Introduction
The concept of sky islands, proposed by Heald (1951), refers to geographically isolated mountainous areas at medium and high altitudes (McCormack et al. 2009; Warshall 1994). These areas are surrounded by extensive lowlands unsuitable for the survival of species adapted to the unique conditions of sky islands (He and Jiang 2014). Due to the considerable geographic distances between these islands, genetic exchange between species through wind or insect vectors alone is limited (Gillespie and Roderick 2002). Research on both animal and plant species has consistently shown significant genetic differentiation between different sky islands, influenced by geographic isolation (DeChaine and Martin 2005; Gálvez-Reyes et al. 2020; Halbritter et al. 2019). During glacial periods, different populations of a sky-island species can migrate to the lowlands and thus have a better connection to each other. Subsequent climate warming leads to habitat compression and isolation of sky-island species (Chen et al. 2019; Wiens et al. 2019). Moreover, the expansion and contraction of the species distributions during glacial cycles may promote genetic exchange between different sky islands (Hewitt 1996). Geographic isolation and genetic drift within species, driven by the sky island effect, could contribute to exotic differentiation and species formation (Bai et al. 2015; Chen et al. 2019; Missoup et al. 2015). Recent studies have shown that sky islands can be a global tool for predicting the ecological and evolutionary consequences of climate change (Love et al. 2023), thus understanding the evolution history and future distribution of sky island species is crucial.
The mountainous region of southwest China, encompassing the Hengduan Mountains, the Yunnan-Kweichow Plateau, and the Himalayas, has emerged as a contemporary hotspot for biodiversity research (Myers et al. 2000; Ye et al. 2019). Concurrently, they shape a distinctive landscape characterized by towering mountains, deep valleys, clear vertical zonation, and a pronounced sky island effect in high-altitude zones. He and Jiang (2014) identified the Hengduan Mountains, the Yunnan-Kweichow Plateau, and the Bashan Mountains surrounding the Sichuan Basin as the sky island regions of southwestern China. A key feature of these sky islands is the interspersed distribution of river valleys between mountain ranges, illustrating a pattern of alternating high mountains and deep valleys. The highly fragmented habitats of this region have affected the speciation or genetic structure of many plant groups, such as subalpine oaks (Meng et al. 2017), Gaultheria ser. Trichophyllae (Cheng et al. 2024), Acanthocalyx alba (Mu et al. 2022), Corybas taliensis (Liu et al. 2023), etc. When examined in detail, these complex factors may include some or all of the following: environmental diversity, glacial cycles, human exploitation, and various climatic changes or tectonic movements (He and Jiang 2014; Pan et al. 2019; Wiens et al. 2019; Yue and Sun 2014; Zhang et al. 2019).
Alpine periglacial vegetation thrives in an ecosystem located above alpine meadows yet below the perpetual snowline (Körner and Kèorner 1999; Xu, Li, and Sun 2014). This unique habitat features a sparse soil matrix and a harsh ecological environment that favors the growth of cold-adapted and drought-resistant species (Billings 1974; Zhang et al. 2023). Numerous sky islands are dispersed throughout the alpine periglacial regions of the mountains of southwest China. Extensive research has been conducted on species formation and population differentiation within the alpine periglacial vegetation of the Hengduan Mountains (Li et al. 2022; Li and Sun 2017; Luo et al. 2016). Adjacent to the Hengduan Mountain region, the high mountains of the Yunnan-Kweichow Plateau (e.g., Jiaozi Mountain) exhibit notable species similarity to the Hengduan Mountain. What is less well-known, however, is that these high mountains host periglacial vegetation and represent the easternmost range of many alpine plants in southwest China. The periglacial vegetation of the Yunnan-Kweichow Plateau is restricted to a few isolated mountains, resulting in a greater degree of geographic isolation than that observed in the Hengduan Mountains. However, species within the periglacial vegetation of the Yunnan-Kweichow Plateau have rarely been included in previous phylogeographic studies.
In the context of significant declines in biodiversity (Díaz et al. 2019; Patil, Sharma, and Mhatre 2021), alpine flora shows increased sensitivity to environmental changes (Dirnböck, Essl, and Rabitsch 2011; Verrall and Pickering 2020). The main drivers of declining biodiversity in the Hengduan Mountains are climate warming and human activities (Zhang et al. 2021). Research on the effects of climate change on alpine orchids indicates that global warming will lead to population declines and range shifts, especially considering that more than 50% of species cannot effectively track climate change (Geppert et al. 2020). Furthermore, climate warming is forcing a significant number of plant species worldwide to migrate to higher altitudes (Koide et al. 2017; Niskanen et al. 2019; Zu et al. 2021). If current warming trends continue, some of these species might lose their habitats within a century (Auld et al. 2022; Wang et al. 2022). In addition, for certain marginal populations already residing at the summits of sky islands, there may not be enough space to ensure their long-term survival (Geppert et al. 2020).
Pleurospermum foetens Franch. (Apiaceae) (Figure 1a) has historically been placed in two different genera, Pleurospermum and Hymenidium, due to differing opinions by different authors (Drude 1897; Peng et al. 2023; Pimenov and Kljuykov 2000). According to the definition by Pimenov and Kljuykov (2000), the genus Pleurospermum contains only two species (P. austriacum and P. uralense), with
[IMAGE OMITTED. SEE PDF]
Materials and Methods
Plant Materials
In our study, we collected 59 samples from a total of 9 populations. The sampling scope was designed to encompass the primary distribution of
Genomic
We extracted DNA from 62 samples using the CTAB method, followed by sonication to fragment the genomic DNA. The sheared DNA fragments were used to construct 350 bp short-insert libraries. The DNA libraries were sequenced on the Illumina platform, generating 150 bp paired-end reads and yielding at least 2 GB RAW reads for each sample. The sequencing process was conducted at Novogene (Tianjin, China) using an Illumina NovaSeq 6000 platform. To ensure data quality, we employed fastp v0.20.1 to remove sequence artifacts (Chen et al. 2018).
For the assembly of cpDNA and nrDNA genomes, we utilized GetOrganelle v.1.7.5.2 with K values of 105 and 121, and R set to 15 (Jin et al. 2020). Annotation of plastid sequences involved referencing plastid genome of P. linearilobum in the PGA master (Qu et al. 2019). Manual corrections were performed in Geneious R 9.0.2 (Kearse et al. 2012) to enhance annotation accuracy, resulting in a set of 62 whole plastid genome sequences. Due to a discrepancy in the length between the nrDNA internal transcribed spacer (ITS) sequence from GenBank and the nrDNA sequence obtained from our sequencing, we performed trimming using Geneious R9.0.2. Subsequently, we utilized the Export Annotations and Concatenate tools in Geneious R9.0.2 to extract and concatenate 79 cpDNA protein-coding sequences and nrDNA ITS sequence. We then performed alignment of both nrDNA ITS and cpDNA protein-coding sequences (CDS) utilizing Mafft v7.490 (Katoh and Standley 2013). The resulting cpDNA CDS alignment is 68,289 bp, and the nrDNA ITS alignment is 611 bp.
Genetic Diversity and Phylogeographic Analyses
Haplotype diversity (Hd) and nucleotide diversity (Pi) were calculated for each population using DnaSP v6 (Rozas et al. 2017). Nucleotide diversity (Pi) of three clades is calculated based on sliding window analysis enabled by DnaSP v6 software with parameter settings of a 600-bp window length and 200-bp step length. To assess phylogeographic structure among populations, GST and NST were evaluated using Permut v1.2.1. We further explored the genetic structure of
Phylogenetic Analyses and Divergence Time Estimation
To investigate phylogenetic relationships and potential conflicts between nuclear and plastid genomes, maximum likelihood (ML) and Bayesian inference (BI) analyses were conducted using nrDNA ITS and cpDNA CDS sequences. ML analyses were implemented using IQ Tree v2.1.3 (Nguyen et al. 2015) with 1000 bootstrap replicates. For BI analyses, we utilized MrBayes v3.1.2 (Ronquist and Huelsenbeck 2003), and best fitting models were determined by Modeltest 3.7 based on the Akaike information criterion (AIC). Three independent Markov Chain Monte Carlo (MCMC) runs were performed for 10,000,000 generations, with the first 30% of generations being discarded as burn-in. Although the taxonomic relationships within the genus Pleurospermum are relatively complex, recent research by Peng et al. (2023) indicates that P. uralense is positioned in an early and stable branch in both cpDNA and nrDNA datasets. Therefore, we selected P. uralense as the outgroup for both phylogenetic analyses.
The divergence time estimation utilized the BEAST 2 package (Bouckaert et al. 2014) with a GTR model determined by jModelTest 2 (Darriba et al. 2012), a strict clock with rate of 1.0, and a Yule process model speciation. The calibration of divergence time was based on
Ecological Niche Modeling
Due to the limited geographical distribution of
For our research, climate data for different time periods are downloaded from WorldClim. CMIP5 data covered three past periods and one present period: last interglacial (LIG, ca. 128,000 years ago), last glaciation maximum (LGM, ca. 21,000 years ago), Middle Holocene (MidH, ca. 6000 years ago), and 1960–1990 (present). CMIP6 data covered two shared socioeconomic pathways (SSP126 and SSP585) across four future periods (2021–2040, 2041–2060, 2061–2080, and 2081–2100) and one present period (1970–2000). All layers were standardized to a 30 arc sec spatial resolution (ca. 1 × 1 km resolution at ground level) and trimmed to the shape of the six provinces using ArcGIS. To minimize potential correlations between climate factors, we calculated the correlation between the 19 parameters of the WorldClim CMIP5 and CMIP6 versions using R. Ecological niche modeling involved 15 replicates for each run using maximum entropy models (MaxEnt v3.4.1) (Phillips, Anderson, and Schapire 2006).
Our ecological niche modeling results are highly convincing, with all AUC values above 0.95 (Figure S2), indicating that our predictions could well simulate the actual distribution patterns of the species. In addition, we performed ecological niche modeling specifically for the narrow niche habitats (PJZ and PSZ) using the mask extraction tool in ArcGIS. Different geographic boundaries and the same grid cells (625 km2) were used for PJZ and PSZ. Visualization process and results were imported into ArcGIS, and habitat suitability was categorized into four levels based on Natural Breaks Classification method: unsuitable habitat (0–0.2397), poorly suitable habitat (0.2397–0.4799), moderately suitable habitat (0.4799–0.7199), and highly suitable habitat (0.7199–0.9599).
Results
Phylogeography and Genetic Diversity
The parameters of NST and GST (Table S4) indicated a significant phylogeographic structure in
TABLE 1 Haplotype analysis based on cpDNA of
Group | Sample size | Haplotype diversity (Hd) | Nucleotide diversity (Pi) | Haplotype composition |
Population | ||||
PDX | 6 | 0.00000 | 0.00000000 | H17(6) |
PTB | 12 | 0.30300 | 0.00000449 | H3(10), H4(2) |
PYL | 10 | 0.00001 | 0.00000527 | H1(8), H2(2) |
PJZ | 10 | 0.75600 | 0.00002000 | H7(5), H8(2), H9(1), H10(1), H11(1) |
PSZ | 8 | 0.53600 | 0.00002000 | H5(5), H6(3) |
PBM | 2 | 1.00000 | 0.00003000 | H18(1), H19(1) |
PSK | 5 | 0.40000 | 0.00004000 | H12(4), H13(1) |
PML | 5 | 0.80000 | 0.00006000 | H14(2), H15(2), H16(1) |
PJC | 1 | 0.00000 | 0.00000000 | H20(1) |
SAMOVA groups | ||||
Group1 | 19 | 0.86500 | 0.00015000 | H12–H20 |
Group2 | 18 | 0.84300 | 0.00018000 | H5–H11 |
Group3 | 22 | 0.67500 | 0.00003000 | H1–H4 |
Complete dataset | ||||
9 populations | 59 | 0.92900 | 0.00166000 | H1–H20 |
The haplotype networks of cpDNA and nrDNA datasets were different (Figure 2). The cpDNA haplotype network identified 20 haplotypes in 59 individuals from 9 populations. The three groups identified in the haplotype network analysis were found to be consistent with the classifications determined by SAMOVA analysis. The haplotypes from nine populations were divided into three groups by a triangular structure formed with three mutational steps. Haplotype group 1 comprised nine Hengduan Mountain haplotypes (H12–H20), group 2 included seven Yunnan-Kweichow Plateau haplotypes (H5–H11), and group 3 consisted of four Hengduan Mountain haplotypes (H1–H4). No haplotypes were shared between any two populations or groups. The PJZ population exhibited the highest number of haplotypes (H7–H11), while the other populations had one (PDX and PJC), two (PBM, PSK, PSZ, PYL, and PTB), or three haplotypes (PML) each. The haplotype network based on nrDNA identified seven haplotypes among nine populations. Two (H1 and H2) out of seven haplotypes were shared by population PDX, PML, PSK, PBM, PTB, and PYL. The remaining five haplotypes are exclusive to three populations: PJC (H3), PSZ (H4 and H5), and PJZ (H6 and H7).
[IMAGE OMITTED. SEE PDF]
Phylogenetic Analysis and Divergence Time Estimation
The average Pi of 600 bp sliding windows (Figure 3a) varied from 0 to 0.00436, with a hotspot region exhibiting varying positions across different clades. Specifically, in clade 2, the hotspot region is concentrated in the two gene regions of psbI-atpA, while in clade 1 and 3, it is mainly concentrated in the two gene regions of petB-petD. Notably, the maximum Pi of clade3 (0.00208) is much lower than that of clade1 (0.00419) and clade2 (0.00436). In the maximum likelihood (ML) and Bayesian inference (BI) phylogenies based on cpDNA dataset, all populations of
[IMAGE OMITTED. SEE PDF]
In contrast, the ML and BI phylogenies based on nrDNA dataset showed that all populations of
The estimate of divergence time based on nrDNA and cpDNA datasets showed different patterns (Figure S4 and Figure 4a). According to the nrDNA analysis,
[IMAGE OMITTED. SEE PDF]
The topology of the BEAST-derived phylogeny based on cpDNA recovered the divergence of the sampled populations of
Ecological Niche Modeling of
The five common bioclimatic variables (Figure S5) included BIO1 (annual mean temperature), BIO3 (isothermality), BIO15 (precipitation seasonality), BIO18 (precipitation of warmest quarter), and BIO19 (precipitation of coldest quarter). Under the different past periods (LIG, LGM, and MidH), the suitable habitats (highly suitable habitat and moderately suitable habitat) of the
When examining two scenarios (SSP 126 and 585) and five periods (1970–2000, 2021–2040, 2041–2060, 2061–2080, and 2081–2100), the ecological niche modeling showed a declining trend (Figure 5 and Figure S6). Under SSP 126, the highly suitable habitat decreased from 7135 km2 (1970–2000) to 2014 km2 (2061–2080), and the moderately suitable habitat decreased from 139,479 km2 (1970–2000) to 39,583 km2 (2061–2080), except for the period 2081–2100 (highly suitable areas: 4063 km2 and moderately suitable areas: 63941 km2). SSP 585 showed a similar trend to SSP 126, with a greater loss of suitable habitats, especially in the periods of 2041–2060 (48.54% smaller than SSP 126) and 2081–2100 (34.57% smaller than SSP 126). The changes in suitable habitat for marginal populations also followed a parallel trend to the study area. The suitable area (highly suitable and moderately suitable habitat) for PSZ decreased from 27.8% to 0 across the five periods of both scenarios, except for the period of 2081–2100 under SSP 126 (2.78%). Similarly, the suitable area (highly suitable habitat and moderately suitable habitat) for PJZ decreased from 47.22% to 16.67%, except for the period of 2081–2100 under SSP 126 (30.56%).
[IMAGE OMITTED. SEE PDF]
Discussion
Cyto-Nuclear Discordance Within
We observed significant cyto-nuclear discordance not only across species of the genus Pleurospermum but also within clades of
The observed patterns do not indicate an ongoing introgression between
Biogeographical Patterns of
No haplotype was shared between any two populations of
The populations in the Yunnan-Kweichow Plateau do not share haplotypes in the nrDNA ITS haplotype network. Additionally, the divergence time between two populations of
Species Range Modeling
Pleurospermum foetens is found only in the alpine scree habitats of the Hengduan Mountains and the Yunnan-Kweichow Plateau. The highly specialized habitats and relatively limited geographic distribution significantly increase the risk of extinction under climate warming scenarios. Our ecological niche modeling, which includes two scenarios (SSP126 and SSP585) over five periods, indicates a progressive decrease in its suitable habitats (Figure 5). Specifically,
Ecological niche modeling indicates that the PJZ population experienced the greatest shift in suitable habitat from the LGM to the current era of global warming (Figure 5). During the LGM, this region's highly suitable and moderately suitable habitat in this region accounted for 94.44% of the total area, potentially serving as a refuge for
Author Contributions
Shuliang Yu: data curation (equal), formal analysis (equal), investigation (equal), visualization (equal), writing – original draft (equal). Jieyu Zhang: data curation (equal), formal analysis (equal), investigation (equal), resources (equal), writing – original draft (supporting). Zhimin Li: conceptualization (equal), funding acquisition (equal), writing – review and editing (equal). Wensheng Li: data curation (supporting), writing – review and editing (supporting). Xiangguang Ma: conceptualization (equal), funding acquisition (equal), resources (equal), supervision (equal), writing – review and editing (equal). Wenguang Sun: conceptualization (equal), funding acquisition (equal), resources (equal), supervision (equal), writing – review and editing (equal).
Acknowledgements
The authors thank Zhe Chen, Zemin Guo, and Yang Niu for their help in material collection.
Conflicts of Interest
The authors declare no conflicts of interest.
Data Availability Statement
Sequencing data of Pleurospermum foetens are available on China National Center for Bioinformation () under accession number CRA014327.
Auld, J., S. E. Everingham, F. A. Hemmings, and A. T. Moles. 2022. “Alpine Plants Are on the Move: Quantifying Distribution Shifts of Australian Alpine Plants Through Time.” Diversity and Distributions 28, no. 5: 943–955. [DOI: https://dx.doi.org/10.1111/ddi.13494].
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
© 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Abstract
ABSTRACT
Sky islands provide insights on how glacial–interglacial cycles have shaped species distribution and help for predicting species' responses to climate warming. The alpine subnival belt of southwest China, especially in the Hengduan Mountains and adjacent areas, is sky island‐like. Among them, the Yunnan‐Kweichow Plateau harbors several isolated mountains with well‐developed alpine subnival vegetation, sharing a similar species composition with the Hengduan Mountains. However, the relationship between the sky islands of the Hengduan Mountains and the Yunnan‐Kweichow Plateau remains insufficiently explored.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details


1 College of Life Sciences, Yunnan Normal University, Kunming, China, Engineering Research Center of Sustainable Development and Utilization of Biomass Energy, Ministry of Education, Yunnan Normal University, Kunming, China
2 CAS Key Laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China