Field and laboratory validation of new sampling gear to quantify coregonine egg deposition and larval emergence across spawning habitat gradients
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Abstract
The influence of habitat and environmental conditions on Great Lakes coregonine reproduction is not well described, in part, because we lack sampling gears for early life stages that are effective across habitats. We designed new egg and larval emergence traps to quantify coregonine reproductive success across variable depths and substrates and tested them in laboratory and field settings. In the laboratory, our new metal ring egg traps had greater egg retention (94–100%) and faster post-catch processing (5–7 min) relative to a commonly employed fiber mat trap (30–67% and 30–60 min). In Lake Ontario’s Chaumont Bay, egg densities for lake whitefish Coregonus clupeaformis (0–5,832 eggs m−2) and cisco Coregonus artedi (0–426,501 eggs m−2) measured with metal ring traps (n = 112) varied across habitats but were greatest between 2–5 m on rock and dreissenid mussel substrates. Emergence traps used an inverted cone, fine mesh, and a clear collection chamber to capture positively phototactic emerging larvae. In the laboratory, traps captured 69–80% of emerged larvae. In Chaumont Bay, emergence traps deployed for 21 days after ice out caught only cisco larvae. Emergence rates varied across habitats (0–118 larvae m−2 day−1, n = 85) but were highest on dreissenid mussel reef substrate. Our samplers improved processing efficiency and facilitated large sample sizes to quantify variability in egg deposition densities and emergence rates across habitats. These methods can advance coregonine conservation by determining how anthropogenic changes to habitat and environmental conditions influence incubation success.
Publication type | Article |
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Publication Subtype | Journal Article |
Title | Field and laboratory validation of new sampling gear to quantify coregonine egg deposition and larval emergence across spawning habitat gradients |
Series title | Journal of Great Lakes Research |
DOI | 10.1016/j.jglr.2023.06.010 |
Volume | 49 |
Issue | 5 |
Year Published | 2023 |
Language | English |
Publisher | Elsevier |
Contributing office(s) | Great Lakes Science Center |
Description | 10 p. |
First page | 1059 |
Last page | 1068 |
Google Analytic Metrics | Metrics page |