A Comprehensive Method to Integrate Unbiased Fisheries Data in Spatially-Explicit Population Dynamics Models

Why It Matters

Accurate, unbiased inputs enable SEAPODYM to deliver reliable tuna population forecasts, directly supporting sustainable management and policy decisions in the Pacific fisheries sector.

Key Takeaways

• Groups fisheries by consistent catchability and selectivity
• Maintains linear catch‑biomass relationship at grid level
• Uses longline data with hooks‑between‑floats covariate
• Merges fine‑scale and coarse aggregated data
• Enhances SEAPODYM parameter estimation accuracy

Pulse Analysis

Fishery‑dependent data, especially catch‑per‑unit‑effort (CPUE), remain the backbone of many stock assessments, yet their reliability is compromised by variable catchability across vessels and gear types. Traditional models often assume a uniform CPUE‑abundance relationship, ignoring spatial heterogeneity and operational nuances, which can lead to biased mortality estimates. Integrating unbiased, high‑resolution data into spatially‑explicit frameworks like SEAPODYM addresses these gaps, offering a more realistic depiction of how fish populations interact with fishing pressure across oceanic grids.

The proposed methodology tackles two core challenges. First, it clusters fishing records into distinct fisheries that share similar catchability and selectivity profiles, using operational covariates such as hooks‑between‑floats to capture gear‑specific effects. Second, it aligns these grouped datasets with fine‑scale spatial cells, preserving a linear link between catch and biomass density at the grid level. By blending detailed longline observations from Pacific Island nations with coarser, aggregated data from distant‑water fleets, the approach ensures comprehensive coverage of fishing mortality while retaining the spatial granularity required for robust SEAPODYM calibration.

For managers and analysts, the enhanced data pipeline translates into more precise population dynamics estimates, informing quota setting, effort controls, and ecosystem‑based fisheries management. The framework is adaptable to other species and regions where scientific surveys are sparse, positioning it as a scalable solution for global fisheries sustainability. As climate variability reshapes marine ecosystems, the ability to integrate unbiased, spatially explicit data will become increasingly critical for maintaining resilient fish stocks and supporting the livelihoods dependent on them.