Fishing for drifts: detecting buoyancy changes of a top marine predator using a step-wise filtering method.
Fishing for drifts: detecting buoyancy changes of a top marine predator using a step-wise filtering method.
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DOI：
10.1242/jeb.118109
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发表时间：
2015-12
期刊：
影响因子：
--
通讯作者：
Boehme L
中科院分区：
文献类型：
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作者：
Gordine SA;Fedak M;Boehme L
在南象海豹（miounga leonina）中，与禁食和觅食相关的身体成分波动反映在浮力变化上。这种浮力的变化可以通过测量海豹在水柱中被动漂移的速率来监测，即当所有主动游动运动停止时。在这里，我们提出了一种改进的基于知识的方法，用于通过遥测接收压缩和抽象的潜水剖面来检测浮力变化。通过对潜水数据的逐步过滤，开发的算法识别出与动物漂移时间相对应的潜水片段。在南乔治亚州11只南象海豹的潜水记录中，该过滤方法识别出0.8-2.2%的潜水为漂移潜水，表明漂移潜水行为的个体差异较大。得到的漂流率时间序列表明，在每次迁徙开始时，所有个体都有强烈的负浮力。在接下来的75-150天里，所有个体的浮力达到峰值，接近或处于中性浮力，这表明海豹觅食成功。用目测详细的高分辨率潜水数据进行独立验证，证实该方法能够可靠地利用抽象数据检测漂移潜水物种潜水记录中的浮力变化。这也证实了抽象的潜水剖面足够详细地传达了漂移潜水的几何形状，从而可以识别它们。此外，使用这种逐级过滤方法，即使在压缩潜水信息的旧数据集中也可以检测到浮力变化，而传统的漂移潜水分类以前无法检测到浮力变化。摘要：一种检测漂移潜水鳍足动物浮力变化的逐级滤波方法。
In southern elephant seals (Mirounga leonina), fasting- and foraging-related fluctuations in body composition are reflected by buoyancy changes. Such buoyancy changes can be monitored by measuring changes in the rate at which a seal drifts passively through the water column, i.e. when all active swimming motion ceases. Here, we present an improved knowledge-based method for detecting buoyancy changes from compressed and abstracted dive profiles received through telemetry. By step-wise filtering of the dive data, the developed algorithm identifies fragments of dives that correspond to times when animals drift. In the dive records of 11 southern elephant seals from South Georgia, this filtering method identified 0.8–2.2% of all dives as drift dives, indicating large individual variation in drift diving behaviour. The obtained drift rate time series exhibit that, at the beginning of each migration, all individuals were strongly negatively buoyant. Over the following 75–150 days, the buoyancy of all individuals peaked close to or at neutral buoyancy, indicative of a seal's foraging success. Independent verification with visually inspected detailed high-resolution dive data confirmed that this method is capable of reliably detecting buoyancy changes in the dive records of drift diving species using abstracted data. This also affirms that abstracted dive profiles convey the geometric shape of drift dives in sufficient detail for them to be identified. Further, it suggests that, using this step-wise filtering method, buoyancy changes could be detected even in old datasets with compressed dive information, for which conventional drift dive classification previously failed. Summary: A step-wise filtering method to detect buoyancy changes in drift diving pinnipeds.