Data for "Movement patterns of foraging common terns breeding in an urban environment in coastal Virginia"
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Common tern tracking data for analysis in R via the momentumHMM package:*REQUIRES R statistical software which is freely available here: https://cran.r-project.org. The package and data analysis are all within the R statistical framework.For information: dcatlin@vt.edu, reference COTE tracking project # R version 4.3.3 "Angel Food Cake".These are tracking data collected from 18 common terns that were nesting on the South Island of the HRBT tunnel. For full description of the model and the package, see the publication.Also see momentuHMM vignette: https://cran.r-project.org/web/packages/momentuHMM/vignettes/momentuHMM.pdf Also see: McClintock, BT, T Michelot. 2018. momentuHMM: R package for generalized hidden Markov models of animal movement. Methods in Ecology and Evolution 9: 1518–1530. Doi: 10.1111/2041-210X.12995 Common tern tracking repeatability data:*REQUIRES R statistical software which is freely available here: https://cran.r-project.org. The package and data analysis are all within the R statistical framework.Data used for repeatability analysis. We quantified the proportion of the total variation in space associated with the Foraging state that was explained by within-individual level variation relative to among-individual variation. We used a nested, generalized linear mixed effects model (GLMM) to decompose the spatial variance of all model-assigned foraging locations into variance components attributed to variation within and among individuals at four levels.We specified this GLMM within R with the package ‘jagsUI’ to call JAGS. For each model, we generated posterior distributions from four chains of 50,000 iterations (thin = 2) with additional adapt and burn-in periods of 25,000 iterations each.Citation for the method used: Wolak, M.E., D.J. Fairbairn, and Y.R. Paulsen. 2012. Guidelines for estimating repeatability. Methods in Ecology and Evolution 3: 129–137. Analysis code for COTE movement study:This information can be found as supplemental materials to the manuscript.For information: dcatlin@vt.edu, reference COTE tracking project # R version 4.3.3 "Angel Food Cake".These are tracking data collected from 18 common terns that were nesting on the South Island of the HRBT tunnel.Full description of the model and the package. Also see momentuHMM vignette.R package for generalized hidden Markov models of animal movement.Required packages. Install prior to running: install.packages('momentuHMM') install.packages('jagsUI')library(momentuHMM) library(jagsUI)
用于通过momentumHMM包在R软件中开展分析的普通燕鸥追踪数据集*本分析需使用R统计软件，可从https://cran.r-project.org免费获取。本研究的包与数据分析均基于R统计框架。咨询邮箱：dcatlin@vt.edu，参考编号：COTE追踪项目，R版本4.3.3 "Angel Food Cake"（天使蛋糕）。 本数据集采集自HRBT隧道南岛筑巢的18只普通燕鸥的追踪数据。如需了解模型与包的完整说明，请参阅相关发表文献。另可参阅momentuHMM包的vignette文档：https://cran.r-project.org/web/packages/momentuHMM/vignettes/momentuHMM.pdf。 参考文献：McClintock, BT, T Michelot. 2018. momentuHMM：用于动物移动广义隐马尔可夫模型的R包. 生态学与进化方法 9: 1518–1530. Doi: 10.1111/2041-210X.12995 普通燕鸥追踪重复性数据集*本分析需使用R统计软件，可从https://cran.r-project.org免费获取。本研究的包与数据分析均基于R统计框架。 本数据集用于重复性分析。我们量化了空间总变异中，由个体内水平变异相较于个体间变异所解释的觅食状态占比。我们采用嵌套式广义线性混合效应模型（generalized linear mixed effects model, GLMM），将所有模型分配的觅食定位点的空间方差分解为四个层级下个体内与个体间变异对应的方差组分。 我们在R环境中通过‘jagsUI’包调用JAGS实现该广义线性混合效应模型。针对每个模型，我们通过4条链、每条链50000次迭代（薄化间隔thin=2）生成后验分布，且每条链附带25000次迭代的自适应期与燃烧期。 所用方法的参考文献：Wolak, M.E., D.J. Fairbairn, 和 Y.R. Paulsen. 2012. 重复性估计指南. 生态学与进化方法 3: 129–137. COTE移动研究分析代码：该信息可在论文的补充材料中获取。咨询邮箱：dcatlin@vt.edu，参考编号：COTE追踪项目，R版本4.3.3 "Angel Food Cake"（天使蛋糕）。 本数据集采集自HRBT隧道南岛筑巢的18只普通燕鸥的追踪数据。如需了解模型与包的完整说明，请参阅相关发表文献与momentuHMM包的vignette文档。 本包为用于动物移动广义隐马尔可夫模型的R工具。 所需安装的前置依赖包： install.packages('momentuHMM') install.packages('jagsUI') 加载包代码： library(momentuHMM) library(jagsUI)
