# How Grouping Data over Time Can Hide Signs of Stock Status: A Case Study Using LBSPR on Frigate Tuna (Auxis thazard, Lacépède, 1800) in the Northeast Atlantic Ocean

## TL;DR

This paper shows that how fish size data is grouped over time can hide or reveal important changes in fish populations, using frigate tuna in the Northeast Atlantic as an example.

## Contribution

The study demonstrates that temporal data grouping significantly affects fish stock assessments and should be treated as a critical sensitivity factor in data-limited fisheries.

## Key findings

- Finer temporal groupings reveal extended periods of reproductive depletion and recovery in frigate tuna stocks.
- Broader data groupings can mask overfishing signals and lead to misleading stock status conclusions.
- Temporal grouping is a structural decision that influences length-based stock assessment outcomes.

## Abstract

It is important to understand the status of fish populations for the well-being of marine ecosystems and for regions reliant on fishing. Scientists face challenges in assessing some fish stocks due to insufficient data. A significant challenge of using length-based spawning potential ratio (LBSPR) lies in how to organise and group the collected data. Broad data groupings, such as merging multiple years, can hide crucial changes in fish populations, including overfishing periods and stock recovery. This study used LBSPR, a method that estimates fish population conditions using fishery-dependent size data from the Northeast Atlantic Ocean Frigate tuna (Auxis thazard) as an illustrative case to examine how different temporal grouping choices ranging from broad multi-year pooling to finer time blocks (groups) affect the interpretation of an illustrative case stock status within the same assessment model. Our research demonstrated that using finer, more detailed groupings provides a clearer and more accurate assessment of fish stocks. In contrast, broader aggregations may lead to incorrect conclusions about stock status. These findings emphasise the value of employing precise data grouping methods when examining fish stocks. Our research aids managers in making better informed decisions, contributing to the sustainability of fish populations and supporting the communities that depend on marine resources.

Length-based stock assessment methods are widely applied in data-limited fisheries, yet the effects of how length-frequency data are temporally grouped prior to analysis remain poorly examined. Temporal grouping is routinely used to increase sample size and approximate equilibrium conditions, but it may also alter the size structure presented to assessment models and bias inference. In this study, we evaluate how alternative temporal grouping schemes influence stock status inference within a single length-based framework, using the length-based spawning potential ratio (LBSPR) model as a diagnostic tool. Using a 30-year length-frequency dataset from a tropical purse seine fishery in the Northeast Atlantic as an illustrative case, we applied LBSPR under four practice-relevant temporal grouping schemes: full-period pooling, a broad regime-based scheme, decadal blocks, and five-year blocks. Life history parameters and model settings were held constant across schemes to isolate the effect of temporal grouping. A sensitivity analysis of biological parameters was conducted for the finest temporal scheme to contextualise robustness. Results show that temporal grouping alone can substantially alter the inferred status of the illustrative case. The fully pooled scheme produced an apparently favourable status signal, whereas finer temporal groupings revealed extended periods of inferred reproductive depletion, followed by a more recent recovery. Sensitivity analyses indicate that, while biological parameter uncertainty influences the magnitude of estimates, it does not overturn the dominant effect of temporal grouping on inferred status patterns. This study demonstrates that temporal grouping is not a neutral preprocessing step but a structural decision with the potential to conceal or reveal exploitation signals in length-based assessments. We argue that temporal grouping should be treated as an explicit sensitivity dimension in data-limited assessment workflows. By shifting attention from stock-specific outcomes to data-structuring choices, this work provides practical guidance for improving transparency and robustness in length-based stock status inference.