Persistence of sucker (Xyrauchen texanus) in the lower Colorado River relies almost entirely upon stocking programs in place today. Only a small proportion of fish stocked are ever encountered in the wild through annual or biannual sampling efforts. In Lake Havasu, recent telemetry studies have found large spawning aggregations of razorback sucker outside of habitat suitable for the standard net-based sampling that occurs in this reach. Contacting a greater proportion of the population is vital to assess the current at large population and the factors that affect their survival once stocked.
The use of remote passive integrated transponder (PIT) scanning has been a successful tool in both riverine and slack waters throughout the lower Colorado River. This technology was deployed biweekly for the spawning period (January–early April 2012) in the fast flowing waters from Davis Dam downstream to Needles, California. We contacted 763 individual razorback sucker, 651 of which had a release with a 134-kilohertz tag record.
The combination of remote PIT scanning and regular sampling methodologies totaled 1,006 fish contacts in 2012. Of these, 675 individuals met criteria to be included in a 2011 population estimate, which produced an estimate of 2,659 (2,069 to 3,414, 95-percent confidence interval) individuals.
The relative capture rates of razorback sucker were directly related to the size of fish at release. Fish released in the higher size classes, ≥ 500 millimeters (mm), were contacted at a rate of 3.2 to 10.3 times greater than fish released in any other individual size class of fish ≤ 449 mm. Individuals were also significantly more likely to be contacted if released in the spring months than the autumn.
Monitoring of the MSCP River Reach 3 razorback sucker stocking program should continue and emphasize seasonal application of remote PIT tag scanning augmented by biannual physical sampling that utilizes electrofishing and netting. Recommendations to improve post-release survival should accrue after multiple iterations of data collection, analysis, and interpretation.