Environmental and biological factors influence migratory Sea Lamprey catchability: Implications for tracking abundance in the Laurentian Great Lakes

Sea Lamprey Petromyzon marinus population trends in the Great Lakes are tracked by trapping migratory adults in tributaries and using mark and recapture techniques to estimate abundance. Understanding what environmental and biological factors influence Sea Lamprey capture in tributaries is crucial to developing efficient trapping methods and reliable abundance estimates. We analyzed data from trapping sites located on eight Great Lakes tributaries using Cormack-Jolly-Seber models and examined how water temperature, discharge, sex, and length influenced Sea Lamprey apparent survival and capture probability. Sea Lamprey apparent survival was negatively associated with water temperature in all tributaries. Additionally, the odds of small Sea Lamprey (≤45 cm) remaining available to capture were 39% less (95% CI: 63% decrease – 1% increase) than large (>45 cm) lamprey odds. These observed relationships were used to investigate if bias in abundance estimates using the pooled-Petersen estimator and Jolly-Seber models was expected to be similar across trapping locations or influenced by variable environmental conditions and biological traits. Pooled-Petersen abundance estimates had a positive bias when datasets were generated from simulated populations with empirical relationships between environmental characteristics and catchability. The degree of bias depended upon changes in stream warming patterns and was not consistent among trapping locations. Jolly-Seber models using data from either weekly-batch-marked or uniquely-marked individuals generated abundance estimate with low bias when data quality was high, but performed poorly in scenarios with few recaptured Sea Lamprey. This research can promote improved Sea Lamprey monitoring efforts by providing insight into the reliability of the pooled-Petersen abundance estimator as a tool for tracking Sea Lamprey populations and demonstrating the limitations of adopting more robust methods when data are sparse.