The contribution of pollinator-mediated vs pollinator-independent interactions to plant reproduction
Data files
Dec 05, 2024 version files 64.01 KB
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clarkia_data_2017_kingsnake.csv
21.72 KB
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codeToPublish_16Sep.R
34.47 KB
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README.md
7.81 KB
Abstract
Pollinator-mediated and pollinator-independent interactions both affect plant reproductive success but are often studied independently. Evaluating the separate and cumulative effect of both types of interactions is necessary to understand population dynamics and species coexistence. Here we ask how interactions during growth and flowering contribute to pollinator-mediated and pollinator-independent density dependence in components of reproduction and total fecundity in communities of Clarkia species. Using experimental plots embedded in natural communities of forbs and grasses, we examine the response of flower number, ovule number per flower, seed set (% of ovules in a fruit that are filled seed) and total fecundity (total seed number per plant) of focal plants of four Clarkia species to varying densities of background Clarkia, forbs and grasses, with (control) or without supplemental pollination of focal flowers. A comparison of seed set and total fecundity between control and pollen-supplemented flowers provided an estimate of pollen limitation to reproduction, which was largely pollinator mediated in this study. Forbs and grasses exerted a density-dependent, pollinator-independent competitive effect on all reproductive components and on total fecundity. By contrast, interactions between focal and background Clarkia were entirely density-independent, pollinator-mediated, and affected only seed set. Pollinator-mediated effects on seed set between pairs of focal and background Clarkia species were largely competitive, and in line with the known pollination biology of Clarkia species. Our results point to the importance of evaluating the pollinator-mediated interactions in the context of natural communities, and that pollinator-mediated interactions between Clarkia species, while strong, are not likely to affect population dynamics at the scale of the small local neighborhood but may do so at larger spatial and/or temporal scales.
README: The contribution of pollinator-mediated vs pollinator-independent interactions to plant reproduction
https://doi.org/10.5061/dryad.k3j9kd56r
Data collected as a part of an experiment testing how pollinator interactions affect plant reproduction and code of the analyses performed.
Description of the data and file structure
Data on components of reproduction from focal plants of four Clarkia species in experimental plots with varying densities of each of the background Clarkia species. Each row in the datafile corresponds to a fruit harvested from a focal plant and includes information on flower number for the focal plant, whether the fruit received supplemental pollination or not, ovule number of the fruit, seed number of the fruit, and whether the fruit was damaged by herbivores. Column variables are as follows:
Variable | Description and measurement units |
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block | ID number of experimental block (81 to 120); identity of background Clarkia species (bg_sp) varies across blocks |
plot | ID number of plot within block; background Clarkia seeding density and biomass (bg_wtC) vary at the plot level |
focal_sp | ID of focal Clarkia species – C: cylindrica; S: speciosa; U: unguiculata; X: xantiana |
bg_sp | ID of background Clarkia species – same as focal species |
bg_Cwt | Dry biomass of background Clarkia plants (g) |
n_grass | Number of grass individuals within 5 cm radius of focal plant |
n_forb | Number of non-Clarkia forb individuals within 5 cm radius of focal plant |
treatment | Pollination treatment applied to flower – cont_sd: open pollinated flower; supp_seed: supplemental pollen applied to flower stigma |
n_flower | Count of number of flowers on focal plant |
n_ovule | Count of number of ovules in flower; NA for missing values; ovule number equals the sum of unfertilized ovules, number of aborted seed and number of filled seed (n_seed) |
aver_n_ovule | Average number of ovules across both treatment fruits on focal plant, if present; otherwise, equal to the ovule number of the single treatment fruit. Unit is in ovules. |
n_ovule_assigned | Indicator variable showing whether the ovule number was assigned (1), rather than directly counted (0). Ovule number was assigned when fruit was too shriveled to directly count ovules. See methods for explanation; NA for missing values |
n_seed | Count of number of filled seed in fruits; NA for missing values; seed set is the proportion of ovules that are filled seed. |
herbivory | Variable showing whether fruit was undamaged (0) or undamaged (1); fruits with no seed are excluded (NA) because herbivores do not attack empty fruits |
trial | Variable indicating whether fruit was subject to herbivory (1); NA for missing fruits |
Description of the code file
Code was run on R version 4.4.1 (2024-06-24) - "Race for Your Life"
R Core Team (2024). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical
Computing, Vienna, Austria. https://www.R-project.org/.
Packages (and versions) are cited below.
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Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. R package
version 1.1.4, https://CRAN.R-project.org/package=dplyr.
Wickham H, Vaughan D, Girlich M (2024). _tidyr: Tidy Messy Data_. R package version 1.3.1,
<https://CRAN.R-project.org/package=tidyr>.
Statistical modeling and model analysis:
Hartig F (2022). _DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models_. R package
version 0.4.6, https://CRAN.R-project.org/package=DHARMa.
Lenth R (2024). _emmeans: Estimated Marginal Means, aka Least-Squares Means_. R package version 1.10.4,
<https://CRAN.R-project.org/package=emmeans>.
Mazerolle MJ (2023). _AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c)_. R package version
2.3.3, https://cran.r-project.org/package=AICcmodavg.
Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN
0-387-95457-0
Skaug, Martin Maechler and Benjamin M. Bolker (2017). glmmTMB Balances Speed and Flexibility Among Packages for
Zero-inflated Generalized Linear Mixed Modeling. The R Journal, 9(2), 378-400. doi: 10.32614/RJ-2017-066.
Lüdecke et al., (2021). performance: An R Package for Assessment, Comparison and Testing of Statistical Models.
Figures:
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
Kassambara A (2023). _ggpubr: 'ggplot2' Based Publication Ready Plots_. R package version 0.6.0,
<https://CRAN.R-project.org/package=ggpubr>.
Pedersen T (2024). _patchwork: The Composer of Plots_. R package version 1.3.0,
<https://CRAN.R-project.org/package=patchwork>.
Mollie E. Brooks, Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen, Hans J.
Journal of Open Source Software, 6(60), 3139. https://doi.org/10.21105/joss.03139
Wilke C (2024). _cowplot: Streamlined Plot Theme and Plot Annotations for 'ggplot2'_. R package version 1.1.3,
<https://CRAN.R-project.org/package=cowplot>.