A new device for monitoring the activity of freely swimming flatfish, Japanese flounder Paralichthys olivaceus

Abstract

The tail beat and activity behavior of four captive Japanese flounder Paralichthys olivaceus, were monitored with acceleration data‐loggers while the fish swam in an aquarium. Depth, swimming speeds and two‐axis acceleration data were collected continuously for approximately 20 h per fish. Simultaneously, the swimming behaviors of the fish were filmed at different angles. Using the specific characteristic of the acceleration profiles, in tandem with other types of data (e.g. speed and depth), four behavioral patterns could be distinguished: (i) ‘active’ swimming; (ii) burying patterns; (iii) ‘inactive’ gliding; and (iv) lying on the bottom. Tail beat frequency ranged from 1.65 ± 0.47 to 2.04 ± 0.25 Hz (mean ± SD; n = 4). Using the relationship between tail beat frequency and swimming speed, the ‘preferred’ swimming speed of the fish was estimated to be between 0.6 and 1.2 body lengths (BL)/s. Additionally, fish rarely swam faster than 1.2 BL/s. This study shows that the acceleration data‐loggers represent a useful and reliable system for accurately recording the tail beat of free‐ranging fish and estimating flatfish behavior.

Accelerometer tags: detecting and identifying activities in fish and the effect of sampling frequency

“…If a fish species that moves more in the vertical domain had been used, such as a cruise predator, we would expect vertical acceleration to make a greater contribution to the parameter set (e.g. Kawabe et al, 2003a). Fast-start event detection may be more complex if the vertical acceleration contribution to spontaneous movement increased, which could decrease the power of the standard deviation as a stand-alone detection parameter and thus other parameters may be required to detect fast-start events.…”

“…We neglected the statistical identification of simple locomotion, such as resting or swimming, because comparable acceleration studies in fish have shown that FFT analyses will detect and identify these movements (Kawabe et al, 2003a;Whitney et al, 2010).…”

“…In fish, acceleration metrics have been linked to heart rate and energy expenditure (Clark et al, 2010), spawning behaviour (Tsuda et al, 2006), activity (Kawabe et al, 2003a;Kawabe et al, 2003b) and, more recently, feeding behaviour (Føre et al, 2011). Generally, accelerometer tags continuously record data at some defined frequency or record a defined time-average of the data, which are either digitally stored or transmitted for subsequent postprocessing.…”

“…rates of change) are then combined to estimate activity and energy budgets or to classify various behaviours such as resting, swimming, etc. Many accelerometer studies involving fish (Kawabe et al, 2003a;Kawabe et al, 2003b;Tsuda et al, 2006;Murchie et al, 2010;O'Toole et al, 2010) employ sampling frequencies ≤32Hz because of battery, data-storage and size constraints associated with commercially available tags. Observations obtained at such frequencies may allow for the identification of relatively simple behaviours such as resting and swimming or some complex behaviours such as spawning in large salmon (Tsuda et al, 2006) or mating in large sharks (Whitney et al, 2010).…”

Post-tagging behaviour and habitat use in shortnose sturgeon measured with high-frequency accelerometer and PSATs

“…Such data can be used to quantify variation in behaviour, energetics and habitat use and provide objective measurements of how animals interact with each other and respond to their environment [1]. Most recently, accelerometer sensors have been used for remote measurements of fine-scale movements among free-swimming fish [4][5][6] in controlled mesocosms [7][8][9][10][11], as well as in the wild [1, 4-6, 12, 13]. Typically, in situ measurements are collected using implanted archival tags or pop-up satellite tags (PSATs) that are deployed over weeks to years [14].…”