The central insight behind RIRC is deceptively simple: knowing how damaging a species can be is not the same as knowing where it will actually do damage. Invasion risk, the researchers argue, is not an inherent property of a species but an emergent outcome of interactions between invader traits and the ecological and socioenvironmental characteristics of the receiving region. The same non-native plant may cause severe ecological disruption in one province while remaining rare and inconsequential in another, because impacts depend on biotic interactions, abiotic conditions, and human pressures that all vary across the landscape. Trait-based or globally derived assessments alone, therefore, cannot reliably predict local ecological outcomes.

To bridge this gap, the framework combines two established tools. The first is the Environmental Impact Classification for Alien Taxa, known as EICAT, a standardized system developed under the International Union for Conservation of Nature that ranks alien species from Minimal Concern to Massive impact based on documented harm to native biodiversity. The second is species distribution modeling, or SDM, which uses environmental variables such as climate, topography, and land cover to predict where a species could establish and spread. By multiplying the two scores on aligned one-to-five scales, RIRC produces a regional risk value ranging from one to twenty-five, which is then binned into six categories from Very Low to Extreme. The multiplicative approach is deliberately conservative: an area scores high risk only when both impact severity and climatic suitability are high, avoiding the compensatory behavior of additive methods where a high value in one dimension can mask a low value in the other.

The technical machinery behind the maps is substantial. The researchers compiled verified occurrence records from the Global Biodiversity Information Facility, the CABI Invasive Species Compendium, and Iranian literature in both English and Persian, applying rigorous quality control that retained only georeferenced records with spatial uncertainty of a kilometer or less and thinned the data to a single record per square kilometer. Climatic suitability was modeled using random forest algorithms trained on nineteen bioclimatic variables from the CHELSA database at roughly one-kilometer resolution, with multicollinearity filtered out through Spearman rank correlations and variance inflation factor analysis. Pseudo-absence points were generated at a one-to-one ratio to presence records and placed at least two degrees away from known occurrences, a strategy shown to optimize random forest performance for species with limited data. Hyperparameters were tuned through five-fold cross-validation with spatially structured folds, and model performance was assessed using out-of-bag error, area under the receiver operating characteristic curve, and the True Skill Statistic.

The models performed remarkably well. Test accuracies ranged from 90.3 percent for snake vine to 99.2 percent for golden wreath wattle, with twelve species exceeding 95 percent. AUC values varied between 0.928 and 0.992, and TSS values ranged from 0.857 to 0.984. Notably, predictive performance did not differ systematically by growth form, photosynthetic pathway, habitat preference, or EICAT impact magnitude, suggesting the framework is robust across ecological types. Predicted high-suitability areas varied enormously among species, from 18,432 square kilometers for the Chinese empress tree to a staggering 476,298 square kilometers for Cynanchum acutum, a clonal perennial vine known as stranglewort.

The spatial patterns that emerged are striking. Three main invasion hotspots dominate the maps: the Caspian northern lowlands and Alborz foothills in the north, the Persian Gulf and Gulf of Oman coasts in the south, and the Zagros foothills in the southwest. In contrast, the central plateau and the hyperarid basins of Dasht-e Kavir and Dasht-e Lut are unsuitable for most species. Each functional group tells its own story. Salt- and drought-tolerant species such as mesquite, with 279,376 square kilometers of high suitability, dominate the southern coasts and southwestern lowlands. Tree invaders like tree-of-heaven concentrate in the northern provinces along the Caspian coast. Aquatic species, including water lettuce, water hyacinth, and water fern, cluster around the Caspian littoral wetlands, while succulents such as Indian fig opuntia and the halophytic shoreline purslane form nearly continuous bands along the southern shoreline.

When the individual risk maps were overlaid to produce a cumulative picture, the Caspian coastal plain emerged as the country’s most urgent priority, with overlapping extreme- and high-impact zones supporting tree-of-heaven, western ragweed, stranglewort, fiddleneck, and the aquatic invaders. The southern and southeastern regions, including Sistan and Baluchestan and the Zagros foothills, ranked as the second most critical area, driven by mesquite, North Indian rosewood, cogon grass, and valley redstem. Under mid-century climate projections, the number of species contributing to rising risk in the northern coastal regions climbs from four to six under the intermediate RCP 4.5 scenario to seven to nine under the high-emission RCP 8.5 scenario, while the central deserts show declining contributions.

Perhaps the most surprising finding is what did not happen. Rather than a uniform upward shift toward higher impact categories, climate change projections revealed a pronounced bidirectional redistribution among risk classes. Some species expand dramatically in low-risk areas while contracting in moderate and very high zones, as seen with cogon grass, which gained roughly 370,000 square kilometers in low-risk areas while losing ground in higher categories. Stranglewort showed the most dramatic turnover of all, expanding by about 640,000 square kilometers in moderate-risk areas while losing 425,000 square kilometers from very high zones. Aquatic species each gained nearly 300,000 square kilometers in low-impact areas under RCP 8.5 while losing up to 360,000 square kilometers from moderate and high classes. Extreme-risk zones, meanwhile, remained geographically restricted and did not expand proportionally under either scenario, indicating that climate change reshuffles invasion risk as much as it amplifies it.

The researchers are candid about the framework’s limitations. EICAT assessments carry epistemic uncertainty from incomplete ecological knowledge and linguistic uncertainty from standardized terminology, challenges amplified in regions where local impact evidence is scarce. Most of the EICAT scores in this study were drawn from global datasets, meaning local impacts may differ from the global maximum categories used. Species distribution models introduce their own uncertainties, including biased occurrence data, uneven sampling, and pseudo-absence selection. By combining the two tools, RIRC necessarily accumulates uncertainty from both sources. The authors also defended their choice of equal weighting for impact and suitability, arguing that any alternative weighting scheme would embed subjective judgments about which component matters more, and that situations of high impact with low suitability, or the reverse, ultimately require normative decisions about acceptable levels of risk.

Even with these caveats, the practical implications are clear. The findings point to region-specific strategies built around early detection and rapid response, with monitoring prioritized for hydrological corridors in the north, plantations and ports in the south, and sentinel plots at high-elevation boundaries where species like spotted spurge and valley redstem are beginning to climb. At the national level, the framework supports predictive pathway management, climate-informed reprioritization of hotspots, and strategically deployed detection teams. RIRC is presented as an evolving tool rather than a fixed model: as EICAT assessments become more locally informed and distribution models improve with expanded sampling and higher-resolution environmental data, its reliability will grow. Future refinements could integrate land-use dynamics, soil traits, hydrological variables, and anthropogenic disturbances. For now, the framework offers something invasion biology has long needed: a transparent, scalable way to translate global impact assessments into locally actionable maps, before the invaders arrive.

Subject of Research: A predictive framework combining EICAT impact scores with species distribution models to classify regional invasion risks of alien plants in Iran under current and future climates

Article Title: Regional impact risk classification (RIRC): A predictive framework for plant invasion impacts

Article References: Oveisi, M., Hazrati, Z., Sun, Y., Hejazi, M., Bacher, S., & Mueller Schaerer, H. (2026). Regional impact risk classification (RIRC): A predictive framework for plant invasion impacts. NeoBiota, 107, 243-268. https://doi.org/10.3897/neobiota.107.182605

Image Credits: AI Generated

DOI: 10.3897/neobiota.107.182605

Keywords: invasive species, plant invasion, risk classification, EICAT, species distribution models, climate change, Iran, biodiversity, invasion hotspots, NeoBiota, ecological impact, conservation planning