Evaluating spawning migration patterns and predicting spawning success of shovelnose sturgeon in the Lower Missouri River

Abstract

Approaches using telemetry, precise reproductive assessments, and surgically implanted data storage tags (DSTs) were used in combination with novel applications of analytical techniques for fish movement studies to describe patterns in migratory behavior and predict spawning success of gravid shovelnose sturgeon. From 2004 to 2007, over 300 gravid female shovelnose sturgeon ( Scaphirhynchus platorynchus ) from the Lower Missouri River, that were expected to spawn in the year they were collected, were surgically implanted with transmitters and archival DSTs. Functional cluster modeling of telemetry data from the spawning season suggested two common migration patterns of gravid female shovelnose sturgeon. Fish implanted from 958 to 1181 river kilometer (rkm) from the mouth of the Missouri River (or northern portion of the Lower Missouri River within 354 rkm of the lowest Missouri River dam at rkm 1305) had one migration pattern. Of fish implanted from 209 to 402 rkm from the mouth of the Missouri River (or southern portion of the Lower Missouri River), half demonstrated a movement pattern similar to the northern fish while the other half demonstrated a migration pattern that covered more of the river. There was no apparent difference in migration patterns between successful and unsuccessful spawners. Multiple hypotheses exist to explain differences in migratory patterns among fish from different river reaches. Additional work is required to determine if observed differences are due to multiple adapted strategies, environmental alteration, and/or initial tagging date. Hierarchical Bayesian modeling of DST data indicated that variation in depth usage patterns was consistently different between successful and unsuccessful spawners, as indicated by differences in likelihood of switching between high and low variability states. Analyses of DST data, and data collected at capture, were sufficient to predict 8 of 10 non‐spawners/incomplete spawners and all 30 spawners in the absence of telemetry location data. Together, the results of these two separate analyses suggest that caution is necessary in extrapolating spawning success from broad‐scale movement data alone. More direct measures of spawning success may be necessary to precisely determine spawning success and to evaluate the effects of management actions.