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Modelling the sound production of narwhals using a point process framework with memory effects
Annals of Applied Statistics ( IF 1.3 ) Pub Date : 2020-12-19 , DOI: 10.1214/20-aoas1379
Aleksander Søltoft-Jensen , Mads Peter Heide-Jørgensen , Susanne Ditlevsen

Obtaining an adequate description of the behaviour of narwhals in a pristine environment is important to understand natural behaviour as well as providing the means to determine potential changes in behaviour directly or indirectly caused by human activity. Based on $\text{Acousonde}^{\text{TM}}$ data from five narwhals in Scoresby Sound, this paper aims at modelling buzzing and calling rates of East Greenland narwhals as functions of time, space and, possibly, autoregressive memory. Both buzzing and calling are sounds produced by narwhals. Buzzing is a way for the whale to navigate and locate prey using echolocation, while calling is associated with social communication between whales. Logistic regression models without and with autoregressive components are compared based on AIC and comparatively assessed using diagnostics from point process theory. Adding an autoregressive component appears to improve the models, and further improvements for the buzzing model are made with a non-GLM extension. Effects of extrinsic covariates and memory are presented and interpreted. Buzzing occurs at deeper depths, and initiations of buzzes are separated by refractory periods. A possible feeding area is identified. Calling occurs closer to the surface, and, while the probability of calling in general is lower than buzzing, it is more likely that calls are clustered together rather than spread randomly.

中文翻译:

使用具有记忆效应的点处理框架对独角鲸的声音产生进行建模

对原始环境中的独角鲸行为进行充分描述对于理解自然行为以及提供确定人类活动直接或间接引起的潜在行为变化的手段非常重要。本文基于Scoresby Sound中五个独角鲸的$ \ text {Acousonde} ^ {\ text {TM}} $数据,本文旨在将东格陵兰岛独角鲸的嗡嗡声和召唤率建模为时间,空间以及可能的自回归记忆的函数。嗡嗡声和叫声都是独角鲸发出的声音。嗡嗡声是鲸鱼使用回声定位导航和定位猎物的一种方式,而通话则与鲸鱼之间的社交交流相关联。基于AIC对不具有和具有自回归分量的Logistic回归模型进行比较,并使用点过程理论的诊断进行比较评估。添加自回归组件似乎可以改善模型,并且通过非GLM扩展对嗡嗡声模型进行了进一步的改进。呈现并解释了外部协变量和记忆的影响。嗡嗡声发生在更深的深度,并且嗡嗡声的启动由不应期分隔。确定可能的进食区域。呼叫发生在靠近表面的位置,尽管呼叫的可能性通常比嗡嗡声低,但呼叫更可能聚集在一起而不是随机分布。使用非GLM扩展对蜂鸣模型进行了进一步的改进。呈现并解释了外部协变量和记忆的影响。嗡嗡声发生在更深的深度,并且嗡嗡声的启动由不应期分隔。确定可能的进食区域。呼叫发生在靠近表面的位置,尽管呼叫的可能性通常比嗡嗡声低,但呼叫更可能聚集在一起而不是随机分布。使用非GLM扩展对蜂鸣模型进行了进一步的改进。呈现并解释了外部协变量和记忆的影响。嗡嗡声发生在更深的深度,并且嗡嗡声的启动由不应期分隔。确定可能的进食区域。呼叫发生在靠近表面的位置,尽管呼叫的可能性通常比嗡嗡声低,但呼叫更可能聚集在一起而不是随机分布。
更新日期:2020-12-20
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