GLOSSA：一种用户友好的R Shiny应用，用于贝叶斯机器学习分析海洋物种分布
GLOSSA: a user-friendly R Shiny application for Bayesian machine learning analysis of marine species distribution
AI总结：
GLOSSA是一种基于贝叶斯加法回归树的用户友好型R Shiny应用，旨在通过直观界面简化海洋物种分布建模，提升生态研究的可操作性与实用性。
摘要：
物种分布模型（SDMs）是评估物种占据和地理分布模式最常用的统计方法之一。随着生态数据复杂性的增加，许多方法学方法已被开发，通常通过命令行界面或图形用户界面（GUI）提供。然而，很少有物种分布建模工具被设计成文档完善、用户友好、灵活且可重复的。在这里，我们介绍了GLOSSA，一个开源的R包和Shiny应用，用于使用物种发生和环境数据进行物种分布建模。GLOSSA的用户友好界面引导用户完成包括数据上传、处理、模型拟合、空间和时间投影以及结果的交互式可视化等步骤。该应用还计算变量重要性，生成与环境变量相关的响应曲线，并进行交叉验证。在核心上，GLOSSA的建模方法基于贝叶斯加法回归树（BART），一种创新的机器学习方法。我们通过三个案例研究展示了GLOSSA的功能和灵活性，涵盖了区域和全球尺度的多种生态场景。除了全面的文档、示例和教程外，这些案例研究还说明了直观的图形界面如何使物种分布建模对广泛受众变得容易使用。GLOSSA作为一种易于使用的工具，提供了直观的界面、详细的文档、灵活的建模和交互式的结果探索和导出选项。此外，其输出可以直接用于告知海洋生态系统模型（MEMs），增强了其在生态研究和应用中的实用性。
英文摘要：
Species distribution models (SDMs) are one of the most common statistical methods to assess species occupancy and geographic distribution patterns. With the increasing complexity of ecological data, many methodological approaches have been developed, often accessible through command-line interfaces or graphical user interfaces (GUIs). However, few species distribution modeling tools are designed to be well-documented, user-friendly, flexible, and reproducible. Here we introduce GLOSSA, an open-source R package and Shiny app designed for species distribution modeling using species occurrence and environmental data. GLOSSA's user-friendly interface guides users through steps including data uploading, processing, model fitting, spatial and temporal projections, and interactive visualization of results. The app also calculates variable importance, generates response curves with environmental variables, and performs cross-validation. At its core, GLOSSA modeling approach is based on Bayesian Additive Regression Trees (BART), an innovative machine learning method. We present the functionality and versatility of GLOSSA through three case studies, addressing a range of ecological scenarios at regional and global scales. Along with comprehensive documentation, examples, and tutorials, these case studies illustrate how an intuitive graphical interface can make species distribution modeling accessible to a broad audience. GLOSSA stands out as an easy-to-use tool for species distribution modeling, providing an intuitive interface, detailed documentation, flexible modeling, and interactive result exploration and export options. Additionally, its outputs can be used directly to inform marine ecosystem models (MEMs), enhancing its utility in ecological research and applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
