Detection of invasive species is critical for management but is often limited by challenges associated with capture, processing and identification of early life stages. DNA metabarcoding facilitates large‐scale monitoring projects to detect establishment early. Here, we test the use of DNA metabarcoding to monitor invasive species by sequencing over 5000 fishes in bulk ichthyoplankton samples (larvae and eggs) from four rivers of ecological and cultural importance in southern Canada. We were successful in detecting species known from each river and three invasive species in two of the four rivers. This includes the first detection of early life‐stage rudd in the Credit River. We evaluated whether sampling gear affected the detection of invasive species and estimates of species richness, and found that light traps outperform bongo nets in both cases. We also found that the primers used for the amplification of target sequences and the number of sequencing reads generated per sample affect the consistency of species detections. However, these factors have less impact on detections and species richness estimates than the number of samples collected and analysed. Our analyses also show that incomplete reference databases can result in incorrectly attributing DNA sequences to invasive species. Overall, we conclude that DNA metabarcoding is an efficient tool for monitoring the early establishment of invasive species by detecting evidence of reproduction but requires careful consideration of sampling design and the primers used to amplify, sequence and classify the diversity of native and potentially invasive species.

Fishes assessed as Threatened or Endangered by the Committee on the Status of Endangered Wildlife in Canada are disproportionately less likely to be listed under the federal Species at Risk Act (SARA) compared to other taxa. We examined the extent to which the amount and type of science advice in a Recovery Potential Assessment (RPA) contributes to SARA-listing decisions for 34 wildlife species of freshwater fishes in Canada. We used a generalized linear mixed model to describe SARA listing status as a function of RPA completeness. Principal coordinates analyses were conducted to assess similarity in answers to RPA questions among listed and nonlisted species. The amount and type of science advice within an RPA were weakly related to SARA status. RPA completeness accounted for only 7.4% of model variation when family was included as a random effect, likely because nine species not listed under SARA (64%) belong to the sturgeon family. Our results suggest that, while potentially useful for informing recovery strategies, RPAs do not appear to be driving listing status for freshwater fishes in Canada. Factors beyond scientific advice likely contribute to nonlisted species and delays in listing decisions.