Using Benford's Law to Detect Possible Biases in Reported Catches of Tropical Tuna From the Indian Ocean

Abstract

Accuracy of catch landing data reported by captains of commercial vessels is crucial in the assessment of marine species stocks and in fisheries policy and management. However, this data can be subject to estimation biases, such as a tendency to inflate some catches (to the detriment of others) and refusal to fill in logbooks. We assessed the accuracy of catch reports from the Spanish tropical tuna purse seine fleet (which accounts for 26% of catches in the Indian Ocean) using Benford's law, a mathematical principle effective for detecting irregularities across multiple datasets. During 2013–2020, including periods before and after the implementation of total allowable catch (TAC) limits in 2017, reported catches differed from Benford's expected distribution, especially for bigeye tuna, indicating potential inaccuracies in reported catches. Changes in data reporting after TAC limits were imposed in 2017 suggested modifications in fishing operations and reporting practices. Use of Benford's law highlighted its potential as an auditing tool in fisheries management and provided insights into data integrity that are crucial for sustainable fisheries governance.

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