Evidence map›Paper›PMID 42056133›Full record

ArticleScientific reports2026

Predicting the impact of climate change on the suitable habitat of Populus qiongdaoensis in Hainan Island using MaxEnt modeling.

Yang Tian, Tong Zhang, Qinuo Li, Baochang Cheng, Ruobing Wei, Yong Zeng, Jingkai Zhang, Jianguo Zhang, Zhaoshan Wang

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Article in Scientific reports, 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

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yang Tian *Key Laboratory of Silviculture of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China. 1850813803@qq.com.
Tong Zhang *Powerchina Northwest Engineering Corporation Limited, Xi'an, 710100, China.
Qinuo LiChina Three Gorges University, Yichang, 443002, China.
Baochang ChengKey Laboratory of Silviculture of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.
Ruobing WeiKey Laboratory of Silviculture of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.
Yong ZengKey Laboratory of Silviculture of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.
Jingkai ZhangKey Laboratory of Silviculture of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.
Jianguo ZhangKey Laboratory of Silviculture of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China. zhangjg@caf.ac.cn.
Zhaoshan WangKey Laboratory of Silviculture of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China. w@caf.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Populus qiongdaoensis is a rare and endemic tree species exclusively located in Hainan Island, China, holding important value for species conservation and ecosystem function. However, its natural populations are threatened by habitat fragmentation and climate change. Based on 42 occurrence records and environmental data, we employed the MaxEnt model to predict the species' suitable habitats, analyze the dominant environmental factors influencing its distribution, and assess changes under future climate scenarios (using the BCC-CSM2-MR model for the 2050s and 2090s under four SSP pathways). The results showed that elevation, mean temperature of the driest quarter (bio9), and precipitation seasonality (bio15) were the most crucial factors affecting its distribution. Currently, the suitable habitats are primarily located in the Bawangling and Wuzhishan mountain regions, covering approximately 9.6% of the island's area. Under future scenarios, the total suitable habitat area is projected to increase by 7.4% to 34.2% across most projections. However, the area of highly suitable habitat is predicted to decrease notably under the low-emission scenario (SSP126) in the 2050s. Furthermore, the centroid of the species' distribution is expected to shift northeastward over time, though remaining within the Wuzhishan region. This study delineates priority conservation areas and provides a critical scientific basis for the habitat protection, population restoration, and sustainable management of P. qiongdaoensis under climate change.

Indexed as

Climate ChangeEcosystemPopulusChinaConservation of Natural ResourcesIslandsModels, TheoreticalSeasonsTemperatureClimate changeEnvironmental factorsMaxEntPopulus qiongdaoensisSuitable habitat

Identifiers

PMID42056133
PMCPMC13226693

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