Data for: Effective population size mediates the impact of pollination services on pollen limitation
Data files
Jan 25, 2024 version files 284.99 KB
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greenhouse_crossdata2.xlsx
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Pollinator_data_inputR2.xlsx
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polpist_final2019-2021.xlsx
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pop_level_dataset.xlsx
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README.md
Abstract
Inadequate pollen receipt limits flowering plant reproduction worldwide. Ecological causes of pollen limitation (‘PL’), like pollinator scarcity and low plant abundance, have been a primary focus of research. The genetic diversity of plant populations could impact both quantity and quality components of PL in concert with ecological factors, yet empirical examples are lacking. We evaluated joint effects of ecological factors (flower abundance, pollinator visitation) and genetic effective population size (NE) on PL across 13 populations of a common herb. We used a histological approach with 5504 styles from 1137 flowers to separate quantity and quality components of PL, and link these to reproductive output. NE and pollinator visitation interacted to shape PL, but NE had stronger direct effects. Effectively smaller populations experienced stronger quantity PL, and controlled crosses in a pollinator-free environment revealed that pollen quantity was an intrinsic population-level attribute that increased with NE. Pollinator visitation enhanced pollen quality, but only in effectively larger populations. Quantity and quality PL negatively impacted fruit and seed set, respectively. Results highlight that PL is dictated by plant population genetic diversity in addition to commonly evaluated ecological factors. Efforts to support pollinators will only enhance plant reproduction in genetically diverse plant populations.
README: Data for: Effective population size mediates the impact of pollination services on pollen limitation
https://doi.org/10.5061/dryad.98sf7m0pk
Datasets include pollinator visitation data from natural populations, histological data on pollen-pistil interactions from flowers collected in the field and population attribute data (effective population size, flower abundance, elevation etc.). R code for analyses in the manuscript are also provided.
Description of the data and file structure
In this study we related pollen limitation with pollinator visitation, flower abundance, and effective population size of 13 populations of Argentina anserina. The datasets include data from the field (pollinator visitation, flower abundance), histological data on pollen-pistil interactions from field-collected flowers, and population-level data on effective population size generated from ddRADseq analyses (see https://doi.org/10.1038/s41437-023-00610-z). Data were collected across the span of three years (2019-2021).
Excel Files:
Name: pollinator_data_inputR2.xlsx
Description: data on pollinator visitation in the field used for calculation of mean visitation rate per population. Metadata describing each column of data are in the ‘metadata’ tab in the excel file. Data in this file are associated with r code entitled “Visitation_Rate_and SEM analyses.R”
Name: polpist_final2019-2021.xlsx
Description: data on pollen pistil interactions from the field used to calculate estimates of pollen quantity and quality limitation for each population. Metadata describing each column of data are in the ‘metadata’ tab in the excel file. Data in this file are associated with r code entitle “segmented_code.R”
Name: pop_level_dataset.xlsx
Description: population-level average data of pollen limitation metrics, effective population size, anther and stigma number, pollinator visitation rate, flower abundance, and reproductive output used for structural equation model. Metadata describing each column of data are in the ‘metadata’ tab in the excel file. Data in this file are associated with r code entitled “Visitation_Rate_and SEM analyses.R”
Name: greenhouse_crossdata.xlsx
Description: pollen deposition data from hand-pollinations in a pollinator free greenhouse. Average deposition per population was calculated from this dataset. Metadata describing each column of data are in the ‘metadata’ tab.
Sharing/Access information
A previous paper that details population genomic analyses used to generate effective population size are here:
https://www.nature.com/articles/s41437-023-00610-z
Code/Software
R files:
Name: Visitation_Rate_and SEM analyses.R
Description: code used to model pollinator visitation rate to obtain mean visitation rate per population, and code to run structural equation model linking ecological and population genetic factors to pollen limitation and ultimately reproductive success. Input data are in “pollinator_data_inputR2.xlsx” and “pop_level_dataset.xlsx”. All datasets were read into R by pasting to the clipboard and then using read.table(pipe("pbpaste"),header=TRUE)
Name: Segmented_code.R
Description: code used to estimate pollen quality (b1 slope) from segmented regression of pollen tubes on pollen grains deposited. Code is provided for one population that was repeated across all populations. Data were read into R by pasting to the clipboard and then using read.table(pipe("pbpaste"),header=TRUE)
Methods
Datasets include field-collected data in natural plant populations on pollinator visitation rates and floral density, data on pollen-pistil interactions from field-collected flowers, population genomic metrics dervied from RAD-Seq data (previously published), and crosses performed in greenhouse conditions.
Usage notes
R v. 4.2.1