Artificial Intelligence for Climate Change Biology: From Data Collection to Predictions.

IF 2.2 3区 生物学 Q1 ZOOLOGY Integrative and Comparative Biology Pub Date : 2024-09-27 DOI:10.1093/icb/icae127
Ofir Levy, Shimon Shahar
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Abstract

In the era of big data, ecological research is experiencing a transformative shift, yet big-data advancements in thermal ecology and the study of animal responses to climate conditions remain limited. This review discusses how big data analytics and artificial intelligence (AI) can significantly enhance our understanding of microclimates and animal behaviors under changing climatic conditions. We explore AI's potential to refine microclimate models and analyze data from advanced sensors and camera technologies, which capture detailed, high-resolution information. This integration can allow researchers to dissect complex ecological and physiological processes with unprecedented precision. We describe how AI can enhance microclimate modeling through improved bias correction and downscaling techniques, providing more accurate estimates of the conditions that animals face under various climate scenarios. Additionally, we explore AI's capabilities in tracking animal responses to these conditions, particularly through innovative classification models that utilize sensors such as accelerometers and acoustic loggers. For example, the widespread usage of camera traps can benefit from AI-driven image classification models to accurately identify thermoregulatory responses, such as shade usage and panting. AI is therefore instrumental in monitoring how animals interact with their environments, offering vital insights into their adaptive behaviors. Finally, we discuss how these advanced data-driven approaches can inform and enhance conservation strategies. In particular, detailed mapping of microhabitats essential for species survival under adverse conditions can guide the design of climate-resilient conservation and restoration programs that prioritize habitat features crucial for biodiversity resilience. In conclusion, the convergence of AI, big data, and ecological science heralds a new era of precision conservation, essential for addressing the global environmental challenges of the 21st century.

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气候变化生物学人工智能:从数据收集到预测
在大数据时代,生态研究正经历着一场变革,但热生态学和动物对气候条件反应的研究进展仍然有限。本综述讨论了大数据分析和人工智能(AI)如何极大地增强我们对气候变化条件下的微气候和动物行为的理解。我们探讨了人工智能在完善微气候模型和分析来自先进传感器和摄像技术的数据方面的潜力,这些技术可以捕捉到详细的高分辨率信息。这种整合使研究人员能够以前所未有的精度剖析复杂的生态和生理过程。我们介绍了人工智能如何通过改进的偏差校正和降尺度技术来增强微气候建模,从而更准确地估计动物在各种气候情景下所面临的条件。此外,我们还探讨了人工智能在跟踪动物对这些条件的反应方面的能力,特别是通过利用加速度计和声学记录仪等传感器的创新分类模型。此外,人工智能驱动的图像分类模型可以准确识别动物的体温调节反应,如阴凉处的使用和喘气,这对相机陷阱的广泛使用大有裨益。因此,人工智能有助于监测动物如何与环境互动,为了解动物的适应性行为提供重要依据。最后,我们将讨论这些先进的数据驱动方法如何为保护战略提供信息和帮助。详细绘制不利条件下物种生存所必需的微生境图,可以指导设计气候适应性保护和恢复计划,优先考虑对生物多样性恢复至关重要的生境特征。总之,人工智能、大数据和生态科学的融合预示着一个新的精准保护时代的到来,这对于应对 21 世纪的全球环境挑战至关重要。
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来源期刊
CiteScore
4.70
自引率
7.70%
发文量
150
审稿时长
6-12 weeks
期刊介绍: Integrative and Comparative Biology ( ICB ), formerly American Zoologist , is one of the most highly respected and cited journals in the field of biology. The journal''s primary focus is to integrate the varying disciplines in this broad field, while maintaining the highest scientific quality. ICB''s peer-reviewed symposia provide first class syntheses of the top research in a field. ICB also publishes book reviews, reports, and special bulletins.
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