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Steve Davis

Publications and source records attributed to Steve Davis.

3 recordsLinked to original sources

Whitefishes—Coregonus spp.

Coregonines represent a diverse group of fishes of the Salmonidae subfamily Coregoninae that have ecological, cultural, and economical importance throughout their range. They are prey for other commercially and recreationally important predatory fish and are harvested for human consumption (Cleland 1982). Coregonines are generally cool- and coldwater species distributed across the northern hemisphere. The Laurentian Great Lakes of North America experienced considerable loss of coregonine biodiversity due to overfishing, the effects of nonnative species, and habitat loss (Bunnell et al. 2023). This resulted in the extinction of several species (Deepwater Cisco, Longjaw Cisco, and Kiyi) and local extirpations of other species (Blackfin Cisco, Shortnose Cisco, Shortjaw Cisco, Bloater, and Cisco) across all lakes (Bunnell et al. 2023). Renewed interest in restoring coregonines as the native prey base in the Laurentian Great Lakes prompted the development of a multi-agency restoration framework (Bunnell et al. 2023, 2024), and stocking of hatchery-produced coregonines is one of the management tools being used in this large restoration effort. This chapter focuses on culture methods for Lake Whitefish, Cisco, and Bloater currently being propagated for restoration and reintroduction in the Great Lakes (Bunnell et al. 2023). The techniques used for these species can serve as a starting point for the culture of other cisco species, such as Kiyi, which fisheries managers may consider for restoration stocking. Lake Whitefish, Cisco, and Bloater (Figure 1) are similar in appearance but have distinctively different life histories. They are silvery-white-colored fish and possess an adipose fin. Lake Whitefish are distinguished from Cisco and Bloater by a snout that overhangs the lower jaw (subterminal), whereas Cisco and Bloater have a lower jaw that extends beyond the snout (terminal). Lake Whitefish can reach lengths up to 79 cm (Phillips et al. 1982). Cisco and Bloater are smaller, reaching lengths of 34 and 30 cm, respectively (Eshenroder et al. 2016; Bunnell et al. 2023). Cisco and Bloater are similar in appearance but can be differentiated by head profiles, number of gill rakers (47 for Cisco and 42 for Bloater; Bunnell et al. 2023), and body morphometry. Lake Whitefish spawn from September through January in water depths of 2–4 m (Phillips et al. 1982). Cisco spawn from November through December in water depths of 1–47 m (Paufve et al. 2022), whereas Bloater spawn later in winter (February through May) at depths of 37–92 m (Koelz 1929; Becker 1983; Eshenroder et al. 2016). After eggs of all three species hatch in the spring, larvae feed on zooplankton and then switch to larger invertebrate prey as they grow; some will begin to feed on other fish species as they reach maturity (Warren and Lehman 1988; Brown and Taylor 1992; Breaker et al. 2020; Gatch et al. 2021). All three species typically mature at ages 4–5, with some maturing beginning at age 2 (Beauchamp 2002; Gorman 2012; DeCosta 2016).

Book chapter

Results of the collaborative Lake Ontario bloater restoration stocking and assessment, 2012–2020

Bloater, Coregonus hoyi , are deepwater planktivores native to the Laurentian Great Lakes and Lake Nipigon. Interpretations of commercial fishery time series suggest they were common in Lake Ontario through the early 1900s but by the 1950s were no longer captured by commercial fishers. Annual bottom trawl surveys that began in 1978 and sampled extensively across putative bloater habitat only yielded one individual (1983), suggesting that the species had been locally extirpated. In 2012, a multiagency restoration program stocked bloater into Lake Ontario from gametes collected in Lake Michigan. From 2012 to 2020, 1,028,191 bloater were stocked into Lake Ontario. Bottom trawl surveys first detected stocked fish in 2015, and through 2020 ten bloater have been caught (total length mean = 129 mm, s.d. = 44 mm, range: 96–240 mm). Hatchery applied marks and genetic analyses confirmed the species identification and identified stocking location for some individuals. Trawl capture locations and acoustic telemetry suggested that stocked fish dispersed throughout the main lake within months or sooner, and the depth distribution of recaptured bloater was similar to historic distributions in Lake Ontario and other Great Lakes. Predicted bloater trawl catches, based on modeled population abundance and trawl survey efficiency, were similar to observed catches, suggesting that post-stocking survival is less than 20% and contemporary bottom trawl surveys can quantify bloater abundance at low densities and track restoration.

Lake Ontario

Upper Auglaize watershed AGNPS modeling project

The Upper Auglaize Watershed agricultural non-point source modeling project was an interagency effort to use a Geographic Information System (GIS)-based modeling approach for assessing and reducing pollution from agricultural runoff and other non-point sources. This project applied the U.S. Department of Agriculture (USDA), Agricultural Research Service’s AGricultural Non-Point Source (AGNPS) suite of models to the Upper Auglaize River Watershed, a major watershed within the Maumee River Basin. This modeling project was conducted by an interagency team consisting of a partnership between the: (1) USDA, Agricultural Research Service (ARS); (2) USDA, Natural Resources Conservation Service (NRCS); (3) U.S. Army Corps of Engineers (USACE); (4) U.S. Geological Survey (USGS); (5) Ohio State University; (6) University of Toledo (UT); (7) Heidelberg College; (8) Ohio Department of Natural Resources (ODNR), Division of Soil and Water Conservation; (9) Ohio Environmental Protection Agency (OEPA); and (10) Allen, Auglaize, Van Wert, and Putnam Soil and Water Conservation Districts. The partnership was the first step in a process to eventually apply the model in a portioned subset of watersheds for the Maumee Basin, and then to link them to form a comprehensive basin-wide model This work was performed under the authority of Section 516(e) of the Water Resources Development Act (WRDA) of 1996, as amended, for the purpose of assisting State and local watershed managers with their evaluation, prioritization and implementation of alternatives for soil conservation, sediment trapping and non-point source pollution prevention in the Upper Auglaize River watershed. The project team, working in a cooperative effort, used the models to determine sediment sources, contributing locations, and the effect of application of best management practices (BMPs) on rates of sediment delivery to the mouth of the watershed. The results will be used to guide conservation incentive and land treatment programs. The team relied heavily on Geographic Information System (GIS)-based applications to expedite the application of the model. The results of the analysis demonstrated that the application of BMPs would have a positive effect on reducing the loadings of sediment leaving the mouth of the Upper Auglaize Watershed. An application of 17 percent new no-till acres and eight percent new grassland acres, when randomly applied to the watershed, reduced loadings at the mouth to 82 percent of the simulated existing condition loadings. No-till, conversion of cropland to grassland, other uses including grass buffers, and reforestation of parts of the watershed, were all shown by the model to have a measurable effect on reducing sediment loads. Conversion of all of the cropland in the watershed to no-till would reduce the average unit load (tons of sediment per acre) leaving the mouth of the watershed to a level that is 42 percent of the simulated existing condition load. Ephemeral gullies were found to be the primary source of erosion (72 percent), sediment yield (73 percent), and sediment loading (73 percent). Controlling sediment load means controlling gully erosion and possibly trapping sediment yield before it reaches the stream system. Most BMPs (e.g., no-till, conversion of cropland, etc.) that reduce sheet and rill erosion and its sediment yield will also reduce gully erosion and its sediment yield. However, grassed waterways, which have no effect on sheet and rill erosion, are frequently an effective BMP to prevent ephemeral gullies. And, of course, riparian vegetation and sediment traps would reduce the delivery ratios of all types of landscape erosion. New techniques were developed by the team to quantify the ephemeral gully erosion within the model. When calibrated to available stream gage data the model suggests that more (73% in the existing condition simulation) of the sediment load originates from ephemeral gully erosion than from traditional sheet and rill erosion. The model quantified the value of tile drainage in reducing the sediment load from the watershed. Loadings under drained conditions were always less than loadings under undrained conditions for otherwise identical land uses. The average sediment load of all alternatives for drained loadings was 89.2 percent of the load for the corresponding undrained loadings. The model established that while many conservation incentive programs treat tile drainage as a production practice, significant erosion and sediment control benefits are provided by the practice in comparison to cultivation in an undrained state.

Ohio