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Ontogenetic shifts in olfactory rosette morphology of the sockeye salmon, Oncorhynchus nerka.

Sarah E RheinsmithThomas C QuinnAndrew H DittmanKara E Yopak
Published in: Journal of morphology (2022)
Sockeye salmon, Oncorhynchus nerka, are anadromous, semelparous fish that breed in freshwater - typically in streams, and juveniles in most populations feed in lakes for one or two years, then migrate to sea to feed for two or three additional years, before returning to their natal sites to spawn and die. This species undergoes important changes in behavior, habitat, and morphology through these multiple life history stages. However, the sensory systems that mediate these migratory patterns are not fully understood, and few studies have explored changes in sensory function and specialization throughout ontogeny. This study investigates changes in the olfactory rosette of sockeye salmon across four different life stages (fry, parr, smolt, and adult). Development of the olfactory rosette was assessed by comparing total rosette size (RS), lamellae number, and lamellae complexity from scanning electron microscopy (SEM) images across life stages, as a proxy for olfactory capacity. Olfactory rosette size increased linearly with lamellae number and body size (p < 0.001). Complexity of the rosette, including the distribution of sensory and non-sensory epithelia and the appearance of secondary lamellar folding, varied between fry and adult life stages. These differences in epithelial structure may indicate variation in odor processing capacity between juveniles imprinting on their natal stream and adults using those odor memories in the final stages of homing to natal breeding sites. These findings improve our understanding of the development of the olfactory system throughout life in this species, highlighting that ontogenetic shifts in behavior and habitat may coincide with shifts in nervous system development. This article is protected by copyright. All rights reserved.
Keyphrases
  • south africa
  • electron microscopy
  • climate change
  • machine learning
  • genetic diversity
  • water quality