Seismic information transfer in African Elephants - CASE Project

非洲大象的地震信息传递以地震振动编码的信息：非洲大象如何感知地震振动？他们如何将它们用作信息源和进行信息传输？这能用来保护大象吗？其他动物是否参与了生物颤动学网络？不断增长的人类人口，特别是在发展中国家，正日益引发人与野生动物之间的冲突。特别是大象，由于农作物掠夺，大象经常与人与野生动物的冲突有关，此外还面临严重的偷猎威胁。结合基于生物振动学的实验和GPS跟踪提供了一种特别有希望的跨学科方法，可以发现减少人与大象冲突的新方法，同时增加我们对大象行为和生态以及草原生态系统中其他地震相互作用的理解。在典型的行为和生态研究中，检测和使用通过底物传播的振动是一种被基本上忽视的感觉模式(Cocroft等人，2014)。然而，有迹象表明，这种振动被广泛的动物使用(Hill&Wessel，2016；Mortimer 2017)，对其进行研究的是生物震动学。其中包括非洲象(O‘Connell-Rodwell，2007年)证明大象可以通过次声振动在地面上传递明显详细的信息。具体地说，大象可以区分熟悉的和陌生的个体发出的警报信号的地震成分。理论上，这些底物上的振动比类似的空气上的振动衰减得慢，模型表明大象可以使用地震振动来进行几公里外的交流(Mortimer等人，2018年)。对于理解草原生态系统中的生物振动学很重要；大象显然不是唯一产生大量地震信号的大型哺乳动物，地震通信一直被假设为(O‘Connell-Rodwell等人，2001年)。具体地说，O‘Connell-Rodwell等人。(2001)提出，黑犀牛可能会使用地震信号来形成松散的群体，以协调夜间同步到达饮水池。技术的进步使地震通信的研究变得更容易处理，我们建议使用这种工作方式来更新我们的图像，将大象作为大草原生态系统及其哺乳动物的重点动物。为此，我们将使用GPS跟踪、生物震动学技术以及现场观察和实验相结合的方法。GPS跟踪技术可以独特地洞察大象的空间分布，因此可以远程指示个体之间的互动。对被追踪的公象在几个小时内平行移动但相隔数公里的观察表明，它们可能通过地面进行交流(道格拉斯-汉密尔顿和沃尔拉斯未发表的观察结果)。然而，GPS数据最终证明相互作用的能力将取决于新的生物颤动学技术，以证明基于实验和观察相结合的因果关系(Mortimer等人，2018年)。

Seismic information transfer in African ElephantsInformation encoded in seismic vibrations: How do African elephants perceive seismic vibrations? How do they use them as an information source and for information transfer? Can this be used for elephant conservation? Are other animals participating in biotremology networks?Growing human populations, particularly in developing countries, are increasingly causing human-wildlife conflict. Elephants in particular, are often associated with human-wildlife conflict due to crop raiding and are additionally faced with severe poaching threats.Combining biotremology-based experiments and GPS tracking offers a particularly promising interdisciplinary approach to discover novel ways to reduce human-elephant conflict while at the same time increasing our understanding of elephant behaviour and ecology as well as other seismic interactions in the savannah ecosystem.Detecting and using vibrations that travel through substrates is a largely ignored sensory mode in typical studies of behaviour and ecology (Cocroft et al., 2014). Yet it emerges that such vibrations are used by a wide range of animals (Hill & Wessel, 2016; Mortimer 2017), the study of which is known as biotremology. These include African elephants with O'Connell-Rodwell (2007) demonstrating that elephants can transfer apparently detailed information via infrasonic vibrations through the ground. Specifically, elephants can distinguish between the seismic components of alarm calls generated by familiar and unfamiliar individuals. In theory, these substrate-borne vibrations attenuate less rapidly than comparable air-borne vibrations, and models suggest that elephants can use seismic vibrations to communicate over distances of several kilometres (Mortimer et al., 2018). Importantly for an understanding of biotremology in savannah ecosystems; elephants are apparently not the only large mammals that generate substantial seismic signals for which seismic communication has been hypothesised (O'Connell-Rodwell et al., 2001). Specifically, O'Connell-Rodwell et al. (2001) suggest that black rhino may use seismic signals to form loose groups that coordinate a synchronised arrival at watering holes at night. Advances in technology have made the study of seismic communication more tractable and we propose to use this modus operandi to update our image, using elephants as a focal animal of the savannah ecosystem and its mammals. For this we will use a combination GPS tracking, biotremology techniques, and in-situ observation and experiments.GPS tracking technology provides unique insight into the spatial distribution of elephants and can consequently remotely indicate interactions between individuals. Observations of tracked male elephants moving in parallel over several hours but kilometres apart suggest that they communicate probably via the ground (Douglas-Hamilton and Vollrath unpublished observations). However, the ability of GPS data to conclusively demonstrate interactions will rely on novel biotremology technology to demonstrate cause and effect based on a combination of experiments and observation (Mortimer et al., 2018).