Calibrating Accelerometer Tags with Oxygen Consumption Rate of Rainbow Trout (Oncorhynchus mykiss) and Their Use in Aquaculture Facility: A Case Study

Abstract

Metabolic rates are linked to the energetic costs of different activities of an animal’s life. However, measuring the metabolic rate in free-swimming fish remains challenging due to the lack of possibilities to perform these direct measurements in the field. Thus, the calibration of acoustic transmitters with the oxygen consumption rate (MO2) could be promising to counter these limitations. In this study, rainbow trout (Oncorhynchus mykiss Walbaum, 1792; n = 40) were challenged in a critical swimming test (Ucrit) to (1) obtain insights about the aerobic and anaerobic metabolism throughout electromyograms; and (2) calibrate acoustic transmitters’ signal with the MO2 to be later used as a proxy of energetic costs. After this calibration, the fish (n = 12) were implanted with the transmitter and were followed during ~50 days in an aquaculture facility, as a case study, to evaluate the potential of such calibration. Accelerometer data gathered from tags over a long time period were converted to estimate the MO2. The MO2 values indicated that all fish were reared under conditions that did not impact their health and welfare. In addition, a diurnal pattern with higher MO2 was observed for the majority of implanted trout. In conclusion, this study provides (1) biological information about the muscular activation pattern of both red and white muscle; and (2) useful tools to estimate the energetic costs in free-ranging rainbow trout. The use of acoustic transmitters calibrated with MO2, as a proxy of energy expenditure, could be promising for welfare assessment in the aquaculture industry.

Mapping the Energetic Costs of Free-Swimming Gilthead Sea Bream (Sparus aurata), a Key Species in European Marine Aquaculture

“…Indeed, the sigmoid function describes the initial exponential pattern of the consumption rate and the slowdown near the U crit . This relationship between MO 2 and swimming speed was already observed in sea bream [ 40 , 43 ], in similar species such as sea bass ( Dicentrarchus Labrax ) [ 9 ], or in more distant species such as rainbow trout ( Oncorhynchus mykiss ) [ 32 ] or chinook salmon ( Oncorhynchus tshawytscha ) [ 53 ]. These differences in MO 2 modelling may be due to the different protocol methodologies, because our protocol and those cited earlier were based on a shorter time step than those cited previously.…”

“…Additionally, the tags used by Arechavala-Lopez et al [ 38 ] recorded the three dimensions of movement (i.e., x -, y -, and z -axes), whereas our algorithm only recorded the x - and z -axes, removing the backward/forward acceleration ( y -axis). Removing the y -axis from the algorithm calculation allows for accounting only for the undulation movements associated to the movements of the tail (‘tail-beat beat’ mode); thus, more reliable estimates of energetic costs could be obtained [ 29 , 32 , 67 ]. This highlights that the tag implantation location and the associated algorithms measuring acceleration are crucial for such correlations and for estimating energetic expenditure.…”

“…These technologies have been proven to be sensitive for remote monitoring of health and welfare in farmed fish [ 14 , 15 , 16 , 20 , 21 , 22 , 23 , 24 ] and could be promising tools in the context of precision livestock farming [ 25 , 26 , 27 , 28 ]. However, the use of such devices in free-swimming fish for estimating energetic costs related to swimming, including those in aquaculture conditions, requires mapping with physiological data such as MO 2 [ 29 , 30 , 31 , 32 , 33 ]. As the MO 2 is known to vary as a function of different biotic and abiotic factors (e.g., species, life stage, stress state, temperature, water quality, and oxygen concentration), the inference of MO 2 using accelerometer tags needs an accurate calibration for a specific species, at a given size and temperature, before it can be used in aquaculture environments [ 1 , 2 ].…”

Automatic Monitoring of Relevant Behaviors for Crustacean Production in Aquaculture: A Review

“…The signal detection method based on machine learning can extract the time–frequency characteristics of the sound and filter out the interference of noisy sounds. In addition, post-processing and analysis of large datasets are current difficulties [ 68 , 69 ]. In order for passive acoustics to be better applied to crustacean behavioral monitoring, specialists can use big data technology to achieve intelligent data processing and analysis and develop user-friendly software that can be used by fishermen and ecologists.…”

Comparison of Metabolic Rates of Young of the Year Beluga (Huso huso), Sterlet (Acipenser ruthenus) and Bester Hybrid Reared in a Recirculating Aquaculture System

“…The metabolic rates of fish are influenced by multiple factors [11,12]. Between those factors, the most important are body weight and temperature [13].…”