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Background: Quantifying metabolic rate in free-living animals is invaluable in understanding the costs of behaviour and movement for individuals and communities. Dynamic body acceleration (DBA) metrics, such as vectoral DBA ( VeDBA), are commonly used as proxies for the energy expenditure of movement but are of limited applicability for slow-moving species. It has recently been suggested that metrics based on angular velocity might be better suited to characterise their energetics. We investigated whether a novel metric—the ‘Rate of change of Rotational Movement (RocRM)’, calculated from the vectoral sum of change in the pitch, roll and yaw/heading axes over a given length of time, is a suitable proxy for energy expenditure. Results: We found that RocRM can be used as an alternative energy expenditure proxy in a slow-moving benthic invertebrate. Eleven Giant spider conchs Lambis truncata (collected in the Red Sea) were instrumented with multiple channel (Daily Diary) tags and kept in sealed chambers for 5 h while their oxygen consumption, V̇O , was measured. We found RocRM to be positively correlated with V̇O , this relationship being affected by the time-step (i.e. the range of the calculated differential) of the RocRM. Time steps of 1, 5, 10 and 60 s yielded an explained variability of between 15 and 31%. The relationship between V̇O and VeDBA was not statistically significant, suggesting RocRM to provide more accurate estimations of metabolic rates in L. truncata. Conclusions: RocRM proved to be a statistically significant predictor of V̇O where VeDBA did not, validating the approach of using angular-based metrics over dynamic movement-based ones for slower moving animals. Further work is required to validate the use of RocRM for other species, particularly in animals with minimally dynamic move- ment, to better understand energetic costs of whole ecosystems. Unexplained variability in the models might be a consequence of the methodology used, but also likely a result of conch activity that does not manifest in movement of the shell. Additionally, density plots of mean RocRM at each time-step suggest differences in movement scales, which may collectively be useful as a species fingerprint of movement going forward. Keywords: Angular velocity, Dynamic body acceleration (DBA), Energy expenditure, Movement costs, RocRM, Rotational movement *Correspondence: lloyd.william.hopkins@gmail.com Department of Biosciences, Singleton Campus, Swansea University, Wallace Building, Swansea SA2 8PP, UK Full list of author information is available at the end of the article © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Hopkins et al. Anim Biotelemetry (2021) 9:30 Page 2 of 13 more active periods of movement, but had limited capac- Background ity to resolve metabolic rates in periods when movement Animal movements, behaviours and life processes was slower. account for much of an organism’s energy demands, with Recently, a comprehensive review of DBA highlighted the process-linked metabolism of an individual deter- the need for alternative tag-derived metrics to address mining fitness, which in turn can effect ecosystem struc - the problem posed by slow-moving species [27]. In par- ture and functioning [1–5]. Accurate determination of ticular, the authors suggested that rates of change of energy expenditure in animal species is therefore pivotal rotational axes (pitch, roll and yaw) might offer better to understanding the value and costs of behaviours and insights into the degree of movement—and by proxy how these relate to the ecology of species within ecosys- energy expenditure [17]—for such species compared tems [6–8]. to DBA (cf. [31]). In other words, the movement of, for However, directly measuring the rate at which an ani- example, a benthic invertebrate, where dynamic motion mal expends energy in the field, where it is unrestrained is consistently negligible and often only one locomotion and allowed to exhibit a full suite of behaviours, is chal- type is used, may be manifest most notably in the speed lenging [9, 10]. Doubly labelled water (DLW)—meas- at which it changes its whole-body orientation. Move- uring the rate of CO production, V̇CO [10–12]—is 2 2 ment along these rotational axes (assuming no drift or the only direct measure, while other methods, such as external factors are acting upon the animal), regardless of recording heart rate, use proxies for energy expended the speed at which this happens, still requires the exer- [10, 13, 14]. All such methods have limitations. For exam- tion of force and, therefore, the expenditure of energy. ple, heart rate monitors are generally intrusive, often Indeed, Wilson et al. [27] presented preliminary data requiring implantation, and are less suitable for smaller showing clearer changes in body angular velocity during animals [15]. The doubly labelled water method, on the movement bouts compared to DBA in the Giant spider other hand, is logistically difficult to conduct and has lim - conch Lambis truncata. Variability in the body roll angle ited temporal resolution [16]. A fairly recent approach has since been shown to describe slow-moving (walking) has used tri-axial accelerometers in externally attached activity patterns of the European spiny lobster (Palinu- tags to provide a less invasive and high-resolution alter- rus elephas) much better than DBA metrics, due to very native deriving ‘dynamic body acceleration’ (DBA) as a smooth acceleration signals [29]. proxy for movement-based energy expenditure [17, 18]. We investigate the validity of a new metric, derived DBA is calculated from the dynamic acceleration (i.e. the from the rates of change in rotational movement (which acceleration signal that remains following the subtrac- we term the ‘Rate of Change of Rotational Movement’, or tion of static acceleration, or the gravitational component ‘RocRM’), as an alternative to DBA for estimating energy [19]) summed over all three dimension axes (x, y, z; surge, expenditure in a slow-moving benthic invertebrate, the heave, sway) [20, 21]. Giant spider conch, L. truncata. Lambis truncata is a DBA has been repeatedly shown to correlate strongly gastropod of the Strombidae family, a family that is com- with the rate of oxygen consumption (ṀO and V̇O ) in 2 2 mercially important and heavily overexploited in many a diverse range of taxa including, inter alia, birds [20], countries [32–34]. Like many other conch species, it cephalopods [22], bivalves [23], fish [24, 25] and mam- moves mostly via ‘jumps’ and rotational (about the yaw mals [26]. These laboratory studies have produced sta - axis) ‘drifts’ [28, 35, 36]. In other species of conch, jumps tistical relationships between DBA and V̇O that can be have been shown to accompany increased oxygen con- used to calculate energy expenditure from DBA extracted sumption [37]. These jumps do produce a notable accel - from accelerometer tags used on wild animals. eration signal and the metric of mean DBA is able to Despite the strength of this relationship and its appli- differentiate between leaps and drifts in a movement rec - cability across many taxa, DBA is less likely to be a useful ognition model [28]. Whether DBA is an accurate proxy proxy for energy expended for species that move slowly for oxygen consumption in conchs, however, is unknown. because of their inherently weak dynamic acceleration For example, ODBA during a drift is typically much signals. This limitation is indeed significant, because lower than during a leap [28], but might actually demand many marine benthic invertebrates, such as gastropods a considerable energetic commitment in practice [31, 38]. and crustaceans move slowly [27–29]. Indeed, where With enough drift movements, therefore, it is possible dynamism occurs, activity and energy expenditure within that a large proportion of energetic expenditure would these periods may be measured by DBA metrics [28], simply be missed by DBA metrics. An alternative met- but these may be a small percentage of the overall time ric that can capture the energetic expenditure involved budget for these animals. Lyons et al. [30], for example, in either mode of movement would therefore be prefer- showed that DBA correlated with oxygen consumption in able. The major focus of this paper is accurately capturing the American lobster (Homarus americanus) during the Hopk ins et al. Anim Biotelemetry (2021) 9:30 Page 3 of 13 Table 1 Conch shell measurements Conch Max. shell width Max. shell length Calculated (cm) (cm) approximate volume (cm ) 1 11 17 1047 2 12 19 1277 3 11 14 739 4 10 14 1008 5 11 15 1188 Fig. 1 Schematic diagram of the static respirometry setup used in 6 9 14 806 this study. H = the total height of the tank; L = the total length of 7 12 16 1382 the tank; P = the water circulation pump, powered externally; Wl = the water level in the tank (shaded area); Lt = L. truncata test animal; 8 11 16 1126 DD = the Daily Diary tag, positioned on the ‘front’ of the animal; O = 9 11 16 1126 the miniDOT O logger, weighed down to the bottom of the tank. 10 12 17 1469 The white rectangle at the top of the water line depicts the plastic 11 13 17 1591 sheeting cover used to seal the tank Mean 11.2 (± 1.1 sd) 15.9 (± 1.6 sd) 1159.9 (± 261.9) Measurements, to the nearest whole cm, of maximum length and width of conchs used in this study and their approximate volumes further cleaning was carried out when deemed neces- sary throughout the experimental period to minimise the effect of non-conch oxygen consumption during these drift movements. We used laboratory-based static respirometry. However, conchs were not cleaned within respirometry to test the hypothesis that RocRM corre- 24 h of being experimentally tested, to reduce the poten- lates with the rate of oxygen consumption in a slow-mov- tial stress of handling. ing animal. We further examined how RocRM compared to DBA as a predictor of V̇O in this species. We hypoth- esised that RocRM would correlate more strongly with Respirometry experiments V̇O than DBA. Overview The tanks used in the respirometry experiments (see Methods above) were chosen to encourage ‘normal’ conch move- Animal collection and holding facilities ment during the experiments; the tanks were large Eleven Giant spider conchs, Lambis truncata sebae enough to allow short periods of traversal. Before each (Kiener 1843), were collected by snorkellers from areas trial, the water flow into the tank was stopped and the of rubble and coarse sand on a shallow (< 1 m) reef tanks drained to a water level of 20 cm. This water height bed near the KAUST campus, Saudi Arabia, in the Red was more than sufficient to allow ‘jumping’ movement Sea during February 2019. Similarly sized conchs were without the conch touching the plastic top covering. Flow selected (maximum width and length of shells, measured was restored between experiments and left long enough to the nearest cm, are shown in Table 1). A GPS fix (using to completely replace the water. Air was bubbled through a handheld GPS 73 unit; Garmin, Schaffhausen, Swit - the water prior to the start of the experiment to fully oxy- zerland) was taken of what was deemed to be the most genate the water. A water pump was used to circulate the representative of the area where the specimens were col- water within the tank to reduce both microbial build-up o o lected from (22 17′22.7″ N, 39 03′25.5″ E), with all indi- on the sensor foil and oxygen stratification inside the viduals being found within a 50 m radius. respirometer (tank), providing more consistent estimates Specimens were housed in two separate holding tanks of both background respiration and the V̇O of the study (dimensions 118 × 56 × 46 cm L × W × H) supplied animal [39]. MiniDOT O sensor loggers (PME, Califor- with a continuous flow of seawater, pumped directly from nia, USA) were anchored to the bottom of the tank at the the neighbouring Red Sea and then micro-filtered. The end opposite to the pump (Fig. 1), with the sensing face water level in the holding tanks was 40 cm (ca. 264 L). pointing towards the tank centre. Tanks of the same size were used during experiments. Respirometry trials were conducted using closed cham- Animals were kept in their holding tanks for 3 days ber/static respirometer techniques [40]. For the control/ prior to experiments and fed throughout the trial on blank runs, initial trials (i.e. when calibrating equipment) the algal film that was allowed to coat the tank walls. and in the experiments themselves, aquaria were sealed The shells of the conchs were cleaned with a rigid brush with plastic sheeting (similar to those described in [25] to remove as much biological growth as possible and and [41]) so as to be gas tight. Four layers of 1-mm-thick Hopkins et al. Anim Biotelemetry (2021) 9:30 Page 4 of 13 sheeting were bonded together with duct tape (taking and the DO and conch movement (via the Daily Diaries) care that the complete material was watertight) to ensure recorded throughout this time. To increase the number that the final cover had enough weight to rest against the of tests, conchs were tested four times across two general water and push out trapped air. The sheet was sealed to time periods (twice each): (1) ‘afternoon’ (average start the tank walls as close as possible to the water level using and end times = 11:04 and 15:59, respectively—Saudi duct tape and the seal inspected for any obvious gaps or Arabia local time = GMT + 3) and ‘evening’ (aver- unwanted gas exchange. Air bubbles between the water age start and end times = 17:36 and 22:30, respectively). and plastic were pushed out by smoothing the plastic These times were used as preliminary work had shown towards the edges until none remained. Care was taken L. truncata collected from this area appear to slowly to seal around the water pump cable in such a way as to increase their movement through the late afternoon and also be airtight. The salinity of the water was measured evening (unpublished data), therefore giving the best following each experiment and control using a Pro Plus chance of capturing different ‘levels’ of movement fre - Quatro salinity probe (YSI, Ohio, USA). quency. Conchs were not reused in an experiment for at least 24 h. All conchs were released following respirometry exper- Data loggers iments and returned as close to their collection loca- Water-dissolved oxygen concentration (mg/L DO) and tion as possible. The Velcro patch and glue were easily water temperature were measured with the miniDOT removed, leaving only superficial markings. loggers, with temperature corrections applied automati- cally by the sensor. Salinity corrections of DO required manual input of salinity using provided ‘miniDOT con- Calibrations and controls catenate’ software. MiniDOT loggers took one reading A control/blank tank, without conchs, was included for of each variable every 30 s—the minimum time that O both time periods mentioned (3 in the afternoon and 4 was predicted to take to diffuse through the sensing foil in the evening), lasting at least 5 h each. Blank runs were (PME, pers. comm.) and therefore the shortest inter- used to assess microbial (‘background’) V̇O . The aver - val that could be confidently used. The sensing foil was age conch respirometry experiment start and end times cleaned between each trial. informed the times used in calculations of corrections. Conch movement was recorded using an ‘elongated’ Two mean control slopes, for afternoon and evening model Daily Diary tag (Wildbyte Technologies Ltd., UK), periods, were calculated from linear regression of each recording tri-axial acceleration (surge, heave and sway) control run. Each conch V̇O calculation was subse- and tri-axial magnetometry at 20 Hz. Each Daily Diary quently corrected for microbial V̇O through subtraction was powered by a 750 mAh, 3.6 V single-use battery (EVE of control rates (see [39]). The lowest recorded DO con - Energy Co., China). The complete package was enclosed centration in any experiment was 5.75 mg/L (87.54% O within a vacuform polystyrene plastic housing and sealed saturation). using Poly Cement (Humbrol, Hornby Hobbies, UK). The overall maximum package dimensions were 38 × 20 × 13 mm L × W × H with a weight in air of 6 g. Velcro Data analysis (Velcro BVBA, UK) was glued to the Daily Diary package. V̇O2 All conchs had a 1.5 × 1.5 cm patch of complementary The strength of the relationship of V Ȯ as a function of time Velcro fixed to their shell using super glue (Loctite Power (as a linear model) was evaluated immediately after each Flex, Loctite, Germany), at an approximately 25°–30° experiment run, to check that the system was sealed suf- angle on the anterior shell spine (as it was an area con- ficiently and that there was a generally consistent negative served between individuals, close to the centre of mass, correlation. Origin 2019 (Origin Lab Corp., Massachusetts, and relatively flat for ease of attachment). The Daily Diary USA) was used to compute R values for this. Experiment package was attached to the animal during transfer of the runs were repeated if plots suggested erratic DO readings conch from holding to experiment tanks and removed (potentially due to poor chamber sealing) or a low R value when conchs were returned to holding tanks. comparing oxygen concentration and time (in the final analysis, all slopes had an R of > 0.87). As a result of the Respirometry experiments corrections discussed in 2.2.3, three experiments were dis- The Daily Diary-equipped conchs were allowed to set - counted due to background-corrected slopes turning posi- tle within their testing tanks for at least 30 min prior tive (suggesting breaches in the tank sealing), all of which to experimentation, with the miniDOT loggers placed were in the afternoon period, leaving 41 slopes across 11 in the tank at the same time. Tanks were sealed for 5 h conchs (3 conchs with 3 slopes, all others with 4 slopes) to Hopk ins et al. Anim Biotelemetry (2021) 9:30 Page 5 of 13 be used in statistical analysis. Of these, five experiments where S , S and S are the static acceleration as x y z did not record for the complete 5 hours (240, 267, 271, 286, recorded on the heave, surge and sway axes, respectively. 295 min, respectively), but were included in all analyses. Heading was derived as: VȮ for a single organism in a static system was calcu- lated as in [40]: Heading (h) = mod atan2 −m , m · , 360 , y x VO = [O ].V , (5) (1) 2 2 ind where m and m refer to the x and y magnetometer x y where Δ[O ] is the regression slope of O in milli- 2 2 channels following normalisation (to bring the data onto grammes per litre per hour, V is the water volume in ind a sphere) and rotational correction (using pitch and roll litres specific to the tested individual and V̇O is the rate to account for device tilt) and mod refers to the modulo of O consumption for that individual in the same time operator [44]. −1 −1 units as the slope (mg ind h ). V was calculated ind There was some magnetic noise within the tanks due as the effective tank volume (1,32,160 cm ) minus the to the pump, so heading was smoothed over 5 s. For volume of the miniDOT logger (314.16 cm ), the water consistency, pitch and roll were also smoothed over 5 s. pump (179.59 c m ) and the conch (calculated for each RocRM was subsequently calculated for four different individual; see Table 1). time-steps (i.e. the vectoral sum of the change in these smoothed channels); over 1 s (Ro cRM ), 5 s (Ro cRM ), 1 5 Movement 10 s (RocRM ) and 60 s (RocRM ). 10 60 Daily Diary data were preliminarily analysed and data The dynamic component of acceleration, used in calcu - extracted using DDMT (Daily Diary Multiple Trace) soft- lating VeDBA, was calculated as: ware (Wildbyte Technologies Ltd, UK). Acceleration (1 g = 9.81 m/s ) was separated into dynamic and static (postural) Dynamic acceleration components using a smoothing window of 3 s [the win- = total acceleration − static acceleration. (6) dow length informed by Shepard et al. [19] and previously VeDBA (measured in g) was calculated using the for- collected data on the movement durations of this species mulation proposed by Qasem et al. [21]: (unpublished data)]. Daily Diary data were used to calculate two move- 2 2 2 VeDBA = A + A + A , (7) ment metrics for each conch experiment run: RocRM and x y z VeDBA. where A , A and A are the dynamic accelerations from The RocRM metric was calculated from the vectoral sum x y z each of the tri-axial channels at any given time. To be of the change (differential) in pitch, roll and heading over a consistent with pitch, roll and heading, VeDBA was specified time-step given by: also subsequently smoothed over 5 s prior to statistical 2 2 2 analysis. RocRM = Roc + Roc + Roc , (2) p r Overall means for VeDBA and each RocRM measure were calculated for each experiment run and matched to where Roc, Roc and Roc refer to the rate of change in p r h their corresponding V̇O measurement. the angles of pitch, roll and heading, over the given time- 2 Kernel density distributions of the four different step, respectively. The rate of change over a time-step is RocRM time-steps were calculated to provide an over- calculated within a moving window, therefore RocRM is view of the differences between timescales of conch presented as a continuous variable. Pitch and roll angle movement. were derived from postural acceleration data [42] while heading is derived from the body orientation data in tan- Statistical analyses dem with the geomagnetic field strength measured in Statistical analyses were conducted in R [45] version three axes [43]. Specifically, pitch and roll were calculated 3.6.0. Five linear mixed models (LMMs) were constructed as: to investigate the relationship between V̇O and each one 2 2 of the movement metrics independently, using the lme4 Pitch(p) = atan2 S , S + S · , (3) y z package [46] version 1.1.21. Thus, in each model, V̇O was the response variable and log(VeDBA), log(mean RocRM ), log(mean RocRM ), log(mean RocRM ) and 2 2 1 5 10 Roll (r) = atan2 S , S + S · , y (4) x z log(mean RocRM ) were included as predictor vari- ables in their respective models (subscript refers to the differential time-step period in seconds). To account for Hopkins et al. Anim Biotelemetry (2021) 9:30 Page 6 of 13 potential dependency of observations obtained from the denominator calculates the relative evidence for a given same individual, ‘conch individual’ was included as a ran- model. dom-effect term in all models. All models included ran - dom intercepts only (i.e. only the by-individual intercept, Results and not the slope, were allowed to vary), as attempts to Controls fit random slopes for the conch ID term had issues with The mean background V̇O slopes of the ‘afternoon’ −1 singularity. Mean experiment temperature was initially and ‘evening’ controls were − 2.98 and − 4.03 mg h , included as a fixed effect in each model, but was removed respectively. Despite the use of micro-filtered water in from all of them through stepwise elimination of terms these experiments, there remained a clear presence of through ANOVA comparisons of models (see Additional background/microbial respiration, particularly in the file 1), as well as judged on Akaike Information Criterion evening period, requiring the use of these corrections. (AIC) values, starting with the full model [47]. Likelihood ratio tests (R function ANOVA, with Maxi- Differences in movement traces between VeDBA mum Likelihood) were used to determine the validity, and RocRM effect size and significance of full versus null models. Movement data traces of rotational movement axes, Graphical procedures (Q–Q plot, histograms, and resid- VeDBA and RocRM reveal appreciable differences in the ual vs fitted value plots) were used to visually assess the RocRM signal with different movement types (Fig. 2). fit of the model and adherence to assumptions of nor - RocRM movement peaks differed between movements mality and homoscedasticity of residuals Zuur et al. predominantly in the pitch and roll axes, exhibiting split [48]. RocRM was log-transformed to better meet these peaks, and those predominantly in the heading/yaw axis, assumptions. VeDBA was also log-transformed, to exhibiting peaks that are more singular. VeDBA, in con- make comparisons between models fairer and clearer, trast, produced relatively consistent peaks. although in practice the VeDBA models were near identi- All RocRM time-steps (1, 5, 10 or 60 s) had a posi- 2 2 cal. Pseudo-R values in the form of marginal (R ) and tive skew of mean values (Fig. 3), with increasing values conditional (R ) values—where the conditional value of RocRM with increasing time-step as well as increas- considers the full model and the marginal value considers ing interquartile range (IQR) relative to the median the fixed model effects alone relative to the conditional value (RocRM : median = 0.13, IQR = 0.07; RocRM : 1 5 value—were calculated according to Nakagawa and Schi- median = 0.37, IQR = 0.26; RocRM : median = 0.49, elzeth [49]. 95% confidence intervals were computed via IQR = 0.43, RocRM : median = 1.43, IQR = 1.76). a bootstrapping method using the ‘confint’ (type = ‘per- centile’, n = 500) function within lme4 [46]. The 95% RocRM as a proxy for oxygen consumption prediction interval was also calculated (using the calcu- Mean temperature was removed from all five models lator provided in [50]) for each reported p value, allow- due following ANOVA tests between models with and ing comment on the p value uncertainty and replicability 2 without the term (VeDBA model: χ (1) = 0.05, p = [51]. 2 2 0.83; RocRM : χ (1) = 0.001, p = 0.97; RocRM : χ 1 5 Where p ≤ 0.37, p values were additionally converted 2 (1) = 0.22, p = 0.63; RocRM : χ (1) = 0.01, p = 0.91; into Bayes factor upper bounds B values, giving the 2 RocRM : χ (1) = 0.09, p = 0.77). See Additional file 1 odds of the alternative hypothesis being true over the null for further details. hypothesis [51, 52]. B values were calculated following Log-transformed mean VeDBA was not a statistically the equation in [51]: −1 −1 significant predictor of V̇O mg ind h with large con- fidence interval bands (which include 0) and an R of −1 B ≤ , (8) 0.01 (Table 2; Fig. 4). In contrast, log-transformed mean e × p × ln(p) RocRM increased with V̇O (Table 2; Fig. 4) at all time- where ‘p’ is the model p value. steps, with smaller (statistically significant) p values for AIC evidence ratios, comparing the relative likelihood larger time-steps. R values increased with increasing of one model over another [51], were calculated accord- RocRM time-step (0.15, 0.18, 0.23 and 0.31). 95% predic- ing to: tion interval bands for these p values indicated increas- ing replicability of the model comparison outcome with increasing time-step of RocRM. ER = , (9) −0.5×AIC where ΔAIC is the difference in AIC between the two models of the interest. Note that the equation Hopk ins et al. Anim Biotelemetry (2021) 9:30 Page 7 of 13 0.6 Acc. x (g) 0.6 0.0 0.3 Acc. y (g) 0.0 1.2 Acc. z (g) 0.7 0.4 0.20 VeDBA (g) 0.05 -10 Pitch (°) -30 -10 Roll (°) 0.2 -16 Heading (°) RocRM 0 5 10 15 20 0.0 Time (mins) Fig. 2 Example of the movement of a single conch over 20 min with data from the animal-attached tag being recorded at 20 Hz. Nineteen distinct movements are identifiable. Displayed are the three acceleration axes (x, y, z) and components of the VeDBA metric, the three rotational axes components of the RocRM metric (pitch, roll and heading angles), the vectoral sum of the dynamic acceleration ( VeDBA) commonly used as a proxy of energy expenditure and the vectoral sum of the rotational axes (RocRM) over a 10 s step length. Pitch, roll, heading and VeDBA were subject to a smoothing window of 5 s evidence for the full model was just 2.66 times as strong B values for RocRM models also increased with as for the null model. increasing time-step. For RocRM time-steps of 1, 5, 10 and 60 s, the alternative hypothesis (that there is a dif- Discussion ference between the full and null models) was at most Dynamic body acceleration is the prevalent tag-derived 16.66, 53.26, 83,86 and 685.31 times as likely as the null metric by which to quantify energetic expenditure of hypothesis (that there is no difference between the full animal movement. This methodology appears flawed in and null models). BFUB could not be reported for the slow-moving organisms. Novel ways of assessing move- VeDBA model as the p value was > 0.37. ment and their effects on metabolic rates are needed AIC-derived evidence ratios showed similar trends, to better understand slow-moving species movements. with increasing evidence for the full model over the Here we have shown that RocRM, a movement metric null model with increasing RocRM time-step. ER for based on angular velocity about the three rotational RocRM time-steps of 10 and 60 s were particularly axes (pitch, roll, yaw), is a good proxy for estimating large. By contrast, ER for the VeDBA showed that the Acc_x sm Hopkins et al. Anim Biotelemetry (2021) 9:30 Page 8 of 13 that fine-scale behaviours in diving loggerhead turtles are manifest most noticeably in the angular velocity about the yaw axis [44]. Clearly, however, there is a sizeable source of variation around the predicted relationship not accounted for by RocRM, indicated by the confidence intervals and mod - est marginal R values for shorter RocRM time-steps (particularly 1 and 5 s). Firstly, the need to place Daily Diaries on the hard shell of the conch inevitably means that conch activity that does not manifest in the move- ment of the shell is unaccounted for. For example, conchs feed using their proboscis whilst grazing [35, 53], which does not result in the movement of the shell [28]. The proportion of time, and energetic expenditure, that such movements account for is unknown, but might explain Fig. 3 Density distributions of the 5-h mean RocRM values for the variance in V̇O at the lowest RocRM values. We also each time-step (where ‘time-step’ refers to the time interval of the differential that RocRM was calculated over- either 1, 5, 10 or 60 s) note that we did not consider the non-movement ener- Table 2 Linear mixed model results 2 2 2 a Model Predictor Est CI t p (PI) χ (df) ΔAIC (ER) R /R m c 1 Intercept 4.20 19.24–27.64 2.49 . log(mean VeDBA) − 0.37 − 6.74–5.99 − 0.51 (0.0001–1). 0.03 (1) 1.96 (2.66) 0.01/0.10 2 Intercept 10.54 6.74–14.27 5.82 *** −6 log(mean RocRM ) 2.59 0.73–4.28 2.87 (7.11 –0.56)* 16.66 6.40 (1) 4.4 (9.02) 0.15/0.41 3 Intercept 7.61 6.10–9.19 9.27 *** −6 log(mean RocRM ) 2.26 1.01–3.55 3.19 (1.1 –0.34)** 53.26 8.46 (1) 6.46 (25.28) 0.18/0.40 4 Intercept 7.06 5.67–8.22 11.00 *** −8 log(mean RocRM ) 2.32 1.03–3.54 3.84 (6.909 –0.14)*** 83.86 11.79 (1) 9.79 (133.62) 0.23/0.46 5 Intercept 5.25 4.27–6.09 10.74 *** −9 log(mean RocRM ) 1.68 0.95–2.37 4.57 (2.04 –0.04)*** 685.31 16.28 (1) 14.28 (1261.43) 0.31/0.51 −1 −1 Results of the final (following removal of terms through stepwise elimination) linear mixed models where VO mg ind h is predicted by either log-transformed mean VeDBA or log-transformed mean RocRM (over either 1 s, 5 s, 10 s or 60 s stepping value) PI refers to the prediction interval of the p value ΔAIC refers to the difference between the null and full models, as shown by ANOVA model comparison, and ER refers to the AIC evidence ratio B refers to Bayes factor upper bound. A B was not calculated for Model 1 due to the p value being > 0.37 a 2 2 2 2 R (marginal R ) refers to the variance of the (final) linear mixed model explained by only the fixed-effect terms, whereas R (conditional R ) refers to the variance m c explained by both fixed- and random-effect model terms [49] ‘.’ , ‘*’ , ‘**’ and ‘***’ refer to p values of > 0.05, < 0.05, < 0.01 and < 0.001, respectively energy expenditure in free-living spider conchs. The getic contributions of specific dynamic action (SDA) [54]. evidence is most convincing for time-steps of 10 and As conchs were allowed to graze whilst in their holding 60 s. Given the lack of a relationship between V̇O tanks on the growing algal film, the metabolic demands and VeDBA in this study, it appears that rotational- of digestion could well be relevant here. Indeed, other based metrics may provide a critical movement-linked studies on gastropod species have found a postprandial energy metric where dynamism-based ones do not. peak following algal film feeding (see Table 2 of [54] and These results support the suggested advantage of rota - the references therein). Disentangling this effect, whether tional metrics from preliminary data on conch move- by explicitly studying the SDA of L. truncata in this con- ment presented in Wilson et al. [27]. They also build text or by eliminating any food source in studies to vali- upon previous studies that show how slow-moving date our results, would certainly help clarify the extent of data in the European spiny lobster, P. elephas, is best this potential variation. described in terms of pitch and roll metrics [29] and Hopk ins et al. Anim Biotelemetry (2021) 9:30 Page 9 of 13 Fig. 4 Oxygen consumption in Lambis truncata is well explained by RocRM but not VeDBA. Relationships between oxygen consumption ( VO mg −1 −1 ind h ) and either mean log-transformed VeDBA or four different time-steps of log-transformed mean RocRM (1, 5, 10 or 60 s). Trend lines show the fitted relationships calculated from linear mixed models (LMMs) with 95% confidence intervals We also suggest that the method used here, using an The sensor probes employed here could reliably meas - overall mean taken over approximately 5 h, misses the ure O concentration only to a minimum resolution of shorter, more intense bouts of movement that might once every 30 s, due to the traversal time of O across produce higher RocRM. However, reducing the time the sensing foil. Other studies regularly employ probes period over which V̇O is measured is likely to increase taking measurements once per second [55, 56] and the error around V̇O estimates derived from the limi- their use here would certainly have allow a more finely tations in detecting very small changes in O in a rela- resolved V̇O –RocRM relationship to be quantified. 2 2 tively large volume of water, especially as ‘high’ activity Conversely, reduction of the water volume, which periods are brief. Nonetheless, further tests using more might mitigate some of this problem, will tend to con- sensitive oxygen sensors may refine our work further. strain animal movement. Our experimental set-up Hopkins et al. Anim Biotelemetry (2021) 9:30 Page 10 of 13 differed from many ‘traditional’ respirometry studies many conch species prefer [36]. Simulating such environ- that use fixed-volume, solid-walled, smaller (relative ments in respirometry conditions poses difficulties in, to the animal’s body size) chambers, which would con- for example, the proper mixing of water and calculations strain animal movement. The tank volume employed of water volume when using fixed-volume chambers, as here, approximately 114 times the volume of the well as providing more substrate for microbes which may conchs, allowed enough room for the animals to move affect O measures. Nonetheless, comparison of differ - in concentrated bouts. We note previous studies [25, ent substrate rugosity/complexity in future studies would 41] on blacktip (Carcharhinus limbatus) and lemon certainly improve the application of the RocRM–V̇O2 (Negaprion brevirostris) sharks encouraged consist- relationship for a given subject species. The movement ent movement by having an animal to chamber vol- bout of the individual depicted in Fig. 2 suggests that ume ratio approximately two times larger than ours, changes in yaw/heading correspond closely to that of although these animals are considerably more mobile pitch, contrasting with a more negligible change in the than the spider conchs tested here. Any tank setup roll. This is unsurprising, as the way in which the conch will restrict normal movement, of course. When com- raises itself up onto its foot during a directional change ing up against a barrier to movement, conchs will often will lead to a pitch change of the animal [28, 35]. As very continue to try to move up against it until they can few, if any, benthic animals move without any change move again [28]—if this happened here, then it is pos- whatsoever in at least two rotational axes, it would seem sible that conchs would have expended large amounts prudent to include all three axes as part of RocRM pre- of energy for what would appear, from the Daily Diary dictions of energy expenditure until further work indi- data, to be limited movement of the shell. cates otherwise. As the respirometry chamber had a soft plastic top in Apparent circadian rhythms were responsible for dif- place of a solid lid, it is feasible that any conchs climb- ference in movement levels, matching subsequently ing up the tank sides could break the airtight seal by collected data on free-roaming L. truncata showing noc- pushing the plastic covering away from the water’s sur- turnal preferences in movement (unpublished data; see face. Conchs were regularly observed throughout the also Additional file 1 on the difference in VeDBA and actual trials, however, and were never seen to climb the RocRM between afternoon and evening periods). This experiment tank sides beyond propping themselves at was good from a perspective of finding a range of activi - an angle between the sides and tanks bottom. As well as ties (i.e. data representative of both higher and lower their greater weight and size compared to many gastro- movement levels). Interpretation of this would have been pods, the lack of tortuous substrate may have stopped enhanced had we known more about the natural circa- the conchs, whose shells are bigger and more protruding dian behaviour of this study species, so that we might than many gastropods, from being able to traverse a 90° have been able to target periods of greater movement angle. intensity. Chamber volume may not be the only factor affect - In the frame of slow-moving animals that exhibit lit- ing results. An important consideration when applying tle-to-no dynamic acceleration signal (sensu Gleiss et al. this relationship to free-living conchs is how the animal [17]), the actual ‘jump’ of L. truncata is surprisingly moves in relation to, and as a consequence of, its environ- dynamic—at least by comparison to the steadier motion ment. Specifically, the substrate across which the conch of other invertebrates, such as crustaceans or urchins. travels will alter the relative contributions of changes in These jumps do produce spikes in VeDBA, as seen in pitch, roll and yaw/heading. Further, the substrate may Fig. 2. However, the instances of these jumps were seem- also negate or emphasise the inclusion of one or more ingly insufficiently large, or did not occur often enough, rotational axes as part of RocRM. In simple respirometry to make a difference in the relationship between VeDBA chambers with flat bottoms as used here, pitch and roll and V̇O over the 5-h time scale employed here—unsur- changes are likely to make a smaller contribution towards prising when surge, heave and sway axes produce a vastly the overall mean RocRM than if the animal was travers- lower signal during these drifts [28]. In fact, in many ing rocky, uneven substrate such as the coral rubble that instances, changes of heading were not even accompa- nied by a discernible change in acceleration, pitch or roll. The apparently greater contribution of drifts compared ‘Bouts’ were considered to be groups of movements that interspersed non- to jumps might also be a result of context—exploration movement periods (the non-movement periods lasting at least several min- of a small tank rather than an open benthic environment utes). Bouts lasted anywhere from < 1 min to > 15 min. Unpublished data leading to the conch having less of a straight line path on this species in the field have shown that 28.5% and 81.2% of movements occurred after an interval of < 1 min and < 10 min, respectively, with minimum on which to jump continuously. It is also reasonable to and maximum between-movement times of 0.01 and 508.6 min, respectively. assume that these jumps are at least partly anaerobic in Hopk ins et al. Anim Biotelemetry (2021) 9:30 Page 11 of 13 nature, leading to a degree of post-exercise oxygen con- integral to the differentials may well identify instances of sumption (EPOC) [57]. Indeed, leaping bursts in other the animal turning back on itself. conch species lead to anaerobic-metabolism enzyme A natural next step using L. truncata would be to build-up [58] and EPOC up to 12 h post-burst [37]. We derive estimates of energy expenditure through RocRM would suggest that our 5-h should account for at least in free-living individuals to make ecologically relevant part of this effect. However, more finely resolved V̇O observations and inferences about this and related spe- measurements under different conditions should eluci - cies. For example, ‘energy landscapes’ have typically been date this (e.g., an experimental procedure more similar visualised and described for highly mobile species such to [37]), as would further study specifically to quantify as birds [59, 60] but, by using RocRM-derived estimates, the length and size of EPOC. We suggest that researchers spatial use and daily movement patterns could be framed might consider a ‘scale’ of taxa to compare the compet- more widely within the context of slow-moving benthic ing suitabilities of VeDBA and RocRM according to the invertebrates. Additionally, RocRM may well convey types of motion exhibited by the animals, whereby ani- much more information about an animal’s movement mal movement changes from being mostly described by type than VeDBA when movement data are matched to its dynamic component of acceleration towards predomi- behavioural observations; here, there are clear differences nantly the change in the static component. in the observed peak profiles depending on the relative Beyond energy expenditure estimations, the use of sizes of change in the pitch, roll and heading axes. different timescales (i.e. differentials over varying time- steps) in analysis of animal movement rotations may be Conclusions a valuable tool in inferring, and in turn describing, scales The particular viability of RocRM to predict oxygen over which an animal moves (Fig. 2). Smaller time-steps consumption (V̇O ) over different time intervals would in RocRM, for example over just a few seconds, can pick indicate that it could work for a variety of differentially up the instantaneous changes that occur during a jump, slow-moving species, even if their angular velocity is where larger time-steps (for example, in this instance appreciably different from that of spider conchs. Further 60 s) might best highlight longer-term changes in body work should be conducted to understand the mecha- posture (particularly if such body posture changes, rela- nisms behind the variability around the mean trend of tive to the plane of the sea bed, are a summation of slower, RocRM vs V̇O . Nonetheless, we have presented evidence gradual, less dynamic movements such as drifts [28]). suggesting that rotational movement metrics better cor- Indeed, different time scales of yaw-change have been relate to energetic expenditure in slow-moving conchs shown to reveal fine-scale movement differences in Log - than DBA. Using similar experiments to ours on species gerhead turtles (Caretta caretta) that can be indicative that will have fewer sources of movement not measured of behaviour types better than DBA metrics alone [44]. by attached tags would be an obvious first step to validat - Visualising these scales of movement, such as through ing these results. density plots as shown here, may then show movement over different scales simultaneously to produce a ‘finger - Supplementary Information print’ for a species, individual or context and potentially The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s40317- 021- 00255-x. be used as a means of comparing these against others. In Fig. 3, there is a clear bias towards scales of movement Additional file 1: Table S1. Criteria for model selection during stepwise manifest at the largest time-step tested (Ro cRM , i.e. removal of insignificant terms in linear mixed models. Models with and 60 s). This is supported by the much larger Bayes factor without the fixed-effect term ‘mean temperature’ were compared by upper bounds values and AIC evidence ratios for RocRM ANOVA with maximum likelihood to produce the final models shown in Table 2. time-steps of 10 and 60 s compared to 1 or 5 s. Addi- tionally, the p value prediction interval for Ro cRM has bounds entirely below 0.05, suggesting very high replica- Acknowledgements We thank the Coastal & Marine Resources Core Lab Services for their invalu- bility and therefore that a time-step of 60 s does appear to able assistance in the setup and running of the experimental procedures. be the most suitable for linking rotational movement to Thanks to Dr Hannah Williams for her assistance with statistical analysis. The O consumption. We note, however, that increasing time Daily Diary tag housings were made by Phil Hopkins from Swansea University. We also thank Dr. Andrea Anton for assistance with the PME miniDOT loggers. intervals also allow the animal time to turn back on itself, We thank the three anonymous reviewers whose comments greatly improved affecting the angle traversed within the time interval this manuscript. and therefore the estimated angular costs. Researchers Authors’ contributions concerned about this miscalculation of the differentials LWH and RPW conceived and designed the study with input from NRG, ECP might consider instead calculating the integral of each and CMD. LWH and NRG conducted the static respirometry work and capture rotational axis over a given time-step; comparing the and release of specimens. LWH conducted data analysis with input from RPW, Hopkins et al. Anim Biotelemetry (2021) 9:30 Page 12 of 13 EPC and ML. MDH provided software support and implemented the RocRM 12. Speakman JR, Hambly C. Using doubly-labelled water to measure metric within the DDMT software. All authors contributed to manuscript free-living energy expenditure: some old things to remember and preparation. All authors read and approved the final manuscript. some new things to consider. Comp Biochem Physiol Part A Mol Integr Physiol. 2016;202:3–9. Funding 13. Gessaman JA. An evaluation of heart rate as an indirect measure of This work was Funded by a KAUST-funded studentship from the Office for daily energy metabolism of the American kestrel. Comp Biochem Sponsored Research, as part of the CAASE project and overarching Sensor Physiol Part A Physiol. 1980;65:273–89. Initiative. 14. Green JA, Halsey LG, Wilson RP, Frappell PB. Estimating energy expenditure of animals using the accelerometry technique: activity, Availability of data and materials inactivity and comparison with the heart-rate technique. J Exp Biol. The dataset supporting the conclusions of this article is available in a Figshare 2009;212:471–82. repository, https:// doi. org/ 10. 6084/ m9. figsh are. 96626 09 15. Green JA. The heart rate method for estimating metabolic rate: review and recommendations. Comp Biochem Physiol Part A Mol Integr Physiol. 2011;158:287–304. Declarations 16. Butler PJ, Green JA, Boyd IL, Speakman JR. Measuring metabolic rate in the field: the pros and cons of the doubly labelled water and heart rate Ethics approval and consent to participate methods. Funct Ecol. 2004;18:168–83. All applicable international, national, and/or institutional guidelines for sam- 17. Gleiss AC, Wilson RP, Shepard ELC. Making overall dynamic body pling, care and experimental use of animals were followed and all necessary acceleration work: on the theory of acceleration as a proxy for energy approvals obtained. All procedures performed in studies involving animals expenditure. Methods Ecol Evol. 2011;2:23–33. were approved by the Swansea University Ethics Committee (SU-Ethics- 18. Halsey LG, Shepard ELC, Wilson RP. Assessing the development and Student-220119/1248). This article does not contain any studies with human application of the accelerometry technique for estimating energy participants. expenditure. Comp Biochem Physiol Part A Mol Integr Physiol. 2011;158:305–14. Consent for publication 19. Shepard ELC, Wilson RP, Halsey LG, Quintana F, Laich AG, Gleiss AC, et al. Not applicable. Derivation of body motion via appropriate smoothing of acceleration data. Aquat Biol. 2008;4:235–41. Competing interests 20. Wilson RP, White CR, Quintana F, Halsey LG, Liebsch N, Martin GR, et al. The authors declare that they have no competing interests. Moving towards acceleration for estimates of activity-specific meta- bolic rate in free-living animals: the case of the cormorant. J Anim Ecol. Author details 2006;75:1081–90. 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Animal Biotelemetry – Springer Journals
Published: Sep 7, 2021
Keywords: Angular velocity; Dynamic body acceleration (DBA); Energy expenditure; Movement costs; RocRM; Rotational movement
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