One-hundred seventy-one models of connectivity across Scotland

Scotland Landscape Connectivity: Stochastic Permeability Analysis## Data and Code Archive — Ecological Informatics **Title:** One-hundred seventy-one models of connectivity across Scotland: Influences of method, source points, dispersal threshold, and functional shape on connectivity predictions **Authors:** Samuel A. Cushman, Zaneta Kaszta, Kerry Kilshaw, Roo Campbell, David W. Macdonald (Department of Biology, Southern Denmark University, Odense, Denmark; Wildlife Conservation Research Unit, University of Oxford, UK) **Journal:** Ecological Informatics (2026) **Data DOI (Zenodo):** https://doi.org/10.5281/zenodo.XXXXXXX **Code repository (GitHub):** https://github.com/USERNAME/scotland_connect **Data licence:** [Creative Commons Attribution 4.0 International (CC-BY 4.0)](https://creativecommons.org/licenses/by/4.0/) **Code licence:** [MIT License](https://opensource.org/licenses/MIT) --- ## Study overview This archive contains all data inputs, intermediate products, analysis scripts, andoutputs for the above manuscript. The study applies **stochastic permeabilityanalysis** to quantify landscape connectivity across Scotland using a resistancesurface derived from national habitat mapping. The central methodological questionis how strongly connectivity estimates depend on the spatial density of randomsource points used to parameterise the stochastic random walks, tested across**nine sample-density treatments** spanning more than two orders of magnitude(1/1.56 km² to 400k points). Stochastic permeability follows the framework of Cushman et al. (2010, 2013):large numbers of random walks are launched from randomly placed source pointsacross the resistance surface; path density is accumulated cell-by-cell toproduce a permeability surface representing functional landscape connectivitywithout requiring predefined source or destination patches. --- ## Archive structure ```zenodo_deposit/├── README.md This file├── LICENSE_data.txt CC-BY 4.0├── LICENSE_code.txt MIT├── CITATION.cff Machine-readable citation├── zenodo_metadata.json Zenodo deposit metadata (API / form)├── manifest.csv Full file inventory with sizes and roles│├── data/│ ├── 01_input/ Resistance surface + sample point XY files│ │ ├── resist1.rsg Landscape resistance surface (110 MB, RSG)│ │ ├── pts100k.xy … pts1_56k.xy Sample points at 7 standard densities│ │ └── resist1_pts_390k.xy / _780k.xy Additional subset point files│ ││ ├── 02_intermediate/ Resistance values extracted at each point set│ │ └── resist1_pts*.csv One file per density treatment│ ││ ├── 03_outputs/ Permeability rasters, path rasters, summary TIFs,│ │ │ correlation matrices, statistical tables│ │ ├── resist1_pts*.asc Stochastic permeability surface per density (~165 MB each)│ │ ├── paths_pts*.asc Path density surface per density (~91 MB each)│ │ ├── meanR.tif / cvR.tif Mean and CV of permeability across densities│ │ ├── perm1_sum/7sum/17sum.tif Cumulative permeability variants│ │ ├── perm_meanR.tif / perm_cvR.tif Replicate mean and CV permeability│ │ ├── paths_meanR.tif / paths_cvR.tif Mean and CV of path density│ │ ├── paths_meanRs.tif / paths_cvRs.tif Scaled path density surfaces│ │ ├── paths6_25ps.tif / paths_390ps.tif / paths_50ps.tif Selected density surfaces│ │ ├── mean1.tif / mean2.tif / mean3.tif Per-replicate-set means│ │ ├── corr_matrix.csv Pairwise Pearson r between permeability surfaces│ │ ├── corr_matrix_no_labels.csv Numeric-only version│ │ ├── corr_matrix_no_mean.csv Excluding grand-mean surface│ │ ├── corr_matrix_paths.csv Pairwise Pearson r between path surfaces│ │ ├── paths_corr_matrix.csv Alternative path correlation layout│ │ ├── dispd.csv Dispersal distance statistics per density│ │ ├── shaped.csv Long-format data for modelling│ │ ├── model_matrices.xlsx Pearson / Kendall / RMSE comparison matrices│ │ ├── time_tracking.xlsx Computation time by step and density│ │ ├── make_transformed_surface.xlsx Resistance transformation workbook│ │ ├── reclass1.csv … reclass5.csv Habitat-to-resistance look-up tables│ │ ├── resist1.rsg / .aux.xml Resistance surface copy (for workflow provenance)│ │ └── outputs.qgz QGIS 3.x project for visualisation│ ││ ├── 04_outputs_full/ Full 400k-point analysis│ │ ├── corr_matrix_full_no_labels.csv Highest-density pairwise correlation matrix (346 kB)│ │ ├── pcolor(corr_matrix_no_labels).txt Pseudo-colour R script│ │ └── pcolor_corr_matrix.jpg Visualisation of full correlation matrix│ ││ └── 05_revision_working/ Large R workspace objects (peer review revision)│ ├── stacked.RData Stacked permeability array, all densities (~8.0 GB)│ └── mat.RData Pairwise matrix object (~3.8 GB)│└── code/ All analysis scripts (identical to GitHub repo) ├── 01_make_resistance_surface.R ├── 02_generate_random_sample_points.R ├── 03_run_stochastic_permeability.R ├── 04_process_permeability_outputs.R ├── 05_process_path_outputs.R ├── 06_make_full_stacked_correlation.R ├── 07_run_cluster_mantel.R ├── 08_make_stack_run_comparisons.R └── 09_statistics_on_stack.docx``` --- ## Reproducing the analysis ### Software | Software | Version tested | Purpose ||---|---|---|| R | ≥ 4.2 | All statistical analyses and post-processing || UNICOR (or equivalent) | 2.x | Stochastic permeability path accumulation (Step 03) || QGIS | ≥ 3.22 | Visualisation (outputs.qgz) | Required R packages: `terra`, `raster`, `vegan`, `ggplot2`, `data.table`, `dplyr`, `tidyr`, `corrplot`, `dendextend`. ```rinstall.packages(c("terra","raster","vegan","ggplot2","data.table", "dplyr","tidyr","corrplot","dendextend"))``` ### Workflow | Step | Script | Inputs | Key outputs ||---|---|---|---|| 1 | 01_make_resistance_surface.R | Habitat raster, reclass*.csv | resist1.rsg || 2 | 02_generate_random_sample_points.R | resist1.rsg extent | pts*.xy || 3 | 03_run_stochastic_permeability.R | resist1.rsg, pts*.xy | resist1_pts*.asc, paths_pts*.asc || 4 | 04_process_permeability_outputs.R | resist1_pts*.asc | meanR.tif, cvR.tif, corr_matrix.csv || 5 | 05_process_path_outputs.R | paths_pts*.asc | paths_meanR.tif, paths_cvR.tif || 6 | 06_make_full_stacked_correlation.R | All *.asc | corr_matrix_full_no_labels.csv || 7 | 07_run_cluster_mantel.R | corr_matrix.csv | Cluster plots, Mantel r || 8 | 08_make_stack_run_comparisons.R | All *.asc | stacked.RData, mat.RData || 9 | 09_statistics_on_stack.docx | stacked.RData, mat.RData | Pearson/Kendall/RMSE tables | Step 03 is computationally intensive: allow 2–12 hours per density treatmentdepending on hardware. Steps 08–09 require ≥32 GB RAM to load stacked.RData. --- ## Spatial reference system All spatial data use **British National Grid, EPSG:27700**.No reprojection is needed to use files together. --- ## File format notes | Extension | Format | Open with ||---|---|---|| `.rsg` | ESRI binary raster | ArcGIS, QGIS, `terra::rast()` || `.asc` | ESRI ASCII grid | Any GIS or `terra::rast()` || `.tif` | GeoTIFF | Any GIS or `terra::rast()` || `.xy` | Tab-delimited text (Easting, Northing) | R, Excel, any GIS || `.csv` | Comma-delimited | R, Excel || `.RData` | R binary workspace | `load("file.RData")` in R || `.qgz` | Compressed QGIS project | QGIS 3.x || `.xlsx` | Excel workbook | Excel, LibreOffice Calc | --- ## Large file notice | File | Size | RAM required ||---|---|---|| stacked.RData | ~8.0 GB | ≥32 GB || mat.RData | ~3.8 GB | ≥16 GB || resist1_pts*.asc (each) | ~160–173 MB | — || paths_pts*.asc (each) | ~90–91 MB | — | **Total archive: ~14.6 GB.** Zenodo supports files up to 50 GB per record.If your account quota is exceeded, request an increase athttps://zenodo.org/help/faq before uploading. --- ## Citation > Cushman S.A., Kaszta Z., Kilshaw K., Campbell R., Macdonald D.W. (2026). One-hundred seventy-one models of connectivity across Scotland: Influences of method, source points, dispersal threshold, and functional shape on connectivity predictions.> *Ecological Informatics*. <https://doi.org/JOURNAL_DOI>>> Data archive: https://doi.org/10.5281/zenodo.XXXXXXX> Code repository: https://github.com/USERNAME/scotland_connect --- ## Licences **Data:** CC-BY 4.0. You are free to share and adapt the data for any purposeprovided you give appropriate credit. Full text: https://creativecommons.org/licenses/by/4.0/ **Code:** MIT License. See LICENSE_code.txt. --- ## Contact Samuel A. Cushman, Zaneta Kaszta, Kerry Kilshaw, Roo Campbell, David W. Macdonald | [e-mail] | Department of Biology, Southern Denmark University, Odense, Denmark; Wildlife Conservation Research Unit, University of Oxford, UKORCID: https://orcid.org/0000-0002-2742-065X