Data for "Movement patterns of foraging common terns breeding in an urban environment in coastal Virginia"

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: [e-mail], 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.pdfAlso 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.12995Common 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: [e-mail], 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)

本数据集包含了对18只在南岛HRBT隧道栖息的普通燕鸥的追踪数据，旨在R统计软件框架内进行数据分析，具体使用momentuHMM软件包。该软件包可在https://cran.r-project.org处免费获取。数据分析和软件包均在R统计框架内完成。如需进一步信息，请联系[e-mail]，参考COTE追踪项目编号# R版本4.3.3“天使蛋糕”。这些数据是对18只普通燕鸥的追踪数据，用于构建和包的详细描述，请参阅相关出版物。此外，请参阅momentuHMM的示例文档：https://cran.r-project.org/web/packages/momentuHMM/vignettes/momentuHMM.pdf。相关文献还包括：McClintock, BT, T Michelot. 2018. momentuHMM: R包用于动物运动的一般隐马尔可夫模型。生态与进化方法 9: 1518–1530. Doi: 10.1111/2041-210X.12995。普通燕鸥追踪可重复性数据：需使用R统计软件，该软件可在https://cran.r-project.org处免费获取。数据分析和软件包均在R统计框架内完成。用于可重复性分析的数据。我们量化了与觅食状态相关的总空间变异性中，由个体内部变异性相对于个体间变异性所解释的比例。我们使用嵌套的广义线性混合效应模型（GLMM）将所有模型分配的觅食位置的空间变异性分解为四个层面的个体内部和个体间变异性的方差成分。我们使用R中的'jagsUI'包调用JAGS来指定此GLMM。对于每个模型，我们生成了来自四个链的50,000次迭代（薄=2）的后验分布，并附加了25,000次迭代的适应和预热期。所使用方法的引用：Wolak, M.E., D.J. Fairbairn, and Y.R. Paulsen. 2012. 估计可重复性的指南。生态与进化方法 3: 129–137。COTE运动研究的分析代码：此信息可在手稿的补充材料中找到。如需进一步信息，请联系[e-mail]，参考COTE追踪项目编号# R版本4.3.3“天使蛋糕”。这些是收集自18只普通燕鸥的追踪数据，它们在南岛HRBT隧道栖息。关于模型和包的详细描述，请参阅相关文献。此外，请参阅momentuHMM的示例文档。R包用于动物运动的一般隐马尔可夫模型。所需包：在运行前安装：install.packages('momentuHMM') install.packages('jagsUI') library(momentuHMM) library(jagsUI)