Collaborative Research: BoCP-Implementation: forecasting functional biodiversity change with satellite remote sensing and modeling

合作研究：BoCP-实施：利用卫星遥感和建模预测功能性生物多样性变化

• 批准号: 2326014
• 负责人: Zhe Zhu
• 金额: $ 96.18万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326014&HistoricalAwards=false
• 关键词: Collaborative; Research; BoCP; Implementation; forecasting

This project will use satellite imaging and species location records in primary forests of the Caribbean to determine which species are threatened with extinction and how their loss will affect ecosystem function across the landscape. The outcomes will help focus limited resources on protecting the most vulnerable and functionally important species in these highly vulnerable areas. Biodiversity is essential for the processes that support life, including food security, medicines and public health. However, with a changing climate, forests are disappearing around the world, leading to a major loss of species. This research will improve on current methods of assessing extinction risk by increasing precision and automation of methods, and by extending the application to all regional species. The project will also map the location of primary forest habitats, which are regions of original forest not previously disturbed that hold the most biodiversity with the greatest number of ecological functions. An additional outcome of the project will be to identify the most critical areas for better protection of biodiversity. Greater knowledge of functional biodiversity will increase understanding of how ecosystems work and how they will change in coming decades through human modifications of habitat and climate, and through other threats to species. Knowing how, when, and where biodiversity changes will occur will help society prepare for the future and mitigate the losses. This project brings together expertise from satellite remote sensing, distribution modeling, organismal biology, and conservation biology to integratively address biodiversity loss through the development of innovative analytical methods. The project will focus on primary forest, which has more functional diversity than secondary forests, because it includes more structure and ecological specialists. The high-resolution land cover data sets with primary forest included that will be produced are currently rarely available. The system, which updates as new satellite imagery, climatic, and biodiversity data become available, is ideally suited for large areas of biodiversity, where remote sensing and environmental data are the primary sources of information for extinction risk. This project focuses on animals and plants in the Caribbean region to test hypotheses of functional diversity change across landscapes and through time.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.

该项目将利用加勒比地区原始森林的卫星成像和物种位置记录来确定哪些物种面临灭绝威胁，以及它们的消失将如何影响整个景观的生态系统功能。结果将有助于集中有限的资源来保护这些高度脆弱地区最脆弱和功能最重要的物种。生物多样性对于维持生命的过程至关重要，包括粮食安全、药品和公共卫生。然而，随着气候变化，世界各地的森林正在消失，导致物种大量流失。这项研究将通过提高方法的精确度和自动化程度以及将应用范围扩展到所有区域物种来改进目前评估灭绝风险的方法。该项目还将绘制原始森林栖息地的位置图，这些栖息地是以前未受干扰的原始森林区域，拥有最多的生物多样性和最多的生态功能。该项目的另一项成果将是确定更好地保护生物多样性的最关键领域。对功能性生物多样性的更多了解将加深对生态系统如何运作以及未来几十年它们将如何通过人类对栖息地和气候的改变以及对物种的其他威胁而发生变化的了解。了解生物多样性变化如何、何时、何地发生将有助于社会为未来做好准备并减轻损失。该项目汇集了卫星遥感、分布模型、有机生物学和保护生物学的专业知识，通过开发创新的分析方法综合解决生物多样性丧失问题。该项目将重点关注原始林，它比次生林具有更多的功能多样性，因为它包含更多的结构和生态专家。将产生的包含原始森林的高分辨率土地覆盖数据集目前很少可用。该系统随着新的卫星图像、气候和生物多样性数据的出现而更新，非常适合大面积的生物多样性，其中遥感和环境数据是灭绝风险信息的主要来源。该项目重点关注加勒比地区的动植物，以测试不同景观和时间范围内功能多样性变化的假设。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。