2 Methods

2.1 Design principles (Hyatt-aligned)

Hyatt (1997) intended estimate-type assignment to reflect (i) method properties, (ii) statistical properties, and (iii) documentation (Hyatt 1997). The updated guidance encodes these dimensions explicitly as decision gates, method-family checks, and final evidence requirements.

Table 2.1: Mapping of Hyatt (1997) intent to the updated, property-first guidance implementation.
Hyatt (1997) dimension How the updated guidance encodes it
Method properties (survey + analysis) Method families + property checks (coverage, effort, timing, visibility, cross-section coverage)
Statistical properties (units, accuracy, precision) Type eligibility by method family and a final precision/accuracy gate (supports CV/SE where available)
Documentation Documentation qualifier + final documentation enforcement

2.2 Decision key design

The classification follows a property-first sequence:

  1. Data format gate: non-numeric presence/not-detected is classified as Type 6.
  2. Method known gate: if survey and analytical methods are not identified, the estimate is provisionally Type 5 and flagged as method-unknown.
  3. Method family selection: a primary method family scopes the applicable questions.
  4. Method-family checks: coverage, effort, visibility, timing, and related criteria drive conservative downgrades.
  5. Final checks: documentation and uncertainty evidence are evaluated; where evidence is missing, conservative downgrades apply.

This structure is intended to reduce subjective table interpretation by turning common qualifiers into explicit questions and recorded qualifier codes.

2.3 Software availability (implementation of the guidance)

The guidance is implemented in the SMN Escapement Estimates Toolkit (R Shiny application) (NuSEDS Escapement Estimates Toolkit Working Group 2026). The toolkit executes one canonical decision key and produces (i) a final estimate Type (1–6) and (ii) explicit qualifier codes explaining conservative downgrades.

In this report, the software is treated as a companion implementation rather than a primary publication artifact. To avoid over-emphasizing engineering details for the intended audience, the implementation is referenced only at a high level.

2.4 Handling common operational complications

The updated guidance treats several recurring complications as explicit qualifiers rather than implicit judgement calls.

2.4.1 Breaches/bypass and infilling

Breach/bypass events at counting sites, and periods of missing observation due to outages or missed visits, can materially affect interpretability unless the magnitude and correction method are documented. The updated guidance treats bypass/breach risk and incomplete coverage as explicit downgrade triggers and records whether defensible infilling/interpolation was required (Vélez-Espino et al. 2010; Holmes, Cronkite, and Enzenhofer 2005; See, Kinzer, and Ackerman 2021).

2.4.2 Survey timing relative to run timing

For survey-based indices, visit count alone is not sufficient: surveys must bracket the period when fish are present, and visibility constraints and observer effects can govern bias and comparability (K. R. Holt and Cox 2008; Jones, Quinn, and Van Alen 1998; Korman et al. 2002; Hill 1997). The updated guidance includes timing and visibility checks within method families where these factors are primary drivers of interpretability.

2.4.3 Combined-method estimates

Combined-method workflows (e.g., system-wide sonar with tributary visual apportionment, or fences supplemented during breach periods) should be recorded as explicit components. The updated guidance applies a conservative rule: where multiple components contribute to the final estimate, the assigned Type should not exceed that implied by the weakest component unless a documented integration method supports a higher classification (Parsons and Skalski 2010).

2.4.4 Calibration and historical revisions

Where historical values have been recalibrated or revised, the updated guidance treats calibration as an analysis layer that should be captured in metadata (calibration source, diagnostics, and revision history) rather than as a new estimate Type. This supports interpretation of time-series values by downstream users.

2.4.5 Quantitative uncertainty (CV/SE)

Hyatt (1997) distinguished higher-quality absolute-abundance estimates in part by qualified precision (variance evidence) (Hyatt 1997). Where available, reporting quantitative uncertainty (e.g., CV or SE) improves interpretability for downstream use. For example, AUC and peak-count approaches have established approaches for incorporating uncertainty and evaluating sensitivity to survey timing and frequency (English, Bocking, and Irvine 1992; Hilborn, Bue, and Sharr 1999; Hill 1997; Parken, Bailey, and Irvine 2003; Millar, McKechnie, and Jordan 2012). The updated guidance includes a final precision/accuracy check and recommends capturing quantitative uncertainty when available.

2.6 NuSEDS data dictionary alignment

The NuSEDS data dictionary defines the database fields used to store enumeration methods, estimation methods, estimate classification (Types 1–6), and supporting metadata (e.g., inspections/effort and timing fields) (Fisheries and Oceans Canada 2025). The updated guidance is designed to be expressible using existing NuSEDS fields where possible, and to clearly identify gaps where additional metadata would improve interpretation and reproducibility.

Table 2.3: NuSEDS fields relevant to estimate-type classification and how they relate to the updated guidance (see the NuSEDS data dictionary in docs/context/).
Field Name Field Definition Role in updated guidance
4 ESTIMATE_CLASSIFICATION This categorizes estimates based on their levels of accuracy and precision (Type-1 are the most accurate, Type-6 the least accurate). There… Stores Type 1–6 estimate classification (includes some legacy non-Type labels)
3 ENUMERATION_METHODS The enumeration method used to observe fish. The first method listed is the primary method. Values are: Bank Walk, Based on Angling Catch, … Primary field method (enumeration) used to scope method-family checks
5 ESTIMATE_METHOD There are several standard methods to chose from. Cumulative CPUE - Created for Stikine Sockeye Fixed Site Census - Combining one or more r… Primary analysis method (estimation) and special cases (combined, calibrated, unknown)
2 ADULT_PRESENCE Values are present if adults were observed, none observed if no adults were observed during the stream inspections, not inspected if adults… Supports presence/not-detected pathways (Type 6 context)
8 JACK_PRESENCE Values are present if jacks were observed, none observed if no jacks were observed during the stream inspections, not inspected if jacks we… Supports presence/not-detected pathways (Type 6 context)
9 NO_INSPECTIONS_USED This is the number of stream inspection logs that are linked to the SEN or were used in the analysis. E.g. 10 stream inspections and a fixe… Supports effort/visit thresholds (VISITS and related downgrades)
13 START_DTT This is the time stream inspections began e.g. 2000-10-15 means that the first inspection for this season’s estimate started on October 15 … Supports timing/coverage interpretation (inspection start date)
12 RUN_TYPE Run_Type indicates the run timing for different runs within the same season. In some cases, the runs may be well documented enough to label… Supports timing context when multiple runs occur in a season
7 INDEX_YN This indicates whether the estimates are for a portion of the population. This is usually due by purposely limiting enumeration to a portio… Flags index (partial coverage) estimates (relative-abundance context)
1 ACCURACY This is the ability of a measurement to match the actual value of the quantity being measured. Some historical estimates that were imported… Legacy qualitative field; not a substitute for quantified uncertainty metadata
10 PRECISION This is the ability of a measurement to be consistently reproduced, or put another way, the number of significant digits to which a value h… Legacy qualitative field; not a substitute for quantified uncertainty metadata
11 RELIABILITY This field was added for the inclusion of historical data from an external source. It is the level of reliability that the person placed in… Legacy/import field (historical); not consistently present
6 ESTIMATE_STAGE Preliminary SENs are the first drafts of summary estimate documents. Source data may be incomplete and their accuracy has not been verified… QA/workflow stage (preliminary/near final/final); not a type determinant

2.7 Method families

Table 2.4: Method families encoded in the property-first guidance and the best attainable Type before conservative downgrades.
Code Method family Best attainable type
FS Fixed site census (manual or electronic) 1
V Visual ground or snorkel count 2
A Aerial survey count 3
S Hydroacoustic sonar count (modelled) 2
T Trap model (non-spanning) 2
R Redd survey 2
P Electrofishing CPUE index 3
M Mark-recapture program 2

D References

English, K. K., R. C. Bocking, and J. R. Irvine. 1992. “A Robust Procedure for Estimating Salmon Escapement Based on the Area-Under-the-Curve Method.” Canadian Journal of Fisheries and Aquatic Sciences 49 (10): 1982–89. https://doi.org/10.1139/f92-220.
———. 2025. “NuSEDS (New Salmon Escapement Database System) Data Dictionary (English).” https://api-proxy.edh-cde.dfo-mpo.gc.ca/catalogue/records/c48669a3-045b-400d-b730-48aafe8c5ee6/attachments/Data_Dictionary_NuSEDS_EN.csv.
Hilborn, Ray, Brian G. Bue, and Samuel Sharr. 1999. “Estimating Spawning Escapements from Periodic Counts: A Comparison of Methods.” Canadian Journal of Fisheries and Aquatic Sciences 56 (5): 888–96. https://doi.org/10.1139/f99-013.
Hill, Ryan A. 1997. “Optimizing Aerial Count Frequency for the Area-Under-the-Curve Method of Estimating Escapement.” North American Journal of Fisheries Management 17 (2): 461–66. https://doi.org/10.1577/1548-8675(1997)017<0461:OACFFT>2.3.CO;2.
Holmes, J. A., G. Cronkite, and H. J. Enzenhofer. 2005. “Feasibility of Deploying a Dual-Frequency Identification Sonar (DIDSON) System to Estimate Salmon Spawning Ground Escapement in Major Tributary Systems of the Fraser River, British Columbia.” 2592. Canadian Technical Report of Fisheries and Aquatic Sciences. Fisheries; Oceans Canada. https://publications.gc.ca/site/eng/9.617451/publication.html.
Holt, K. R., and S. P. Cox. 2008. “Evaluation of Visual Survey Methods for Monitoring Pacific Salmon (Oncorhynchus Spp.) Escapement in Relation to Conservation Guidelines.” Canadian Journal of Fisheries and Aquatic Sciences 65 (2): 212–26. https://doi.org/10.1139/f07-160.
Hyatt, K. D. 1997. “Salmon Escapement Classification Key.” Internal memo, Fisheries and Oceans Canada, Pacific Biological Station.
Jones, Edgar L., Terrance J. Quinn, and Benjamin W. Van Alen. 1998. “Observer Accuracy and Precision in Aerial and Foot Survey Counts of Pink Salmon in a Southeast Alaska Stream.” North American Journal of Fisheries Management 18 (4): 832–46. https://doi.org/10.1577/1548-8675(1998)018<0832:OAAPIA>2.0.CO;2.
Korman, Josh, Robert N. M. Ahrens, Paul S. Higgins, and Carl J. Walters. 2002. “Effects of Observer Efficiency, Arrival Timing, and Survey Life on Estimates of Escapement for Steelhead Trout (Oncorhynchus Mykiss) Derived from Repeat Mark-Recapture Experiments.” Canadian Journal of Fisheries and Aquatic Sciences 59 (7): 1116–31. https://doi.org/10.1139/f02-081.
Millar, Russell B., Sam McKechnie, and Chris E. Jordan. 2012. “Simple Estimators of Salmonid Escapement and Its Variance Using a New Area-Under-the-Curve Method.” Canadian Journal of Fisheries and Aquatic Sciences 69 (6). https://doi.org/10.1139/f2012-034.
NuSEDS Escapement Estimates Toolkit Working Group. 2026. “SMN Escapement Estimates Toolkit (r Shiny Application).”
Parken, C. K., R. E. Bailey, and J. R. Irvine. 2003. “Incorporating Uncertainty into Area-Under-the-Curve and Peak Count Salmon Escapement Estimation.” North American Journal of Fisheries Management 23 (1): 78–90. https://doi.org/10.1577/1548-8675(2003)023<0078:IUIAUT>2.0.CO;2.
Parsons, A. L., and J. R. Skalski. 2010. “Quantitative Assessment of Salmonid Escapement Techniques.” Reviews in Fisheries Science 18 (4): 301–14. https://doi.org/10.1080/10641262.2010.513020.
See, Kevin E., Ryan N. Kinzer, and Michael W. Ackerman. 2021. “State-Space Model to Estimate Salmon Escapement Using Multiple Data Sources.” North American Journal of Fisheries Management 41 (5): 1360–74. https://doi.org/10.1002/nafm.10649.
Vélez-Espino, L. A. et al. 2010. “Mark-Recapture Experiment for the 2009 Chinook Salmon Spawning Escapement in the Atnarko River.” 2930. Canadian Manuscript Report of Fisheries and Aquatic Sciences. Fisheries; Oceans Canada. https://publications.gc.ca/site/eng/9.620001/publication.html.