Data and materials for "Climate limits the niche, settlement mosaics expand it: implications for the Asian range expansion of invasive Spanish slug Arion vulgaris"

This repository contains the data and supplementary materials associated with a manuscript currently in preparation, provisionally entitled “Climate limits the niche, settlement mosaics expand it: implications for the Asian range expansion of invasive Spanish slug Arion vulgaris”. The final title and bibliographic details will be updated upon acceptance. The materials include two major components:(1) species distribution modelling outputs, including environmental significance tables, AUC summaries, permutation importance heatmaps, partial dependence analyses, and global projections; and(2) phylogenetic data supporting species identification, including the COI alignment and resulting maximum-likelihood and Bayesian trees. All modelling scripts used to generate these results are openly available on GitHub (see Related identifiers). Data and results deposited here allow full reproduction of the analyses presented in the manuscript. Repository structure 1. Modeling/ Contains species distribution modelling results for the Arion vulgaris–A. rufus–A. ater complex: 01_environmental_significance.csv – Relative contributions of predictors in ensemble models. 02_AUC_all_runs_models.csv – Cross-validation AUC results for all algorithms and repetitions. 03_PI_heatmap_variables_by_region.pdf – Regional permutation importance across Asian subregions. 04_PD_all_regions_comparison.pdf – Partial dependence curves illustrating regional effects. Asia_0.5arcmin.tif – Final suitability projection for Asia at 0.5 arc-minute resolution. World_2.5arcmin.tif – Global suitability projection at 2.5 arc-minute resolution. Arion_niche_projections.qgz – QGIS project file containing the final layers and maps. 2. Phylogeny/ Supporting phylogenetic data for the identification of Arion vulgaris: 01_COI_alignment.fa – COI sequence alignment used in phylogenetic analyses. 02_ML_slugs_484bp_10kboot.pdf – Maximum likelihood tree with 10,000 bootstrap replicates. 03_BI_all_genes_MAP.tree.pdf – Bayesian MAP tree (all available markers). Methods summary Molecular techniques and phylogenetic analysis.DNA was extracted from foot tissues of two specimens, and COI was amplified using standard Folmer primers. Bidirectional Sanger sequencing was performed in Almaty. Reads were assembled in Ugene; final sequences (484 bp) are deposited in GenBank. The alignment was produced with MAFFT and filtered with gBlocks. The optimal substitution model (TPM3u+G+I) was selected using phangorn in R. Maximum-likelihood analysis with 100,000 bootstrap replicates was conducted in R, and Bayesian inference was run in RevBayes with three independent MCMC runs. Convergence diagnostics were performed in R. All phylogeny scripts are available on GitHub. Occurrence data.Distribution modelling used GBIF records for Arion vulgaris, A. rufus and A. ater, supplemented by visually verified iNaturalist observations from Asia. Due to unreliable external morphological separation and strong ecological niche overlap among these taxa, all records were treated as a single species complex. Occurrences were merged, visually checked, and aggregated into 25 × 25 km grid cells to reduce spatial autocorrelation, resulting in 2757 final presence points. Environmental predictors.We used two predictor groups: (1) bioclimatic variables from WorldClim 2.1, reduced to five weakly correlated predictors (Bio1, Bio2, Bio7, Bio12, Bio14), and (2) land-cover fractions from the CDR and Sentinel-3 CCI Land Cover datasets (2022), aggregated into intensive agriculture, mosaic agro-natural landscapes and urban areas. Environmental layers were processed in R using terra and geodata. Species distribution modelling.Global niche models for the Arion vulgaris–A. rufus–A. ater complex were built using biomod2 in R at 2.5 arc-minute resolution and projected onto Asia at 0.5 arc minutes. Four algorithms were used (GLM, GBM, RF, Maxent) with 12,000 pseudo-absences sampled outside a 200 km buffer around occurrences. Each model was trained with ten 70/30 cross-validation splits, and evaluated using AUC. An ensemble prediction was constructed using accuracy-weighted means. All modelling scripts are available on GitHub. Regional predictor assessment.To analyse localised effects of climatic and anthropogenic factors, the Asian study region was subdivided into 12 subregions (3 latitudinal × 4 longitudinal bands) with a European control area representing the native range. For each subregion, permutation importance (PI) of predictors and partial dependence (PD) curves were computed. PD curves were based on 100,000 random pixels per region. Analyses were performed in R (biomod2), and visualisations were produced with ggplot2.