Quantifying imperfect detection in an invasive pest fish and the implications for conservation management

In managing non-native species, surveillance programmes aim to minimise the opportunity for invasions to develop from initial introductions through early detection. However, this is dependent on surveillance methods being able to detect species at low levels of abundance to avoid false-negative recordings through imperfect detection. We investigated through field experimentation the ability to detect Pseudorasbora parva, a highly invasive pest fish in Europe, in relation to their known density and sampling method. Secure pond mesocosms of area 100 m2 contained P. parva densities from 0.02 to 5.0 m−2; each density was in triplicate. These were searched using point sampling electric fishing and deployment of fish traps (non-baited and baited). No fish were captured at densities <0.5 m−2 using any method and this was considered their detection threshold. Point sample electric fishing was the least effective detection method, producing high proportions of false-negative data even at high fish densities. Baited traps were the most effective detection method. Probability of detection of P. parva was 1.0 for baited traps at all densities >0.5 m−2, whereas for electric fishing it only exceeded 0.95 at 5.0 m−2 using high searching effort. These data reveal that small pest fishes such as P. parva may be prone to imperfect detection when at low densities and this is consistent with a number of other invasive species. This indicates the importance of designing surveillance programmes using methods of known statistical power to optimise conservation resource expenditure and enhance management outcomes.