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Mass mortality events, due to a variety of natural and anthropogenic causes, usually result in population (and associated fishery) crashes. Recovery from such events may not occur for many years, if at all. We have witnessed a mass die-off of adult (1+ yr) bay scallops Argopecten irradians irradians in the Peconic Bays, New York, USA, from 2019-2022, with declines in population density from spring to fall of 90-99%. Similar declines in commercial landings have occurred since 2018, with severe economic consequences for fishermen. Observed mortality levels are well above those seen prior to 2019. However, since die-offs of adult scallops have been occurring after the first seasonal spawning cycle, larval and benthic juvenile (0+ yr) recruitment have remained robust through 2021. Nevertheless, with lower numbers of adults surviving to spawn in September-October, resulting in fewer fall recruits, potential buffering of marked annual fluctuations in abundance is now less likely for this short-lived species. Peconic bay scallops are again in a precarious state as these recurring die-offs, likely driven by changing environmental conditions, present further challenges to the persistence of robust populations and the likelihood of successful restoration efforts. 

Addressing continental scale challenges affecting inland aquatic systems requires data at comparable scales. Critically, local in-situ observations for both lotic and lentic ecosystems are frequently fragmented across federal, state and local agencies, and nonprofit or academic organizations and must be linked to other geospatial data to be useful. To advance macro-scale aquatic ecosystem science, better tools are needed to facilitate dataset integration. Key to integration of aquatic data is the linking of spatial data to the hydrologic network. This integration step is challenging as hydrologic network data are large and cumbersome to manage. Here we develop a new R package, hydrolinks, to ease linking aquatic data to the hydrologic network. We use hydrolinks to evaluate the spatial data quality for all lake and stream sites available through the U.S. Water Quality Portal. We find that 76.5% of lake sites and 13.9% of stream sites do not correspond with mapped waterbodies.