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，数以百万计的植物和动物物种表现出独特的地理分布，受到其独特但相互交织的生物学和栖息地需求的影响。这一开创性的举措旨在通过采用先进的生成式人工智能（AI）方法，在全面的全球环境和物种发生数据的支持下，增强对这些模式的预测性理解。生成式人工智能的变革潜力已经通过工具建立起来，这些工具可以用类似人类的反应回答问题，或者根据文本描述创建令人惊叹的图像。该项目将应用类似的人工智能技术来预测物种如何在地球上的环境中分布。这对生物多样性保护至关重要，有助于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一个基础模型--一个多功能的人工智能模型，可供科学家、环保主义者和教育工作者使用，以更好地理解和保护自然世界。通过在大量数据上训练这个模型，它将学会模仿物种分布的模式，预测不同物种可能在哪里被发现。一旦开发出来，研究人员将向公众发布模型，允许广泛使用和持续改进。通过为科学界提供这一工具，该项目可以共同为保护生物多样性和地球的福祉做出贡献。利用一种称为扩散模型的生成人工智能方法-以处理复杂的条件概率而闻名-研究人员旨在开发一种能够理解高维环境空间中复杂物种-生态位关系的模型，展示了在广泛的物种中的多样性。为了支持这一目标，该项目还将使用全球数据库中经过清理的发生记录创建一个规模空前的广泛培训数据集，其中包括各种鸟类，哺乳动物，两栖动物和爬行动物。这种新方法将增强物种分布模型（SDM），这是生态学，进化生物学和保护的传统工具，提供了一个在拟合过程中在物种之间共享信息的版本，并且可以在无需重新训练的情况下针对新数据集进行微调，即使输入最少也能确保准确性。这种生成式人工智能驱动的SDM方法有望促进对生物多样性的理解，实现对各种景观和条件下物种分布的准确预测和可视化，包括数据稀缺地区。除了自然保护之外，该工具还将用于教育目的，并促进公众与自然世界的互动。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。