Supporting data for: Spatial biodiversity rasters, modeling datasets, and occurrence records for California butterflies

This dataset supports the manuscript “Spatial integration of taxonomic and evolutionary diversity refines butterfly conservation in California.” It includes cleaned georeferenced occurrence records for 211 butterfly species across California, derived spatial rasters of expected species richness (SRexp), probability-weighted evolutionary distinctiveness (EDexp), and probability-weighted pendant lineage length (PLexp), and binary top-decile hotspot layers for each metric and their spatial consensus.
The repository also contains a spatially subsampled modeling dataset (n = 2,948 grid cells) used in spatial generalized linear mixed models (GLMMs), including raw and standardized environmental predictors and response variables. A predictor correlation matrix derived from this subsampled dataset is provided.
Analytical R scripts are included for evolutionary metric calculation, spatial GLMM fitting with Matérn correlation structures, and phylogenetic community structure analyses (Net Relatedne..., , # Supporting Data for: Spatial integration of taxonomic and evolutionary diversity refines butterfly conservation in California

## Creator

Dr. Khuram Zaman\
Bakersfield College

## Associated Manuscript

Zaman, K. 2026. Spatial integration of taxonomic and evolutionary diversity refines butterfly conservation in California. *Insect Conservation and Diversity*.

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## Dataset Overview

This repository contains occurrence records, derived spatial raster outputs, subsampled modeling datasets, and analytical scripts used to quantify taxonomic and evolutionary diversity of 211 butterfly species across California.

The dataset includes:

1. Raw species occurrence records
2. Expected species richness raster (SRexp)
3. Probability-weighted evolutionary distinctiveness raster (EDexp)
4. Probability-weighted pendant lineage length raster (PLexp)
5. Top-decile hotspot rasters
6. Consensus hotspot raster
7. Spatially subsampled GLMM dataset (n = 2,948 grid cells)
8. Predictor correlation matrix..., ,