Data from: Do past and present abiotic conditions explain variation in the nutritional quality of wildflower pollens for bees?

GENERAL INFORMATION
This README.txt file was updated on August 23, 2024
Citation: Past and present season abiotic conditions predict pollen nutritional quality
Brief abstract: Plant intraspecific trait variation (ITV) is mostly unknown regarding food rewards that angiosperms offer pollinators, including pollen which bees collect for protein and lipid nutrition. This gap limits our understanding of how spatiotemporal and climate-induced variation in reward quality affects mutualisms. Manipulative experiments show that pollen chemistry changes with extreme stress, yet whether different pollen species in natural populations exhibit ITV related to local conditions is unknown and has ecological implications. Using Bayesian sparse regression, we explored the relationship between site-specific climate variables and variation in pollen protein and lipid content from 35 native wildflower species across multiple sites in sagebrush steppe habitat (NV/CA, USA). We found that pollen nutrient ITV across sites was related to current season below-ground (climatic water deficit) and previous season above-ground (dewpoint) conditions. We discuss the implication of these findings considering plant physiological responses to climate stress and potential downstream effects on pollinator community interactions.
Names, institutions of all authors:
Anthony D. Vaudo1,2, Eva Lin1, Jillian A. Luthy1, Eliza M. Grames1,3, and Anne S. Leonard1
1. Department of Biology, University of Nevada, Reno, NV 89557
2. Rocky Mountain Research Station, USDA Forest Service, Moscow, ID 83843
3. Department of Biological Sciences, Binghamton University, Binghamton, NY 13902

Dates of data collection: 2021
Geographic location(s) of data collection: Nevada, California, USA
Funding Sources: This work was supported by the National Science Foundation (NSF) grant number NSF IOS-1755096 and NSF REPS Supplement number IOS-1755096 to A.S.L. and in part by the U.S.D.A. Forest Service.

ACCESS INFORMATION
These code and data are licensed under a Creative Commons Attribution 4.0 International License. You are free to share and adapt the material for any purpose, provided that you give appropriate credit, provide a link to the license, and indicate if any changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Recommended citation for this data/code archive
Vaudo, A.D., E. Lin, J.A. Luthy, A.S. Leonard, and E.M. Grames. (2024). Do past and present abiotic conditions explain variation in the nutritional quality of wildflower pollens for bees? Evolutionary Ecology, https://doi.org/10.1007/s10682-024-10313-4

DATA & CODE FILE OVERVIEW
This data repository consist of 6 data files, 4 code scripts, and this README document, with the following data and code filenames and variables
Data files and variables
1. pollen_nutrition_population: a .csv file containing raw data of pollen nutrition samples and metadata
2. westernstates: a .rda file containing a spatial polygon of western states in which sites are located
3. awc: a .tif raster file containing soil available water content in the region
4. dem: a .tif raster file of elevation
5. PRISM_ppt_tmin_tmean_tmax_tdmean_vpdmin_vpdmax_stable_4km_20210101_20211231: a .csv file containing weather data for the sites in 2021 downloaded from PRISM
5. PRISM_ppt_tmin_tmean_tmax_tdmean_vpdmin_vpdmax_stable_4km_20200101_20201231: a .csv file containing weather data for the sites in 2020 downloaded from PRISM

Code scripts and workflow
1. pollen-nutrition-climate-analysis: a .R script for the main analyses, including calculations of local abiotic conditions, running stage one and stage two analyses, and generating figures
2. jags-pollen-model: a .R file containing JAGS code specifying the sparse regression model for climate variable selection
3. jags-step2-model-lipid: a .R file containing JAGS code specifying the stage two model for estimating the effects of selected variables on lipid concentrations
4. jags-step2-model-protein: a .R file containing JAGS code specifying the stage two model for estimating the effects of selected variables on protein concentrations

SOFTWARE VERSIONS
PRISM (PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu, data created and accessed 11 Nov 2022)
U.S. Geological Survey, 2022, USGS 3D Elevation Program Digital Elevation Model, accessed June 7, 2020 at URL https://elevation.nationalmap.gov/arcgis/rest/services/3DEPElevation/ImageServer.
USGS Soil Properties Dataset based on the Gridded National Soil Survey (gNATSGO) and Gridded Soil Survey (gSSURGO) Geographic Databases (Boiko et al. 2021)
Code was developed in R version 4.1.2 on x86_64-pc-linux-gnu (64-bit)
R packages
raster v3.5-11 (Hijmans et al. 2021)
saveJAGS v0.0.4.9002 (Meredith 2021)
R2jags v0.6-1 (Su and Yajima, 2020)

REFERENCES
Boiko, O., Kagone S., Senay G.B., 2021, Soil properties dataset in the United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9TI3IS8.
Hijmans, R. J., J. van Etten, M. Mattiuzzi, M. Sumner, J. A. Greenberg, O. P. Lamingueiro, A. Bevan, E. B. Racine, and A. Shortridge. 2021. raster: Geographic Data Analysis and Modeling, Version 2.9-23, R package.
Lu, Z. and W. Lou. 2022. Bayesian approaches to variable selection: a comparative study from practical perspectives. The International Journal of Biostatistics. 18: 83-108.
Lutz, J. A., J. W. van Wagtendonk, and J. F. Franklin. 2010. Climatic water deficit, tree species ranges, and climate change in Yosemite National Park. Journal of Biogeography 37:936–950
Meredith, M. 2021. saveJAGS: Run JAGS and Regularly Save Output to Files. R package version 0.0.4.9002.
Plummer, M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd international workshop on distributed statistical computing. 124, No. 125.10, pp. 1-10).
Redmond, M. D. 2022. CWD and AET function (Version V1.0.3). Zenodo. https://doi.org/10.5281/zenodo.6416352.
Roberts, E. and L. Zhao. 2022. A Bayesian mixture model for changepoint estimation using ordinal predictors. The International Journal of Biostatistics. 18: 57-72.
Su, Y.S. and M. Yajima. 2021. R2jags: Using R to Run ‘JAGS’. R package version 0.6-1; 2020.