Biotelemetry of shortnose sturgeon Acipenser brevirostrum and Atlantic sturgeon A. oxyrinchus oxyrinchus was used to study fish uses of habitat in several hierarchical classes in the Connecticut and Merrimack rivers. Hierarchical classes were geomorphological region (straight river run, run with an island, and river curve), river cross section (channel or shoal), and microhabitat (water depth, bottom current, substrate, and illumination). Coastal wandering juvenile Atlantic sturgeon were summer visitors to the Merrimack River, where they used a narrow range of habitat on all spatial scales, e.g., run-with-island, the channel portion of the cross section, and sand substrate. Shortnose sturgeon, year-round residents in both rivers, showed great individual variation in habitat use, and all ages selected a broad range of habitats on all spatial scales. However, shortnose sturgeon in both rivers preferred curves with sand or cobble substrate and avoided runs regardless of substrate. Individuals used channel or shoal at rates ranging from 0 to 100% on a weekly time scale in an unpredictable manner. Connecticut River shortnose sturgeon increased their use of curves, channels (deep water), and sand substrate in the fall. This strategy may conserve energy because these conditions usually reflect slow water velocity. Winter habitat selection continued the fall pattern, but was less variable because habitat affinity was highest among wintering fish. Documenting individual fish use of large-scale habitat revealed habitat relationships that would not have been discovered if only fish use of microhabitat had been studied.

Successful fisheries management practices developed for one ecosystem can often be used in similar ecosystems. We developed a flexible lake classification framework in collaboration with ~100 fisheries biologists for improved fisheries conservation management in Wisconsin, USA. In total, 5,950 lakes were classified into 15 lake classes using a two‐tiered approach. In tier‐one, lakes were clustered into “simple” and “complex” sportfish assemblages. In tier‐two, lakes were further clustered using accumulated degree days, water clarity, and special cases. We focus on temperature and clarity because these factors often drive fisheries change over time—thus a lake's class can change over time. Lake class assignments were refined through a vetting process where fisheries biologists with expert knowledge provided feedback. Relative abundance, size‐structure, and growth rates of fishes varied significantly across classes. Biologists are encouraged to utilize class interquartile ranges in fisheries metrics to make improved fisheries assessments. We highlight hard‐won lessons from our effort including: (1) the importance of co‐developing classification frameworks alongside fisheries biologists; and (2) encouraging frameworks where lakes can shift classes and fisheries expectations over time due to factors like climate change and eutrophication.

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