MaxEnt-Based Distribution Modeling of the Invasive Species <i>Phragmites australis</i> Under Climate Change Conditions in Iraq
<i>Phragmites australis</i> (common reed), a recently introduced invasive species in Iraq, has swiftly established itself as a vigorous perennial plant, significantly impacting the biodiversity and ecosystem functions of Iraqi ecoregions with alarming consequences. There is an insufficie...
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MDPI AG
2025-03-01
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| author | Nabaz R. Khwarahm |
| author_facet | Nabaz R. Khwarahm |
| author_sort | Nabaz R. Khwarahm |
| collection | DOAJ |
| description | <i>Phragmites australis</i> (common reed), a recently introduced invasive species in Iraq, has swiftly established itself as a vigorous perennial plant, significantly impacting the biodiversity and ecosystem functions of Iraqi ecoregions with alarming consequences. There is an insufficient understanding of both the current distribution and possible future trends under climate change scenarios. Consequently, this study seeks to model the current and future potential distribution of this invasive species in Iraq using machine learning techniques (i.e., MaxEnt) alongside geospatial tools integrated within a GIS framework. Land-cover features, such as herbaceous zones, wetlands, annual precipitation, and elevation, emerged as optimal conditioning factors for supporting the species’ invasiveness and habitat through vegetation cover and moisture retention. These factors collectively contributed by nearly 85% to the distribution of <i>P. australis</i> in Iraq. In addition, the results indicate a net decline in high-suitability habitats for <i>P. australis</i> under both the SSP126 (moderate mitigation; 5.33% habitat loss) and SSP585 (high emissions; 6.74% habitat loss) scenarios, with losses concentrated in southern and northern Iraq. The model demonstrated robust reliability, achieving an AUC score of 0.9 ± 0.012, which reflects high predictive accuracy. The study area covers approximately 430,632.17 km<sup>2</sup>, of which 64,065.66 km<sup>2</sup> (14.87% of the total region) was classified as the optimal habitat for <i>P. australis</i>. While climate projections indicate an overall decline (i.e., SSP126 (5.33% loss) and SSP585 (6.74% loss)) in suitable habitats for <i>P. australis</i> across Iraq, certain localized regions may experience increased habitat suitability, reflecting potential gains (i.e., SSP126 (3.58% gain) and SSP585 (1.82% gain)) in specific areas. Policymakers should focus on regions with emerging suitability risks for proactive monitoring and management. Additionally, areas already infested by the species require enhanced surveillance and containment measures to mitigate ecological and socioeconomic impacts. |
| format | Article |
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| institution | DOAJ |
| issn | 2223-7747 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Plants |
| spelling | doaj-art-4d4c9d7438d04fdb93b7f207350568162025-08-20T02:59:15ZengMDPI AGPlants2223-77472025-03-0114576810.3390/plants14050768MaxEnt-Based Distribution Modeling of the Invasive Species <i>Phragmites australis</i> Under Climate Change Conditions in IraqNabaz R. Khwarahm0Department of Biology, College of Education, University of Sulaimani, Sulaimani 334, Kurdistan Region, Iraq<i>Phragmites australis</i> (common reed), a recently introduced invasive species in Iraq, has swiftly established itself as a vigorous perennial plant, significantly impacting the biodiversity and ecosystem functions of Iraqi ecoregions with alarming consequences. There is an insufficient understanding of both the current distribution and possible future trends under climate change scenarios. Consequently, this study seeks to model the current and future potential distribution of this invasive species in Iraq using machine learning techniques (i.e., MaxEnt) alongside geospatial tools integrated within a GIS framework. Land-cover features, such as herbaceous zones, wetlands, annual precipitation, and elevation, emerged as optimal conditioning factors for supporting the species’ invasiveness and habitat through vegetation cover and moisture retention. These factors collectively contributed by nearly 85% to the distribution of <i>P. australis</i> in Iraq. In addition, the results indicate a net decline in high-suitability habitats for <i>P. australis</i> under both the SSP126 (moderate mitigation; 5.33% habitat loss) and SSP585 (high emissions; 6.74% habitat loss) scenarios, with losses concentrated in southern and northern Iraq. The model demonstrated robust reliability, achieving an AUC score of 0.9 ± 0.012, which reflects high predictive accuracy. The study area covers approximately 430,632.17 km<sup>2</sup>, of which 64,065.66 km<sup>2</sup> (14.87% of the total region) was classified as the optimal habitat for <i>P. australis</i>. While climate projections indicate an overall decline (i.e., SSP126 (5.33% loss) and SSP585 (6.74% loss)) in suitable habitats for <i>P. australis</i> across Iraq, certain localized regions may experience increased habitat suitability, reflecting potential gains (i.e., SSP126 (3.58% gain) and SSP585 (1.82% gain)) in specific areas. Policymakers should focus on regions with emerging suitability risks for proactive monitoring and management. Additionally, areas already infested by the species require enhanced surveillance and containment measures to mitigate ecological and socioeconomic impacts.https://www.mdpi.com/2223-7747/14/5/768alien speciesclimate changeIraq<i>Phragmites australis</i>machine learning spatial distribution |
| spellingShingle | Nabaz R. Khwarahm MaxEnt-Based Distribution Modeling of the Invasive Species <i>Phragmites australis</i> Under Climate Change Conditions in Iraq Plants alien species climate change Iraq <i>Phragmites australis</i> machine learning spatial distribution |
| title | MaxEnt-Based Distribution Modeling of the Invasive Species <i>Phragmites australis</i> Under Climate Change Conditions in Iraq |
| title_full | MaxEnt-Based Distribution Modeling of the Invasive Species <i>Phragmites australis</i> Under Climate Change Conditions in Iraq |
| title_fullStr | MaxEnt-Based Distribution Modeling of the Invasive Species <i>Phragmites australis</i> Under Climate Change Conditions in Iraq |
| title_full_unstemmed | MaxEnt-Based Distribution Modeling of the Invasive Species <i>Phragmites australis</i> Under Climate Change Conditions in Iraq |
| title_short | MaxEnt-Based Distribution Modeling of the Invasive Species <i>Phragmites australis</i> Under Climate Change Conditions in Iraq |
| title_sort | maxent based distribution modeling of the invasive species i phragmites australis i under climate change conditions in iraq |
| topic | alien species climate change Iraq <i>Phragmites australis</i> machine learning spatial distribution |
| url | https://www.mdpi.com/2223-7747/14/5/768 |
| work_keys_str_mv | AT nabazrkhwarahm maxentbaseddistributionmodelingoftheinvasivespeciesiphragmitesaustralisiunderclimatechangeconditionsiniraq |