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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. For. Glob. Change</journal-id>
<journal-title>Frontiers in Forests and Global Change</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. For. Glob. Change</abbrev-journal-title>
<issn pub-type="epub">2624-893X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/ffgc.2025.1539025</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Forests and Global Change</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Ecological assessment of <italic>Iris hookeriana</italic> across subalpine and alpine regions of the Hindu-Himalayas</article-title>
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<name><surname>Khan</surname> <given-names>Muhammad Shakeel</given-names></name>
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<name><surname>Khan</surname> <given-names>Shujaul Mulk</given-names></name>
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<name><surname>Abdullah</surname></name>
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<name><surname>Liu</surname> <given-names>Jie</given-names></name>
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<name><surname>Wu</surname> <given-names>Zeng-Yuan</given-names></name>
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<name><surname>Hussain</surname> <given-names>Jawad</given-names></name>
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<name><surname>Zeb</surname> <given-names>Shakil Ahmad</given-names></name>
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<name><surname>Mohammad</surname> <given-names>Nazir</given-names></name>
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<name><surname>Batool</surname> <given-names>Zareena</given-names></name>
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<name><surname>Saqib</surname> <given-names>Zafeer</given-names></name>
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<name><surname>Afza</surname> <given-names>Rabia</given-names></name>
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<name><surname>Manan</surname> <given-names>Fazal</given-names></name>
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<name><surname>Ali</surname> <given-names>Shahab</given-names></name>
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<aff id="aff1"><sup>1</sup><institution>Department of Plant Sciences, Quaid-i-Azam University</institution>, <addr-line>Islamabad</addr-line>, <country>Pakistan</country></aff>
<aff id="aff2"><sup>2</sup><institution>CAS Key Laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming</institution>, <addr-line>Yunnan</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Germplasm Bank of Wild Species &#x0026; Yunnan Key Laboratory of Crop Wild Relatives Omics, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming</institution>, <addr-line>Yunnan</addr-line>, <country>China</country></aff>
<aff id="aff4"><sup>4</sup><institution>Department of Environmental Science, International Islamic University</institution>, <addr-line>Islamabad</addr-line>, <country>Pakistan</country></aff>
<aff id="aff5"><sup>5</sup><institution>Department of Botany, Hazara University</institution>, <addr-line>Mansehra</addr-line>, <country>Pakistan</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Zishan Ahmad Wani, Baba Ghulam Shah Badshah University, India</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Tajamul Islam, University of Kashmir, India</p><p>Renu Rawal, Soban Singh Jeena University, India</p></fn>
<corresp id="c001">&#x002A;Correspondence: Shujaul Mulk Khan, <email>shuja60@gmail.com</email></corresp>
<corresp id="c002">Jie Liu, <email>liujie@mail.kib.ac.cn</email></corresp>
<corresp id="c003">Shahab Ali, <email>shahabali@bs.qau.edu.pk</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>13</day>
<month>03</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>8</volume>
<elocation-id>1539025</elocation-id>
<history>
<date date-type="received">
<day>03</day>
<month>12</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>19</day>
<month>02</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2025 Khan, Khan, Abdullah, Liu, Wu, Hussain, Zeb, Mohammad, Batool, Saqib, Afza, Manan and Ali.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Khan, Khan, Abdullah, Liu, Wu, Hussain, Zeb, Mohammad, Batool, Saqib, Afza, Manan and Ali</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>The Hindu-Himalayan region, a global biodiversity, hotspot harbors numerus endemic plant species that contribute significantly to ecosystem stability. Among these, <italic>Iris hookeriana</italic>, an endemic perennial herb, thrives in the subalpine and alpine zones of the Hindu-Himalayas. The study aimed to assess the population dynamics of <italic>Iris hookeriana</italic>, assesses the primary threats to its survival, and explores its ecological associations along with with indicator species.</p>
</sec>
<sec>
<title>Methods</title>
<p>Fieldwork was conducted across 35 distinct sites, at the elevation range of 2,500 m&#x2013;3,900 m above sea level. A random stratified sampling method using quadrats, was employed to assess both qualitative and quantitative plant characteristics. Edaphic, topographic, geographic coordinates, and elevation Global Positioning System (GPS) data were recorded on each sampling site. Plant species associations were analyzed using 5 m<sup>2</sup> &#x00D7; 5 m<sup>2</sup> quadrat for shrubs and 1 m<sup>2</sup> &#x00D7; 1 m<sup>2</sup> quadrat for herbaceous vegetation. The relationships between species composition and environmental variables were analyzed via Statistical analyses, including Detrended Correspondence Analysis (DCA), Canonical Correspondence Analysis (CCA), Two-Way Cluster Analysis (TWCA), and Indicator Species Analysis (ISA), were conducted using PCORD version 5.0.</p>
</sec>
<sec>
<title>Results</title>
<p>Seventy associated plant species from 26 families were identified across 35 sampling sites, revealing four major plant associations: (1) <italic>Arenaria-Viola</italic> association, (2) <italic>Nepeta-Oxytropis</italic> association, (3) <italic>Phlomis-Prunella</italic> association, and (4) <italic>Bistorta-Carex</italic> association. These associations exhibited significant correlations (<italic>p</italic> &#x003C; 0.05). The identification of these four novel plant associations provides a crucial baseline for understanding the ecological dynamics of the subalpine and alpine ecosystems. The study emphasizes the significant role of environmental factors i.e., altitude, organic matter, pH, humidity, total dissolved solid, electrical conductivity, nitrogen, phosphorus, silt, and clay content in shaping the <italic>Iris hookeriana</italic> population within the subalpine and alpine zone of Hindu-Himalayan Mountains.</p>
</sec>
<sec>
<title>Discussion</title>
<p>The findings show that indicator species and plant associations vary with changing environmental conditions, providing valuable insights for sustainable biodiversity management in subalpine and alpine ecosystems of the Hindu-Himalayan mountains.</p>
</sec>
</abstract>
<kwd-group>
<kwd>Hindu-Himalayas</kwd>
<kwd>indicator species</kwd>
<kwd>plant associations</kwd>
<kwd>alpine zone</kwd>
<kwd>environmental variables</kwd>
<kwd>plant biodiversity</kwd>
<kwd>ecosystem services</kwd>
<kwd>medicinal plant</kwd>
</kwd-group>
<counts>
<fig-count count="11"/>
<table-count count="1"/>
<equation-count count="8"/>
<ref-count count="96"/>
<page-count count="15"/>
<word-count count="8914"/>
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<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Forest Management</meta-value>
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</front>
<body>
<sec id="S1" sec-type="intro">
<title>1 Introduction</title>
<p>The Himalayas are recognized as a major biodiversity hotspot region, presenting diversified fauna and flora with several endemic species. The rich biodiversity of the Himalayas is because of its unique geography and diversified microclimatic weather (<xref ref-type="bibr" rid="B89">Tali et al., 2019</xref>; <xref ref-type="bibr" rid="B15">Dar and Khuroo, 2020</xref>). Currently, the biodiversity of the Himalayas is under intense pressure because of anthropogenic activities, drastic changes in land use systems, and climatic changes (<xref ref-type="bibr" rid="B27">Hamid et al., 2020</xref>; <xref ref-type="bibr" rid="B32">Hassan et al., 2021</xref>). Loss of flora in the Himalayas is because of unsustainable medicinal uses which make species vulnerable and ultimately species lead to extinction (<xref ref-type="bibr" rid="B24">Ganie et al., 2019</xref>; <xref ref-type="bibr" rid="B89">Tali et al., 2019</xref>; <xref ref-type="bibr" rid="B1">Abdullah and Khan, 2023</xref>). Such variation in the environment influences the plants to bring changes and adjust to the environment, especially plants found in specific habitats of the Himalayan region (<xref ref-type="bibr" rid="B16">Davis, 1992</xref>). Comparable to plant adaptation environmental changes are rapid in the environment (<xref ref-type="bibr" rid="B17">Davis and Shaw, 2001</xref>). In the field of vegetation science, assessment of medicinal plants is important, and other scientific disciplines as well as management, bioprospecting, and conservation of Phyto resources (<xref ref-type="bibr" rid="B92">Teixidor-Toneu et al., 2025</xref>; <xref ref-type="bibr" rid="B53">Magray et al., 2022</xref>; <xref ref-type="bibr" rid="B75">Rashid S. et al., 2023</xref>). Natural communities are crucial to studying the composition of the species, structure, abundance, population, and interaction of species in the environment (<xref ref-type="bibr" rid="B12">Chawla et al., 2012</xref>; <xref ref-type="bibr" rid="B60">Mishra et al., 2012</xref>). Alpine zones occur where trees are unable to grow however, several plant species thrive due to their adaptations to the harsh climatic conditions of the alpine zone (<xref ref-type="bibr" rid="B62">Nagy and Grabherr, 2009</xref>). Approximately 3% of the Earth&#x2019;s vegetated land area is covered by the alpine zone (<xref ref-type="bibr" rid="B49">K&#x00F6;rner, 2011</xref>). Which hosts about 4% of the world&#x2019;s plant species, with an estimated 8,000&#x2013;10,000 species (<xref ref-type="bibr" rid="B63">Nagy et al., 2012</xref>). Most of the species are perennial and have adapted to the extremely harsh habitat (<xref ref-type="bibr" rid="B25">Grabherr et al., 2010</xref>). The life forms of alpine plants include succulents, graminoids, forbs, and mat-forming cushions (<xref ref-type="bibr" rid="B36">Jabis, 2018</xref>). The composition and diversity of species within the alpine zone depend on various factors, including different mountain ranges, as well as altitudinal and latitudinal gradients (<xref ref-type="bibr" rid="B54">Malanson et al., 2020</xref>). Geographically, an alpine ecosystem is classified based on water availability, seasonality, and elevation; all these factors play a crucial role in shaping the biotic composition of plant associations (<xref ref-type="bibr" rid="B25">Grabherr et al., 2010</xref>; <xref ref-type="bibr" rid="B49">K&#x00F6;rner, 2011</xref>).</p>
<p>In the Himalayan region, the genus <italic>Iris</italic> contributes significantly to plant diversity, thriving abundantly in open grasslands, sub-alpine and alpine environments, graveyards, gardens, saffron fields, and along stream banks. Some species within the <italic>Iris</italic> genus are cultivated, while others grow wild such as <italic>Iris hookeriana</italic> (<xref ref-type="bibr" rid="B96">Zeerak and Wani, 2007</xref>). Among the species of <italic>Iris</italic>, a notable and endemic variety in the Himalayas is <italic>Iris hookeriana</italic>, a member of the family Iridaceae, small herb that features a long stem, a short perianth tube, a slender rhizome, and blue-purple flowers adorned with darker blotches, typically found on open grassy slopes at high altitudes above sea level, locally referred as <italic>Mazarmund</italic> in India, and <italic>Toori</italic> in Swat and Shangla district of Pakistan (<xref ref-type="bibr" rid="B68">Polunin and Stainton, 1984</xref>). Climate change significantly impacts various species, particularly as temperatures decrease at high altitudes in the Himalayas and precipitation patterns shift, both of which have detrimental effects (<xref ref-type="bibr" rid="B64">Negi et al., 2012</xref>; <xref ref-type="bibr" rid="B18">Devi et al., 2024</xref>; <xref ref-type="bibr" rid="B80">Sekar et al., 2024</xref>). Overharvesting is another critical issue, as local communities often utilize <italic>Iris hookeriana</italic> for ornamental purposes due to its attractive purple flowers. Phytochemicals such as isoflavones (iridin, junipigenin-A, irisflorentin, irigenin) and stilbene glycoside (piceid) have commonly isolated chemicals form rhizomes of <italic>Iris hookeriana</italic> (<xref ref-type="bibr" rid="B90">Tariq et al., 2008</xref>). Additionally, the rhizome is harvested for medicinal uses, further contributing to the decline of the species (<xref ref-type="bibr" rid="B34">Hopping et al., 2018</xref>). Other factors exacerbating the situation include habitat degradation, infrastructure development, habitat loss, and fragmentation.</p>
<p><italic>Iris hookeriana</italic> is a significant species in the Hindu-Himalayan Mountain ranges, playing a crucial role in the ecosystem. Studies on <italic>Iris hookeriana</italic> are remarkably scarce, particularly regarding the specific plant associations involved. The distribution of <italic>Iris hookeriana</italic> and how these plant species are influenced by environmental factors. Factors remain largely unexplored. This study addresses this critical gap by providing the first comprehensive characterization of plant associations linked to <italic>Iris hookeriana</italic>. Hindu-Himalayan Mountain ranges have been studied, leading to the identification of four distinct and previously unreported plant species associations.</p>
<p>The study was designed with specific objectives to evaluate the population dynamics of <italic>Iris hookeriana</italic> across the subalpine and alpine Hindu-Himalaya Mountains range to enlighten conservation management strategies for this species. This study also investigates the associated plant species of <italic>Iris hookeriana</italic>, with a particular focus on identifying indicator species that could provide insights into their ecological preferences and habitat conditions within the studied subalpine and alpine region of Hindu-Himalaya Mountains eventually contributing to ecosystem conservation and sustainability (<xref ref-type="fig" rid="F1">Figure 1</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption><p>Life stages of <italic>Iris hookeriana</italic>: <bold>(A)</bold> Juvenile, <bold>(B)</bold> Mature, <bold>(C)</bold> Flowering, <bold>(D)</bold> In the fruiting stage.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-08-1539025-g001.tif"/>
</fig>
</sec>
<sec id="S2" sec-type="materials|methods">
<title>2 Materials and methods</title>
<sec id="S2.SS1">
<title>2.1 Study area</title>
<p>This study was conducted in the Hindu-Himalaya Mountains, which are characterized by high-altitude meadows, extensive green cultivated fields, towering mountain peaks, snow-capped glaciers, and scenic tributary rivers, streams, lakes, and ponds. The region hosts a diverse range of flora throughout the seasons, driven by its varied topography, geological features, habitats, and climatic conditions (<xref ref-type="bibr" rid="B8">Ali et al., 2024</xref>). The climate is characterized by distinct seasons, with moist summers and cold, snowy winters. The area boasts a diverse range of vegetation, including a significant population of medicinal and aromatic plants (<xref ref-type="bibr" rid="B93">Thapa et al., 2021</xref>; <xref ref-type="bibr" rid="B80">Sekar et al., 2024</xref>). Additionally, there are several endemic plant species, as well as a rich variety of wildlife. Due to the unique climatic and topography conditions, plant diversity is notably high. Indigenous communities engage in livestock breeding and agriculture (<xref ref-type="bibr" rid="B73">Rao et al., 2003</xref>; <xref ref-type="bibr" rid="B21">Farooquee et al., 2004</xref>; <xref ref-type="fig" rid="F2">Figure 2</xref>).</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption><p>Map of the study area in the Hindu-Himalayan region, showing (top left) location within Pakistan, (bottom) the province, and on the right, a detailed map of the specific area where the field work was conducted.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-08-1539025-g002.tif"/>
</fig>
</sec>
<sec id="S2.SS2">
<title>2.2 Plants collection</title>
<p>Thirty-five sampling sites were selected to represent the study area. Within each sampling site, quadrats were established to assess plant associations and composition. The size of the quadrat depend on the <italic>Iris hookeriana</italic> where it was found if the <italic>Iris hookeriana</italic> was growing with herbaceous plants (herbs), we used (1 m &#x00D7; 1 m<sup>2</sup>) quadrat. Where <italic>Iris hookeriana</italic> was growing with shrubs, we used quadrats of (5 m &#x00D7; 5 m<sup>2</sup>) (<xref ref-type="bibr" rid="B42">Khan et al., 2013c</xref>; <xref ref-type="bibr" rid="B39">Khan M. et al., 2017</xref>; <xref ref-type="bibr" rid="B61">Mumshad et al., 2021</xref>) growing. Soil samples were collected from all the 35 sampling sites for edaphic analysis. We calculated density, relative density, cover, relative cover, frequency, and relative frequency. The Importance Value Index (IVI) was derived from all quadrats based on their relative indices with the help of the following formulas (<xref ref-type="bibr" rid="B61">Mumshad et al., 2021</xref>; <xref ref-type="bibr" rid="B75">Rashid S. et al., 2023</xref>). Plant samples were labeled, pressed, and dried before being mounted on herbarium sheets and stored at the Plant Ecology and Conservation Laboratory at Quaid-i-Azam University Islamabad for future research. A Global Positioning System (GPS) (model Garmin eTrex. HC series, vista HCx) were used to measure geographic coordinates, including altitude, latitude, and longitude.</p>
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</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>individuals</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi mathvariant="normal">a</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>species</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>in</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>all</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>quadrats</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Total</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>number</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>quadrats</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>studied</mml:mi>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:math>
</disp-formula>
<disp-formula id="S2.Ex2">
<mml:math id="M2">
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Relative</mml:mi>
</mml:mpadded>
<mml:mpadded width="+5pt">
<mml:mi>Density</mml:mi>
</mml:mpadded>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mo>%</mml:mo>
<mml:mo rspace="5.8pt" stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo rspace="5.8pt">=</mml:mo>
<mml:mpadded width="+3.3pt">
<mml:mfrac>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Density</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>the</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>species</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Total</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>Density</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>all</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>species</mml:mi>
</mml:mrow>
</mml:mfrac>
</mml:mpadded>
<mml:mpadded width="+3.3pt">
<mml:mtext>&#x00D7;</mml:mtext>
</mml:mpadded>
<mml:mn>100</mml:mn>
</mml:mrow>
</mml:math>
</disp-formula>
<disp-formula id="S2.Ex3">
<mml:math id="M3">
<mml:mrow>
<mml:mpadded width="+3.3pt">
<mml:mtext>Cover</mml:mtext>
</mml:mpadded>
<mml:mo rspace="5.8pt">=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Total</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>number</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>individuals</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi mathvariant="normal">a</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>species</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>in</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>all</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>quadrats</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Total</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>number</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>quadrats</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>in</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>which</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>species</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>occure</mml:mi>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:math>
</disp-formula>
<disp-formula id="S2.Ex4">
<mml:math id="M4">
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Relative</mml:mi>
</mml:mpadded>
<mml:mpadded width="+5pt">
<mml:mi>cover</mml:mi>
</mml:mpadded>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mo>%</mml:mo>
<mml:mo rspace="5.8pt" stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo rspace="5.8pt">=</mml:mo>
<mml:mpadded width="+3.3pt">
<mml:mfrac>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Abundance</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>the</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>species</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Total</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>abundance</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>all</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>species</mml:mi>
</mml:mrow>
</mml:mfrac>
</mml:mpadded>
<mml:mpadded width="+3.3pt">
<mml:mtext>&#x00D7;</mml:mtext>
</mml:mpadded>
<mml:mn>100</mml:mn>
</mml:mrow>
</mml:math>
</disp-formula>
<disp-formula id="S2.Ex5">
<mml:math id="M5">
<mml:mrow>
<mml:mpadded width="+3.3pt">
<mml:mtext>Frequency</mml:mtext>
</mml:mpadded>
<mml:mo rspace="5.8pt">=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Total</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>number</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>quadrats</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>in</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>which</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>species</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>occure</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Total</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>no</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>quadrats</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>studied</mml:mi>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:math>
</disp-formula>
<disp-formula id="S2.Ex6">
<mml:math id="M6">
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Relative</mml:mi>
</mml:mpadded>
<mml:mpadded width="+5pt">
<mml:mi>frequecny</mml:mi>
</mml:mpadded>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mo>%</mml:mo>
<mml:mo rspace="5.8pt" stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo rspace="5.8pt">=</mml:mo>
<mml:mpadded width="+3.3pt">
<mml:mfrac>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Frequency</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>the</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>species</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Total</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>frequency</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>of</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>all</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>species</mml:mi>
</mml:mrow>
</mml:mfrac>
</mml:mpadded>
<mml:mo rspace="5.8pt">&#x00D7;</mml:mo>
<mml:mn>100</mml:mn>
</mml:mrow>
</mml:math>
</disp-formula>
<disp-formula id="S2.Ex7">
<mml:math id="M7">
<mml:mrow>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Importance</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>Valve</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>Index</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>IVI</mml:mi>
<mml:mo rspace="5.8pt" stretchy="false">)</mml:mo>
</mml:mrow>
</mml:mrow>
<mml:mo rspace="5.8pt">=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mi>RD</mml:mi>
<mml:mo>+</mml:mo>
<mml:mi>RC</mml:mi>
<mml:mo>+</mml:mo>
<mml:mi>Rf</mml:mi>
</mml:mrow>
<mml:mn>3</mml:mn>
</mml:mfrac>
</mml:mrow>
</mml:math>
</disp-formula>
<p>Moisture content of the soil samples was found out on oven dry soil weight (Model: DSO-300D) as per the method of <xref ref-type="bibr" rid="B74">Rashid K. et al. (2023)</xref> with the help of the following formula</p>
<disp-formula id="S2.Ex8">
<mml:math id="M8">
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Soil</mml:mi>
</mml:mpadded>
<mml:mi>moisture</mml:mi>
<mml:mrow>
<mml:mo>(</mml:mo>
<mml:mo>%</mml:mo>
<mml:mo rspace="5.8pt">)</mml:mo>
</mml:mrow>
<mml:mo rspace="5.8pt">=</mml:mo>
<mml:mpadded width="+3.3pt">
<mml:mfrac>
<mml:mrow>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Wet</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>soil</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>weight</mml:mi>
</mml:mrow>
<mml:mo>-</mml:mo>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Dry</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>soil</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>weight</mml:mi>
</mml:mrow>
</mml:mrow>
<mml:mrow>
<mml:mpadded width="+5pt">
<mml:mi>Dry</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mpadded width="+5pt">
<mml:mi>soil</mml:mi>
</mml:mpadded>
<mml:mo>&#x2062;</mml:mo>
<mml:mi>weight</mml:mi>
</mml:mrow>
</mml:mfrac>
</mml:mpadded>
<mml:mo rspace="5.8pt">&#x00D7;</mml:mo>
<mml:mn>100</mml:mn>
</mml:mrow>
</mml:math>
</disp-formula>
</sec>
<sec id="S2.SS3">
<title>2.3 Soil sampling and field observation</title>
<p>All the collected soil samples from 35 different sampling sites were replicated three times to ensure reliability and reproducibility Before analysis, the samples were cleaned of stones, plant roots, and other extraneous materials, then stored in polythene bags and properly labeled with their respective codes (<xref ref-type="bibr" rid="B10">Bergeron et al., 2013</xref>; <xref ref-type="bibr" rid="B5">Ahmad et al., 2019</xref>). The soil samples were analyzed in the Plant Ecology and Conservation Laboratory. The following parameters were determined: For measuring electrical conductivity, the samples were saturated with water, filtered, and readings were recorded using an electrical conductivity meter was measured by (model: AD 1030), Total Dissolved Solids (TDS) was measured by (Model: AD 3000), pH was measured by (model: AD 1030 pH/mV), Moisture Content (MC), Humidity, Organic Matter (OM), Nitrogen (N), Phosphorus (P), Potassium (K) and Sieve meshes were employed to assess the soil texture (Sand, Silt, and Clay) of each sample (<xref ref-type="bibr" rid="B35">Iqbal et al., 2018</xref>; <xref ref-type="bibr" rid="B9">Anum et al., 2019</xref>; <xref ref-type="bibr" rid="B66">Noreen et al., 2019</xref>; <xref ref-type="bibr" rid="B77">Rhoades, 1996</xref>; <xref ref-type="bibr" rid="B14">Corwin and Lesch, 2005</xref>). Soil organic matter was quantified using a standardized solution of iron (II) sulfate (FeSO<sub>4</sub>) and potassium dichromate (K<sub>2</sub>Cr<sub>2</sub>O<sub>7</sub>) (<xref ref-type="bibr" rid="B65">Nelson and Sommers, 1982</xref>; <xref ref-type="bibr" rid="B23">Gallardo et al., 1987</xref>). The ammonium bicarbonate-diethylenetriaminepentaacetic acid (AB-DTPA) soil test was conducted to analyze Phosphorus, Nitrogen, and Potassium, following the method described by <xref ref-type="bibr" rid="B88">Soltanpour (1991)</xref>. In addition to soil analysis, we also documented other features in each of the 35 sampling sites, including aspect, soil erosion, grazing pressure, habitat destruction, uprooting, and other anthropogenic pressures in the field (<xref ref-type="bibr" rid="B47">Khan et al., 2016</xref>; <xref ref-type="bibr" rid="B48">Khan W. et al., 2017</xref>).</p>
</sec>
<sec id="S2.SS4">
<title>2.4 Statistical analysis</title>
<p>All the collected data of plant species, environmental variables, and edaphic factors data were organized, to find out the relationship between them (<xref ref-type="bibr" rid="B58">Mayor et al., 2017</xref>). Importance value index (IVI) data of all the recorded plant species (70 species) and the data from 35 different sampling sites were compiled via the MS EXCEL spread sheet and arranged according to the software requirement. CANOCO version 5.0 software was used to perform Detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA) Cluster analysis, two-way cluster analysis and indicator species analysis were performed through PC-ORD version 5.0 to find out major plant associations (<xref ref-type="bibr" rid="B30">Haq et al., 2017</xref>). These analyss were based on the pattern of similarity through Sorenson Distance Measurement using Ward&#x2019;s linkage Method (<xref ref-type="bibr" rid="B28">Haq, 2018</xref>; <xref ref-type="bibr" rid="B72">Rahman et al., 2023</xref>) Presence-absence (1, 0) data were utilized to classify fields and associations through cluster analysis (CA) and two-way cluster analysis (TWCA) which is based on the associated abundance data. The abiotic factor and species abundance data matrices were analyzed together to assess the impact of edaphic factors and another environmental variable on association formation through indicator species analysis (ISA) of various associations. Several abiotic parameters were examined to evaluate their effects on species composition, diversity, and aggregation. Independent abiotic parameters were employed to find indicator species within individual associations, with selected indicator species exhibiting a probability of <italic>P</italic> &#x003C; 0.05. Additionally, a generalized linear model (GLM) was applied to explore the relationships between abiotic factors and species abundance across the variables.</p>
</sec>
</sec>
<sec id="S3" sec-type="results">
<title>3 Results</title>
<p>A total of seventy different plant species associated with <italic>Iris hookeriana</italic> were recorded from 35 sampling sites from the Hindu-Himalaya Mountains. Almost 95% of the associated flora consists of herbaceous species, with only a small proportion of shrubs. No climbers or other growth forms were seen among the associated plants.</p>
<sec id="S3.SS1">
<title>3.1 Species distribution patterns through detrended correspondence and Canonical Correspondence Analysis</title>
<p>Detrended Correspondence Analysis (DCA) was applied to assess the distribution patterns of 70 plant species along the detrended gradient analysis. Axis 1 and Axis 2 show the primary ecological gradients changing the composition of the species relating to variations in biotic and abiotic properties of the environment. The DCA ordination plot shows distinct clustering of the species that show similar environmental preferences and requirements of the habitat among closely related taxa. The species that are broadly separated along the ordination axes show distinct ecological requirements (<xref ref-type="fig" rid="F3">Figure 3</xref>).</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption><p>Detrended Correspondence Analysis (DCA) plot illustrates species distribution along the ecological gradient.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-08-1539025-g003.tif"/>
</fig>
<p>The Canonical Correspondence Analysis (CCA) a multivariate statistical technique to analyse the relationships between species and environmental variables including altitude, anthropogenic pressure, aspect, electrical conductivity, grazing pressure, humidity, moisture content, nitrogen, organic matter, pH, phosphorus, potassium, sand, silt, clay, soil erosion, and total dissolved solids. These findings illustrate the dependence of the distribution of species across the environmental variables (<xref ref-type="fig" rid="F4">Figure 4</xref>).</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption><p>Canonical Correspondence Analysis (CCA) ordination showing the relationships between plant species and environmental variables.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-08-1539025-g004.tif"/>
</fig>
</sec>
<sec id="S3.SS2">
<title>3.2 Generalized linear model</title>
<p>We employed a generalized linear model (GLM) to investigate the impact of various environmental factors on the abundance of <italic>Iris hookeriana</italic>. The (GLM) graphs illustrate the predicted species&#x2019; abundance across different environmental gradients, with the red line standing for the model&#x2019;s output. This approach allowed us to find key environmental variables that influence the species&#x2019; distribution and evaluate its abundance under diverse ecological conditions. The red line provides a clear representation of the interactions between the species and its surrounding environment (<xref ref-type="fig" rid="F5">Figure 5</xref>).</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption><p>Effects of environmental variables on species abundance [Generalized Linear Model (GLM) results].</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-08-1539025-g005.tif"/>
</fig>
</sec>
<sec id="S3.SS3">
<title>3.3 Plant associations and habitat types</title>
<p>Owing to environmental factors, plant species are confined to specific habitats. A cluster analysis of the seventy plant species and the environmental variables divided the thirty-five sampling sites into four main groups, which we refer as plant associations (<xref ref-type="fig" rid="F6">Figure 6</xref>).</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption><p>Cluster analysis based on the Sorensen measure, conducted using PC-ORD version 5 software, revealed four different plant associations.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-08-1539025-g006.tif"/>
</fig>
<p>The Two-Way Cluster Analysis (TWCA) provides a comprehensive overview of species distribution across the sampling points. In (TWCA) white dots represent the absence of species, while black dots indicate their presence in a specific area of fieldwork (<xref ref-type="fig" rid="F7">Figure 7</xref>).</p>
<fig id="F7" position="float">
<label>FIGURE 7</label>
<caption><p>The dendrogram is based on two-way cluster analysis, illustrating the distribution pattern of seventy species across thirty-five different sampling sites.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-08-1539025-g007.tif"/>
</fig>
<p>The indicator species analysis revealed that all top indicator species in the subalpine and alpine zones are herbs, as these areas are herbaceous. Each of the four associations is characterized by two top indicator herb species, all with significant values (<italic>P</italic> &#x003C; 0.05) and observed indicator values greater than 20, highlighting the dominance of herbs in these ecosystems (<xref ref-type="table" rid="T1">Table 1</xref>). An inclusive description of identified associations, their co-indicator plant species, and the corresponding environmental factors is discussed below.</p>
<table-wrap position="float" id="T1">
<label>TABLE 1</label>
<caption><p>The top two indicator species for each association were identified based on the association&#x2019;s name.</p></caption>
<table cellspacing="5" cellpadding="5" frame="box" rules="all">
<thead>
<tr>
<td valign="top" align="left" style="color:#ffffff;background-color: #7f8080;">Associations</td>
<td valign="top" align="left" style="color:#ffffff;background-color: #7f8080;">Indicator species</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">OIV</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Standard deviation</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;"><italic>P</italic>&#x002A;-value</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Association 01</td>
<td valign="top" align="left"><italic>Arenaria serpyllifolia</italic></td>
<td valign="top" align="center">27.9</td>
<td valign="top" align="center">6.9</td>
<td valign="top" align="center">0.04</td>
</tr>
<tr>
<td/>
<td valign="top" align="left"><italic>Viola canescens</italic></td>
<td valign="top" align="center">50</td>
<td valign="top" align="center">10.27</td>
<td valign="top" align="center">0.039</td>
</tr>
<tr>
<td valign="top" align="left">Association 02</td>
<td valign="top" align="left"><italic>Nepeta erecta</italic></td>
<td valign="top" align="center">68.4</td>
<td valign="top" align="center">13.7</td>
<td valign="top" align="center">0.042</td>
</tr>
<tr>
<td/>
<td valign="top" align="left"><italic>Oxytropis cachemiriana</italic></td>
<td valign="top" align="center">50</td>
<td valign="top" align="center">8.18</td>
<td valign="top" align="center">0.0591</td>
</tr>
<tr>
<td valign="top" align="left">Association 03</td>
<td valign="top" align="left"><italic>Phlomis bracteosa</italic></td>
<td valign="top" align="center">44</td>
<td valign="top" align="center">7.85</td>
<td valign="top" align="center">0.009</td>
</tr>
<tr>
<td/>
<td valign="top" align="left"><italic>Prunella vulgaris</italic></td>
<td valign="top" align="center">39.6<break/></td>
<td valign="top" align="center">11.97</td>
<td valign="top" align="center">0.046</td>
</tr>
<tr>
<td valign="top" align="left">Association 04</td>
<td valign="top" align="left"><italic>Bistorta affinis</italic></td>
<td valign="top" align="center">52.6</td>
<td valign="top" align="center">9.86</td>
<td valign="top" align="center">0.01</td>
</tr>
<tr>
<td/>
<td valign="top" align="left"><italic>Carex infuscate</italic></td>
<td valign="top" align="center">66.7</td>
<td valign="top" align="center">11.12</td>
<td valign="top" align="center">0.022</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn><p>OIV, observed indicator analysis, <italic>P</italic>&#x002A;, probability.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="S3.SS4">
<title>3.4 Association 01 - <italic>Arenaria serpyllifolia - Viola canescens</italic></title>
<p>The leading indicator species of this association are <italic>Arenaria serpyllifolia</italic> (OIV = 27.9) and <italic>Viola canescens</italic> (OIV = 50), with significant <italic>p</italic>-values of 0.04 and 0.039, respectively. This association, is found in the subalpine and alpine zones at elevation ranges between 2,500 m and 2,900 m. Other indicator species within this association include <italic>Achillea millefolium, Salix karelinii, Artemisia absinthium, Berberis pseudumbellata</italic>, and <italic>Juniperus communis</italic>. <italic>Arenaria serpyllifolia</italic> and <italic>Viola canescens</italic> are under the influence of edaphic gradients, such as altitude, organic matter, clay content, humidity, and electrical conductivity, significantly influence the structure of association 01 and the distribution of associated plant species in the study area (<xref ref-type="fig" rid="F8">Figure 8</xref>).</p>
<fig id="F8" position="float">
<label>FIGURE 8</label>
<caption><p>Data attribute plots (from left to right) of <italic>indicator species for Association 1</italic> (<italic>Arenaria serpyllifolia and Viola canescens</italic>).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-08-1539025-g008.tif"/>
</fig>
</sec>
<sec id="S3.SS5">
<title>3.5 Association 02 - <italic>Nepeta erecta - Oxytropis cachemiriana</italic></title>
<p>The leading indicator species of association 02 are <italic>Nepeta erecta</italic> (OIV = 68.4) and <italic>Oxytropis cachemiriana</italic> (OIV = 50), with significant <italic>p</italic>-values of <italic>P</italic> = 0.042 and <italic>P</italic> = 0.0591, respectively, both below the 0.05 <italic>P</italic>-value. This association is found at an elevation range between 2,900 m and 3,200 m. Other indicator species in this association include <italic>Corydalis diphylla, Filipendula vestita, Geum elatum, Caltha alba, Jurinea dolomiaea, Lindelofia longiflora, Nepeta laevigata</italic>, and <italic>Origanum vulgare</italic>. Electrical conductivity, clay, silt, and nitrogen concentration play a vital role in the determination of this association 02 (<xref ref-type="fig" rid="F9">Figure 9</xref>).</p>
<fig id="F9" position="float">
<label>FIGURE 9</label>
<caption><p>Data attribute plots (from left to right) of indicator species for Association 2 (<italic>Nepeta erecta</italic> and <italic>Oxytropis cachemiriana</italic>).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-08-1539025-g009.tif"/>
</fig>
</sec>
<sec id="S3.SS6">
<title>3.6 Association 03 - <italic>Phlomis bracteosa</italic> and <italic>Prunella vulgaris</italic></title>
<p>The leading indicator species of association 03 are <italic>Phlomis bracteosa</italic> (OIV = 44) and Prunella vulgaris (OIV = 39.6), with significant <italic>p</italic>-values of 0.009 and 0.046, respectively. This association is found an elevation range between 3,200 m and 3,500 m. Other indicator species in this association include <italic>Poa alpina, Polygonum arenastrum, Potentilla nepalensis, Pseudomertensia moltkioides</italic>, and <italic>Anaphalis margaritacea</italic>. Prominent levels of total dissolved solids, phosphorus, and organic matter strongly influence the composition and structure of this association 03 (<xref ref-type="fig" rid="F10">Figure 10</xref>).</p>
<fig id="F10" position="float">
<label>FIGURE 10</label>
<caption><p>Data attributes plots (from left to right) of indicator species for Association 3 (<italic>Phlomis bracteosa</italic> and <italic>Prunella vulgaris</italic>).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-08-1539025-g010.tif"/>
</fig>
</sec>
<sec id="S3.SS7">
<title>3.7 Association 04 - <italic>Bistorta affinis - Carex infuscata</italic></title>
<p>The primary indicator species of association 04 are <italic>Bistorta affinis</italic> (OIV = 52.6) and <italic>Carex infuscata</italic> (OIV = 66.7), with significant <italic>p</italic>-values of 0.01 and 0.022, respectively. This association is found an elevation range between 3,500 m and 3,900 m. Other indicator species within this association include <italic>Sedum ewersii, Senecio chrysanthemoides, Sibbaldia procumbens, Thymus linearis</italic>, and <italic>Viola biflora</italic>. This association is heavily influenced by high levels of total dissolved solids, phosphorus, and organic matter. However, minimal contributions from other edaphic factors, such as potassium, nitrogen, silt, clay, and electrical conductivity, were observed in this association 04 (<xref ref-type="fig" rid="F11">Figure 11</xref>).</p>
<fig id="F11" position="float">
<label>FIGURE 11</label>
<caption><p>Data attributes plots (from left to right) of indicator species for Association 4 (<italic>Bistorta affinis</italic>, and <italic>Carex infuscate</italic>).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-08-1539025-g011.tif"/>
</fig>
</sec>
</sec>
<sec id="S4" sec-type="discussion">
<title>4 Discussion</title>
<p>Our study documented seventy plant species across 26 different plant families associated with <italic>Iris hookeriana</italic>. The most dominant families were Asteraceae and Lamiaceae, each contributing eight species, followed by Rosaceae with seven species. The next most prominent families were Apiaceae and Polygonaceae, each with five species. Caryophyllaceae and Poaceae contained four species each, while Plantaginaceae and Ranunculaceae had three species each. Families such as Boraginaceae, Crassulaceae, Cyperaceae, Fabaceae, and Violaceae were represented by two species each. Additionally, Adoxaceae, Balsaminaceae, Caprifoliaceae, Cupressaceae, Gentianaceae, Iridaceae, Onagraceae, Orobanchaceae, Oxalidaceae, Papaveraceae, Primulaceae, and Salicaceae were each represented by one species. Our findings are supported by <xref ref-type="bibr" rid="B71">Rahman et al. (2020)</xref>. Various environmental variables and anthropogenic activities typically influence high-altitude species distribution, composition, and association. This study examines several factors, including edaphic, topographic, and environmental variables, to understand their roles in shaping subalpine and alpine species composition, association formation, and identifying indicator species. Many such kinds of studies have been undertaken to study wild plants in ecosystems other than tree lines. A notable gap in research focused on such types of high-altitude species in different regions of the alpine zone. Several research articles have also documented alpine species of the Himalayas across various habitats (<xref ref-type="bibr" rid="B7">Ali et al., 2022</xref>). Similarly, other studies have focused on individual subalpine species across different habitats, as <xref ref-type="bibr" rid="B55">Manzoor et al. (2024)</xref>. Investigated the effect of various environmental variables on the distribution of <italic>Iris hookeriana</italic> using TWINSPAN and DECORANA as classification and ordination techniques (<xref ref-type="bibr" rid="B85">Shehata and Galal, 2014</xref>). In the present study, the four major plant associations have resulted. The use of such a robust procedure can be observed quite often for the classification of natural vegetation (<xref ref-type="bibr" rid="B83">Shaheen et al., 2011</xref>; <xref ref-type="bibr" rid="B45">Khan et al., 2012</xref>; <xref ref-type="bibr" rid="B84">Shaheen et al., 2015</xref>; <xref ref-type="bibr" rid="B87">Singh and Yadav, 2024</xref>). The top indicator species of each association were recorded based on environmental gradients i.e., edaphic factors, topography, and climatic variables. Such a methodology for association was also followed by <xref ref-type="bibr" rid="B43">Khan et al. (2013a</xref>,<xref ref-type="bibr" rid="B44">2014</xref>), <xref ref-type="bibr" rid="B81">Shah et al. (2015)</xref>, <xref ref-type="bibr" rid="B4">Ahmad et al. (2016a)</xref>, <xref ref-type="bibr" rid="B6">Ahmad et al. (2016b)</xref>, <xref ref-type="bibr" rid="B47">Khan et al. (2016)</xref>, <xref ref-type="bibr" rid="B39">Khan M. et al. (2017)</xref>. The classification of various plant species into different plant associations, along with their indicator species, was conducted in this study.</p>
<p>A study conducted in the subalpine zone of the Himalayas, specifically in Manoor Valley, identified 56 plant species across twenty-one sampling sites. The researchers distinguished four major plant associations, with soil gradients playing a crucial role in shaping and supporting these associations. In current phytosociological research vegetation at higher elevations features key indicator species and characteristic plants typical of sub-alpine and alpine zone Such as <italic>Arenaria serpyllifolia, viola canescens, Nepeta erecta, Oxytropis cachemiriana, Phlomis bracteosa, Prunella vulgaris, Bistorta affinis, and Carex infuscate</italic> (<xref ref-type="bibr" rid="B91">Tasser et al., 2008</xref>; <xref ref-type="bibr" rid="B70">Qiong et al., 2010</xref>; <xref ref-type="bibr" rid="B78">Rixen et al., 2022</xref>; <xref ref-type="bibr" rid="B82">Shaheen et al., 2023</xref>) Various environmental factors define the boundaries of altitudinal zones, including altitude, humidity, temperature, and species interactions. These factors contribute to the formation of distinct associations along with the elevation gradient. Similar plant populations influenced by these environmental parameters have also been reported by <xref ref-type="bibr" rid="B29">Haq et al. (2015)</xref>, <xref ref-type="bibr" rid="B40">Khan and Ahmad (2015)</xref>.</p>
<p>Our research shows that the endemic plant <italic>Iris hookeriana</italic> shows a significant positive correlation with elevation. The species is more abundant at higher altitudes, where cooler temperatures, favorable soil moisture, and higher humidity are present. These environmental factors contribute to their increased presence at elevated sites, while their occurrence diminishes at lower altitudes. This pattern highlights the species&#x2019; preference for specific ecological conditions associated with higher elevations (<xref ref-type="bibr" rid="B3">Ahmad et al., 2015</xref>; <xref ref-type="bibr" rid="B93">Thapa et al., 2021</xref>). Average levels of silt in the soil positively affect the abundance of <italic>Iris hookeriana</italic> by improving moisture retention, which supports the species growth. A moderate amount of clay enhances species abundance by retaining moisture. However, excessive clay can hinder root development and reduce abundance. There is a negative correlation between sandy soil content and the abundance of <italic>Iris hookeriana</italic>. Higher sand content reduces moisture retention, which is unfavorable for the species as a similar result is also given by <xref ref-type="bibr" rid="B95">Wiens and Donoghue (2004)</xref>, <xref ref-type="bibr" rid="B31">Harrison et al. (2006)</xref>. <italic>Iris hookeriana</italic> is more abundant on north-facing slopes, which provide cooler and moist conditions due to reduced sunlight exposure these conditions are optimal for the species growth consistent with alpine plants (<xref ref-type="bibr" rid="B11">Chauhan et al., 2019</xref>).</p>
<p>Moreover, (<xref ref-type="bibr" rid="B22">Gairola et al., 2012</xref>) reported that slightly acidic soil supports <italic>Trillium govanianum</italic>, as an endemic sub-alpine Himalayan plant, and Similarly, in the present study, <italic>Iris hookeriana</italic>, also found in the same habitat, exhibited a preference for slightly acidic soils, with an optimal pH range of 5.6&#x2013;6.2 as extremely acidic or alkaline conditions were observed negatively affect its abundance, highlighting the importance of moderately acidic to neutral pH levels for the species growth and distribution. In certain areas, grazing pressure on herbaceous plants is particularly high, especially where livestock goats, sheep, and horses are abundant conversely, other regions have lower grazing pressure due to landowners enforcing restrictions to protect their properties. Additionally, grazing pressure in the Himalayas varies with the seasons; high-altitude meadows are only accessible during the summer. Increased grazing pressure significantly reduces the abundance of <italic>Iris hookeriana</italic>, as grazing damages the plants and disrupts soil conditions similar patterns of seasonal variation and grazing impacts have been reported by <xref ref-type="bibr" rid="B67">Peer et al. (2001)</xref>, <xref ref-type="bibr" rid="B56">Maselli (2004)</xref>, <xref ref-type="bibr" rid="B50">Kukshal et al. (2009)</xref>. Consequences of grazing pressure, increase in temperature, and alteration in climate change lead to a significant decline in the population of <italic>Iris hookeriana</italic> the findings are similar with the <xref ref-type="bibr" rid="B76">Rawat et al. (2024)</xref>, <xref ref-type="bibr" rid="B26">Grandin (2006)</xref>. Climate change is expected to considerably affect the Hindu-Himalayan Mountains, with rising temperatures, altered rainfall patterns, and glacial retreat posing significant threats to subalpine and alpine plant species such as <italic>Iris hookeriana</italic> the projected climate changes in the Hindu-Himalayan region, is essential to perform climate adaptation strategies into conservation efforts for <italic>Iris hookeriana</italic> (<xref ref-type="bibr" rid="B52">Liu et al., 2025</xref>). There is a strong negative correlation between anthropogenic activities and species abundance. Human activities like over-exploitation, urbanization, and pollution degrade habitats, leading to declining <italic>Iris hookeriana</italic> populations in the study area. Due to its attractive purple flowers, locals often extract the plant&#x2019;s rhizome from its natural habitat. The flowers are sold in markets, while the rhizomes are supplied to traditional healers (hakims) to prepare crude medicines. Additionally, local traders sell rhizome of <italic>Iris hookeriana</italic> to larger merchants for distribution to national and international markets, a trend documented in previous research (<xref ref-type="bibr" rid="B37">Jones, 2000</xref>; <xref ref-type="bibr" rid="B67">Peer et al., 2001</xref>; <xref ref-type="bibr" rid="B56">Maselli, 2004</xref>; <xref ref-type="bibr" rid="B57">Maurer et al., 2006</xref>; <xref ref-type="bibr" rid="B50">Kukshal et al., 2009</xref>; <xref ref-type="bibr" rid="B13">Chowdhury and Koike, 2010</xref>; <xref ref-type="bibr" rid="B38">Kassam et al., 2010</xref>; <xref ref-type="bibr" rid="B41">Khan et al., 2011</xref>).</p>
<p>One of the primary challenges contributing to species overexploitation in the Himalayas is the dependence of local communities on ethnoecological knowledge for their economic well-being and the production of traditional medicines (<xref ref-type="bibr" rid="B1">Abdullah and Khan, 2023</xref>). The region&#x2019;s isolation from urban centers exacerbates this issue, leading to unsustainable resources used for economic gain. Similar trends of resource exploitation have been highlighted in other research (<xref ref-type="bibr" rid="B46">Khan et al., 2013b</xref>). Higher humidity levels and increased soil moisture content have a significant positive impact on the abundance of <italic>Iris hookeriana</italic>. The species thrives in moist environments, where elevated atmospheric moisture contributes to its growth. Similarly, as soil moisture levels rise, particularly in well-drained soils, <italic>Iris hookeriana</italic> exhibits greater abundance such favorable conditions enhance the species&#x2019; physiological processes, enabling it to thrive in habitats with higher humidity and optimal soil moisture similar findings have been reported by <xref ref-type="bibr" rid="B94">Wani et al. (2023)</xref>.</p>
<p>Increased soil erosion has a significant negative impact on species abundance, as habitat degradation leads to the decline of <italic>Iris hookeriana</italic> these findings are consistent with the results of <xref ref-type="bibr" rid="B20">Ekka et al. (2023)</xref>. A significant negative relationship exists between electrical conductivity and species abundance as conductivity increases, indicating higher soil salinity in the soil so, an abundance of <italic>Iris hookeriana</italic> declines, as it prefers non-saline environments (<xref ref-type="bibr" rid="B19">Dong et al., 2022</xref>). <italic>Iris hookeriana</italic> tolerates moderate levels of total dissolved solids (TDS), but higher concentrations, often associated with salinity, negatively affect its abundance our results align with those of <xref ref-type="bibr" rid="B2">Adjovu et al. (2023)</xref>. Organic matter (OM) positively correlates with the abundance of <italic>Iris hookeriana</italic> as organic matter improves soil fertility and moisture retention, supporting greater species abundance comparable findings were documented by <xref ref-type="bibr" rid="B59">Melese et al. (2017)</xref>. Adequate nitrogen levels promote healthy growth in <italic>Iris hookeriana</italic>, leading to higher abundance however, excessive nitrogen can disrupt nutrient uptake, negatively affecting the species the findings of <xref ref-type="bibr" rid="B51">Leghari et al. (2016)</xref> are consistent with ours, particularly regarding the role of nitrogen in promoting species abundance.</p>
<p>Moderate phosphorus levels benefit <italic>Iris hookeriana</italic> as they support root development however, extremely low or high levels of phosphorus negatively affect the species&#x2019; abundance (<xref ref-type="bibr" rid="B33">Hill et al., 2006</xref>). Potassium (k) positively influences the abundance of <italic>Iris hookeriana</italic> by promoting overall plant health as moderate potassium levels are associated with a higher abundance of species (<xref ref-type="bibr" rid="B79">Sardans and Pe&#x00F1;uelas, 2021</xref>). Due to combination of factors, including anthropogenic pressure, species overexploitation, climate change, and unsustainable resource use, the populations of various Himalayan species are steadily declining (<xref ref-type="bibr" rid="B69">Premlata et al., 2024</xref>; <xref ref-type="bibr" rid="B87">Singh and Yadav, 2024</xref>). Urgent biodiversity strategies and sustainable development policies are needed to protect these species, particularly the endemic ones, in their native habitats. Our research highlights indicator species in the study area, providing key insights and recommendations for prioritizing biodiversity conservation efforts in the Hindu-Himalayan mountains.</p>
<p>Conservation of <italic>Iris hookeriana</italic> not only supports its ecological role but also contributes to local economies by promoting sustainable harvesting practices and ecotourism, and ensures the long-term availability of <italic>Iris hookeriana</italic> for commercial and medicinal uses. However, initiatives of conservation strategies should involve local stakeholders and policymakers to develop sustainable trade or community-led necessaries which give alternative income sources and ultimately reduce overharvesting pressure for the sustainability of <italic>Iris hookeriana</italic> in the natural habitat.</p>
</sec>
<sec id="S5" sec-type="conclusion">
<title>5 Conclusion</title>
<p>It is concluded that cluster analysis, two-way cluster analysis, and indicator species analysis, identified key plant associations and their indicator species within the subalpine and alpine zones of the Hindu-Himalayas, specifically those associated with <italic>Iris hookeriana</italic> The primary indicators include various herbs such as <italic>Arenaria serpyllifolia, Viola canescens, Nepeta erecta, Oxytropis cachemiriana, Phlomis bracteosa, Prunella vulgaris, Bistorta affinis</italic>, and <italic>Carex infuscata</italic>. The distribution of these indicator species in the study area is closely linked to numerous environmental factors, including altitude, electrical conductivity (EC), total dissolved solid (TDS), Nitrogen (N), Phosphorus (P), Potassium (K), Silt, Organic Matter (OM) and clay content. Our findings suggest a critical role of such environmental variables in shaping plant associations and inducing the distribution of <italic>Iris hookeriana</italic> within sub alpine and alpine ecosystems where each environmental variable converges to create optimal conditions. This emphasizes the sensitivity of <italic>Iris hookeriana</italic> to variations in environmental parameters, particularly in the face of ongoing climate change and increasing anthropogenic pressures in the Hindu-Himalayan region. The observed shifts in species associations and the potential impact on <italic>Iris hookeriana</italic> underscore the need for further research to understand the long-term consequences of environmental change on this important species and the broader alpine ecosystem Future work can be carried out to address the durable outcomes of plant biodiversity loss for the biological ecosystem&#x2019;s sustainability, integrating remote sensing and species distribution modeling (SDM) to improve the understanding of <italic>Iris hookeriana</italic> habitat preferences and expand conservation planning by predicting species distribution under changing environmental conditions.</p>
</sec>
</body>
<back>
<sec id="S6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="S7" sec-type="author-contributions">
<title>Author contributions</title>
<p>MK: Data curation, Project administration, Writing &#x2013; original draft, Writing &#x2013; review and editing, Formal Analysis, Investigation, Methodology, Software, Conceptualization, Resources. SMK: Formal Analysis, Investigation, Writing &#x2013; original draft, Writing &#x2013; review and editing, Funding acquisition, Project administration, Conceptualization, Supervision, Validation, Visualization. Abdullah: Investigation, Writing &#x2013; review and editing, Methodology, Software. JL: Writing &#x2013; review and editing, Project administration. Z-YW: Investigation, Writing &#x2013; review and editing. JH: Writing &#x2013; review and editing, Conceptualization. SZ: Writing &#x2013; review and editing, Software. NM: Writing &#x2013; review and editing, Methodology. ZB: Writing &#x2013; review and editing, Formal Analysis. ZS: Writing &#x2013; review and editing, Formal Analysis, Methodology. RA: Writing &#x2013; review and editing. FM: Writing &#x2013; review and editing. SA: Writing &#x2013; review and editing, Data curation, Funding acquisition, Project administration, Writing &#x2013; original draft.</p>
</sec>
<sec id="S8" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This research was part of the Joint Exchange Program of Pakistan Science Foundation (PSF) and National Science Foundation China (NSFC) via project number (PSF-NSFC/JSEP/BIO/C-OAU(04) and authors acknowledge the aforementioned organizations for their support.</p>
</sec>
<ack><p>We are very thankful to the local community for their support and guidance during the field work. We are also thankful to Dr. Zahid Ullah, University of Swat for helping us in identification of associated plant species and Dr. Abdul Bari, director Agriculture Research Institute, Swat for his support during the soil analyses.</p>
</ack>
<sec id="S9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="S10" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The authors declare that no Generative AI was used in the creation of this manuscript.</p>
</sec>
<sec id="S11" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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