ModelEco: Integrated Software for Species Distribution Analysis and Modeling

ModelEco：物种分布分析和建模集成软件

• 批准号: 0742986
• 负责人: Qinghua Guo
• 金额: $ 17.32万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0742986&HistoricalAwards=false
• 关键词: ModelEco; Integrated; Software; Species; Distribution

The University of California at Merced is awarded a grant to develop ModelEco, a freely available and user-friendly software that enables researchers and students to fully and easily explore the rapidly increasing wealth of species distribution data. ModelEco will consist of four major components: data management and visualization, feature analysis, model training and prediction, and accuracy assessment. The proposed tools will incorporate a range of methods for species niche modeling as well as several feature selection methods to improve the prediction accuracy. This combination of features will foster better understanding of the relationship between species distribution and environmental variables. Spatial uncertainty of both species localities and environmental layers will be taken into consideration for the first time to improve spatial prediction of species distributions. The development of these tools will result in greater participation in species distribution modeling and its related activities in conservation, monitoring, planning, and climate change research and education. By creating tools that are free and easy to use, course and curriculum development will be fostered. With the proposed software, users will easily be able to appreciate the value of data quality, to understand the limitations of the data at hand, and to get guidance to obtain additional data necessary to answer priority-based questions.

位于默塞德的加州大学获得了开发ModelEco的赠款，ModelEco是一款免费提供的用户友好型软件，使研究人员和学生能够充分轻松地探索迅速增加的物种分布数据。ModelEco将由四个主要组件组成：数据管理和可视化、特征分析、模型训练和预测以及准确性评估。拟议的工具将纳入一系列的物种生态位建模方法，以及几个功能选择方法，以提高预测精度。这些特征的结合将有助于更好地了解物种分布与环境变量之间的关系。首次将物种分布地和环境层的空间不确定性考虑在内，以提高物种分布的空间预测能力。这些工具的开发将导致更多地参与物种分布建模及其在保护，监测，规划和气候变化研究和教育方面的相关活动。通过创建免费和易于使用的工具，将促进课程和课程设置。有了拟议的软件，用户将能够很容易地认识到数据质量的价值，了解手头数据的局限性，并获得指导，以获得回答基于优先事项的问题所需的额外数据。