{"title":"从年龄到死亡数据的灵活参数建模:一个混合线性回归框架","authors":"E. Rouby, V. Ridoux, M. Authier","doi":"10.1002/1438-390x.12069","DOIUrl":null,"url":null,"abstract":"Correspondence Etienne Rouby, Centre d'études Biologiques de Chizé, UMR 7372, Villiersen-Bois, France. Email: etienne.rouby@univ-lr.fr Abstract Many long-lived vertebrate species are under threat in the Anthropocene, but their conservation is hampered by a lack of demographic information to assess population long-term viability. When longitudinal studies (e.g., Capture-MarkRecapture design) are not feasible, the only available data may be cross-sectional, for example, stranding for marine mammals. Survival analysis deals with age at death (i.e., time to event) data and allows to estimate survivorship and hazard rates assuming that the cross-sectional sample is representative. Accommodating a bathtub-shaped hazard, as expected in wild populations, was historically difficult and required specific models. We identified a simple linear regression model with individual frailty that can fit a bathtub-shaped hazard, take into account covariates, allow goodness-of-fit assessments and give accurate estimates of survivorship in realistic settings. We first conducted a Monte Carlo study and simulated age at death data to assess the accuracy of estimates with respect to sample size. Secondly, we applied this framework on a handful of case studies from published studies on marine mammals, a group with many threatened and data-deficient species. We found that our framework is flexible and accurate to estimate survivorship with a sample size of 300. This approach is promising for obtaining important demographic information on data-poor species.","PeriodicalId":54597,"journal":{"name":"Population Ecology","volume":" ","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/1438-390x.12069","citationCount":"1","resultStr":"{\"title\":\"Flexible parametric modeling of survival from age at death data: A mixed linear regression framework\",\"authors\":\"E. Rouby, V. Ridoux, M. Authier\",\"doi\":\"10.1002/1438-390x.12069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Correspondence Etienne Rouby, Centre d'études Biologiques de Chizé, UMR 7372, Villiersen-Bois, France. Email: etienne.rouby@univ-lr.fr Abstract Many long-lived vertebrate species are under threat in the Anthropocene, but their conservation is hampered by a lack of demographic information to assess population long-term viability. When longitudinal studies (e.g., Capture-MarkRecapture design) are not feasible, the only available data may be cross-sectional, for example, stranding for marine mammals. Survival analysis deals with age at death (i.e., time to event) data and allows to estimate survivorship and hazard rates assuming that the cross-sectional sample is representative. Accommodating a bathtub-shaped hazard, as expected in wild populations, was historically difficult and required specific models. We identified a simple linear regression model with individual frailty that can fit a bathtub-shaped hazard, take into account covariates, allow goodness-of-fit assessments and give accurate estimates of survivorship in realistic settings. We first conducted a Monte Carlo study and simulated age at death data to assess the accuracy of estimates with respect to sample size. Secondly, we applied this framework on a handful of case studies from published studies on marine mammals, a group with many threatened and data-deficient species. We found that our framework is flexible and accurate to estimate survivorship with a sample size of 300. This approach is promising for obtaining important demographic information on data-poor species.\",\"PeriodicalId\":54597,\"journal\":{\"name\":\"Population Ecology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2020-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1002/1438-390x.12069\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Population Ecology\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1002/1438-390x.12069\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Population Ecology","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1002/1438-390x.12069","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECOLOGY","Score":null,"Total":0}
Flexible parametric modeling of survival from age at death data: A mixed linear regression framework
Correspondence Etienne Rouby, Centre d'études Biologiques de Chizé, UMR 7372, Villiersen-Bois, France. Email: etienne.rouby@univ-lr.fr Abstract Many long-lived vertebrate species are under threat in the Anthropocene, but their conservation is hampered by a lack of demographic information to assess population long-term viability. When longitudinal studies (e.g., Capture-MarkRecapture design) are not feasible, the only available data may be cross-sectional, for example, stranding for marine mammals. Survival analysis deals with age at death (i.e., time to event) data and allows to estimate survivorship and hazard rates assuming that the cross-sectional sample is representative. Accommodating a bathtub-shaped hazard, as expected in wild populations, was historically difficult and required specific models. We identified a simple linear regression model with individual frailty that can fit a bathtub-shaped hazard, take into account covariates, allow goodness-of-fit assessments and give accurate estimates of survivorship in realistic settings. We first conducted a Monte Carlo study and simulated age at death data to assess the accuracy of estimates with respect to sample size. Secondly, we applied this framework on a handful of case studies from published studies on marine mammals, a group with many threatened and data-deficient species. We found that our framework is flexible and accurate to estimate survivorship with a sample size of 300. This approach is promising for obtaining important demographic information on data-poor species.
期刊介绍:
Population Ecology, formerly known as Researches on Population Ecology launched in Dec 1952, is the official journal of the Society of Population Ecology. Population Ecology publishes original research articles and reviews (including invited reviews) on various aspects of population ecology, from the individual to the community level. Among the specific fields included are population dynamics and distribution, evolutionary ecology, ecological genetics, theoretical models, conservation biology, agroecosystem studies, and bioresource management. Manuscripts should contain new results of empirical and/or theoretical investigations concerning facts, patterns, processes, mechanisms or concepts of population ecology; those purely descriptive in nature are not suitable for this journal. All manuscripts are reviewed anonymously by two or more referees, and the final editorial decision is made by the Chief Editor or an Associate Editor based on the referees'' evaluations.