Calibrating PSAT tridimensional acceleration data for the estimation of fish swimming speed and activity

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Benoît, Jonathan A.D. Fisher, Dominique Robert This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7304544/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Understanding marine species distribution and habitat use contributes to the development of effective population conservation and management. Data from biotelemetry can provide valuable insights on individual movement, behaviour and swimming speed. These data can link movement ecology and ecophysiology, thereby describing life strategies from a bioenergetic perspective. Technical limitations often constrain the use of biologgers over large spatial and temporal scales in free-ranging marine animals, hindering detailed examination of movements over seasonal or annual migrations, for instance. Pop-up Satellite Archival Tags (PSATs) equipped with triaxial accelerometers offer a promising solution to these challenges in aquatic animals by providing a platform recoverable at-sea to aid the collection and interpretation of high frequency, year-long accelerometry data. Results Using two approaches, this study calibrated the relationship between PSAT tridimensional tilt and speed of movement, while addressing potential limitations related to interpreting movement rates via PSAT accelerometry. First, we tested five PSATs under different attachment designs, attachment positions, and flow rates in a controlled calibration flume tank experiment. We found that properties of the tags and attachment had a minor effect except at no and low flow, and that an asymptotic model accurately described the relationship between tilt and flow speed. Second, we analysed field data from 43 tagged Atlantic halibut ( Hippoglossus hippoglossus ) and two stationary moored PSATs in the Gulf of St. Lawrence (Canada). We found that slow swim speeds up to a threshold of about 0.25 m s − 1 could not be distinguished from tag movement caused by ocean currents; however, above this threshold average swimming speed was related to halibut fork length. Conclusion This study validates the reliability of PSATs accelerometry data for estimating moderate and fast, but not slow swimming speeds, across long-term movements of halibut. Further research is needed to accurately characterize slow swimming speeds given variability in tag inclination even in the absence of movement, and confounding with water current-induced values. PSAT accelerometry offers prospects for investigating species distribution, life strategies and habitat selection from a bioenergetic perspective through an individual-focused approach for the wide diversity of aquatic taxa that can be equipped with PSATs. Acceleration accelerometry Atlantic halibut biotelemetry movement ecology Pop-up Satellite Archival Tags PSATs swimming speed Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Biotelemetry provides fine-scale data at the individual level, revealing species' use of space, habitat, and associated spatiotemporal changes in distribution [ 1 – 3 ]. This addresses key questions in movement ecology, particularly where, when, and how animals move [ 4 – 6 ]. These insights hold significant potential for the development of effective conservation and management strategies [ 7 – 10 ]. Combining accelerometry and bioenergetics describes how movement, activity and swimming speed relate to metabolic rate, heart rate or tail-beat frequency and consequently energy expenditure and cost of transport [ 11 – 14 ]. This approach offers opportunities to examine species distribution from a bioenergetic perspective, as conceptualized more generally by the Ideal Free Distribution model [ 15 , 16 ] or the Equal Fitness Paradigm [ 17 – 19 ]. Behavioural studies require data sampling frequencies sufficient to accurately describe specific recurring behaviours [ 13 , 20 ]. In the context of biotelemetry, battery life often constrains the quantity of data collected and transmitted either in duration, sampling frequency or minimum battery size [ 21 – 23 ]. As a result, electronic tagging projects often span short periods of time (hours to days) or target less mobile, more predictable species [ 24 ]. This leads to significant gaps in data representation across taxa and geographical areas [ 13 , 22 , 23 ]. In aquatic animals, for instance, water constrains satellite telemetry data transmission, requiring tags to reach the surface to transmit summarised data to satellites [ 23 , 25 ]. Therefore, these tags are often programmed to autonomously detach from the animal for satellite transmission or for manual retrieval, which allows access to the full dataset [ 20 , 24 , 25 ]. Recent developments using Argos goniometers have facilitated high retrieval rates of Pop-up Satellite Archival Tags (PSATs) floating at sea [ 20 ]. In the Gulf of St. Lawrence (GSL), Canada, these recoveries have provided near-continuous high-frequency data over periods of up to a year on 126 Atlantic halibut ( Hippoglossus hippoglossus ) [ 26 – 30 ]. The first GSL deployments involved PSATs equipped with depth and temperature data loggers, allowing for the development and application of geolocation models to estimate daily location and therefore migration patterns and the identification of putative halibut spawning grounds [ 26 ]. These estimates revealed partial-migration and homing behaviours between winter and summer seasons covering distances ranging from dozens to hundreds of kilometers [ 27 ]. In addition, high temporal resolution depth data allowed for the quantification of putative spawning behaviours [ 29 ]. Within this research program, deployments of 43 PSATs since 2017 included accelerometer data loggers. This additional technology presents an opportunity to potentially estimate activity and energy expenditure over timescales relevant to the study of both short-term (hourly, daily) and long-term behaviours (seasonal or annual migrations). These data revealed high variability in daily, seasonal and annual activity [ 30 ] and associations between the timing of acceleration peaks and spawning [ 29 ]. Relating contrasting life strategies, such as migrations, to energy expenditure could provide a unique bioenergetic perspective to better understand shifts in aquatic animal distribution. Despite the potential utility of acceleration data, the requirement of PSAT attachments external to fish may influence interpretations of accelerometry data. PSATs are often anchored to fish via a monofilament tether inserted through the muscles and anchored to the fish [ 31 – 34 ] (Fig. 1 ). Due to their positive buoyancy, PSATs float above the animal, normally remaining in a vertical position at rest (and at zero current speed), tilting away from vertical with increasing water velocity [ 32 ]. This attachment method differs from the direct attachment typically used with accelerometers in aquatic environments to estimate activity, swimming speed, and energy expenditure [ 35 – 38 ]. Tags employed to monitor Dynamic Body Acceleration - a metric derived from acceleration data - are either externally attached in a fixed location using glue, tape, a collar or a harness, or surgically implanted internally [ 14 , 39 , 40 ]. This has several implications for the interpretation of acceleration data, as these methods can potentially affect tag stability and introduce acceleration components related to body movement, thereby making recorded acceleration data sensitive to tag position [ 14 , 35 ]. The main advantage of PSATs tethered externally compared to many of these tags are their battery capacity and programmed release mechanisms, enabling long-term deployments with good recovery capabilities after pop-off [ 20 ]. To use accelerometry data in a context of bioenergetics, it is important to derive reliable estimates of swimming speed [ 14 ]. PSAT acceleration data was converted into speed estimates for the first time by Nielsen et al. [ 32 ], who described a sigmoidal relationship between the vertical component of acceleration and towing speed using a mechanical rotating arm in an experimental tank. To fully use the potential of PSAT accelerometry towards the precise estimation of swimming speed in marine animals, the three components of acceleration should be considered [ 11 ]. In addition, further research is needed to ensure that interpretation of PSAT accelerometry data accurately reflect animal movement and not other confounding factor resulting from the tethered external attachment, such as water current-induced tag movement. To our knowledge, no studies have specifically evaluated the effects of PSAT attachment choices on acceleration data interpretation, despite significant differences in recorded accelerations based on tag position being reported in other accelerometers [ 14 , 41 ]. Using controlled flume tank experiments and at-sea trials, we aim to address these uncertainties to advance the reliability and calibration of PSAT triaxial acceleration data to estimate swimming speed, and, in future studies, energy expenditure related to movement. To achieve our calibration goals, we employed five PSATs attached to halibut-like dummies positioned at the bottom of a large flume tank, measuring triaxial acceleration tilts at calibrated moving water velocities ranging from 0 to 0.93 m s⁻¹. Those trials quantified potential sources of variability on tilt measurements and were then compared to acceleration data from PSATs recovered from 43 free-ranging GSL Atlantic halibut and two additional PSATs moored on the GSL sea floor that measured accelerations associated solely with water currents. We estimated the swimming speed of halibut during medium to high accelerations given these experimental calibrations, anticipating a positive correlation between halibut fork length and mean individual swimming speed. By integrating the results of experimental and at-sea trials, we explore the strengths and limitations of PSAT accelerometers to estimate fine scale movement. Methods Pop‑up Satellite Archival Tags We used PSATs (MiniPAT) manufactured by Wildlife Computers, Inc. (“WC”, Redmond, Washington, USA), similar to those used by Nielsen et al. [ 32 ]. These tags record high resolution data for depth, temperature, light, and since 2017, have been equipped with a tri-axial accelerometer (range: -2 to 2 g, accuracy: ±0.05 g). PSATs release after a pre-programmed duration or sensing of specific environmental conditions, transmitting subsets or summaries of data via the Argos satellite archival time series, and complete archived data series can be downloaded from the MiniPATs if they are physically recovered [ 20 , 27 , 42 ]. When oriented vertically (tilt of 0°), the accelerometer records values near − 1 g on the z-axis, representing gravity, also referred to as static acceleration [ 12 , 43 ]. When oriented horizontally, the static acceleration shifts to the x- and/or y-axis, corresponding to a tilt of 90°; see tilt formula below. During the experiment, tags recorded data at 3-second intervals (0.33 Hz). Although this frequency differs slightly from the one used in field deployments (0.2 Hz), it is not expected to influence the results, as experimental recordings were conducted under continuous water flow conditions. We employed two variants of these tags, differing in the physical design of the attachment point between the tag and tether (Fig. 2 , inset). The first design, previously used in halibut deployments, feature a ‘direct’ tether-to-PSAT attachment point. The second design has a tether-to-PSAT attachment via a very low friction ‘disc’ that allows greater freedom of movement of the PSAT relative to the tether. This updated design prevents jamming of PSATs in a specific angle at the PSAT-tether connection at low velocities, which can cause elevated tilt values of approximately 20 to 30° in the absence of water movement, as occasionally observed with the first design in experimental trials (Fig. 2 a, central tag). Initially, we planned to employ only the first tag design in the flume experiments. However, limited tag availability required the use of both designs. The small sample size limited the scope for statistical tests of differences associated with the two tag attachment point designs. We used Kruskal–Wallis tests solely to assess whether there was any indication of differences between the two designs based on available tag behaviour information. Tests were applied to mean tilt values at each velocity step, as the high number of observations (1200 per velocity step) would lead to very low variance and therefore low p-values. Flume tank The experiment took place in the flume tank (22.25 m long, 8 m wide, 4 m deep, 1.7 million L volume) located at the Centre for Sustainable Aquatic Resources, Fisheries and Marine Institute of Memorial University of Newfoundland (St. John’s, Newfoundland and Labrador, Canada). The tank is supplied with freshwater, and water velocity is adjusted from computer-controlled pumps, stabilising within a few minutes after velocity change [ 44 ]. Uniform water flow is provided via screens, vanes, a deflector, and a wave damper. We reduced water depth from 4 m to 3 m to achieve higher water velocities, calibrated the flow rate at the bottom of the tank using a rotary flow meter averaged across three positions spanning the tank width, and recorded seven stable flow rates between 0 m s⁻¹ and 0.93 m s⁻¹. One side of the tank features 20 m wide acrylic windows, enabling direct observation and video recording. Design of halibut-like dummies and tag setup We designed five dummies using a rubber mat wrapped around PVC pipe (9 cm diameter × 35.6 cm long) to mimic the curvature of the dorsal, right eyed side of a halibut (Fig. 2 ), as PSATs are attached on this side of this asymmetrical flatfish (Fig. 1 ). To attach the tether, we fastened an eye-bolt at the bottom front of each cylinder and drilled two holes above it through each pipe, one vertically (0°) and the other angled leftward toward the left flank (30°). These variations, referred as tag position, account for potential differences in tag attachment associated with differences in size among individual halibut. In the field experiments in the GSL, mean tagged halibut fork length was 150.39 ± 3.46 cm (range: 87–202 cm), with noticeable variations in body thickness and curvature of the dorsal, right flank. These observed differences potentially alter the water flow around the tag and slightly change the angle of the emerging tether, reinforcing the need to experimentally account for potential variation in tether position. To further represent sources of tilt variability, we changed PSAT orientation by rotating the eye bolts after PSATs were tethered. This is to account for possible differences in tag movement associated with tether shape and the attachment point of the tether to the tag, as we observed substantial variability in tether shape between tags due to the shape retention of the monofilament tether (i.e., permanent curvature of the tether). We hypothesized that these differences could potentially alter the relationship between tilt and speed among individuals, thus impairing interindividual comparison. We secured all five dummies to a wider welded steel structure designed to remain stable at the bottom of the flume tank even at maximum water velocities (Fig. 2 ). The design, including 60 cm spacing between dummies prevented tags from colliding with each other and/or affecting local water flows. The use of a single structure simplified employment and recovery of 5 PSATs at once while ensuring consistent control of dummy position and orientation in the flume tank. Experimental setup We assigned each PSAT to a specific dummy for the duration of the experiment. Tether length from the outer part of the cylinder to the tag was set to 3.8 cm for all tags to ensure comparability to that used on halibut tagged at-sea. The experiment consisted of twelve consecutive trials (Table 1 ), during each of which water velocity was increased stepwise from 0 to 0.93 m s⁻¹, with increments of approximately 0.15 m s⁻¹. We ensured water velocity stabilised at each velocity step for a minimum duration of 60 s, corresponding to 20 successive tilt measurements before recording tilt angles. We further registered each period by time-matched video recordings. After recording the period at maximum velocity, water flow was returned to 0 m s⁻¹. The structure was retrieved between trials to adjust tag position and orientation before redeployment. Upon completing all trials, data were downloaded from PSATs and filtered to isolate recordings for all tags during each trial and velocity step. Table 1 Experimental design trial descriptions as related to tag position and tag orientation. Trial Tag position (°) Tag orientation (°) 1 0 0 2 0 30 3 0 330 4 0 180 5 0 150 6 0 210 7 30 0 8 30 30 9 30 330 10 30 180 11 30 150 12 30 210 Tag position is relative to vertical, while tag orientation is relative to perpendicular to water flow. Description of tilt and speed relationship in halibut-like dummies We calculated tri-dimensional tilts using the following formula from Pedley [ 45 ]: $$\:Tilt=\text{acos}\left(\frac{Az}{\sqrt{\left(A{x}^{2}+A{y}^{2}+A{z}^{2}\right)}}\right)\bullet\:57.2958-180$$ Where Ax and Ay represent the horizontal acceleration components of the tag and Az is the vertical acceleration component. We modelled data from each tag and trial separately, resulting in 60 models (12 trials × 5 PSATs). We compared overall model performance for sigmoid, logistic and asymptotic equations using non-linear least square models. Sigmoidal and logistic models showed convergence issues, likely due to the overall asymptotic pattern observed in our data, which differs from the sigmoidal relationship described by Nielsen et al. [ 32 ] for tilt derived solely from vertical acceleration (Az). Therefore, we focus on two asymptotic equations, both modified to account for a positive intercept ( \(\:{y}_{0}\) ): Modified one phase exponential association equation: \(\:Tilt={y}_{0}+lmax\bullet\:\left(1-{e}^{\left(-\beta\:\bullet\:\:U\right)}\right)\) Modified Michaelis-Menten equation: \(\:Tilt={y}_{0}+lmax\frac{U}{\beta\:+U}\) Where \(\:{y}_{0}\) is the intercept, \(\:lmax\) is the difference between the upper asymptote and \(\:{y}_{0}\) , \(\:\beta\:\) controls the slope of the relationship and U is the speed. We selected these equations for their ability to model tilt values from the intercept at 0 m s⁻¹ up to the asymptote. Although both describe an asymptotic relationship, they differ slightly in the shape of their response curves and in the interpretation of the parameters: in the first equation, \(\:\beta\:\) represents the rate at which saturation is approached, while in the second, it corresponds to the speed at which 50% of the tilt range ( \(\:{y}_{0}+lmax\) ) is reached. Asymptotic models were fitted by groups of tags with similar attachment designs to assess potential tether attachment style influence on the relationship and model parameters. We tested for the presence of significant differences between tag positions and orientations using Kruskal-Wallis tests. Swimming speed estimation for in situ movement-related and current-related tilt in PSAT data In PSATs recovered from at-liberty halibut, tilt data exhibit a bimodal distribution, with one mode at low tilt values corresponding to a mixture of resting and low activity level behaviour, and water current velocities (i.e. presumed inactive behaviour), and another mode at medium to high tilt values associated with medium to high activity levels (i.e. presumed active behaviour) [ 30 , 32 ]. We estimated speed for the mean value of each mode in each of the 43 halibut, separated using a threshold of 38.5°, as defined by Boulanger et al. [ 30 ]. Based on an analysis of Gaussian mixture models conducted on 25 of the 43 halibut presented here, as well as on the two moored PSATs, Boulanger et al. [ 30 ] determined this threshold to distinguish between both modes and to ensure that existing elevated water current-induced tilt values be identified as resting periods. Their work provides additional context on water current patterns, particularly in relation to tidal cycles, justifying the need for caution when interpreting low tilt values. To estimate the influence of current velocities, we estimated speeds related to percentiles of recorded tilt values for the two stationary moored PSATs in the GSL, deployed at depths of 342 m (M1) and 48 m (M2), respectively [ 28 ]. These estimates were compared, along with the one corresponding to the threshold, to modelled average water current velocities in the GSL [ 46 ]. Spearman rank correlation tests served to assess the strength of potential correlations between halibut fork length, mean swimming speed, and percentage of estimated time spent actively swimming. Results Model selection Both asymptotic models showed a strong average fit across all 60 data subsets, with a mean R² of 0.97 in both cases. However, the modified one phase exponential association equation performed slightly better overall, as indicated by a lower mean AIC (767.83 for Eqs. 1 and 780.92 for Eq. 2). Therefore, we use this equation for the remainder of this study. Across the 60 data subset fits, the average 95% confidence interval was 1.95°, indicating a low level of individual tag variability at a given speed under standardised conditions over a minute. Influence of tag attachment Attachment design primarily affects tilts at lower velocities (p ≤ 0.001 at 0 m s − 1 , p ≤ 0.01 at 0.17 m s − 1 and p > 0.05 at higher speeds) (Fig. 3 , Table 2 ). This is likely related to the PSAT 21C (direct attachment design) recording higher tilt values at these velocities, up to 27.2° at 0 m s − 1 . These higher tilt values appear to be mitigated progressively at higher speeds although they persist somewhat at higher velocities. This result aligns with the expected behaviour of tags related to attachment design. Table 2 Comparison of model parameter by tag and attachment design. Attachment design ID y 0 lmax 𝛃 R² Direct 21A 7.53 70.11 2.21 0.96 21B 6.87 77.05 1.85 0.96 21C 13.89 64.82 2.23 0.91 Group 9.44 70.43 2.09 0.93 Disc 24A 2.11 75.80 2.18 0.95 24B 3.00 75.32 2.65 0.96 Group 2.58 75.34 2.41 0.94 Tag position and orientation have a limited influence on tilt values between trials, globally (p > 0.05). However, several trials indicate some level of difference (Fig. 4 ). For example, (1) tilt values were consistently greater at all velocities for the 7th trial of 21A (tag position: 30°, tag orientation: 0°), (2) the first two trials for 24A (tag position: 0°, tag orientation 0 and 30°) stand out at several water velocities, and (3) tilt values at the three highest velocities for 24B show a repetitive pattern between trials. These elements, among others evident in Fig. 4 represent a consistent pattern of tilt differences across speeds that indicate a persistent, though not predominant, effect of individual tags/tether combination differences. Using field PSAT acceleration data to refine estimation of activity and swimming speed Moored PSATs M1 and M2 exhibited tilt values that were entirely induced by local water currents, providing data that aid in the interpretation of fish movement vs. current influences on PSAT tilt (Supplementary Data, Figure A1). Mean tilt values are 5.5 and 8.35° respectively, corresponding to current speeds of 0 m s − 1 as the model (Fig. 3 ) estimates that tilts under 9.44°, and potentially 9.8° when considering 95% confidence interval, correspond to an absence of motion in PSATs with the direct attachment design. Tilt values over 9.8° correspond to 41.50 and 22.73% of all tilt values, respectively. The 95th percentile of tilt values, representing those presumed to result from the highest current speeds of the year, are 25.25° and 30.02° respectively, corresponding to current speeds of 0.12 and 0.17 m s − 1 (Supplementary Data, Figure A1). The threshold of 38.5° reported by Boulanger et al. [ 30 ] to identify presumed inactivity and activity behaviour in halibut correspond to a velocity of 0.25 m s − 1 . The mean tilt for the respective inactive and active periods in the free-swimming halibut are 8.18 ± 0.38 and 63.83 ± 0.61°, corresponding to a mean velocity of 0 m s⁻¹ and a mean swimming speed 0.72 ± 0.02 m s⁻¹ (min: 0.43 m s⁻¹, max: 1.13 m s⁻¹) (Supplementary Data, Figure A1, Table A1). Individual mean velocity for the inactive mode in halibut isn’t reported as all the estimates are below 0.03 m s⁻¹. There was a significant positive correlation (r = 0.36, p ≤ 0.05) between mean swimming speed during the active mode and halibut fork length (Fig. 5 ), but no significant correlation between mean swimming speed and either percentage of estimated time spent actively swimming or halibut fork length (p > 0.05). Discussion The present study identified different sources of variability on PSAT triaxial accelerometry tilt data, with a focus on their implications for the estimation of swimming speed and activity. We used two complementary approaches: (1) a calibration experiment in a flume tank, where we assessed the relationship between tilt and flow speed for five PSATs under different attachment designs, positions, and orientations; and (2) an analysis of field data from 43 halibut PSATs and two stationary moored PSATs, highlighting the influence of environmental currents and identifying potential challenges for the interpretation of specific behaviours at low activity levels. An asymptotic model, fit to 60 different data subsets, accurately described the relationship between tilt and water velocity across all setups and PSATs in the flume tank experiment. This differs from the sigmoidal relationship reported by Nielsen et al. [ 32 ], who estimated tilt solely via vertical acceleration, when we used the three acceleration components. The mean 95% confidence interval across these models was 1.95°, outlining the potential for precise swimming speed estimation over short periods of time (minutes) using PSAT acceleration data. This aspect is particularly important in accelerometry studies, where reliable estimates depend on multiple consecutive measurements, with finer movements requiring higher sampling frequencies [ 21 ]. Tag attachment design influenced tilt both in the absence of current and at low velocities, with the direct attachment design used in previous generation of tags from Wildlife Computers, Inc. exhibiting higher tilts at these velocities. This effect was mainly observed at low velocities and became less pronounced at medium velocities. This positive inclination, which likely results from reduced flexibility of the direct tether attachment design, thus constitutes a small bias. While this has a limited impact for the study of large-scale movements, such as migrations, it could affect identification and analyses of behaviours associated with low activity levels and inactivity. The new attachment design mitigates this issue, enhancing the potential of accelerometry data from PSATs in future deployments. Our results indicate that differences in tilt between tag positions or orientations are limited and partly consistent across speeds, although more pronounced at lower velocities. Nonetheless, we cannot rule out such differences due to the limited number of PSATs tested. This overall suggests that swimming speed estimates and interindividual comparisons should not be affected by variations in tag positioning on the fish, particularly when compared to the influence of water current. This is a promising feature of PSAT accelerometry, as tag attachment and position are often identified as potential sources of variability and interpretation mistakes in acceleration data [ 11 , 14 , 35 , 47 ]. Tilt data from moored PSATs and the first mode of tilt values for PSATs attached to halibut primarily correspond to near-zero speeds, with 95th percentile values of 0.12 and 0.17 m s⁻¹ for moored PSATs. These values are generally lower than average modelled water currents in the GSL, which range from 0.25 to 0.5 m s⁻¹ depending on depth [ 46 ]. Given the limited number (n = 2) of moored PSATs and the inherent spatial variability in water current, it is likely that these values do not fully represent the range of water current velocities in the GSL. The overlap between movement- and current-related tilts represents a challenge for the accurate distinction of movement and behaviour at resting and low activity levels, as these tilt values likely reflect the combined influence of water currents and animal movement. PSATs are likely more sensitive to environmental currents than tags directly attached to the body of the animal given that PSATs can tilt independently from the tagged animal. Our findings emphasise the importance of accounting for this variability when studying animal-driven accelerometers [ 48 ]. This is particularly important given the unknown, potentially wide, spatiotemporal variability in current [ 30 , 46 ]. Without knowing true local current speed and the orientation of the animal with respect to the current, disentangling current and movement driven acceleration for low tilt value does not appear possible. Our flume tank experiments revealed that the 38.5° threshold for active/inactive halibut behaviour derived from field studies by Boulanger et al. [ 30 ] corresponds to a flow rate of 0.25 m s⁻¹, which also approximates modelled mean current speeds from 100 m to the seafloor in the GSL [ 46 ]. Given this known, current-induced tilt, accelerometry data collected from PSATs below this threshold of 0.25 m s⁻¹ should not be interpreted as active halibut movement. In addition to increasing the risk of misidentifying water current-induced tilt values as movement, lowering this threshold would likely yield little to no difference in terms of interpretations of overall bioenergetic costs as the corresponding velocities represent infrequent low-activity behaviour. For instance, tilt values between 20 and 38.5° represent, overall, across the 43 halibut timeseries, 7.4% of all data despite several cases of frequently elevated water current-induced tilt values in several halibut (see Boulanger et al. [ 30 ] for more details on observations ascribed to water currents). Overall, PSAT tilt data reliably capture medium to high activity levels in halibut and align with speed estimates from the flume tank. During active behaviour, the studied halibut exhibited an overall mean swimming speed of 0.72 ± 0.02 m s⁻¹, and a significant correlation between individual mean swimming speed and fork length, as in all fish species [ 6 ]. However, given the unknown direction of current, this mean estimated swimming speed should be interpreted with caution. Assuming a mean current speed of 0.25 m s⁻¹ in the GSL [ 46 ], it is possible that the mean swimming speed of a halibut could vary more than estimated, at least locally. Nonetheless, our observations on 43 halibut suggest that current speed are low most of the time. This implies that medium to high swimming speed estimates in halibut are accurate most of the time. These estimates do not account for specific halibut behaviours, as individuals may adopt different strategies to exploit strong water currents to optimise cost of transport [ 49 – 52 ]. Through the estimation of activity and swimming speed from PSAT tilt values, it is possible to estimate some aspects of bioenergetic costs in marine animals. We anticipate two possible main approaches, depending on knowledge of species-specific metabolic rates with respect to swimming speeds. The first approach, when lacking knowledge of metabolic rates, is to estimate relative bioenergetic costs through the consideration of individual relative activity duration, assuming a near-equivalent bioenergetic cost among individuals. For example, relative costs linked to migrations over the year could be estimated by considering the relative frequency of active moments over the full seasonal cycle. The second approach requires prior knowledge of species metabolic rates as a function of swimming speeds quantified via respirometry experiments [ 14 ]. Such an approach could be undertaken based on the relationship between tilt values and swimming speed described in the present study. The advantage of relying on metabolic rates, in contrast to relative activity cost, is that it accounts for bioenergetic costs related to speed, individual characteristics such as body weight [ 53 – 55 ], and/or the effect of environmental factors such as water temperature [ 56 , 57 ]. Metabolic costs can be converted from oxygen consumption units to energetic units (i.e. calories, Joules) using oxycalorific equivalents, such as the average 3.24 cal mg O 2 in fish species [ 58 , 59 ]. This could yield promising perspectives encompassing field metabolic rates, respirometry and energetic gains estimated for example through growth rates estimated by otolithometry [ 60 , 61 ] to further bridge biotelemetry and ecophysiological knowledge. Furthermore, respirometry studies typically measure critical swimming speed, an estimate of the maximum speed individuals can sustain before exhaustion [ 62 , 63 ]. Future PSAT studies could possibly describe maximum swimming speed of targeted species as estimated in marine animals using other accelerometry devices [ 64 , 65 ], thereby allowing for direct comparison with swimming capacity described in respirometry studies [ 59 , 66 ]. In adult Atlantic halibut, the estimation of maximum swimming speed through PSAT acceleration data is currently not possible given our observation that some tagged halibut PSAT frequently reach the maximum possible tilt value of ~ 80 o . This implies that these individuals likely achieve velocities higher than values that can be estimated through PSAT accelerometers, at least with the current attachment design. Conclusion Overall, results from the present study validate the reliability of PSAT accelerometry data for estimating moderate and fast, but not slow swimming speeds, across long-term movements of halibut. The main limitation for measuring slow swimming speed arises from measuring a mixture of individual movement and water current, with water current velocities inducing inclinations that would otherwise be consistent with slow swimming speeds in still waters. In addition, some PSATs, primarily those deployed with the direct attachment design, may inherently have moderate positive inclinations (i.e. up to 30°) related to jamming via friction at the PSAT-tether connection. These limitations are most pronounced at low velocities and, to a lesser extent, at medium velocities. Therefore, tilt values below 38.5°, corresponding to a velocity of 0.25 m s − 1 , should be interpreted with caution as they may contain an unquantifiable mixture of true animal movement, influence of water currents and artefacts of the tags themselves. Most of these low velocities however approach 0 m s − 1 , implying that these periods likely correspond to resting behaviour with minimal water current speed, thus resulting in a limited potential effect on estimated swimming speed during active behaviour. The present study offers perspectives for studying bioenergetic costs in marine animals by quantifying swimming speed at high frequency and then relating these results to oxygen (energy) consumption from laboratory respirometry studies. Abbreviations Gulf of St. Lawrence GSL Pop-up Satellite Archival Tags PSAT/PSATs Declarations Ethics approval and consent to participate Data analysed in this study were collected through tagging projects carried out under Fisheries and Oceans Canada Experimental Licenses and Memorial University Animal Care Protocols. Consent for publication Not applicable. Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the Newfoundland and Labrador Department of Fisheries and Aquaculture, by the Research and Development Corporation of Newfoundland and Labrador Ignite Grants to J.A.D.F. and D.R., by the NSERC Discovery Grant Program and Robert and Edith Skinner Fund Wildlife Management Fund to J.A.D.F, by Fisheries and Oceans Canada's, by the Université du Québec à Rimouski, and by Ressources Aquatiques Québec. Authors' contributions The experiment was conducted by L.M., J.A.D.F. and D.R. Data analysis were carried out by L.M. with support from H.P.B. and D.R. Result interpretation and writing was led by L.M. with support from all co-authors. All authors read and approved the final manuscript. Acknowledgements We thank T. Araya Schmidt, D. Gosse, M. Santos, and P. Winger from the Centre for Sustainable Aquatic Resources, Fisheries and Marine Institute of Memorial University of Newfoundland, for their advice and guidance during the development and execution of the flume tank experimental trials. Tagging operations were conducted during Fisheries and Oceans Canada's halibut longline survey coordinated by M. Desgagnés. We thank the fishing associations that contributed to satellite tag deployments and recoveries, namely: the Fish Food and Allied Workers (FFAW), the Prince Edward Island Fishing Association (PEIFA) and the Association des Capitaines Propriétaires de la Gaspésie (ACPG). We also thank the captains and crews involved in both the longline survey where the tagging occurred and the tag recovery efforts that occurred afterwards. References Aarts G, MacKenzie M, McConnell B, Fedak M, Matthiopoulos J. Estimating space-use and habitat preference from wildlife telemetry data. Ecography (Cop). 2008;31(1):140–60. 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Brown DD, Kays R, Wikelski M, Wilson R, Klimley AP. Observing the unwatchable through acceleration logging of animal behavior. Anim Biotelemetry. 2013;1(1):1–16. Wilson RP, Borger L, Holton MD, Scantlebury DM, Gomez-Laich A, Quintana F, et al. Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. J Anim Ecol. 2020;89(1):161–72. Fretwell SD, Lucas HL. On territorial behavior and other factors influencing habitat distribution in birds - I. Theoretical development. Acta Biotheor. 1969;19(1):16–36. MacCall, AD. Dynamic geography of marine fish populations. Washington sea grant program; 1990. Brown JH, Hall CAS, Sibly RM. Equal fitness paradigm explained by a trade-off between generation time and energy production rate. Nat Ecol Evol. 2018;2(2):262–8. Burger JR, Hou C, Brown JH. Toward a metabolic theory of life history. Proc Natl Acad Sci U S A. 2019;116(52):26653–61. Burger JR, Hou C, A. S. Hall C, Brown JH. 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Movements of electronically tagged shortfin mako sharks ( Isurus oxyrinchus ) in the eastern North Pacific Ocean. Anim Biotelemetry [Internet]. 2019;7(1):1–26. Available from: https://doi.org/10.1186/s40317-019-0174-6 Klöcker CA, Albert OT, Ferter K, Bjelland O, Lennox RJ, Albretsen J, et al. Seasonal habitat use and diel vertical migration in female spurdog in Nordic waters. Mov Ecol [Internet]. 2024;12(1). Available from: https://doi.org/10.1186/s40462-024-00498-2 Halsey LG, Shepard ELC, Wilson RP. Assessing the development and application of the accelerometry technique for estimating energy expenditure. Comp Biochem Physiol - A Mol Integr Physiol [Internet]. 2011;158(3):305–14. Available from: http://dx.doi.org/10.1016/j.cbpa.2010.09.002 Andrzejaczek S, Gleiss AC, Lear KO, Pattiaratchi CB, Chapple TK, Meekan MG. Biologging tags reveal links between fine-scale horizontal and vertical movement behaviors in Tiger sharks ( Galeocerdo cuvier ). Front Mar Sci. 2019;6. Ste-Marie E, Watanabe YY, Semmens JM, Marcoux M, Hussey NE. Life in the slow lane: field metabolic rate and prey consumption rate of the Greenland shark ( Somniosus microcephalus ) modelled using archival biologgers. J Exp Biol. 2022;225(7). Logan RKK, Luongo SMM, Vaudo JJJ, Wetherbee BMM, Shivji MSS. Hunting behavior of a solitary sailfish Istiophorus platypterus and estimated energy gain after prey capture. Sci Rep. 2023;13(1). Thorstad EB, Rikardsen AH, Alp A, Okland F. The use of electronic tags in fish research - An overview of fish telemetry methods. Turkish J Fish Aquat Sci. 2013;13(SI):881–96. Jepsen N, Thorstad EB, Havn T, Lucas MC. The use of external electronic tags on fish: An evaluation of tag retention and tagging effects. Anim Biotelemetry. 2015;3(1). Bridger CJ, Booth RK. The effects of biotelemetry transmitter presence and attachment procedures on fish physiology and behavior. Rev Fish Sci. 2003;11(1):13–34. Ransier, KT, Gatti, P, Le Bris A, den Heyer, CE, Claireaux G, Wringe B, Fisher JAD. Electronic tags reveal high migratory diversity within the largest Atlantic halibut ( Hippoglossus hippoglossus ) stock Can J Fish Aquat Sci. 2024;81:828–846. Thiem JD, Dawson JW, Gleiss AC, Martins EG, Haro A, Castro-Santos T, et al. Accelerometer-derived activity correlates with volitional swimming speed in lake sturgeon ( Acipenser fulvescens ). Can J Biol. 2015;93(8):645–54. Winger PD, DeLouche H, Legge G. Designing and testing new fishing gears: the value of a flume tank. Mar Technol Soc J. 2006;40(3):44–9. Pedley M. Tilt sensing using a three-axis accelerometer. Free Semicond Appl notes. 2013;1–22. Galbraith, PS, Chassé, J, Shaw, J-L, Dumas, J and Bourassa, M-N. Physical oceanographic conditions in the Gulf of St. Lawrence during 2022. Vol. 354, Can. Tech. Rep. Hydrogr. Ocean Sci. 2024. v + 91 p. Qasem L, Cardew A, Wilson A, Griffiths I, Halsey LG, Shepard ELC, et al. Tri-axial dynamic acceleration as a proxy for animal energy expenditure; should we be summing values or calculating the vector? PLoS One. 2012;7(2). Shiomi K, Sato K, Mitamura H, Arai N, Naito Y, Ponganis PJ. Effect of ocean current on the dead-reckoning estimation of 3-D dive paths of emperor penguins. Aquat Biol. 2008;3(3):265–70. Montgomery J, Carton G, Voigt R, Baker C, Diebel C. Sensory processing of water currents by fishes. Philos Trans R Soc B Biol Sci. 2000;355(1401):1325–7. Chapman JW, Klaassen RHG, Drake VA, Fossette S, Hays GC, Metcalfe JD, et al. Animal orientation strategies for movement in flows. Curr Biol [Internet]. 2011;21(20):R861–70. Available from: http://dx.doi.org/10.1016/j.cub.2011.08.014 Shepard ELC, Wilson RP, Rees WG, Grundy E, Lambertucci SA, Vosper SB. Energy landscapes shape animal movement ecology. Am Nat. 2013;182(3):298–312. Hintz WD, Porreca AP, Garvey JE. Water velocity shapes fish movement behavior. J Fish Biol. 2024;104(4):1223–30. Peters RH. Physiological correlates of size. In: The ecological implications of body size. Cambridge: Cambridge University Press; 1983. p. 24–44. (Cambridge Studies in Ecology). Clarke A, Johnston NM. Scaling of metabolic rate with body mass and temperature in teleost fish. J Anim Ecol. 1999;68(5):893–905. Tirsgaard B, Svendsen JC, Steffensen JF. Effects of temperature on specific dynamic action in Atlantic cod Gadus morhua . FISH Physiol Biochem. 2015;41(1):41–50. Clarke A, Pörtner HO. Temperature, metabolic power and the evolution of endothermy. Biol Rev. 2010;85(4):703–27. Lefevre S, Wang T, McKenzie DJ. The role of mechanistic physiology in investigating impacts of global warming on fishes. J Exp Biol. 2021;224(1, SI). Beamish FWH. 2 - Swimming capacity. In: Hoar WS, Randall DJ, editors. Locomotion. Academic Press; 1978. p. 101–87. (Fish Physiology; vol. 7). Available from: https://www.sciencedirect.com/science/article/pii/S1546509808601648 Claireaux G, Couturier C, Groison A-L. Effect of temperature on maximum swimming speed and cost of transport in juvenile European sea bass ( Dicentrarchus labrax ). J Exp Biol. 2006;209(17):3420–8. Pannella G. Fish otoliths: Daily growth layers and periodical patterns. Adv Sci. 1971;173(4002):1124–7. Campana SE, Neilson JD. Microstructure of fish otoliths. Can J Fish Aquat Sci. 1985;42(5):1014–32. Plaut I. Critical swimming speed: Its ecological relevance. Comp Biochem Physiol - A Mol Integr Physiol. 2001;131(1):41–50. Cano-Barbacil C, Radinger J, Argudo M, Rubio-Gracia F, Vila-Gispert A, García-Berthou E. Key factors explaining critical swimming speed in freshwater fish: a review and statistical analysis for Iberian species. Sci Rep [Internet]. 2020;10(1):1–12. Available from: https://doi.org/10.1038/s41598-020-75974-x Block BA, Booth D, Carey FG. Direct measurement of swimming speeds and depth of Blue marlin. J Exp Biol. 1992;166:267–84. Marras S, Noda T, Steffensen JF, Svendsen MBS, Krause J, Wilson ADM, et al. Not so fast: Swimming behavior of sailfish during predator-prey interactions using high-speed video and accelerometry. Integr Comp Biol. 2015;55(4):719–27. Tudorache C, de Boeck G, Claireaux G. Forced and preferred swimming speeds of fish: A methodological approach. In: Palstra AP, Planas J V. Swimming physiology of fish: Towards using exercise to farm a fit fish in sustainable aquaculture. Springer. 2013;(January):1–429. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7304544","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":503830489,"identity":"c557109f-9608-44bc-a61a-6c95cbc350b5","order_by":0,"name":"Lucas Martin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACPgSTsYHhAzFa2BAsxgbGGSRqYWBg5iFKi0Tyw48/GLbJm89vbnts22Ytb97A/BCvA9kk0oyleRhuG845xthunNuWbjjnAJuxBH4tOQzSDAy3GWewMbZJ57YdBvqHh4GQFuafPxhu24O1WLYdtgdqYf5BQAubBNBhiWAtjG2HE4Fa2PDbwvPMzJrH4HbyDLbENsmec+nJM5jZzCzwaeFnT35880fFbdsZzMefSfwos7adwd78+AY+LRBgAGcxgxFJgFT1o2AUjIJRMBIAAHErO2BCIktzAAAAAElFTkSuQmCC","orcid":"","institution":"Université du Québec à Rimouski","correspondingAuthor":true,"prefix":"","firstName":"Lucas","middleName":"","lastName":"Martin","suffix":""},{"id":503830490,"identity":"ce7be5ec-2d29-48ec-b71b-826526612e3e","order_by":1,"name":"Hugues P. Benoît","email":"","orcid":"","institution":"Maurice Lamontagne Institute, Fisheries and Oceans Canada","correspondingAuthor":false,"prefix":"","firstName":"Hugues","middleName":"P.","lastName":"Benoît","suffix":""},{"id":503830491,"identity":"db2c5bca-6c51-4119-8ba2-efddf10bc959","order_by":2,"name":"Jonathan A.D. Fisher","email":"","orcid":"","institution":"Memorial University of Newfoundland","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"A.D.","lastName":"Fisher","suffix":""},{"id":503830493,"identity":"323a3b65-666a-49ec-a8b3-4469709f537e","order_by":3,"name":"Dominique Robert","email":"","orcid":"","institution":"Université du Québec à Rimouski","correspondingAuthor":false,"prefix":"","firstName":"Dominique","middleName":"","lastName":"Robert","suffix":""}],"badges":[],"createdAt":"2025-08-06 01:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7304544/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7304544/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89953133,"identity":"d3a6aebf-a4a5-4bde-81ba-f9a8e9362d6f","added_by":"auto","created_at":"2025-08-26 20:23:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10954763,"visible":true,"origin":"","legend":"\u003cp\u003ePSAT tagging on Atlantic halibut. This illustrates the right (eyed) side of this approximately 130 cm flatfish and relative sizes of halibut and PSAT. Next to the tagger’s right hand is a PSAT tethered external to the halibut’s right, dorsal side.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7304544/v1/17138fa6c718c633de2ffddd.png"},{"id":89953389,"identity":"2f67d575-5d46-4504-bb3b-120c9480ee1c","added_by":"auto","created_at":"2025-08-26 20:31:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1239265,"visible":true,"origin":"","legend":"\u003cp\u003eSimultaneous employment of 5 PSATs on halibut-like dummies in a flume tank. Direction of flow is left to right, parallel to markings on the tank floor. (a) shows PSATs experiencing a flow rate of 0 m\u0026nbsp;s\u003csup\u003e-1\u003c/sup\u003e, while (b) shows PSATs experiencing a flow rate of 0.78 m\u0026nbsp;s\u003csup\u003e-1\u003c/sup\u003e. In (a), the PSAT at the center is inclined, unlike the other PSATs, despite the absence of flow. Ropes in the photo are positioned for deployment and recovery of the steel structure. The inset shows the two types of tether attachments, direct (left) and disc (right).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7304544/v1/85f45708aca731064de4235c.png"},{"id":89953390,"identity":"50ce5b09-e1d0-48be-b028-4d04f28d0d2c","added_by":"auto","created_at":"2025-08-26 20:31:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":297937,"visible":true,"origin":"","legend":"\u003cp\u003eTilt as a function of speed for direct (blue) and disc (red) attachment designs. Solid lines show group-level adjustments and dashed lines show adjustments for individual PSATs.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7304544/v1/877ea70dc686ec6cc78863f0.png"},{"id":89953132,"identity":"48b7fc13-b5c5-4a11-9481-12a0a033e381","added_by":"auto","created_at":"2025-08-26 20:23:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":438917,"visible":true,"origin":"","legend":"\u003cp\u003eVariability in tilt values at each velocity step across trials for each tested PSAT. Each boxplot represents a trial corresponding to a specific tag orientation (from left to right: 0, 30, 330, 180, 210, 150°). Red boxplots indicate trials conducted at a 0° position, and blue boxplots represent trials at a 30° position.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7304544/v1/f9e21d98d6d4d12ab76cc408.png"},{"id":89953127,"identity":"8b410ecb-88d0-46bf-b351-7957f0f61c11","added_by":"auto","created_at":"2025-08-26 20:23:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50520,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between halibut fork length and mean swimming speed for the active mode throughout the deployment period.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7304544/v1/ebc6b879dce74754cc5bf2b1.png"},{"id":95527961,"identity":"ea40dc8c-e033-4e2b-b7d6-b6b9ed928651","added_by":"auto","created_at":"2025-11-10 10:15:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18785412,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7304544/v1/491eae32-313a-43a0-998b-5802b273beda.pdf"},{"id":89953782,"identity":"dfa86343-5059-4eff-94e4-97a06992b848","added_by":"auto","created_at":"2025-08-26 20:39:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":910078,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-7304544/v1/8050b1ef4bff1c9b062ac09f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Calibrating PSAT tridimensional acceleration data for the estimation of fish swimming speed and activity","fulltext":[{"header":"Background","content":"\u003cp\u003eBiotelemetry provides fine-scale data at the individual level, revealing species' use of space, habitat, and associated spatiotemporal changes in distribution [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This addresses key questions in movement ecology, particularly where, when, and how animals move [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These insights hold significant potential for the development of effective conservation and management strategies [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Combining accelerometry and bioenergetics describes how movement, activity and swimming speed relate to metabolic rate, heart rate or tail-beat frequency and consequently energy expenditure and cost of transport [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This approach offers opportunities to examine species distribution from a bioenergetic perspective, as conceptualized more generally by the Ideal Free Distribution model [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] or the Equal Fitness Paradigm [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBehavioural studies require data sampling frequencies sufficient to accurately describe specific recurring behaviours [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the context of biotelemetry, battery life often constrains the quantity of data collected and transmitted either in duration, sampling frequency or minimum battery size [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. As a result, electronic tagging projects often span short periods of time (hours to days) or target less mobile, more predictable species [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This leads to significant gaps in data representation across taxa and geographical areas [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In aquatic animals, for instance, water constrains satellite telemetry data transmission, requiring tags to reach the surface to transmit summarised data to satellites [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, these tags are often programmed to autonomously detach from the animal for satellite transmission or for manual retrieval, which allows access to the full dataset [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent developments using Argos goniometers have facilitated high retrieval rates of Pop-up Satellite Archival Tags (PSATs) floating at sea [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the Gulf of St. Lawrence (GSL), Canada, these recoveries have provided near-continuous high-frequency data over periods of up to a year on 126 Atlantic halibut (\u003cem\u003eHippoglossus hippoglossus\u003c/em\u003e) [\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The first GSL deployments involved PSATs equipped with depth and temperature data loggers, allowing for the development and application of geolocation models to estimate daily location and therefore migration patterns and the identification of putative halibut spawning grounds [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These estimates revealed partial-migration and homing behaviours between winter and summer seasons covering distances ranging from dozens to hundreds of kilometers [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, high temporal resolution depth data allowed for the quantification of putative spawning behaviours [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Within this research program, deployments of 43 PSATs since 2017 included accelerometer data loggers. This additional technology presents an opportunity to potentially estimate activity and energy expenditure over timescales relevant to the study of both short-term (hourly, daily) and long-term behaviours (seasonal or annual migrations). These data revealed high variability in daily, seasonal and annual activity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and associations between the timing of acceleration peaks and spawning [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Relating contrasting life strategies, such as migrations, to energy expenditure could provide a unique bioenergetic perspective to better understand shifts in aquatic animal distribution.\u003c/p\u003e\u003cp\u003eDespite the potential utility of acceleration data, the requirement of PSAT attachments external to fish may influence interpretations of accelerometry data. PSATs are often anchored to fish via a monofilament tether inserted through the muscles and anchored to the fish [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Due to their positive buoyancy, PSATs float above the animal, normally remaining in a vertical position at rest (and at zero current speed), tilting away from vertical with increasing water velocity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This attachment method differs from the direct attachment typically used with accelerometers in aquatic environments to estimate activity, swimming speed, and energy expenditure [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Tags employed to monitor Dynamic Body Acceleration - a metric derived from acceleration data - are either externally attached in a fixed location using glue, tape, a collar or a harness, or surgically implanted internally [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This has several implications for the interpretation of acceleration data, as these methods can potentially affect tag stability and introduce acceleration components related to body movement, thereby making recorded acceleration data sensitive to tag position [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The main advantage of PSATs tethered externally compared to many of these tags are their battery capacity and programmed release mechanisms, enabling long-term deployments with good recovery capabilities after pop-off [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo use accelerometry data in a context of bioenergetics, it is important to derive reliable estimates of swimming speed [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. PSAT acceleration data was converted into speed estimates for the first time by Nielsen et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], who described a sigmoidal relationship between the vertical component of acceleration and towing speed using a mechanical rotating arm in an experimental tank. To fully use the potential of PSAT accelerometry towards the precise estimation of swimming speed in marine animals, the three components of acceleration should be considered [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, further research is needed to ensure that interpretation of PSAT accelerometry data accurately reflect animal movement and not other confounding factor resulting from the tethered external attachment, such as water current-induced tag movement. To our knowledge, no studies have specifically evaluated the effects of PSAT attachment choices on acceleration data interpretation, despite significant differences in recorded accelerations based on tag position being reported in other accelerometers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUsing controlled flume tank experiments and at-sea trials, we aim to address these uncertainties to advance the reliability and calibration of PSAT triaxial acceleration data to estimate swimming speed, and, in future studies, energy expenditure related to movement. To achieve our calibration goals, we employed five PSATs attached to halibut-like dummies positioned at the bottom of a large flume tank, measuring triaxial acceleration tilts at calibrated moving water velocities ranging from 0 to 0.93 m s⁻\u0026sup1;. Those trials quantified potential sources of variability on tilt measurements and were then compared to acceleration data from PSATs recovered from 43 free-ranging GSL Atlantic halibut and two additional PSATs moored on the GSL sea floor that measured accelerations associated solely with water currents. We estimated the swimming speed of halibut during medium to high accelerations given these experimental calibrations, anticipating a positive correlation between halibut fork length and mean individual swimming speed. By integrating the results of experimental and at-sea trials, we explore the strengths and limitations of PSAT accelerometers to estimate fine scale movement.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003ePop‑up Satellite Archival Tags\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used PSATs (MiniPAT) manufactured by Wildlife Computers, Inc. (\u0026ldquo;WC\u0026rdquo;, Redmond, Washington, USA), similar to those used by Nielsen et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These tags record high resolution data for depth, temperature, light, and since 2017, have been equipped with a tri-axial accelerometer (range: -2 to 2 g, accuracy: \u0026plusmn;0.05 g). PSATs release after a pre-programmed duration or sensing of specific environmental conditions, transmitting subsets or summaries of data via the Argos satellite archival time series, and complete archived data series can be downloaded from the MiniPATs if they are physically recovered [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. When oriented vertically (tilt of 0\u0026deg;), the accelerometer records values near \u0026minus;\u0026thinsp;1 g on the z-axis, representing gravity, also referred to as static acceleration [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. When oriented horizontally, the static acceleration shifts to the x- and/or y-axis, corresponding to a tilt of 90\u0026deg;; see tilt formula below. During the experiment, tags recorded data at 3-second intervals (0.33 Hz). Although this frequency differs slightly from the one used in field deployments (0.2 Hz), it is not expected to influence the results, as experimental recordings were conducted under continuous water flow conditions.\u003c/p\u003e\u003cp\u003eWe employed two variants of these tags, differing in the physical design of the attachment point between the tag and tether (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, inset). The first design, previously used in halibut deployments, feature a \u0026lsquo;direct\u0026rsquo; tether-to-PSAT attachment point. The second design has a tether-to-PSAT attachment via a very low friction \u0026lsquo;disc\u0026rsquo; that allows greater freedom of movement of the PSAT relative to the tether. This updated design prevents jamming of PSATs in a specific angle at the PSAT-tether connection at low velocities, which can cause elevated tilt values of approximately 20 to 30\u0026deg; in the absence of water movement, as occasionally observed with the first design in experimental trials (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, central tag). Initially, we planned to employ only the first tag design in the flume experiments. However, limited tag availability required the use of both designs. The small sample size limited the scope for statistical tests of differences associated with the two tag attachment point designs. We used Kruskal\u0026ndash;Wallis tests solely to assess whether there was any indication of differences between the two designs based on available tag behaviour information. Tests were applied to mean tilt values at each velocity step, as the high number of observations (1200 per velocity step) would lead to very low variance and therefore low p-values.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFlume tank\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe experiment took place in the flume tank (22.25 m long, 8 m wide, 4 m deep, 1.7\u0026nbsp;million L volume) located at the Centre for Sustainable Aquatic Resources, Fisheries and Marine Institute of Memorial University of Newfoundland (St. John\u0026rsquo;s, Newfoundland and Labrador, Canada). The tank is supplied with freshwater, and water velocity is adjusted from computer-controlled pumps, stabilising within a few minutes after velocity change [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Uniform water flow is provided via screens, vanes, a deflector, and a wave damper. We reduced water depth from 4 m to 3 m to achieve higher water velocities, calibrated the flow rate at the bottom of the tank using a rotary flow meter averaged across three positions spanning the tank width, and recorded seven stable flow rates between 0 m s⁻\u0026sup1; and 0.93 m s⁻\u0026sup1;. One side of the tank features 20 m wide acrylic windows, enabling direct observation and video recording.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDesign of halibut-like dummies and tag setup\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe designed five dummies using a rubber mat wrapped around PVC pipe (9 cm diameter \u0026times; 35.6 cm long) to mimic the curvature of the dorsal, right eyed side of a halibut (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), as PSATs are attached on this side of this asymmetrical flatfish (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To attach the tether, we fastened an eye-bolt at the bottom front of each cylinder and drilled two holes above it through each pipe, one vertically (0\u0026deg;) and the other angled leftward toward the left flank (30\u0026deg;). These variations, referred as tag position, account for potential differences in tag attachment associated with differences in size among individual halibut. In the field experiments in the GSL, mean tagged halibut fork length was 150.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46 cm (range: 87\u0026ndash;202 cm), with noticeable variations in body thickness and curvature of the dorsal, right flank. These observed differences potentially alter the water flow around the tag and slightly change the angle of the emerging tether, reinforcing the need to experimentally account for potential variation in tether position.\u003c/p\u003e\u003cp\u003eTo further represent sources of tilt variability, we changed PSAT orientation by rotating the eye bolts after PSATs were tethered. This is to account for possible differences in tag movement associated with tether shape and the attachment point of the tether to the tag, as we observed substantial variability in tether shape between tags due to the shape retention of the monofilament tether (i.e., permanent curvature of the tether). We hypothesized that these differences could potentially alter the relationship between tilt and speed among individuals, thus impairing interindividual comparison.\u003c/p\u003e\u003cp\u003eWe secured all five dummies to a wider welded steel structure designed to remain stable at the bottom of the flume tank even at maximum water velocities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The design, including 60 cm spacing between dummies prevented tags from colliding with each other and/or affecting local water flows. The use of a single structure simplified employment and recovery of 5 PSATs at once while ensuring consistent control of dummy position and orientation in the flume tank.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExperimental setup\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe assigned each PSAT to a specific dummy for the duration of the experiment. Tether length from the outer part of the cylinder to the tag was set to 3.8 cm for all tags to ensure comparability to that used on halibut tagged at-sea. The experiment consisted of twelve consecutive trials (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), during each of which water velocity was increased stepwise from 0 to 0.93 m s⁻\u0026sup1;, with increments of approximately 0.15 m s⁻\u0026sup1;. We ensured water velocity stabilised at each velocity step for a minimum duration of 60 s, corresponding to 20 successive tilt measurements before recording tilt angles. We further registered each period by time-matched video recordings. After recording the period at maximum velocity, water flow was returned to 0 m s⁻\u0026sup1;. The structure was retrieved between trials to adjust tag position and orientation before redeployment. Upon completing all trials, data were downloaded from PSATs and filtered to isolate recordings for all tags during each trial and velocity step.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExperimental design trial descriptions as related to tag position and tag orientation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrial\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTag position (\u0026deg;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTag orientation (\u0026deg;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTag position is relative to vertical, while tag orientation is relative to perpendicular to water flow.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDescription of tilt and speed relationship in halibut-like dummies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe calculated tri-dimensional tilts using the following formula from Pedley [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Tilt=\\text{acos}\\left(\\frac{Az}{\\sqrt{\\left(A{x}^{2}+A{y}^{2}+A{z}^{2}\\right)}}\\right)\\bullet\\:57.2958-180$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere Ax and Ay represent the horizontal acceleration components of the tag and Az is the vertical acceleration component.\u003c/p\u003e\u003cp\u003eWe modelled data from each tag and trial separately, resulting in 60 models (12 trials \u0026times; 5 PSATs). We compared overall model performance for sigmoid, logistic and asymptotic equations using non-linear least square models. Sigmoidal and logistic models showed convergence issues, likely due to the overall asymptotic pattern observed in our data, which differs from the sigmoidal relationship described by Nielsen et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] for tilt derived solely from vertical acceleration (Az). Therefore, we focus on two asymptotic equations, both modified to account for a positive intercept (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{0}\\)\u003c/span\u003e\u003c/span\u003e):\u003c/p\u003e\u003cp\u003eModified one phase exponential association equation: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Tilt={y}_{0}+lmax\\bullet\\:\\left(1-{e}^{\\left(-\\beta\\:\\bullet\\:\\:U\\right)}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eModified Michaelis-Menten equation: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Tilt={y}_{0}+lmax\\frac{U}{\\beta\\:+U}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the intercept, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lmax\\)\u003c/span\u003e\u003c/span\u003e is the difference between the upper asymptote and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{0}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e controls the slope of the relationship and \u003cem\u003eU\u003c/em\u003e is the speed. We selected these equations for their ability to model tilt values from the intercept at 0 m s⁻\u0026sup1; up to the asymptote. Although both describe an asymptotic relationship, they differ slightly in the shape of their response curves and in the interpretation of the parameters: in the first equation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e represents the rate at which saturation is approached, while in the second, it corresponds to the speed at which 50% of the tilt range (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{0}+lmax\\)\u003c/span\u003e\u003c/span\u003e) is reached.\u003c/p\u003e\u003cp\u003eAsymptotic models were fitted by groups of tags with similar attachment designs to assess potential tether attachment style influence on the relationship and model parameters. We tested for the presence of significant differences between tag positions and orientations using Kruskal-Wallis tests.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSwimming speed estimation for\u003c/b\u003e \u003cb\u003ein situ\u003c/b\u003e \u003cb\u003emovement-related and current-related tilt in PSAT data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn PSATs recovered from at-liberty halibut, tilt data exhibit a bimodal distribution, with one mode at low tilt values corresponding to a mixture of resting and low activity level behaviour, and water current velocities (i.e. presumed inactive behaviour), and another mode at medium to high tilt values associated with medium to high activity levels (i.e. presumed active behaviour) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We estimated speed for the mean value of each mode in each of the 43 halibut, separated using a threshold of 38.5\u0026deg;, as defined by Boulanger et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Based on an analysis of Gaussian mixture models conducted on 25 of the 43 halibut presented here, as well as on the two moored PSATs, Boulanger et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] determined this threshold to distinguish between both modes and to ensure that existing elevated water current-induced tilt values be identified as resting periods. Their work provides additional context on water current patterns, particularly in relation to tidal cycles, justifying the need for caution when interpreting low tilt values. To estimate the influence of current velocities, we estimated speeds related to percentiles of recorded tilt values for the two stationary moored PSATs in the GSL, deployed at depths of 342 m (M1) and 48 m (M2), respectively [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These estimates were compared, along with the one corresponding to the threshold, to modelled average water current velocities in the GSL [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Spearman rank correlation tests served to assess the strength of potential correlations between halibut fork length, mean swimming speed, and percentage of estimated time spent actively swimming.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eModel selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBoth asymptotic models showed a strong average fit across all 60 data subsets, with a mean R\u0026sup2; of 0.97 in both cases. However, the modified one phase exponential association equation performed slightly better overall, as indicated by a lower mean AIC (767.83 for Eqs.\u0026nbsp;1 and 780.92 for Eq.\u0026nbsp;2). Therefore, we use this equation for the remainder of this study. Across the 60 data subset fits, the average 95% confidence interval was 1.95\u0026deg;, indicating a low level of individual tag variability at a given speed under standardised conditions over a minute.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInfluence of tag attachment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAttachment design primarily affects tilts at lower velocities (p\u0026thinsp;\u0026le;\u0026thinsp;0.001 at 0 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, p\u0026thinsp;\u0026le;\u0026thinsp;0.01 at 0.17 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 at higher speeds) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is likely related to the PSAT 21C (direct attachment design) recording higher tilt values at these velocities, up to 27.2\u0026deg; at 0 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. These higher tilt values appear to be mitigated progressively at higher speeds although they persist somewhat at higher velocities. This result aligns with the expected behaviour of tags related to attachment design.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of model parameter by tag and attachment design.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttachment design\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ey\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003elmax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026#120515;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDirect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDisc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTag position and orientation have a limited influence on tilt values between trials, globally (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, several trials indicate some level of difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For example, (1) tilt values were consistently greater at all velocities for the 7th trial of 21A (tag position: 30\u0026deg;, tag orientation: 0\u0026deg;), (2) the first two trials for 24A (tag position: 0\u0026deg;, tag orientation 0 and 30\u0026deg;) stand out at several water velocities, and (3) tilt values at the three highest velocities for 24B show a repetitive pattern between trials. These elements, among others evident in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e represent a consistent pattern of tilt differences across speeds that indicate a persistent, though not predominant, effect of individual tags/tether combination differences.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eUsing field PSAT acceleration data to refine estimation of activity and swimming speed\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMoored PSATs M1 and M2 exhibited tilt values that were entirely induced by local water currents, providing data that aid in the interpretation of fish movement vs. current influences on PSAT tilt (Supplementary Data, Figure A1). Mean tilt values are 5.5 and 8.35\u0026deg; respectively, corresponding to current speeds of 0 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as the model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) estimates that tilts under 9.44\u0026deg;, and potentially 9.8\u0026deg; when considering 95% confidence interval, correspond to an absence of motion in PSATs with the direct attachment design. Tilt values over 9.8\u0026deg; correspond to 41.50 and 22.73% of all tilt values, respectively. The 95th percentile of tilt values, representing those presumed to result from the highest current speeds of the year, are 25.25\u0026deg; and 30.02\u0026deg; respectively, corresponding to current speeds of 0.12 and 0.17 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Supplementary Data, Figure A1). The threshold of 38.5\u0026deg; reported by Boulanger et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to identify presumed inactivity and activity behaviour in halibut correspond to a velocity of 0.25 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The mean tilt for the respective inactive and active periods in the free-swimming halibut are 8.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 and 63.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u0026deg;, corresponding to a mean velocity of 0 m s⁻\u0026sup1; and a mean swimming speed 0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 m s⁻\u0026sup1; (min: 0.43 m s⁻\u0026sup1;, max: 1.13 m s⁻\u0026sup1;) (Supplementary Data, Figure A1, Table A1). Individual mean velocity for the inactive mode in halibut isn\u0026rsquo;t reported as all the estimates are below 0.03 m s⁻\u0026sup1;. There was a significant positive correlation (r\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;\u0026le;\u0026thinsp;0.05) between mean swimming speed during the active mode and halibut fork length (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), but no significant correlation between mean swimming speed and either percentage of estimated time spent actively swimming or halibut fork length (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study identified different sources of variability on PSAT triaxial accelerometry tilt data, with a focus on their implications for the estimation of swimming speed and activity. We used two complementary approaches: (1) a calibration experiment in a flume tank, where we assessed the relationship between tilt and flow speed for five PSATs under different attachment designs, positions, and orientations; and (2) an analysis of field data from 43 halibut PSATs and two stationary moored PSATs, highlighting the influence of environmental currents and identifying potential challenges for the interpretation of specific behaviours at low activity levels.\u003c/p\u003e\u003cp\u003eAn asymptotic model, fit to 60 different data subsets, accurately described the relationship between tilt and water velocity across all setups and PSATs in the flume tank experiment. This differs from the sigmoidal relationship reported by Nielsen et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], who estimated tilt solely via vertical acceleration, when we used the three acceleration components. The mean 95% confidence interval across these models was 1.95\u0026deg;, outlining the potential for precise swimming speed estimation over short periods of time (minutes) using PSAT acceleration data. This aspect is particularly important in accelerometry studies, where reliable estimates depend on multiple consecutive measurements, with finer movements requiring higher sampling frequencies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTag attachment design influenced tilt both in the absence of current and at low velocities, with the direct attachment design used in previous generation of tags from Wildlife Computers, Inc. exhibiting higher tilts at these velocities. This effect was mainly observed at low velocities and became less pronounced at medium velocities. This positive inclination, which likely results from reduced flexibility of the direct tether attachment design, thus constitutes a small bias. While this has a limited impact for the study of large-scale movements, such as migrations, it could affect identification and analyses of behaviours associated with low activity levels and inactivity. The new attachment design mitigates this issue, enhancing the potential of accelerometry data from PSATs in future deployments.\u003c/p\u003e\u003cp\u003eOur results indicate that differences in tilt between tag positions or orientations are limited and partly consistent across speeds, although more pronounced at lower velocities. Nonetheless, we cannot rule out such differences due to the limited number of PSATs tested. This overall suggests that swimming speed estimates and interindividual comparisons should not be affected by variations in tag positioning on the fish, particularly when compared to the influence of water current. This is a promising feature of PSAT accelerometry, as tag attachment and position are often identified as potential sources of variability and interpretation mistakes in acceleration data [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTilt data from moored PSATs and the first mode of tilt values for PSATs attached to halibut primarily correspond to near-zero speeds, with 95th percentile values of 0.12 and 0.17 m s⁻\u0026sup1; for moored PSATs. These values are generally lower than average modelled water currents in the GSL, which range from 0.25 to 0.5 m s⁻\u0026sup1; depending on depth [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Given the limited number (n\u0026thinsp;=\u0026thinsp;2) of moored PSATs and the inherent spatial variability in water current, it is likely that these values do not fully represent the range of water current velocities in the GSL. The overlap between movement- and current-related tilts represents a challenge for the accurate distinction of movement and behaviour at resting and low activity levels, as these tilt values likely reflect the combined influence of water currents and animal movement.\u003c/p\u003e\u003cp\u003ePSATs are likely more sensitive to environmental currents than tags directly attached to the body of the animal given that PSATs can tilt independently from the tagged animal. Our findings emphasise the importance of accounting for this variability when studying animal-driven accelerometers [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This is particularly important given the unknown, potentially wide, spatiotemporal variability in current [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Without knowing true local current speed and the orientation of the animal with respect to the current, disentangling current and movement driven acceleration for low tilt value does not appear possible.\u003c/p\u003e\u003cp\u003eOur flume tank experiments revealed that the 38.5\u0026deg; threshold for active/inactive halibut behaviour derived from field studies by Boulanger et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] corresponds to a flow rate of 0.25 m s⁻\u0026sup1;, which also approximates modelled mean current speeds from 100 m to the seafloor in the GSL [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Given this known, current-induced tilt, accelerometry data collected from PSATs below this threshold of 0.25 m s⁻\u0026sup1; should not be interpreted as active halibut movement. In addition to increasing the risk of misidentifying water current-induced tilt values as movement, lowering this threshold would likely yield little to no difference in terms of interpretations of overall bioenergetic costs as the corresponding velocities represent infrequent low-activity behaviour. For instance, tilt values between 20 and 38.5\u0026deg; represent, overall, across the 43 halibut timeseries, 7.4% of all data despite several cases of frequently elevated water current-induced tilt values in several halibut (see Boulanger et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] for more details on observations ascribed to water currents).\u003c/p\u003e\u003cp\u003eOverall, PSAT tilt data reliably capture medium to high activity levels in halibut and align with speed estimates from the flume tank. During active behaviour, the studied halibut exhibited an overall mean swimming speed of 0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 m s⁻\u0026sup1;, and a significant correlation between individual mean swimming speed and fork length, as in all fish species [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, given the unknown direction of current, this mean estimated swimming speed should be interpreted with caution. Assuming a mean current speed of 0.25 m s⁻\u0026sup1; in the GSL [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], it is possible that the mean swimming speed of a halibut could vary more than estimated, at least locally. Nonetheless, our observations on 43 halibut suggest that current speed are low most of the time. This implies that medium to high swimming speed estimates in halibut are accurate most of the time. These estimates do not account for specific halibut behaviours, as individuals may adopt different strategies to exploit strong water currents to optimise cost of transport [\u003cspan additionalcitationids=\"CR50 CR51\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThrough the estimation of activity and swimming speed from PSAT tilt values, it is possible to estimate some aspects of bioenergetic costs in marine animals. We anticipate two possible main approaches, depending on knowledge of species-specific metabolic rates with respect to swimming speeds. The first approach, when lacking knowledge of metabolic rates, is to estimate relative bioenergetic costs through the consideration of individual relative activity duration, assuming a near-equivalent bioenergetic cost among individuals. For example, relative costs linked to migrations over the year could be estimated by considering the relative frequency of active moments over the full seasonal cycle.\u003c/p\u003e\u003cp\u003eThe second approach requires prior knowledge of species metabolic rates as a function of swimming speeds quantified via respirometry experiments [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Such an approach could be undertaken based on the relationship between tilt values and swimming speed described in the present study. The advantage of relying on metabolic rates, in contrast to relative activity cost, is that it accounts for bioenergetic costs related to speed, individual characteristics such as body weight [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], and/or the effect of environmental factors such as water temperature [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Metabolic costs can be converted from oxygen consumption units to energetic units (i.e. calories, Joules) using oxycalorific equivalents, such as the average 3.24 cal mg O\u003csub\u003e2\u003c/sub\u003e in fish species [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. This could yield promising perspectives encompassing field metabolic rates, respirometry and energetic gains estimated for example through growth rates estimated by otolithometry [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] to further bridge biotelemetry and ecophysiological knowledge.\u003c/p\u003e\u003cp\u003eFurthermore, respirometry studies typically measure critical swimming speed, an estimate of the maximum speed individuals can sustain before exhaustion [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Future PSAT studies could possibly describe maximum swimming speed of targeted species as estimated in marine animals using other accelerometry devices [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], thereby allowing for direct comparison with swimming capacity described in respirometry studies [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. In adult Atlantic halibut, the estimation of maximum swimming speed through PSAT acceleration data is currently not possible given our observation that some tagged halibut PSAT frequently reach the maximum possible tilt value of ~\u0026thinsp;80\u003csup\u003eo\u003c/sup\u003e. This implies that these individuals likely achieve velocities higher than values that can be estimated through PSAT accelerometers, at least with the current attachment design.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, results from the present study validate the reliability of PSAT accelerometry data for estimating moderate and fast, but not slow swimming speeds, across long-term movements of halibut. The main limitation for measuring slow swimming speed arises from measuring a mixture of individual movement and water current, with water current velocities inducing inclinations that would otherwise be consistent with slow swimming speeds in still waters. In addition, some PSATs, primarily those deployed with the direct attachment design, may inherently have moderate positive inclinations (i.e. up to 30\u0026deg;) related to jamming via friction at the PSAT-tether connection. These limitations are most pronounced at low velocities and, to a lesser extent, at medium velocities. Therefore, tilt values below 38.5\u0026deg;, corresponding to a velocity of 0.25 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, should be interpreted with caution as they may contain an unquantifiable mixture of true animal movement, influence of water currents and artefacts of the tags themselves. Most of these low velocities however approach 0 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, implying that these periods likely correspond to resting behaviour with minimal water current speed, thus resulting in a limited potential effect on estimated swimming speed during active behaviour. The present study offers perspectives for studying bioenergetic costs in marine animals by quantifying swimming speed at high frequency and then relating these results to oxygen (energy) consumption from laboratory respirometry studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGulf of St. Lawrence\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGSL\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePop-up Satellite Archival Tags\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePSAT/PSATs\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysed in this study were collected through tagging projects carried out under Fisheries and Oceans Canada Experimental Licenses and Memorial University Animal Care Protocols.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Newfoundland and Labrador Department of Fisheries and Aquaculture, by the Research and Development Corporation of Newfoundland and Labrador Ignite Grants to J.A.D.F. and D.R., by the NSERC Discovery Grant Program and Robert and Edith Skinner Fund Wildlife Management Fund to J.A.D.F, by Fisheries and Oceans Canada's, by the Université du Québec à Rimouski, and by Ressources Aquatiques Québec.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experiment was conducted by L.M., J.A.D.F. and D.R. Data analysis were carried out by L.M. with support from H.P.B. and D.R. Result interpretation and writing was led by L.M. with support from all co-authors. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank T. Araya Schmidt, D. Gosse, M. Santos, and P. Winger from the Centre for Sustainable Aquatic Resources, Fisheries and Marine Institute of Memorial University of Newfoundland, for their advice and guidance during the development and execution of the flume tank experimental trials. Tagging operations were conducted during Fisheries and Oceans Canada's halibut longline survey coordinated by M. Desgagnés. We thank the fishing associations that contributed to satellite tag deployments and recoveries, namely: the Fish Food and Allied Workers (FFAW), the Prince Edward Island Fishing Association (PEIFA) and the Association des Capitaines Propriétaires de la Gaspésie (ACPG). We also thank the captains and crews involved in both the longline survey where the tagging occurred and the tag recovery efforts that occurred afterwards.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAarts G, MacKenzie M, McConnell B, Fedak M, Matthiopoulos J. Estimating space-use and habitat preference from wildlife telemetry data. Ecography (Cop). 2008;31(1):140\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGriffiths CA, Patterson TA, Blanchard JL, Righton DA, Wright SR, Pitchford JW, et al. Scaling marine fish movement behavior from individuals to populations. 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Springer. 2013;(January):1\u0026ndash;429.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acceleration, accelerometry, Atlantic halibut, biotelemetry, movement ecology, Pop-up Satellite Archival Tags, PSATs, swimming speed","lastPublishedDoi":"10.21203/rs.3.rs-7304544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7304544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eUnderstanding marine species distribution and habitat use contributes to the development of effective population conservation and management. Data from biotelemetry can provide valuable insights on individual movement, behaviour and swimming speed. These data can link movement ecology and ecophysiology, thereby describing life strategies from a bioenergetic perspective. Technical limitations often constrain the use of biologgers over large spatial and temporal scales in free-ranging marine animals, hindering detailed examination of movements over seasonal or annual migrations, for instance. Pop-up Satellite Archival Tags (PSATs) equipped with triaxial accelerometers offer a promising solution to these challenges in aquatic animals by providing a platform recoverable at-sea to aid the collection and interpretation of high frequency, year-long accelerometry data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eUsing two approaches, this study calibrated the relationship between PSAT tridimensional tilt and speed of movement, while addressing potential limitations related to interpreting movement rates via PSAT accelerometry. First, we tested five PSATs under different attachment designs, attachment positions, and flow rates in a controlled calibration flume tank experiment. We found that properties of the tags and attachment had a minor effect except at no and low flow, and that an asymptotic model accurately described the relationship between tilt and flow speed. Second, we analysed field data from 43 tagged Atlantic halibut (\u003cem\u003eHippoglossus hippoglossus\u003c/em\u003e) and two stationary moored PSATs in the Gulf of St. Lawrence (Canada). We found that slow swim speeds up to a threshold of about 0.25 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e could not be distinguished from tag movement caused by ocean currents; however, above this threshold average swimming speed was related to halibut fork length.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study validates the reliability of PSATs accelerometry data for estimating moderate and fast, but not slow swimming speeds, across long-term movements of halibut. Further research is needed to accurately characterize slow swimming speeds given variability in tag inclination even in the absence of movement, and confounding with water current-induced values. PSAT accelerometry offers prospects for investigating species distribution, life strategies and habitat selection from a bioenergetic perspective through an individual-focused approach for the wide diversity of aquatic taxa that can be equipped with PSATs.\u003c/p\u003e","manuscriptTitle":"Calibrating PSAT tridimensional acceleration data for the estimation of fish swimming speed and activity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 20:23:27","doi":"10.21203/rs.3.rs-7304544/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1bd93e71-0430-41fd-a12f-b9d7a2ef510d","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-09T23:23:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-26 20:23:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7304544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7304544","identity":"rs-7304544","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}