Integrating multiple surveys for abundance indices: single- and multi-survey spatio-temporal models for Hudson River anadromous fish abundance

Combining catch data from multiple surveys may overcome limitations of individual surveys and produce improved abundance indices. However, maintaining multiple surveys and developing integrated indices is resource intensive and may not be necessary if a single survey provides reliable trend estimates. We applied a spatiotemporal modeling framework to derive single- and multi-survey abundance indices from two long-term fisheries-independent monitoring programs in the Hudson River Estuary: an inshore beach seine survey and an offshore multi-gear trawl survey. We compared these indices, along with the geometric mean index currently used in stock assessments, by evaluating abundance estimates and precision for young-of-year Alewife ( Alosa pseudoharengus), American Shad ( Alosa sapidissima), Blueback Herring ( Alosa aestivalis), and Striped Bass ( Morone saxatillis). We found that integrating surveys did not consistently alter trends or improve precision, suggesting that a single survey may suffice for some scenarios. However, we suggest that maintaining complementary surveys when feasible is valuable, as cross-index comparisons provide insight into species ecology and increase confidence in abundance trends.