1 Introduction

NuSEDS (National Salmon Escapement Database) stores salmon spawner survey records, spawner abundance estimates, and linkages between them for Pacific Region salmon populations (Fisheries and Oceans Canada 2016). NuSEDS estimate Types 1–6 are intended to communicate the interpretability of annual escapement estimates for downstream uses such as stock assessment and status evaluation. However, the current table-only guidance is often applied inconsistently and does not explicitly encode common, operational complications that strongly influence interpretation (e.g., incomplete run coverage, breaches/bypass at counting sites, variable visibility, and incomplete documentation).

This report documents an updated, Hyatt-aligned guidance implementation intended for biologists and analysts who upload escapement data to New SEDS/NuSEDS and require a consistent, transparent basis for assigning Types 1–6.

1.1 Hyatt (1997) intent and foundation

Hyatt (1997) developed a draft, hierarchical escapement classification key intended to be “unambiguous, easy to apply and reliable” for both field personnel and informed data users (Hyatt 1997). Hyatt framed classification around three linked dimensions: (i) properties of the survey and analytical methods used, (ii) statistical properties of the estimates (units of expression, accuracy, and precision), and (iii) the level of documentation supporting the estimate.

The NuSEDS estimate type table is derived from this foundation, but the table-only representation can be read as a method lookup. This creates ambiguity about how to incorporate timing, partial coverage/uptime, breaches/bypass, infilling, combined-method workflows, and uncertainty evidence.

1.2 Estimate type summary (Types 1–6)

Table 1.1: High-level summary of estimate Types 1–6 for orientation. Types summarize interpretability given method properties, uncertainty evidence, and documentation; they do not guarantee unbiasedness.
Type Label Units Interpretation
1 Census / near-census (high resolution) Absolute abundance A direct count (or near-census) with high confidence that missed fish are negligible or well constrained.
2 Absolute abundance estimate (qualified) Absolute abundance (often with uncertainty) A standardized estimation method producing absolute abundance with qualified accuracy and, where available, quantified precision.
3 Relative abundance index (high resolution) Relative units (index, within-series) A consistent, high-effort index that supports within-series comparisons; absolute abundance and uncertainty may not be fully quantified.
4 Relative abundance index (medium resolution) Relative units (index, within-series) A lower-effort index where comparability is more sensitive to timing, visibility, and coverage.
5 Relative abundance / estimate (low evidence) Relative units (uncertain) A numeric estimate where method information, documentation, or key qualifiers are insufficient to support stronger interpretation.
6 Presence / not detected
  • / -
A non-numeric record indicating adults present or not detected with reliable species identification.

1.3 Why an update is needed

In operational datasets, the same nominal estimate Type can be assigned to methodologically different estimates, and the table-only guidance does not encode several recurring, decision-relevant qualifiers. Key drivers of interpretation include:

  • Modern method coverage: hydroacoustic imaging sonar approaches (e.g., DIDSON/ARIS pipelines) are increasingly used for escapement estimation but are not always clearly represented in table-only guidance, encouraging ad hoc interpretation (Holmes, Cronkite, and Enzenhofer 2005).
  • Incomplete coverage and breaches/bypass: device outages, partial run-window coverage, and breach/bypass events can materially affect interpretability unless the magnitude and correction method are documented (See, Kinzer, and Ackerman 2021).
  • Timing, visibility, and effort standardization: for survey-based indices, timing relative to run timing, observer efficiency, and visibility conditions often govern comparability and potential bias (K. R. Holt and Cox 2008; Jones, Quinn, and Van Alen 1998; Korman et al. 2002).
  • Combined-method workflows: multi-component estimates (e.g., sonar combined with tributary visual apportionment; fences supplemented during breach periods) require explicit component recording to avoid concealing a weaker component (Parsons and Skalski 2010).
  • Downstream filtering sensitivity: estimate Types are used as data-quality filters in Wild Salmon Policy (WSP) rapid status workflows; inconsistent assignment can create discontinuities near threshold cutoffs (C. A. Holt et al. 2009).

1.4 How this guidance should be used when uploading to New SEDS/NuSEDS

This update is intended to support consistent type assignment during data entry and review by making key qualifiers explicit.

In practice:

  • Record methods explicitly (enumeration and estimation). If methods are unknown, the guidance assigns a conservative Type 5 and records a method-unknown qualifier.
  • Record key qualifiers that affect interpretation (e.g., run-window coverage, device uptime, breach/bypass context, timing/visibility constraints, infilling/interpolation method).
  • Provide documentation evidence sufficient for independent interpretation (e.g., field logs, methods/QA notes). Where evidence is missing, the guidance applies conservative downgrades.
  • Report quantitative uncertainty when available (e.g., CV or SE) to support qualified precision for Type 2 where appropriate (e.g., AUC and mark–recapture uncertainty reporting) (English, Bocking, and Irvine 1992; Hilborn, Bue, and Sharr 1999; Parken, Bailey, and Irvine 2003; Schwarz et al. 1993).

1.5 What this report provides

This report documents an updated, Hyatt-aligned guidance implementation that:

  • Preserves Hyatt’s Types 1–6 while making eligibility rules explicit.
  • Separates enumeration methods (field data collection) from estimation methods (analysis and modelling).
  • Uses a property-first decision sequence (data format gate; method-known gate; method-family checks; final documentation and uncertainty checks).
  • Records qualifier codes for common downgrade causes (e.g., breaches/bypass, coverage/uptime, timing/visibility, documentation).
  • Identifies recommended metadata additions for New SEDS/NuSEDS and notes that these additions are implemented in supporting software (see Methods).

1.6 Scope

This report documents classification logic, evidence expectations, and recommended metadata. It does not re-estimate escapement, evaluate biological status, or replace program-specific expert judgement.

D References

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———. 2016. “NuSEDS–New Salmon Escapement Database System.” Open Government Portal dataset. https://open.canada.ca/data/en/dataset/c48669a3-045b-400d-b730-48aafe8c5ee6.
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.
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.
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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.