Biometric relationships as a fisheries management tool: A case study on the bullseye puffer (Sphoeroides annulatus) in an artisanal fishery

In artisanal fisheries, many species are marketed in diverse commercial presentations, yet few biometric relationships exist to predict live weight from landed categories. Typically, biometric relationships are analysed using ordinary least squares (OLS), but outliers can violate OLS assumptions. Robust regression model (RRM) is designed to minimize the impact of outliers and larger residuals, leading to more reliable and accurate results. Using the bullseye puffer Sphoeroides annulatus as a case study, we developed RRM to predict: a) live weight from different landed categories, b) fillet yield from body measurements, and c) the utility of fillet yield as a management reference point. Sampling 1397 individuals from Sinaloa, Mexico (2014–2015), we fitted nine morphological relationships with high precision R 2 adjusted ≥ 0.97 . Our conversions highlighted significant underreporting in the 2023 Mexican Pacific landings, with official records showing 1368 metric tons that underestimated live weight by approximately 950 tons. The largest discrepancies were found in fresh-gutted landings (449 versus 1007 tons) and frozen-gutted landings (361 versus 813 tons). Fillet yield analyses established a 250 g benchmark for recreational fisheries (limit 5 fish/day), requiring a minimum total length L T of 20 cm fillet weight W fi ≈ 50 g / fish to achieve this without exceeding bag limits. Smaller fish would necessitate keeping more than five individuals. Provides standardized conversion factors and intuitive benchmarks, enhancing data quality for stock assessments in data-poor artisanal fisheries. By addressing underreporting and linking fillet yield to maturity-based reference points, these tools support sustainable management policies, such as size limits to optimize yield and protect spawning stocks. • Benchmark could be a suitable option when the harvested resources lack reference points. • Conversion factors allow landing size or weight to be standardized into live fish sizes. • Skewed data constitute valid information about the stocks under study. • Robust regression method can handle outliers and other types of skewed data.