Study system and data sources

The occurrence records used in this study were compiled from multiple sources that are subject to different licensing and data-sharing conditions. Because these conditions do not permit redistribution of the compiled dataset under a single license, the occurrence dataset is not included in this repository and cannot be released under the CC0 waiver applied to Dryad submissions.

Occurrence records were compiled from sources including:

• Global Biodiversity Information Facility (GBIF)
• iNaturalist
• Barcode of Life Data Systems (BOLD)
• National Center for Biotechnology Information (NCBI)
• Published taxonomic literature
• Regional butterfly monographs
• Museum voucher specimens
• Field surveys conducted between 2021 and 2025

Occurrence records for Melitaea species were acquired from the sources above and used for buffered convex hull or buffered occurrence range mapping. Species represented by more than ten occurrence records were used as input for species distribution modelling (MaxEnt).

Occurrence records were taxonomically verified, visually inspected, spatially thinned using the spThin R package, and cleaned using CoordinateCleaner before range map construction.

More details are available in the associated research article (https://doi.org/10.1111/jbi.70307), particularly in the Methods section. Researchers requiring additional information about the occurrence data may contact the corresponding author.

Dataset organization

Range_maps_stack.rds

Stacked binary species range maps aggregated to a spatial resolution of 100 × 100 km and used in richness, turnover, and biogeographical analyses.

Bioregionalization

Variables_for_Cluster_bioregions.csv

Environmental and diversity variables used for clustering-based bioregionalization.

Variables

• x, y – Geographic coordinates of grid-cell centroids.
• cluster – Assigned biogeographical cluster.
• richness – Number of Melitaea species.
• bio1 – Annual mean temperature (°C).
• bio6 – Minimum temperature of the coldest month (°C).
• bio4 – Temperature seasonality (standard deviation ×100; WorldClim convention).
• bio12 – Annual precipitation (mm).
• bio14 – Precipitation of the driest month (mm).
• bio15 – Precipitation seasonality (coefficient of variation, %).
• anom_temp – Temperature anomaly since the Last Glacial Maximum (°C).
• anom_percip – Precipitation anomaly since the Last Glacial Maximum (mm).
• velocity_temp – Velocity of temperature change (km/year).
• velocity_percip – Velocity of precipitation change (km/year).
• stability_temp – Temperature stability (unitless index scaled between 0 and 1).
• stability_percip – Precipitation stability (unitless index scaled between 0 and 1).

Contemporary bioclimatic variables (BIO1, BIO4, BIO6, BIO12, BIO14, and BIO15) were obtained from WorldClim version 2.1 (Fick & Hijmans, 2017). Mean annual temperature (BIO1) and minimum temperature of the coldest month (BIO6) are expressed in degrees Celsius (°C). Annual precipitation (BIO12) and precipitation of the driest month (BIO14) are expressed in millimetres (mm). Temperature seasonality (BIO4) is reported as the standard deviation of monthly temperature multiplied by 100 (following the WorldClim convention), and precipitation seasonality (BIO15) is reported as the coefficient of variation (%).

Historical climate variables were derived from paleoclimatic reconstructions (Beyer et al., 2020). Temperature anomaly (anom_temp) is expressed in °C, precipitation anomaly (anom_percip) in millimetres (mm), temperature and precipitation velocities (velocity_temp and velocity_percip) are expressed in km/year, and temperature and precipitation stability (stability_temp and stability_percip) are reported as unitless stability indices scaled between 0 and 1.

Variables_for_Network_bioregions.csv

Environmental and diversity variables used for network-based bioregionalization. This file intentionally contains the same predictor variables as Variables_for_Cluster_bioregions.csv, because the same environmental variables were used to evaluate the relationships between climatic conditions and species assemblages (species richness) within the bioregions identified by the two different bioregionalization methods. 

Network-Based_Bioregionalization.txt

Purpose

Network-based bioregionalization using the Infomap algorithm.

Inputs

Species-by-grid-cell incidence matrix generated from the occurrence records used in the analyses.

Outputs

Network-derived biogeographical regions.

Clustering-Based_Bioregionalization.txt

Purpose

Hierarchical clustering-based bioregionalization using Simpson dissimilarity.

Inputs

Species occurrence matrix generated from the occurrence records used in the analyses and climatic variables.

Outputs

Biogeographical clusters.

mycto_clusters.tree

Hierarchical clustering tree generated during clustering analyses.

final_bioregion.zip

Polygon shapefile containing the final biogeographical regions.

Climate-related files

ClimateStability.txt

Purpose

Calculation of historical climate stability.

Inputs

Paleoclimate reconstructions obtained through the pastclim R package.

Outputs

Temperature and precipitation stability indices.

The_velocity_of_climate_change.txt

Purpose

Calculation of climate velocity.

Inputs

Paleoclimate reconstructions obtained through the pastclim R package.

Outputs

Temperature and precipitation velocity metrics.

Koppen_giegger_overlap_Bioregions.txt

Results of overlap analyses between biogeographical regions and Köppen–Geiger climate classifications.

Statistical analyses

Varpart_Regression_Variables.txt

Purpose

Ordinary least squares regression and variation partitioning analyses.

Inputs

Species richness, contemporary climate variables, historical climate variables, and biogeographical regions.

Outputs

Regression models and variation partitioning results.

Maxent_Range_Map.txt

Purpose

Species distribution modelling using MaxEnt.

Inputs

Cleaned occurrence records, WorldClim bioclimatic variables, and SRTM-derived elevation, slope, and aspect.

Outputs

Continuous habitat suitability maps, binary range maps, and model evaluation statistics (AUC and TSS).

convex_hull_Buffer_Range_Map.txt

Purpose

Construction of species range maps for species with limited occurrence records.

Inputs

Cleaned occurrence records and SRTM elevation data.

Outputs

Buffered convex hull range maps and buffered occurrence-based range maps.

Spatial resolution

All analyses were conducted using a 100 × 100 km equal-area grid.

Additional external data required

Some scripts included in this repository reference external raster datasets that are not deposited in Dryad because they are publicly available from their original providers.

Users wishing to reproduce the analyses should obtain these datasets from the following sources:

• WorldClim version 2.1 bioclimatic variables: https://www.worldclim.org/
• Shuttle Radar Topography Mission (SRTM) Digital Elevation Model: https://www2.jpl.nasa.gov/srtm/
• PastClim paleoclimate reconstructions (accessed through the pastclim R package)

Climate stability and climate velocity were calculated using the climateStability and VoCC R packages based on these publicly available climatic datasets.

Software

Analyses were performed in R using, among others, the following packages:

• dismo
• spThin
• CoordinateCleaner
• adehabitatHR
• usdm
• vegan
• bioregion
• biogeonetworks
• climateStability
• VoCC
• pastclim

Species distribution models were generated using MaxEnt version 3.4.4.

Missing values

Missing values are represented as NA or empty cells where appropriate.

Data coding

Binary variables:

• 1 = presence
• 0 = absence

Logical variables:

• TRUE = record passed the corresponding filtering procedure
• FALSE = record failed the corresponding filtering procedure

Limitations

Species range maps were generated from occurrence records compiled from multiple sources. Although occurrence records were taxonomically verified, spatially thinned, and cleaned using CoordinateCleaner prior to analysis, residual sampling biases may remain. Species range estimates should therefore be interpreted in light of the limitations inherent to occurrence-based modelling approaches.

References

Beyer, R. M., Krapp, M., & Manica, A. (2020). High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years. Scientific Data, 7, 236. https://doi.org/10.1038/s41597-020-0552-1

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086