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<journal-id journal-id-type="publisher-id">Front. Ecol. Evol.</journal-id>
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<journal-title>Frontiers in Ecology and Evolution</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Ecol. Evol.</abbrev-journal-title>
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<issn pub-type="epub">2296-701X</issn>
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<article-id pub-id-type="doi">10.3389/fevo.2025.1631747</article-id>
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<subj-group subj-group-type="heading">
<subject>Original Research</subject>
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</article-categories>
<title-group>
<article-title>Forthcoming risk of invasive species <italic>Arundo donax</italic>: global invasion driven by climate change</article-title>
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<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Poudel</surname><given-names>Anil</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
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<name><surname>Lee</surname><given-names>Yong Ho</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<name><surname>Adhikari</surname><given-names>Prabhat</given-names></name>
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<name><surname>Adhikari</surname><given-names>Pradeep</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
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<name><surname>Hong</surname><given-names>Sun Hee</given-names></name>
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<aff id="aff1"><label>1</label><institution>Department of Plant Resources and Landscape Architecture, College of Agriculture and Life Sciences, Hankyong National University</institution>, <city>Anseong</city>,&#xa0;<country country="check-value">Republic of Korea</country></aff>
<aff id="aff2"><label>2</label><institution>Institute of Humanities and Ecology Consensus Resilience Lab, Hankyong National University</institution>, <city>Anseong</city>,&#xa0;<country country="check-value">Republic of Korea</country></aff>
<aff id="aff3"><label>3</label><institution>OJEong Resilience Institute, Korea University</institution>, <city>Seoul</city>,&#xa0;<country country="check-value">Republic of Korea</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Pradeep Adhikari, <email xlink:href="mailto:pdp2042@gmail.com">pdp2042@gmail.com</email>; Sun Hee Hong, <email xlink:href="mailto:shhong@hknu.ac.kr">shhong@hknu.ac.kr</email></corresp>
<fn fn-type="equal" id="fn003">
<label>&#x2020;</label>
<p>These authors have contributed equally to this work</p></fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2025-11-18">
<day>18</day>
<month>11</month>
<year>2025</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>13</volume>
<elocation-id>1631747</elocation-id>
<history>
<date date-type="received">
<day>20</day>
<month>05</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>10</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Poudel, Lee, Adhikari, Adhikari and Hong.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Poudel, Lee, Adhikari, Adhikari and Hong</copyright-holder>
<license>
<ali:license_ref start_date="2025-11-18">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. 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.</license-p>
</license>
</permissions>
<abstract>
<p><italic>Arundo donax</italic> ranks among the world&#x2019;s 100 most invasive weed species, posing significant threats to native biodiversity, agriculture, and natural ecosystems. Global climate change, characterized by increasing temperatures and increased human activities, is projected to amplify the risk of <italic>A. donax</italic> invasion worldwide. In this study, species distribution modeling via the maximum entropy algorithm was employed to predict the potential distributions of species under current and future climate scenarios on the basis of the following shared socioeconomic pathways (SSPs): SSP2-4.5 and SSP5-8.5. Our study revealed that the human influence index (HII), annual mean temperature (Bio1), and ultraviolet radiation (UV-B) were the top contributors to the model output, with contribution rates of 59%, 23.6%, and 7.3%, respectively. Currently, approximately 10.15% of the total land mass is invaded by <italic>A. donax</italic>, with 21 countries, including France, Croatia, Italy, and Spain, identified as exhibiting more than 75% of their territories at high risk of invasion. However, the future projections for 2041&#x2013;2060 and 2081&#x2013;2100 indicated substantial expansion in suitable habitats, covering land mass proportions of 18.40% and 24.26%, respectively, under SSP2-4.5 and 19.39% and 25.66%, respectively, under SSP5-8.5. Notably, 41 countries (SSP2-4.5) and 42 countries (SSP5-8.5) were projected to shift from low to high or very high invasion risk categories from 2081&#x2013;2100. Moreover, invasion risk was projected to increase across all continents, with Africa demonstrating the most significant increase (312.08%). These findings highlight the escalating threat of <italic>A. donax</italic> under global climate change and human activities, emphasizing the urgent need for proactive management strategies, including enhanced quarantine measures and effective control programs, to limit its spread and mitigate associated risks.</p>
</abstract>
<kwd-group>
<kwd>countries</kwd>
<kwd>maximum entropy</kwd>
<kwd>risk assessment</kwd>
<kwd>species distribution models</kwd>
<kwd>shared socioeconomic pathways</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declare financial support was received for the research and/or publication of this article. This study was supported by the Rural Development Administration (RS-2024-00428455).</funding-statement>
</funding-group>
<counts>
<fig-count count="8"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="97"/>
<page-count count="15"/>
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<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Models in Ecology and Evolution</meta-value>
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</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>The global spread of invasive alien plants (IAPs) is intricately linked with human activities and climate change, posing significant threats to biodiversity and ecosystem stability (<xref ref-type="bibr" rid="B23">Catford et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B13">Bellard et&#xa0;al., 2012</xref>). The IAPs are introduced through human-mediated pathways such as agricultural practices, global trade and tourism, and the development of transportation corridors. These activities facilitate the spread of these species into various ecosystems (<xref ref-type="bibr" rid="B30">Dukes and Mooney, 1999</xref>; <xref ref-type="bibr" rid="B65">Pejchar and Mooney, 2009</xref>; <xref ref-type="bibr" rid="B5">Adhikari et&#xa0;al., 2021</xref>). Changing climatic conditions, coupled with human-induced habitat disturbances, accelerate the spread of invasive plant species by altering ecosystem dynamics and resource availability (<xref ref-type="bibr" rid="B84">Walther et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B14">Bellard et&#xa0;al., 2013</xref>). Furthermore, human intervention not only shapes societal progress but also significantly impacts ecosystem stability and biodiversity (<xref ref-type="bibr" rid="B37">Gonz&#xe1;lez-Moreno et&#xa0;al., 2014</xref>).</p>
<p>In addition to human activities, rapid climate warming and globalization have accelerated the invasion and dispersal of IAPs worldwide. With global temperatures projected to increase by approximately 2.7&#xa0;&#xb0;C by the end of the century (<xref ref-type="bibr" rid="B84">Walther et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B14">Bellard et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B74">Ripple et&#xa0;al., 2024</xref>), the changing climate can alter ecological conditions, thereby shifting suitable habitats and enhancing the establishment and spread of IAPs (<xref ref-type="bibr" rid="B14">Bellard et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B67">Petitpierre et&#xa0;al., 2016</xref>). Human-induced environmental changes, combined with increasing temperatures, exacerbate the spread of IAPs, which highlights the need for proactive strategies to mitigate their impact on global ecosystems (<xref ref-type="bibr" rid="B97">Zimmermann et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B54">Liu et&#xa0;al., 2024</xref>).</p>
<p><italic>Arundo donax</italic>, also known as giant reed, is a highly invasive, fast-growing perennial grass native to East Asia (<xref ref-type="bibr" rid="B66">Perdue, 1958</xref>; <xref ref-type="bibr" rid="B70">Pilu et&#xa0;al., 2012</xref>). It has been cultivated for thousands of years across Asia and Europe for various traditional applications, including the provision of construction materials, fencing, basket weaving, and reeds in woodwind instruments (<xref ref-type="bibr" rid="B66">Perdue, 1958</xref>; <xref ref-type="bibr" rid="B12">Bell, 1998</xref>; <xref ref-type="bibr" rid="B39">Guthrie, 2007</xref>; <xref ref-type="bibr" rid="B48">Jim&#xe9;nez-Ruiz et&#xa0;al., 2021</xref>). Over time, the species has been introduced into tropical, subtropical, and warm temperate regions worldwide, where it has naturalized and subsequently become invasive (<xref ref-type="bibr" rid="B48">Jim&#xe9;nez-Ruiz et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B38">Goolsby et&#xa0;al., 2023</xref>). Historical records indicate its introduction into North America during the early 1800s, South Africa during the late 1700s, and Australia during the mid-1800s, primarily for ornamental purposes and erosion control (<xref ref-type="bibr" rid="B39">Guthrie, 2007</xref>; <xref ref-type="bibr" rid="B83">Virtue et&#xa0;al., 2010</xref>; <xref ref-type="bibr" rid="B21">CABI, 2022</xref>). In recent decades, <italic>A. donax</italic> has been widely planted for additional purposes, including the production of walking sticks, fishing poles and musical instruments and as a potential biofuel source (<xref ref-type="bibr" rid="B21">CABI, 2022</xref>).</p>
<p>The species exhibits rapid growth, with an observed height increase of up to 10&#xa0;cm per day, reaching a maximum height of approximately 7.62&#xa0;m within a year and yielding nearly 25 tons of biomass per acre per harvest (<xref ref-type="bibr" rid="B12">Bell, 1998</xref>; <xref ref-type="bibr" rid="B76">Sidella, 2013</xref>; <xref ref-type="bibr" rid="B21">CABI, 2022</xref>; <xref ref-type="bibr" rid="B26">CISR, 2022</xref>). The primary mode of reproduction is vegetative propagation through rhizomes and plant fragments, enabling rapid colonization and regrowth following disturbance (<xref ref-type="bibr" rid="B64">Ozudogru et&#xa0;al., 2023</xref>). Dispersal occurs through both natural and anthropogenic mechanisms; notably, hydrological events such as flooding facilitate the downstream transport of rhizome fragments, whereas human-mediated activities, including soil displacement and intentional planting, significantly contribute to its widespread establishment (<xref ref-type="bibr" rid="B12">Bell, 1998</xref>; <xref ref-type="bibr" rid="B39">Guthrie, 2007</xref>; <xref ref-type="bibr" rid="B38">Goolsby et&#xa0;al., 2023</xref>).</p>
<p><italic>Arundo donax</italic> is a highly adaptable species capable of thriving under a wide range of climatic and soil conditions, making it one of the most invasive plants globally. It can grow in regions with annual precipitation amounts ranging from 300 to 4000&#xa0;mm and temperatures between 9&#xa0;&#xb0;C and 29&#xa0;&#xb0;C (<xref ref-type="bibr" rid="B66">Perdue, 1958</xref>; <xref ref-type="bibr" rid="B88">Weber, 2017</xref>; <xref ref-type="bibr" rid="B41">Haworth et&#xa0;al., 2019</xref>). Its remarkable drought resistance is due to its extensive underground system of rhizomes and deep roots that can penetrate up to 5&#xa0;m into the soil, enabling it to access water and nutrients even under arid conditions (<xref ref-type="bibr" rid="B75">S&#xe1;nchez et&#xa0;al., 2021</xref>). <italic>A. donax</italic> also demonstrates remarkable ecological plasticity, thriving across a wide range of soil types, including loose sandy soils, gravelly substrates, heavy clay, and riverine sediments (<xref ref-type="bibr" rid="B48">Jim&#xe9;nez-Ruiz et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B38">Goolsby et&#xa0;al., 2023</xref>). Its physiological adaptations, including a base growth temperature threshold of 9&#xa0;&#xb0;C and a high carbon dioxide exchange capacity, enable it to persist under diverse climatic conditions, ranging from warm to cold environments and humid to arid environments (<xref ref-type="bibr" rid="B66">Perdue, 1958</xref>; <xref ref-type="bibr" rid="B78">Spencer and Ksander, 2006</xref>). This extensive adaptability has led to its classification as one of the world&#x2019;s 100 most invasive alien species (<xref ref-type="bibr" rid="B47">ISSG, 2011</xref>; <xref ref-type="bibr" rid="B21">CABI, 2022</xref>).</p>
<p>As a highly invasive species, <italic>A. donax</italic> exhibits profound ecological and economic impacts, particularly in riparian ecosystems, where it aggressively displaces native vegetation, alters soil nutrient dynamics, and increases fire hazards due to its high flammability (<xref ref-type="bibr" rid="B9">Ambrose and Rundel, 2007</xref>; <xref ref-type="bibr" rid="B18">Bruno et&#xa0;al., 2019</xref>). This highly invasive grass forms dense monospecific stands along waterways, thereby consuming nearly three times more water than native plants do, which is a major concern in areas with large-scale invasions, such as Australia, New Zealand, South Africa, the United States, and Mexico (<xref ref-type="bibr" rid="B50">Lambert et&#xa0;al., 2010</xref>; <xref ref-type="bibr" rid="B83">Virtue et&#xa0;al., 2010</xref>; <xref ref-type="bibr" rid="B40">Haddadchi et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B61">Nkuna et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B48">Jim&#xe9;nez-Ruiz et&#xa0;al., 2021</xref>). In South Africa, <italic>A. donax</italic> is the most abundant and widespread invasive alien grass species, and compulsory inclusion is legally required in invasive species control programs. Its dense growth disrupts the hydrological system, further reducing water availability in already water-scarce regions, making it a nationally recognized problematic invasive weed (<xref ref-type="bibr" rid="B61">Nkuna et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B81">Tshapa et&#xa0;al., 2021</xref>). Similarly, in California, <italic>A. donax</italic> exacerbates flood risks and fire hazards in riparian zones, with its dense, dormant biomass serving as a vertical fuel source that promotes the occurrence of wildfires and increases fire severity (<xref ref-type="bibr" rid="B29">Dudley, 2000</xref>; <xref ref-type="bibr" rid="B73">Quinn et&#xa0;al., 2007</xref>). Given its significant ecological and economic impacts, <italic>A. donax</italic> has become a priority for control and management efforts to mitigate further environmental and economic losses.</p>
<p>In recent years, species distribution models (SDMs) have emerged as critical tools for predicting the potential geographical distributions of species under changing climatic conditions (<xref ref-type="bibr" rid="B10">Ara&#xfa;jo and Guisan, 2006</xref>; <xref ref-type="bibr" rid="B36">Franklin, 2013</xref>). Among the various SDMs, such as the maximum entropy (MaxEnt) algorithm, CLIMEX model, random forest (RF) models, generalized linear models (GLMs), and artificial neural networks (ANNs), the MaxEnt algorithm has gained widespread recognition for its effectiveness in modeling species distributions, particularly for IAPs (<xref ref-type="bibr" rid="B15">Booth et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B20">Byeon et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B2">Adhikari et&#xa0;al., 2022</xref>, <xref ref-type="bibr" rid="B4">2025</xref>). The MaxEnt model is especially advantageous for analyzing presence-only data and provides notable performance even with limited sample sizes (<xref ref-type="bibr" rid="B69">Phillips et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B95">Yi et&#xa0;al., 2016</xref>). Despite its limitations, including the risk of overfitting, the MaxEnt model remains an effective tool for assessing invasion risk and guiding IAP management (<xref ref-type="bibr" rid="B68">Phillips et&#xa0;al., 2017</xref>). Its ability to integrate bioclimatic variables and simulate species distributions under current and future climate scenarios makes it invaluable for understanding and mitigating the ecological impacts of invasive species (<xref ref-type="bibr" rid="B69">Phillips et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B85">Wang et&#xa0;al., 2023</xref>).</p>
<p>Considering the highly invasive nature and global impact of <italic>A. donax</italic> on agriculture, nature, and the economy, further studies are needed to support the control and management of invasive weeds. In this study, global species occurrence records of <italic>A. donax</italic> are employed to estimate its invasion risk in various countries and predict invasion risk on the basis of bioclimatic variables under current and potential future shared socioeconomic pathway (SSP) climate change scenarios (SSP2-4.5 and SSP5-8.5). This research is designed to achieve three primary aims: (1) identify the key environmental variables influencing the distribution of <italic>A. donax</italic>, (2) predict the potential geographical distribution of <italic>A. donax</italic> under current and future climate change scenarios, and (3) classify the invasion risk levels of <italic>A. donax</italic> in different countries. By elucidating the invasion dynamics of this highly invasive species, this study aims to contribute to the development of early warning systems, prevention strategies, and targeted management plans. Ultimately, the findings of the study could support global efforts to mitigate the ecological and socioeconomic disruptions caused by <italic>A. donax</italic> invasions.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Occurrence records</title>
<p>In this study, 51,430 occurrence records for <italic>A. donax</italic> were obtained from the Global Biodiversity Information Facility (GBIF) (<ext-link ext-link-type="uri" xlink:href="https://www.gbif.org">https://www.gbif.org</ext-link>; accessed on 3 December 2024). To minimize duplicate coordinates and reduce sampling biases, we applied the spatially rarefy occurrence tool within the ArcGIS SDM toolbox v.2.4, thereby retaining a single observation within each 2.5-minute resolution grid cell (<xref ref-type="bibr" rid="B16">Brown et&#xa0;al., 2017</xref>). Ultimately, the number of occurrence record points was reduced to 7,975 (<xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref>), which is sufficient for MaxEnt modeling (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S1</bold></xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Global distribution points of <italic>A. donax</italic> (n=7,975). The red points indicate recorded global occurrence locations worldwide, illustrating its widespread distribution and invasive potential.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-13-1631747-g001.tif">
<alt-text content-type="machine-generated">World map showing presence points of a species in red across various regions, including parts of North and South America, Europe, Africa, Asia, and Australia. Countries are shaded in gray, with a scale indicator at the bottom.</alt-text>
</graphic></fig>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Environmental factor variables</title>
<p>The distribution of species is influenced by various factors, including climatic conditions, habitat, human activities, and radiation effects (<xref ref-type="bibr" rid="B93">Yang et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B6">Adhikari et&#xa0;al., 2023b</xref>, <xref ref-type="bibr" rid="B3">2024</xref>; <xref ref-type="bibr" rid="B59">Mansinhos et&#xa0;al., 2024</xref>). In this study, we analyzed the global distribution of <italic>A. donax</italic> on the basis of 19 bioclimatic variables, the human influence index (HII), and ultraviolet radiation (UV-B) via the MaxEnt model (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S2</bold></xref>). Current climatic data (1979&#x2013;2013) were obtained from the PaleoClim v1.2 dataset (<ext-link ext-link-type="uri" xlink:href="http://www.paleoclim.org/">http://www.paleoclim.org/</ext-link>; accessed on 10 March 2024) at a 2.5-minute resolution (<xref ref-type="bibr" rid="B17">Brown et&#xa0;al., 2018</xref>). Future climate projections for 2041&#x2013;2060 and 2081&#x2013;2100, under the SSP2-4.5 and SSP5-8.5 scenarios, respectively, were sourced from World Climate (<ext-link ext-link-type="uri" xlink:href="http://worldclim.org/">http://worldclim.org/</ext-link>; accessed on 15 January 2024) (<xref ref-type="bibr" rid="B42">Hijmans et&#xa0;al., 2005</xref>), with datasets representing the Beijing Climate Center Climate System Model (BCC-CSM2-MR) under the Coupled Model Intercomparison Project Phase 6 (CMIP6) (<xref ref-type="bibr" rid="B92">Wu et&#xa0;al., 2019</xref>).</p>
<p>Human activities, such as global trade, transportation, and habitat alteration, play a significant role in controlling the invasion and spread of invasive species by facilitating their unintentional or intentional movement to new areas, often disrupting local ecosystems (<xref ref-type="bibr" rid="B31">Early et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B24">Chen et&#xa0;al., 2021</xref>). Therefore, we considered human factors when selecting environmental variables for modeling the distribution of <italic>A. donax</italic>. We downloaded data for the human influence index, with a resolution of 30 arc seconds (~1 km&#xb2;) (<ext-link ext-link-type="uri" xlink:href="http://sedac.ciesin.columbina.edu/">http://sedac.ciesin.columbina.edu/</ext-link>; accessed on 4 April 2024), and resampled the data to a 2.5-minute resolution for our analysis. This variable, representing human impacts from 1995&#x2013;2004, was used in modeling and includes data on the population density, land development, infrastructure, artificial lighting, and transportation networks sourced from the Wildlife Conservation Society (<xref ref-type="bibr" rid="B87">WCS, 2005</xref>). Additionally, UV-B radiation data were obtained from the gIUV database (<ext-link ext-link-type="uri" xlink:href="http://www.ufz.de/gluv">http://www.ufz.de/gluv</ext-link>; accessed on 5 May 2024), highlighting plant physiology, growth, and ecosystem dynamics under climate change (<xref ref-type="bibr" rid="B59">Mansinhos et&#xa0;al., 2024</xref>). Similarly, we employed the global biome as a key variable, sourced from the Earth Engine data catalog (<xref ref-type="bibr" rid="B28">Dinerstein et&#xa0;al., 2017</xref>), to refine the species distribution modeling process and better understand invasive plant dynamics. Biomes, characterized by unique climatic conditions, vegetation patterns, and ecological processes, serve as fundamental drivers of species distributions and play a critical role in shaping the spread of invasive species (<xref ref-type="bibr" rid="B63">Olson et&#xa0;al., 2001</xref>; <xref ref-type="bibr" rid="B27">Conradi et&#xa0;al., 2020</xref>). As integrative environmental determinants, biomes encapsulate the interplay among climate and habitat factors, offering a holistic framework for predicting species invasions (<xref ref-type="bibr" rid="B31">Early et&#xa0;al., 2016</xref>). Combining environmental data with biome variables can enhance our ability to map ecological niches, assess invasion risks, and inform effective conservation strategies, providing greater insights into the mechanisms driving invasive species success.</p>
<p>Pearson&#x2019;s correlation analysis was conducted to reduce multicollinearity and increase the accuracy of the estimates and predictions. We used band collection statistics obtained with the spatial analyst tool in ArcGIS 10.8 (Esri, Redlands, CA, USA) to perform Pearson&#x2019;s correlation analysis. We analyzed 19 bioclimatic variables along with three additional variables: HII, UV-B, and Biome. Variables with low contribution rates and high correlations (r &gt; 0.75, P&#xa0;=&#xa0;0.05) were excluded (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S3</bold></xref>). On the basis of the analysis, six climatic variables, namely, annual mean temperature (Bio1), mean diurnal temperature range (Bio2), isothermality (Bio3), annual precipitation (Bio12), precipitation in the wettest month (Bio13), and precipitation in the driest month (Bio14), along with three additional variables, i.e., HII, UV-B, and Biome, were selected (<xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>List of the selected bioclimatic variables used in this study.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Category</th>
<th valign="middle" align="center">Variable</th>
<th valign="middle" align="center">Description</th>
<th valign="middle" align="center">Unit</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="6" align="left">Bioclimatic variables</td>
<td valign="middle" align="left">Bio1</td>
<td valign="middle" align="center">Annual mean temperature</td>
<td valign="middle" align="center">&#xb0;C</td>
</tr>
<tr>
<td valign="middle" align="left">Bio2</td>
<td valign="middle" align="center">Mean diurnal temperature range</td>
<td valign="middle" align="center">&#xb0;C</td>
</tr>
<tr>
<td valign="middle" align="left">Bio3</td>
<td valign="middle" align="center">Isothermality (BIO2/BIO7) (* 100)</td>
<td valign="middle" align="center">%</td>
</tr>
<tr>
<td valign="middle" align="left">Bio12</td>
<td valign="middle" align="center">Annual precipitation</td>
<td valign="middle" align="center">mm</td>
</tr>
<tr>
<td valign="middle" align="left">Bio13</td>
<td valign="middle" align="center">Precipitation in the wettest month</td>
<td valign="middle" align="center">mm</td>
</tr>
<tr>
<td valign="middle" align="left">Bio14</td>
<td valign="middle" align="center">Precipitation in the driest month</td>
<td valign="middle" align="center">mm</td>
</tr>
<tr>
<td valign="middle" align="left">Human influence variable</td>
<td valign="middle" align="left">HII</td>
<td valign="middle" align="center">Human influence index</td>
<td valign="middle" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="middle" align="left">Radiation variable</td>
<td valign="middle" align="left">UV-B</td>
<td valign="middle" align="center">Annual mean UV-B radiation</td>
<td valign="middle" align="center">kJ/m<sup>2</sup></td>
</tr>
<tr>
<td valign="middle" align="left">Biome variable</td>
<td valign="middle" align="left">Biome</td>
<td valign="middle" align="center">Biome</td>
<td valign="middle" align="center">&#x2013;</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>The key variables in our study, which were selected on the basis of their importance and minimal multicollinearity, were determined by Pearson&#x2019;s correlation analysis (r&gt; 0.75).</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Model development</title>
<p>We modeled the global distribution patterns of <italic>A. donax</italic> under current and future climate scenarios using the MaxEnt model version 3.4.4, a robust machine learning algorithm for species distribution modeling with presence-only datasets, which is particularly useful for mapping invasive species ranges (<xref ref-type="bibr" rid="B69">Phillips et&#xa0;al., 2006</xref>). Background points were determined to establish a standard protocol in ArcGIS 10.8 (ESRI, Redlands, CA, USA) (<xref ref-type="bibr" rid="B11">Barbet-Massin et&#xa0;al., 2012</xref>). The occurrence data were split into datasets for model training (75%) and validation (25%) (<xref ref-type="bibr" rid="B10">Ara&#xfa;jo and Guisan, 2006</xref>). We replicated the model 100 times using optimized parameters via a cross-validation method to obtain an average outcome similar to that in the literature (<xref ref-type="bibr" rid="B6">Adhikari et&#xa0;al., 2023b</xref>, <xref ref-type="bibr" rid="B3">2024</xref>; <xref ref-type="bibr" rid="B71">Poudel et&#xa0;al., 2024</xref>).</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Model result evaluation and validation</title>
<p>The effectiveness of the MaxEnt model was evaluated, with a focus on the area under the curve (AUC) derived from receiver operating characteristic (ROC) curve analysis (<xref ref-type="bibr" rid="B56">Liu et&#xa0;al., 2011</xref>). The AUC, with values ranging from 0 to 1, provides a threshold-independent measure of the model performance, classified as failing (0.5&#x2013;0.6), poor (0.6&#x2013;0.7), fair (0.7&#x2013;0.8), good (0.8&#x2013;0.9), or excellent (0.9&#x2013;1) (<xref ref-type="bibr" rid="B79">Swets, 1988</xref>; <xref ref-type="bibr" rid="B34">Fielding and Bell, 1997</xref>; <xref ref-type="bibr" rid="B32">Elith et&#xa0;al., 2006</xref>). By representing the area under the ROC curve, the AUC reflects the ability of the model to distinguish suitable from unsuitable habitats.</p>
<p>Additionally, the jackknife test was employed to evaluate the relative contributions of individual environmental variables to the species distribution modeling results. In this method, each variable is systematically excluded to measure its impact on the model performance, identifying the most influential predictors shaping the potential distribution of the species (<xref ref-type="bibr" rid="B69">Phillips et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B60">Merow et&#xa0;al., 2013</xref>).</p>
</sec>
<sec id="s2_5">
<label>2.5</label>
<title>Assessing the current and future potential distribution of <italic>A. donax</italic> worldwide</title>
<p>Binary habitat distribution maps for <italic>A. donax</italic> were developed by converting the MaxEnt probability outputs into suitable and unsuitable habitat classes via the maximum training sensitivity plus specificity cloglog threshold (<xref ref-type="bibr" rid="B55">Liu et&#xa0;al., 2016</xref>). These distribution maps were generated for three temporal scenarios, i.e., current (1979&#x2013;2013), mid-century (2041&#x2013;2060), and end-century scenarios (2081&#x2013;2100), under two climate pathways, namely, SSP2-4.5 and SSP5-8.5.</p>
<p>Via the use of the Zonal Statistics tool in ArcGIS Desktop 10.8, the number of grid cells representing suitable habitats was calculated for each continent across the different scenarios. Each grid cell, covering approximately 4.5&#xa0;km at the equator, was used to estimate the total area of suitable habitats. Changes in the suitable habitat area in each country were then estimated under the SSP2-4.5 and SSP5-8.5 scenarios for the 2041&#x2013;2060 and 2081&#x2013;2100 periods.</p>
<p>To evaluate the global invasion risk of <italic>A. donax</italic>, the invasion risk in each country was estimated on the basis of the average habitat suitability and classified into five categories: no invasion risk (0), low invasion risk (0.001&#x2013;0.25), moderate invasion risk (0.26&#x2013;0.50), high invasion risk (0.51&#x2013;0.75), and very high invasion risk (0.76&#x2013;1). These categories provide a framework for analyzing global invasion risks and identifying areas of vulnerability. Comparative analyses across periods revealed varying degrees of invasion risk under the current and projected climate conditions, highlighting regions requiring targeted management and intervention strategies. The overall research methodology explained in this section is summarized in a flowchart (<xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2</bold></xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Flowchart showing the overall process of data preparation, modeling, and analysis.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-13-1631747-g002.tif">
<alt-text content-type="machine-generated">Flowchart illustrating the modeling process for Arundo donax habitat using MaxEnt. It begins with data preparation, processing occurrence records, and climate models for different scenarios and time periods. After processing, the model validation involves Jackknife tests and AUC/TSS metrics. Results include potential habitat maps, habitat distribution, contributing variables, and areas estimation with a world map showing data distribution.</alt-text>
</graphic></fig>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Model contribution, evaluation, and validation</title>
<p>Among the selected variables, HII, Bio1, and UV-B were the top contributors, with contribution rates of 59%, 23.6%, and 7.3%, respectively, accounting for a cumulative contribution of 89.9% (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3A</bold></xref>). The remaining variables exerted a minimal influence on the model performance. Bio1, HII, and UV-B exhibited relatively high contributions to the model and were confirmed as the most influential predictors based on their impact on species distribution modelling (<xref ref-type="fig" rid="f4"><bold>Figure&#xa0;4</bold></xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Estimating the contribution of bioclimatic variables in species distribution modeling of <italic>A</italic>. <italic>donax</italic>. <bold>(A)</bold> Radar chart showing the average percent contribution and permutation importance of the selected environmental variables in the MaxEnt model for <italic>A</italic>. <italic>donax</italic> under current and future climatic scenarios (SSP2-4.5 and SSP5-8.5, respectively) across the 2041&#x2013;2060 and 2081&#x2013;2100 periods. <bold>(B)</bold> Jackknife test results showing the relative significance of the bioclimatic variables used in the MaxEnt model.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-13-1631747-g003.tif">
<alt-text content-type="machine-generated">(A) Radar chart showing percent contribution and permutation importance for environmental variables such as HII, Bio1, and UV-B. Red squares represent percent contribution, and green circles indicate permutation importance. (B) Bar chart of jackknife regularized training gain for environmental variables like Bio1 and HII. Bars are colored based on categories: without variable (blue), with only variable (green), and with all variables (red).</alt-text>
</graphic></fig>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Highly contributing variables in the MaxEnt model for the global distribution of <italic>A</italic>. <italic>donax</italic>. <bold>(A)</bold> Human influence index (HII), <bold>(B)</bold> annual mean temperature (Bio1), and <bold>(C)</bold> annual ultraviolet radiation (UV-B). The colors corresponding to the legend indicate the varying levels of HII, Bio1 and UV-B across different parts of the world.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-13-1631747-g004.tif">
<alt-text content-type="machine-generated">Three world maps illustrate different environmental data. Map A shows the Human Influence Index (HII) with a gradient from green (low influence) to red (high influence). Map B displays Bio1, a biological climate index, ranging from green (cold) to red (hot). Map C depicts UV-B radiation exposure, with green for low exposure and red for high exposure. Each map includes a color-coded legend, latitudinal and longitudinal lines, and a north arrow.</alt-text>
</graphic></fig>
<p>The permutation importance analysis results also highlighted Bio1, HII, and Bio3 as key variables, with importance values of 47.54%, 29.26%, and 10.65%, respectively, underscoring their critical role in determining the model performance. The jackknife test outcomes supported these findings, identifying Bio1, HII, and UV-B as the most significant contributors (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3B</bold></xref>), demonstrating the relative contribution of each variable to the potential distribution of <italic>A. donax</italic>. The model performance was evaluated using AUC values. The predictions based on rarefied species occurrence points achieved a higher AUC score (0.901) than those based on all occurrence points (0.755), indicating that the use of rarefied data increases the prediction accuracy and reduces the risk of over- or underestimation.</p>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Potential distribution of <italic>A. donax</italic> under current and future climate change scenarios</title>
<p>The MaxEnt model was used to predict the current and future distributions of <italic>A. donax</italic> (<xref ref-type="fig" rid="f5"><bold>Figure&#xa0;5</bold></xref>). Currently, <italic>A. donax</italic> occupies a substantial global range, spanning 884,899 grid cells, equivalent to 10.15% of the world&#x2019;s total land area (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6</bold></xref>). The geographical reach of this species extends across 145 countries, with notable high-coverage regions, including South Africa, Belgium, Italy, France, Portugal, Israel, and Uruguay, where <italic>A. donax</italic> potentially occupies 50&#x2013;100% of the land area (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S4</bold></xref>).</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Potential geographical distribution of <italic>A</italic>. <italic>donax</italic> under current and future climate change scenarios (SSP2-4.5 and SSP5-8.5, respectively) for the 2041&#x2013;2060 and 2081&#x2013;2100 periods. <bold>(A)</bold> Current (1979&#x2013;2013), <bold>(B)</bold> SSP-4.5 (2041&#x2013;2060), <bold>(C)</bold> SSP2-4.5 (2081&#x2013;2100), <bold>(D)</bold> SSP5-8.5 (2041&#x2013;2060), and <bold>(E)</bold> SSP2-4.5 (2081&#x2013;2100). Red and gray in the legend indicate the presence and absence, respectively, of <italic>A</italic>. <italic>donax</italic>.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-13-1631747-g005.tif">
<alt-text content-type="machine-generated">Five world maps labeled A to E depict habitat presence and absence. Red indicates habitat presence, while gray shows absence. Each map presents a different distribution pattern across continents, highlighting variations in habitat zones globally. Maps include latitude and longitude markers and an arrow indicating north.</alt-text>
</graphic></fig>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Bar graph showing the proportion of the global area occupied by <italic>A. donax</italic> under current and future climate change scenarios (SSP2-4.5 and SSP5-8.5, respectively) from 2041&#x2013;2060 and 2081&#x2013;2100. The analysis is based on a grid with a resolution of 2.5 minutes, equivalent to approximately 4.5&#xa0;km at the equator.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-13-1631747-g006.tif">
<alt-text content-type="machine-generated">Bar chart comparing the percentage of area covered under different climate change scenarios (SSP2-4.5 and SSP5-8.5) across three time periods: 1979-2013 (green), 2041-2060 (blue), and 2081-2100 (red). The red bars, representing 2081-2100, show the highest area coverage in both scenarios.</alt-text>
</graphic></fig>
<p>Under the future climate change scenarios, the potential distribution of <italic>A. donax</italic> is projected to increase significantly. By 2041&#x2013;2060, under SSP2-4.5, the species is projected to occupy 1,603,606 grid cells, accounting for 18.40% of the global land area. This expansion is projected to increase to 2,113,752 grid cells (24.26%) by 2081&#x2013;2100. Similarly, under SSP5-8.5, the potential distribution increases to 1,690,048 cells (19.39%) from 2041&#x2013;2060 and increases to 2,235,916 cells (25.66%) from 2081&#x2013;2100 (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6</bold></xref>). These projections highlight the extensive potential spread of <italic>A. donax</italic> under global climate change.</p>
<p>Potential habitat expansion was also evaluated across continents for the two future periods under SSP2-4.5 and SSP5-8.5 (<xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref>). Under SSP2-4.5, Africa and Asia exhibited the most significant increases in habitat suitability, with increases of 133.97% and 132.54%, respectively, from 2041&#x2013;2060, increasing to 273.22% and 225.85%, respectively, from 2081&#x2013;2100. South America also exhibited a notable upward trend, whereas Europe, Australia, North America, and Oceania exhibited moderate increases ranging from 38.41% to 85.82%.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Continental changes in the habitat distribution (%) of <italic>A. donax</italic> compared with that under the current climatic conditions (1979&#x2013;2013).</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="3" align="center">Continents</th>
<th valign="middle" rowspan="3" align="center">Current<xref ref-type="table-fn" rid="fnT2_1"><sup>a</sup></xref> (1979&#x2013;2013) Total no. of cells</th>
<th valign="middle" colspan="4" align="center">Change in habitat categories (%)</th>
</tr>
<tr>
<th valign="middle" colspan="2" align="center">SSP2-4.5</th>
<th valign="middle" colspan="2" align="center">SSP5-8.5</th>
</tr>
<tr>
<th valign="middle" align="center">2041&#x2013;2060</th>
<th valign="middle" align="center">2081&#x2013;2100</th>
<th valign="middle" align="center">2041&#x2013;2060</th>
<th valign="middle" align="center">2081&#x2013;2100</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Africa</td>
<td valign="middle" align="center">101,665.69</td>
<td valign="middle" align="center">133.97</td>
<td valign="middle" align="center">273.22</td>
<td valign="middle" align="center">152.03</td>
<td valign="middle" align="center">312.08</td>
</tr>
<tr>
<td valign="middle" align="left">Asia</td>
<td valign="middle" align="center">199,530.62</td>
<td valign="middle" align="center">132.54</td>
<td valign="middle" align="center">225.85</td>
<td valign="middle" align="center">152.60</td>
<td valign="middle" align="center">233.12</td>
</tr>
<tr>
<td valign="middle" align="left">Australia</td>
<td valign="middle" align="center">63,043.26</td>
<td valign="middle" align="center">45.67</td>
<td valign="middle" align="center">85.54</td>
<td valign="middle" align="center">44.78</td>
<td valign="middle" align="center">80.32</td>
</tr>
<tr>
<td valign="middle" align="left">Europe</td>
<td valign="middle" align="center">148,964.67</td>
<td valign="middle" align="center">49.60</td>
<td valign="middle" align="center">85.82</td>
<td valign="middle" align="center">55.12</td>
<td valign="middle" align="center">91.15</td>
</tr>
<tr>
<td valign="middle" align="left">North America</td>
<td valign="middle" align="center">232,255.05</td>
<td valign="middle" align="center">38.41</td>
<td valign="middle" align="center">69.19</td>
<td valign="middle" align="center">46.98</td>
<td valign="middle" align="center">73.93</td>
</tr>
<tr>
<td valign="middle" align="left">Oceania</td>
<td valign="middle" align="center">7,736.33</td>
<td valign="middle" align="center">46.87</td>
<td valign="middle" align="center">68.31</td>
<td valign="middle" align="center">43.30</td>
<td valign="middle" align="center">62.31</td>
</tr>
<tr>
<td valign="middle" align="left">South America</td>
<td valign="middle" align="center">140,919.31</td>
<td valign="middle" align="center">81.08</td>
<td valign="middle" align="center">129.30</td>
<td valign="middle" align="center">81.67</td>
<td valign="middle" align="center">123.76</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="fnT2_1">
<p><sup>a</sup>The numbers in the column denote the current total number of cells covered by <italic>A. donax</italic> on different continents.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>Under SSP5-8.5, even greater expansions in the habitat distribution of <italic>A. donax</italic> were observed. Africa exhibited the largest increase, with habitat suitability increasing to 152.03% from 2041&#x2013;2060 and 312.08% from 2081&#x2013;2100. Asia demonstrated a similar pattern, increasing from 152.60% to 233.12%, whereas South America revealed an increase from 81.67% to 123.76% over the same periods. Europe, Australia, North America, and Oceania continued to exhibit moderate but consistent increases. Antarctica showed an absence of habitat suitability under the current or future scenarios and was therefore excluded from the analysis.</p>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Classification of global invasion risk of <italic>A. donax</italic> in different countries</title>
<p>The mean habitat suitability of <italic>A. donax</italic> was assessed across all countries under the current and future climate scenarios (SSP2-4.5 and SSP5-8.5, respectively), categorizing countries into five risk levels: no invasion risk, low invasion risk, moderate invasion risk, high invasion risk, and very high invasion risk (<xref ref-type="fig" rid="f7"><bold>Figure&#xa0;7</bold></xref>). Under the current climatic conditions, 39 countries exhibited no invasion risk, 84 countries exhibited low invasion risks, 24 countries exhibited moderate invasion risks, 16 countries exhibited high invasion risks, and 21 countries exhibited very high invasion risks. However, the future climate change scenarios revealed a decrease in the no- and low-invasion risk areas and a progressive increase in high- and very high-invasion risk countries (<xref ref-type="table" rid="T3"><bold>Table&#xa0;3</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S5</bold></xref>). Under SSP2-4.5, the number of very high-invasion risk countries increased to 44 from 2041&#x2013;2060 and increased to 71 from 2081&#x2013;2100. Under the more extreme SSP5-8.5 scenario, the trend increased, with 47 countries projected to exhibit very high invasion risks from 2041&#x2013;2060, with the number of countries reaching 72 from 2081&#x2013;2100.</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>Mean habitat suitability of <italic>A</italic>. <italic>donax</italic> estimated for different countries worldwide under current and future climate change scenarios (SSP2-4.5 and SSP5-8.5, respectively) for the 2041&#x2013;2060 and 2081&#x2013;2100 periods. <bold>(A)</bold> Current (1979&#x2013;2013), <bold>(B)</bold> SSP-4.5 (2041&#x2013;2060), <bold>(C)</bold> SSP2-4.5 (2081&#x2013;2100), <bold>(D)</bold> SSP5-8.5 (2041&#x2013;2060), and <bold>(E)</bold> SSP2-4.5 (2081&#x2013;2100). The risk levels are categorized as follows: no invasion risk (0), low invasion risk (0.001&#x2013;0.25), moderate invasion risk (0.26&#x2013;0.50), high invasion risk (0.51&#x2013;0.75), and very high invasion risk (0.76&#x2013;1).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-13-1631747-g007.tif">
<alt-text content-type="machine-generated">World maps labeled A to E illustrate invasion risk levels across different regions, indicated by colors: gray (no risk), green (low), yellow (moderate), orange (high), and red (very high). Each map displays varying distributions of invasion risk, with notable areas in red and orange in parts of Southern Europe, Central America, and parts of Africa and Asia. Maps include a compass rose and scale bar for distance.</alt-text>
</graphic></fig>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Number of countries invaded by <italic>A. donax</italic> globally under current and future climate change scenarios on the basis of the different risk categories.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="center">Risk category</th>
<th valign="middle" rowspan="2" align="center">Current</th>
<th valign="middle" colspan="2" align="center">SSP2-4.5</th>
<th valign="middle" colspan="2" align="center">SSP5-8.5</th>
</tr>
<tr>
<th valign="middle" align="center">2041&#x2013;2060</th>
<th valign="middle" align="center">2081&#x2013;2100</th>
<th valign="middle" align="center">2041&#x2013;2060</th>
<th valign="middle" align="center">2081&#x2013;2100</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">No invasion risk</td>
<td valign="middle" align="center">39</td>
<td valign="middle" align="center">20</td>
<td valign="middle" align="center">11</td>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">10</td>
</tr>
<tr>
<td valign="middle" align="left">Low invasion risk</td>
<td valign="middle" align="center">84</td>
<td valign="middle" align="center">60</td>
<td valign="middle" align="center">49</td>
<td valign="middle" align="center">61</td>
<td valign="middle" align="center">44</td>
</tr>
<tr>
<td valign="middle" align="left">Moderate invasion risk</td>
<td valign="middle" align="center">24</td>
<td valign="middle" align="center">30</td>
<td valign="middle" align="center">22</td>
<td valign="middle" align="center">25</td>
<td valign="middle" align="center">28</td>
</tr>
<tr>
<td valign="middle" align="left">High invasion risk</td>
<td valign="middle" align="center">16</td>
<td valign="middle" align="center">30</td>
<td valign="middle" align="center">31</td>
<td valign="middle" align="center">33</td>
<td valign="middle" align="center">30</td>
</tr>
<tr>
<td valign="middle" align="left">Very high invasion risk</td>
<td valign="middle" align="center">21</td>
<td valign="middle" align="center">44</td>
<td valign="middle" align="center">71</td>
<td valign="middle" align="center">47</td>
<td valign="middle" align="center">72</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Invasion risk categories of <italic>A. donax</italic> under the current and future climate change scenarios (SSP2-4.5 and SSP5-8.5, respectively) for the 2041&#x2013;2060 and 2081&#x2013;2100 periods. The risk levels are categorized as follows: no invasion risk (0), low invasion risk (0.001&#x2013;0.25), moderate invasion risk (0.26&#x2013;0.50), high invasion risk (0.51&#x2013;0.75), and very high invasion risk (0.76&#x2013;1); SSP, shared socioeconomic pathway.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>Under the current climatic conditions, 21% of the global land area occurs within the no invasion risk category, whereas the areas with a low invasion risk, moderate invasion risk, high invasion risk, and very high invasion risk account for 46%, 14%, 8%, and 11%, respectively, of the total land area (<xref ref-type="fig" rid="f8"><bold>Figure&#xa0;8</bold></xref>). The proportion of the global land area with a very high risk of invasion from <italic>A. donax</italic> is projected to increase substantially under the future climate scenarios. Compared with the value of 11% under the current conditions (1979&#x2013;2013), this category increased to 23% (2041&#x2013;2060) and 38% (2081&#x2013;2100) under SSP2-4.5, which is similar to values of 26% and 38%, respectively, under SSP5-8.5. Areas at high invasion risk also increased slightly, reaching 17&#x2013;18% during the future periods compared with the value of 8% currently. In contrast, the proportion of land in the no-risk and low-risk categories is projected to decrease under the future climate change scenarios. These results emphasize the increasing global exposure to severe invasion risk under changing climate conditions.</p>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>Percentage of the global area categorized by invasion risk level for <italic>A. donax</italic> under current and future climate scenarios (SSP2-4.5 and SSP5-8.5, respectively) for the 2041&#x2013;2060 and 2081&#x2013;2100 periods. Gray indicates no invasion risk, green indicates low invasion risk, blue indicates moderate invasion risk, purple indicates high invasion risk, and red indicates very high invasion risk.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-13-1631747-g008.tif">
<alt-text content-type="machine-generated">Stacked bar chart showing the percentage of area covered by invasion risk categories from 1979 to 2100. Categories include no invasion risk (gray), low invasion risk (green), moderate invasion risk (blue), high invasion risk (purple), and very high invasion risk (red) under scenarios SSP2-4.5 and SSP5-8.5. The chart illustrates increasing areas of high and very high invasion risks over time, with significant increases in the red and purple sections in future periods.</alt-text>
</graphic></fig>
<p>The model projected a significant expansion of <italic>A. donax</italic> habitats and a shift in invasion risk patterns, affecting multiple countries worldwide. Under the SSP2-4.5 scenario, of the 39 countries currently at no invasion risk, 25 countries, including Norway, Senegal, Brunei, and Poland, will shift to the low or moderate risk category. Similarly, under the SSP5-8.5 scenario, 20 countries, such as Sweden, Gabon, Guinea, and Denmark, will exhibit similar changes in risk level, affecting up to 50% of their land area (<xref ref-type="table" rid="T3"><bold>Tables&#xa0;3</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>S6</bold></xref>). Additionally, 41 countries may transition from the low risk category to the high or very high invasion risk category under SSP2-4.5, including Austria, India, and Bangladesh, whereas 42 countries, such as Sri Lanka, Germany, and the Netherlands, could experience a similar shift under SSP5-8.5, covering between 50% and 100% of their total land area. These findings suggest that climate change may cause a shift in the risk level of <italic>A. donax</italic> invasion, placing multiple countries at greater threat and highlighting its potential for global expansion.</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>This study provides critical insights into the global invasion dynamics of <italic>A. donax</italic>, with significant implications for biodiversity conservation and invasive species management under the global environment change. The identification of HII as the most significant predictor of species distribution underscores the growing role of anthropogenic activities in facilitating biological invasions (<xref ref-type="bibr" rid="B24">Chen et&#xa0;al., 2021</xref>). Environmental factors such as temperature, soil quality, and resource availability govern habitat. Human activities such as urbanization, transportation network establishment, land use changes, and intentional species introductions, catalyze the establishment and spread of invasive species (<xref ref-type="bibr" rid="B49">Kueffer, 2017</xref>; <xref ref-type="bibr" rid="B24">Chen et&#xa0;al., 2021</xref>). These anthropogenic activities not only disrupt the ecological balance but also create pathways for colonization, particularly through coastal waterways, highways, and navigable rivers, which serve as conduits for invasive propagules (<xref ref-type="bibr" rid="B91">With, 2002</xref>; <xref ref-type="bibr" rid="B22">Catford et&#xa0;al., 2012</xref>). Furthermore, ecosystem modifications such as infrastructure expansion, nocturnal lighting, and hydrological changes increase invasion risk through the generation of disturbed habitats that favor nonnative species over native species (<xref ref-type="bibr" rid="B62">Okorondu et&#xa0;al., 2022</xref>).</p>
<p><italic>Arundo donax</italic> exemplifies the profound influence of humans on invasion dynamics. Historically dispersed through intentional introductions for erosion control, biofuel production, and ornamental use, its global spread has been intensified by accidental transport via agricultural machinery and the nursery trade (<xref ref-type="bibr" rid="B66">Perdue, 1958</xref>; <xref ref-type="bibr" rid="B12">Bell, 1998</xref>; <xref ref-type="bibr" rid="B40">Haddadchi et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B48">Jim&#xe9;nez-Ruiz et&#xa0;al., 2021</xref>). Human-induced disturbances, including land development, agricultural runoff, and water management practices, create ideal conditions for <italic>A. donax</italic> dominance. Nutrient enrichment resulting from wastewater discharge and irrigation promotes soil fertility, enabling <italic>A. donax</italic> to outcompete native species, whereas mechanical disturbances, including bulldozing and tilling, can fragment <italic>A. donax</italic> rhizomes and stems, facilitating their vegetative reproduction and spread (<xref ref-type="bibr" rid="B12">Bell, 1998</xref>; <xref ref-type="bibr" rid="B90">Wijte et&#xa0;al., 2005</xref>). In fire-prone regions, human-induced burns enhance the competitive edge of <italic>A. donax</italic>, allowing rapid postfire recolonization while suppressing native vegetation recovery (<xref ref-type="bibr" rid="B73">Quinn et&#xa0;al., 2007</xref>; <xref ref-type="bibr" rid="B38">Goolsby et&#xa0;al., 2023</xref>). Given the notable influence of human activities on biological invasion, incorporating human factors, such as the HII, into invasion risk models increases the prediction accuracy, especially in regions with high levels of human activity (<xref ref-type="bibr" rid="B19">Bucklin et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B96">Zhu et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B44">Hong et&#xa0;al., 2021</xref>). In our study, the HII was the most critical environmental variable, with a significant contribution of 59% to the <italic>A. donax</italic> distribution modeling results (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3A</bold></xref>), underscoring the centrality of anthropogenic drivers in shaping its spread. Understanding these human-driven mechanisms is crucial for developing effective strategies to mitigate the spread and impact of IAPs and to preserve biodiversity and ecological balance.</p>
<p>Similarly, the annual mean temperature has second highest contribution in model prediction. Increasing temperature facilitates to expand the habitat of <italic>A. donax</italic> into the higher altitude and higher latitudes. The optimal temperature for <italic>A. donax</italic> germination varies between 20&#xa0;&#xb0;C and 30&#xa0;&#xb0;C (<xref ref-type="bibr" rid="B12">Bell, 1998</xref>), although it can tolerate a broader range of temperatures. However, temperatures below 0&#xa0;&#xb0;C can be detrimental to its growth (<xref ref-type="bibr" rid="B12">Bell, 1998</xref>; <xref ref-type="bibr" rid="B21">CABI, 2022</xref>). Given its preference for warm climates, <italic>A. donax</italic> has thrived in tropical, subtropical, and warm temperate regions, where it has adapted well to diverse soil types, including sandy gravel, heavy clay, and river sediments (<xref ref-type="bibr" rid="B21">CABI, 2022</xref>). Species resilience is highlighted by its ability to withstand increasing global temperatures, which have increased by approximately 1.1&#xa0;&#xb0;C since the late 19th century (<xref ref-type="bibr" rid="B51">Legg, 2021</xref>). This adaptability has facilitated an increase in the range of <italic>A. donax</italic> even under shifting climatic conditions. In support of this process, Bio 1 contributed significantly (23.60%) to the potential distribution of <italic>A. donax</italic> (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3A</bold></xref>), highlighting the dominant role of temperature in shaping its geographical spread.</p>
<p>Similarly, UV-B radiation plays a crucial role in determining the distribution of IAPs by influencing their competitive ability and survival (<xref ref-type="bibr" rid="B43">Hock et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B59">Mansinhos et&#xa0;al., 2024</xref>). While UV-B exposure can cause damage to DNA and cellular structures, leading to reduced growth rates and impaired reproduction, certain invasive species, such as <italic>A. donax</italic>, have developed adaptive mechanisms to thrive under high-UV and high-drought conditions (<xref ref-type="bibr" rid="B52">Liakoura et&#xa0;al., 1997</xref>; <xref ref-type="bibr" rid="B58">Mann et&#xa0;al., 2013</xref>). These adaptations include the production of UV-absorbing flavonoids and antioxidants, allowing them to exploit UV-driven changes in plant structure, nutrient cycling, and microbial interactions. In our study, UV-B radiation contributed 7.30% to the potential distribution of <italic>A. donax</italic> (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3A</bold></xref>). Although UV-B radiation can suppress invasive species by impairing their reproductive success, resilient species can withstand its effects, rendering UV-B radiation a key factor in predicting and managing plant invasions (<xref ref-type="bibr" rid="B43">Hock et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B59">Mansinhos et&#xa0;al., 2024</xref>).</p>
<p>With respect to bioclimatic factors, <italic>A. donax</italic> exhibits rapid growth, extensive roots, clonal reproduction via rhizomes, and notable tolerance to temperature and drought, increasing its invasiveness (<xref ref-type="bibr" rid="B66">Perdue, 1958</xref>; <xref ref-type="bibr" rid="B39">Guthrie, 2007</xref>; <xref ref-type="bibr" rid="B21">CABI, 2022</xref>). Initially, introduced for erosion control, bioenergy production, and ornamental landscaping, <italic>A. donax</italic> has become invasive because of its high germination rate, ability to regenerate roots, and adaptability to diverse climates (<xref ref-type="bibr" rid="B83">Virtue et&#xa0;al., 2010</xref>; <xref ref-type="bibr" rid="B21">CABI, 2022</xref>). By outcompeting native species, it reduces biodiversity and disrupts ecosystem functions such as hydrology, soil chemistry, and fire regimes. <italic>A. donax</italic> monopolizes sunlight, water, and nutrients, altering ecosystem dynamics and posing a threat to native flora and fauna (<xref ref-type="bibr" rid="B86">Waterworth, 2015</xref>). Our model revealed its presence in 145 countries under current climate conditions, covering approximately 10.15% of the global land surface. This study supports global risk assessments identifying <italic>A. donax</italic> as one of the most pervasive and damaging invasive species (<xref ref-type="bibr" rid="B31">Early et&#xa0;al., 2016</xref>), highlighting the urgent need for coordinated management strategies to curb its spread and mitigate its ecological impacts. Our findings indicated that areas in Africa, Asia, and South America are likely to exhibit a significant increase in suitable habitats for <italic>A. donax</italic>, with its spatial distribution projected to increase by up to 312.08% from 2081&#x2013;2100 (<xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref>). Increasing anthropogenic activities and temperatures could result in habitat loss for native species while facilitating a shift from unsuitable areas to those that are highly suitable for <italic>A. donax</italic>. This expansion of suitable conditions, combined with its rapid growth, will likely increase the invasion range of <italic>A. donax</italic>, as similar to other IAPs, such as <italic>Lantana camara, Oxalis latifolia</italic>, <italic>Parthenium hysterophorus</italic> and <italic>Acacia mearnsii</italic> (<xref ref-type="bibr" rid="B8">Ahmad et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B80">Tiwari et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B72">Poudel et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B3">Adhikari et&#xa0;al., 2024</xref>; <xref ref-type="bibr" rid="B71">Poudel et&#xa0;al., 2024</xref>).</p>
<p>The SSP5-8.5 scenario produced a greater invasion risk for <italic>A. donax</italic> from 2081&#x2013;2100 than the SSP2-4.5 scenario did, with notable increases in South Korea, Mexico, Vietnam, and Turkey and new risks emerging in Bangladesh, India, the Netherlands, Sri Lanka, Japan, and Bhutan, where the invasion risk may exceed 50% (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S4</bold></xref>). Extreme climate changes under SSP5-8.5 cause expansion in suitable habitats, especially at relatively high latitudes and elevations, while adversely affecting native plant communities, increasing their vulnerability to invasion (<xref ref-type="bibr" rid="B25">Chen et&#xa0;al., 2022</xref>). The synergistic effects of climate change and human-driven factors, such as land use changes, exacerbate the invasion risk under SSP5-8.5 (<xref ref-type="bibr" rid="B1">Adhikari et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B94">Yang et&#xa0;al., 2023</xref>), resulting in a greater threat of global invasion than that under SSP2-4.5.</p>
<p>Countries were classified into five categories on the basis of their risk of invasion by <italic>A. donax</italic>. Currently, 39 countries, including Denmark, Guinea, Kuwait, and Norway, have no recorded presence of <italic>A. donax</italic> (<xref ref-type="table" rid="T3"><bold>Table&#xa0;3</bold></xref>). However, climate change and human-driven activities, such as research, exploration, exploitation, and tourism activities, have significantly increased over the past 200 years, increasing the likelihood of invasion in several of these countries (<xref ref-type="bibr" rid="B89">Weir, 2017</xref>; <xref ref-type="bibr" rid="B7">Adhikari et&#xa0;al., 2023a</xref>, <xref ref-type="bibr" rid="B3">2024</xref>). Projections for 2081&#x2013;2100 indicated a substantial shift in invasion risk patterns, with the proportion of global land classified as no-risk areas expected to decrease to just 5%. Conversely, areas categorized as high-invasion risk areas and very high-invasion risk areas are projected to expand, encompassing 38% of the global land area under both the SSP2-4.5 and SSP5-8.5 scenarios (<xref ref-type="fig" rid="f8"><bold>Figure&#xa0;8</bold></xref>). These findings highlight countries at potential risk of invasion, emphasizing the need for the implementation of quarantine measures to prevent the introduction and spread of <italic>A. donax</italic>.</p>
<p>The limited northward distribution of <italic>A. donax</italic> is due primarily to climatic and environmental constraints that restrict its growth and survival at relatively high latitudes. As a warm-season C4 grass, <italic>A. donax</italic> is highly sensitive to low temperatures and cannot tolerate freezing conditions, which are common in northern regions such as Canada, northern Europe, and Siberia (<xref ref-type="bibr" rid="B73">Quinn et&#xa0;al., 2007</xref>; <xref ref-type="bibr" rid="B57">Mack, 2008</xref>). These areas experience harsh winters, shorter growing seasons, and reduced solar radiation, all of which limit the ability of plants to establish and thrive (<xref ref-type="bibr" rid="B66">Perdue, 1958</xref>; <xref ref-type="bibr" rid="B12">Bell, 1998</xref>). Additionally, water availability is often restricted in frozen soils, and repeated freeze&#x2013;thaw cycles can cause plant rhizome damage, impeding plant spread (<xref ref-type="bibr" rid="B29">Dudley, 2000</xref>).</p>
<p>Human-mediated pathways, such as urbanization, agriculture, and transportation networks, typically facilitate the introduction and proliferation of invasive species such as <italic>A. donax</italic>. However, such activities are less prevalent in colder, sparsely populated northern regions, significantly reducing opportunities for anthropogenic dispersal and colonization (<xref ref-type="bibr" rid="B29">Dudley, 2000</xref>; <xref ref-type="bibr" rid="B46">Hulme, 2009</xref>; <xref ref-type="bibr" rid="B31">Early et&#xa0;al., 2016</xref>). Additionally, the competitive advantage of <italic>A. donax</italic> in warmer, disturbed ecosystems decreases in areas with colder climates, where native flora are better adapted to local environmental stressors (<xref ref-type="bibr" rid="B12">Bell, 1998</xref>; <xref ref-type="bibr" rid="B38">Goolsby et&#xa0;al., 2023</xref>).</p>
<p>The key limiting factors for <italic>A. donax</italic> at northern latitudes include low average annual temperatures, extreme cold during the winter months, and minimal human activity (<xref ref-type="bibr" rid="B35">Franco et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B21">CABI, 2022</xref>; <xref ref-type="bibr" rid="B38">Goolsby et&#xa0;al., 2023</xref>). These conditions collectively reduce habitat suitability and restrict species range expansion. The interplay of climatic stressors, physiological limitations, and reduced anthropogenic influence underscores the challenges facing <italic>A. donax</italic> in colonizing higher-latitude areas (<xref ref-type="bibr" rid="B38">Goolsby et&#xa0;al., 2023</xref>).</p>
<p>Despite its negative impacts, <italic>A. donax</italic> has significant economic value. It is utilized for biomass energy production, edible and medicinal fungus cultivation, windbreak construction, soil conservation, and soil remediation (<xref ref-type="bibr" rid="B77">Speck, 2003</xref>; <xref ref-type="bibr" rid="B53">Liu et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B33">Fagnano et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B82">Vasmara et&#xa0;al., 2023</xref>). Various methods have been employed to control the spread of <italic>A. donax</italic> (for example, mechanical control, such as repeated mowing, may be relatively effective, but even small root fragments can regenerate new growth) (<xref ref-type="bibr" rid="B12">Bell, 1998</xref>). Systemic herbicides are often applied after flowering, either as a cut-stump treatment or a foliar spray, with late summer or fall as the most effective period for application (<xref ref-type="bibr" rid="B45">Hoshovsky, 1986</xref>; <xref ref-type="bibr" rid="B12">Bell, 1998</xref>). Since <italic>A. donax</italic> rarely produces viable seeds, it spreads primarily through vegetative fragments, with rhizome clumps and culm nodes serving as key colonization sources (<xref ref-type="bibr" rid="B12">Bell, 1998</xref>). Control efforts are labor intensive, requiring multiple sessions to eliminate root fragments and prevent regrowth, often making it a costly, long-term process that can last up to 20 years (<xref ref-type="bibr" rid="B45">Hoshovsky, 1986</xref>; <xref ref-type="bibr" rid="B21">CABI, 2022</xref>). To prevent ecological disruption caused by <italic>A. donax</italic>, comprehensive border control measures are essential. The implementation of rigorous biosecurity protocols can restrict its spread, protect native plant biodiversity, and create opportunities for ecosystem restoration.</p>
<p>Although our study provides valuable insights into the invasion risk of <italic>A. donax</italic>, several limitations should be noted. First, we used a single GCM model, and incorporating multiple models in future analyses could improve the robustness of projections. Second, our global-scale analysis employed a 2.5-minute resolution, which may not accurately capture invasion dynamics in small or geographically isolated areas such as islands. Third, our study relied on model-based projections, and while the identified risk areas are informative, field validation is currently lacking. Finally, we applied only the Maxent model, which does not account for dispersal barriers and may therefore lead to under- or overestimation of potential distributions.</p>
<p>Future research should address the limitations identified in this study by incorporating multiple GCMs to enhance the robustness of climate projections, applying higher spatial resolutions for regional and island-scale analyses, and integrating dispersal mechanisms to more realistically capture invasion dynamics. Beyond the MaxEnt framework, employing ensemble modeling approaches that combine multiple species distribution models could help reduce biases and provide more reliable predictions. Moreover, field-based validation of the predicted high-risk areas is essential to ground-truth model outputs and refine management strategies. Together, these efforts will improve the accuracy of invasion risk assessments for <italic>A. donax</italic> and provide stronger scientific guidance for prevention, monitoring, and control under current and future climate change scenarios.</p>
<p>Our study highlights the urgent need for coordinated international strategies to manage the invasion risk of <italic>A. donax</italic>, particularly in regions highly vulnerable to its spread. We recommend implementing early detection, rapid response, and biosecurity measures, supported by strict regulations on its trade and cultivation. The species could be introduced through commercial grass seeds used in pasturelands; therefore, quarantine systems should be properly managed. Our study provides a list of countries potentially at risk of future invasion, which could serve as a reference for policymakers. As the study is based on model predictions, the results may differ from real-world scenarios and should therefore be applied with caution, especially in areas lacking field evidence. Furthermore, collaborative research on sustainable management approaches, along with awareness programs for stakeholders and the public, will be essential to minimize the ecological and economic impacts of <italic>A. donax</italic> under changing climate conditions.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion</title>
<p><italic>Arundo donax</italic> poses a significant threat to biodiversity, ecosystems, and local economies. The risk of invasion is heightened by global climate change and human activities. In this study, species distribution modeling was applied to assess the invasion potential of <italic>A. donax</italic> globally under various climate change scenarios. Among the nine environmental variables considered, the HII contributed the most to the model output, emphasizing the role of human activities in facilitating its spread. Currently, <italic>A. donax</italic> occupies approximately 10.15% of the world&#x2019;s land area, but projections suggest that this value will increase to 25.66% from 2081&#x2013;2100. The rate of invasion is particularly high in Africa, where it is expected to increase by up to 312.08%. Several countries, including South Africa, Morocco, Belgium, and Israel, are at high risk of invasion. The study identified regions with varying levels of invasion risk&#x2014;low, moderate, high, and very high&#x2014;along with emerging risks and expansion areas between 2041&#x2013;2060 and 2081&#x2013;2100. The future risk assessment results indicated that at least 20 countries will transition from no risk to low risk categories, and 42 countries will move from low to high and very high invasion risk categories. The results highlight the urgent need for the formulation of stringent quarantine measures and proactive management strategies to prevent the spread of these IAPs and mitigate their environmental and economic impacts.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Material</bold></xref>.</p></sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>AP: Conceptualization, Data curation, Formal analysis, Writing &#x2013; original draft. YL: Conceptualization, Methodology, Validation, Writing &#x2013; review &amp; editing. PrabA: Writing &#x2013; review &amp; editing, Data curation, Formal analysis. PradA: Conceptualization, Investigation, Supervision, Writing &#x2013; review &amp; editing. SH: Funding acquisition, Project administration, Resources, Supervision, Writing &#x2013; review &amp; editing.</p></sec>
<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 author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If&#xa0;you identify any issues, please contact us.</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&#xa0;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>
<sec id="s12" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fevo.2025.1631747/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fevo.2025.1631747/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Table1.xlsx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"/>
<supplementary-material xlink:href="Table2.xlsx" id="SM2" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"/>
<supplementary-material xlink:href="Table3.xlsx" id="SM3" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"/>
<supplementary-material xlink:href="Table4.xlsx" id="SM4" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"/>
<supplementary-material xlink:href="Table5.xlsx" id="SM5" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"/>
<supplementary-material xlink:href="Table6.docx" id="SM6" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/></sec>
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