DOI: 10.5061/dryad.gqnk98szd
title: Urbanization drives habitat suitability of the invasive Cuban Knight Anole (<em>Anolis equestris</em>) in Florida, USA

description: This dataset supports the study Urbanization Drives Habitat Suitability of
 the Invasive Cuban Knight Anole (Anolis equestris) in Florida, which
 evaluated climatic and anthropogenic drivers of habitat suitability in
 both the species’ native range (Cuba) and invasive range (Florida). We
 developed replicated species distribution models (SDMs) using eight
 algorithms and ten independent pseudo-absence sets (1:1 PA:presence
 ratio), with regional stratification and 10-fold cross-validation (100
 runs per algorithm). Predictors included climate, topography, vegetation
 indices, human population density, urban land cover, and a standardized
 iNaturalist observer-effort layer. An ensemble modeling framework was
 applied at both the algorithm-specific and global levels. Model
 performance was assessed with True Skill Statistic (TSS) and Boyce Index
 (BI) calculated separately for each region. The best-performing model, a
 Random Forest ensemble, was validated with an independent long-term
 monitoring dataset from South Florida. Results show that urbanization
 variables had a stronger influence in Florida, while climate and
 vegetation played larger roles in Cuba. Predicted high-suitability areas
 for A. equestris overlapped with occurrence points of three protected
 invertebrates: Florida tree snail (Liguus fasciatus), Schaus’ swallowtail
 (Papilio aristodemus ponceanus), and Miami tiger beetle (Cicindelidia
 floridana). This package contains cleaned occurrence records,
 environmental layers generated for the analysis, habitat suitability
 rasters for both regions, model performance summaries, invertebrate
 overlap results, and the R script used to reproduce the workflow.
description: # Urbanization drives habitat suitability of the invasive Cuban Knight
 Anole (*Anolis equestris*) in Florida, USA [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.gqnk98szd) This dataset supports the
 study published in *Ecology and Evolution*\ (Romer et al. 2025;
 [https://doi.org/10.1002/ece3.72334](https://doi.org/10.1002/ece3.72334)),
 which evaluated climatic and anthropogenic drivers of habitat suitability
 for the invasive Cuban knight anole (*Anolis equestris*) across its native
 (Cuba) and invasive (Florida, USA) ranges.\ Replicated
 species-distribution models (SDMs) were constructed using eight algorithms
 and ten independent pseudo-absence sets, with regional stratification and
 10-fold cross-validation.\ A Random Forest ensemble showed the highest
 performance and was validated with a 15-year independent monitoring
 dataset from South Florida. --- ## Descriptions ###
 **ECE-2025-02-00429.R** * *Type*: R script * *Purpose*: Implements the
 full SDM workflow, including data import, preprocessing, model training
 (eight algorithms × 10 replicates), ensemble generation, evaluation, and
 figure export. * *Key packages*: `biomod2`, `terra`, `sf`, `randomForest`,
 `ggplot2`, `glmmTMB`, `emmeans`. * *Output*: Performance metrics,
 variable-importance plots, and suitability rasters. --- ###
 **independent_survey_data.csv** * *Type*: Presence/absence dataset
 (2008–2023). * *Source*: long-term (2008–2023) monitoring effort for
 invasive ectotherms in South Florida. * *Columns*: * *SiteID*: Survey
 location code. * *Latitude*, *Longitude*: decimal degrees (WGS 84). *
 *Presence*: 1 = present, 0 = absent. --- ###
 **ThreatenedInvertDataObscured.csv** * *Type*: Occurrence data for three
 protected invertebrates\ (*Liguus fasciatus*, *Papilio aristodemus
 ponceanus*, *Cicindelidia floridana*). * *Coordinates*: Randomly displaced
 ≤ 10 km to protect sensitive sites. * *Columns*: * *Species*: Scientific
 name. * *Latitude*, *Longitude*: decimal degrees (WGS 84). * *Source*:
 Originating agency (FWC or FDEP). --- ### **Algo_Selection.png** *
 *Content*: Comparison of algorithm-level and ensemble-level performance
 using True Skill Statistic (TSS) and Boyce Index (BI). ###
 **Variable_Importance.png** * *Content*: Regional variable-importance
 scores for the final Random Forest ensemble. ### **Cuba_Map.jpg** and
 **Florida_Map.jpg** * *Content*: Habitat-suitability rasters (0–1 scale)
 for each region. * *Resolution*: 30 arc-sec (~1 km²). ###
 **invert_cosuitability_annotated_plot.png** * *Content*: Predicted
 suitability of *A. equestris* at locations of the three threatened
 invertebrates. ### **SFig1.jpg – SFig4.jpg** * *Content*: Individual
 exports of supplementary figures (e.g., correlation matrix, diagnostics).
 ### **Supplementary_Figures_ECE-2025-02-00429.pdf** * *Content*: Combined
 supplemental figures ### **Supplementary_Tables_ECE-2025-02-00429.xlsx** *
 *Sheets*: * *Model_Performance*: TSS and BI for all algorithms × regions.
 * *Variable_Importance*: Scaled importance scores per predictor. *
 *Predictor_Correlations*: Pairwise Pearson r values (|r| &gt; 0.7
 flagged). --- ## Key Variables * *Latitude / Longitude*: decimal degrees
 (WGS 84) * *Presence*: 1 = present, 0 = absent * *TSS*, *BI*: unitless
 model-performance metrics (0–1) * *Suitability*: predicted habitat
 suitability (0–1 continuous scale) * *BIO1 – BIO19*: WorldClim bioclimatic
 predictors (°C or mm) * *Elevation*: meters above sea level (m) * *NDVI*,
 *EVI*: vegetation indices (unitless −1 to 1) * *Settlement Model Grid*,
 *Built-up Surface*, *Population Density*: urbanization metrics (GHSL;
 fraction or people per km²) * *iNaturalist Effort*: log-transformed
 observation density ln(records per km² + 1) --- ## Key Information Sources
 Environmental and ancillary data were obtained from publicly available
 repositories: * **WorldClim v2.1 Bioclimatic Variables:**
 [https://www.worldclim.org/data/worldclim21.html](https://www.worldclim.org/data/worldclim21.html) * **Global Multi-resolution Terrain Elevation Data (GMTED2010):** [https://topotools.cr.usgs.gov/gmted/](https://topotools.cr.usgs.gov/gmted/) * **Global Human Settlement Layers (GHSL):** [https://ghsl.jrc.ec.europa.eu/](https://ghsl.jrc.ec.europa.eu/) * **MODIS EVI and NDVI Products:** [https://modis.gsfc.nasa.gov/data/dataprod/](https://modis.gsfc.nasa.gov/data/dataprod/) * **Florida Administrative Boundaries (FL DOT):** [https://hub.arcgis.com/datasets/519e0a0ed5984bedba53695e1f56c1ee](https://hub.arcgis.com/datasets/519e0a0ed5984bedba53695e1f56c1ee) * **Cuba Administrative Boundaries (OCHA):** [https://data.humdata.org/dataset/cod-ab-cub](https://data.humdata.org/dataset/cod-ab-cub) * **GBIF Occurrence Records for *Anolis equestris*:** [https://www.gbif.org/species/2458613](https://www.gbif.org/species/2458613) * **iNaturalist Observation Data (Phylum Chordata):** [https://www.inaturalist.org/](https://www.inaturalist.org/) All listed sources are open-access and compatible with CC0. Derived layers (e.g., scaled rasters, effort surfaces) were authored by the dataset creators and are likewise released under CC0. --- ## Code / Software R (version 4.3.1 or later) is required to run **ECE-2025-02-00429.R**.\ The script is fully annotated and self-contained: 1. Library loading 2. Data import and cleaning 3. Model fitting and ensemble construction 4. Validation and evaluation 5. Figure and table export Hardware used: Windows 11 OS, Intel i7 CPU, 32 GB RAM. --- ## Licensing and Attribution * **Data:** Released under CC0 Public Domain Dedication ([https://creativecommons.org/publicdomain/zero/1.0/](https://creativecommons.org/publicdomain/zero/1.0/)). * **Creative works and figures ( Zenodo supplements ):** Licensed under CC BY 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). * All data were generated by the authors or derived from sources permitting unrestricted reuse; no copyrighted or restricted materials are included.