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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">5</journal-id>
      <journal-id journal-id-type="index">urn:lsid:arphahub.com:pub:8D1BC1DD-8175-5933-B147-C839B202D5BA</journal-id>
      <journal-id journal-id-type="aggregator">urn:lsid:zoobank.org:pub:C132FA7C-A22D-4474-AAAF-B7A6889D0FF9</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">NeoBiota</journal-title>
        <abbrev-journal-title xml:lang="en">NB</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">1619-0033</issn>
      <issn pub-type="epub">1314-2488</issn>
      <publisher>
        <publisher-name>Pensoft Publishers</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.3897/neobiota.109.192296</article-id>
      <article-id pub-id-type="publisher-id">192296</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group subj-group-type="biological_taxon">
          <subject>Cerambycidae</subject>
          <subject>Coleoptera</subject>
        </subj-group>
        <subj-group subj-group-type="scientific_subject">
          <subject>Biological Invasions</subject>
          <subject>Ecology &amp; Environmental sciences</subject>
        </subj-group>
        <subj-group subj-group-type="geological_era">
          <subject>Neogene</subject>
        </subj-group>
        <subj-group subj-group-type="geographical_area">
          <subject>Europe</subject>
          <subject>North America</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Assessing invasion risk of two North American <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part></tp:taxon-name></italic> species (<tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order" reg="Coleoptera">Coleoptera</tp:taxon-name-part></tp:taxon-name>, <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="family" reg="Cerambycidae">Cerambycidae</tp:taxon-name-part></tp:taxon-name>) in Europe using joint bioclimatic-habitat species distribution models and maritime-inland connectivity</article-title>
      </title-group>
      <contrib-group content-type="authors">
        <contrib contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Ruzzier</surname>
            <given-names>Enrico</given-names>
          </name>
          <email xlink:type="simple">enrico.ruzzier@uniroma3.it</email>
          <uri content-type="orcid">https://orcid.org/0000-0003-1020-1247</uri>
          <xref ref-type="aff" rid="A1">1</xref>
          <xref ref-type="aff" rid="A2">2</xref>
          <xref ref-type="aff" rid="A3">3</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Rassati</surname>
            <given-names>Davide</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0000-0001-7778-0349</uri>
          <xref ref-type="aff" rid="A4">4</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Di Giulio</surname>
            <given-names>Andrea</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0000-0003-0508-0751</uri>
          <xref ref-type="aff" rid="A1">1</xref>
          <xref ref-type="aff" rid="A3">3</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Santoiemma</surname>
            <given-names>Giacomo</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0000-0002-3226-9253</uri>
          <xref ref-type="aff" rid="A4">4</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Pureswaran</surname>
            <given-names>Deepa S.</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0000-0002-4040-7708</uri>
          <xref ref-type="aff" rid="A5">5</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Sweeney</surname>
            <given-names>Jon</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0000-0003-3391-2375</uri>
          <xref ref-type="aff" rid="A5">5</xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Bani</surname>
            <given-names>Luciano</given-names>
          </name>
          <email xlink:type="simple">luciano.bani@unimib.it</email>
          <uri content-type="orcid">https://orcid.org/0000-0001-5795-9499</uri>
          <xref ref-type="aff" rid="A2">2</xref>
          <xref ref-type="aff" rid="A3">3</xref>
          <xref ref-type="aff" rid="A6">6</xref>
        </contrib>
      </contrib-group>
      <aff id="A1">
        <label>1</label>
        <addr-line content-type="verbatim">Department of Science, Roma Tre University, Rome, Italy</addr-line>
        <institution>Department of Science, Roma Tre University</institution>
        <addr-line content-type="city">Rome</addr-line>
        <country>Italy</country>
        <uri content-type="ror">https://ror.org/05vf0dg29</uri>
      </aff>
      <aff id="A2">
        <label>2</label>
        <addr-line content-type="verbatim">World Biodiversity Association (WBA), Verona, Italy</addr-line>
        <institution>World Biodiversity Association (WBA)</institution>
        <addr-line content-type="city">Verona</addr-line>
        <country>Italy</country>
        <uri content-type="ror">https://ror.org/01p1n0337</uri>
      </aff>
      <aff id="A3">
        <label>3</label>
        <addr-line content-type="verbatim">National Biodiversity Future Center (NBFC), Palermo, Italy</addr-line>
        <institution>National Biodiversity Future Center (NBFC)</institution>
        <addr-line content-type="city">Palermo</addr-line>
        <country>Italy</country>
      </aff>
      <aff id="A4">
        <label>4</label>
        <addr-line content-type="verbatim">Department of Agronomy, Food, Natural resources, Animals, Environment (DAFNAE), University of Padua, Padova, Italy</addr-line>
        <institution>Department of Agronomy, Food, Natural resources, Animals, Environment (DAFNAE), University of Padua</institution>
        <addr-line content-type="city">Padova</addr-line>
        <country>Italy</country>
        <uri content-type="ror">https://ror.org/00240q980</uri>
      </aff>
      <aff id="A5">
        <label>5</label>
        <addr-line content-type="verbatim">Natural Resources Canada, Canadian Forest Service, Atlantic Forestry Center, Fredericton, New Brunswick, Canada</addr-line>
        <institution>Natural Resources Canada, Canadian Forest Service, Atlantic Forestry Center</institution>
        <addr-line content-type="city">Fredericton</addr-line>
        <country>Canada</country>
      </aff>
      <aff id="A6">
        <label>6</label>
        <addr-line content-type="verbatim">Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy</addr-line>
        <institution>Department of Earth and Environmental Sciences, University of Milano-Bicocca</institution>
        <addr-line content-type="city">Milan</addr-line>
        <country>Italy</country>
        <uri content-type="ror">https://ror.org/01ynf4891</uri>
      </aff>
      <author-notes>
        <fn fn-type="corresp">
          <p>Corresponding author: Enrico Ruzzier (<email xlink:type="simple">enrico.ruzzier@uniroma3.it</email>)</p>
        </fn>
        <fn fn-type="edited-by">
          <p>Academic editor: Victoria Lantschner</p>
        </fn>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>17</day>
        <month>09</month>
        <year>2026</year>
      </pub-date>
      <volume>109</volume>
      <fpage>177</fpage>
      <lpage>201</lpage>
      <uri content-type="arpha" xlink:href="http://openbiodiv.net/F2C54CFB-7662-5EC1-8957-9E717D0A6330">F2C54CFB-7662-5EC1-8957-9E717D0A6330</uri>
      <uri content-type="zenodo_dep_id" xlink:href="https://zenodo.org/record/0">0</uri>
      <history>
        <date date-type="received">
          <day>26</day>
          <month>03</month>
          <year>2026</year>
        </date>
        <date date-type="accepted">
          <day>06</day>
          <month>08</month>
          <year>2026</year>
        </date>
      </history>
      <permissions>
        <license license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/share-your-work/public-domain/cc0/" xlink:type="simple">
          <license-p>This is an open access article distributed under the terms of the CC0 Public Domain Dedication.</license-p>
        </license>
      </permissions>
      <abstract>
        <label>Abstract</label>
        <p>The introduction and establishment of non-native wood-boring insects pose a significant threat to forest ecosystems, urban trees and agricultural systems worldwide. In this study, we assessed the invasion risk of two North American longhorn beetles, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>, to Europe by integrating joint bioclimatic–habitat species distribution models with maritime connectivity data. Both species can infest live trees; <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> is primarily associated with hosts in the <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="family" reg="Rosaceae">Rosaceae</tp:taxon-name-part></tp:taxon-name>, whereas <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> mainly attacks <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Ulmus">Ulmus</tp:taxon-name-part></tp:taxon-name></italic> species, making both species of primary phytosanitary concern to Europe. We developed ensemble models that separately quantified the effects of broad-scale climatic variables and fine-scale habitat factors, including land cover and host plant availability and then combined them into overall suitability maps. These were further integrated with a proxy of propagule pressure, based on international shipping connectivity to generate spatially explicit invasion risk maps. Both species showed strong model performance and high transferability. Results indicate that large areas of Europe, particularly central and western regions, offer suitable environmental conditions for establishment, although climatic constraints limit suitability in southern and northern extremes. Host plant availability emerged as a key determinant of habitat suitability, while climate acted as the primary limiting factor in Europe. When combined with maritime connectivity, invasion risk was highest in central Europe, especially in regions with dense trade networks and high environmental suitability. Our findings highlight that invasion risk is driven by the interaction between environmental suitability and human-mediated pathways, emphasising the importance of integrating ecological modelling with trade data. This approach provides a robust framework for improving surveillance, prioritising high-risk areas and supporting preventative phytosanitary strategies against invasive wood-boring insects.</p>
      </abstract>
      <kwd-group>
        <label>Key words:</label>
        <kwd>Bioclimatic suitability model</kwd>
        <kwd>
          <tp:taxon-name>
            <tp:taxon-name-part taxon-name-part-type="family" reg="Cerambycidae">Cerambycidae</tp:taxon-name-part>
          </tp:taxon-name>
        </kwd>
        <kwd>habitat suitability model</kwd>
        <kwd>host plants</kwd>
        <kwd>insect pest</kwd>
        <kwd>invasive species</kwd>
        <kwd>Species Distribution Model (SDM)</kwd>
      </kwd-group>
    </article-meta>
    <notes>
      <sec sec-type="Citation" id="sec1">
        <title>Citation</title>
        <p>Ruzzier E, Rassati D, Di Giulio A, Santoiemma G, Pureswaran DS, Sweeney J, Bani L (2026) Assessing invasion risk of two North American Saperda species (Coleoptera, Cerambycidae) in Europe using joint bioclimatic-habitat species distribution models and maritime-inland connectivity. NeoBiota 109: 177–201. <ext-link xlink:type="simple" ext-link-type="doi" xlink:href="10.3897/neobiota.109.192296">https://doi.org/10.3897/neobiota.109.192296</ext-link></p>
      </sec>
    </notes>
  </front>
  <body>
    <sec sec-type="Introduction" id="sec2">
      <title>Introduction</title>
      <p>Non-native insects that become invasive in their introduced range are amongst the leading drivers of forest and urban-tree decline worldwide (<xref ref-type="bibr" rid="B13">Brockerhoff and Liebhold 2017</xref>). Wood-boring beetles (<tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order" reg="Coleoptera">Coleoptera</tp:taxon-name-part></tp:taxon-name>) are particularly damaging because some species can attack living trees, disrupt timber and fruit production and generate large management costs due to severe ecological and economic impacts when established outside their native range (<xref ref-type="bibr" rid="B46">Lovett et al. 2016</xref>; <xref ref-type="bibr" rid="B38">Kenis et al. 2017</xref>; <xref ref-type="bibr" rid="B44">Linnakoski and Forbes 2019</xref>; <xref ref-type="bibr" rid="B30">Haubrock et al. 2021</xref>). The increase in container shipping and movement of wood-packaging material since 1980 have resulted in a corresponding increase in detection of wood-boring insects in recent decades (<xref ref-type="bibr" rid="B3">Aukema et al. 2010</xref>). Cryptic invaders that include several longhorn beetles (<tp:taxon-name><tp:taxon-name-part taxon-name-part-type="family" reg="Cerambycidae">Cerambycidae</tp:taxon-name-part></tp:taxon-name>), jewel beetles (<tp:taxon-name><tp:taxon-name-part taxon-name-part-type="family" reg="Buprestidae">Buprestidae</tp:taxon-name-part></tp:taxon-name>) and bark and ambrosia beetles (<tp:taxon-name><tp:taxon-name-part taxon-name-part-type="family" reg="Curculionidae">Curculionidae</tp:taxon-name-part></tp:taxon-name>, <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="subfamily" reg="Scolytinae">Scolytinae</tp:taxon-name-part></tp:taxon-name>), have repeatedly been transported outside their native ranges through global trade, often in wood or wood-packaging material (<xref ref-type="bibr" rid="B28">Haack 2006</xref>; <xref ref-type="bibr" rid="B42">Lantschner et al. 2020</xref>; <xref ref-type="bibr" rid="B62">Ruzzier et al. 2023a</xref>). Such repeated introductions facilitate successful establishments (<xref ref-type="bibr" rid="B14">Brockerhoff et al. 2014</xref>), underscoring the central role of international trade in facilitating biological invasions and the importance of stringent phytosanitary measures, early detection and rapid response strategies to mitigate ecological and economic impacts (<xref ref-type="bibr" rid="B13">Brockerhoff and Liebhold 2017</xref>; <xref ref-type="bibr" rid="B23">Epanchin-Niell et al. 2021</xref>; <xref ref-type="bibr" rid="B79">Wray et al. 2024</xref>). Preventing the arrival of non-native insects and their early detection are significantly more cost-effective than post-establishment control, making risk-based tools that predict where an organism could survive and reproduce outside its native range essential to plant health planning (<xref ref-type="bibr" rid="B24">Epanchin-Niell et al. 2014</xref>; <xref ref-type="bibr" rid="B10">Blaalid et al. 2021</xref>).</p>
      <p>Species distribution models (<abbrev xlink:title="Species distribution models">SDMs</abbrev>) are used to identify the relationship between species and environmental conditions (<xref ref-type="bibr" rid="B27">Guisan et al. 2017</xref>). They can serve as useful tools to produce habitat- and climate-suitability maps for invasive taxa, including <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="family" reg="Cerambycidae">Cerambycidae</tp:taxon-name-part></tp:taxon-name> (e.g. <xref ref-type="bibr" rid="B69">Shatz et al. (2016)</xref>; <xref ref-type="bibr" rid="B41">Krishnankutty et al. (2020)</xref>; Zhao et al. (<xref ref-type="bibr" rid="B81">2023a</xref>, <xref ref-type="bibr" rid="B82">2023b</xref>); <xref ref-type="bibr" rid="B40">Kim et al. (2025)</xref>). <abbrev xlink:title="Species distribution models">SDMs</abbrev> can, therefore, provide information for surveillance prioritisation, pathway risk assessments and scenario testing under climate change. When combined with information on trade pathways, host availability and dispersal, they support targeted inspections, quarantine zoning and contingency planning (e.g. <xref ref-type="bibr" rid="B71">Soubeyrand et al. (2024)</xref>). However, <abbrev xlink:title="Species distribution models">SDMs</abbrev> must be applied with caution when modelling invasive species. Sampling bias and georeferencing accuracy in occurrence data, the allocation of pseudo-absence/background points, covariate selection, the omission of biotic interactions and dispersal limitations and uncertainty in climate scenarios can all hinder model transferability, leading to an over- or underestimation of areas potentially susceptible to invasion (e.g. <xref ref-type="bibr" rid="B80">Yates et al. (2018)</xref>; <xref ref-type="bibr" rid="B59">Rousseau and Betts (2022)</xref>; <xref ref-type="bibr" rid="B74">Valavi et al. (2022)</xref>; <xref ref-type="bibr" rid="B1">Adde et al. (2023)</xref>).</p>
      <p>The spatial heterogeneity of climate, host availability and the distribution of trade hubs across Europe creates a complex mosaic of sites with variable invasion risks for non-native cerambycid beetles (e.g. <xref ref-type="bibr" rid="B57">Roques et al. (2020)</xref>; <xref ref-type="bibr" rid="B58">Rosace et al. (2025)</xref>). As a consequence, multiple non-native cerambycids potentially threaten European forestry, urban trees and fruit production (e.g. <xref ref-type="bibr" rid="B49">Malumphy et al. (2020)</xref>; <xref ref-type="bibr" rid="B47">Lupi et al. (2023</xref>); <xref ref-type="bibr" rid="B35">Horrocks et al. (2024)</xref>; <xref ref-type="bibr" rid="B67">Sarto i Monteys et al. (2024)</xref>). Moreover, Europe continues to experience repeated incursions of longhorn beetles, representing a significant risk for further establishment and invasion (e.g. <xref ref-type="bibr" rid="B17">Cocquempot and Lindelöw (2010)</xref>; <xref ref-type="bibr" rid="B39">Keszthelyi et al. (2019)</xref>; <xref ref-type="bibr" rid="B61">Ruzzier et al. (2020)</xref>; <xref ref-type="bibr" rid="B68">Seidel et al. (2021)</xref>; <xref ref-type="bibr" rid="B63">Ruzzier et al. (2023b)</xref>). These realities make integrative risk assessment, relying on <abbrev xlink:title="Species distribution models">SDMs</abbrev>, pathway analysis and surveillance data, critical to international and national plant-health strategies.</p>
      <p>Recent detections in Europe of two North American cerambycid beetles, the round-headed apple-tree borer, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> Fabricius, 1787 and the elm borer, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> Olivier, 1795 (<tp:taxon-name><tp:taxon-name-part taxon-name-part-type="subfamily" reg="Lamiinae">Lamiinae</tp:taxon-name-part></tp:taxon-name>, <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="tribe" reg="Saperdini">Saperdini</tp:taxon-name-part></tp:taxon-name>), have placed these species under European surveillance due to their potential threat to plant health on the continent (<xref ref-type="bibr" rid="B21">EFSA PLH Panel et al. 2020</xref>; <xref ref-type="bibr" rid="B25">EPPO 2021</xref>). <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> is native to much of eastern North America, ranging from south-eastern Canada west to central Alberta and south through many U.S. States (<xref ref-type="bibr" rid="B11">Bousquet et al. 2013</xref>; <xref ref-type="bibr" rid="B52">Monné and Nearns 2023</xref>). Its primary hosts are fruit and ornamental trees in the family <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="family" reg="Rosaceae">Rosaceae</tp:taxon-name-part></tp:taxon-name>, including <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Malus">Malus</tp:taxon-name-part></tp:taxon-name></italic>, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Pyrus">Pyrus</tp:taxon-name-part></tp:taxon-name></italic>, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Crataegus">Crataegus</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Amelanchier">Amelanchier</tp:taxon-name-part></tp:taxon-name></italic> (<xref ref-type="bibr" rid="B70">Solomon 1995</xref>). It has also been reported from <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Betula">Betula</tp:taxon-name-part></tp:taxon-name></italic> sp. (<tp:taxon-name><tp:taxon-name-part taxon-name-part-type="family" reg="Betulaceae">Betulaceae</tp:taxon-name-part></tp:taxon-name>) once (<xref ref-type="bibr" rid="B75">Vlasak and Vlasakova 2002</xref>), but reports of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Cotoneaster">Cotoneaster</tp:taxon-name-part></tp:taxon-name></italic> (Hess, 1940), <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Prunus">Prunus</tp:taxon-name-part></tp:taxon-name></italic> (Becker, 1918) and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Pyracantha">Pyracantha</tp:taxon-name-part></tp:taxon-name></italic> (Linsley &amp; Chemsak, 1995) as larval hosts have not been substantiated (<xref ref-type="bibr" rid="B48">Maier 2020</xref>). Infestations cause extensive tunnelling in trunks and roots, leading to branch dieback and even tree mortality in orchards and nurseries (<xref ref-type="bibr" rid="B25">EPPO 2021</xref>). In Europe, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> has already been detected in the wild, with the first confirmed record in Germany in 2008 (<xref ref-type="bibr" rid="B19">DEFRA 2025</xref>; <xref ref-type="bibr" rid="B37">Julius Kühn-Institut 2025</xref>). Since then, active eradication measures have been implemented to prevent further establishment and spread (<xref ref-type="bibr" rid="B37">Julius Kühn-Institut 2025</xref>). The detection of the species in natural environments highlights its ability to survive outside its native range and indicates a potential risk to fruit crops, ornamental trees and natural vegetation (<xref ref-type="bibr" rid="B25">EPPO 2021</xref>).</p>
      <p><italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> occurs across eastern North America, with records from the United States and southern Canada (<xref ref-type="bibr" rid="B11">Bousquet et al. 2013</xref>; <xref ref-type="bibr" rid="B52">Monné and Nearns 2023</xref>). It is primarily associated with elm species (e.g. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Ulmus">Ulmus</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="americana">americana</tp:taxon-name-part></tp:taxon-name></italic>, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Ulmus">U.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="rubra">rubra</tp:taxon-name-part></tp:taxon-name></italic>, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Ulmus">U.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="crassifolia">crassifolia</tp:taxon-name-part></tp:taxon-name></italic>) (<xref ref-type="bibr" rid="B70">Solomon 1995</xref>). Early isolated records reporting <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Acer">Acer</tp:taxon-name-part></tp:taxon-name></italic> (<xref ref-type="bibr" rid="B29">Harrington 1883</xref>) and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Populus">Populus</tp:taxon-name-part></tp:taxon-name></italic> (<xref ref-type="bibr" rid="B78">Washburn 1910</xref>) as larval host plants are considered doubtful and their potential role as substrates for larval feeding and development is currently under investigation (Santoiemma et al., personal communication). Damage results from larval feeding beneath the bark, causing bark splitting, frass exudation, canopy thinning and progressive branch decline (<xref ref-type="bibr" rid="B21">EFSA PLH Panel et al. 2020</xref>). In contrast to <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> does not have known adventive populations in Europe, despite repeated interceptions in imported <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Ulmus">Ulmus</tp:taxon-name-part></tp:taxon-name></italic> logs from North America (<xref ref-type="bibr" rid="B9">Binazzi et al. 2019</xref>). These interceptions underscore the ongoing risk of accidental introduction and establishment through international trade in wood and plant material. The repeated detection of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> in imported materials demonstrates the species’ potential for entry and emphasises the need for continued surveillance and early warning systems to prevent establishment and mitigate future impacts. Both species pose a potential invasion risk to Europe through trade in infested wood or live plants, prompting their inclusion in European surveillance and pest risk analyses (<xref ref-type="bibr" rid="B21">EFSA 2020</xref>; <xref ref-type="bibr" rid="B25">EPPO 2021</xref>). Given their biology with cryptic larval stages inside trunks/roots, multiple suitable hosts, the likelihood of detection is low until damage becomes noticeable.</p>
      <p>The present study aimed to assess the invasion risk of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> in Europe. We hypothesised that Europe provides climatic and habitat conditions similar to those of the native ranges of both species, potentially allowing their establishment. However, invasion risk was expected to increase with the intensity of commercial trade and transport, for which maritime and inland connectivity provide suitable proxies. We tested these hypotheses by: (1) developing, for both species, a data-driven joint bioclimatic–habitat species distribution model (<abbrev xlink:title="joint bioclimatic–habitat species distribution model">JBH-SDM</abbrev>), based on two complementary <abbrev xlink:title="Species distribution models">SDMs</abbrev>: a bioclimatic suitability model (<abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>) and a habitat suitability model (<abbrev xlink:title="habitat suitability model">HSM</abbrev>), both developed using an ensemble framework that explicitly accounted for sampling bias and the georeferencing accuracy of occurrence records; and (2) integrating <abbrev xlink:title="joint bioclimatic–habitat species distribution model">JBH-SDM</abbrev> predictions with maritime and inland connectivity linking the species’ native ranges to potentially invadable (i.e. environmentally suitable) areas in Europe.</p>
    </sec>
    <sec sec-type="materials|methods" id="sec3">
      <title>Material and methods</title>
      <sec sec-type="Occurrence data and variable selection" id="sec4">
        <title>Occurrence data and variable selection</title>
        <p>Species occurrences</p>
        <p>Occurrences of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> were downloaded from the Global Biodiversity Information Facility and iNaturalist via the <italic>spocc</italic> R package (<xref ref-type="bibr" rid="B54">Owens et al. 2024</xref>). Occurrence data from GBIF and iNaturalist used in the <abbrev xlink:title="Species distribution models">SDMs</abbrev> were updated as of 14 December 2025. The datasets were checked for all potential issues (e.g. duplicate records, misidentifications etc.).</p>
        <p>For <abbrev xlink:title="Species distribution models">SDMs</abbrev> calibrated in the native range of the species, we used the available occurrence records (686 for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and 922 for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>), with geographic coordinates expressed in degrees and a spatial resolution of at least two decimal places. The rationale of the procedure relies on the capability of associating each occurrence with habitat data derived from land cover cartography with a grid resolution of 0.04167° (corresponding to approximately 5 km at the Equator). Before modelling, the occurrences recurring in the calibration area were thinned through the function “thin” of the package <italic>GeoThinneR</italic> (<xref ref-type="bibr" rid="B51">Mestre-Tomás 2025</xref>), with a thinning parameter of 5 km in R, version 4.4.2 to remove occurrences within the same raster cell.</p>
        <sec sec-type="Bioclimatic and habitat covariates (elevation, land cover and host plants layer)" id="sec5">
          <title>Bioclimatic and habitat covariates (elevation, land cover and host plants layer)</title>
          <p>Nineteen bioclimatic layers and a digital elevation model (<abbrev xlink:title="digital elevation model">DEM</abbrev>) with a 0.00833° grid resolution (averaged per anni 1970–2000) were obtained from WorldClim 2.1 (<ext-link xlink:href="http://www.worldclim.com/version2" ext-link-type="uri">http://www.worldclim.com/version2</ext-link>).</p>
          <p>Twelve land-cover layers were sourced from the Global 1-km Consensus Land Cover dataset (<ext-link xlink:href="http://www.earthenv.org" ext-link-type="uri">http://www.earthenv.org</ext-link>), representing the percentage cover of each land-cover class per 30-arc-second (~ 0.00833°) pixel: evergreen/deciduous needleleaf trees (<abbrev xlink:title="evergreen/deciduous needleleaf trees">EDNT</abbrev>), evergreen broadleaf trees (<abbrev xlink:title="evergreen broadleaf trees">EBT</abbrev>), deciduous broadleaf trees (<abbrev xlink:title="deciduous broadleaf trees">DBT</abbrev>), mixed/other trees (<abbrev xlink:title="mixed/other trees">MOT</abbrev>), shrubs (<abbrev xlink:title="shrubs">SHR</abbrev>), herbaceous vegetation (<abbrev xlink:title="herbaceous vegetation">HERB</abbrev>), cultivated/managed vegetation (<abbrev xlink:title="cultivated/managed vegetation">CMV</abbrev>), regularly flooded vegetation (<abbrev xlink:title="regularly flooded vegetation">RFV</abbrev>), urban/built-up (<abbrev xlink:title="urban/built-up">BU</abbrev>), barren lands (<abbrev xlink:title="barren lands">BR</abbrev>), open waters (<abbrev xlink:title="open waters">OW</abbrev>) and snow/ice (<abbrev xlink:title="snow/ice">SI</abbrev>). To reduce computational demand, all bioclimatic, elevation and land-cover layers were upscaled to 0.04167° using the “aggregate” function in the <italic>terra</italic> package (<xref ref-type="bibr" rid="B33">Hijmans 2025</xref>). For the habitat suitability modelling (<abbrev xlink:title="habitat suitability model">HSM</abbrev>), <abbrev xlink:title="open waters">OW</abbrev> was excluded <italic>a priori</italic>.</p>
          <p>To evaluate whether host availability could improve habitat suitability model (<abbrev xlink:title="habitat suitability model">HSM</abbrev>), we added a host-plants (<abbrev xlink:title="host-plant">HP</abbrev>) layer following the approach of Ruzzier et al. (<xref ref-type="bibr" rid="B64">2024</xref>, <xref ref-type="bibr" rid="B65">2025a</xref>, <xref ref-type="bibr" rid="B66">2025b</xref>). For both <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part></tp:taxon-name></italic> species, we created a species-specific host-plant layer considering the genus of each of all reported host plants (<italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Amelanchier">Amelanchier</tp:taxon-name-part></tp:taxon-name></italic> Medik., <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Aronia">Aronia</tp:taxon-name-part></tp:taxon-name></italic> Medik., <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Betula">Betula</tp:taxon-name-part></tp:taxon-name></italic> L., <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Hedlundia">Hedlundia</tp:taxon-name-part></tp:taxon-name></italic> Sennikov &amp; Kurtto, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Cotoneaster">Cotoneaster</tp:taxon-name-part></tp:taxon-name></italic> Medik., <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Crataegus">Crataegus</tp:taxon-name-part></tp:taxon-name></italic> L., <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Cydonia">Cydonia</tp:taxon-name-part></tp:taxon-name></italic> Mill., <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Malus">Malus</tp:taxon-name-part></tp:taxon-name></italic> Mill., <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Prunus">Prunus</tp:taxon-name-part></tp:taxon-name></italic> L., <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Pyrus">Pyrus</tp:taxon-name-part></tp:taxon-name></italic> L. and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Sorbus">Sorbus</tp:taxon-name-part></tp:taxon-name></italic> L. for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>; <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Acer">Acer</tp:taxon-name-part></tp:taxon-name></italic> L., <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Populus">Populus</tp:taxon-name-part></tp:taxon-name></italic> L. and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Ulmus">Ulmus</tp:taxon-name-part></tp:taxon-name></italic> L. for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>). Secondary hosts, even when uncertain, were retained to avoid underestimating the host range for each <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part></tp:taxon-name></italic> species in both native and potentially invadable areas; this strategy was adopted to account for the possible presence of congeneric (vicariant) plant species in case <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part></tp:taxon-name></italic> species were to shift to alternative, phylogenetically related hosts. For each host-plant genus, occurrence records were retrieved from GBIF (accessed 15 September 2025) using the “occ_data” function of the <italic>rgbif</italic> R package (<xref ref-type="bibr" rid="B16">Chamberlain et al. 2025</xref>). Records were converted to georeferenced point patterns (“as.ppp”) and transformed into density maps via kernel interpolation (“density.ppp”), with kernel bandwidth estimated by “bw.ppl”; all functions were implemented in the <italic>spatstat</italic> R package (<xref ref-type="bibr" rid="B4">Baddeley and Turner 2005</xref>; <xref ref-type="bibr" rid="B5">Baddeley et al. 2005</xref>). Kernel density maps were log-transformed to reduce the effect of potential oversampling (<xref ref-type="bibr" rid="B65">Ruzzier et al. 2025a</xref>, <xref ref-type="bibr" rid="B66">2025b</xref>). All host-plant kernel density maps were then combined into a single layer by retaining, for each grid cell, the maximum density value amongst all host-plant layers. As different host plants may locally replace one another, the maximum value was retained instead of summing all density layers to avoid overestimating local host-plant density. The resulting maximum-density layer was then multiplied by a land-cover layer comprising all land-cover classes associated with the habitats of the host taxa and, where applicable, with cultivated or managed environments (<abbrev xlink:title="evergreen/deciduous needleleaf trees">EDNT</abbrev>, <abbrev xlink:title="evergreen broadleaf trees">EBT</abbrev>, <abbrev xlink:title="deciduous broadleaf trees">DBT</abbrev>, <abbrev xlink:title="mixed/other trees">MOT</abbrev>, <abbrev xlink:title="shrubs">SHR</abbrev>, <abbrev xlink:title="herbaceous vegetation">HERB</abbrev>, <abbrev xlink:title="cultivated/managed vegetation">CMV</abbrev>), thereby reducing overestimation of host-plant distribution. This procedure generated the final host-plant (<abbrev xlink:title="host-plant">HP</abbrev>) layer (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: figs S1, S2).</p>
          <p>Before <abbrev xlink:title="Species distribution models">SDMs</abbrev> calibration, all layers were masked (using the “mask” function of the <italic>terra</italic> package) to exclude areas within the native range of both species that are inaccessible due to geographic barriers (e.g. the sea), thereby preventing the allocation of background points or pseudo-absences in those regions. In addition, these layers were cropped to an extent encompassing the native distribution of both <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> (15°N – 60°N, -125°E – -50°E). This extent spans longitudinally and latitudinally to include all continuous areas (i.e. not interrupted by ecological barriers) from the East Coast to the continental divide of the Rocky Mountains and from the southern regions of North America to the subarctic areas of the continent.</p>
          <p>Bioclimatic variables exhibiting noisy or bimodal frequencies of observed presence (<abbrev xlink:title="frequencies of observed presence">FOP</abbrev>) were excluded, retaining only predictors that produced ecologically interpretable response curves (<xref ref-type="bibr" rid="B76">Vollering et al. 2019</xref>). This screening was conducted using the R package <italic>MIAmaxent</italic> (<xref ref-type="bibr" rid="B76">Vollering et al. 2019</xref>). The variables retained after <abbrev xlink:title="frequencies of observed presence">FOP</abbrev> were Bio13 (precipitation of the wettest month), Bio15 (precipitation seasonality), Bio18 (precipitation of the warmest quarter) and Bio19 (precipitation of the coldest quarter) for both species, with the addition of Bio10 (mean temperature of the warmest quarter) for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>.</p>
          <p>To reduce collinearity, the bioclimatic variables retained after <abbrev xlink:title="frequencies of observed presence">FOP</abbrev> and the habitat covariates were evaluated separately using variance inflation factor (<abbrev xlink:title="variance inflation factor">VIF</abbrev>) analysis with the “vifcor” function of the <italic>usdm</italic> R package (<xref ref-type="bibr" rid="B53">Naimi et al. 2014</xref>). In each group, highly correlated variables (Pearson’s |r| &gt; 0.7) were excluded (<xref ref-type="bibr" rid="B20">Dormann et al. 2013</xref>).</p>
        </sec>
      </sec>
      <sec sec-type="Joint bioclimatic–habitat species distribution modelling" id="sec6">
        <title>Joint bioclimatic–habitat species distribution modelling</title>
        <p>The modelling procedure for each species consisted of two distinct modelling pipelines. Each pipeline included two models, a bioclimatic suitability model (<abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>) and a habitat suitability model (<abbrev xlink:title="habitat suitability model">HSM</abbrev>), which were used to assess the influence of broad-scale predictors (bioclimatic variables) and fine-scale predictors (e.g. land cover, host plants and elevation) on species distributions, respectively. The suitability maps produced by the <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> and <abbrev xlink:title="habitat suitability model">HSM</abbrev> were subsequently integrated to generate an overall environmental suitability map.</p>
        <sec sec-type="Pseudo-absence and background points selection" id="sec7">
          <title>Pseudo-absence and background points selection</title>
          <p>In each modelling pipeline, pseudo-absences (<abbrev xlink:title="pseudo-absences">PAs</abbrev>) were generated for <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>, whereas background points (<abbrev xlink:title="background points">BPs</abbrev>) were used as absence-like data in <abbrev xlink:title="habitat suitability model">HSM</abbrev>. Pseudo-absences were used for <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> to effectively sample the broad bioclimatic gradient across the entire modelling domain, thereby improving characterisation of the species’ climatic niche. Conversely, <abbrev xlink:title="background points">BPs</abbrev> were used for <abbrev xlink:title="habitat suitability model">HSM</abbrev> to represent local habitat availability around occurrence records, allowing habitat suitability to be estimated within the accessible area, while minimising the influence of broad-scale climatic conditions. In both cases, the total number of <abbrev xlink:title="pseudo-absences">PAs</abbrev> or <abbrev xlink:title="background points">BPs</abbrev> was set to 3 times the number of occurrences (<xref ref-type="bibr" rid="B77">Warton and Shepherd 2010</xref>; <xref ref-type="bibr" rid="B56">Renner et al. 2015</xref>). For <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>, <abbrev xlink:title="pseudo-absences">PAs</abbrev> were generated across a geographical extent encompassing the species’ native ranges, as defined above. This choice aimed to evaluate the effects of bioclimatic variables within a continuous landmass, characterised by the absence of ecological barriers (<xref ref-type="bibr" rid="B65">Ruzzier et al. 2025a</xref>, <xref ref-type="bibr" rid="B66">2025b</xref>; <xref ref-type="bibr" rid="B60">Ruzzier and Uliana 2025</xref>). Within this extent, <abbrev xlink:title="pseudo-absences">PAs</abbrev> were allocated with a spatial density inversely proportional to the occurrence points density. To estimate occurrence density, we used the same kernel estimation strategy adopted for host plants. The natural logarithm of the resulting kernel density surface was used to reduce the influence of potential occurrence aggregation/oversampling that could generate local density peaks. The resulting kernel map was then used as a proxy for species abundance in the native calibration area. In contrast, for <abbrev xlink:title="habitat suitability model">HSM</abbrev>, <abbrev xlink:title="background points">BPs</abbrev> were randomly generated within a buffer around occurrence points, with inner and outer radii of 30 km and 300 km, respectively. The inner buffer prevented the placement of <abbrev xlink:title="background points">BPs</abbrev> too close to occurrence records, thereby reducing the likelihood of allocating <abbrev xlink:title="background points">BPs</abbrev> within potentially suitable habitats, whereas the outer buffer allowed the exploration of a wide range of environmental conditions without placing <abbrev xlink:title="background points">BPs</abbrev> beyond the bioclimatic envelope. This procedure was implemented using the “disk” strategy available in the <italic>biomod2</italic> R package (version 4.2-5-2; <xref ref-type="bibr" rid="B73">Thuiller et al. (2024)</xref>).</p>
        </sec>
        <sec sec-type="Ensemble modelling and overall suitability map" id="sec8">
          <title>Ensemble modelling and overall suitability map</title>
          <p>For both <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> and <abbrev xlink:title="habitat suitability model">HSM</abbrev>, five modelling algorithms were implemented using the “bigboss” settings in <italic>biomod2</italic> (version 4.2.5.2), without further manual tuning: Artificial Neural Networks (<abbrev xlink:title="Artificial Neural Networks">ANN</abbrev>), Generalised Additive Models (<abbrev xlink:title="Generalised Additive Models">GAM</abbrev>), Generalised Boosting Models (<abbrev xlink:title="Generalised Boosting Models">GBM</abbrev>), Random Forest (<abbrev xlink:title="Random Forest">RF</abbrev>) and Maximum Entropy (<abbrev xlink:title="Maximum Entropy">MAXNET</abbrev>). Model outputs generated by each algorithm were subsequently combined using an ensemble modelling approach (<xref ref-type="bibr" rid="B2">Araújo and New 2007</xref>; <xref ref-type="bibr" rid="B27">Guisan et al. 2017</xref>), as implemented in <italic>biomod2</italic>.</p>
          <p><abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> used five sets of <abbrev xlink:title="pseudo-absences">PAs</abbrev>, whereas <abbrev xlink:title="habitat suitability model">HSM</abbrev> employed 10 sets of <abbrev xlink:title="background points">BPs</abbrev>. All models were validated using a random five-fold cross-validation (80% training and 20% testing), according to the area under (<abbrev xlink:title="area under">AUC</abbrev>) the receiver operating characteristic curve (<abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev>) and the true skill statistic (<abbrev xlink:title="true skill statistic">TSS</abbrev>). Model calibration was performed without fixing the random seed. In total, for each species, 125 models were developed for the <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> (5 algorithms × 5 PA sets × 5 CV folds) and 250 models for the <abbrev xlink:title="habitat suitability model">HSM</abbrev> (5 algorithms × 10 BP sets × 5 CV folds), all evaluated using both <abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> and <abbrev xlink:title="true skill statistic">TSS</abbrev>. Models with a <abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> score ≥ 0.8 and a <abbrev xlink:title="true skill statistic">TSS</abbrev> score ≥ 0.6 were retained to build the final ensemble models using the EMwmean method, whereby ensemble predictions were calculated as the weighted mean of individual model predictions with weights assigned proportionally to their respective evaluation scores (<abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> or <abbrev xlink:title="true skill statistic">TSS</abbrev>), using a proportional weighting decay.</p>
          <p>Variable importance was assessed through a three-run permutation procedure.</p>
          <p>Both <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> and <abbrev xlink:title="habitat suitability model">HSM</abbrev> were projected on to the calibration area to obtain native habitat and native bioclimatic suitability maps, respectively. <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> and <abbrev xlink:title="habitat suitability model">HSM</abbrev> outputs are suitability maps expressed as probabilities of occurrence derived from two independent models. This approach assumes that ecological filters operating at scenopoetic (climatic) and local (habitat) scales contribute independently to species suitability. To obtain an overall suitability map (representing the overall probability of two independent, <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> and <abbrev xlink:title="habitat suitability model">HSM</abbrev>, probabilities) that accounts for the simultaneous effects of habitat and bioclimate on species occurrence probability, the <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> and <abbrev xlink:title="habitat suitability model">HSM</abbrev> maps were combined as a spatial product (see <xref ref-type="bibr" rid="B64">Ruzzier et al. (2024)</xref>).</p>
          <p>The predictive performance of these overall suitability maps within the calibration area was then evaluated using the Boyce index (<xref ref-type="bibr" rid="B12">Boyce et al. 2002</xref>) implemented within the <italic>modEvA</italic> R package (<xref ref-type="bibr" rid="B7">Barbosa et al. 2013</xref>).</p>
          <p>The projection of <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> and <abbrev xlink:title="habitat suitability model">HSM</abbrev> on to the European (non-native) range (34°N–72°N, -12°E–43°E) and the overall suitability map were realised following the same procedure described above for the native range projection.</p>
          <p>To identify potential extrapolation areas in the European projections of the <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> and <abbrev xlink:title="habitat suitability model">HSM</abbrev>, we calculated the multivariate environmental similarity surface (<abbrev xlink:title="multivariate environmental similarity surface">MESS</abbrev>) index (<xref ref-type="bibr" rid="B22">Elith et al. 2010</xref>) using the <italic>dismo</italic> R package (<xref ref-type="bibr" rid="B34">Hijmans et al. 2024</xref>).</p>
        </sec>
      </sec>
      <sec sec-type="Development of the invasion risk map, based on maritime connectivity by vessel traffic and inland infrastructural network" id="sec9">
        <title>Development of the invasion risk map, based on maritime connectivity by vessel traffic and inland infrastructural network</title>
        <p>Maritime connectivity between North America and Europe was quantified using the Liner Shipping Bilateral Connectivity Index (<abbrev xlink:title="Liner Shipping Bilateral Connectivity Index">LSBCI</abbrev>), available from the United Nations Conference on Trade and Development (<abbrev xlink:title="United Nations Conference on Trade and Development">UNCTAD</abbrev>; <ext-link xlink:href="https://unctadstat.unctad.org/datacentre/dataviewer/US.LSBCI" ext-link-type="uri">https://unctadstat.unctad.org/datacentre/dataviewer/US.LSBCI</ext-link>). The <abbrev xlink:title="Liner Shipping Bilateral Connectivity Index">LSBCI</abbrev> describes the degree of liner shipping connectivity between pairs of countries, based on the structure of the global maritime transport network. In this study, the <abbrev xlink:title="Liner Shipping Bilateral Connectivity Index">LSBCI</abbrev> was used as a proxy for the potential propagule pressure associated with maritime trade, under the assumption that stronger shipping connectivity increases the likelihood of species introduction.</p>
        <p>Bilateral connectivity values between the United States and European countries and between Canada and European countries, were considered separately. To obtain a more stable estimate of long-term maritime connectivity, while minimising the influence of annual fluctuations, the average <abbrev xlink:title="Liner Shipping Bilateral Connectivity Index">LSBCI</abbrev> over the period 2015–2025 was calculated for each country pair.</p>
        <p>European ports connected to the United States and Canada were subsequently identified and georeferenced. For each port, maritime connections with the two source countries were classified as either direct or indirect. Ports with direct connections were assigned the corresponding averaged <abbrev xlink:title="Liner Shipping Bilateral Connectivity Index">LSBCI</abbrev> value for the respective country pair, whereas ports connected only indirectly received half of that value, to reflect the lower expected contribution of indirect shipping routes to propagule transfer (Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>).</p>
        <p>Finally, a maritime connectivity index was calculated for each European port by summing its weighted connectivity with the United States and Canada. This index was used as a spatial proxy for the potential propagule pressure associated with maritime trade and served as the basis for transferring connectivity values to the European terrestrial transport network.</p>
        <p>European road and railway networks were obtained from the Natural Earth database using the <italic>rnaturalearth</italic> package in R (<xref ref-type="bibr" rid="B50">Massicotte and South 2026</xref>). The vector layers were clipped to the European extent, geometries were validated and all spatial data were projected to the ETRS89 / Lambert Azimuthal Equal Area coordinate system (EPSG:3035) to ensure accurate distance calculations. The road and railway layers were subsequently merged into a single transport network representing the major terrestrial transport infrastructure across Europe.</p>
        <p>The centroid of each road and railway segment was used as a representative location and Euclidean distances between all transport segments and European ports were calculated using the <italic>sf</italic> package in R (<xref ref-type="bibr" rid="B55">Pebesma 2018</xref>). The contribution of each port to every transport segment was weighted using a negative exponential distance-decay function (l = 200 km; to account for propagule pressure within an infrastructure network in which goods are transported over relatively long distances), assuming that the influence of maritime propagule pressure decreases with increasing distance from the port. The weighted contributions from all ports were subsequently summed to obtain a cumulative connectivity value for each transport segment.</p>
        <p>The transport network was then intersected with the European landmass to remove offshore sections and rasterised to a 5-km spatial resolution using the <italic>terra</italic> package, ensuring consistency with the resolution of the species’ overall suitability maps. Raster cells intersecting the transport network retained the cumulative connectivity value assigned to the corresponding transport segment.</p>
        <p>To generate a continuous map of propagule pressure given by inland connectivity, the Euclidean distance from every terrestrial raster cell to the nearest transport segment was calculated. Each raster cell inherited the connectivity value of its nearest transport segment, which was subsequently reduced according to a second negative exponential distance-decay function to account for the decreasing influence of transport infrastructure with increasing distance (l = 100 km; to prevent overlapping effects from different segments in areas with a high infrastructure density). This procedure assumes that propagules introduced through maritime trade are subsequently dispersed along the terrestrial transport network and that the probability of dispersal decreases progressively away from major transport corridors.</p>
        <p>The resulting propagule pressure map was multiplied with the European overall suitability maps for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> to obtain the two maps of invasion risk.</p>
      </sec>
    </sec>
    <sec sec-type="Results" id="sec10">
      <title>Results</title>
      <sec sec-type="SDM performance, effect of covariates and transferability" id="sec11">
        <title>SDM performance, effect of covariates and transferability</title>
        <p>For both <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>, BSMs and HSMs exhibited strong predictive performance, based on cross‐validation metrics (Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: tables S1–S4). For <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>, all <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> models (125 of 125; 100%) achieved both <abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> scores ≥ 0.8 and <abbrev xlink:title="true skill statistic">TSS</abbrev> values ≥ 0.6. Similarly, for the HSMs, although some models did not meet the predefined validation thresholds, the overall modelling performance remained very high: <abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> scores resulted ≥ 0.8 in 248 of 250 cases (99.2%), while 227 of 250 models (90.8%) reached a <abbrev xlink:title="true skill statistic">TSS</abbrev> ≥ 0.6. For <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>, <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>, <abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> scores ≥ 0.8 were obtained in all models (125 of 125; 100%) and 122 of 125 models (97.6%) achieved a <abbrev xlink:title="true skill statistic">TSS</abbrev> ≥ 0.6. For the <abbrev xlink:title="habitat suitability model">HSM</abbrev>, models achieved <abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> scores ≥ 0.8 in all cases (250 of 250; 100%), whereas only 106 of 250 models (42.4%) reached a <abbrev xlink:title="true skill statistic">TSS</abbrev> ≥ 0.6.</p>
        <p>The ensemble modelling procedure provided variable importance scores and response curves for both species (Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: tables S5–S8; Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: figs S5–S8).</p>
        <p>Bioclimatic suitability for both species was primarily associated with precipitation seasonality (coefficient of variation), which showed the highest relative importance (0.72 for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and 0.61 for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>; Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: figs S5, S6). Additional variables with high relative importance included precipitation of the warmest quarter (0.41) for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and mean temperature of the warmest quarter (0.55) together with precipitation of the warmest quarter (0.24) for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>. Overall, higher predicted suitability for both species was associated with humid temperate climatic conditions, characterised by relatively high and evenly distributed precipitation throughout the year. For <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>, mean temperature of the warmest quarter also showed high relative importance. Model response curves indicated that higher predicted suitability was associated with mean temperatures of the warmest quarter exceeding approximately 18 °C, with predicted suitability increasing as temperature increased.</p>
        <p>For <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>, predicted occurrence probability was most strongly associated with host plants (importance = 0.64), followed by cultivated/managed vegetation (0.32), evergreen/deciduous needleleaf trees (0.27) and herbaceous vegetation (0.26) (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S7). Response curves indicated a positive association between host plants and predicted occurrence probability, whereas cultivated/managed vegetation, evergreen/deciduous needleleaf trees and herbaceous vegetation were negatively associated with predicted occurrence probability.</p>
        <p>For <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>, predicted occurrence probability was most strongly associated with host plants (importance = 0.53), followed by mixed/other trees (0.42), cultivated/managed vegetation (0.24), the digital elevation model (0.24), herbaceous vegetation (0.15) and evergreen/deciduous needleleaf trees (0.12) (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S8). Response curves indicated a positive association between host plants and predicted occurrence probability, whereas all remaining variables were negatively associated with predicted occurrence probability.</p>
        <p>Within their respective calibration areas, <abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> and <abbrev xlink:title="true skill statistic">TSS</abbrev> projection maps for both <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> and <abbrev xlink:title="habitat suitability model">HSM</abbrev> exhibited very high internal consistency (Pearson’s correlation coefficients always &gt; 0.99 for both species); therefore, we presented only the <abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> maps. Bioclimatic and habitat suitability maps, together with their 95% confidence intervals, are presented in Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: figs S11–S14.</p>
        <p>The overall suitability maps of each species showed excellent calibration according to the Boyce index. Boyce index values reached 0.998 for both <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>, indicating strong agreement between observed occurrences and predicted suitability (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: figs S9, S10).</p>
        <p><abbrev xlink:title="multivariate environmental similarity surface">MESS</abbrev> analyses revealed that extrapolations for <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev> and <abbrev xlink:title="habitat suitability model">HSM</abbrev> were negligible across the European projections for both species (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: figs S15, S16).</p>
        <p>These results indicate that the <abbrev xlink:title="joint bioclimatic–habitat species distribution model">JBH-SDM</abbrev> framework provides robust and transferable predictions across both native ranges that could be credible for the potential invadable European ranges.</p>
      </sec>
      <sec sec-type="JBH-SDM – projections" id="sec12">
        <title>JBH-SDM – projections</title>
        <sec sec-type="Saperda candida: species suitability for the native range" id="sec13">
          <title><italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>: species suitability for the native range</title>
          <p><bold>Bioclimatic suitability</bold>. The bioclimatic suitability map identified a broad and largely continuous area of moderate to high suitability across eastern North America (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S11), extending from the south-eastern to the north-eastern United States and parts of the Midwest. Suitability progressively declined towards western and more continental regions.</p>
          <p><bold>Habitat suitability</bold>. Habitat suitability showed a heterogeneous and fragmented spatial pattern (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S13). High suitability was concentrated in the eastern United States, particularly along the Appalachian Region, the eastern Midwest and the north-eastern forested areas, whereas large portions of North America exhibited low or negligible suitability.</p>
          <p><bold>Overall suitability</bold>. Compared with the bioclimatic suitability projection, the overall suitability map showed a reduced extent of highly suitable areas, with habitat acting as a limiting factor and producing a more spatially structured pattern of suitability (Fig. <xref ref-type="fig" rid="F1">1A</xref>). The highest suitability values remained concentrated in the eastern United States and showed strong spatial correspondence with known occurrence records (Boyce index; Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S9).</p>
          <fig id="F1">
            <object-id content-type="arpha">ED85E7D9-B113-5EB5-9EA1-6132B8CD4704</object-id>
            <label>Figure 1.</label>
            <caption>
              <p>Overall suitability map (native range) of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> (<bold>A</bold>) and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> (<bold>B</bold>). Colours represent predicted suitability, ranging from low (white) to high (green).</p>
            </caption>
            <graphic xlink:href="neobiota-109-177_article-192296__-g001.jpg" id="oo_1777676.jpg">
              <uri content-type="original_file">https://binary.pensoft.net/fig/1777676</uri>
            </graphic>
          </fig>
        </sec>
        <sec sec-type="Saperda tridentata: species suitability for the native range" id="sec14">
          <title><italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>: species suitability for the native range</title>
          <p><bold>Bioclimatic suitability</bold>. The bioclimatic suitability map identified a broad and largely continuous area of high suitability across eastern North America (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S12), extending throughout most of the eastern United States, including the south-eastern region, the Midwest and the north-eastern areas. Suitability gradually declined towards western and more continental regions.</p>
          <p><bold>Habitat suitability</bold>. Habitat suitability showed a heterogeneous, but relatively continuous spatial pattern across the eastern portion of North America (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S14). High suitability was predicted throughout much of the eastern United States, extending from the south-eastern region through the Midwest to the north-eastern forested areas, with an additional narrow belt of suitable habitat along the western coast of North America. Spatial discontinuities were evident within parts of the eastern United States, although extensive suitable areas remained connected.</p>
          <p><bold>Overall suitability</bold>. Compared with the bioclimatic suitability projection, the overall suitability map showed a reduced extent of suitable areas, with habitat acting as a limiting factor and producing a more spatially structured pattern of suitability (Fig. <xref ref-type="fig" rid="F1">1B</xref>). Moderate to high suitability remained concentrated across the eastern United States, extending from the south-eastern region through the Midwest to the north-eastern areas and showed strong spatial correspondence with observed occurrence records (Boyce index; Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S10).</p>
        </sec>
        <sec sec-type="Saperda candida: species suitability for Europe" id="sec15">
          <title><italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>: species suitability for Europe</title>
          <p><bold>Bioclimatic suitability</bold>. The bioclimatic suitability map identified a spatially restricted pattern of suitability across Europe (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S17). High suitability was concentrated in western and central Europe, particularly across eastern France, southern Germany, Switzerland, Austria, northern Italy and parts of the western Balkans. Additional areas of moderate suitability occurred in the British Isles and parts of the Atlantic and Alpine regions, whereas northern, eastern and southern Europe were generally characterised by low suitability.</p>
          <p><bold>Habitat suitability</bold>. Habitat suitability map showed a rather broad and continuous spatial pattern (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S18). High suitability extended across much of western and central Europe, including France, Belgium, the Netherlands, Luxembourg, Germany, Switzerland, Austria, northern Italy, the British Isles, northern Spain and parts of southern Scandinavia.</p>
          <p><bold>Overall suitability</bold>. The overall suitability map identifies spatially structured potentially invadable areas for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> across Europe (Fig. <xref ref-type="fig" rid="F2">2A</xref>). The most suitable areas are concentrated in a core region spanning eastern France, Belgium, the Netherlands, Luxembourg, southern and western Germany, Switzerland, Austria and northern Italy, forming a continuous belt of highly suitable areas across central Europe; outside this core area, suitability declines progressively. Moderate suitability extends into parts of western France, the British Isles, southern Scandinavia and the western Balkans, reflecting either locally favourable habitat conditions or climatically suitable pockets. In contrast, in Eastern Europe and northern Fennoscandia, the joint influence of bioclimate and habitat conditions determines the availability of suitable areas, whereas in southern Europe, bioclimate acts as the primary limiting factor governing the extent of the overall suitability.</p>
          <fig id="F2">
            <object-id content-type="arpha">02F33E0B-CC1E-54E4-847F-B3756FA3A96C</object-id>
            <label>Figure 2.</label>
            <caption>
              <p>Predicted overall suitability of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> (<bold>A</bold>) and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> (<bold>B</bold>). Colours represent predicted suitability, ranging from low (white) to high (green).</p>
            </caption>
            <graphic xlink:href="neobiota-109-177_article-192296__-g002.jpg" id="oo_1777677.jpg">
              <uri content-type="original_file">https://binary.pensoft.net/fig/1777677</uri>
            </graphic>
          </fig>
        </sec>
        <sec sec-type="Saperda tridentata: species suitability for Europe" id="sec16">
          <title><italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>: species suitability for Europe</title>
          <p><bold>Bioclimatic suitability</bold>. The bioclimatic suitability map identified broad and largely continuous areas of high suitability across much of Europe (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S19). High suitability extended from western through central and eastern Europe, whereas lower suitability occurred in northern Europe, mountainous regions and the southernmost Mediterranean areas.</p>
          <p><bold>Habitat suitability</bold>. The habitat suitability map showed a spatially structured, but relatively continuous pattern across Europe (Suppl. material <xref ref-type="supplementary-material" rid="S3">3</xref>: fig. S20). High suitability was predicted across large portions of western and central Europe, including France, Germany, northern Italy and parts of eastern Europe, while localised reductions were mainly associated with mountainous regions.</p>
          <p><bold>Overall suitability</bold>. Areas of moderate to high overall suitability were concentrated across western and central Europe (Fig. <xref ref-type="fig" rid="F2">2B</xref>). Compared to the habitat projection alone, the extent of suitable areas was reduced, although remaining broadly continuous across the continent. Several regions, characterised by high habitat suitability, exhibited lower overall suitability, reflecting the effect of bioclimate as a limiting factor. Nevertheless, large and connected regions of moderate to high overall suitability persisted across Europe, indicating extensive areas where habitat and climate are jointly predicted to be suitable for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>.</p>
        </sec>
      </sec>
      <sec sec-type="Risk assessment" id="sec17">
        <title>Risk assessment</title>
        <p>The propagule pressure map shows a high level of interconnection and consequently high propagule pressure, between the native ranges of both <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part></tp:taxon-name></italic> species and the European countries bordering the Atlantic Ocean, except for the Scandinavian Peninsula and Ireland. High propagule pressure is also observed for Italy and the north-eastern part of the Adriatic coast (Fig. <xref ref-type="fig" rid="F3">3</xref>).</p>
        <fig id="F3">
          <object-id content-type="arpha">4F679863-D284-502A-B42C-B8CE1A43AEF2</object-id>
          <label>Figure 3.</label>
          <caption>
            <p>Spatial representation of propagule pressure across Europe obtained by combining maritime connectivity (Liner Shipping Bilateral Connectivity Index, <abbrev xlink:title="Liner Shipping Bilateral Connectivity Index">LSBCI</abbrev>) with inland transport network connectivity (roads and railways). Warmer colours indicate higher estimated propagule pressure, whereas cooler colours indicate lower estimated propagule pressure.</p>
          </caption>
          <graphic xlink:href="neobiota-109-177_article-192296__-g003.jpg" id="oo_1777678.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1777678</uri>
          </graphic>
        </fig>
        <p>The integration of environmental suitability with maritime connectivity between the USA/Canada and Europe, together with European inland transport connectivity (road and railway networks), provides a spatially explicit assessment of the invasion risk posed by <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> across Europe. Although environmentally suitable conditions occur over extensive areas, the resulting invasion risk maps reveal strong spatial heterogeneity, with the highest invasion risk occurring where high environmental suitability coincides with major maritime connections and densely interconnected road and railway networks.</p>
        <p>For <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>, the highest invasion risk forms an extensive and largely continuous core across western and central Europe (Fig. <xref ref-type="fig" rid="F4">4A</xref>). High invasion risk extends from northern and eastern France through Belgium, the Netherlands and Luxembourg into much of Germany, Switzerland and northern Italy. Elevated risk is also evident in south-western France and north-eastern Spain, particularly along the Pyrenean Region. Outside this main core, distinct high-risk areas occur across much of England and Wales, along the northern and eastern Adriatic coast, particularly in north-eastern Italy, Slovenia and Croatia and in southern Sweden. These geographically separated hotspots indicate that high invasion risk is not restricted to the central European corridor, but also occurs in regions combining suitable conditions with major coastal gateways and well-developed inland transport connections.</p>
        <fig id="F4">
          <object-id content-type="arpha">B70A7C4F-6DB7-5429-9748-0D5A6AD2413A</object-id>
          <label>Figure 4.</label>
          <caption>
            <p>Invasion risk maps of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> (<bold>A</bold>) and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> (<bold>B</bold>). Warmer colours denote higher invasion risk, whereas cooler colours denote lower invasion risk.</p>
          </caption>
          <graphic xlink:href="neobiota-109-177_article-192296__-g004.jpg" id="oo_1777679.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1777679</uri>
          </graphic>
        </fig>
        <p>For <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>, the highest-risk area is similarly centred on western and central Europe, but displays a somewhat different spatial configuration (Fig. <xref ref-type="fig" rid="F4">4B</xref>). A broad, highly connected zone extends from north-eastern France through Belgium, the Netherlands and Luxembourg, across Germany and Switzerland and into northern and central Italy. High values also occur along the Atlantic coast of southern western France. In Great Britain, the principal hotspot is more geographically restricted than for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and is concentrated mainly in central eastern England. Elevated risk is also present around the northern Adriatic and along parts of the Croatian coast, although it is more fragmented and less extended than that predicted for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>. In contrast, the pronounced hotspot observed in southern Sweden for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> is almost absent for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>.</p>
        <p>Overall, the two species share a major high-risk core across western and central Europe, but differ in the extent and spatial continuity of secondary hotspots. These differences suggest that species-specific environmental responses modulate the spatial consequences of exposure to a shared transport network. In this context, maritime connectivity facilitates long-distance introductions, whereas road and railway networks promote subsequent inland dispersal.</p>
      </sec>
    </sec>
    <sec sec-type="Discussion" id="sec18">
      <title>Discussion</title>
      <p>The modelling framework we employed in this study showed strong overall performance, delivering consistent and robust predictions for both <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part></tp:taxon-name></italic> species across their native ranges as well as for projections in Europe. Forecasting invasion risk is key to effectively managing invasive species (<xref ref-type="bibr" rid="B72">Thomas et al. 2024</xref>), although limitations and challenges of <abbrev xlink:title="Species distribution models">SDMs</abbrev> have been questioned (<xref ref-type="bibr" rid="B6">Barbet-Massin et al. 2018</xref>; <xref ref-type="bibr" rid="B43">Lee-Yaw et al. 2022</xref>; <xref ref-type="bibr" rid="B26">Fenollosa et al. 2025</xref>). Our modelling framework allowed us to evaluate separately the effect of scenopoetic variables and local variables on the ecological niche of the investigated species, thereby avoiding potential confounding effects in reconstructing the two niche components (bioclimatic and habitat-related) that may arise when jointly considering variables describing processes operating at different spatial scales, as previously highlighted by <xref ref-type="bibr" rid="B1">Adde et al. (2023)</xref> and Ruzzier et al. (<xref ref-type="bibr" rid="B64">2024</xref>, <xref ref-type="bibr" rid="B65">2025a</xref>, <xref ref-type="bibr" rid="B66">2025b</xref>). This modelling approach is particularly important when estimating the actual climatic and habitat suitability in areas that are potentially invadable and differ from those used for model calibration (i.e. the native range).</p>
      <p>Moreover, a particularly important aspect concerns modelling, based exclusively on presence data within the species’ native range; such occurrences can be assumed to reflect a species distribution of a population that is in equilibrium with the environment as a whole (<xref ref-type="bibr" rid="B36">Hui 2023</xref>). Consequently, this allows for the estimation of the realized niche occupied by the species within its native range and the identification of areas outside this range where analogous environmental conditions occur. In addition, for species whose biology is closely associated with a specific set of host plants, including these variables in habitat modelling further improved the ability to identify and refine suitability at a local scale.</p>
      <p>While the joint bioclimatic–habitat models identified extensive areas of environmental suitability across Europe, the invasion-risk maps revealed a more spatially structured pattern. Rather than being uniformly distributed across all environmentally suitable regions, the highest invasion risk became concentrated within a limited number of geographically coherent hotspots where suitable environmental conditions overlapped with high values of transport-based proxies of propagule pressure. This framework highlights the added value of integrating environmental suitability with these proxies, allowing environmentally suitable areas to be distinguished from those that also exhibit greater potential for introduction and subsequent spread.</p>
      <p>The modelled native range of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> primarily encompasses eastern North America, extending from the south-eastern and central-eastern United States to the Midwest and north-eastern regions, with partial inclusion of adjacent areas in southern Canada. Bioclimatic models indicate a strong association with humid temperate climates, characterised by low precipitation seasonality and relatively stable conditions throughout the year. In particular, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> is closely associated with stable precipitation regimes and high moisture availability during the warmest quarter. These results are consistent with suitability assessments reported by <xref ref-type="bibr" rid="B25">EPPO (2021)</xref>. From a habitat perspective, the species is strongly linked to the availability of host plants, which represents the primary constraint on its distribution. It is narrowly associated with host-dominated forest habitats, avoiding cultivated and managed areas lacking host plants, as well as coniferous forests. Overall, its native range corresponds predominantly to mesic temperate forests of eastern North America, where suitable climatic conditions and host availability co-occur. When projected to Europe, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> shows a wide extent of suitable and relatively continuous areas, largely driven by the distribution of host plants and associated land-cover types. However, bioclimatic projections alone indicate a more restricted suitability, suggesting that climate may act as a filtering factor. In particular, the species appears more strongly associated with the moister climates of central Europe, characterised by precipitation distributed throughout the year.</p>
      <p>The modelled native range of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> largely overlaps with that of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>, encompassing eastern North America from the south-eastern and central-eastern United States to the Midwest and north-eastern regions, with some extension into southern Canada. Similarly, bioclimatic models indicate an association with humid temperate climates and low precipitation seasonality. However, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> shows a greater association with temperature conditions, occurring across temperate to warm environments during the warmest quarter, suggesting a stronger influence of temperature gradients in shaping its climatic niche. These findings are also consistent with <xref ref-type="bibr" rid="B21">EFSA PLH Panel et al. (2020)</xref>. As for habitat, the species remains dependent on host plant availability, but exhibits a broader ecological tolerance compared to <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>, occurring in mixed forests and, to a limited extent, in cultivated and managed areas, with a preference for low-elevation environments. As with <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>, its native distribution reflects the overlap between suitable climate and host availability within mesic temperate forests. When projected to Europe, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> also shows extensive areas of potential suitability driven by host plant distribution. However, bioclimatic projections indicate a more constrained pattern, highlighting the role of climate as a limiting factor. In Europe, the species appears associated with milder and more temperate conditions, such as those found in central Italy. Nevertheless, the potential host plant range still requires further investigation, as precise information on host use is currently lacking. Indeed, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Ulmus">Ulmus</tp:taxon-name-part></tp:taxon-name></italic> is currently the only confirmed host genus, whereas the inclusion of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Acer">Acer</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Populus">Populus</tp:taxon-name-part></tp:taxon-name></italic> is based on sporadic occurrence records rather than confirmed host associations (<xref ref-type="bibr" rid="B78">Washburn 1910</xref>; <xref ref-type="bibr" rid="B21">EFSA 2020</xref>).</p>
      <p>Overall, for both species, habitat availability emerges as a primary environmental filter within the native range, while climate plays a stronger constraining role in shaping potential areas of establishment under a European invasion scenario. This pattern is consistent with findings for other invasive longhorn beetles, such as <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Aromia">Aromia</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="bungii">bungii</tp:taxon-name-part></tp:taxon-name></italic> (Faldermann, 1835) (<tp:taxon-name><tp:taxon-name-part taxon-name-part-type="subfamily" reg="Cerambycinae">Cerambycinae</tp:taxon-name-part></tp:taxon-name>, <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="tribe" reg="Callichromatini">Callichromatini</tp:taxon-name-part></tp:taxon-name>) and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Psacothea">Psacothea</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="hilaris">hilaris</tp:taxon-name-part></tp:taxon-name></italic> (Pascoe, 1857) (<tp:taxon-name><tp:taxon-name-part taxon-name-part-type="subfamily" reg="Lamiinae">Lamiinae</tp:taxon-name-part></tp:taxon-name>, <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="tribe" reg="Monochamini">Monochamini</tp:taxon-name-part></tp:taxon-name>), for which host plant availability represents a key determinant of distribution (<xref ref-type="bibr" rid="B64">Ruzzier et al. 2024</xref>, <xref ref-type="bibr" rid="B65">2025a</xref>). Moreover, habitat may be even more limiting; indeed, habitat suitability may be slightly overestimated due to the inclusion in the <abbrev xlink:title="habitat suitability model">HSM</abbrev> of host plants whose use by the two <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part></tp:taxon-name></italic> species remains to be verified.</p>
      <p>Considering the results presented in this study, the current presence of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> on the island of Fehmarn (northern Germany) represents a clear phytosanitary concern. This situation is consistent with previous assessments by competent authorities and fully supports the inclusion of the species in the European Union A1 Quarantine Pest list (Annex II A). Our modelling results provide quantitative and spatially explicit evidence that reinforces the high-risk status attributed to this species.</p>
      <p>The invasion-risk map identifies a broad high-risk corridor extending across western and central Europe, including much of Germany and neighbouring countries. Several of these high-risk areas occur in close proximity to the current outbreak on Fehmarn, potentially increasing the likelihood of further spread if containment and eradication measures prove unsuccessful. The continuity of environmentally suitable habitats, together with the high connectivity characterising these regions, suggests that <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> could establish and persist over extensive areas of continental Europe if propagules disperse from the current infestation site, either naturally or through human-mediated movement of infested materials. Propagule pressure is, in fact, a key factor influencing the establishment and spread of non-native species (<xref ref-type="bibr" rid="B14">Brockerhoff et al. 2014</xref>).</p>
      <p>However, the comparatively moderate suitability predicted for the island of Fehmarn suggests that containment and eradication measures could be successfully implemented. The predominance of agricultural land use reduces habitat continuity and the availability of host plants required to sustain populations capable of generating high propagule pressure. Moreover, the local bioclimatic conditions, although not entirely unfavourable, are less suitable than those predicted for the neighbouring continental high-risk areas. Together, these factors indicate that the currently established population likely occurs under ecologically marginal conditions, where both long-term persistence and spatial expansion are constrained. Under such circumstances, the timely implementation of targeted phytosanitary measures could substantially increase the likelihood of complete eradication.</p>
      <p>The elevated invasion risk predicted for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> across western and central Europe, together with the sustained and well-documented propagule pressure resulting from repeated interceptions (<xref ref-type="bibr" rid="B9">Binazzi et al. 2019</xref>), indicates that continued vigilance is necessary to prevent its establishment in Europe. In particular, the predicted high-risk corridor extending from France through Belgium, the Netherlands and Luxembourg to Germany, Switzerland and northern Italy, together with secondary hotspots in eastern England and along the northern Adriatic coast, identifies priority areas where surveillance and early detection efforts should be concentrated. For both species, the comparison between overall suitability and invasion-risk maps demonstrates that environmental suitability alone does not fully explain the geography of invasion risk. Although environmentally suitable conditions occur across extensive portions of Europe, invasion risk is highest in hotspots where favourable environmental conditions coincide with intense transport connectivity. Both species share a broad high-risk core across western and central Europe, but differ in the extent and distribution of secondary hotspots. In particular, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> exhibits more extensive high-risk areas across Great Britain, southern Sweden and the northern Adriatic Region, whereas <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> remains more strongly concentrated within the central European corridor. These differences indicate that species-specific environmental responses continue to shape the spatial pattern of invasion risk despite both species sharing similar introduction pathways.</p>
      <p>We acknowledge that the outputs outlined here represent a potential invasion scenario that does not account for possible limiting factors within European ecosystems that could counteract the invasion process. Amongst these, biotic resistance may play a key role. For instance, the presence of native congeners in invaded areas may limit the establishment and spread of non-native species, as natural enemies associated with native congeners could shift hosts and regulate populations of introduced relatives, preventing rapid spread or high population densities (<xref ref-type="bibr" rid="B15">Cavender-Bares et al. 2009</xref>; <xref ref-type="bibr" rid="B18">Dearborn et al. 2016</xref>). In Europe, nine species and three subspecies of the genus <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part></tp:taxon-name></italic> are currently recorded. If <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> were to become sympatric with native <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part></tp:taxon-name></italic> spp., interactions with the resident community could potentially reduce their invasiveness. Similar dynamics have been observed following the establishment of the European brown spruce longhorned beetle, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Tetropium">Tetropium</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fuscum">fuscum</tp:taxon-name-part></tp:taxon-name></italic> (Fabricius, 1787), in eastern Canada, where native natural enemies contributed to limiting its population growth (<xref ref-type="bibr" rid="B18">Dearborn et al. 2016</xref>; <xref ref-type="bibr" rid="B32">Heustis et al. 2018</xref>).</p>
    </sec>
    <sec sec-type="Conclusions" id="sec19">
      <title>Conclusions</title>
      <p>The risk maps produced in this study indicate a substantial potential for both <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part></tp:taxon-name></italic> species to establish and spread across Europe, although invasion risk is far from uniform. The highest-risk areas are concentrated within a broad western and central European corridor, where favourable environmental conditions, widespread host availability and intense maritime and terrestrial connectivity converge. Additional regional hotspots, particularly in Great Britain, the northern Adriatic Region and, for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>, southern Sweden, further demonstrate that invasion risk extends beyond the main continental core. Conversely, large portions of northern, eastern and southern Europe remain characterised by comparatively low invasion risk, reflecting lower environmental suitability, weaker transport connectivity or a combination of both.</p>
      <p>Taken together, these results demonstrate that, although Europe offers extensive environmentally suitable areas for both <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>, environmental suitability alone does not fully explain the geography of invasion risk. The integration of transport connectivity concentrates the highest-risk areas into a limited number of geographically coherent hotspots, providing a more realistic representation of where introduction and establishment are most likely to occur. This finding underscores the limitations of suitability-based projections alone and highlights the added value of integrated risk frameworks that explicitly incorporate dispersal pathways. Such approaches provide a more robust and operational basis for prioritising surveillance, early detection and preventative phytosanitary measures, ultimately supporting more effective management of invasive wood-boring insects.</p>
    </sec>
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    <ack>
      <title>Acknowledgements</title>
      <p>The authors express their sincere gratitude to the reviewers and the editor for their thorough and expert evaluation, which has substantially enhanced the quality and clarity of the manuscript.</p>
    </ack>
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    <sec sec-type="Additional information" id="sec20">
      <title>Additional information</title>
      <sec sec-type="Conflict of interest" id="sec21">
        <title>Conflict of interest</title>
        <p>The authors have declared that no competing interests exist.</p>
      </sec>
      <sec sec-type="Ethical statement" id="sec22">
        <title>Ethical statement</title>
        <p>No ethical statement was reported.</p>
      </sec>
      <sec sec-type="Artificial Intelligence (AI) use" id="sec23">
        <title>Artificial Intelligence (AI) use</title>
        <p>The authors accept full responsibility for the content of the manuscript, including the disclosure of any use of AI.</p>
        <p>Regarding the use of AI in the preparation of this manuscript, the authors declare the following: GPT, used for assistance with language, style and writing.</p>
      </sec>
      <sec sec-type="Funding" id="sec24">
        <title>Funding</title>
        <p>ER, ADG, LB acknowledge the support of NBFC to University of Roma Tre, Department of Science and to University of Milano-Bicocca, Department of Earth and Environmental Sciences. Funder: Project funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 – Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of the Italian Ministry of University and Research funded by the European Union – NextGenerationEU. Award Number: Project title “National Biodiversity Future Center –NBFC”, Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP: F83C22000730006, CUP: H43C22000530001. DR, JS, GS, DP were supported by the project “<tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name> on European elms and other tree species – SAPTREES”, funded by EFSA (European Food Safety Authority) under the call GP/EFSA/ALPHA/2021/07 – Lot 1.</p>
      </sec>
      <sec sec-type="Author contributions" id="sec25">
        <title>Author contributions</title>
        <p>All authors have contributed equally.</p>
      </sec>
      <sec sec-type="Author ORCIDs" id="sec26">
        <title>Author ORCIDs</title>
        <p>E. Ruzzier <ext-link xlink:href="https://orcid.org/0000-0003-1020-1247" ext-link-type="uri">https://orcid.org/0000-0003-1020-1247</ext-link></p>
        <p>D. Rassati <ext-link xlink:href="https://orcid.org/0000-0001-7778-0349" ext-link-type="uri">https://orcid.org/0000-0001-7778-0349</ext-link></p>
        <p>A. Di Giulio <ext-link xlink:href="https://orcid.org/0000-0003-0508-0751" ext-link-type="uri">https://orcid.org/0000-0003-0508-0751</ext-link></p>
        <p>G. Santoiemma <ext-link xlink:href="https://orcid.org/0000-0002-3226-9253" ext-link-type="uri">https://orcid.org/0000-0002-3226-9253</ext-link></p>
        <p>D.S. Pureswaran <ext-link xlink:href="https://orcid.org/0000-0002-4040-7708" ext-link-type="uri">https://orcid.org/0000-0002-4040-7708</ext-link></p>
        <p>J. Sweeney <ext-link xlink:href="https://orcid.org/0000-0003-3391-2375" ext-link-type="uri">https://orcid.org/0000-0003-3391-2375</ext-link></p>
        <p>L. Bani <ext-link xlink:href="https://orcid.org/0000-0001-5795-9499" ext-link-type="uri">https://orcid.org/0000-0001-5795-9499</ext-link></p>
      </sec>
      <sec sec-type="Data availability" id="sec27">
        <title>Data availability</title>
        <p>All data supporting the findings of this study are available in the main text and the Suppl. materials <xref ref-type="supplementary-material" rid="S1">1</xref>–3. The R scripts, relevant spatial layers, GeoTIFF files and ODMAP (<xref ref-type="bibr" rid="B83">Zurell et al. 2020</xref>) are freely available on Zenodo (<ext-link xlink:href="10.5281/zenodo.21406645" ext-link-type="doi">https://doi.org/10.5281/zenodo.21406645</ext-link>).</p>
      </sec>
    </sec>
    <sec sec-type="supplementary-material">
      <title>Supplementary materials</title>
      <supplementary-material id="S1" position="float" orientation="portrait" xlink:type="simple">
        <object-id content-type="doi">10.3897/neobiota.109.192296.suppl1</object-id>
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        <object-id content-type="arpha">EF99FAE8-5B01-5158-8EDD-D6E2C8FCACA3</object-id>
        <label>Supplementary material 1</label>
        <caption>
          <p>European ports connected to the United States and Canada</p>
        </caption>
        <statement content-type="dataType">
          <label>Data type</label>
          <p>xlsx</p>
        </statement>
        <media xlink:href="neobiota-109-177_article-192296__-s001.xlsx" mimetype="application" mime-subtype="vnd.openxmlformats-officedocument.spreadsheetml.sheet" position="float" orientation="portrait" id="oo_1777680.xlsx">
          <uri content-type="original_file">https://binary.pensoft.net/file/1777680</uri>
        </media>
        <permissions>
          <license>
            <license-p>This dataset is made available under the Open Database License (<ext-link ext-link-type="uri" xlink:href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</ext-link>). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.</license-p>
          </license>
        </permissions>
        <attrib specific-use="authors"> Enrico Ruzzier, Davide Rassati, Andrea Di Giulio, Giacomo Santoiemma, Deepa S. Pureswaran, Jon Sweeney, Luciano Bani</attrib>
      </supplementary-material>
      <supplementary-material id="S2" position="float" orientation="portrait" xlink:type="simple">
        <object-id content-type="doi">10.3897/neobiota.109.192296.suppl2</object-id>
        <object-id content-type="zenodo_dep_id">0</object-id>
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        <label>Supplementary material 2</label>
        <caption>
          <p>Excel file including single model performances and variables importance</p>
        </caption>
        <statement content-type="dataType">
          <label>Data type</label>
          <p>xlsx</p>
        </statement>
        <statement content-type="notes">
          <label>Explanation note</label>
          <p><bold>table S1</bold>. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> – Evaluation <abbrev xlink:title="habitat suitability model">HSM</abbrev>. Table of the two evaluation metrics used for <abbrev xlink:title="habitat suitability model">HSM</abbrev>. <bold>table S2</bold>. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> – Evaluation <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>. Table of the two evaluation metrics used for <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>. <bold>table S3</bold>. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> – Evaluation <abbrev xlink:title="habitat suitability model">HSM</abbrev>. Table of the two evaluation metrics used for <abbrev xlink:title="habitat suitability model">HSM</abbrev>. <bold>table S4</bold>. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> – Evaluation <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>. Table of the two evaluation metrics used for <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>. <bold>table S5</bold>. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> - Importance of the covariates included in the <abbrev xlink:title="habitat suitability model">HSM</abbrev>. <bold>table S6</bold>. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> -Importance of the covariates included in the <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>. <bold>table S7</bold>. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> - Importance of the covariates included in the <abbrev xlink:title="habitat suitability model">HSM</abbrev>. <bold>table S8</bold>. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> -Importance of the covariates included in the <abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>.</p>
        </statement>
        <media xlink:href="neobiota-109-177_article-192296__-s002.xlsx" mimetype="application" mime-subtype="vnd.openxmlformats-officedocument.spreadsheetml.sheet" position="float" orientation="portrait" id="oo_1777681.xlsx">
          <uri content-type="original_file">https://binary.pensoft.net/file/1777681</uri>
        </media>
        <permissions>
          <license>
            <license-p>This dataset is made available under the Open Database License (<ext-link ext-link-type="uri" xlink:href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</ext-link>). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.</license-p>
          </license>
        </permissions>
        <attrib specific-use="authors"> Enrico Ruzzier, Davide Rassati, Andrea Di Giulio, Giacomo Santoiemma, Deepa S. Pureswaran, Jon Sweeney, Luciano Bani</attrib>
      </supplementary-material>
      <supplementary-material id="S3" position="float" orientation="portrait" xlink:type="simple">
        <object-id content-type="doi">10.3897/neobiota.109.192296.suppl3</object-id>
        <object-id content-type="zenodo_dep_id">0</object-id>
        <object-id content-type="arpha">3274CFEE-2BB6-5D18-A091-81A3A6D40F38</object-id>
        <label>Supplementary material 3</label>
        <caption>
          <p>Variable response curves, prediction and projection maps, <abbrev xlink:title="multivariate environmental similarity surface">MESS</abbrev></p>
        </caption>
        <statement content-type="dataType">
          <label>Data type</label>
          <p>pdf</p>
        </statement>
        <statement content-type="notes">
          <label>Explanation note</label>
          <p><bold>fig. S1</bold>. Spatial distribution of the maximum kernel density of host plants (<abbrev xlink:title="host-plant">HP</abbrev>.max) of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> in North America. <bold>fig. S2</bold>. Spatial distribution of the maximum kernel density of host plants (<abbrev xlink:title="host-plant">HP</abbrev>.max) of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> in Europe. <bold>fig. S3</bold>. Spatial distribution of the maximum kernel density of host plants (<abbrev xlink:title="host-plant">HP</abbrev>.max) of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> in North America. <bold>fig. S4</bold>. Spatial distribution of the maximum kernel density of host plants (<abbrev xlink:title="host-plant">HP</abbrev>.max) of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> in Europe. <bold>fig. S5</bold>. Response curves of covariates included in the <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> bioclimatic suitability model (<abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>) obtained from the two evaluation metrics (<abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> in green, <abbrev xlink:title="true skill statistic">TSS</abbrev> in red). <bold>fig. S6</bold>. Response curves of covariates included in the <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> bioclimatic suitability model (<abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>) obtained from the two evaluation metrics (<abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> in green, <abbrev xlink:title="true skill statistic">TSS</abbrev> in red). <bold>fig. S7</bold>. Response curves of covariates included in the <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> habitat suitability model (<abbrev xlink:title="habitat suitability model">HSM</abbrev>) obtained from the two evaluation metrics (<abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> in green, <abbrev xlink:title="true skill statistic">TSS</abbrev> in red). <bold>fig. S8</bold>. Response curves of covariates included in the <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> habitat suitability model (<abbrev xlink:title="habitat suitability model">HSM</abbrev>) obtained from the two evaluation metrics (<abbrev xlink:title="receiver operating characteristic curve">ROC</abbrev> in green, <abbrev xlink:title="true skill statistic">TSS</abbrev> in red). <bold>fig. S9</bold>. Boyce index of the overall suitability map of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> native range. <bold>fig. S10</bold>. Boyce index of the overall suitability map of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> native range. <bold>fig. S11</bold>. Bioclimatic suitability map for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> in its native range obtained projecting the bioclimatic suitability model (<abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>); a) lower limit of the 95% confidence interval; b) mean; c) upper limit of the 95% confidence interval. <bold>fig. S12</bold>. Bioclimatic suitability map for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> in its native range obtained projecting the bioclimatic suitability model (<abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>); a) lower limit of the 95% confidence interval; b) mean; c) upper limit of the 95% confidence interval. <bold>fig. S13</bold>. Habitat suitability map for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> in its native range obtained projecting the habitat suitability model (<abbrev xlink:title="habitat suitability model">HSM</abbrev>); a) lower limit of the 95% confidence interval; b) mean; c) upper limit of the 95% confidence interval. <bold>fig. S14</bold>. Habitat suitability map for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> in its native range obtained projecting the habitat suitability model (<abbrev xlink:title="habitat suitability model">HSM</abbrev>); a) lower limit of the 95% confidence interval; b) mean; c) upper limit of the 95% confidence interval. <bold>fig. S15</bold>. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic>: Multivariate Environmental Similarity Surfaces (<abbrev xlink:title="multivariate environmental similarity surface">MESS</abbrev>) index for Europe. <bold>fig. S16</bold>. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">Saperda</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic>: Multivariate Environmental Similarity Surfaces (<abbrev xlink:title="multivariate environmental similarity surface">MESS</abbrev>) index for Europe. <bold>fig. S17</bold>. Bioclimatic suitability maps for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> across Europe, generated by projecting the bioclimatic suitability model (<abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>): (a) lower 95% confidence limit; (b) mean suitability; (c) upper 95% confidence limit. <bold>fig. S18</bold>. Habitat suitability maps for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="candida">candida</tp:taxon-name-part></tp:taxon-name></italic> across Europe, generated by projecting the habitat suitability model (<abbrev xlink:title="habitat suitability model">HSM</abbrev>): (a) lower 95% confidence limit; (b) mean suitability; (c) upper 95% confidence limit. <bold>fig. S19</bold>. Bioclimatic suitability maps for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> across Europe, generated by projecting the bioclimatic suitability model (<abbrev xlink:title="bioclimatic suitability model">BSM</abbrev>): (a) lower 95% confidence limit; (b) mean suitability; (c) upper 95% confidence limit. <bold>fig. S20</bold>. Habitat suitability maps for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Saperda">S.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="tridentata">tridentata</tp:taxon-name-part></tp:taxon-name></italic> across Europe, generated by projecting the habitat suitability model (<abbrev xlink:title="habitat suitability model">HSM</abbrev>): (a) lower 95% confidence limit; (b) mean suitability; (c) upper 95% confidence limit.</p>
        </statement>
        <media xlink:href="neobiota-109-177_article-192296__-s003.pdf" mimetype="application" mime-subtype="pdf" position="float" orientation="portrait" id="oo_1777682.pdf">
          <uri content-type="original_file">https://binary.pensoft.net/file/1777682</uri>
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        <permissions>
          <license>
            <license-p>This dataset is made available under the Open Database License (<ext-link ext-link-type="uri" xlink:href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</ext-link>). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.</license-p>
          </license>
        </permissions>
        <attrib specific-use="authors"> Enrico Ruzzier, Davide Rassati, Andrea Di Giulio, Giacomo Santoiemma, Deepa S. Pureswaran, Jon Sweeney, Luciano Bani</attrib>
      </supplementary-material>
    </sec>
  </back>
</article>
