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doi: 10.1525/elementa.388
Studying the distribution of zooplankton in relation to their prey and predators is challenging, especially in situ. Recent developments in underwater imaging enable such fine-scale research. We deployed the Lightframe On-sight Keyspecies Investigation (LOKI) image profiler to study the fine-scale (1 m) vertical distribution of the copepods Calanus hyperboreus and C. glacialis in relation to the subsurface chlorophyll maximum (SCM) at the end of the grazing season in August in the North Water and Nares Strait (Canadian Arctic). The vertical distribution of both species was generally consistent with the predictions of the Predator Avoidance Hypothesis. In the absence of a significant SCM, both copepods remained at depth during the night. In the presence of a significant SCM, copepods remained at depth in daytime and a fraction of the population migrated in the SCM at night. All three profiles where the numerically dominant copepodite stages C4 and C5 of the two species grazed in the SCM at night presented the same intriguing pattern: the abundance of C. hyperboreus peaked in the core of the SCM while that of C. glacialis peaked just above and below the core SCM. These distributions of the same-stage congeners in the SCMs were significantly different. Lipid fullness of copepod individuals was significantly higher in C. hyperboreus in the core SCM than in C. glacialis above and below the core SCM. Foraging interference resulting in the exclusion from the core SCM of the smaller C. glacialis by the larger C. hyperboreus could explain this vertical partitioning of the actively grazing copepodite stages of the two species. Alternatively, specific preferences for microalgal and/or microzooplankton food hypothetically occupying different layers in the SCM could explain the observed partitioning. Investigating the observed fine-scale co-distributions further will enable researchers to better predict potential climate change effects on these important Arctic congeners.
arctic copepods, automated zooplankton identification using machine learning, Environmental sciences, predator avoidance, Arctic copepods; Underwater imaging; Automated zooplankton identification using machine learning; Fine scale vertical distribution; Resource partitioning; Predator avoidance, GE1-350, fine scale vertical distribution, underwater imaging, resource partitioning, lcsh: lcsh:GE1-350, lcsh: lcsh:Environmental sciences
arctic copepods, automated zooplankton identification using machine learning, Environmental sciences, predator avoidance, Arctic copepods; Underwater imaging; Automated zooplankton identification using machine learning; Fine scale vertical distribution; Resource partitioning; Predator avoidance, GE1-350, fine scale vertical distribution, underwater imaging, resource partitioning, lcsh: lcsh:GE1-350, lcsh: lcsh:Environmental sciences
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citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 11 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |