Evidence map›Paper›PMID 42494626›Full record

ArticleFrontiers in plant science2026

Simulating the distribution changes and future climate responses of a key invasive plant in Northwest China's natural grasslands using an optimized MaxEnt model.

Gulsum Rixit, Rukeya Sawut, Alimujiang Kasimu, Ainiwaer Aimaier, Ma Bao Yue

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Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Gulsum RixitCollege of Geographic Science and Tourism, Xinjiang Normal University, Ürümqi, China.
Rukeya SawutCollege of Geographic Science and Tourism, Xinjiang Normal University, Ürümqi, China.
Alimujiang KasimuCollege of Geographic Science and Tourism, Xinjiang Normal University, Ürümqi, China.
Ainiwaer AimaierXinjiang Uygur Autonomous Region Academy of Animal Sciences, Grassland Research Institute, Ürümqi, China.
Ma Bao YueXinyuan County Forestry and Grassland Bureau, Yili, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Global biodiversity and ecosystem services are threatened by invasive alien plants. Ragweed (Ambrosia artemisiifolia L.), a globally problematic weed, has rapidly spread across Xinjiang's Ili Prefecture-particularly in Xinyuan County-and presents serious obstacles to local agriculture, animal husbandry, and human health. However, in the context of climate change, the fine-scale distribution dynamics and key driving mechanisms of this species remain poorly understood. Methods: Based on field survey data from Xinyuan County, a Kuenm-optimized MaxEnt model was combined with four shared socioeconomic pathway (SSP) scenarios from CMIP6 to systematically simulate the spatiotemporal evolution of the potential habitat of ragweed at present and from the 2030s to the 2090s. Results: (1) The optimized model exhibited exceptional predictive accuracy (AUC = 0.991; Partial ROC ratio = 1.959, p < 0.001; empirical omission rate = 0.0267), with precipitation seasonality (BIO15) and isothermality (BIO3) as the primary environmental factors constraining ragweed distribution. (2) Current high-risk zones predominantly exhibit distributions along river valley alluvial plains and road networks. Overlay analysis revealed that mountain steppe grasslands face the most severe stress, followed by low-middle mountain meadow grasslands, whereas mountain meadow steppe is least affected. (3) In the future, the total area of highly suitable ragweed habitats will decrease, with a significant decrease in the area of core suitable zones (mountain steppe). Under most future scenarios and periods, the distribution centroid is projected to exhibit predominantly westward and northwestward passive displacement (maximum displacement: 0.829 km under the SSP585 scenario in the 2050s), driven by the contraction of suitable habitat in low-elevation river valleys rather than by active colonization of high-elevation zones. High-elevation alpine grassland ecosystems (mountain meadow steppe and mountain desert steppe) consistently demonstrated strong resilience against ragweed invasion across all projected periods and scenarios. Discussion: This study reveals the response pattern of ragweed, characterized by "contraction in low-elevation core areas, passive westward-northwestward displacement of the distribution centroid, and maintenance of the alpine barrier." A zoned control strategy focused on implementing physical eradication and replacement restoration in river valley core areas and establishing early warning systems in the transitional zones between mountain grasslands and meadows is recommended to safeguard regional ecological security.

Indexed as

climate changekuenm optimizationMAXENT modelragweedsuitable habitat distributionXinyuan County

Identifiers

PMID42494626
PMCPMC13391865

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.