Volpov Beth L, Rosen David A S, Trites Andrew W, Arnould John P Y
School of Life and Environmental Sciences, Deakin University, Burwood, VIC, 3125, Australia,
J Comp Physiol B. 2015 Aug;185(6):695-708. doi: 10.1007/s00360-015-0911-y. Epub 2015 May 23.
We tested the ability of overall dynamic body acceleration (ODBA) to predict the rate of oxygen consumption ([Formula: see text]) in freely diving Steller sea lions (Eumetopias jubatus) while resting at the surface and diving. The trained sea lions executed three dive types-single dives, bouts of multiple long dives with 4-6 dives per bout, or bouts of multiple short dives with 10-12 dives per bout-to depths of 40 m, resulting in a range of activity and oxygen consumption levels. Average metabolic rate (AMR) over the dive cycle or dive bout calculated was calculated from [Formula: see text]. We found that ODBA could statistically predict AMR when data from all dive types were combined, but that dive type was a significant model factor. However, there were no significant linear relationships between AMR and ODBA when data for each dive type were analyzed separately. The potential relationships between AMR and ODBA were not improved by including dive duration, food consumed, proportion of dive cycle spent submerged, or number of dives per bout. It is not clear whether the lack of predictive power within dive type was due to low statistical power, or whether it reflected a true absence of a relationship between ODBA and AMR. The average percent error for predicting AMR from ODBA was 7-11 %, and standard error of the estimated AMR was 5-32 %. Overall, the extensive range of dive behaviors and physiological conditions we tested indicated that ODBA was not suitable for estimating AMR in the field due to considerable error and the inconclusive effects of dive type.
我们测试了整体动态身体加速度(ODBA)预测北海狗(Eumetopias jubatus)在水面休息和潜水时的耗氧率([公式:见正文])的能力。经过训练的海狮执行三种潜水类型——单次潜水、每回合进行4 - 6次潜水的多次长潜水回合,或每回合进行10 - 12次潜水的多次短潜水回合——下潜至40米深度,从而产生一系列活动和耗氧量水平。根据[公式:见正文]计算出潜水周期或潜水回合的平均代谢率(AMR)。我们发现,当将所有潜水类型的数据合并时,ODBA能够在统计学上预测AMR,但潜水类型是一个显著的模型因素。然而,当分别分析每种潜水类型的数据时,AMR与ODBA之间不存在显著的线性关系。通过纳入潜水持续时间、消耗的食物量、潜水周期中在水下花费的比例或每回合潜水次数,AMR与ODBA之间的潜在关系并未得到改善。目前尚不清楚在潜水类型内缺乏预测能力是由于统计能力不足,还是反映了ODBA与AMR之间确实不存在关系。从ODBA预测AMR的平均百分比误差为7 - 11%,估计的AMR的标准误差为5 - 32%。总体而言,我们测试的广泛潜水行为和生理条件表明,由于存在相当大的误差以及潜水类型的不确定影响,ODBA不适用于在野外估计AMR。