Data and code for "Pollen wars: Explosive pollination removes pollen deposited from previously visited flowers

This data consist of 02 data files, 01 code script, and this README document, with the following data and code filenames and variables Data files and variables1. [red flower experiment.csv] [Date: the date the data was taken; Flower number: the flower identity; labelled Pollen count on beak: number of pollen grains placed on hummingbird’s bill; Total unlabelled pollen grains on beak: number of unlabelled pollen grains on the hummingbird’s bill after visit; labelled pollen on flower keel: number of pollen grains on flower keel after visit; labelled pollen on petals: number of labelled pollen on petals after visit; labelled pollen on flower hairs: number of labelled pollen on flower hairs after visit; Before or After treatment: whether the pollen grains were counted before or after floral visit; Treatment: whether the visit was done on triggered or untriggered flower; Beak Photo number: photo identity of the bill; Keel photo number: photo identity for the keel (none was taken); hair photo number: photo identity for the floral hairs (none was taken); comment: any observation on the experiment; Labelled grains transferred to stigma: number of labelled pollen grains on the stigma after explosion (only one data point); unlabelled grains transferred to stigma: number of unlabelled pollen grains on the stigma after explosion (only one data point)]. 2. 2. [explosion_data.csv] [Flower number: the flower identity; Before count: number of pollen grains before floral explosion; After Count: number of pollen grains after explosion; Before Minus after: the subtraction of the last two values; % pollen removed: percentage of pollen grains removed by the explosion; Proportion pollen removed: proportion of pollen grains removed by the explosion; % removed (arcsin root transformed): arcsin root transformation for the last values; Total unlabelled pollen grains on beak: total number of pollen grains counted on hummingbird’s bill; % removed (arcsin root transformed): arcsin root transformation for the percentage of pollen removed]. Code scripts and workflow[script_analysis_Hypenea.R: code for data analysis]1. libraries used on the analysis;2. data loading and processing for explosion analysis;3. modelling; checking model adjustment; anova table; estimation of marginal means; getting predicted values by the model.4. plotting figure;5. data loading and processing for pollen removal;6. modelling; checking model adjustment; anova table; getting predicted values by the model.7. plotting figure; SOFTWARE VERSIONS All the statistical analyses were run in R environment version 4.3.1 (R Development Core Team, 2023) using the default and the following packages: glmmTMB (Brooks et al., 2017), emmeans (Russell, 2022) and car (Fox & Weisberg, 2019). Residual dispersion around the fitted models was checked using Dharma package (Hartig, 2022). REFERENCESBrooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Mächler, M., and Bolker, B. M. 2017. glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. The R Journal, 9(2), 378-400. http://dx.doi.org/10.32614/RJ-2017-066 Fox, J., and Weisberg, S. 2019. An {R} Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. URL: https://socialsciences.mcmaster.ca/jfox/Books/Companion/ Hartig, F. 2022. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. URL https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html R Development Core Team. 2023. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. URL https://www.r-project.org/ Russell, V. L. 2022. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.7.4-1. https://CRAN.R-project.org/package=emmeans本数据集包含2份数据文件、1份代码脚本以及本README文档，以下为相关数据与代码文件名称及变量说明。 1. [红花实验.csv（red flower experiment.csv）] 变量说明如下： - Date：数据采集日期 - Flower number：花朵编号 - labelled Pollen count on beak：蜂鸟喙部标记花粉粒数 - Total unlabelled pollen grains on beak：访花后蜂鸟喙部未标记花粉粒总数 - labelled pollen on flower keel：访花后花龙骨瓣上的标记花粉粒数 - labelled pollen on petals：访花后花瓣上的标记花粉粒数 - labelled pollen on flower hairs：访花后花毛上的标记花粉粒数 - Before or After treatment：花粉计数是在花访前还是访后进行 - Treatment：访花对象为触发型还是未触发型花朵 - Beak Photo number：喙部照片编号 - Keel photo number：龙骨瓣照片编号（未拍摄） - hair photo number：花毛照片编号（未拍摄） - comment：实验相关观测记录 - Labelled grains transferred to stigma：花爆开展后柱头上的标记花粉粒数（仅1条数据） - unlabelled grains transferred to stigma：花爆开展后柱头上的未标记花粉粒数（仅1条数据） 2. [爆开展数据.csv（explosion_data.csv）] 变量说明如下： - Flower number：花朵编号 - Before count：花爆开展前的花粉粒数 - After Count：花爆开展后的花粉粒数 - Before Minus after：前两项数值的差值 - % pollen removed：爆开展移除的花粉粒百分比 - Proportion pollen removed：爆开展移除的花粉粒占比 - % removed (arcsin root transformed)：花粉移除百分比的反正弦平方根转换值 - Total unlabelled pollen grains on beak：蜂鸟喙部统计的未标记花粉粒总数 - % removed (arcsin root transformed)：花粉移除百分比的反正弦平方根转换值 ### 代码脚本与工作流程 [script_analysis_Hypenea.R：数据分析代码] 工作流程如下： 1. 分析所用的R包加载 2. 爆开展分析的数据加载与预处理 3. 模型构建、模型适配性检验、方差分析表生成、边际均值估算、获取模型预测值 4. 绘图 5. 花粉移除分析的数据加载与预处理 6. 模型构建、模型适配性检验、方差分析表生成、边际均值估算、获取模型预测值 7. 绘图 ### 软件版本 所有统计分析均在R 4.3.1版本环境（R开发核心团队，2023）中完成，使用了默认设置及以下R包：glmmTMB（Brooks等，2017）、emmeans（Russell，2022）与car（Fox & Weisberg，2019）。模型拟合后的残差离散程度通过DHARMa包（Hartig，2022）进行检验。 ### 参考文献 1. Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Mächler, M.及Bolker, B. M.，2017。glmmTMB：兼顾速度与灵活性的零膨胀广义线性混合模型包。《R期刊》，9(2)，378-400。http://dx.doi.org/10.32614/RJ-2017-066 2. Fox, J.及Weisberg, S.，2019。《应用回归R指南》（第三版）。加利福尼亚州千橡市：Sage出版社。网址：https://socialsciences.mcmaster.ca/jfox/Books/Companion/ 3. Hartig, F.，2022。DHARMa：分层（多水平/混合）回归模型残差诊断。网址：https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html 4. R开发核心团队，2023。R：统计计算语言与环境。奥地利维也纳：R统计计算基金会。网址：https://www.r-project.org/ 5. Russell, V. L.，2022。emmeans：估计边际均值，又称最小二乘均值。R包版本1.7.4-1。https://CRAN.R-project.org/package=emmeans