Regional impact risk classification (RIRC): a predictive framework for plant invasion impacts
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Abstract
Regional prediction of impact risk is essential for prioritizing targeted management of invasive alien plants. We present the Regional Impact Risk Classification (RIRC) framework, which combines the Environmental Impact Classification for Alien Taxa (EICAT) with species distribution models (SDMs) to connect ecological impact severity with the spatial likelihood of establishment and spread under current and future climates. Applying RIRC to 20 alien plants in Iran, we produced species-specific and aggregated regional maps, along with detailed climatic suitability projections. The analysis revealed significant regional contrasts: very high to extreme risks for salt- and drought-tolerant taxa along the southern coasts, a dominance of tree invaders in the southwestern lowlands and the Zagros, and hotspots of trees, woody climbers, and aquatic species in the northern provinces. Aggregated maps identified the Caspian coast and southern regions as hotspots for invasion risk. Under RCP 4.5 and 8.5 climate scenarios, RIRC levels increase along the northern and southern coasts but decline in central deserts, with species-specific responses ranging from strong expansions (e.g., Cynanchum acutum ) to projected contractions (e.g., Paulownia fortunei ). By linking impact severity with spatial dynamics, RIRC provides a scalable decision-support framework for guiding invasion management and conservation planning under current and future climates.
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ARPHA Preprints
https://doi.org/10.3897/arphapreprints.e182608 (16 Dec 2025)
https://doi.org/10.3897/arphapreprints.e182608 (16 Dec 2025)
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doi:
10.3897/arphapreprints.e182608
First posted
16 Dec 2025
Authors
Mostafa Oveisi
- Corresponding author
University of Tehran, Karaj, Iran
University of Tehran, Karaj, Iran
University of Fribourg, Fribourg, Germany
University of Tehran, Karaj, Iran
University of Fribourg, Fribourg, Switzerland
Heinz Müller-Schärer
- Corresponding author
Ecology & Evolution, Fribourg, Switzerland
Prof. Dr., Fribourg, Switzerland
Conflict of interest
The authors have declared that no competing interests exist.
Supporting agencies
MO acknowledges support from the Swiss National Science Foundation (Scientific Exchange grant IZSEZ0_204050). YS acknowledges funding from the National Natural Science Foundation of China (grant 32522062) and the Fundamental Research Funds for the Central Universities (grant 2662025ZHPY003). SB acknowledges support from the Swiss National Science Foundation (grant IC00I0-231475). HMS acknowledges funding from the Swiss National Science Foundation (grant 31003A_166448).
This is an open access preprint distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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