Dataset DOI: 10.5061/dryad.m37pvmdgh

Description of the data and file structure

Files and variables

1. File: Data_Increased_atmospheric-CO2_Khan_et_al.csv

Description: This comma-separated values file contains pollinator survey data and associated environmental measurements collected from 25 study sites across the Pothwar Plateau, Punjab, Pakistan. The file includes sampling date, geographic location, elevation, land-use and habitat information, atmospheric CO₂ concentration, climatic variables, pollinator species richness, pollinator abundance, and pollinator body-size category. The dataset was used to examine size-dependent responses of pollinating insects to atmospheric CO₂ variation.

Variables:

• Date: Date of field sampling.
• Location: Sampling district or site location within the Pothwar Plateau, including Murree, Rawalpindi/Islamabad, Jhelum, Chakwal, and Attock.
• Latitude: Latitude of the sampling site in decimal degrees.
• Longitude: Longitude of the sampling site in decimal degrees.
• Altitude (m): Elevation of the sampling site above sea level, measured in metres.
• Local Land Use: Dominant land-use type surrounding the sampling site, such as agricultural, forested, semi-natural, or human-modified land.
• Land Structure: Description of habitat or landscape structure at the sampling site.
• Temperature: Ambient air temperature recorded during pollinator sampling, measured in degrees Celsius (°C).
• Rainfall (mm): Rainfall recorded for the relevant sampling period or district, measured in millimetres.
• Humidity (%): Relative humidity recorded during sampling, expressed as a percentage.
• CO2 (PPM): Atmospheric carbon dioxide concentration measured during sampling, expressed in parts per million (ppm).
• Pollinator species: Number of pollinator species recorded during the sampling event; used as a measure of species richness.
• Abundance: Total number of pollinating insect individuals recorded during the sampling event.
• Size: Body-size category of pollinators. Pollinators are classified as either large or small. Bees were classified as large when body length was ≥15 mm and small when <15 mm. Hoverflies were classified as large when body length was ≥10 mm and small when <10 mm.

Missing values, if present, are indicated by blank cells or NA.

2. File: Plots_for_GE_B_journal.R

Description: Generates scatterplots, shaded CO₂ response plots, combined figures, correlation outputs, model summary tables, and body-size histograms. Output files were exported as .png, .jpg, and .csv files using standard R functions such as ggsave() and write.csv().

Code/software

The analyses were conducted using R version 4.3.1 in RStudio version 2023.6.1.524.

The following R packages were used:

• ggplot2 for data visualization
• dplyr for data manipulation
• patchwork for combining figures
• Hmisc for correlation analysis
• car for multicollinearity assessment
• lubridate for date handling
• lme4 for mixed-effects modelling
• DHARMa for residual diagnostics

Workflow Description:

Data Loading:

The data was loaded from a CSV file using the read.csv() function.

After loading the data, the necessary columns were checked for accuracy, and some columns were converted to numeric or factor types as needed.

Data Preprocessing:

The data was cleaned by removing rows with missing values using the na.omit() function.

Various transformations were performed, such as converting categorical variables (like Size, Location) into factors.

Exploratory Data Analysis (EDA):

Initial visualizations, such as scatter plots and histograms, were generated using ggplot2. These plots were used to explore the relationships between CO₂ levels, pollinator abundance, and other environmental factors.

Correlation matrices were computed using the rcorr() function from the Hmisc package to explore relationships between continuous variables.

Statistical Modeling:

Linear models (lm) and generalized linear mixed-effects models (glmer) were used to analyze the impact of environmental factors (e.g., CO₂, temperature, humidity) on pollinator abundance and diversity.

Random effects were included in the models to account for variations across different locations.

Model diagnostics were conducted to check for multicollinearity using VIF (from car) and overdispersion using residual simulations (from DHARMa).

Model Comparison:

The performance of different models was compared using AIC (Akaike Information Criterion) to identify the best-fitting model for pollinator abundance and diversity.

Visualization and Export:

Final plots were saved in various formats (PNG, JPEG) using the ggsave() function. The plots were customized with titles, axis labels, legends, and color schemes for clarity.

The results of the model summary (coefficients, standard errors, p-values) were formatted into a table and saved to a CSV file for reporting.

Result Interpretation:

The results, including correlation coefficients and model summaries, were interpreted to understand how environmental variables (like CO₂) impact pollinator populations in the study area.

Working Directory:

Ensure that your working is set to the location where the data file and the script are stored. You can set the working directory in R using the following command:

RCopy

setwd("path/to/your/directory")

Outputs:

The analysis generates several output files saved in the "outputs" folder, including:

1. Plots:
   • plot_CO2_abundance_by_size.png: Scatter plot showing the relationship between CO2 concentration and pollinator abundance, colored by body size.
   • plot_CO2_species_richness_by_size.png: Scatter plot showing the relationship between CO2 concentration and pollinator species richness, colored by body size.
   • combined_CO2_pollinator_plots.png: A combined plot of abundance and species richness against CO2 concentration.
2. Statistical Results:
   • correlation_coefficients.csv: Correlation coefficients among selected environmental variables and pollinator metrics.
   • correlation_p_values.csv: P-values corresponding to the correlation coefficients.
   • vif_values.csv: Variance Inflation Factor (VIF) values to check for multicollinearity among predictors.
   • model_abundance_summary.txt: Summary of the generalized linear mixed model for pollinator abundance.
   • model_abundance_overdispersion_test.txt: Results of the overdispersion test for the abundance model.
   • aic_model_comparison.csv: AIC values for model comparison, assessing the impact of different predictors on abundance.
   • model_species_richness_summary.txt: Summary of the linear model for species richness.
   • body_size_summary.csv: Summary statistics for pollinator abundance and species richness by body size.
   • cleaned_pollinator_CO2_data.csv: The cleaned dataset ready for further analysis.