Suppr超能文献

COVERater - 一款免费应用程序,用于培训研究人员准确估算陆地和水生生态系统中的物种覆盖率。

COVERater-A Free Application for Training Researchers to Accurately Estimate Species Cover in Terrestrial and Aquatic Ecosystems.

作者信息

Cruickshank Madelon M, Moles Angela T, Debono Samuel A, Xirocostas Zoe A

机构信息

Evolution & Ecology Research Centre, School of Biological, Earth and Environmental Sciences UNSW Sydney Sydney New South Wales Australia.

Pegleg Software (Peglegsoftware.com.au) Sydney New South Wales Australia.

出版信息

Ecol Evol. 2024 Oct 16;14(10):e70447. doi: 10.1002/ece3.70447. eCollection 2024 Oct.

Abstract

Visual estimates of cover are widely used among ecologists, from describing vegetation communities to tracking and monitoring species' abundance. However, despite the known bias associated with visual estimates, no standardised training is available to improve these measurements. We developed a free online training tool, the COVERater, that effectively teaches users to visually estimate the percent cover of species in a variety of ecosystems (including alpine heath, arid lands, coral reefs, temperate reefs and wetlands). Prior to training, users with prior professional experience estimated species cover to an average inaccuracy of 5.2%, while users with no experience estimated cover to an average inaccuracy of 7.6%. COVERater training took an average of 31 min and 68 images, and reduced the estimate inaccuracy of users with no prior experience to 5.2%. There was no significant loss of estimate accuracy over 100 days following training. The COVERater can be used anywhere in the world, by data collectors of all experience levels, for projects spanning all spatial scales. By providing researchers with standardised training, our application can reduce variation in cover estimates that arise from human biases, allowing for comparable estimates across global collaborative projects and data syntheses. We encourage all relevant scientists to include COVERater training in their protocols to quantify cover with greater accuracy, improve the veracity of their results and make better inferences about our biosphere.

摘要

在生态学家中,从描述植被群落到跟踪和监测物种丰度,视觉估计盖度的方法被广泛使用。然而,尽管已知视觉估计存在偏差,但却没有标准化的培训来改进这些测量。我们开发了一种免费的在线培训工具COVERater,它能有效地教会用户在各种生态系统(包括高山石南荒原、干旱地区、珊瑚礁、温带礁石和湿地)中视觉估计物种的盖度百分比。在培训之前,有专业经验的用户估计物种盖度的平均误差为5.2%,而没有经验的用户估计盖度的平均误差为7.6%。COVERater培训平均耗时31分钟,使用68张图像,并将没有经验的用户的估计误差降低到5.2%。在培训后的100天内,估计准确性没有显著下降。COVERater可被世界各地所有经验水平的数据收集者用于涵盖所有空间尺度的项目。通过为研究人员提供标准化培训,我们的应用程序可以减少因人为偏差而产生的盖度估计差异,从而在全球合作项目和数据综合中实现可比的估计。我们鼓励所有相关科学家在其方案中纳入COVERater培训,以便更准确地量化盖度,提高结果的准确性,并对我们的生物圈做出更好的推断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f6/11483595/b5a700706683/ECE3-14-e70447-g002.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验