Importance of accounting for imperfect detection of plants in the estimation of population growth rates

IF 3.1 2区 环境科学与生态学 Q2 ECOLOGY Oikos Pub Date : 2024-09-18 DOI:10.1111/oik.10708
Jorge A. Martínez‐Villegas, Irene Pisanty, Carlos Martorell, Mariana Hernández‐Apolinar, Teresa Valverde, Luisa A. Granados‐Hernández, Mariana Rodríguez‐Sánchez, J. Jaime Zúñiga‐Vega
{"title":"Importance of accounting for imperfect detection of plants in the estimation of population growth rates","authors":"Jorge A. Martínez‐Villegas, Irene Pisanty, Carlos Martorell, Mariana Hernández‐Apolinar, Teresa Valverde, Luisa A. Granados‐Hernández, Mariana Rodríguez‐Sánchez, J. Jaime Zúñiga‐Vega","doi":"10.1111/oik.10708","DOIUrl":null,"url":null,"abstract":"Detection of plant individuals is imperfect. Not accounting for this issue can result in biased estimates of demographic parameters as important as population growth rates. In mobile organisms, a common practice is to explicitly account for detection probability during the estimation of most demographic parameters, but no study in plant populations has examined the consequences of ignoring imperfect detectability on the estimation of population growth rates. The lack of accounting for detection probability occurs because plant demographers have frequently assumed that detection is perfect, and because there is a scarcity of studies that formally compare the performance of estimation methods that incorporate detection probabilities with respect to methods that ignore detectabilities. Based on field data of five plant species and data simulations, we compared the performance of three methods that estimate population growth rates, two that do not estimate detection probabilities (direct counts of individuals and the minimum‐number‐alive method) and the other that explicitly accounts for detection probabilities (temporal symmetry models). Our aims were 1) to estimate detection probabilities, and 2) to evaluate the performance of these three methods by calculating bias, accuracy, and precision in their estimates of population growth rates. Our five plant species had imperfect detection. Estimates of population growth rates that explicitly incorporate detectabilities had better performance (less biased estimates, with higher accuracy and precision) than those obtained with the two methods that do not calculate detection probabilities. In these latter methods, bias increases as detection probability decreases. Our findings highlight the importance of using robust analytical methods that account for detection probability of plants during the estimation of critical demographic parameters such as population growth rates. In this way, estimates of plant population parameters will reliably indicate their actual status and quantitative trends.","PeriodicalId":19496,"journal":{"name":"Oikos","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oikos","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1111/oik.10708","RegionNum":2,"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

Abstract

Detection of plant individuals is imperfect. Not accounting for this issue can result in biased estimates of demographic parameters as important as population growth rates. In mobile organisms, a common practice is to explicitly account for detection probability during the estimation of most demographic parameters, but no study in plant populations has examined the consequences of ignoring imperfect detectability on the estimation of population growth rates. The lack of accounting for detection probability occurs because plant demographers have frequently assumed that detection is perfect, and because there is a scarcity of studies that formally compare the performance of estimation methods that incorporate detection probabilities with respect to methods that ignore detectabilities. Based on field data of five plant species and data simulations, we compared the performance of three methods that estimate population growth rates, two that do not estimate detection probabilities (direct counts of individuals and the minimum‐number‐alive method) and the other that explicitly accounts for detection probabilities (temporal symmetry models). Our aims were 1) to estimate detection probabilities, and 2) to evaluate the performance of these three methods by calculating bias, accuracy, and precision in their estimates of population growth rates. Our five plant species had imperfect detection. Estimates of population growth rates that explicitly incorporate detectabilities had better performance (less biased estimates, with higher accuracy and precision) than those obtained with the two methods that do not calculate detection probabilities. In these latter methods, bias increases as detection probability decreases. Our findings highlight the importance of using robust analytical methods that account for detection probability of plants during the estimation of critical demographic parameters such as population growth rates. In this way, estimates of plant population parameters will reliably indicate their actual status and quantitative trends.
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
在估算种群增长率时考虑植物检测不完善因素的重要性
对植物个体的检测并不完美。不考虑这个问题可能会导致对人口统计参数(如种群增长率)的估计出现偏差。在移动生物中,通常的做法是在估算大多数人口统计参数时明确考虑探测概率,但在植物种群中还没有研究忽视不完全探测性对种群增长率估算的影响。之所以没有考虑探测概率,是因为植物种群统计学家经常假定探测是完美的,也因为很少有研究能正式比较包含探测概率的估算方法与忽略探测概率的方法的性能。基于五个植物物种的实地数据和模拟数据,我们比较了三种估算种群增长率的方法的性能,其中两种方法不估算检测概率(个体直接计数法和最小存活数法),另一种方法明确考虑了检测概率(时间对称模型)。我们的目标是:1)估算检测概率;2)通过计算这三种方法对种群增长率估算的偏差、准确性和精确度来评估其性能。我们的五个植物物种的探测并不完美。与不计算探测概率的两种方法相比,明确包含探测概率的种群增长率估算结果性能更好(估算结果偏差更小、准确度和精确度更高)。在后两种方法中,偏差随着检测概率的降低而增加。我们的研究结果突出表明,在估算种群增长率等关键人口参数时,使用考虑植物检测概率的稳健分析方法非常重要。这样,植物种群参数的估算结果才能可靠地反映其实际状况和数量趋势。
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
Oikos
Oikos 环境科学-生态学
CiteScore
6.20
自引率
5.90%
发文量
152
审稿时长
6-12 weeks
期刊介绍: Oikos publishes original and innovative research on all aspects of ecology, defined as organism-environment interactions at various spatiotemporal scales, so including macroecology and evolutionary ecology. Emphasis is on theoretical and empirical work aimed at generalization and synthesis across taxa, systems and ecological disciplines. Papers can contribute to new developments in ecology by reporting novel theory or critical empirical results, and "synthesis" can include developing new theory, tests of general hypotheses, or bringing together established or emerging areas of ecology. Confirming or extending the established literature, by for example showing results that are novel for a new taxon, or purely applied research, is given low priority.
期刊最新文献
Linking fine‐root diameter across root orders with climatic, biological and edaphic factors in the Northern Hemisphere Do plants respond to multi‐year disturbance rhythms and are we missing the beat? Importance of accounting for imperfect detection of plants in the estimation of population growth rates Landscape structures and stand attributes jointly regulate forest productivity Evolutionary cycles in a model of nestmate recognition
×
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