2006
DOI: 10.1111/j.1600-0633.2006.00147.x
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Monitoring the spawning behaviour of chum salmon with an acceleration data logger

Abstract: –  The continuous observation of salmon behaviour in some wild environments can be extremely difficult. We recorded spawning behaviour of female chum salmon (Oncorhynchus keta) in the field simultaneously using visual observation and fish‐borne data loggers with two‐axis accelerometer sensors. Using only acceleration records, behaviours were successfully classified into the eight well‐known components of spawning behaviour: swimming, nosing, exploratory digging, nest digging, probing, oviposition, covering and… Show more

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Cited by 96 publications
(96 citation statements)
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“…These results agree with those of Tsuda et al (2006) who found a similar decrease in typical chum salmon spawning behaviors and a complete cessation of nest digging during a high discharge event. In that study, flows increased water depths by 80 cm, but the authors were unable to measure accompanying velocities.…”
Section: Discussionsupporting
confidence: 83%
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“…These results agree with those of Tsuda et al (2006) who found a similar decrease in typical chum salmon spawning behaviors and a complete cessation of nest digging during a high discharge event. In that study, flows increased water depths by 80 cm, but the authors were unable to measure accompanying velocities.…”
Section: Discussionsupporting
confidence: 83%
“…The increase in swimming activity (i.e., tailbeat frequency) we observed at higher velocities suggests that chum salmon were incurring greater energetic costs to maintain position on their redds. Similarly, Tsuda et al (2006) found a substantial increase in tailbeat frequency of spawning chum salmon when flows were high in a Japanese river, and speculated that significant depletion of energy reserves may occur under such conditions. The three female chum salmon that left their redds when velocities exceeded 1 m/s may have done so to reduce energy expenditure.…”
Section: Discussionmentioning
confidence: 99%
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“…Specific behaviours can be readily identified from acceleration data collected from free-ranging fish without direct observation (e.g. Tsuda et al, 2006;Whitney et al, 2010Whitney et al, , 2012Gleiss et al, 2013;Broell et al, 2013). These behaviours can then be paired with metabolic rate estimates through ODBA, allowing the construction of high-resolution time-energy budgets for free-ranging fish.…”
Section: Application To Estimates Of Fmrmentioning
confidence: 99%