Wild state secrets: ultra‐sensitive measurement of micro‐movement can reveal internal processes in animals

Wilson, Rory P.; Grundy, Edward; Massy, Richard; Soltis, Joseph; Tysse, Brenda; Holton, Mark D.; Cai, Yuzhi; Parrott, Andrew C.; Downey, Luke A.; Qasem, Lama; Butt, Tariq M.

doi:10.1890/140068

Cited by 48 publications

(56 citation statements)

References 30 publications

(53 reference statements)

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“…In these situations, human observers are superior because they are able to acquire additional information (e.g., posture of ears, body tension, external stimuli) that facilitate correct discrimination. Unfortunately, we lacked this information with our sensors, although techniques like measurements of micromovements [29] or heart beat [30] promise to fill this gap soon. Additionally, animals sometimes gradually transition from one state to another or display active behaviors (e.g., grooming) after lying down.…”

Section: Discussionmentioning

confidence: 99%

Remote monitoring of vigilance behavior in large herbivores using acceleration data

Kröschel

Reineking

Werwie³

et al. 2017

Anim Biotelemetry

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Background: Biotelemetry offers an increasing set of tools to monitor animals. Acceleration sensors in particular can provide remote observations of animal behavior at high temporal resolution. While recent studies have demonstrated the capability of this technique for a wide range of species and behaviors, a coherent methodology is still missing (1) for behavior monitoring of large herbivores that are usually tagged with neck collars and frequently switch between diverse behaviors and (2) for monitoring of vigilance behavior. Here, we present an approach that aims at remotely monitoring different types of large herbivore behavior including vigilance with acceleration data. Methods:We pioneered this approach with field observations of eight collared roe deer (Capreolus capreolus). First, we trained a classification model for distinguishing seven structural behavior categories: lying, standing, browsing, walking, trotting, galloping and 'others' . Second, we developed a model that predicted the internal states, active and resting, based on the predicted sequence of structural behaviors and expert-based rules. Further, we applied both models to automatically monitor vigilance behavior and compared model predictions with expert judgment of vigilance behavior. To exemplify the practical application of this approach, we predicted behavior, internal state and vigilance continuously for a collared roe deer. Results:The structural behaviors were predicted with high accuracy (overall cross-validated accuracy 71%). Only behaviors that are similar in terms of posture and dynamic body movements were prone to misclassification. Active and resting states showed clear distinction and could be utilized as behavioral context for the detection of vigilance behavior. Here, model predictions were characterized by excellent consistency with expert judgment of vigilance behavior (mean accuracy 96%). Conclusion:In this study, we demonstrated the strong potential and practical applicability of acceleration data for continuous, high-resolution behavior monitoring of large herbivores and showed that vigilance behavior is well detectable. In particular, when combined with spatial data, automated behavior recognition will enrich many fields in behavioral ecology by providing extensive access to behaviors of animals in the wild.

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Section: Discussionmentioning

confidence: 99%

Remote monitoring of vigilance behavior in large herbivores using acceleration data

Kröschel

Reineking

Werwie³

et al. 2017

Anim Biotelemetry

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“…Our group previously attached accelerometers in collars to African elephants at Disney's Animal Kingdom ® , showing that common elephant behaviors (resting, feeding, bathing, and walking) can be differentiated from one another using dynamic acceleration and the periodicity of movement (Soltis et al 2012). We also showed that elephants walk with a different posture dependent on whether they are in a positive or negative emotional state (Wilson et al 2014). …”

Section: Introductionmentioning

confidence: 90%

Accelerometers and simple algorithms identify activity budgets and body orientation in African elephants Loxodonta africana

Soltis¹,

King²,

Vollrath³

et al. 2016

Endang. Species. Res.

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“…An animal's internal state relates to the physiological and psychological drivers of movement [7,34]. This includes desire to eat, find shelter, find mates, escape predation or avoid competition.…”

Section: Internal Statementioning

confidence: 99%

Coupling movement and landscape ecology for animal conservation in production landscapes

Doherty¹,

Driscoll²

2018

Proc. R. Soc. B.

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Habitat conversion in production landscapes is among the greatest threats to biodiversity, not least because it can disrupt animal movement. Using the movement ecology framework, we review animal movement in production landscapes, including areas managed for agriculture and forestry. We consider internal and external drivers of altered animal movement and how this affects navigation and motion capacities and population dynamics. Conventional management approaches in fragmented landscapes focus on promoting connectivity using structural changes in the landscape. However, a movement ecology perspective emphasizes that manipulating the internal motivations or navigation capacity of animals represents untapped opportunities to improve movement and the effectiveness of structural connectivity investments. Integrating movement and landscape ecology opens new opportunities for conservation management in production landscapes.

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Wild state secrets: ultra‐sensitive measurement of micro‐movement can reveal internal processes in animals

Cited by 48 publications

References 30 publications

Remote monitoring of vigilance behavior in large herbivores using acceleration data

Remote monitoring of vigilance behavior in large herbivores using acceleration data

Accelerometers and simple algorithms identify activity budgets and body orientation in African elephants Loxodonta africana

Coupling movement and landscape ecology for animal conservation in production landscapes

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