Geographic principles applied to population dynamics: A spatially interpolated integrated population model

IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Methods in Ecology and Evolution Pub Date : 2024-06-27 DOI:10.1111/2041-210X.14334
Brian G. Prochazka, Peter S. Coates, Shawn T. O'Neil, Shawn P. Espinosa, Cameron L. Aldridge
{"title":"Geographic principles applied to population dynamics: A spatially interpolated integrated population model","authors":"Brian G. Prochazka,&nbsp;Peter S. Coates,&nbsp;Shawn T. O'Neil,&nbsp;Shawn P. Espinosa,&nbsp;Cameron L. Aldridge","doi":"10.1111/2041-210X.14334","DOIUrl":null,"url":null,"abstract":"<p>\n \n </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"15 8","pages":"1394-1407"},"PeriodicalIF":6.3000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14334","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.14334","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
应用于人口动态的地理原理:空间内插综合种群模型
从定量的角度来看,野生动物保护和管理的一个主要障碍是处理与种群估计相关的高度不确定性。综合种群模型(IPMs)可以帮助缓解这一挑战,但由于获取所需数据集所带来的经济和后勤负担,这些模型往往局限于狭窄的空间或时间窗口。为了扩大 IPM 实际应用的时空范围,我们开发了一种新方法,利用空间自相关的地理原理来表达取样地点和未取样地点之间的人口相关性。我们利用来自数据信息地点的参数估计,对未采样地点的人口统计参数进行内插。利用联合似然法和当地记录的计数数据("便宜 "且分布广泛)对内插法过程中产生的误差进行了校正。我们使用模拟数据和 "留空交叉验证"(LOOCV)技术评估了空间内插 IPM(SIIPM)在不同空间自相关水平下的精度和准确性。传统的 IPM 和状态空间模型(SSM)被拟合到相同的模拟数据集上,以便对新方法进行比较评估。在最后的实证演示中,我们将 SIIPM 与 2013-2021 年间从美国内华达州大松鸡(Centrocercus urophasianus; sag-grouse)种群收集的数据进行了拟合。在拟合具有中度到高度空间自相关性的数据时,SIIPMs 的表现优于传统 IPMs。在中等自相关水平下,参数估计的平均改进率分别为:存活率13.6%、招募率65.3%和种群变化率23.7%()。当空间自相关性较低时,在与取样地点地理位置较近(67 千米)的区域,SIIPM 的性能仍优于当代方法。在低自相关性-近距离情况下,我们观察到 SIIPM 参数比当代模型精确 30.8%(招募)、32.5%(IPM 比较)和 54.0%(SSM 比较)。在比较大地域范围内的种群动态时,通常会假设空间自相关性,但很少进行测试。我们证明,当将模型推断外推到有长期监测数据的种群之外时,SIIPMs 可以提高物种生命率估计的精确度。具体到松鸡,这些结果支持了以前的结论,即种群动态的大尺度空间自相关性和以前在较小尺度上记录的繁殖-生存权衡。
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