Assessing the spawning ecology of fish in situ using a benthic pump sampler
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Abstract
In situ observations of incubating fish eggs can identify spawning sites and spawning habitat preferences, informing the ecology of fishes with benthic eggs. Suction pumps have been used to sample benthic-incubating, non-adhesive fish eggs, yet their sampling efficiency is not well known. Imperfect or systematically variable egg detection could bias resulting ecological inference if left unaddressed. Here we present results from replicate field trials and examine the effects of varying substrate type, intake design, sampling effort, and egg density on the ability of a gasoline-powered diaphragm pump, rated for 66 gal/min (approx. 250 L/min), to detect egg presence and estimate relative or absolute abundance. A wider box-shaped intake was effective at detecting the presence of eggs on fine, silty substrates, but had limited effectiveness on larger-grained substrates. A narrower cone-shaped intake consistently detected the presence of eggs on silt, gravel, and shallow cobble, and demonstrated potential to measure relative and absolute egg abundance. Neither intake design was able to collect eggs from deep interstitial spaces (e.g. several layers of cobble), indicating that there are limitations to the types of substrates on which benthic sampling pumps can operate. Both intake designs often collected eggs from outside the edge of the intake opening. Most eggs were collected within a two minute period, but increasing pumping time to four minutes produced better egg detection outcomes. Our results suggest that stationary benthic sampling pumps are viable tools for directly sampling incubating eggs on most substrates, but have imperfect and varying sampling efficiency resulting from intake design and substrate type.
Publication type | Article |
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Publication Subtype | Journal Article |
Title | Assessing the spawning ecology of fish in situ using a benthic pump sampler |
Series title | Fisheries Research |
DOI | 10.1016/j.fishres.2019.01.029 |
Volume | 214 |
Year Published | 2019 |
Language | English |
Publisher | Elsevier |
Contributing office(s) | Coop Res Unit Leetown, Great Lakes Science Center |
Description | 6 p. |
First page | 19 |
Last page | 24 |
Google Analytic Metrics | Metrics page |