Data from: Group density, disease, and season shape territory size and overlap of social carnivores

Here we provide the R code (as R Markdown documents) used to analyze variables predicting group-level territory size and overlap for Serengeti lions and Yellowstone wolves. We show the structure of the datasets, interpretation of the code, and summaries of the Serengeti lion and Yellowstone wolf metadata. We also provide the code for our generalized additive models (GAM) and model outputs. Specifically, 'Wolf_Datasets_Markdown.doc' demonstrates how we created the final dataset used in our GAMs from the raw locational data for Yellowstone wolves. This is where we parse down the data used, calculate certain network measures (e.g., degree), and estimate spatial measures (e.g., territory size). 'GAM_Markdown.doc' provides the code for our machine-learning imputations and exploration, GAMs and GAM outputs (e.g., model fit), and model selection.本研究提供用于分析塞伦盖蒂狮与黄石狼群体领地规模及领地重叠度预测变量的R代码（以R Markdown文档形式呈现）。本研究同时展示了数据集的结构、代码解读内容，以及塞伦盖蒂狮与黄石狼的元数据汇总信息。我们还提供了广义可加模型（Generalized Additive Models，GAM）的代码与模型输出结果。具体而言，Wolf_Datasets_Markdown.doc展示了我们如何从黄石狼的原始位置数据中生成用于广义可加模型分析的最终数据集；该文档中完成了所用数据的筛选整理、部分网络指标（如节点度）的计算，以及空间指标（如领地规模）的估算工作。GAM_Markdown.doc则提供了本研究所用的机器学习插补与探索性分析、广义可加模型及其输出结果（如模型拟合度）、模型选择相关代码。