10.1098/rsbl.2012.0919
Accelerometry predicts daily energy expenditure in a bird with high activity levels
Kyle Elliott, Maryline Le Vaillant, Akiko Kato, John Speakman, Yan Ropert-Coudert
SUPPLEMENTARY MATERIALS: METHOD DETAILS
From July 9 to August 1, 2009 we captured 20 breeding (4 incubating, 16 chick-rearing) thick-billed
murres at the Coats Island colony in Nunavut, Canada, and injected them in the brood patch with 0.5 mL
of doubly-labelled water (50% H2O18 and 25% D2O). We then attached an accelerometer (M190-D2GT,
12 bit resolution, 52·15 mm, 16 g, 0.1 m depth accuracy, Little Leonardo, Tokyo, Japan; average murre
body mass = 1014 g, SD = 66 g) to the lower back of each bird using Tesa tape. We also attached a timedepth-temperature recorder (LAT1500, 3.2 g, resolution = 0.25⁰C, time constant = 0.055 s -1, Lotek
Wireless, St. John’s, Canada) to the leg band. The accelerometer recorded depth and temperature at 1
Hz and acceleration along the head-tail (surging) and dorso-ventral (heaving) axes at 16 Hz. Past
experiments at our colony showed that the optimal method for measuring equilibrium isotopic values
(total body water) was the plateau method at 90 minutes using the oxygen-18 equilibrium value (Jacobs
et al. 2012). Therefore, we obtained 100 µL equilibrium blood samples 90 min after injection from the
tarsal vein.
We released the birds between the initial and equilibrium blood samples and were unable to recapture
three birds for the equilibrium blood sample. In those cases, we estimated the isotopic concentrations
from the equation between isotopic concentration and body mass (R2> 0.98; see Jacobs et al. 2012). We
recaptured the birds, retrieved accelerometers and obtained a second 100 µL blood sample from the
brachial vein 24 hours later. Based on known diet of murres at our study colony, we used a respiratory
quotient of 0.85.
Capillaries that contained the blood samples were then distilled [1], and water from the resulting distillate
was used to produce CO2 and H2 (methods in [2] for CO2 and [3] for H2). The isotope ratios 18O: 16O and 2H:
1
H were analysed using gas source isotope ratio mass spectrometry (Optima, Micromass IRMS and
Isochrom μG, Manchester, UK). Samples were run alongside three lab standards for each isotope
(calibrated to international standards) to correct delta values to ppm. Isotope enrichments were
converted to values of daily energy expenditure using a single pool model as recommended for this size
of animal by Speakman [4]. There are several alternative approaches for the treatment of evaporative
water loss in the calculation [5]. We chose the assumption of a fixed evaporation of 25% of the water
flux (equation 7.17: [6]), which has been established to minimise error in a range of conditions [5,7]. All
of the isotopic concentrations and daily energy expenditure calculations are attached in the Supporting
Information.
Validations of doubly-labelled water against respirometry usually indicate an absolute error of ±3% for
average values across groups and ±10% for individuals [6]. A recent paper on closely related rhinoceros
auklets [8] suggests a similar error for auks. Absolute values of doubly-labelled water measurements are
strongly dependent on the equation used to estimate energy expenditure, on the respiratory quotient
and on the estimate for total body water. We directly measured respiratory quotient (see above) and
total body water [9], and so our error is predicted to be smaller than many other studies that do not
directly measure those values [6]. Furthermore, values for daily energy expenditure estimated from
different equations vary in absolute estimates but are strongly correlated (R2> 0.97 for our study for the
equations listed in [6]). Thus, even if a different equation were used, the relative magnitudes of energy
expenditure as estimated from doubly-labelled water would be largely unaffected and therefore the R2
and AIC values would not change. In short, our central question—whether PDBA is correlated with field
metabolic rate as measured via doubly-labelled water—is unaffected by typical alterations in how
energy expenditure is calculated from the doubly-labelled water measurements. Estimates of
uncertainty follow Speakman [7].
Overall Dynamic Body Acceleration (ODBA) is calculated from triaxial acceleration (surge, sway and
heave) [10] and Partial Dynamic Body Acceleration (PDBA) is calculated from one or two-axis
acceleration data [11].
In our study, acceleration was recorded in 2 axes, corresponding surge and heave, at 16 Hz. Wing stroke
and thrust can be recorded on heave and surge accelerations of the body [12]. As the flapping frequency
of guillemots during flight and diving was ~6-7 Hz and 1-2 Hz, respectively, 16 Hz is below the Nyquist
sampling frequency for both locomotory modes. Thus, although PDBA could be underestimated
comparing to higher-frequency sampling data, the underestimation would be similar across all
individuals and both wing beat frequency and amplitude can be obtained reliably.
To calculate the PDBA, at first the static acceleration was approximated by applying a smoothing
function to the total acceleration recorded for each axis [13]. We used the box smooth function with 31
points of IGOR Pro, which is similar to running mean of 2 sec [14], to generate the static component
(𝐴̅).The dynamic acceleration is then determined by subtracting the static component from the total
acceleration, A ([13], see also Fig. 3 in [13]). PDBAsum is the sum of the absolute values of the dynamic
accelerations from two axes [13], although acceleration is a vectorial quantity. PDBAvectorial could be
more appropriate in cases where accelerometers cannot be accurately placed on the animal, or
movements occur in a variable manner and along various planes [14].
𝑃𝐷𝐵𝐴𝑠𝑢𝑚 = |𝐴𝑥 − ̅𝐴̅̅̅𝑥 | + |𝐴𝑦 − ̅̅̅̅
𝐴𝑦 |
2
𝑃𝐷𝐵𝐴𝑣𝑒𝑐𝑡𝑜𝑟𝑖𝑎𝑙 = √(𝐴𝑥 − ̅𝐴̅̅̅𝑥 )2 + (𝐴𝑦 − ̅̅̅̅
𝐴𝑦 )
Those two measures of PDBA were closely correlated with one another (R2 = 0.9986) and we therefore
used PDBAvectorial as a better indicator of dynamic body acceleration because it is less sensitive to position
of the logger on the body.
We used the acceleration profiles coupled with the temperature and pressure logs to determine when
the bird was in one of four different locomotory modes: flying, diving, at the water surface and on land
[15]. After accounting for the temperature drift on depth data (never more than 1 m), we used the
pressure log to determine when the bird was diving. We then used a computer script based on [16] to
determine when the bird is flying (high amplitude, high frequency constant between 6-8 Hz), swimming
on the water (intermediate amplitude and variable frequency) or resting on the land (low amplitude and
variable frequency) via filters designed to outline the distinctive frequencies and amplitudes associated
with each of those behaviours. Next, we used the temperature log as a “check” on the ethograms
determined via acceleration, as temperature is high when the bird is at the colony, relatively low and
variable when the bird is flying and very low and stable when the bird is on the ocean surface (see
detailed description of the technique in [17] and [18]).
We specifically used general linear models with the intercept set to zero for the time budget model (no
energy expended if no time had occurred) to test the following two models for predicting daily energy
expenditure (DEE):
𝐷𝐸𝐸 = 𝑀𝐿 𝑇𝐿 + 𝑀𝑆 𝑇𝑆 + 𝑀𝐹 𝑇𝐹 + 𝑀𝐷 𝑇𝐷 (1)
2
(2),
Where Mx is the average metabolic rate during mode x, Tx is the time spent in mode x, β is the intercept,
ηx is the efficiency coefficient converting accelerometry (a proxy of mechanical work) into daily energy
expenditure (metabolic work) and Ax is the integrated PDBA across all time spent in a particular
locomotory mode (the sum of all PDBA values for that activity). We partitioned time budgets into the
following locomotory modes: resting on land (L), swimming on surface (S), flying (F) and diving (D) and,
for each individual, calculated the time spent in each mode and total PDBA (time spent in the mode *
average PDBA for a particular individual for a particular locomotory mode) for each mode. The
coefficients were then determined by entering the data into a general linear model, with the intercept
set at zero for the time budget model, and selecting the value that reduced the log-likelihood
(essentially, the model that reduced the sum of square deviances). Equation (1), which involved just
time budgets and no accelerometry, was considered the null model. If accelerometry did not improve
predictability of energy expenditure over time budgets alone, then the ability of equation (2) to predict
DEE would be no better than equation (1).
We used Akaike’s information criterion (AIC) to compare among models, as AIC penalizes models that
are needlessly complex. Specifically, we compared among different models to see whether different
models could be simplified. For example, if metabolic rate during swimming at the surface and diving
were not significantly different, then Model (1) could be simplified by stating that MS = MD. Likewise, if
the same linear regression could convert PDBA during both swimming and diving into DEE, then Model
(2) could be simplified by stating that ηS = ηD. In either case, the AIC would select the more
parsimonious, simplified model for describing daily energy expenditure as a function of time budgets or
accelerometry. Statistical tests occurred in R 2.10.1.
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