Abstract
BackgroundBiotelemetry 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.MethodsWe 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.ResultsThe 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%).ConclusionIn 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.
Highlights
Biotelemetry offers an increasing set of tools to monitor animals
The primary aim of this study is to develop an approach for an automated detection of vigilance behavior in large herbivores, based on acceleration data
We develop and evaluate a classification model that aims at classifying behavior of roe deer into structural behavior categories, based on video-documented field observations of collared animals
Summary
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. Unlike GPS sensors, which only allow for detecting behaviors that produce distinct movement patterns, acceleration sensors allow for monitoring of a wide variety of behaviors by providing information about an animal’s posture and motion. This is technically feasible because acceleration sensors measure two types of acceleration: static body acceleration and dynamic body acceleration [6]. Modeling is usually based on supervised classification models that are trained with a ground-truthed dataset [7,8,9] In this case, the behavior of tagged animals must be observed in the field, which allows for assigning a behavioral category to each segment of the training dataset. When several behaviors are targeted (e.g., browsing and standing), segments of fixed width are unable to account for behavior-specific signatures of different length
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