Leveraging camera traps and artificial intelligence to explore thermoregulation behaviour

Leveraging camera traps and artificial intelligence to explore thermoregulation behaviour

Behavioural thermoregulation has critical ecological and physiological consequences that profoundly influence individual fitness and species distributions, particularly in the context of climate change. However, field monitoring of this behaviour remains labour‐intensive and time‐consuming. With the...

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Bibliographic Details
Published in:The Journal of animal ecology Vol. 93; no. 9; pp. 1246 - 1261
Main Authors: Shermeister, Ben, Mor, Danny, Levy, Ofir
Format: Journal Article
Language:English
Published: England Blackwell Publishing Ltd 01-09-2024
Subjects:
Artificial intelligence
camera traps
Cameras
Classification
climate
Climate change
Computer vision
Data analysis
Data collection
Deep learning
Environmental risk
Image classification
lizard
Lizards
Machine learning
Microclimate
Performance evaluation
Performance measurement
Shade
Sun
Thermoregulation
Thermoregulatory behavior
shade
deep learning
climate
lizard
artificial intelligence
camera traps
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Summary:Behavioural thermoregulation has critical ecological and physiological consequences that profoundly influence individual fitness and species distributions, particularly in the context of climate change. However, field monitoring of this behaviour remains labour‐intensive and time‐consuming. With the rise of camera‐based surveys and artificial intelligence (AI) approaches in computer vision, we should try to build better tools for characterizing animals' behavioural thermoregulation. In this study, we developed a deep learning framework to automate the detection and classification of thermoregulation behaviour. We used lizards, the Rough‐tail rock agama (Laudakia vulgaris), as a model animal for thermoregulation. We colour‐marked the lizards and curated a diverse dataset of images captured by trail cameras under semi‐natural conditions. Subsequently, we trained an object‐detection model to identify lizards and image classification models to determine their microclimate usage (activity in sun or shade), which may indicate thermoregulation preferences. We then evaluated the performance of each model and analysed how the classification of thermoregulating lizards performed under different solar conditions (sun or shade), times of day and marking colours. Our framework's models achieved high scores in several performance metrics. The behavioural thermoregulation classification model performed significantly better on sun‐basking lizards, achieving the highest classification accuracy with white‐marked lizards. Moreover, the hours of activity and the microclimate choices (sun vs shade‐seeking behaviour) of lizards, generated by our framework, are closely aligned with manually annotated data. Our study underscores the potential of AI in effectively tracking behavioural thermoregulation, offering a promising new direction for camera trap studies. This approach can potentially reduce the labour and time associated with ecological data collection and analysis and help gain a deeper understanding of species' thermal preferences and risks of climate change on species behaviour. By utilizing camera traps for lizard monitoring, we introduce an artificial intelligence framework designed to identify thermoregulation behaviour from captured images. This innovative approach, applicable across animal species, opens a new avenue in thermal ecology, significantly enriching future studies and the analysis of existing, extensive image datasets.
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ISSN:0021-8790
1365-2656
1365-2656
DOI:10.1111/1365-2656.14139