angler-ratio parts are inside `se` but not listed, because the bootstrap replicates cannot separate them. ### Confidence interval (CI) A **confidence interval** is a range of plausible values for the quantity being estimated, given the sample and the assumed estimation method. ### Relative standard error (RSE) **Relative standard error** is the standard error divided by the estimate, usually expressed as a proportion. It is a common precision target in survey planning. ## Planning terms ### Sample size planning **Sample size planning** means deciding how many days, interviews, or other sampling units are needed before the survey begins. tidycreel provides `power_creel()`, `creel_n_effort()`, `creel_n_cpue()`, and `creel_power()` for this work. ### Power **Power** is the probability that a study will detect a meaningful change when that change is truly present. In tidycreel this is typically used for planning a future comparison in catch rate. ### Design comparison A **design comparison** is a side-by-side comparison of estimates or precision from alternative survey designs or alternative variance methods. `compare_designs()` provides this surface. ### Hybrid interviews **Hybrid interviews** are on-site interviews that mix access-point and roving interviews within one creel survey — typically access-point interviews for boat anglers and roving interviews for bank anglers. *Access* and *roving* are properties of the **interview**, not of the count: access interviews intercept completed trips as anglers leave, roving interviews intercept incomplete trips while anglers fish, and the two require different catch-rate estimators. tidycreel records this on `add_interviews()` via `interview_type`. ### Hybrid design A **hybrid design** in the sense of `as_hybrid_svydesign()` is a different thing: it combines two or more **count** series covering disjoint parts of a fishery, treating each as a stratum with its own within-day sampling fraction, while all expand to the same population of days. It estimates a period total rather than a sampled-day total. The totals may be added only if the frames observe disjoint sets of angler trips. The frames are named by a column you nominate with `frame_col` — an angler-type column, typically — rather than by the interview vocabulary; counts themselves are instantaneous, progressive, bus-route, camera or aerial. ## Survey-type terms ### Instantaneous count survey An **instantaneous count survey** samples counts at selected moments and uses those observations to estimate total effort over a season. ### Progressive count survey A **progressive count survey** moves through a route or circuit over time rather than taking one instantaneous snapshot. tidycreel handles this through `add_counts()` plus progressive-count logic in the effort pipeline. ### Bus-route survey A **bus-route survey** samples access points or circuits with known inclusion probabilities and uses Horvitz-Thompson style expansion to estimate totals. ### Camera survey A **camera survey** uses automated image or timestamp data to index or estimate fishing effort. tidycreel supports both counter-style and ingress-egress camera workflows. ### Aerial survey An **aerial survey** estimates effort from counts collected during overflights. The package supports both simple aerial estimation and a GLMM-based correction path for non-random flight timing. ## Where to go next Use this glossary as a map to the rest of the package: - `vignette("tidycreel")` for the core workflow - `vignette("interview-estimation")` for interview-based estimators - `vignette("unextrapolated-summaries")` for raw interview summaries - `vignette("survey-design-toolbox")` for planning and design comparison tools - `?creel_design` for the main entry point into the analysis pipeline