Applying information theory to increase the efficiency of life-history sampling in fisheries data

Effective monitoring of fish populations is critical for understanding life-history traits such as maturation or growth, which directly impact population dynamics and fisheries management. Long-term monitoring efforts around the world are faced with rising survey costs and a more uncertain future, as environmental variability is projected to increase. Together, these pressures necessitate more efficient sampling designs. In this study, we use information theory (Fisher Information) to evaluate and optimize life-history sampling, focusing on how variation in fish growth or maturation can guide more informed sampling decisions. Fisher Information is closely related to variance, in that samples with higher information result in greater precision. Using life-history parameters from eight commercially important groundfish species assessed on the U.S. West Coast, we develop three case studies illustrating how sampling strategies can be improved using Fisher Information. The first example focuses on estimating maturity ogives in the absence of spatial and temporal variability; our second case study incorporates spatial and temporal variation, and the third adds a second life-history trait (growth) to jointly optimize sampling for maturity. Our results indicate that for informing maturity ogives, Fisher Information is maximized at lengths near the inflection point, and that the range of most informative lengths can shift across space and time due to habitat or environmental variation. When both growth and maturation are considered, optimal sampling windows broaden for some species but remain narrow for others. Our simulation results also show that sampling fish in proportion to Fisher Information, rather than random sampling, can reduce sampling effort by more than 50% while maintaining or improving the precision of parameter estimates. While the benefits of using Fisher Information are species specific, these findings suggest that adaptive sampling based on Fisher Information can substantially increase the efficiency and effectiveness of monitoring programs, especially under budgetary or logistical constraints.