Evidence map›Paper›PMID 41436504›Full record

ArticleScientific reports2025

Environmental heterogeneity and its influence on fern diversity in a low-altitude mountain forest in central Taiwan.

Pei-Hsuan Lee, Yao-Moan Huang, Li-Wan Chang, Jian-Hong Yang, Wen-Liang Chiou, Yen-Hsueh Tseng, Hsy-Yu Tzeng

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Article in Scientific reports, 2025. 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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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Pei-Hsuan LeeDepartment of Forestry, National Chung Hsing University, No. 145, Xingda Rd., South Dist, Taichung, 402202, Taiwan.
Yao-Moan HuangTaiwan Forestry Research Institute, No. 53, Nanhai Rd, Taipei, 100051, Taiwan.
Li-Wan ChangTaiwan Forestry Research Institute, No. 53, Nanhai Rd, Taipei, 100051, Taiwan.
Jian-Hong YangTaiwan Forestry Research Institute, No. 53, Nanhai Rd, Taipei, 100051, Taiwan.
Wen-Liang ChiouTaiwan Forestry Research Institute, No. 53, Nanhai Rd, Taipei, 100051, Taiwan.
Yen-Hsueh TsengDepartment of Forestry, National Chung Hsing University, No. 145, Xingda Rd., South Dist, Taichung, 402202, Taiwan.
Hsy-Yu TzengDepartment of Forestry, National Chung Hsing University, No. 145, Xingda Rd., South Dist, Taichung, 402202, Taiwan. erecta@nchu.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Environmental heterogeneity plays a crucial role in shaping the distribution and composition of natural vegetation, including understory ferns. This study investigated the influence of environmental variation on understory fern communities within a one-hectare permanent plot in a low-altitude mountain forest in central Taiwan. Twenty-two environmental factors, including topographic, soil, and biotic variables, were recorded. Multiple regression, cluster (two-way indicator species analysis, TWINSPAN), and ordination (detrended correspondence analysis, DCA, and canonical correspondence analysis, CCA) analyses were conducted. A total of 51 fern species (including Lycophytes) belonging to 20 families and 30 genera were recorded. Among these, 43 were terrestrial, and eight were epiphytic; however, only terrestrial species were analyzed because of the limited representation of epiphytes. Multiple regression analyses revealed that environmental variables significantly affected fern richness, abundance, and community composition. Specifically, stream distance and the importance value (IV) of the saplings significantly influenced fern richness; herb/vine IV affected abundance; and the carbon-to-nitrogen ratio (C/N), manganese concentration (Mn), and herb/vine IV impacted the first axis of the DCA. Furthermore, the elevation, curvature, slope, and topographic wetness index (TWI) significantly influenced the second axis of the DCA. In all the models, topographic variables-particularly stream distance-were one of the most influential drivers. TWINSPAN categorized the ferns into four distinct groups (Diplazium donianum var. donianum [DIPLDO], D. donianum var. aphanoneuron [DIPLAP], Blechnopsis orientalis [BLECOR], and Angiopteris lygodiifolia [ANGILY]), and CCA revealed that environmental factors structured the community compositions in line with the TWINSPAN grouping. The DIPLDO and DIPLAP groups were associated with ridges and upper slope habitats characterized by higher elevations and drier conditions. In contrast, the BLECOR and ANGILY groups were associated with lower elevations, stream proximity, steeper slopes, and higher humidity. This study highlights the role of topographic and soil C/N heterogeneity in structuring fern communities in fine-scale plots for future ecological monitoring in subtropical forest ecosystems.

Indexed as

BiodiversityFernsForestsAltitudeEcosystemSoilTaiwanSoilBiotic factorsFern communitySoil propertiesTopography

Identifiers

PMID41436504
PMCPMC12728214

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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.