Chakravarty Pritish, Cozzi Gabriele, Scantlebury David Michael, Ozgul Arpat, Aminian Kamiar
Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Department of Evolutionary Biology and Environmental Studies, Universität Zürich, Zurich, Switzerland.
Mov Ecol. 2023 May 30;11(1):29. doi: 10.1186/s40462-023-00395-0.
All behaviour requires energy, and measuring energy expenditure in standard units (joules) is key to linking behaviour to ecological processes. Animal-borne accelerometers are commonly used to infer proxies of energy expenditure, termed 'dynamic body acceleration' (DBA). However, converting acceleration proxies (m/s) to standard units (watts) involves costly in-lab respirometry measurements, and there is a lack of viable substitutes for empirical calibration relationships when these are unavailable.
We used past allometric work quantifying energy expenditure during resting and locomotion as a function of body mass to calibrate DBA. We used the resulting 'power calibration equation' to estimate daily energy expenditure (DEE) using two models: (1) locomotion data-based linear calibration applied to the waking period, and Kleiber's law applied to the sleeping period (ACTIWAKE), and (2) locomotion and resting data-based linear calibration applied to the 24-h period (ACTIREST24). Since both models require locomotion speed information, we developed an algorithm to estimate speed from accelerometer, gyroscope, and behavioural annotation data. We applied these methods to estimate DEE in free-ranging meerkats (Suricata suricatta), and compared model estimates with published DEE measurements made using doubly labelled water (DLW) on the same meerkat population.
ACTIWAKE's DEE estimates did not differ significantly from DLW (t(19) = - 1.25; P = 0.22), while ACTIREST24's estimates did (t(19) = - 2.38; P = 0.028). Both models underestimated DEE compared to DLW: ACTIWAKE by 14% and ACTIREST by 26%. The inter-individual spread in model estimates of DEE (s.d. 1-2% of mean) was lower than that in DLW (s.d. 33% of mean).
We found that linear locomotion-based calibration applied to the waking period, and a 'flat' resting metabolic rate applied to the sleeping period can provide realistic joule estimates of DEE in terrestrial mammals. The underestimation and lower spread in model estimates compared to DLW likely arise because the accelerometer only captures movement-related energy expenditure, whereas DLW is an integrated measure. Our study offers new tools to incorporate body mass (through allometry), and changes in behavioural time budgets and intra-behaviour changes in intensity (through DBA) in acceleration-based field assessments of daily energy expenditure.
所有行为都需要能量,以标准单位(焦耳)测量能量消耗是将行为与生态过程联系起来的关键。动物携带的加速度计通常用于推断能量消耗的替代指标,即“动态身体加速度”(DBA)。然而,将加速度替代指标(米/秒)转换为标准单位(瓦特)需要进行成本高昂的实验室呼吸测量,并且当无法获得经验校准关系时,缺乏可行的替代方法。
我们利用过去的异速生长研究,将休息和运动期间的能量消耗量化为体重的函数,以校准DBA。我们使用由此得到的“功率校准方程”,通过两种模型来估计每日能量消耗(DEE):(1)将基于运动数据的线性校准应用于清醒期,将克莱伯定律应用于睡眠期(ACTIWAKE),以及(2)将基于运动和休息数据的线性校准应用于24小时周期(ACTIREST24)。由于这两种模型都需要运动速度信息,我们开发了一种算法,根据加速度计、陀螺仪和行为注释数据来估计速度。我们将这些方法应用于自由放养的狐獴(Suricata suricatta),以估计其DEE,并将模型估计值与使用双标记水(DLW)对同一狐獴种群进行的已发表的DEE测量值进行比较。
ACTIWAKE的DEE估计值与DLW没有显著差异(t(19) = -1.25;P = 0.22),而ACTIREST24的估计值有显著差异(t(19) = -2.38;P = 0.028)。与DLW相比,两种模型都低估了DEE:ACTIWAKE低估了14%,ACTIREST低估了26%。模型估计的DEE个体间差异(标准差为平均值的1 - 2%)低于DLW(标准差为平均值的33%)。
我们发现,将基于运动的线性校准应用于清醒期,将“固定”的休息代谢率应用于睡眠期,可以为陆生哺乳动物的DEE提供现实的焦耳估计值。与DLW相比,模型估计值的低估和较低差异可能是因为加速度计仅捕获与运动相关的能量消耗,而DLW是一种综合测量。我们的研究提供了新工具,可在基于加速度的每日能量消耗现场评估中纳入体重(通过异速生长)、行为时间预算的变化以及行为强度的内部变化(通过DBA)。