当观鸟热点变得太热:野火相关干扰对公民科学数据时空偏差的地理评估

IF 4.6 2区 环境科学与生态学 Q1 BIODIVERSITY CONSERVATION
Edwin A. Jacobo, Jeffrey A. Manning
{"title":"当观鸟热点变得太热:野火相关干扰对公民科学数据时空偏差的地理评估","authors":"Edwin A. Jacobo,&nbsp;Jeffrey A. Manning","doi":"10.1111/ddi.70021","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aim</h3>\n \n <p>Long-term monitoring is critical for ecology and conservation, especially as non-stationary climatic conditions increase. Citizen science projects offer long-term georeferenced data from thousands of observers across diverse geographic areas. Despite the attraction of these datasets for biogeographical research and conservation planning, data collection commonly lacks standardised probabilistic sampling, which can increase observer bias, decrease precision of parameter estimates, and increase risk of spurious results when using the associated species data. Additionally, environmental disturbance may affect observer behaviour, confounding observed patterns in species responses. We aimed to test the effects of wildfire disturbance on observer biases in locality selection and return rates by citizen scientists registered with eBird, a globally available bird observation database.</p>\n </section>\n \n <section>\n \n <h3> Location</h3>\n \n <p>Western USA.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We used a long-term (10-year) dataset of 47,662 localities from 1788 eBird observers to calculate resource selection functions and explain observer locality selection as a function of wildfire and non-fire-related environmental covariates. We calculated spatiotemporally explicit covariates from the Monitoring Trends in Burn Severity program and also developed generalised linear mixed models to predict the probability of observers returning to localities in response to fire and non-fire variables.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Our results show that fire characteristics predicted locality selection and the probability of returning to a locality. Closer, more recent, larger and more severe fires showed the greatest effects on spatiotemporal patterns of observer sampling bias. Other non-fire-related variables related to locality attractiveness, land use, convenience and accessibility were also important.</p>\n </section>\n \n <section>\n \n <h3> Main Conclusions</h3>\n \n <p>Our results demonstrate that landscape disturbance introduces spatiotemporal biases in citizen scientist locality selection and revisitation. Researchers using citizen science data can follow our modelling approach to quantify disturbance-related observer sampling biases and estimate bias-corrected parameters necessary for ecological studies. Without this, observer biases inherent in these data can lead to increased bias, decreased precision in parameter estimates and spurious results. We propose recommendations to enhance the value of citizen science data for biological monitoring and conservation.</p>\n </section>\n </div>","PeriodicalId":51018,"journal":{"name":"Diversity and Distributions","volume":"31 4","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ddi.70021","citationCount":"0","resultStr":"{\"title\":\"When Birding Hotspots Get Too Hot: A Geographic Evaluation of Wildfire-Related Disturbance on Spatiotemporal Biases in Citizen Science Data\",\"authors\":\"Edwin A. Jacobo,&nbsp;Jeffrey A. Manning\",\"doi\":\"10.1111/ddi.70021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Aim</h3>\\n \\n <p>Long-term monitoring is critical for ecology and conservation, especially as non-stationary climatic conditions increase. Citizen science projects offer long-term georeferenced data from thousands of observers across diverse geographic areas. Despite the attraction of these datasets for biogeographical research and conservation planning, data collection commonly lacks standardised probabilistic sampling, which can increase observer bias, decrease precision of parameter estimates, and increase risk of spurious results when using the associated species data. Additionally, environmental disturbance may affect observer behaviour, confounding observed patterns in species responses. We aimed to test the effects of wildfire disturbance on observer biases in locality selection and return rates by citizen scientists registered with eBird, a globally available bird observation database.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Location</h3>\\n \\n <p>Western USA.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We used a long-term (10-year) dataset of 47,662 localities from 1788 eBird observers to calculate resource selection functions and explain observer locality selection as a function of wildfire and non-fire-related environmental covariates. We calculated spatiotemporally explicit covariates from the Monitoring Trends in Burn Severity program and also developed generalised linear mixed models to predict the probability of observers returning to localities in response to fire and non-fire variables.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Our results show that fire characteristics predicted locality selection and the probability of returning to a locality. Closer, more recent, larger and more severe fires showed the greatest effects on spatiotemporal patterns of observer sampling bias. Other non-fire-related variables related to locality attractiveness, land use, convenience and accessibility were also important.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Main Conclusions</h3>\\n \\n <p>Our results demonstrate that landscape disturbance introduces spatiotemporal biases in citizen scientist locality selection and revisitation. Researchers using citizen science data can follow our modelling approach to quantify disturbance-related observer sampling biases and estimate bias-corrected parameters necessary for ecological studies. Without this, observer biases inherent in these data can lead to increased bias, decreased precision in parameter estimates and spurious results. We propose recommendations to enhance the value of citizen science data for biological monitoring and conservation.</p>\\n </section>\\n </div>\",\"PeriodicalId\":51018,\"journal\":{\"name\":\"Diversity and Distributions\",\"volume\":\"31 4\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ddi.70021\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diversity and Distributions\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/ddi.70021\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIODIVERSITY CONSERVATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diversity and Distributions","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/ddi.70021","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIODIVERSITY CONSERVATION","Score":null,"Total":0}
引用次数: 0

摘要

目的长期监测对生态和保护至关重要,特别是随着非平稳气候条件的增加。公民科学项目提供了来自不同地理区域的数千名观测者的长期地理参考数据。尽管这些数据集对生物地理研究和保护规划具有吸引力,但数据收集通常缺乏标准化的概率抽样,这可能会增加观察者的偏差,降低参数估计的精度,并增加使用相关物种数据时产生虚假结果的风险。此外,环境干扰可能影响观察者的行为,混淆物种反应中观察到的模式。通过在eBird(一个全球可用的鸟类观测数据库)注册的公民科学家,我们旨在测试野火干扰对观测者在地点选择和返回率方面的偏差的影响。地点:美国西部。方法利用1788名eBird观测者的47,662个地点的长期(10年)数据集计算资源选择函数,并解释观测者地点选择作为野火和非火灾相关环境协变量的函数。我们从烧伤严重程度监测趋势项目中计算了时空显式协变量,并开发了广义线性混合模型来预测观察者在火灾和非火灾变量下返回地点的概率。结果研究结果表明,火灾特征预测了局部选择和返回局部的概率。更近、更近、更大、更严重的火灾对观察者抽样偏差的时空模式影响最大。与地点吸引力、土地利用、便利性和可达性有关的其他与火灾无关的变量也很重要。研究结果表明,景观干扰在公民科学家的地点选择和访问中引入了时空偏差。使用公民科学数据的研究人员可以遵循我们的建模方法来量化与干扰相关的观察者抽样偏差,并估计生态研究所需的偏差校正参数。如果没有这一点,这些数据中固有的观察者偏差可能导致偏差增加,参数估计精度降低和虚假结果。我们提出了提高公民科学数据在生物监测和保护中的价值的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

When Birding Hotspots Get Too Hot: A Geographic Evaluation of Wildfire-Related Disturbance on Spatiotemporal Biases in Citizen Science Data

When Birding Hotspots Get Too Hot: A Geographic Evaluation of Wildfire-Related Disturbance on Spatiotemporal Biases in Citizen Science Data

Aim

Long-term monitoring is critical for ecology and conservation, especially as non-stationary climatic conditions increase. Citizen science projects offer long-term georeferenced data from thousands of observers across diverse geographic areas. Despite the attraction of these datasets for biogeographical research and conservation planning, data collection commonly lacks standardised probabilistic sampling, which can increase observer bias, decrease precision of parameter estimates, and increase risk of spurious results when using the associated species data. Additionally, environmental disturbance may affect observer behaviour, confounding observed patterns in species responses. We aimed to test the effects of wildfire disturbance on observer biases in locality selection and return rates by citizen scientists registered with eBird, a globally available bird observation database.

Location

Western USA.

Methods

We used a long-term (10-year) dataset of 47,662 localities from 1788 eBird observers to calculate resource selection functions and explain observer locality selection as a function of wildfire and non-fire-related environmental covariates. We calculated spatiotemporally explicit covariates from the Monitoring Trends in Burn Severity program and also developed generalised linear mixed models to predict the probability of observers returning to localities in response to fire and non-fire variables.

Results

Our results show that fire characteristics predicted locality selection and the probability of returning to a locality. Closer, more recent, larger and more severe fires showed the greatest effects on spatiotemporal patterns of observer sampling bias. Other non-fire-related variables related to locality attractiveness, land use, convenience and accessibility were also important.

Main Conclusions

Our results demonstrate that landscape disturbance introduces spatiotemporal biases in citizen scientist locality selection and revisitation. Researchers using citizen science data can follow our modelling approach to quantify disturbance-related observer sampling biases and estimate bias-corrected parameters necessary for ecological studies. Without this, observer biases inherent in these data can lead to increased bias, decreased precision in parameter estimates and spurious results. We propose recommendations to enhance the value of citizen science data for biological monitoring and conservation.

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
Diversity and Distributions
Diversity and Distributions 环境科学-生态学
CiteScore
8.90
自引率
4.30%
发文量
195
审稿时长
8-16 weeks
期刊介绍: Diversity and Distributions is a journal of conservation biogeography. We publish papers that deal with the application of biogeographical principles, theories, and analyses (being those concerned with the distributional dynamics of taxa and assemblages) to problems concerning the conservation of biodiversity. We no longer consider papers the sole aim of which is to describe or analyze patterns of biodiversity or to elucidate processes that generate biodiversity.
×
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