Illegal, unreported, and unregulated fishing: A risk scoring method for prioritizing inspection of fish imported to Australia for zoonotic parasites

Abstract

The global burden of illegal fishing is estimated to be extensive. Intricately constructed trade routes introduce illegally caught fish products into the global commercial market, including Australia. To date, no studies have investigated the potential for illegally caught fish to harbor zoonotic parasites. Any tests applied to fish imported to Australia must be scientifically justified according to international trade agreements. The primary aim of this study was to develop a risk scoring method that provides a scientific basis for the development of protocols to examine fish imported to Australia for zoonotic parasites. The secondary aim was to estimate and prioritize the provenance of fish, which may be high-risk areas for illegal, unreported, and unregulated (IUU)11Illegal, unreported, unregulated (IUU) fishing. The third aim was to calculate the amount of unreported catch from each of the ten highest-risk countries. Scoring was conducted using seven predictor variables, which were identified in the published literature as important, within the forensics of IUU fishing, for identifying the “IUU or unreported catch risk” of each provenance. The unreported catch (UC)22Unreported catch (UC) for the highest scoring provenances (1–10) was calculated after risk scoring. The highest and second highest scoring provenances, 30 and 67, had 39.8% and 41.55% UC, respectively; Provenance 79, which had the tenth highest risk score, had 6.9% UC. Linear regression analysis showed a non-significant association between the size of the exclusive economic zone and UC. Number of commercial spp. was the greatest indicator of UC. The analysis showed that for every unit increase in the number of different commercial spp. available, there was an increase of 5.28 units in the percentage of UC. Mean provenance risk scores and percentage of UC were linearly related. There was a 79.4% decrease in the mean risk scores between provenances 1–5 and 6–10; a decrease was also observed in the UC between the two groups (33.7% and 15.5%, respectively). The proposed scoring method appears to be a good predictor of UC, with a clear association between the mean risk scores for each provenance and percentage UC.