Evidence map›Paper›PMID 37791293›Full record

ArticleEcology and evolution2023

Impacts of physiological characteristics and human activities on the species distribution models of orchids taking the Hengduan Mountains as a case.

Xue-Man Wang, Pei-Hao Peng, Mao-Yang Bai, Wen-Qian Bai, Shi-Qi Zhang, Yu Feng, Juan Wang, Ying Tang

Abstract read
In one paragraph

Article in Ecology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xue-Man WangCollege of Earth Sciences Chengdu University of Technology Chengdu China.ORCID https://orcid.org/0000-0002-7063-9729
Pei-Hao PengCollege of Earth Sciences Chengdu University of Technology Chengdu China.
Mao-Yang BaiCollege of Earth Sciences Chengdu University of Technology Chengdu China.
Wen-Qian BaiCollege of Earth Sciences Chengdu University of Technology Chengdu China.
Shi-Qi ZhangCollege of Earth Sciences Chengdu University of Technology Chengdu China.
Yu FengCollege of Earth Sciences Chengdu University of Technology Chengdu China.
Juan WangCollege of Tourism and Urban-rural Planning Chengdu University of Technology Chengdu China.
Ying TangCollege of Tourism and Urban-rural Planning Chengdu University of Technology Chengdu China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The biogeography research of orchids through species distribution models (SDMs), a vital tool in the biogeography field, is critical to understanding the fundamental geographic distribution patterns and identifying conservation priorities. The correspondence between species occurrence and environmental information is crucial to the model's performance. However, ecological preferences unique to different orchid species, such as their life forms, are often overlooked during the modeling process. This oversight can introduce bias and increase model uncertainty. Additionally, human activities, as an important potential predictor, have not been quantified in any orchid SDMs. Taking the Hengduan Mountains as an example, we preprocessed all orchid species' occurrences based on physiological characteristics. Choosing five spatial factors related to human activities to quantify the interference and enter into models as HI factor. Using different modeling methods (GLM, MaxEnt, and RF) and evaluation indices (AUC, TSS, and Kappa), diverse modeling strategies have been constructed in the study. A double-ranking method has been adopted to select the critical orchid distribution regions. The results showed that classification models based on physiological characteristics significantly improved the model's accuracy while adding the HI factor had the same effect but the absence of enough significance. Suitability maps indicated that highly heterogeneous mountainous areas were vital for the distribution of orchids in the Hengduan Mountains. Different distribution patterns and critical regions existed between various orchid life forms geographically - terrestrial orchids were dominant in the mountain, and mycoherterophical orchids were primarily located in the north, more influenced by vegetation and temperature. Critical regions of epiphytic orchids were in the south due to a greater dependence on precipitation and temperature. These studies are informative for understanding the orchids' geographic distribution patterns in the Hengduan Mountains, promoting conservation and providing references for similar research beyond orchids.

Indexed as

biogeographyconservationhuman activitiesorchidsphysiological characteristicsspecies distribution models (SDMs)

Identifiers

PMID37791293
PMCPMC10542477

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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.