bioclim_example: Example Bioclimatic Data for Ecological Modeling

Description

A simulated dataset with the 19 WorldClim bioclimatic variables (https://www.worldclim.org/data/bioclim.html) measured at 100 geographic locations, with species richness as the response variable. Variables are organized into correlated blocks representing temperature (BIO1-BIO11) and precipitation (BIO12-BIO19).

Usage

bioclim_example

Arguments

Format

A data frame with 100 rows and 20 variables:

species_richness: Integer. Number of species observed (response variable)
BIO1: Numeric. Annual Mean Temperature
BIO2: Numeric. Mean Diurnal Range
BIO3: Numeric. Isothermality
BIO4: Numeric. Temperature Seasonality
BIO5: Numeric. Max Temperature of Warmest Month
BIO6: Numeric. Min Temperature of Coldest Month
BIO7: Numeric. Temperature Annual Range
BIO8: Numeric. Mean Temperature of Wettest Quarter
BIO9: Numeric. Mean Temperature of Driest Quarter
BIO10: Numeric. Mean Temperature of Warmest Quarter
BIO11: Numeric. Mean Temperature of Coldest Quarter
BIO12: Numeric. Annual Precipitation
BIO13: Numeric. Precipitation of Wettest Month
BIO14: Numeric. Precipitation of Driest Month
BIO15: Numeric. Precipitation Seasonality
BIO16: Numeric. Precipitation of Wettest Quarter
BIO17: Numeric. Precipitation of Driest Quarter
BIO18: Numeric. Precipitation of Warmest Quarter
BIO19: Numeric. Precipitation of Coldest Quarter

Details

This dataset demonstrates a common problem in ecological modeling: bioclimatic predictors are highly correlated within groups (temperature variables BIO1-BIO11 are highly correlated; precipitation variables BIO12-BIO19 are moderately correlated), leading to multicollinearity issues. The species richness response depends on a subset of predictors.

Use case: Demonstrating corrPrune() and modelPrune() for reducing correlated environmental predictors before fitting species distribution models.

Examples

Run this code

data(bioclim_example)

# The 19 WorldClim bioclimatic variables (https://www.worldclim.org/data/bioclim.html)
# Many are highly correlated, making them ideal for pruning

# Remove highly correlated variables
pruned <- corrPrune(bioclim_example[, -1], threshold = 0.7)
ncol(pruned)  # Reduced from 19 to ~8 variables

# Model-based pruning with VIF
model_data <- modelPrune(species_richness ~ .,
                         data = bioclim_example,
                         limit = 5)
attr(model_data, "selected_vars")