Bias in self-reported parasite data from the salmon farming industry.

Many industries are required to monitor themselves in meeting regulatory policies intended to protect the environment. Self-reporting of environmental performance can place the cost of monitoring on companies rather than tax-payers, but there are obvious risks of bias, often addressed through external audits or inspections. Surprisingly, there have been relatively few empirical analyses of bias in industry self-reported data. Here, we test for bias in reporting of environmental compliance data using a unique dataset from Canadian salmon farms, where companies monitor the number of parasitic sea lice on fish in open sea-pens, in order to minimize impacts on wild fish in surrounding waters. We fit a hierarchical population-dynamics model to these sea-louse count data using a Bayesian approach. We found that the industry's monthly counts of two sea-louse species - Caligus clemensi and Lepeophtheirus salmonis - increased by a factor of 1.95 (95% credible interval: 1.57, 2.42) and 1.18 (1.06, 1.31), respectively, in months when counts were audited by the federal fisheries department. Consequently, industry sea-louse counts are less likely to trigger costly but mandated delousing treatments intended to avoid sea-louse epidemics in wild juvenile salmon. These results highlight the potential for combining external audits of industry self-reported data with analyses of their reporting to maintain compliance with regulations, achieve intended conservation goals, and build public confidence in the process.