具有群体异质性的Jolly-Seber-Tag-Loss模型

Selina Beatriz Gonzalez, L. Cowen
{"title":"具有群体异质性的Jolly-Seber-Tag-Loss模型","authors":"Selina Beatriz Gonzalez, L. Cowen","doi":"10.18357/TAR0120103259","DOIUrl":null,"url":null,"abstract":"Mark-recapture experiments are performed to estimate population parameters such as survival probabilities. Animals are captured, tagged, released, and recaptured at subsequent time periods in order to obtain parameter information. The Jolly-Seber-Tag-Loss (JSTL) model (Cowen & Schwarz, 2006) requires some individuals to be double tagged in order to account for the possibility of animals losing their tags. The Jolly-Seber-Tag-Loss model does not, however, consider the possibility of parameters being different among different groups of individuals, that is, group heterogeneity (for example, males may have higher capture probabilities than females). Our research extends the Jolly-Seber-Tag-Loss model to account for this possibility of group heterogeneity among parameters. We use a Newton-Raphson method to obtain maximum likelihood estimators and R software to create a program that estimates population parameters from tag histories. Our simulation study concludes that when group heterogeneity exists, accounting for this group heterogeneity results in more accurate parameter estimates than the original JSTL model. We present the group heterogeneous JSTL (g-hJSTL) for this purpose.","PeriodicalId":143772,"journal":{"name":"The Arbutus Review","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1969-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The Jolly-Seber-Tag-Loss model with group heterogeneity\",\"authors\":\"Selina Beatriz Gonzalez, L. Cowen\",\"doi\":\"10.18357/TAR0120103259\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mark-recapture experiments are performed to estimate population parameters such as survival probabilities. Animals are captured, tagged, released, and recaptured at subsequent time periods in order to obtain parameter information. The Jolly-Seber-Tag-Loss (JSTL) model (Cowen & Schwarz, 2006) requires some individuals to be double tagged in order to account for the possibility of animals losing their tags. The Jolly-Seber-Tag-Loss model does not, however, consider the possibility of parameters being different among different groups of individuals, that is, group heterogeneity (for example, males may have higher capture probabilities than females). Our research extends the Jolly-Seber-Tag-Loss model to account for this possibility of group heterogeneity among parameters. We use a Newton-Raphson method to obtain maximum likelihood estimators and R software to create a program that estimates population parameters from tag histories. Our simulation study concludes that when group heterogeneity exists, accounting for this group heterogeneity results in more accurate parameter estimates than the original JSTL model. We present the group heterogeneous JSTL (g-hJSTL) for this purpose.\",\"PeriodicalId\":143772,\"journal\":{\"name\":\"The Arbutus Review\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1969-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Arbutus Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18357/TAR0120103259\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Arbutus Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18357/TAR0120103259","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

进行标记-再捕获实验来估计种群参数,如生存概率。为了获得参数信息,在随后的时间段捕获、标记、释放和重新捕获动物。Jolly-Seber-Tag-Loss (JSTL)模型(Cowen & Schwarz, 2006)要求对一些个体进行双重标记,以考虑动物丢失标签的可能性。然而,Jolly-Seber-Tag-Loss模型没有考虑在不同个体群体中参数不同的可能性,即群体异质性(例如,男性可能比女性有更高的捕获概率)。我们的研究扩展了Jolly-Seber-Tag-Loss模型,以解释参数之间群体异质性的可能性。我们使用Newton-Raphson方法来获得最大似然估计器,并使用R软件来创建一个从标签历史中估计总体参数的程序。我们的模拟研究得出结论,当群体异质性存在时,考虑这种群体异质性会导致比原始JSTL模型更准确的参数估计。为此,我们提出了组异构JSTL (g-hJSTL)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Jolly-Seber-Tag-Loss model with group heterogeneity
Mark-recapture experiments are performed to estimate population parameters such as survival probabilities. Animals are captured, tagged, released, and recaptured at subsequent time periods in order to obtain parameter information. The Jolly-Seber-Tag-Loss (JSTL) model (Cowen & Schwarz, 2006) requires some individuals to be double tagged in order to account for the possibility of animals losing their tags. The Jolly-Seber-Tag-Loss model does not, however, consider the possibility of parameters being different among different groups of individuals, that is, group heterogeneity (for example, males may have higher capture probabilities than females). Our research extends the Jolly-Seber-Tag-Loss model to account for this possibility of group heterogeneity among parameters. We use a Newton-Raphson method to obtain maximum likelihood estimators and R software to create a program that estimates population parameters from tag histories. Our simulation study concludes that when group heterogeneity exists, accounting for this group heterogeneity results in more accurate parameter estimates than the original JSTL model. We present the group heterogeneous JSTL (g-hJSTL) for this purpose.
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
自引率
0.00%
发文量
0
×
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