SG: Species Distribution Modeling on the A.I. frontier: Deep generative models for powerful, general and accessible SDM

SG：人工智能上的物种分布建模

• 批准号: 2329701
• 负责人: Russell Dinnage
• 金额: $ 19.81万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329701&HistoricalAwards=false
• 关键词: SG; Species; Distribution; Modeling; A.I.

Globally, millions of plant and animal species exhibit unique geographic distributions, influenced by their distinct yet intertwined biology and habitat needs. This pioneering initiative seeks to enhance predictive understanding of these patterns by employing advanced generative Artificial Intelligence (AI) methods, supported by comprehensive global environmental and species occurrence data. The transformative potential of generative AI has already been established with tools that can answer questions with human-like responses or create stunning images based on textual descriptions. The project will apply similar AI techniques to the task of predicting how species are distributed across environments on Earth. This is crucial for biodiversity conservation, and could help identify priority areas for protection and management, and predict species responses to climate change. The heart of the project is the creation of a foundation model - a versatile AI model that can be used by scientists, conservationists, and educators to better understand and protect the natural world. By training this model on a vast array of data, it will learn to mimic the patterns of species distributions, making predictions of where different species are likely to be found. Once developed, the researchers will release the model to the public, allowing for widespread use and continuous improvement. By empowering scientific communities with this tool, the project can collectively contribute to the preservation of biodiversity and the well-being of the planet.Leveraging a generative AI method called diffusion models – known for handling complex conditional probabilities – the researchers aim to develop a model capable of understanding complicated species-niche relationships within a high-dimensional environmental space, demonstrating versatility across a broad spectrum of species. To support this aim, the project will also create an extensive training dataset of unprecedented size using cleaned occurrence records from global databases, encompassing a wide range of birds, mammals, amphibians, and reptiles. This novel approach will enhance Species Distribution Models (SDMs), a traditional tool in ecology, evolutionary biology, and conservation, providing a version that shares information between species during fitting and can be fine-tuned for new datasets without retraining, ensuring accuracy even with minimal input. This generative AI-driven approach to SDMs promises to advance understanding of biodiversity, enabling accurate predictions and visualizations of species distributions across various landscapes and conditions, including data-scarce regions. Beyond conservation, this tool will serve educational purposes and foster public engagement with the natural world.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在全球范围内，数以百万计的动植物物种暴露了独特的地理分布，受其独特但相互交织的生物学和栖息地需求的影响。这项开拓性计划旨在通过采用先进的通用人工智能（AI）方法来增强对这些模式的预测性理解，并得到全球全球环境和物种发生数据的支持。通用AI的变革潜力已经通过工具建立，可以通过类似人类的响应回答问题或基于纹理描述创建令人惊叹的图像。该项目将应用类似的AI技术，以预测物种在地球上如何分布的任务。这对于保护生物多样性至关重要，可以帮助确定保护和管理的优先领域，并预测物种对气候变化的反应。该项目的核心是创建基础模型 - 一种多功能的AI模型，科学家，保护主义者和教育者可以使用，以更好地理解和保护自然世界。通过在大量数据上训练此模型，它将学会模仿物种分布的模式，从而预测可能在哪里可以找到不同物种。一旦开发，研究人员将向公众发布该模型，从而允许使用宽度和持续改进。通过使用该工具赋予科学社区的能力，该项目可以集体促进生物多样性和地球的福祉。掌握一种名为“扩散模型”的通用AI方法（以处理复杂的条件性可能性而闻名） - 研究人员旨在开发一种能够在高维环境空间中理解多种多样的多种多样的多种多样的模型，展现出多种多样的多种多样的多种多样的多种多样的多种多样的频谱，这些模型能够理解高度的复杂性。为了支持这一目标，该项目还将使用全球数据库中清洁的事件记录来创建广泛的培训数据集，以实现前所未有的大小，并涵盖了广泛的鸟类，哺乳动物，两栖动物和爬行动物。这种新颖的方法将增强物种分布模型（SDMS），这是一种传统的生态，进化生物学和保护的工具，提供了一个版本，该版本可以在拟合过程中共享物种之间的信息，并且可以对新数据集进行微调，而无需重新培训，即使最少的输入也可以确保准确性。 SDMS的这种通用AI驱动方法有望提高对生物多样性的理解，从而在包括数据筛分区域在内的各种景观和条件（包括数据筛查区域）对物种分布进行准确的预测和可视化。除了保护之外，该工具还将达到教育目的，并促进与自然世界的公众参与。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，被视为通过评估而被视为珍贵的支持。