|Document Type:||Journal Article|
|Title:||Enzyme activities of demersal fishes from the shelf to the abyssal plain|
|Author:||Jeffrey C. Drazen, Jason R. Friedman, Nicole E. Condon, Erica J. Aus, Mackenzie E. Gerringer, A. A. Keller, M. E. Clarke|
|Journal:||Deep Sea Research Part I: Oceanographic Research|
|Keywords:||metabolism,visual interactions hypothesis,enzymes,locomotory mode,|
The present study examined metabolic enzyme activities of 61 species of demersal fishes (331 individuals) trawled from a 3000 m depth range. Citrate synthase, lactate dehydrogenase, malate dehydrogenase, and pyruvate kinase activities were measured as proxies for aerobic and anaerobic activity and metabolic rate. Fishes were classified according to locomotory mode, either benthic or benthopelagic. Fishes with these two locomotory modes were found to exhibit differences in metabolic enzyme activity. This was particularly clear in the overall activity of citrate synthase, which had higher activity in benthopelagic fishes. Confirming earlier but less comprehensive studies, enzyme activities declined with depth in benthopelagic fishes. For the first time patterns in benthic species could be explored and these fishes also exhibited depth-related declines in enzyme activity, contrary to expectations of the visual interactions hypothesis. Trends were significant when using depth parameters taken from the literature as well as from the present trawl information, suggesting a robust pattern regardless of depth metric used. Potential explanations for the depth trends are discussed but clearly metabolic rate does not vary simply as a function of mass and temperature in fishes as shown by the substantial depth-related changes in enzymatic activities.
The study examined metabolic enzyme activities of 61 species of demersal fishes (331 individuals) trawled from a 3000 m depth range.
|Full Text URL:||http://dx.doi.org/10.1016/j.dsr.2015.02.013|
|Theme:||Recovery and rebuilding of marine and coastal species|
Characterize the population biology of species, and develop and improve methods for predicting the status of populations.
Develop methods to use physiological, biological and behavioral information to predict population-level processes.