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<article xmlns:tp="http://www.plazi.org/taxpub" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" article-type="research-article" xml:lang="en">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">119</journal-id>
      <journal-id journal-id-type="index">urn:lsid:arphahub.com:pub:164696f9-9de4-57df-b939-8dd7e23d8d8f</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">Aquatic Invasions</journal-title>
        <abbrev-journal-title xml:lang="en">AquaInv</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">1798-6540</issn>
      <issn pub-type="epub">1818-5487</issn>
      <publisher>
        <publisher-name>Regional Euro-Asian Biological Invasions Centre</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.3391/ai.2026.21.2.189571</article-id>
      <article-id pub-id-type="publisher-id">189571</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group subj-group-type="biological_taxon">
          <subject>Animalia</subject>
          <subject>Bivalvia</subject>
          <subject>Corbiculidae</subject>
          <subject>Corbiculoidea</subject>
          <subject>Invertebrata</subject>
          <subject>Mollusca</subject>
          <subject>Veneroida</subject>
        </subj-group>
        <subj-group subj-group-type="scientific_subject">
          <subject>Bioinvasions in inland waters</subject>
          <subject>Biological Invasions</subject>
        </subj-group>
        <subj-group subj-group-type="geographical_area">
          <subject>Alabama and Georgia</subject>
          <subject>Americas</subject>
          <subject>North America</subject>
          <subject>North and South Carolina</subject>
          <subject>Southern USA</subject>
          <subject>USA and Canada</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Habitat and landscape variables affecting <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">Corbicula</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence in the upper Savannah River drainage (USA)</article-title>
      </title-group>
      <contrib-group content-type="authors">
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Schumber</surname>
            <given-names>Zachary M.</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0009-0000-2852-2220</uri>
          <xref ref-type="aff" rid="A1">1</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Baker</surname>
            <given-names>Michael A.</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0009-0002-1139-2153</uri>
          <xref ref-type="aff" rid="A1">1</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Irwin</surname>
            <given-names>Brian J.</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0000-0002-0666-2641</uri>
          <xref ref-type="aff" rid="A2">2</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Hamel</surname>
            <given-names>Martin J.</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0000-0001-5013-2907</uri>
          <xref ref-type="aff" rid="A1">1</xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Hazelton</surname>
            <given-names>Peter D.</given-names>
          </name>
          <email xlink:type="simple">phaze@uga.edu</email>
          <uri content-type="orcid">https://orcid.org/0000-0001-7500-3706</uri>
          <xref ref-type="aff" rid="A1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="A1">
        <label>1</label>
        <addr-line content-type="verbatim">Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, USA</addr-line>
        <institution>Warnell School of Forestry and Natural Resources, University of Georgia</institution>
        <addr-line content-type="city">Athens</addr-line>
        <country>United States of America</country>
        <uri content-type="ror">https://ror.org/00te3t702</uri>
      </aff>
      <aff id="A2">
        <label>2</label>
        <addr-line content-type="verbatim">U.S. Geological Survey, Georgia Cooperative Fish and Wildlife Research Unit, Warnell School of Forestry and Natural Resources, University or Georgia, Athens, Georgia, USA</addr-line>
        <institution>U.S. Geological Survey, Georgia Cooperative Fish and Wildlife Research Unit, Warnell School of Forestry and Natural Resources, University or Georgia</institution>
        <addr-line content-type="city">Athens</addr-line>
        <country>United States of America</country>
        <uri content-type="ror">https://ror.org/00te3t702</uri>
      </aff>
      <author-notes>
        <fn fn-type="corresp">
          <p>Corresponding author: Peter D. Hazelton (<ext-link xlink:href="mailto:phaze@uga.edu" ext-link-type="uri">phaze@uga.edu</ext-link>)</p>
        </fn>
        <fn fn-type="edited-by">
          <p>Academic editor: Ian Duggan</p>
        </fn>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>30</day>
        <month>04</month>
        <year>2026</year>
      </pub-date>
      <volume>21</volume>
      <issue>2</issue>
      <fpage>111</fpage>
      <lpage>126</lpage>
      <uri content-type="arpha" xlink:href="http://openbiodiv.net/C7D02E2A-A475-5642-BA90-F943C5AEE4A9">C7D02E2A-A475-5642-BA90-F943C5AEE4A9</uri>
      <history>
        <date date-type="received">
          <day>18</day>
          <month>05</month>
          <year>2025</year>
        </date>
        <date date-type="accepted">
          <day>09</day>
          <month>11</month>
          <year>2025</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>Aquatic invasive species (<abbrev xlink:title="Aquatic invasive species">AIS</abbrev>) are amongst the greatest threats to native aquatic biodiversity. These introduced species often thrive in human-altered environments and spread through human-mediated pathways to invade new watersheds. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">Corbicula</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> is a freshwater bivalve native to southeastern Asia first introduced in North America in Seattle, WA, in 1938 and has spread to nearly every major watershed in the southeastern United States. In the present study, we use an information theoretic framework to compare landscape and stream habitat variables associated with <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence across five <abbrev xlink:title="Hydrologic Unit Code 10">HUC10</abbrev> watersheds in the upper Savannah River basin of South Carolina and Georgia, USA. Predictive models included landscape-level and site-level habitat variables associated with agricultural, developed, and forested landscapes. Models with variables associated with forested and developed landscapes were the top performing models based on <abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev> values. In top performing models <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence was positively correlated with increased stream width, but negatively correlated with substrates dominated by cobble. Lower performing models highlight positive correlations with the presence of upstream reservoirs and increased developed landscape surrounding the site. Identification of habitat and landscape correlates with invasive species presence may lead to more efficient introduction monitoring efforts for conservation managers.</p>
      </abstract>
      <kwd-group>
        <label>Key words:</label>
        <kwd>Freshwater golden clam</kwd>
        <kwd>basket clam</kwd>
        <kwd>land-use change</kwd>
        <kwd>early detection</kwd>
        <kwd>ecosystem vulnerability</kwd>
        <kwd>habitat suitability</kwd>
        <kwd>passive dispersal</kwd>
        <kwd>aquatic hitch-hikers</kwd>
      </kwd-group>
      <funding-group>
        <award-group>
          <funding-source>
            <named-content content-type="funder_name">South Carolina Department of Natural Resources</named-content>
            <named-content content-type="funder_identifier">100010242</named-content>
            <named-content content-type="funder_ror">https://ror.org/043cdzb63</named-content>
            <named-content content-type="funder_doi">http://doi.org/10.13039/100010242</named-content>
          </funding-source>
        </award-group>
        <award-group>
          <funding-source>
            <named-content content-type="funder_name">U.S. Fish and Wildlife Service</named-content>
            <named-content content-type="funder_identifier">100000202</named-content>
            <named-content content-type="funder_ror">https://ror.org/04k7dar27</named-content>
            <named-content content-type="funder_doi">http://doi.org/10.13039/100000202</named-content>
          </funding-source>
        </award-group>
        <award-group>
          <funding-source>
            <named-content content-type="funder_name">University of Georgia</named-content>
            <named-content content-type="funder_identifier">100007699</named-content>
            <named-content content-type="funder_ror">https://ror.org/00te3t702</named-content>
            <named-content content-type="funder_doi">http://doi.org/10.13039/100007699</named-content>
          </funding-source>
        </award-group>
      </funding-group>
    </article-meta>
    <notes>
      <sec sec-type="Citation" id="sec1">
        <title>Citation</title>
        <p>Schumber ZM, Baker MA, Irwin BJ, Hamel MJ, Hazelton PD (2026) Habitat and landscape variables affecting Corbicula fluminea presence in the upper Savannah River drainage (USA). Aquatic Invasions 21(2): 111–126. <ext-link xlink:href="10.3391/ai.2026.21.2.189571" ext-link-type="doi">https://doi.org/10.3391/ai.2026.21.2.189571</ext-link></p>
      </sec>
    </notes>
  </front>
  <body>
    <sec sec-type="Introduction" id="sec2">
      <title>Introduction</title>
      <p>Less than 1% of the Earth is covered by freshwater ecosystems, yet these environments contain nearly 10% of all recognized animal species (<xref ref-type="bibr" rid="B3">Balian et al. 2008</xref>). These systems are critically important to humans for water supply, recreation and tourism, flood control, and food production. The greatest threat to freshwater ecosystems is habitat degradation, specifically the altering of flow patterns, pollution from runoff, and land-use change (<xref ref-type="bibr" rid="B33">Sala et al. 2000</xref>). Habitat degradation may also affect freshwater ecosystems indirectly through the facilitation of species introductions (<xref ref-type="bibr" rid="B39">Strayer 2010</xref>). Consequently, the introduction and proliferation of invasive species may displace native species and can disrupt ecosystem function in the invaded habitat (<xref ref-type="bibr" rid="B39">Strayer 2010</xref>). Human activities, specifically land-use changes from agriculture, urbanization, and the creation of reservoirs have led to an increase in habitat fragmentation and spread of invasive species, further endangering freshwater ecosystems (<xref ref-type="bibr" rid="B48">Vörösmarty et al. 2010</xref>).</p>
      <p>The Basket Clam (<italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">Corbicula</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> Müller, 1774) is a freshwater bivalve native to southeast Asia and introduced to North America, South America, Europe, and some countries of North Africa (<xref ref-type="bibr" rid="B6">Clavero et al. 2012</xref>; <xref ref-type="bibr" rid="B8">Crespo et al. 2015</xref>). In North America, the species first appeared in Seattle, WA in 1938, and quickly expanded across the country. It can now be found in 48 of the 50 United States of America <xref ref-type="bibr" rid="B46">(<named-content content-type="dwc:institutional_code" xlink:title="USGS Patuxent Wildlife Research Center" xlink:href="https://scientific-collections.gbif.org/institution/4be1a1c8-f981-472e-b2f5-e18af997f9ce">USGS</named-content> 2025</xref>). Global dispersal is likely facilitated by the building of reservoirs as well as activities related to trade, including global shipping and the construction of shipping canals (<xref ref-type="bibr" rid="B19">Karatayev et al. 2007</xref>), the pet trade and use as a food resource ( <xref ref-type="bibr" rid="B9">Ferreira-Rodríguez et al. 2019</xref>). Once established in a new region <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> may spread to new waterbodies through attachment on fishing gear and boat hulls, bait-bucket transfers, as well as passive dispersal (<xref ref-type="bibr" rid="B9">Ferreira-Rodríguez et al. 2019</xref>).</p>
      <p>Aquatic invasive species (<abbrev xlink:title="Aquatic invasive species">AIS</abbrev>) invade new habitats through vectors such as shipping ballast water (<xref ref-type="bibr" rid="B13">Holeck et al. 2004</xref>), aquarium releases, water gardens, deliberate stocking, bait buckets, and horticulture (<xref ref-type="bibr" rid="B20">Keller and Lodge 2007</xref>). Certain life history traits improve the likelihood that a species will be a successful <abbrev xlink:title="Aquatic invasive species">AIS</abbrev>, including the following: high reproductive capacity and rates, smaller body size, broad physical habitat tolerances, early maturation, and asexual reproduction or self-fertilization (<xref ref-type="bibr" rid="B22">Kolar and Lodge 2001</xref>). <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> possess many of the same life history traits as other <abbrev xlink:title="Aquatic invasive species">AIS</abbrev>. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> can reproduce through self-fertilization (<xref ref-type="bibr" rid="B38">Strayer 1999</xref>) and can produce 35,000 offspring per breeding season (<xref ref-type="bibr" rid="B25">McMahon 2002</xref>), allowing them to establish new populations from only one individual. Outside of human intervention, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> can disperse passively with water current through the release of juveniles, surviving the gut biome of fish, and by attaching to the legs of waterfowl and shorebirds (<xref ref-type="bibr" rid="B24">McMahon 1982</xref>). Once released from an adult, larval <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> have a short time window (100 hours) where they persist in the water column by “swimming” with an organ called the velum (<xref ref-type="bibr" rid="B23">Mackie and Claudi 2010</xref>). This stage allows juveniles to disperse greater distances. During this time juveniles can also attach to human or wildlife vectors, which is most likely the method for short-distance upstream dispersal (<xref ref-type="bibr" rid="B28">Pernecker et al. 2021</xref>). Further, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> have a high filtration rate, can feed on a variety of algae, and can efficiently incorporate nutrients into somatic and reproductive growth (<xref ref-type="bibr" rid="B25">McMahon 2002</xref>). These dispersal and foraging characteristics of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> likely improve their success when invading new freshwater ecosystems (<xref ref-type="bibr" rid="B29">Pigneur et al. 2012</xref>).</p>
      <p>Early detection and prevention of invasive species are more cost-effective solutions than removal or sustained management (<xref ref-type="bibr" rid="B7">Coughlan et al. 2020</xref>). Surveillance of aquatic environments for invasive species often lags behind the establishment of new populations (<xref ref-type="bibr" rid="B4">Beric and MacIsaac 2015</xref>), but could be improved through a better understanding of habitat suitability of the invader. Across their range, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> are considered habitat generalists, occurring in a wide range of lentic and lotic conditions (<xref ref-type="bibr" rid="B18">Karatayev et al. 2005</xref>), and across various substrate classes (<xref ref-type="bibr" rid="B17">Karatayev et al. 2003</xref>; <xref ref-type="bibr" rid="B35">Schmidlin and Baur 2007</xref>; <xref ref-type="bibr" rid="B21">Kelley et al. 2022</xref>). In the southeastern United States, greater <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> densities have been linked to agricultural land use, including the amount of agriculture in the watershed, increased water temperature, and increased nitrogen pollution (<xref ref-type="bibr" rid="B10">Ferreira-Rodríguez et al. 2022</xref>).</p>
      <p>Our objective in this study is to understand the habitat and landscape variables that affect <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> dispersal and presence in the upper Savannah River watershed in Georgia and South Carolina. Based on previous literature, we expect <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence to be positively associated with the amount of agricultural land cover (<xref ref-type="bibr" rid="B10">Ferreira-Rodríguez et al. 2022</xref>) and the presence of reservoirs upstream. <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> have been associated with reservoirs receiving recreational pressure and seen in higher abundances downstream of dams; therefore, we expect to see the same association in our study (<xref ref-type="bibr" rid="B18">Karatayev et al. 2005</xref>; <xref ref-type="bibr" rid="B31">Robb-Chavez et al. 2022</xref>). Understanding the habitat and landscape variables that affect <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> dispersal may allow more effective monitoring and prioritization of habitats for preventative measures.</p>
    </sec>
    <sec sec-type="methods" id="sec3">
      <title>Methods</title>
      <sec sec-type="Study area" id="sec4">
        <title>Study area</title>
        <p>The upper Savannah River basin begins in the Blueridge region of North Carolina, South Carolina, and Georgia. The Savannah River forms at the convergence of the Seneca and Tugaloo rivers. This portion of the Savannah River drainage is home to five of the largest reservoirs in South Carolina, including Lake Hartwell (227 km<sup>2</sup>) and Lake Keowee (75 km<sup>2</sup>) (<xref ref-type="bibr" rid="B49">Wachob et al. 2009</xref>). The study area spanned five Hydrologic Unit Code 10 (<abbrev xlink:title="Hydrologic Unit Code 10">HUC10</abbrev>) watersheds in the upper Savannah River drainage: the Chattooga, Chauga, Coneross, Lower Tugaloo, and Little River watersheds (Fig. <xref ref-type="fig" rid="F1">1</xref>). Hydrologic Unit Codes serve as a numerical classification system to delineate hydrologic features in the Unites States ranging from regions (2-digit) to subwatersheds (12-digit). The north-western portion of the study area is dominated by forested land cover of the Chattahoochee-Oconee National Forest in Georgia, and the Francis Marion and Sumter National Forests in South Carolina. In the south-eastern portion of the study area near the Hartwell and Keowee reservoirs, higher densities of agricultural land cover and development are present (Fig. <xref ref-type="fig" rid="F1">1</xref>).</p>
        <fig id="F1">
          <object-id content-type="doi">10.3391/ai.2026.21.2.189571.figure1</object-id>
          <object-id content-type="arpha">46D7651B-9F74-5C19-B8B7-7D6A0CB7DCC7</object-id>
          <label>Figure 1.</label>
          <caption>
            <p>Study area detailing the boundaries of the five Hydrologic Unit Code 10 (<abbrev xlink:title="Hydrologic Unit Code 10">HUC10</abbrev>) watersheds and land use classification. The northern region of the study area is dominated by forest, while the southern regions contain more of an agricultural and developed landscape. Inset map shows location of study area within southeastern United States.</p>
          </caption>
          <graphic xlink:href="aquaticinvasions-21-111_article-189571__-g001.jpg" id="oo_1614508.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1614508</uri>
          </graphic>
        </fig>
      </sec>
      <sec sec-type="Site surveys" id="sec5">
        <title>Site surveys</title>
        <p>We selected study sites and conducted biotic and habitat surveys using the methods of the Brook Floater Rapid Assessment Protocol (<xref ref-type="bibr" rid="B37">Sterrett et al. 2018</xref>), originally designed for site selection and survey of native freshwater mussels (<tp:taxon-name><tp:taxon-name-part taxon-name-part-type="class" reg="Bivalvia">Bivalvia</tp:taxon-name-part></tp:taxon-name>: <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order" reg="Unionida">Unionida</tp:taxon-name-part></tp:taxon-name>). Ten sites were sampled in each of five <abbrev xlink:title="Hydrologic Unit Code 10">HUC10</abbrev> watersheds. Briefly, 50 sites were randomly selected from a pool of all possible bridge crossings within a <abbrev xlink:title="Hydrologic Unit Code 10">HUC10</abbrev> watershed using the primary roads layer in the U.S. Census Bureau’s Transportation dataset (<xref ref-type="bibr" rid="B42">USCB 2023</xref>) and the United States Geological Survey National Hydrography Dataset (<xref ref-type="bibr" rid="B45"><named-content content-type="dwc:institutional_code" xlink:title="USGS Patuxent Wildlife Research Center" xlink:href="https://scientific-collections.gbif.org/institution/4be1a1c8-f981-472e-b2f5-e18af997f9ce">USGS</named-content> 2023</xref>). Within each of the 5 watersheds, 40 bridge crossings were randomly chosen, and randomly split into 20 priority sites and 20 replacement sites. Our goal was to sample 10 priority sites; if there were not enough suitable priority sites to reach the goal of 10, then replacement sites were randomly chosen to sample until 10 sampled sites was achieved. We considered sites suitable for sampling if they met criteria of the rapid assessment protocol (<xref ref-type="bibr" rid="B37">Sterrett et al. 2018</xref>), including &lt; 1 m in mean depth and ≥ 3 m in mean width, legally and safely accessible from the bridge, and safe to traverse. Sites that did not meet suitability criteria were replaced with a randomly chosen replacement site that did meet the survey criteria. Upon arrival at a site, 100 meters were measured in the upstream direction from the road crossing, then another randomly chosen distance (using a random number generator) between 0–100 meters was measured to mark the start of the survey site. This protocol was designed to avoid scour pools or other habitat bias associated with bridge crossings (<xref ref-type="bibr" rid="B37">Sterrett et al. 2018</xref>).</p>
        <p>Visual and tactile surveys were conducted along longitudinal transects (i.e., lanes) running the length of the stream reach. The number of lanes was equal to the number of observers (n ≥ 3). The width of each lane was equal to the stream width divided by the number of observers, where lane width was a minimum of 1 meter and a maximum of 3 meters. Surveys were standardized to a total of 2-person hours, and surveyors were trained to maintain a survey search rate of approximately 10 m<sup>2</sup> per minute. Surveyors initiated the search at the downstream origin of the transect and moved in the upstream direction searching the benthos of their lane until the 2-person hours limit was reached, at which point the surveyors would be at the end (upstream) of the transect. Each observer used snorkeling or view buckets to scan the stream substrate within their lane and documented the presence of either shell or live <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic>. Due to the density of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> at sites where they were present, it was not practical to enumerate shells/individuals, but rather to only record presence/absence. Evidence supports snorkeling as a more effective method for detecting freshwater mussels (<xref ref-type="bibr" rid="B26">Nuri et al. 2022</xref>), but many sites in our study area were small and shallow, making clear-bottom view buckets the only feasible method. For that reason, observers used snorkeling whenever stream conditions allowed.</p>
        <p>Habitat variables at each site were assessed as means of lane-specific habitat characteristics, or data collected at the reach level. Stream depth variation at a site was a measure of the coefficient of variation between all measurements of depth at a site. Depth measurements were taken from five locations in each lane (start, 25%, 50%, 75%, end). Similarly, the dominant substrate was measured at the same five locations along each lane. Surveyors classified the substrate into a substrate size class (ranging from fine silt [≤ 0.06 mm] to bedrock [&gt; 4 m]) as characterized by the National Rivers and Streams Assessment 2013–2014 (<xref ref-type="bibr" rid="B43"><named-content content-type="dwc:institutional_code" xlink:title="U.S. Environmental Protection Agency" xlink:href="https://scientific-collections.gbif.org/institution/3214aec2-6c93-4ca4-b233-099a1dbff3a2">USEPA</named-content> 2013</xref>). Large woody debris was quantified as the number of wood pieces &gt;10 cm in diameter and &gt; 1.5m long within each lane. If a lane contained large wood in the form of snags, log jams, or root wads, they were counted as one large wood. Large wood was measured by a surveyor once per lane and averaged for the site. Canopy cover was estimated using a modified spherical densiometer counting the number of 17 intersections covered by canopy vegetation. This measurement was taken at the middle of the reach along both banks and was the average of readings in upstream, downstream, river-right, and river-left directions taken at each bank. Mesohabitat composition was estimated as the approximate percentage of riffle, run, or pool within the study reach. Lane level habitat values were averaged across lanes, except for substrate classes, where we calculated the frequency of occurrence of each substrate type as the dominant substrate.</p>
      </sec>
      <sec sec-type="Spatial data" id="sec6">
        <title>Spatial data</title>
        <p>We classified land cover data for each site at a spatial resolution of 10 m using two metrics, the landcover within a 3 km buffer of the site and the landcover within the delineated catchment of a site. Analysis was conducted using the U.S. Geological Survey (<named-content content-type="dwc:institutional_code" xlink:title="USGS Patuxent Wildlife Research Center" xlink:href="https://scientific-collections.gbif.org/institution/4be1a1c8-f981-472e-b2f5-e18af997f9ce">USGS</named-content>) National Land Cover Database (<xref ref-type="bibr" rid="B44"><named-content content-type="dwc:institutional_code" xlink:title="USGS Patuxent Wildlife Research Center" xlink:href="https://scientific-collections.gbif.org/institution/4be1a1c8-f981-472e-b2f5-e18af997f9ce">USGS</named-content> 2021</xref>). We calculated the percentage of land use of four land cover classes: water (combining open water, woody wetlands, and emergent herbaceous wetlands), agriculture (combining pasture/hay, cultivated crops, and grassland/herbaceous), forest (combining deciduous forest, evergreen forest, and mixed forest) and developed landscape (combining developed open space, developed low intensity, developed medium intensity, developed high intensity, and barren land). We then calculated the percentage of each land cover class within a site’s catchment and the land cover percentage in a 3 km buffer surrounding each site.</p>
        <p>We used the <named-content content-type="dwc:institutional_code" xlink:title="USGS Patuxent Wildlife Research Center" xlink:href="https://scientific-collections.gbif.org/institution/4be1a1c8-f981-472e-b2f5-e18af997f9ce">USGS</named-content> National Hydrography Dataset (<abbrev xlink:title="National Hydrography Dataset">NHD</abbrev>; <xref ref-type="bibr" rid="B45"><named-content content-type="dwc:institutional_code" xlink:title="USGS Patuxent Wildlife Research Center" xlink:href="https://scientific-collections.gbif.org/institution/4be1a1c8-f981-472e-b2f5-e18af997f9ce">USGS</named-content> 2023</xref>) to measure each site’s relationship to reservoirs within the <abbrev xlink:title="Hydrologic Unit Code 10">HUC10</abbrev> watershed using two metrics: the distance to the nearest reservoir (river km) and a binary indicator denoting whether there were any reservoirs present upstream. To be considered in the analysis, reservoirs must have been larger than 0.05 km<sup>2</sup>. <xref ref-type="bibr" rid="B18">Karatayev et al. (2005)</xref> evaluated reservoirs across Texas and found that <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> were uncommon in reservoirs smaller than 0.10 km<sup>2</sup>. However, the majority of reservoirs within our study system were smaller than this. Because of the smaller average size of the reservoirs in our study area, a lower threshold value was chosen to exclude the smallest reservoirs in our study area. All spatial analyses were conducted in ArcGIS Pro (Version 3.0.3, ESRI, Redlands, California). In total, we had 20 descriptor variables, that included the watershed where the site was located, seven landscape-level variables from GIS layers, 10 site-level variables measured directly in the sampling sites during May-September of 2023, and two that were based on a site’s position in the watershed relative to reservoirs (Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>: table SS1).</p>
      </sec>
      <sec sec-type="Statistical analysis" id="sec7">
        <title>Statistical analysis</title>
        <p>We used binomial logistic regression models to evaluate the effects that site specific habitat and landscape variables have on the presence of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic>. We developed multiple competing models for each landscape category (i.e., agricultural, forested, developed), each containing variables shown to be affected by these land use patterns (<xref ref-type="bibr" rid="B14">Jacobson et al. 2001</xref>) (Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>: table SS2). For example, % agricultural and % developed landscapes in a watershed increases fine sediment in streams. Therefore, we included the proportion of sand or fine sediments as a predictor variable in models explaining <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence during agricultural or developed land use dominant cases, but not forested land use. From this, a set of 10 candidate models was constructed using <italic>a priori</italic> hypotheses associated with agricultural, forested, or developed landscapes (Table <xref ref-type="table" rid="T1">1</xref>). A Pearson correlation coefficient of r = 0.6 was used as a threshold to limit the inclusion of two correlated variables in any single model; when two variables were highly correlated, the more biological meaningful variable was retained (Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>: fig. S1). In the case of categorical variables, the generalized variance inflation factor (<abbrev xlink:title="generalized variance inflation factor">GVIF</abbrev>) was calculated when considering which variables to include. Stream width is not characteristic of any dominant landscape type (Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>: fig S1); therefore, we included the variable into subsets of models from each category. All models were ranked using Akaike’s Information Criterion (<xref ref-type="bibr" rid="B5">Burnham and Anderson 2004</xref>) values corrected for small sample size (<abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev>) to determine which model and characteristics most influenced <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence at a site. We chose to only interpret models with a cumulative AIC weight of 95%. A Hosmer-Lemeshow Goodness-of-fit test was used to assess the fit of the interpreted models. An alpha level of 0.05 was used as the level of significance in all statistical tests, which were performed in the R statistical programming language (<xref ref-type="bibr" rid="B30">R Core Team 2024</xref>).</p>
        <table-wrap id="T1" position="float" orientation="portrait">
          <label>Table 1.</label>
          <caption>
            <p><italic>A priori</italic> models used to predict <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> site presence by combining habitat and landscape variables.<bold/></p>
          </caption>
          <table>
            <tbody>
              <tr>
                <th rowspan="1" colspan="1">
                  <bold>Model</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>Model predictors</bold>
                </th>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Agriculture 1</td>
                <td rowspan="1" colspan="1">% agriculture catchment + sand + variation in depth + run</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Agriculture 2</td>
                <td rowspan="1" colspan="1">% agriculture surrounding + sand + fine gravel + canopy cover + stream width</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Agriculture 3</td>
                <td rowspan="1" colspan="1">sand + fine gravel + stream width</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Forest 1</td>
                <td rowspan="1" colspan="1">% forest catchment + cobble + riffle + pool</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Forest 2</td>
                <td rowspan="1" colspan="1">% forest surrounding + riffle + canopy cover + LWD + stream width</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Forest 3</td>
                <td rowspan="1" colspan="1">cobble + riffle + stream width</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Develop 1</td>
                <td rowspan="1" colspan="1">% developed surrounding + upstream reservoir presence + sand + stream width</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Develop 2</td>
                <td rowspan="1" colspan="1">% developed catchment + upstream reservoir presence + reservoir distance</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Develop 3</td>
                <td rowspan="1" colspan="1">upstream reservoir presence + sand + stream width</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Develop 4</td>
                <td rowspan="1" colspan="1">canopy cover + sand + variation in depth</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
      </sec>
    </sec>
    <sec sec-type="Results" id="sec8">
      <title>Results</title>
      <sec sec-type="Watershed characteristics" id="sec9">
        <title>Watershed characteristics</title>
        <p>We found <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> at 20 of the 50 sites sampled (Table <xref ref-type="table" rid="T2">2</xref>). The Chattooga watershed was characterized by nearly 90% forest cover and the lowest agricultural (2.6%) and developed (6.9%) land cover. This watershed also had the fewest reservoirs, with only one site being associated with an upstream reservoir, and was the only watershed where <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> was absent. In contrast, the Coneross watershed had the highest proportion of developed land cover (22%) due to the towns of Seneca, Westminster, and Walhalla. It also contained the greatest number of sites with reservoirs present upstream (eight) and the most number of sites with <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> present (eight). A summary of land cover, reservoir presence, and prevalence of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> for each watershed can be found in Table <xref ref-type="table" rid="T2">2</xref>.</p>
        <table-wrap id="T2" position="float" orientation="portrait">
          <label>Table 2.</label>
          <caption>
            <p>Attributes of 5 <abbrev xlink:title="Hydrologic Unit Code 10">HUC10</abbrev> watersheds surveyed for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic>. Ten survey sites were selected within each <abbrev xlink:title="Hydrologic Unit Code 10">HUC10</abbrev> watershed.<bold/></p>
          </caption>
          <table>
            <tbody>
              <tr>
                <th rowspan="1" colspan="1">
                  <bold>Watershed</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold># of sites w/ <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> present</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>% Developed</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>% Forest</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>% Agriculture</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold># Sites w/ Reservoir Upstream</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold># Reservoirs ≥ 0.05km<sup>2</sup></bold>
                </th>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Chauga</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">7.5</td>
                <td rowspan="1" colspan="1">86.7</td>
                <td rowspan="1" colspan="1">5.1</td>
                <td rowspan="1" colspan="1">6</td>
                <td rowspan="1" colspan="1">6</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Little</td>
                <td rowspan="1" colspan="1">5</td>
                <td rowspan="1" colspan="1">15.5</td>
                <td rowspan="1" colspan="1">67.2</td>
                <td rowspan="1" colspan="1">8.3</td>
                <td rowspan="1" colspan="1">3</td>
                <td rowspan="1" colspan="1">6</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Coneross</td>
                <td rowspan="1" colspan="1">8</td>
                <td rowspan="1" colspan="1">21.9</td>
                <td rowspan="1" colspan="1">53.0</td>
                <td rowspan="1" colspan="1">21.9</td>
                <td rowspan="1" colspan="1">8</td>
                <td rowspan="1" colspan="1">5</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Chattooga</td>
                <td rowspan="1" colspan="1">0</td>
                <td rowspan="1" colspan="1">6.9</td>
                <td rowspan="1" colspan="1">89.8</td>
                <td rowspan="1" colspan="1">2.6</td>
                <td rowspan="1" colspan="1">1</td>
                <td rowspan="1" colspan="1">2</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Lower Tugaloo</td>
                <td rowspan="1" colspan="1">3</td>
                <td rowspan="1" colspan="1">13.1</td>
                <td rowspan="1" colspan="1">47.6</td>
                <td rowspan="1" colspan="1">29.4</td>
                <td rowspan="1" colspan="1">3</td>
                <td rowspan="1" colspan="1">5</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
      </sec>
      <sec sec-type="Model selection and parameter estimation" id="sec10">
        <title>Model selection and parameter estimation</title>
        <p>Models associated with forest and developed environments ranked as the top two models with a cumulative <abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev> weight of 0.949 (Table <xref ref-type="table" rid="T3">3</xref>), with the top model (Forest 3) having an <abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev> weight of 0.906. Stream width was a positive predictor of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> site presence in every model in which it appeared (Table <xref ref-type="table" rid="T4">4</xref>, Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>: table SS3, Fig. <xref ref-type="fig" rid="F2">2</xref>). In almost all cases, the models that contained stream width within a landscape hypothesis type outperformed other models that did not contain the predictor. The inclusion of watershed as a predictor resulted in high <abbrev xlink:title="generalized variance inflation factor">GVIF</abbrev> scores (&gt; 5), indicating a high degree of collinearity with other model predictors and was therefore not included in our final analysis.</p>
        <fig id="F2">
          <object-id content-type="doi">10.3391/ai.2026.21.2.189571.figure2</object-id>
          <object-id content-type="arpha">0509E44C-EE66-5FC2-87AA-4BC462F4B2AE</object-id>
          <label>Figure 2.</label>
          <caption>
            <p>Generalized linear model (GLM) predictions of the probability of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence across various habitat and landscape variables. Each subplot represents the relationship between <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence (binary response) and a specific predictor variable from the best fitting model containing that predictor: (a) cobble substrate composition, (b) developed land within a 3 km radius (%), (c) stream width (meters). The y-axis displays the predicted probability of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence, with 95% confidence intervals indicated by the shaded bands. Variables were selected based on their ecological relevance to <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> habitat preferences.</p>
          </caption>
          <graphic xlink:href="aquaticinvasions-21-111_article-189571__-g002.jpg" id="oo_1614509.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1614509</uri>
          </graphic>
        </fig>
        <table-wrap id="T3" position="float" orientation="portrait">
          <label>Table 3.</label>
          <caption>
            <p>Results from logistic regression models to predict <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence at a site. Models were created using a specific set of habitat and landscape variables (see Table <xref ref-type="table" rid="T1">1</xref>) that represented characteristics of a specific habitat type. <abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev> = Akaike information criterion corrected for small sample size, K = the number of parameters in a model, LogLik = Log likelihood for a given model, Δ<abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev>, <abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev> weight (AICcWT), and cumulative weight (Cum.Wt) are also shown.</p>
          </caption>
          <table>
            <tbody>
              <tr>
                <th rowspan="1" colspan="1">
                  <bold>Model</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>K</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>LogLik</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>
                    <abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev>
                  </bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>Δ<abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev></bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>AICcWT</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>Cum.Wt</bold>
                </th>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Forest 3</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">-18.657</td>
                <td rowspan="1" colspan="1">46.203</td>
                <td rowspan="1" colspan="1">0.000</td>
                <td rowspan="1" colspan="1">0.906</td>
                <td rowspan="1" colspan="1">0.906</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Develop 1</td>
                <td rowspan="1" colspan="1">5</td>
                <td rowspan="1" colspan="1">-20.463</td>
                <td rowspan="1" colspan="1">52.289</td>
                <td rowspan="1" colspan="1">6.086</td>
                <td rowspan="1" colspan="1">0.043</td>
                <td rowspan="1" colspan="1">0.949</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Forest 2</td>
                <td rowspan="1" colspan="1">6</td>
                <td rowspan="1" colspan="1">-19.590</td>
                <td rowspan="1" colspan="1">53.132</td>
                <td rowspan="1" colspan="1">6.929</td>
                <td rowspan="1" colspan="1">0.028</td>
                <td rowspan="1" colspan="1">0.977</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Develop 3</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">-23.309</td>
                <td rowspan="1" colspan="1">55.506</td>
                <td rowspan="1" colspan="1">9.303</td>
                <td rowspan="1" colspan="1">0.009</td>
                <td rowspan="1" colspan="1">0.986</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Develop 2</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">-23.631</td>
                <td rowspan="1" colspan="1">56.152</td>
                <td rowspan="1" colspan="1">9.949</td>
                <td rowspan="1" colspan="1">0.006</td>
                <td rowspan="1" colspan="1">0.992</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Agriculture 3</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">-24.021</td>
                <td rowspan="1" colspan="1">56.930</td>
                <td rowspan="1" colspan="1">10.727</td>
                <td rowspan="1" colspan="1">0.004</td>
                <td rowspan="1" colspan="1">0.996</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Agriculture 2</td>
                <td rowspan="1" colspan="1">6</td>
                <td rowspan="1" colspan="1">-21.809</td>
                <td rowspan="1" colspan="1">57.572</td>
                <td rowspan="1" colspan="1">11.369</td>
                <td rowspan="1" colspan="1">0.003</td>
                <td rowspan="1" colspan="1">0.999</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Forest 1</td>
                <td rowspan="1" colspan="1">5</td>
                <td rowspan="1" colspan="1">-25.437</td>
                <td rowspan="1" colspan="1">62.238</td>
                <td rowspan="1" colspan="1">16.035</td>
                <td rowspan="1" colspan="1">&lt; 0.001</td>
                <td rowspan="1" colspan="1">0.999</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Develop 4</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">-30.009</td>
                <td rowspan="1" colspan="1">68.906</td>
                <td rowspan="1" colspan="1">22.703</td>
                <td rowspan="1" colspan="1">&lt; 0.001</td>
                <td rowspan="1" colspan="1">0.999</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Agriculture 1</td>
                <td rowspan="1" colspan="1">5</td>
                <td rowspan="1" colspan="1">-29.344</td>
                <td rowspan="1" colspan="1">70.053</td>
                <td rowspan="1" colspan="1">23.850</td>
                <td rowspan="1" colspan="1">&lt; 0.001</td>
                <td rowspan="1" colspan="1">1.000</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <table-wrap id="T4" position="float" orientation="portrait">
          <label>Table 4.</label>
          <caption>
            <p>Best fitting models, model statistics, and parameter estimates for logistic regression models of habitat and landscape variables affecting <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence. Parameter estimates provided on the scale of the model using the logit link. <abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev> = Akaike information criterion corrected for small sample size, LogLik = Log likelihood for a given model. K = the number of parameters in a model. <abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev> weight (AICcWT) is also shown.<bold/></p>
          </caption>
          <table>
            <tbody>
              <tr>
                <th rowspan="1" colspan="1">
                  <bold>Model</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>
                    <abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev>
                  </bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>LogLik</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>AICcWT</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>K</bold>
                </th>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Forest 3</td>
                <td rowspan="1" colspan="1">46.203</td>
                <td rowspan="1" colspan="1">-18.657</td>
                <td rowspan="1" colspan="1">0.906</td>
                <td rowspan="1" colspan="1">4</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Parameter</td>
                <td rowspan="1" colspan="1">Estimate</td>
                <td rowspan="1" colspan="1">Standard error</td>
                <td rowspan="1" colspan="1">z value</td>
                <td rowspan="1" colspan="1">p value</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Intercept</td>
                <td rowspan="1" colspan="1">-1.815</td>
                <td rowspan="1" colspan="1">1.184</td>
                <td rowspan="1" colspan="1">-1.534</td>
                <td rowspan="1" colspan="1">0.125</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Cobble</td>
                <td rowspan="1" colspan="1">-0.181</td>
                <td rowspan="1" colspan="1">0.070</td>
                <td rowspan="1" colspan="1">-2.589</td>
                <td rowspan="1" colspan="1">0.009</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Riffle</td>
                <td rowspan="1" colspan="1">-0.056</td>
                <td rowspan="1" colspan="1">0.031</td>
                <td rowspan="1" colspan="1">-1.818</td>
                <td rowspan="1" colspan="1">0.069</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Stream width</td>
                <td rowspan="1" colspan="1">0.512</td>
                <td rowspan="1" colspan="1">0.197</td>
                <td rowspan="1" colspan="1">2.603</td>
                <td rowspan="1" colspan="1">0.009</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Develop 1</td>
                <td rowspan="1" colspan="1">52.289</td>
                <td rowspan="1" colspan="1">-20.463</td>
                <td rowspan="1" colspan="1">0.043</td>
                <td rowspan="1" colspan="1">5</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Parameter</td>
                <td rowspan="1" colspan="1">Estimate</td>
                <td rowspan="1" colspan="1">Standard error</td>
                <td rowspan="1" colspan="1">z value</td>
                <td rowspan="1" colspan="1">p value</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Intercept</td>
                <td rowspan="1" colspan="1">-5.155</td>
                <td rowspan="1" colspan="1">1.551</td>
                <td rowspan="1" colspan="1">-3.324</td>
                <td rowspan="1" colspan="1">&lt;0.001</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Upstream reservoir presence</td>
                <td rowspan="1" colspan="1">1.677</td>
                <td rowspan="1" colspan="1">0.837</td>
                <td rowspan="1" colspan="1">2.002</td>
                <td rowspan="1" colspan="1">0.045</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">% developed surrounding</td>
                <td rowspan="1" colspan="1">0.107</td>
                <td rowspan="1" colspan="1">0.050</td>
                <td rowspan="1" colspan="1">2.143</td>
                <td rowspan="1" colspan="1">0.032</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Sand</td>
                <td rowspan="1" colspan="1">0.022</td>
                <td rowspan="1" colspan="1">0.015</td>
                <td rowspan="1" colspan="1">1.397</td>
                <td rowspan="1" colspan="1">0.112</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Stream width</td>
                <td rowspan="1" colspan="1">0.200</td>
                <td rowspan="1" colspan="1">0.106</td>
                <td rowspan="1" colspan="1">1.891</td>
                <td rowspan="1" colspan="1">0.058</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>Habitat variables related to substrate composition and mesohabitat descriptions appeared as positive and negative predictors of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> site presence. The amount of site composed of cobble was a significant negative predictor of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> site presence in the top model (Table <xref ref-type="table" rid="T4">4</xref>, Fig. <xref ref-type="fig" rid="F2">2</xref>), while sand, fine gravel, and riffle were significantly affecting <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence in lower ranked models (Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>: table SS3).</p>
        <p>The presence of a reservoir upstream of a site was a significant variable in one of the top models (Develop 1, Table <xref ref-type="table" rid="T4">4</xref>) while also appearing significant in lower ranked models (Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>: table SS3) and represented an approximate 3-fold increase in probability of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence at a site (Fig. <xref ref-type="fig" rid="F3">3</xref>). The density of landscape characteristics was a significant predictor of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence when measured in 3km buffers around sites, but never when measured within a site’s catchment in our models. Land cover attributes related to agricultural density did not appear significant in any of our models.</p>
        <fig id="F3">
          <object-id content-type="doi">10.3391/ai.2026.21.2.189571.figure3</object-id>
          <object-id content-type="arpha">0B3204FE-4214-584D-84B9-0767B23AD233</object-id>
          <label>Figure 3.</label>
          <caption>
            <p>Probability of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence and the relationship with upstream reservoir presence. The points represent the observed site occurrences in a corresponding upstream reservoir scenario. The lines with black points indicate the predicted probabilities from a logistic regression model (±95% confidence intervals).</p>
          </caption>
          <graphic xlink:href="aquaticinvasions-21-111_article-189571__-g003.jpg" id="oo_1614510.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1614510</uri>
          </graphic>
        </fig>
      </sec>
    </sec>
    <sec sec-type="Discussion" id="sec11">
      <title>Discussion</title>
      <p><italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">Corbicula</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> are an <abbrev xlink:title="Aquatic invasive species">AIS</abbrev> currently found throughout most major watersheds in the United States (<xref ref-type="bibr" rid="B19">Karatayev et al. 2007</xref>, <xref ref-type="bibr" rid="B46"><named-content content-type="dwc:institutional_code" xlink:title="USGS Patuxent Wildlife Research Center" xlink:href="https://scientific-collections.gbif.org/institution/4be1a1c8-f981-472e-b2f5-e18af997f9ce">USGS</named-content> 2025</xref>), and whose effects on native biota are not fully understood (<xref ref-type="bibr" rid="B11">Haag et al. 2021</xref>). Often considered a habitat generalist (<xref ref-type="bibr" rid="B21">Kelley et al. 2022</xref>), <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> may be associated with varying habitat correlates depending on the system they are invading. Nevertheless, understanding relationships with habitat and spatial variables could aid in targeted management or monitoring prior to the establishment of new populations. To better understand the distribution of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> in the upper Savannah River watershed of South Carolina and Georgia (USA), we used an information theoretic approach to evaluate multiple competing hypotheses of landscape use (agricultural, forest, developed) and site level habitat to best predict <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence. We found <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence was predicted best by increasing stream width, and negatively associated with an increase in cobble as the dominant substrate.</p>
      <p>It is important to note that our study only evaluated presence of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic>, but not abundance or density. We believe that presence only data can be helpful in defining suitable habitat for an invader, but may be improved upon to evaluate optimal habitat through the inclusion of quantitative estimates of density or abundance. We also did not evaluate detection probability in our study, and therefore cannot be certain that a recorded absence of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> is representative of true absence or imperfect detection. We used a rapid assessment protocol to maximize the number of sites that can be surveyed within a day, using a search rate of 10 m<sup>2</sup> per minute (<xref ref-type="bibr" rid="B37">Sterrett et al. 2018</xref>). Others have proposed that an effective search rate for freshwater bivalves in low densities is closer to 1–2 m<sup>2</sup> per minute (<xref ref-type="bibr" rid="B40">Strayer and Smith 2003</xref>; <xref ref-type="bibr" rid="B36">Smith 2006</xref>). While it is possible that the rapid search rate may have added false negatives at sites, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> often reach very high densities (e.g., 1.7–132 clams/m<sup>2</sup>; <xref ref-type="bibr" rid="B21">Kelley et al. 2022</xref>) and thus the likelihood of detecting at least one individual at these densities is high.</p>
      <p>Our findings suggest that stream size was the most important factor associated with the presence of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic>. As stream size increased, the probability of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence also increased (Fig. <xref ref-type="fig" rid="F2">2</xref>). The distribution and abundance of aquatic species is often related to increased stream size due to a greater diversity of habitat and available resources found in larger rivers or streams (<xref ref-type="bibr" rid="B34">Schlosser 1982</xref>; <xref ref-type="bibr" rid="B47">Vaughn and Taylor 2000</xref>). But in this case, the relationship could be more complex. Stream size was also highly correlated with larger catchment areas (Pearson r = 0.9, Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>: fig S1). As catchments increase in size, there may be a greater number of accidental or deliberate introductions of invasive species through human activity simply due to greater area (<xref ref-type="bibr" rid="B22">Kolar and Lodge 2001</xref>). Once established, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> disperses downstream (<xref ref-type="bibr" rid="B28">Pernecker et al. 2021</xref>), aided by its free-swimming juvenile stage that lasts for approximately 100 hours (<xref ref-type="bibr" rid="B23">Mackie and Claudi 2010</xref>), sufficient time for larvae to reach the outflow of reservoirs and be carried further downstream. Therefore, it is possible that the stream width relationship in our study indicates a greater probability of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> being introduced higher in the watershed.</p>
      <p>In our best explanatory model, we found a negative association between <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence and increasing proportion of cobble as the dominant substrate (Fig. <xref ref-type="fig" rid="F2">2</xref>), which may be indicative of smaller, headwater streams in forested watersheds. Successful aquatic invaders may tolerate a wide range of habitats, and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> can withstand a range of flow conditions and substrate types in lentic and lotic systems. Previous studies have indicated increased presence and abundance in slow-moving sandy rivers (<xref ref-type="bibr" rid="B35">Schmidlin and Baur 2007</xref>), rivers with larger substrate (<xref ref-type="bibr" rid="B21">Kelley et al. 2022</xref>), and in reservoirs (<xref ref-type="bibr" rid="B17">Karatayev et al. 2003</xref>; <xref ref-type="bibr" rid="B27">Patrick et al. 2017</xref>). In a behavioral habitat preference experiment, <xref ref-type="bibr" rid="B35">Schmidlin and Baur (2007)</xref> found <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> preferred fine sediments including sand and fine gravel. The behavioral preference corroborated observations of higher natural densities in fine sediments in the Rhine River (<xref ref-type="bibr" rid="B35">Schmidlin and Baur 2007</xref>). In our study, proportion of sandy substrates was also a significant and positive predictor of presence, but only in lower performing models (Agriculture 3, <abbrev xlink:title="Akaike’s Information Criterion">AICc</abbrev> = 56.930, AICcWT = 0.004; Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>: table SS3). The lack of a strong positive relationship between presence and substrate may be indicative of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic>’s ability to colonize multiple substrate types, but it is also difficult to compare our presence data with abundance data reported from other studies.</p>
      <p>Freshwater ecosystems are often affected by changing land use (<xref ref-type="bibr" rid="B33">Sala et al. 2000</xref>), and land cover in the catchment of a site may affect in-stream habitat parameters (<xref ref-type="bibr" rid="B14">Jacobson et al. 2001</xref>). Land use can influence water flow and sediment loads, which in turn may impact benthic organisms, and critical habitat along and within the streams (<xref ref-type="bibr" rid="B1">Allan 2004</xref>). <xref ref-type="bibr" rid="B10">Ferreira-Rodríguez et al. (2022)</xref> found <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> densities were positively associated with the amount of agricultural land cover in a watershed. We did not see any relationship between agricultural land use and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence, but it is possible that agricultural land use is a better predictor of abundance than of presence only. Conversely, we did see a significant negative relationship with the % forest, and a significant positive relationship with % developed land within a 3km buffer around a site. However, these relationships were reported in lesser performing models representing 3% (Forest 2) and 4% (Develop 1, Fig. <xref ref-type="fig" rid="F2">2</xref>) of AICcWT (Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>: table SS3). The association of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence with surrounding land cover may indicate that habitat degradation promotes establishment and proliferation of <abbrev xlink:title="Aquatic invasive species">AIS</abbrev>. For example, increased developed land use, and conversely declining forested land use adjacent to a stream may result in declining habitat quality (<xref ref-type="bibr" rid="B2">Allan et al. 1997</xref>), reduced native biodiversity (<xref ref-type="bibr" rid="B41">Strayer et al. 2003</xref>), and thereby foster an invasion. It is also feasible that increased developed landscape (or decreased forested landscape) may indicate increased introduction and propagule pressure from human aided dispersal. <xref ref-type="bibr" rid="B32">Rodríguez-Rey et al. (2023)</xref> found that anthropogenic variables related to dispersal (e.g., road density, distance to ports or boat ramps, and human population density) provide greater relative importance in predicting the presence of <abbrev xlink:title="Aquatic invasive species">AIS</abbrev> in the United Kingdom. It is possible that the gradient response of forested vs. developed land use in our study represents a gradient of anthropogenic activity aiding in <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> dispersal in the upper Savannah River basin.</p>
      <p>Human activity within and between reservoirs also serves as a potential mechanism of overland dispersal for aquatic hitch-hikers. Reservoirs have been shown to act as stepping stones for <abbrev xlink:title="Aquatic invasive species">AIS</abbrev> invasions by providing accessible environments for human activities serving as introduction vectors (<xref ref-type="bibr" rid="B15">Johnson and Carlton 1996</xref>; <xref ref-type="bibr" rid="B16">Johnson et al. 2008</xref>). As relatively new habitats, reservoirs generally have lower species biodiversity than their original lotic systems, resulting in open niches that invasive species can exploit (<xref ref-type="bibr" rid="B50">Wetzel 1990</xref>; <xref ref-type="bibr" rid="B12">Havel et al. 2015</xref>). <xref ref-type="bibr" rid="B18">Karatayev et al. (2005)</xref> related <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence to landscape characteristics throughout the state of Texas, USA. The authors found that <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> were disproportionally present in larger reservoirs compared to smaller ones due to the amount of human activity that these reservoirs received. We found a positive relationship between the presence of upstream reservoirs and the presence of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> (Fig. <xref ref-type="fig" rid="F3">3</xref>), though this was in a lesser performing model (Develop 1). The initial introduction of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> may have been facilitated by human recreational activities, including boating, swimming, and fishing, and may have spread to nearby reservoirs in the same manner (<xref ref-type="bibr" rid="B12">Havel et al. 2015</xref>), or through passive transport on the feet and feathers of aquatic birds (<xref ref-type="bibr" rid="B25">McMahon 2002</xref>). We did not, however, assess presence of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> in upstream reservoirs and we acknowledge that this pathway is speculative, yet a plausible explanation given support from the literature. The potential of reservoirs as stepping-stones of passive dispersal by <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> warrants continued investigation.</p>
      <p>Our findings suggest that some landscape variables related to forest and developed environments may play a role in determining <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence; however, site level habitat characteristics including substrate and stream width showed clearer relationships to <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> absence or presence. Differences in our results from those previously published may simply be a result of differences in habitat and landscape processes that predict presence vs. abundance. Whereas habitat and landscape correlates of abundance or density are more indicative of optimal or quality habitat, presence data may be helpful in identifying suitable habitat where an invasion may initially occur. Given support in the literature, future research on <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> may benefit from further evaluation of upstream reservoirs and developed landscapes on clam presence and abundance.</p>
    </sec>
    <sec sec-type="Author contribution" id="sec12">
      <title>Author contribution</title>
      <p>ZS: research conceptualization, sample design &amp; methodology, investigation and data collection, data analysis and interpretation, writing – original draft, writing – revisions; MB: research conceptualization, sample design &amp; methodology, investigation and data collection, data analysis and interpretation, writing – revisions; MH: data analysis and interpretation, writing – revisions; BI: data analysis and interpretation, writing – revisions; PH: research conceptualization, sample design &amp; methodology, investigation and data collection, data analysis and interpretation, writing – revisions, funding.</p>
    </sec>
    <sec sec-type="Acknowledgments" id="sec13">
      <title>Acknowledgments</title>
      <p>Ericah Beason and Morgan Kern of the South Carolina Department of Natural Resources (SCDNR) assisted with permits, site selection and access permission. We are indebted to Regina Mondibrown, Molly Martin, Amanda Van Buskirk, Jake Smith, and Hayley Robinson for assistance in data collection, and Robert Ratajczak for logistical support. The authors benefitted from early feedback from Allison Roy (Massachusetts Cooperative Fish &amp; Wildlife Research Unit) and members of the Brook Floater Working Group. We also thank our two anonymous reviewers for their constructive feedback.</p>
    </sec>
    <sec sec-type="Funding declaration" id="sec14">
      <title>Funding declaration</title>
      <p>Funding for this project was provided by a United States Fish &amp; Wildlife Service State Wildlife Grant to the SCDNR, and graduate assistant support from the University of Georgia Warnell School of Forestry and Natural Resources. The Georgia Cooperative Fish and Wildlife Research Unit is sponsored jointly by the Georgia Department of Natural Resources, the University of Georgia, the U.S. Fish and Wildlife Service, the U.S. Geological Survey, and the Wildlife Management Institute. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the United States Government.</p>
    </sec>
    <sec sec-type="Ethical statement" id="sec15">
      <title>Ethical statement</title>
      <p>All sampling activities were permitted by SCDNR under permit number F – 23 – 058, as well as written permission for stream access from private landowners and the United States Forest Service Francis Marion and Sumter National Forest.</p>
    </sec>
    <sec sec-type="Data availability" id="sec16">
      <title>Data availability</title>
      <p>All of the data that support the findings of this study are available in the main text or Supplementary material.</p>
    </sec>
  </body>
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    <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">10.3391/ai.2026.21.2.189571.suppl1</object-id>
        <object-id content-type="arpha">7CF7E7BB-22A2-5D1A-8E79-8BE5E9DDA6BF</object-id>
        <label>Supplementary material 1</label>
        <caption>
          <p>Additional information</p>
        </caption>
        <statement content-type="dataType">
          <label>Data type</label>
          <p>docx</p>
        </statement>
        <statement content-type="notes">
          <label>Explanation note</label>
          <p><bold>table SS1</bold>. Summary table describing all potential predictor variables analyzed to find their effect on <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> site presence. <bold>table SS2</bold>. Hypothesized pathways for how major land-use types could experience different factors that cause changes in stream habitat and result in decreases (-) or increases (+) of several potential predictor variables used in the candidate model set for predicting the probability of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> site presence. <bold>table SS3</bold>. Additional models, model statistics, and parameter estimates for logistic regression models of habitat and landscape variables affecting <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence. <bold>fig. S1</bold>. Correlation matrix of all predictor variables that we considered using in final models for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Corbicula">C.</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="fluminea">fluminea</tp:taxon-name-part></tp:taxon-name></italic> presence. A correlation level of 0.6 was chosen to eliminate redundancy within models. Predictor variables with a value of 0.6 or greater were not used in the same model.</p>
        </statement>
        <media xlink:href="aquaticinvasions-21-111_article-189571__-s001.docx" mimetype="application" mime-subtype="vnd.openxmlformats-officedocument.wordprocessingml.document" position="float" orientation="portrait" id="oo_1614511.docx">
          <uri content-type="original_file">https://binary.pensoft.net/file/1614511</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"> Zachary M. Schumber, Michael A. Baker, Brian J. Irwin, Martin J. Hamel, Peter D. Hazelton</attrib>
      </supplementary-material>
    </sec>
  </back>
</article>
