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