Derived products

Author

VLab Course Team

Overview

This page summarizes derived products currently produced (or planned) from the Global Tuna Atlas workflows. These products build on the harmonized catch and effort datasets and enable analytical use cases (indicators, comparisons, reconciliations).

Status note: at present, no DOI has been assigned to these derived products. They are experimental/iterative/deprecated and may evolve.

CPUE (Catch Per Unit Effort)

The CPUE dataset is derived by combining catch and effort on a common analysis key (e.g., year × area × gear × species, optionally fishing_mode).

Formula (per stratum):

CPUE = Catch / Effort

Important considerations

  • Some strata exist in only one input (catch or effort). Such strata are incomplete and are excluded or flagged during the join.
  • The unit depends on the effort measure (e.g., mt per 1000 hooks, mt per day, mt per set).
  • Multiple CPUE variants are possible depending on the catch dataset used:
    • CPUE from geo‑referenced catch (FIRMS L0 / IRD L2) + Effort L0.
    • CPUE from nominal catch (for diagnostic purposes) + Effort L0.
  • Current scripts (see CPUE step in the GTA launcher) handle joining and exporting standardized CWP‑compatible outputs.

Minimal example (pseudo‑code)

keys <- c("time_start","geographic_identifier","gear_type","species")
joined <- dplyr::inner_join(catch_tbl, effort_tbl, by = keys)
cpue <- dplyr::mutate(joined,
  cpue_value = measurement_value.x / measurement_value.y,
  cpue_unit  = paste0(measurement_unit.x," per ", unique(measurement_unit.y))
)

Level 1 (Harmonized Tons & Numbers)

A Level 1 product harmonizes weights (tons) and numbers of fish onto a common structure, applying conversion factors where necessary.

Key points

  • Provides side‑by‑side tons and numbers for the same strata.
  • Relies on species/gear/region‑specific conversion factors (length‑weight, dress‑to‑live weight, etc.).
  • Scientific caveat: results depend strongly on the chosen conversion factors and their provenance. Assumptions must be documented and sensitivity analyses are recommended.

Intended use - Comparative analyses where both biomass and counts are informative (e.g., standardizing indices, bycatch assessments).

Cross‑Dataset Analyses

Beyond single indicators, several crossings are valuable for QA and interpretation:

  • Nominal vs Geo‑referenced (consistency checks):

    • Compare totals by year/species/gear to identify gaps or raising impacts.
    • Map differences to detect spatial/temporal mismatches.

  • Effort unit diagnostics (per gear, per RFMO):

    • Inventory effort units by stratum; flag potential duplicates (same stratum, different units).
    • Decide on project‑specific unit preferences before aggregating.

  • Raising diagnostics (IRD L2):

    • Inspect strata where georeferenced data were raised to match nominal.
    • Verify rules (exact match vs UNK/NEI/99.9) and year completeness constraints.

Output patterns

  • Summary dashboards (Shiny) for exploratory review.