The intriguing co-distribution of the copepods 'Calanus hyperboreus' and 'Calanus glacialis' in the subsurface chlorophyll maximum of Arctic seas

Abstract

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.

Keywords

• arctic copepods
• underwater imaging
• automated zooplankton identification using machine learning
• fine scale vertical distribution
• resource partitioning
• predator avoidance