Prediction of the Potential Suitable Habitat of Spartina alterniflora in China and Comparison of Ecological Niches Between Its Native and Invaded Ranges Based on Species Distribution Models
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Le résumé fourni par la source
Invasive alien species (IAS) threaten coastal wetland ecosystems, and smooth cordgrass (Spartina alterniflora) is among the most damaging invaders along the coast of China. We compiled occurrence records from the invaded range (China) and native range (United States) and retained 358 and 291 spatially thinned occurrences after quality control and definition of coastal-accessible areas. We assembled climatic, topographic, land use, soil and anthropogenic predictors and fitted species distribution models using the biomod2 ensemble-modeling framework, complemented by an ecospat-based comparison of native and invaded niche spaces. The ensemble model (EM) showed high predictive accuracy (China: AUC = 0.98, TSS = 0.99; USA: AUC = 0.99, TSS = 0.94). Elevation (73.6%) and human influence (6.0%) were the strongest predictors, highlighting the role of intertidal geomorphology and human-mediated propagule pressure. Niche overlap between ranges was low (Schoener’s D = 0.13), and the invaded niche showed substantial unfilling (0.36), indicating additional environmental space at risk of colonization in China. The current suitable habitat forms a continuous coastal belt from the Bohai Rim through the Yellow Sea–East China Sea to the South China Sea. Projections under future climate change suggest predominantly stable suitable areas with localized expansions but potential contractions in some periods. Our results may support the early warning, surveillance prioritization, and adaptive management of S. alterniflora under climate change.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Prediction of the Potential Suitable Habitat of Spartina alterniflora in China and Comparison of Ecological Niches Between Its Native and Invaded Ranges Based on Species Distribution Models
- Date Crossref
- 17/06/2026
- Éditeur
- MDPI AG
- Type
- journal-article
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