Adaptive sampling by citizen scientists improves species distribution model performance: A simulation study

IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Methods in Ecology and Evolution Pub Date : 2024-05-26 DOI:10.1111/2041-210X.14355
Thomas Mondain-Monval, Michael Pocock, Simon Rolph, Tom August, Emma Wright, Susan Jarvis
{"title":"Adaptive sampling by citizen scientists improves species distribution model performance: A simulation study","authors":"Thomas Mondain-Monval,&nbsp;Michael Pocock,&nbsp;Simon Rolph,&nbsp;Tom August,&nbsp;Emma Wright,&nbsp;Susan Jarvis","doi":"10.1111/2041-210X.14355","DOIUrl":null,"url":null,"abstract":"<p>\n \n </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"15 7","pages":"1206-1220"},"PeriodicalIF":6.3000,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14355","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Methods in Ecology and Evolution","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/2041-210X.14355","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
公民科学家的适应性采样提高了物种分布模型的性能:模拟研究
志愿记录者通过公民科学产生了大量生物多样性数据,这些数据被用于保护规划和政策决策。非结构化取样,即志愿者想记录什么就记录什么,想在哪里记录就在哪里记录,会导致这些数据在空间上的不均匀性。虽然有很多统计技术可以解释由此产生的偏差,但也有可能通过引导一部分记录者在信息量最大的地点进行采样来改进数据集,这就是所谓的适应性采样。我们利用模拟生态群落研究了适应性采样在改善基于公民科学数据建立的物种分布模型性能方面的潜力。我们根据当前的蝴蝶数据模拟了大不列颠的生态组合,并建立了每个物种的分布模型。然后,我们模拟了基于五种适应性采样方法(一种基于经验的方法,仅用于填补空白;四种基于模型的方法,使用模型输出中的各种测量方法)和一种非适应性方法(继续按照当前模式记录的方法)的新数据采样,并重新运行了物种分布模型。其中,我们还改变了根据适应性取样分配的记录工作的速率。将使用原始数据和适应性取样数据的模型预测结果与真实的物种分布进行比较,以评估每种方法的性能。我们发现,所有适应性取样方法都提高了模型性能,与经验取样方法(即简单的填补空白)相比,基于模型的方法提高最大。所有四种基于模型的自适应采样方法都为模型输出提供了类似的好处。当适应性取样量从无取样量变为 1%取样量时,模型性能的改善幅度最大,这表明只需要记录者行为的少量改变就能改善模型性能。根据模型输出的信息,将志愿记录者引导到最需要记录的地方,可以改善基于公民科学数据建立的物种分布模型,即使对建议地点的吸收率极低。因此,我们的研究结果表明,记录者的适应性采样对现实世界的公民科学数据集是有益的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
11.60
自引率
3.00%
发文量
236
审稿时长
4-8 weeks
期刊介绍: A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas. MEE publishes methodological papers in any area of ecology and evolution, including: -Phylogenetic analysis -Statistical methods -Conservation & management -Theoretical methods -Practical methods, including lab and field -This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual. A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.
期刊最新文献
Cover Picture and Issue Information Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery Spatially explicit predictions using spatial eigenvector maps SimpleMetaPipeline: Breaking the bioinformatics bottleneck in metabarcoding A LiDAR-driven pruning algorithm to delineate canopy drainage areas of stemflow and throughfall drip points
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1