Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado, United States of America.
U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colorado, United States of America.
PLoS One. 2022 Feb 8;17(2):e0263056. doi: 10.1371/journal.pone.0263056. eCollection 2022.
Narrowing the communication and knowledge gap between producers and users of scientific data is a longstanding problem in ecological conservation and land management. Decision support tools (DSTs), including websites or interactive web applications, provide platforms that can help bridge this gap. DSTs can most effectively disseminate and translate research results when producers and users collaboratively and iteratively design content and features. One data resource seldom incorporated into DSTs are species distribution models (SDMs), which can produce spatial predictions of habitat suitability. Outputs from SDMs can inform management decisions, but their complexity and inaccessibility can limit their use by resource managers or policy makers. To overcome these limitations, we present the Invasive Species Habitat Tool (INHABIT), a novel, web-based DST built with R Shiny to display spatial predictions and tabular summaries of habitat suitability from SDMs for invasive plants across the contiguous United States. INHABIT provides actionable science to support the prevention and management of invasive species. Two case studies demonstrate the important role of end user feedback in confirming INHABIT's credibility, utility, and relevance.
缩小科学数据生产者和使用者之间的沟通和知识差距是生态保护和土地管理中长期存在的问题。决策支持工具（DST），包括网站或交互式网络应用程序，为弥合这一差距提供了平台。当生产者和用户共同协作并反复设计内容和功能时，DST 可以最有效地传播和转化研究成果。数据资源很少纳入 DST 的是物种分布模型（SDM），它可以对栖息地适宜性进行空间预测。SDM 的输出可以为管理决策提供信息，但它们的复杂性和难以理解性可能会限制资源管理者或政策制定者的使用。为了克服这些限制，我们提出了入侵物种栖息地工具（INHABIT），这是一种基于网络的新型 DST，使用 R Shiny 构建，用于显示来自美国大陆入侵植物的 SDM 的栖息地适宜性的空间预测和表格摘要。INHABIT 提供了可操作的科学依据，以支持入侵物种的预防和管理。两个案例研究证明了终端用户反馈在确认 INHABIT 的可信度、实用性和相关性方面的重要作用。
