Proto-OKN Theme 1: Exploiting Federal Data and Beyond: A Multi-modal Knowledge Network for Comprehensive Wildlife Management under Climate Change

这个原型开放知识网络项目旨在创建一个全面的，综合的知识网络，用于在气候变化的背景下管理野生动物，称为KN野生动物。有效的野生动物管理对于保护任何地区的生物多样性、生态系统健康和经济稳定至关重要。气候变化的威胁可能会破坏许多物种的分布，行为和种群动态，特别是那些入侵或受威胁或经济重要的物种。虽然物种分布模型（SDM）已被用于预测物种对气候变化的反应，但其预测能力可能会受到忽略的几个影响因素，如物种间的竞争，土地利用的变化，或各种物种的迁移能力。与这些考虑因素有关的数据来源包括美国地质调查局（USGS）、全球生物多样性信息设施和世界自然保护联盟濒危物种红色名录。然而，由于数据的异质性、空间和时间差异以及质量和完整性参差不齐，目前这些数据的有效利用受到阻碍。KN-Wildlife将通过提供一个开放访问平台来解决这个问题，该平台将数据与可视化工具和预测模型相结合，将复杂的多模态数据提取为管理物种的直观，统一的表示。KN-Wildlife将有助于为决策过程提供信息，同时为利益相关者提供可操作的见解。该项目将开始，为美国这两个州的利益相关者提供有关物种的全面知识和预测模型，即，印第安纳州和佛罗里达。项目合作者包括来自这两个州的鱼类和野生动物委员会（FWC）和卫生部（DoH）的利益相关者。KN-野生动物最初将专注于利益相关者感兴趣的约3，000种管理物种，包括从真菌和细菌到鱼类和哺乳动物的广泛分类。该项目与圣母大学露西家庭研究所合作，将KN-野生动物的使用整合到NSF资助的社会责任和可持续数据科学家跨学科培训（iTREDS）计划和夏季教育和参与数据科学（SEEDS）计划中。这些举措专门旨在提供以社会挑战为中心的本科数据科学培训，并为来自资源不足的学校和社区的学生提供K-12培训机会。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。

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.