Suppr超能文献

用于气候变化生物学的人工智能:从数据收集到预测

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

作者信息

Levy Ofir, Shahar Shimon

机构信息

Tel Aviv University, Faculty of Life Sciences, School of Zoology, Tel Aviv 6997801, Israel.

Tel Aviv University, The AI and Data Science Center, Tel Aviv 6997801, Israel.

出版信息

Integr Comp Biol. 2024 Sep 27;64(3):953-974. doi: 10.1093/icb/icae127.

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.

摘要

在大数据时代,生态研究正在经历变革性的转变,然而热生态学以及动物对气候条件反应研究方面的大数据进展仍然有限。本综述讨论了大数据分析和人工智能(AI)如何能够显著增进我们对不断变化的气候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以及分析来自先进传感器和摄像技术的数据方面的潜力,这些技术能够捕捉详细的高分辨率信息。这种整合能够让研究人员以前所未有的精度剖析复杂的生态和生理过程。我们描述了人工智能如何通过改进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,我们探讨了人工智能在追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级记录仪等传感器的创新分类模型。例如,广泛使用的相机陷阱可以受益于人工智能驱动的图像分类模型,以准确识别体温调节反应,如使用阴凉处和喘气。因此,人工智能有助于监测动物与环境的相互作用,为了解它们的适应性行为提供至关重要的见解。最后,我们讨论了这些先进的数据驱动方法如何为保护策略提供信息并加以改进。特别是,详细绘制在不利条件下物种生存所必需的微生境图,可以指导设计具有气候适应能力的保护和恢复计划,这些计划将优先考虑对生物多样性恢复力至关重要的栖息地特征。总之,人工智能、大数据和生态科学的融合预示着一个精准保护的新时代,这对于应对21世纪的全球环境挑战至关重要。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验