Proto-OKN Theme 1: Exploiting Federal Data and Beyond: A Multi-modal Knowledge Network for Comprehensive Wildlife Management under Climate Change
Proto-OKN Theme 1: Exploiting Federal Data and Beyond: A Multi-modal Knowledge Network for Comprehensive Wildlife Management under Climate Change
批准号:
2333795
负责人:
Xiangliang Zhang
金额:
$150.0万
依托单位:
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-10-01 至 2026-09-30
中文摘要
点击翻译按钮获取中文摘要
英文摘要
This Prototype-Open Knowledge Network project seeks to create a comprehensive, integrative knowledge network for the management of wildlife in the context of climate change, called KN-Wildlife. Effective wildlife management is essential for safeguarding biodiversity, ecosystem health, and economic stability of any region. The looming threat of climate change can disrupt the distribution, behavior, and population dynamics of many species, particularly those that are invasive or threatened or economically important. While Species Distribution Models (SDMs) have been used to predict species responses to climate change, their predictive capability can be hampered by the omission of several influencing factors like interspecies competition, changes in land use, or the migration ability of various species. A number of data sources are available that are relevant to these considerations including the United States Geological Survey (USGS), the Global Biodiversity Information Facility, and the IUCN Red List of Threatened Species. However, the effective use of these data is currently impeded by factors associated with the heterogeneity, spatial and temporal discrepancies, and varying quality and completeness of the data. KN-Wildlife will address this problem by providing an open-access platform that couples data with visualization tools and predictive models to distill complex multimodal data into an intuitive, unified representation of managed species. KN-Wildlife would help inform decision-making processes and while providing stakeholders with actionable insights. The project will begin by providing comprehensive knowledge and predictive models for species of concern to stakeholders in these two US states, viz., Indiana and Florida. Project collaborators include stakeholders from the Fish and Wildlife Commissions (FWC) and Departments of Health (DoH) from both of these states. KN-Wildlife will initially focus on around 3,000 managed species of interest to the stakeholders, encompassing a broad taxonomy from fungi and bacteria to fish and mammals. Working in collaboration with the Lucy Family Institute at the University of Notre Dame, the project will integrate use of KN-Wildlife into the NSF-funded Interdisciplinary Traineeship for Socially Responsible and Engaged Data Scientists (iTREDS) program and the Summer Education and Engagement for Data Science (SEEDS) Program. These initiatives are specifically designed to provide undergraduate data science training centered on societal challenges, and also provide K-12 training opportunities for those from under-resourced schools and communities. All KN-Wildlife resources will be publicly available.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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
CyberTraining: Implementation: Medium: C2D - Cybertraining for Chemical Data scientists
-
批准号:2321054
-
项目类别:Standard Grant
-
资助金额:$100.0万
-
财政年份:2023
-
负责人:Xiangliang Zhang
-
依托单位:
国内基金
海外基金
等亮度彩色运动图象的OKN眼动跟踪的研究
-
批准号:39200038
-
项目类别:青年科学基金项目
-
资助金额:4.5万元
-
批准年份:1992
-
负责人:方烈义
-
依托单位: