Shermeister Ben, Mor Danny, Levy Ofir
Faculty of Life Sciences, School of Zoology, Tel Aviv University, Tel Aviv, Israel.
J Anim Ecol. 2024 Sep;93(9):1246-1261. doi: 10.1111/1365-2656.14139. Epub 2024 Jul 22.
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.
行为体温调节具有关键的生态和生理后果，深刻影响个体适应性和物种分布，尤其是在气候变化背景下。然而，对这种行为的野外监测仍然是劳动密集型且耗时的。随着基于相机的调查和计算机视觉中的人工智能（AI）方法的兴起，我们应该尝试构建更好的工具来表征动物的行为体温调节。在本研究中，我们开发了一个深度学习框架，以实现体温调节行为的自动检测和分类。我们使用蜥蜴，即粗尾岩鬣蜥（Laudakia vulgaris），作为体温调节的模型动物。我们对蜥蜴进行了颜色标记，并整理了在半自然条件下由跟踪相机拍摄的多样化图像数据集。随后，我们训练了一个目标检测模型来识别蜥蜴，并训练图像分类模型来确定它们对微气候的利用情况（在阳光下或阴凉处的活动），这可能表明体温调节偏好。然后，我们评估了每个模型的性能，并分析了在不同太阳条件（阳光或阴凉处）、一天中的时间和标记颜色下，对进行体温调节的蜥蜴的分类情况。我们框架的模型在几个性能指标上取得了高分。行为体温调节分类模型在晒太阳的蜥蜴上表现明显更好，白色标记的蜥蜴达到了最高的分类准确率。此外，我们的框架生成的蜥蜴活动时间和微气候选择（寻求阳光与阴凉处的行为）与人工标注的数据密切一致。我们的研究强调了人工智能在有效跟踪行为体温调节方面的潜力，为相机陷阱研究提供了一个有前景的新方向。这种方法有可能减少与生态数据收集和分析相关的劳动力和时间，并有助于更深入地了解物种的热偏好以及气候变化对物种行为的风险。
