Suitability-space species trait dataset for priority invasive alien plants (Ailanthus altissima; Acer negundo) in the Danubian riparian corridor
Description
Description Woody invasive alien plant species (IAS) can be characterised by their realised suitability niche derived from modelled associations between occurrences and environmental gradients. This dataset serves as a species trait database in the form of model-derived “suitability-space traits” (predictor–suitability association signatures), together with harmonised 1-km predictors, domain tables, and scenario-specific normalised suitability surfaces for the Danubian riparian corridor. This enables users to fit alternative SDM model families, quantify predictor–suitability associations (“species suitability-space traits”), assess transferability across domains, and test bias-correction strategies using accessibility-linked predictors. The release contains: (1) an AOI polygon (Danubian riparian corridor extent); (2) screened environmental predictors aligned to a common 1-km grid in ETRS89-LAEA (EPSG:3035), provided as transformed (where relevant) and z-standardised rasters (mean 0, SD 1) with the scaling table; (3) species-specific occurrence bundles (Ailanthus altissima; Acer negundo) with three 1-km aggregated domain tables: Danube calibration data using a target-group background (TGB) design (presence vs TGB pseudoabsence; includes spatial CV fold IDs), and two independent evaluation domains (Romania and Serbia) with 1-km presence/absence tables. All tables include a stable 1-km cell identifier (cell_id). (4) Scenario-specific PN suitability GeoTIFFs (Figs. S1–S8): each raster is probability-like normalised to sum to 1 within the Danube AOI. PN GeoTIFF naming template PN_{species}_{scenario}_1km_EPSG3035.tif, where species ∈ {Ailanthus_altissima, Acer_negundo} and scenario ∈ {S1_baseline, S2_bias_aware, S3_romania, S4_serbia_riparian}. Primary occurrence sources (Danube calibration; opportunistic citizen science) GBIF (Global Biodiversity Information Facility; aggregated backbone and publisher feeds): https://www.gbif.org iNaturalist: https://www.inaturalist.org Observation.org: https://observation.org Pl@ntNet: https://plantnet.org NABU|naturgucker: https://www.naturgucker.de ; https://www.nabu.de External-domain evaluation data sources Romania: Anastasiu, P. et al. (2024). Alien plant species distribution in Romania… Biodiversity Data Journal, 12, e119539. https://doi.org/10.3897/BDJ.12.e119539 Serbia: Anđelković, A. A. et al. (2022). Plant invasions in riparian areas… NeoBiota, 71, 23–48. https://doi.org/10.3897/neobiota.71.69716 This dataset accompanies the manuscript by Halabuk et al., “From citizen science records to invasive plant indicators: multi-domain evaluation and scenario-based model selection for the Danube corridor”. Acknowledgement Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I01-03-V04-00005.
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Steps to reproduce
How users can use it This dataset is meant to be re-used as a feature set for alternative modelling algorithms, variable importance analyses, bias-correction methods, etc., for example: • learning IAS trait–environment relationships with other model families (tree-based models, regularised regression, Bayesian approaches), • comparing species’ response-curve signatures between domains (transferability diagnostics). Interpretation caveats These “traits” are not intrinsic functional traits measured on individuals; they are model-derived descriptors of realised associations in an specific observation context. They reflect the environmental and observation regimes represented in the input data and preprocessing. They should therefore be interpreted as statistical niche descriptors (“suitability-space traits”) rather than mechanistic traits. The more details are described in the Readme.txt