Pollen specialist bee species are accurately predicted from visitation, occurrence and phylogenetic data

Predicting specialist and generalist bee species

This repository contains data and code for the manuscript Pollen specialist bee species are accurately predicted from visitation, occurrence and phylogenetic data.

Abstract

An animal’s diet breadth is a central aspect of its life history. Yet the factors driving which species have narrow dietary breadths (specialists) and which have broad dietary breadths (generalists) remain poorly understood. Studying diet breadth in herbivorous insects is especially challenging as comprehensive datasets on plant hosts are missing for many taxa and regions. Leveraging interaction data from museum specimens and published literature can help address this gap. Here, we focus on bees to predict pollen diet breadth using machine learning models, supplemented by a bee phylogeny and occurrence data for 682 bee species native to the United States. We found that, on average, 72.9% of the visits made by pollen specialist bees were to their host plants, and that specialist bees can be predicted with high accuracy (mean 94%). Our models predicted generalist bee species, which made up a minority of the species in our dataset, with lower accuracy (mean 70%). Models tested on spatially and phylogenetically blocked data reveal that the most informative predictors of diet breadth are plant phylogenetic diversity, bee species' geographic range, and regional abundance. These results suggest we can predict specialist bee species in regions and for taxonomic groups where diet breadth is unknown, but predicting generalists may be more challenging. Additionally, we found that male bees tend to visit the same species females use as pollen hosts. Overall, our findings align with prior research, indicating that range size is predictive of diet breadth and specialist bees mostly visit their host plants.

Description of data

In final_data

File Name	Description
AppendixS2_20Aug2024-revision.csv	List of USA bee species in GloBI dataset, their diet breadth classifications and references.
globi_speciesLevelFinal-12Aug2024_revision2_genera_removed.csv	Species-level data used to predict specialist and generalist bee species with random forest model.
fowler_formatted-15Aug2024.csv	List of bee species in our dataset and their pollen host records from Jarrod Fowler's dataset. Used to assess how often specialist bees visit their host plants.
russell_formatted-15Aug2024.csv	List of bee species in the GloBI dataset and their pollen host records from Avery's Russell's dataset. Used to assess how often specialist bees visit their host plants.
sources_table28nov2023_revision.csv	Sources of bee interaction data found in GloBI visitation dataset

In Large data files stored on Zenodo

File Name	Description
globi_allNamesUpdated.csv.zip	GloBI dataset with interaction records of bee species from the contiguous United States. Both plant and bee names are updated to the currently accepted species name.
globi_allNamesUpdated_Henriquez_Piskulich.csv.zip	GloBI dataset with interaction records of bee species from the contiguous United States. Both plant and bee names are updated to the currently accepted species names with generic changes in bees to match the phylogeny we used in the analysis.
globi_speciesLevelFinal-12Aug2024_revision2.csv.zip	Species-level data used to predict specialist and generalist bee species with random forest model with additional genera that were not found in the GloBI visitation dataset.

General workflow for running the analyses:

1. Download full GloBI interaction dataset from Zenodo.

2. Filter the dataset to just include bee data using scripts/format_bee_data.sh

   a. Output: all_bee_data.tsv (not included in archive)

3. Reformat the data using scripts/GLOBI_bee_update_clean.Rmd

   a. Output: interactions-14dec2022.csv (not included in archive)

4. Get list of USA native bee species and calculate extent of occurence using scripts/Chesshire2023-beeArea.R

   a. Output: modeling_data/Chesshire2023_nameAlignment.csv

   b. Output: modeling_data/chesshire2023_beeArea-11april2023.csv

5. Update Chesshire2023_nameAlignment.csv to include information about whether name alignment was informed by geography using scripts/name-alignment-chesshire.R

   a. Output: modeling_data/Chesshire2023_nameAlignment-geoUpdate.csv

6. Update bee names and filter to just be native US bees using scripts/update bee names.R

   a. Output: modeling_data/large_data_files/globi_american_native_bees_7march2023.csv.zip

7. Make bee phylogeny using scirpts/make bee phylogeny_Henriquez Piskulich.R

   a. Output: modeling_data/bee_phylogenetic_data_Henriquez_Piskulich_tree.csv

   b. Output: modeling_data/modified_tree_Henriquez_Piskulich.nwk

8. Update plant names and make plant phylogeny using scripts/make plant phylogeny.R

   a. get plant families from WFO, using scripts/update plant names.R

   b. Output: modeling_data/large_data_files/globi_allNamesUpdated.csv

   c. Output: modeling_data/globi_phyloDiv.csv

9. Format and update name of list of specialist bees using scripts/format_fowler_hosts.R and scripts/format_russell_hosts.R

   a. Output: modeling_data/russell_formatted-30nov2023.csv

   b. Output: modeling_data/fowler_formatted-30nov2023.csv

10. Update datasets with taxon names in GloBI to match Henriquez Piskulich bee phylogeny scripts/make bee phylogeny_Henriquez Piskulich.R

    a. Output: globi_allNamesUpdated_Henriquez_Piskulich.csv

11. Make final dataset using scripts/finalDataset.R

    a. Output: modeling_data/large_data_files/globi_speciesLevelFinal-12Aug2024_revision2.csv.zip

12. Run analyses using scripts/main_analyses.R

13. Run analysis to predict specialist host plants using scripts/predict specialist hosts.R

14. Run code to look at how often specialist bees visit their host plants using scripts/specialists-nonhosts.R

Defintions of column names in globi_speciesLevelFinal-12Aug2024_revision2.csv

Column Name	Defintion
scientificName	bee species name
bee_genus	bee species' genus
bee_family	bee species' family
diet_breadth_conservative	whether bee is a pollen specialist or generalist using conservative criteria to define generalists
diet_breadth_liberal	whether bee is a pollen specialist or generalist using liberal criteria to define generalists
phylo_rich	Faith's phylogenetic diversity of plant genera visited
phylo_simp	phylogenetic Simpson diversity of plant genera visited
eigen1_plantGenus	first eigenvalue of Morisita-Horn distance-matrix for plant genera visited
eigen2_plantGenus	second eigenvalue of Morisita-Horn distance-matrix for plant genera visited
eigen1_plantFam	first eigenvalue of Morisita-Horn distance-matrix for plant families visited
eigen2_plantFam	second eigenvalue of Morisita-Horn distance-matrix for plant families visited
rich_genus	number of plant genera visited
simpson_genus	inverse Simpson diversity of plant genera visited
rich_fam	number of plant families visited
simpson_fam	inverse Simpson diversity of plant families visited
n_globi	sample size in GLOBI
med_lat	median latitude of bee species in Chesshire et al 2023
med_long	median longitude of bee species in Chesshire et al 2023
n_chesshire	sample size in Chesshire et al 2023
area_m2	area in m2 of extent of occurrence in Chesshire et al 2023
area_ha	area in hectares of extent of occurrence in Chesshire et al 2023
spherical_geometry	was spherical geometry used to calculate area of bee's extent of occurrence?
mean_doy	mean day of year of collection in Chesshire et al 2023
median_doy	median day of year of collection in Chesshire et al 2023
quant10	10% quantile of day of year of collection in Chesshire et al 2023
quant90	90% quantile of day of year of collection in Chesshire et al 2023
flight_season	'quant90' - 'quant10'
eigen1	first eigenvalue of matrix of bee phylogenetic distance
eigen2	second eigenvalue of matrix of bee phylogenetic distance
all other columns	phylogenetic distance of bee genus to bee genus in column name