利用辅助摄影数据纠正长期航空测量中的测量误差

IF 2.7 3区 环境科学与生态学 Q2 ECOLOGY
Ecosphere Pub Date : 2024-08-27 DOI:10.1002/ecs2.4961
Jamie L. Brusa, Matthew T. Farr, Joseph Evenson, Emily Silverman, Bryan Murphie, Thomas A. Cyra, Heather J. Tschaekofske, Kyle A. Spragens, Sarah J. Converse
{"title":"利用辅助摄影数据纠正长期航空测量中的测量误差","authors":"Jamie L. Brusa,&nbsp;Matthew T. Farr,&nbsp;Joseph Evenson,&nbsp;Emily Silverman,&nbsp;Bryan Murphie,&nbsp;Thomas A. Cyra,&nbsp;Heather J. Tschaekofske,&nbsp;Kyle A. Spragens,&nbsp;Sarah J. Converse","doi":"10.1002/ecs2.4961","DOIUrl":null,"url":null,"abstract":"<p>Long-term, large-scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large-scale, long-term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species-specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large-scale, long-term datasets.</p>","PeriodicalId":48930,"journal":{"name":"Ecosphere","volume":"15 8","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ecs2.4961","citationCount":"0","resultStr":"{\"title\":\"Correcting for measurement errors in a long-term aerial survey with auxiliary photographic data\",\"authors\":\"Jamie L. Brusa,&nbsp;Matthew T. Farr,&nbsp;Joseph Evenson,&nbsp;Emily Silverman,&nbsp;Bryan Murphie,&nbsp;Thomas A. Cyra,&nbsp;Heather J. Tschaekofske,&nbsp;Kyle A. Spragens,&nbsp;Sarah J. Converse\",\"doi\":\"10.1002/ecs2.4961\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Long-term, large-scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large-scale, long-term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species-specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large-scale, long-term datasets.</p>\",\"PeriodicalId\":48930,\"journal\":{\"name\":\"Ecosphere\",\"volume\":\"15 8\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ecs2.4961\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecosphere\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ecs2.4961\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecosphere","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ecs2.4961","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

对野生动物种群进行长期、大规模的监测是保护研究和管理不可或缺的一部分。然而,一些传统的监测方案缺乏在数据分析中考虑测量误差来源所需的信息。忽略部分可用性、不完全检测和物种错误识别等测量误差会导致对种群状态和过程的错误描述。考虑测量误差是对种群进行稳健监测的关键,它可以为各种决策提供信息,包括收获、栖息地恢复和物种法律地位的确定。我们开展了一项辅助研究,以共同估算测量误差的组成部分,从而努力追溯性地将美国华盛顿州萨利什海海洋鸟类大规模长期监测计划中的偏差最小化。我们在贝叶斯框架下建立了一个新模型,该模型同时利用人类观察者和照片数据类型来产生必要的估计值,以纠正部分可用性、不完全检测和物种错误识别的影响。在照片识别的所有 31 个物种中,两位观察者都有少计和多计鸟类的情况,但都倾向于少计(观察者在 69.3%-78.9% 的横断面上少计了所有物种的总数)。我们估计了特定物种的校正因子,这些因子可用于校正自 1992 年开始的咸海调查的历史和未来计数。我们新颖的建模框架可应用于其他多物种监测,在这种情况下,可以收集最少的摄影数据,以校正大规模、长期数据集中的测量误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Correcting for measurement errors in a long-term aerial survey with auxiliary photographic data

Correcting for measurement errors in a long-term aerial survey with auxiliary photographic data

Long-term, large-scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large-scale, long-term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species-specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large-scale, long-term datasets.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Ecosphere
Ecosphere ECOLOGY-
CiteScore
4.70
自引率
3.70%
发文量
378
审稿时长
15 weeks
期刊介绍: The scope of Ecosphere is as broad as the science of ecology itself. The journal welcomes submissions from all sub-disciplines of ecological science, as well as interdisciplinary studies relating to ecology. The journal''s goal is to provide a rapid-publication, online-only, open-access alternative to ESA''s other journals, while maintaining the rigorous standards of peer review for which ESA publications are renowned.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信