Intraspecific competition does not predict horn threshold evolution in introduced populations of the bull-horned dung beetle Onthophagus taurus

https://doi.org/10.5061/dryad.70rxwdcdc

Data and R code for analyses of population density, horn allometry, and temperature-dependent horn threshold variation in Onthophagus taurus.

Overview

This dataset contains the data and R code needed to reproduce the analyses associated with the manuscript. The analyses examine population-level dung beetle density, horn length allometry, wild-caught horn threshold variation, and horn threshold plasticity under common-garden developmental temperatures.

The R script estimates:

• population-level dung beetle density from dung-pad sampling;
• regional density differences using Tweedie generalized linear mixed models;
• wild-caught horn threshold estimates using nonlinear sigmoidal models;
• common-garden horn threshold estimates at 19°C and 27°C;
• density-threshold relationships among matched populations.

File structure

The files should be arranged as follows:

project_folder/
  ALLhorns.csv
  OtPadWUSA.csv
  01_reproduce_density_horn_allometry.R
  README.md

The R script assumes that ALLhorns.csv, OtPadWUSA.csv, and 01_reproduce_density_horn_allometry.R are in the same working directory.

Data files

ALLhorns.csv

Individual-level horn and body-size measurements.

Main columns:

Population codes include Western US populations, Eastern US populations, Western Australian populations, and native-range populations.

OtPadWUSA.csv

Dung-pad density data. This file includes new Western US dung-pad sampling data and comparison data extracted from Moczek (2003).

Main columns:

Code file

01_reproduce_density_horn_allometry.R

This script reproduces the main analyses. It loads the two data files, cleans the data, fits the horn-threshold and density models, prints key tables to the R console, and displays the main plots.

Main objects created by the script:

How to run the analysis

1. Open R or RStudio.
2. Set the working directory to the folder containing the two CSV files and the R script.
3. Run:

source("01_reproduce_density_horn_allometry.R")

The script will print model summaries and tables to the console. It will also display the main plots.

To inspect tables manually after running the script:

View(wc_thresholds)
View(thresholds_19_27)
View(westcoast_density_estimates)
View(west_pop_tweedie_estimates)
View(region_density_emm)
View(density_threshold_dat)

To display plots manually after running the script:

print(p_wc_sigmoids)
print(p_reaction_norm)
print(p_density_region)
print(p_density_threshold_all)

Analysis summary

Horn allometry

Horn allometry is modelled using a four-parameter sigmoid:

HL = c + (d - c) / (1 + exp[-b(log(PW) - log(theta))])

where:

• HL is horn length;
• PW is pronotum width;
• c is the lower asymptote;
• d is the upper asymptote;
• b is the slope around the inflection point;
• theta is the horn threshold, estimated as the sigmoid inflection point.

For wild-caught individuals, the main model shares the lower asymptote, upper asymptote, and slope among populations, while allowing the threshold parameter to vary among populations.

For the 19°C/27°C common-garden data, models compare whether the threshold parameter varies by population, temperature, or their interaction.

Density analysis

Dung beetle density is analysed using DBensEstOt, the estimated number of O. taurus individuals per kg dung.

Regional density differences are estimated using a Tweedie generalized linear mixed model with:

DBensEstOt ~ Region + (1 | Location)

Population-level Western US density estimates are also calculated using a Tweedie model with population as a fixed effect.

Density-threshold analysis

The script joins population-level density estimates with wild-caught horn threshold estimates using matching population and region labels. It then fits a simple linear model of threshold size as a function of log-transformed mean density.

Required R packages

The script requires:

• dplyr
• tidyr
• ggplot2
• purrr
• tibble
• glmmTMB
• emmeans
• ggrepel

Install missing packages using:

install.packages(c(
  "dplyr", "tidyr", "ggplot2", "purrr", "tibble",
  "glmmTMB", "emmeans", "ggrepel"
))

Notes

• The script does not require absolute file paths.
• The CSV files and R script should be placed in the same folder before running.
• Population codes and region labels are preserved from the analysis files.
• Some populations are excluded from the wild-caught threshold model where sample size or body-size range made threshold estimation unstable, following the analysis script.
• The density file includes both this study and comparison data from Moczek (2003), identified by the Ref column.

Citation

We encourage users to cite the associated manuscript when using these data. The density comparison data extracted from Moczek (2003) should also be cited where relevant.