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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">10</journal-id>
      <journal-id journal-id-type="index">urn:lsid:arphahub.com:pub:6BEA2F36-F41A-5C4C-8F71-33F322F204B5</journal-id>
      <journal-id journal-id-type="aggregator">urn:lsid:zoobank.org:pub:0D3F4FBA-8FC4-4738-B942-05DB471A61E6</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">Nature Conservation</journal-title>
        <abbrev-journal-title xml:lang="en">NC</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">1314-6947</issn>
      <issn pub-type="epub">1314-3301</issn>
      <publisher>
        <publisher-name>Pensoft Publishers</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.3897/natureconservation.64.193189</article-id>
      <article-id pub-id-type="publisher-id">193189</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group subj-group-type="biological_taxon">
          <subject>Plantae</subject>
        </subj-group>
        <subj-group subj-group-type="scientific_subject">
          <subject>Biological Invasions</subject>
          <subject>Ecology &amp; Environmental sciences</subject>
          <subject>Plant and habitat conservation</subject>
        </subj-group>
        <subj-group subj-group-type="geographical_area">
          <subject>Africa</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Riparian community perceptions of water hyacinth (<italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Eichhornia">Eichhornia</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="crassipes">crassipes</tp:taxon-name-part></tp:taxon-name></italic>) infestation and its socio-economic impacts in the Rweru-Mugesera wetland complex, Eastern Rwanda</article-title>
      </title-group>
      <contrib-group content-type="authors">
        <contrib contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Tuyishime</surname>
            <given-names>Josine</given-names>
          </name>
          <email xlink:type="simple">josinetuyi@gmail.com</email>
          <uri content-type="orcid">https://orcid.org/0009-0009-4260-5087</uri>
          <xref ref-type="aff" rid="A1">1</xref>
          <role content-type="http://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
          <role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">Writing - original draft</role>
          <role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review and editing</role>
          <role content-type="http://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
          <role content-type="http://credit.niso.org/contributor-roles/formal-analysis/">Formal analysis</role>
          <role content-type="http://credit.niso.org/contributor-roles/funding-acquisition/">Funding acquisition</role>
          <role content-type="http://credit.niso.org/contributor-roles/methodology/">Methodology</role>
          <role content-type="http://credit.niso.org/contributor-roles/resources/">Resources</role>
          <role content-type="http://credit.niso.org/contributor-roles/software/">Software</role>
          <role content-type="http://credit.niso.org/contributor-roles/validation/">Validation</role>
          <role content-type="http://credit.niso.org/contributor-roles/visualization/">Visualization</role>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Rwanyiziri</surname>
            <given-names>Gaspard</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0000-0001-8138-2538</uri>
          <xref ref-type="aff" rid="A2">2</xref>
          <role content-type="http://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
          <role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">Writing - original draft</role>
          <role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review and editing</role>
          <role content-type="http://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
          <role content-type="http://credit.niso.org/contributor-roles/methodology/">Methodology</role>
          <role content-type="http://credit.niso.org/contributor-roles/supervision/">Supervision</role>
          <role content-type="http://credit.niso.org/contributor-roles/visualization/">Visualization</role>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Soro</surname>
            <given-names>Dodiomon</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0000-0003-2260-6030</uri>
          <xref ref-type="aff" rid="A1">1</xref>
          <role content-type="http://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
          <role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">Writing - original draft</role>
          <role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review and editing</role>
          <role content-type="http://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
          <role content-type="http://credit.niso.org/contributor-roles/methodology/">Methodology</role>
          <role content-type="http://credit.niso.org/contributor-roles/supervision/">Supervision</role>
          <role content-type="http://credit.niso.org/contributor-roles/visualization/">Visualization</role>
        </contrib>
      </contrib-group>
      <aff id="A1">
        <label>1</label>
        <addr-line content-type="verbatim">African Center of Excellence in Climate Change, Biodiversity and Sustainable Agriculture (CEA-CCBAD), UFR Biosciences, University Félix Houphouët-Boigny (UFHB), P. O. Box 165, Abidjan 31, Cote d’Ivoire</addr-line>
        <institution>African Center of Excellence in Climate Change, Biodiversity and Sustainable Agriculture (CEA-CCBAD), UFR Biosciences, University Félix Houphouët-Boigny (UFHB)</institution>
        <addr-line content-type="city">Abidjan</addr-line>
        <country>Cote d'Ivoire</country>
      </aff>
      <aff id="A2">
        <label>2</label>
        <addr-line content-type="verbatim">Department of Spatial Planning, School of Architecture and Built Environment, College of Science and Technology, University of Rwanda, P. O. Box 3900, Kigali, Rwanda</addr-line>
        <institution>Department of Spatial Planning, School of Architecture and Built Environment, College of Science and Technology, University of Rwanda</institution>
        <addr-line content-type="city">Kigali</addr-line>
        <country>Rwanda</country>
      </aff>
      <author-notes>
        <fn fn-type="corresp">
          <p>Corresponding author: Josine Tuyishime (<email xlink:type="simple">josinetuyi@gmail.com</email>)</p>
        </fn>
        <fn fn-type="edited-by">
          <p>Academic editor: Renato Martins</p>
        </fn>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>25</day>
        <month>08</month>
        <year>2026</year>
      </pub-date>
      <volume>64</volume>
      <fpage>163</fpage>
      <lpage>183</lpage>
      <uri content-type="arpha" xlink:href="http://openbiodiv.net/C7049A02-1508-55F5-B967-814601C42431">C7049A02-1508-55F5-B967-814601C42431</uri>
      <uri content-type="zoobank" xlink:href="https://zoobank.org/7AA75409-3A7E-4D86-813F-60097E53EED9">7AA75409-3A7E-4D86-813F-60097E53EED9</uri>
      <history>
        <date date-type="received">
          <day>07</day>
          <month>04</month>
          <year>2026</year>
        </date>
        <date date-type="accepted">
          <day>14</day>
          <month>08</month>
          <year>2026</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>Josine Tuyishime, Gaspard Rwanyiziri, Dodiomon Soro</copyright-statement>
        <license license-type="creative-commons-attribution" xlink:href="http://creativecommons.org/licenses/by/4.0/" xlink:type="simple">
          <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
        </license>
      </permissions>
      <self-uri content-type="zoobank" xlink:type="simple">https://zoobank.org/7AA75409-3A7E-4D86-813F-60097E53EED9</self-uri>
      <abstract>
        <label>Abstract</label>
        <p>Water hyacinth (<italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Eichhornia">Eichhornia</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="crassipes">crassipes</tp:taxon-name-part></tp:taxon-name></italic>) is one of the worst aquatic weeds in the world. Its rapid spread harms aquatic ecosystems and the people living nearby. This study was conducted around the Rweru-Mugesera wetland complex in three districts of the Eastern Province of Rwanda, namely Bugesera, Ngoma, and Rwamagana. The study used both secondary and primary data sources. Primary data were collected from a targeted sample of 399 households and 22 key informants across the three districts using structured household questionnaire surveys and an interview guide. The impacts were assessed using content analysis, descriptive statistics, Kruskal–Wallis tests, and chi-square tests. The study revealed that local communities living near areas invaded by water hyacinth had substantial knowledge of water hyacinth, which was not significantly influenced by occupation, education level, or gender. Most respondents perceived an increase in water hyacinth coverage over time, caused mainly by nutrient enrichment from agricultural runoff and increased temperatures due to climate change. Furthermore, the invasion of water hyacinth affected various socio-economic activities around the Rweru-Mugesera wetland complex. For instance, fish harvests in invaded areas dropped sharply from 2,147 to 39 kg per year, and crop yields decreased from 730 to 162 kg per season, with significant increases in fish and crop prices. Transport income also decreased from 68,705 to 30,119 Rwandan francs per year in heavily invaded areas. This study demonstrates that water hyacinth expansion has caused considerable negative effects on the socio-economic activities conducted around the wetland complex, with fishing activities being among the most severely affected. To reverse the situation, this study suggests the development of an integrated water hyacinth management strategy for the Rweru-Mugesera wetland complex that involves local riparian communities and supports their sustainable livelihoods and environmental conservation.</p>
      </abstract>
      <kwd-group>
        <label>Key words:</label>
        <kwd>Anthropogenic factors</kwd>
        <kwd>aquatic weeds</kwd>
        <kwd>climate change</kwd>
        <kwd>invaded areas</kwd>
        <kwd>local knowledge</kwd>
        <kwd>wetland ecosystem</kwd>
      </kwd-group>
      <funding-group>
        <funding-statement>The Partnership for Skills in Applied Sciences, Engineering and Technology-Regional Scholarship and Innovation Fund (PASET-RSIF)</funding-statement>
      </funding-group>
    </article-meta>
    <notes>
      <sec sec-type="Citation" id="sec1">
        <title>Citation</title>
        <p>Tuyishime J, Rwanyiziri G, Soro D (2026) Riparian community perceptions of water hyacinth (Eichhornia crassipes) infestation and its socio-economic impacts in the Rweru-Mugesera wetland complex, Eastern Rwanda. Nature Conservation 64: 163–183. <ext-link xlink:type="simple" ext-link-type="doi" xlink:href="10.3897/natureconservation.64.193189">https://doi.org/10.3897/natureconservation.64.193189</ext-link></p>
      </sec>
    </notes>
  </front>
  <body>
    <sec sec-type="Introduction" id="sec2">
      <title>Introduction</title>
      <p>Known as the most harmful aquatic weed globally (<xref ref-type="bibr" rid="B56">Villamagna and Murphy 2010</xref>; <xref ref-type="bibr" rid="B29">Mbaka 2023</xref>), water hyacinth (<italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Eichhornia">Eichhornia</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="crassipes">crassipes</tp:taxon-name-part></tp:taxon-name></italic>) is native to South America and was introduced to various parts of the world mainly because of its attractive flowers (<xref ref-type="bibr" rid="B61">Yirefu et al. 2007</xref>). Studies have revealed that water hyacinth has invaded freshwater systems in over 50 countries worldwide (<xref ref-type="bibr" rid="B22">Jafari 2010</xref>). Climate change models have shown that its distribution might expand as global temperatures continue to increase (<xref ref-type="bibr" rid="B56">Villamagna and Murphy 2010</xref>; <xref ref-type="bibr" rid="B57">Villamagna et al. 2012</xref>). In Africa, water hyacinth was first recorded in the 1890s in the Nile River in Egypt and has since spread widely across freshwater bodies throughout the continent (<xref ref-type="bibr" rid="B23">Kanyamugenge 2018</xref>). Similarly, because of its high capacity to multiply beyond its native range, water hyacinth has spread to various East African water bodies, including those in Rwanda (<xref ref-type="bibr" rid="B41">Navarro and Phiri 2000</xref>). In Rwanda, water hyacinth was first publicly recorded in the Mukungwa River valley in 1987 and has since infested various lakes and wetlands, such as Akagera, Nyabarongo, Akanyaru, and the Rweru-Mugesera wetland complex (<xref ref-type="bibr" rid="B10">Gashamura 2009</xref>; <xref ref-type="bibr" rid="B51">Seburanga et al. 2014</xref>). However, the invasive nature of water hyacinth and the lack of sustainable management strategies have resulted in persistent environmental, social, and economic challenges nationwide (<xref ref-type="bibr" rid="B51">Seburanga et al. 2014</xref>), which are likely to worsen as climate variability, eutrophication, and increasing anthropogenic disturbances accelerate its expansion across African wetlands, highlighting the urgent need for sustainable management (<xref ref-type="bibr" rid="B6">Dersseh et al. 2019</xref>; <xref ref-type="bibr" rid="B14">Harun et al. 2021</xref>; <xref ref-type="bibr" rid="B21">IPCC 2023</xref>; <xref ref-type="bibr" rid="B46">Parida et al. 2023</xref>). These conditions collectively create a favorable environment for water hyacinth that accelerates its aggressive growth and dominance in aquatic ecosystems. Consequently, once water hyacinth establishes itself under these favorable conditions, its ecological impacts become increasingly pronounced. Its dense mats reduce light penetration and water circulation, lower phytoplankton productivity, decrease dissolved oxygen concentrations, increase sedimentation, and raise evapotranspiration (<xref ref-type="bibr" rid="B6">Dersseh et al. 2019</xref>; <xref ref-type="bibr" rid="B46">Parida et al. 2023</xref>). These mats also hinder sunlight from reaching submerged macrophytes, alter nutrient availability, and intensify competition among aquatic plants (<xref ref-type="bibr" rid="B33">Mengistu et al. 2017</xref>; <xref ref-type="bibr" rid="B58">Wang and Yan 2017</xref>). Beyond its ecological impacts, the socio-economic problems caused by water hyacinth are also severe, particularly for riparian communities that rely on water bodies for their livelihoods. Dense mats of water hyacinth reduce fish catchability, damage fishing equipment, thereby increasing operational costs, block waterways used for transport, and limit the availability of freshwater for domestic and agricultural use (<xref ref-type="bibr" rid="B42">Ndimele 2012</xref>; <xref ref-type="bibr" rid="B6">Dersseh et al. 2019</xref>). Water hyacinth also increases the siltation of dams and rivers, blocks irrigation channels, and decreases the economic value of riparian zones (<xref ref-type="bibr" rid="B25">Kenfack et al. 2019</xref>).</p>
      <p>Despite its negative impacts, water hyacinth has been used in various economic activities, mainly because of its rapid growth and high biomass production. For many years, it has been used not only in bioremediation, the bioaccumulation of heavy metals, and biogas and biofuel production but also as animal feed and in compost production (<xref ref-type="bibr" rid="B50">Sayago and Rodríguez 2018</xref>; <xref ref-type="bibr" rid="B3">Ayanda et al. 2020</xref>; <xref ref-type="bibr" rid="B46">Parida et al. 2023</xref>). Various studies conducted across different parts of Africa have shown that the spread of water hyacinth seriously affects key livelihood resources, such as fisheries, crop production, and transportation (<xref ref-type="bibr" rid="B18">Hungwe et al. 2016</xref>; <xref ref-type="bibr" rid="B25">Kenfack et al. 2019</xref>; <xref ref-type="bibr" rid="B8">Enyew et al. 2020</xref>; <xref ref-type="bibr" rid="B29">Mbaka 2023</xref>).</p>
      <p>Various studies conducted on water hyacinth in Rwanda have focused on different aspects of the issue. First, studies focused either on mapping its distribution using Landsat imagery (<xref ref-type="bibr" rid="B38">Mukarugwiro et al. 2019</xref>) or on assessing its occurrence and extent and selecting appropriate biological controls (<xref ref-type="bibr" rid="B37">Mukarugwiro 2020</xref>). Second, studies focused on its use to improve local livelihoods and restore ecosystems (<xref ref-type="bibr" rid="B23">Kanyamugenge 2018</xref>) and on local community perceptions of its control (<xref ref-type="bibr" rid="B51">Seburanga et al. 2014</xref>). Finally, other studies involved an assessment of biological control efforts for water hyacinth in the Kagera River headwaters of Rwanda (<xref ref-type="bibr" rid="B59">Williams et al. 2007</xref>). In the Rweru-Mugesera wetland complex, where water hyacinth infestation has expanded rapidly in recent years, there is limited empirical evidence not only on how local communities perceive the invasion but also on the extent to which their livelihoods have been affected. In addition, concerns remain about whether current management interventions adequately address local needs. This knowledge gap limits the development of sustainable management strategies for this invasive aquatic plant (<xref ref-type="bibr" rid="B16">Hill et al. 2016</xref>).</p>
      <p>This study addresses this gap by investigating riparian community perceptions of water hyacinth infestation and evaluating its socio-economic impacts in the Rweru-Mugesera wetland complex, Eastern Rwanda. It integrates local ecological knowledge with quantitative socio-economic data collected from the surrounding communities experiencing different levels of water hyacinth infestation. By comparing areas with different water hyacinth invasion levels, the study provides a more comprehensive understanding of how invasion intensity influences community perceptions, livelihood outcomes, and local adaptation and management responses. Such an integrated socio-ecological approach is increasingly recognized as essential for developing effective invasive species management responses because successful interventions depend not only on ecological evidence but also on stakeholder participation and local acceptance (<xref ref-type="bibr" rid="B49">Reed 2008</xref>; <xref ref-type="bibr" rid="B20">IPBES 2023</xref>).</p>
      <p>The findings of this study are expected to provide empirical evidence regarding the human dimensions of water hyacinth invasion in Rwanda, a context where socio-economic assessments are limited despite the increasing threat of invasive aquatic plants. In addition, they will inform policymakers, local government authorities, and conservationists at different levels in the development of participatory, evidence-based management strategies that incorporate local priorities and experiences. They will also support the implementation of Rwanda’s wetland conservation and restoration initiatives by identifying community-informed interventions that enhance livelihood resilience and ecosystem health (<xref ref-type="bibr" rid="B55">UNEP 2021</xref>; <xref ref-type="bibr" rid="B20">IPBES 2023</xref>; <xref ref-type="bibr" rid="B21">IPCC 2023</xref>).</p>
      <p>The aim of this study was to assess local riparian community perceptions of water hyacinth infestation in the Rweru-Mugesera wetland complex and its socio-economic impacts. To achieve this aim, the study considered the following research questions:</p>
      <list list-type="order">
        <list-item>
          <p>How knowledgeable is the local riparian community about the infestation of water hyacinth in the Rweru-Mugesera wetland complex?
</p>
        </list-item>
        <list-item>
          <p>What are the local riparian community’s perceptions of the status of water hyacinth in the wetland complex?
</p>
        </list-item>
        <list-item>
          <p>What are the factors perceived by the local community to influence the spread or decline of water hyacinth in the study area?
</p>
        </list-item>
        <list-item>
          <p>What are the impacts of water hyacinth infestation on important livelihood activities conducted around the Rweru-Mugesera wetland complex?
</p>
        </list-item>
      </list>
    </sec>
    <sec sec-type="materials|methods" id="sec3">
      <title>Materials and methods</title>
      <sec sec-type="Study site description" id="sec4">
        <title>Study site description</title>
        <p>This study was conducted in and around the Rweru-Mugesera wetland complex in three districts of the Eastern Province of Rwanda, namely Bugesera, Ngoma, and Rwamagana (Fig. <xref ref-type="fig" rid="F1">1</xref>). The complex is part of the Nile Basin and Lake Victoria Basin within the Akagera catchment. It comprises 11 lakes and associated wetlands (Mugesera, Gashariga, Kidogo, Rumira, Gashora, Birara, Mirayi, Sake, Kilimbi, Gaharwa, and Rweru) and covers a total area of 13,845 ha (<xref ref-type="bibr" rid="B2">ARCOS 2017</xref>). Socioeconomically, approximately 225,073 people live within the surrounding sectors and depend mainly on farming, livestock, fishing, forestry, quarrying, wage labor and trade. According to the results of the 2022 Population and Housing Census, the mean poverty level across the three districts is approximately 48% (higher than the national average of 39%) (<xref ref-type="bibr" rid="B44">NISR 2022</xref>).</p>
        <fig id="F1">
          <object-id content-type="arpha">1D1E8FA7-25C3-55FD-AFB6-2C598EB2B425</object-id>
          <label>Figure 1.</label>
          <caption>
            <p>Map of the study districts and their location in Rwanda.</p>
          </caption>
          <graphic xlink:href="nature_conservation-64-163_article-193189__-g001.jpg" id="oo_1753561.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1753561</uri>
          </graphic>
        </fig>
      </sec>
      <sec sec-type="Sampling procedures" id="sec5">
        <title>Sampling procedures</title>
        <p>The fieldwork was conducted from 1 June to 30 September 2025 in Bugesera, Ngoma, and Rwamagana districts in Eastern Rwanda. The sampling sites were chosen based on the level of water hyacinth invasion within the three districts: Bugesera (highly invaded), Ngoma (moderately invaded), and Rwamagana (non-invaded). The target population consisted of households living within a 2 km radius of the Rweru-Mugesera wetland complex. From a total of 10,224 households, using the formula of <xref ref-type="bibr" rid="B60">Yamane (1965)</xref> at a 95% confidence level and a 0.05 margin of error, a sample size of 399 respondents was determined.</p>
        <p>This means that, in each district, 133 households were sampled for the household questionnaire survey. The formula is given in Equation 1 below:</p>
        <p>Equation 1,</p>
        <p>where <italic>n</italic> is the required sample size; <italic>N</italic> is the population size; and <italic>e</italic> is the margin error (0.05).</p>
      </sec>
      <sec sec-type="Methods of data collection" id="sec6">
        <title>Methods of data collection</title>
        <p>Data were collected using structured and semi-structured questionnaires containing both closed- and open-ended questions. To minimize interpretation errors and enhance respondent comprehension, the questionnaires were first translated into Kinyarwanda, the primary language spoken in Rwanda (<xref ref-type="bibr" rid="B52">Sha and Immerwahr 2018</xref>). Before the main data collection period, the questionnaires were pretested on a small group of randomly selected households that were excluded from the final sample. The pretest helped ensure that the questions were clear, reduce errors, and generate the intended information (<xref ref-type="bibr" rid="B19">Ikart 2018</xref>).</p>
      </sec>
      <sec sec-type="Household survey" id="sec7">
        <title>Household survey</title>
        <p>For the household survey, questionnaires were administered to both men and women from different households in the three selected districts neighboring the Rweru-Mugesera wetland complex. A structured questionnaire was developed following a review of previous studies on invasive aquatic plants, climate change impacts, and wetland-dependent livelihoods and was adapted to the socio-economic context of the Rweru-Mugesera wetland complex.</p>
        <p>The questionnaire consisted of three sections: (i) socio-demographic characteristics of the respondents, (ii) awareness and perceptions of water hyacinth, and (iii) socio-economic impacts of water hyacinth infestation. Socio-demographic variables included respondents’ ages, gender, level of education, occupation, household size, and years of residence. These variables were selected because they influence respondents’ dependence on wetland resources, environmental awareness, and perceptions of invasive species (<xref ref-type="bibr" rid="B32">McMillan et al. 1997</xref>; <xref ref-type="bibr" rid="B40">Nabahungu and Visser 2011</xref>). Knowledge-related variables assessed respondents’ awareness of water hyacinth, its causes, spread, impacts, and management practices, providing insights into the level of community understanding and preparedness for invasive species management. Climate-related variables, such as respondents’ perceptions of changes in temperature, rainfall, drought frequency, and surface runoff, were included because they influence nutrient availability, hydrological conditions, and the proliferation of water hyacinth (<xref ref-type="bibr" rid="B7">Djihouessi et al. 2023</xref>). Socio-economic variables encompassed changes in production quantity, market prices, household income, transportation, fishing, and agriculture, as well as respondents’ perceptions of shifts in livelihood activities resulting from water hyacinth infestation. These variables were selected as key indicators of the environmental and socio-economic impacts of water hyacinth on wetland ecosystem services and local livelihoods. This survey targeted heads of households (or adult household members) to ensure that the collected responses reflected local experiences within the wetland ecosystem. The survey was conducted by three trained enumerators using a digital data collection tool called Kobo Collect.</p>
      </sec>
      <sec sec-type="Key informant interviews" id="sec8">
        <title>Key informant interviews</title>
        <p>Key informant interviews were conducted to gain clear, in-depth insights into the major drivers and impacts of water hyacinth, mitigation strategies, and policy implications. Environmental professionals, local government officials, community leaders, and cooperative leaders were interviewed using open-ended questions. In total, 22 respondents were interviewed, including one ecologist from a nongovernmental organization (<abbrev xlink:title="nongovernmental organization">NGO</abbrev>) operating in the wetland complex; eight village leaders; three executive secretaries of cells and three agronomists from sectors neighboring the wetland; three district environmental officers; two fishers; and two farmers’ cooperative leaders. The key informant interviews were conducted using a checklist of open-ended questions prepared in advance.</p>
      </sec>
      <sec sec-type="Methods of data analysis" id="sec9">
        <title>Methods of data analysis</title>
        <p>Qualitative data were analyzed using content analysis to identify key themes, patterns, and emerging issues related to water hyacinth invasion and its impacts on socio-economic activities around the wetland complex (<xref ref-type="bibr" rid="B34">Moretti et al. 2011</xref>). The findings were discussed in a narrative format under each of the proposed research themes (<xref ref-type="bibr" rid="B45">Onwuegbuzie et al. 2009</xref>) and then summarized and described qualitatively to complement the quantitative data obtained from household interviews (<xref ref-type="bibr" rid="B5">Debela et al. 2019</xref>). Simple descriptive statistics, such as percentages, frequencies, and means, were calculated for quantitative data. Prior to conducting inferential analysis, diagnostic tests were performed to assess the normality and homogeneity of the collected data. The Shapiro–Wilk test results for all variables showed <italic>p</italic>-values below 0.05, while homoscedasticity tests revealed unequal variances among groups (<italic>p</italic> &lt; 0.05). This implied that the data were not normally distributed. Therefore, the Kruskal–Wallis test was used to assess the impacts of water hyacinth on farming, transport, and fishing activities conducted around the Rweru-Mugesera wetland complex. This non-parametric test was chosen because it does not assume normality of the data and allows comparison of independent groups (<xref ref-type="bibr" rid="B28">Macunluoglu and Ocakoğlu 2023</xref>). When significant differences were detected, Dunn’s post hoc test was conducted to identify pairwise differences. Furthermore, the chi-square (X<sup>2</sup>) test was used to test whether there was an association between respondents’ socio-demographic characteristics and their perceptions of water hyacinth across all study sites. This test was used because the study variables were categorical and expressed as frequencies, allowing the identification of significant relationships between respondents’ characteristics and their perceptions (<xref ref-type="bibr" rid="B31">McHugh 2013</xref>). Data analysis was carried out using R software version 4.4.2.</p>
      </sec>
    </sec>
    <sec sec-type="Results" id="sec10">
      <title>Results</title>
      <sec sec-type="Socio-demographic characteristics of respondents" id="sec11">
        <title>Socio-demographic characteristics of respondents</title>
        <p>A total of 399 respondents were interviewed across Bugesera (high invasion), Ngoma (moderate invasion), and Rwamagana (no invasion) districts. Overall, 56.6% of the respondents were male (<italic>n</italic> = 226), while 43.4% were female (<italic>n</italic> = 173). Bugesera District showed a higher proportion of males (66.2%) than females (33.8%), while Ngoma and Rwamagana exhibited relatively balanced gender representation (Table <xref ref-type="table" rid="T1">1</xref>).</p>
        <table-wrap id="T1" position="float" orientation="portrait">
          <label>Table 1.</label>
          <caption>
            <p>Socio-demographic distribution of the respondents by gender across the three study sites.<bold/></p>
          </caption>
          <table>
            <tbody>
              <tr>
                <th rowspan="1" colspan="1">
                  <bold>Gender</bold>
                </th>
                <th rowspan="1" colspan="2">
                  <bold>Bugesera</bold>
                </th>
                <th rowspan="1" colspan="2">
                  <bold>Ngoma</bold>
                </th>
                <th rowspan="1" colspan="2">
                  <bold>Rwamagana</bold>
                </th>
                <th rowspan="1" colspan="2"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1">Frequency</td>
                <td rowspan="1" colspan="1">%</td>
                <td rowspan="1" colspan="1">Frequency</td>
                <td rowspan="1" colspan="1">%</td>
                <td rowspan="1" colspan="1">Frequency</td>
                <td rowspan="1" colspan="1">%</td>
                <td rowspan="1" colspan="1">Total Frequency</td>
                <td rowspan="1" colspan="1">%</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Male</td>
                <td rowspan="1" colspan="1">88</td>
                <td rowspan="1" colspan="1">66.2</td>
                <td rowspan="1" colspan="1">70</td>
                <td rowspan="1" colspan="1">52.6</td>
                <td rowspan="1" colspan="1">68</td>
                <td rowspan="1" colspan="1">51.1</td>
                <td rowspan="1" colspan="1">226</td>
                <td rowspan="1" colspan="1">56.6</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Female</td>
                <td rowspan="1" colspan="1">45</td>
                <td rowspan="1" colspan="1">33.8</td>
                <td rowspan="1" colspan="1">63</td>
                <td rowspan="1" colspan="1">47.4</td>
                <td rowspan="1" colspan="1">65</td>
                <td rowspan="1" colspan="1">48.9</td>
                <td rowspan="1" colspan="1">173</td>
                <td rowspan="1" colspan="1">43.4</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>Total</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>133</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>100</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>133</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>100</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>133</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>100</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>399</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>100</bold>
                </td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>Regarding the education levels of the respondents, the study found that primary education was the most common, with 69.17% in Ngoma, 64.7% in Rwamagana, and 51.9% in Bugesera. At the same time, a considerable proportion of respondents reported having no formal education, mainly in Bugesera (32.3%) and Rwamagana (24.1%), as presented in Fig. <xref ref-type="fig" rid="F2">2</xref>.</p>
        <fig id="F2">
          <object-id content-type="arpha">6AB26B5E-D5C5-589C-B8CB-2E0BF322DA92</object-id>
          <label>Figure 2.</label>
          <caption>
            <p>Education levels of the respondents in the study sites.</p>
          </caption>
          <graphic xlink:href="nature_conservation-64-163_article-193189__-g002.jpg" id="oo_1753562.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1753562</uri>
          </graphic>
        </fig>
        <p>Local livelihoods were mostly agriculture-based across the three study sites, with farming being the dominant occupation in Bugesera (84.2%), Ngoma (88.0%), and Rwamagana (94.0%). Fishing was the second most common occupation in Bugesera (14.3%) and Ngoma (6.02%), while it was low in Rwamagana District (0.8%). Beekeeping was the least represented occupation in Bugesera and Rwamagana districts (1.5% each) and was not reported in Ngoma District. Transport and small-scale business activities were limited and observed mostly in Ngoma and Rwamagana districts, each accounting for less than 4% of respondents (Fig. <xref ref-type="fig" rid="F3">3</xref>).</p>
        <fig id="F3">
          <object-id content-type="arpha">898510ED-0EAA-50A0-9174-B42B0A52BB72</object-id>
          <label>Figure 3.</label>
          <caption>
            <p>Respondents’ occupations across the three study sites.</p>
          </caption>
          <graphic xlink:href="nature_conservation-64-163_article-193189__-g003.jpg" id="oo_1753563.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1753563</uri>
          </graphic>
        </fig>
      </sec>
      <sec sec-type="Local knowledge of water hyacinth" id="sec12">
        <title>Local knowledge of water hyacinth</title>
        <p>The level of knowledge of water hyacinth varied noticeably across the three districts. In Bugesera District, the majority of respondents (92.49%) reported having knowledge of water hyacinth, while only 7.51% reported having no knowledge of it. In Ngoma District, most respondents (81.2%) confirmed having knowledge of water hyacinth, while a notable proportion (18.8%) reported the opposite. In contrast, Rwamagana District, which was not invaded by water hyacinth, showed low levels of knowledge, with 96.25% of respondents reporting no knowledge of water hyacinth. At the same time, only 3.75% of respondents indicated having knowledge of it, as shown in Fig. <xref ref-type="fig" rid="F4">4</xref>.</p>
        <fig id="F4">
          <object-id content-type="arpha">87FB2128-E0ED-5EAC-BABD-C5D87CBC91F3</object-id>
          <label>Figure 4.</label>
          <caption>
            <p>Riparian community knowledge of water hyacinth by district.</p>
          </caption>
          <graphic xlink:href="nature_conservation-64-163_article-193189__-g004.jpg" id="oo_1753564.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1753564</uri>
          </graphic>
        </fig>
        <p>As presented in Table <xref ref-type="table" rid="T2">2</xref>, the chi-square test showed that occupation and gender were not significantly associated with respondents’ knowledge of water hyacinth across the three study districts. Knowledge levels did not vary significantly across various occupations in Bugesera (χ<sup>2</sup> = 2, df = 4, <italic>p</italic> = 0.731), Ngoma (χ<sup>2</sup> = 5.3, df = 4, <italic>p</italic> = 0.254), and Rwamagana (χ<sup>2</sup> = 0.4, df = 3, <italic>p</italic> = 0.940) districts. Similarly, no significant association was found between gender and knowledge of water hyacinth in any district, although Bugesera showed a possible association between gender and knowledge levels despite the lack of statistical significance (χ<sup>2</sup> = 7.1, df = 4, <italic>p</italic> = 0.084), while clear non-significant relationships were observed in Ngoma (χ<sup>2</sup> = 0.27, df = 4, <italic>p</italic> = 0.668) and Rwamagana (χ<sup>2</sup> = 0.8, df = 4, <italic>p</italic> = 0.445) districts. In contrast, the influence of education level on knowledge of water hyacinth varied by district. There was no significant association in Bugesera (χ<sup>2</sup> = 4.2, df = 4, <italic>p</italic> = 0.346) or Rwamagana (χ<sup>2</sup> = 3.9, df = 2, <italic>p</italic> = 0.116), whereas a statistically significant association was observed in Ngoma District (χ<sup>2</sup> = 12.5, df = 3, <italic>p</italic> = 0.007).</p>
        <table-wrap id="T2" position="float" orientation="portrait">
          <label>Table 2.</label>
          <caption>
            <p>Association between socio-demographic characteristics and knowledge of water hyacinth across the three study districts.</p>
          </caption>
          <table>
            <tbody>
              <tr>
                <th rowspan="1" colspan="1">
                  <bold>Variable</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>District</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>χ<sup>2</sup> Statistic</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>df</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold><italic>p</italic>-value</bold>
                </th>
              </tr>
              <tr>
                <td rowspan="3" colspan="1">Occupation</td>
                <td rowspan="1" colspan="1">Bugesera</td>
                <td rowspan="1" colspan="1">2</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">0.731</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Ngoma</td>
                <td rowspan="1" colspan="1">5.3</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">0.254</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Rwamagana</td>
                <td rowspan="1" colspan="1">0.4</td>
                <td rowspan="1" colspan="1">3</td>
                <td rowspan="1" colspan="1">0.940</td>
              </tr>
              <tr>
                <td rowspan="3" colspan="1">Gender</td>
                <td rowspan="1" colspan="1">Bugesera</td>
                <td rowspan="1" colspan="1">7.3</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">0.084</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Ngoma</td>
                <td rowspan="1" colspan="1">0.3</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">0.668</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Rwamagana</td>
                <td rowspan="1" colspan="1">0.8</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">0.445</td>
              </tr>
              <tr>
                <td rowspan="3" colspan="1">Education level</td>
                <td rowspan="1" colspan="1">Bugesera</td>
                <td rowspan="1" colspan="1">4.2</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">0.346</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Ngoma</td>
                <td rowspan="1" colspan="1">12.5</td>
                <td rowspan="1" colspan="1">3</td>
                <td rowspan="1" colspan="1">0.007***</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Rwamagana</td>
                <td rowspan="1" colspan="1">3.9</td>
                <td rowspan="1" colspan="1">2</td>
                <td rowspan="1" colspan="1">0.116</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn>
              <p>Source: Field Survey (September 2025). Note: *** indicates significance at <italic>p</italic> &lt; 0.05; χ<sup>2</sup> = chi-square statistic; df = degrees of freedom.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
      </sec>
      <sec sec-type="Local community perceptions of the state of water hyacinth infestation" id="sec13">
        <title>Local community perceptions of the state of water hyacinth infestation</title>
        <p>Local community perceptions of the current state of water hyacinth infestation differed across the wetland complex. As shown in Fig. <xref ref-type="fig" rid="F5">5</xref>, infestation was more severe in Bugesera District, where nearly all respondents (95.5%) reported an increase in water hyacinth spread.</p>
        <fig id="F5">
          <object-id content-type="arpha">3B040CED-DE4D-5168-84F1-2A7F2310BF34</object-id>
          <label>Figure 5.</label>
          <caption>
            <p>Local community perceptions of the state of water hyacinth infestation across the Rweru-Mugesera wetland complex.</p>
          </caption>
          <graphic xlink:href="nature_conservation-64-163_article-193189__-g005.jpg" id="oo_1753565.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1753565</uri>
          </graphic>
        </fig>
        <p>In Ngoma District, however, there was variation in respondents’ perceptions of water hyacinth infestation, with almost half of the respondents (48.1%) reporting an increase. At the same time, other respondents observed different scenarios, including a decrease (19.5%), no change (13.5%), or no awareness of any change (18.8%). In contrast, Rwamagana District, which served as a control site, showed a very low level of awareness, with 94.7% of respondents reporting that they were not aware of any change in the state of water hyacinth coverage in the wetland complex.</p>
        <p>A Pearson’s chi-square test was conducted to assess the influence of respondents’ socio-demographic characteristics on their perceptions of the state of water hyacinth infestation across the three districts. As indicated in Table <xref ref-type="table" rid="T3">3</xref>, the results showed that education level significantly affected respondents’ perceptions of trends in water hyacinth only in Ngoma (χ<sup>2</sup> = 28.2, df = 9, <italic>p</italic> &lt; 0.001). In contrast, no significant associations were observed in Bugesera (χ<sup>2</sup> = 3.9, df = 4, <italic>p</italic> = 0.373) or Rwamagana (χ<sup>2</sup> = 1.3, df = 4, <italic>p</italic> = 1.000) districts. Similarly, there was no significant relationship between respondents’ occupations and their perceptions of the state of water hyacinth in any of the three districts. In Bugesera (χ<sup>2</sup> = 1.2, df = 4, <italic>p</italic> = 1), Rwamagana (χ<sup>2</sup> = 0.5, df = 6, <italic>p</italic> = 1), and Ngoma (χ<sup>2</sup> = 13.7, df = 9, <italic>p</italic> = 0.134), respondents’ occupations did not affect how the local community perceived changes in water hyacinth coverage. Regarding gender, a significant association was found only in Bugesera District (χ<sup>2</sup> = 12.7, df = 4, <italic>p</italic> = 0.044). In contrast, the gender of the respondents did not affect how they perceived the state of water hyacinth infestation across the Rweru-Mugesera wetland complex in Ngoma (χ<sup>2</sup> = 2.1, df = 3, <italic>p</italic> = 0.555) and Rwamagana (χ<sup>2</sup> = 0.3, df = 2, <italic>p</italic> = 0.844) districts.</p>
        <table-wrap id="T3" position="float" orientation="portrait">
          <label>Table 3.</label>
          <caption>
            <p>Chi-square test results for the association between socio-demographic factors and local community perceptions of the state of water hyacinth coverage across the three study districts.</p>
          </caption>
          <table>
            <tbody>
              <tr>
                <th rowspan="1" colspan="1">
                  <bold>Variable</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>District</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>χ<sup>2</sup> Statistic</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>df</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold><italic>p</italic>-value</bold>
                </th>
              </tr>
              <tr>
                <td rowspan="3" colspan="1">Education</td>
                <td rowspan="1" colspan="1">Bugesera</td>
                <td rowspan="1" colspan="1">3.9</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">0.373</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Ngoma</td>
                <td rowspan="1" colspan="1">28.2</td>
                <td rowspan="1" colspan="1">9</td>
                <td rowspan="1" colspan="1">&lt;0.001 ***</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Rwamagana</td>
                <td rowspan="1" colspan="1">1.3</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">1.000</td>
              </tr>
              <tr>
                <td rowspan="3" colspan="1">Occupation</td>
                <td rowspan="1" colspan="1">Bugesera</td>
                <td rowspan="1" colspan="1">1.2</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">1.000</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Ngoma</td>
                <td rowspan="1" colspan="1">13.7</td>
                <td rowspan="1" colspan="1">9</td>
                <td rowspan="1" colspan="1">0.134</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Rwamagana</td>
                <td rowspan="1" colspan="1">0.5</td>
                <td rowspan="1" colspan="1">6</td>
                <td rowspan="1" colspan="1">1.000</td>
              </tr>
              <tr>
                <td rowspan="3" colspan="1">Gender</td>
                <td rowspan="1" colspan="1">Bugesera</td>
                <td rowspan="1" colspan="1">12.7</td>
                <td rowspan="1" colspan="1">4</td>
                <td rowspan="1" colspan="1">0.044***</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Ngoma</td>
                <td rowspan="1" colspan="1">2.1</td>
                <td rowspan="1" colspan="1">3</td>
                <td rowspan="1" colspan="1">0.555</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Rwamagana</td>
                <td rowspan="1" colspan="1">0.3</td>
                <td rowspan="1" colspan="1">2</td>
                <td rowspan="1" colspan="1">0.844</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn>
              <p>Source: Field Survey (September 2025). Note: *** indicates significance at <italic>p</italic> &lt; 0.05; χ<sup>2</sup> = chi-square statistic; df = degrees of freedom.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
      </sec>
      <sec sec-type="Community perceptions of drivers of water hyacinth spread and reduction" id="sec14">
        <title>Community perceptions of drivers of water hyacinth spread and reduction</title>
        <p>Local community perceptions of the state of water hyacinth spatial distribution across the Rweru-Mugesera wetland complex were mainly influenced by a combination of anthropogenic and climatic factors. In Bugesera District, respondents mentioned poor water hyacinth disposal in the wetland during agricultural activities (39.1%) and agricultural runoff (35.4%) as the most critical factors contributing to water hyacinth spread in the wetland. Only 19.8% of them mentioned increased temperatures as a major factor. In contrast, respondents in Ngoma District provided a more diverse set of responses. While agricultural runoff was cited as the most important factor by 27.5% of respondents, climate-related factors such as flooding (21.5%) and changes in rainfall patterns (19.5%) were also mentioned. In Rwamagana District, all respondents (100%) were unaware of any factor contributing to water hyacinth spread in the wetland complex.</p>
        <p>Furthermore, respondents identified factors contributing to water hyacinth reduction in the same area. In Bugesera District, respondents mainly attributed water hyacinth reduction to manual removal by fishers and droughts, at 56% and 43%, respectively. In Ngoma District, respondents reported droughts (33%), bag production (17%), basket production (13%), and compost production (11%). In Rwamagana District, all respondents (100%) were unaware of any factor contributing to water hyacinth reduction in the wetland complex.</p>
      </sec>
      <sec sec-type="Socio-economic impacts of water hyacinth in the Rweru-Mugesera wetland complex" id="sec15">
        <title>Socio-economic impacts of water hyacinth in the Rweru-Mugesera wetland complex</title>
        <sec sec-type="Impacts of water hyacinth on the fishing industry" id="sec16">
          <title>Impacts of water hyacinth on the fishing industry</title>
          <p>The study showed that water hyacinth invasion had noticeably affected fishing activities in the Rweru-Mugesera wetland complex. Respondents identified multiple impacts of the invasion, with restricted access to fishing grounds mentioned as the most prevalent effect. Additional reported impacts included increased time required for fishing, damage to fishing nets, increased fish prices, and reductions in both fish size and weight. In contrast, respondents from less affected areas reported minimal or no impact on fishing activities.</p>
          <p>As presented in Table <xref ref-type="table" rid="T4">4</xref>, water hyacinth invasion also had a significant impact on both fish harvest quantity and fish price across the study sites. In Bugesera and Ngoma districts, the annual fish harvest quantity declined substantially from 2,147 kg/year to only 39 kg/year (<italic>p</italic> &lt; 0.001) and from 1,200 kg/year to 419 kg/year (<italic>p</italic> &lt; 0.001), respectively. In contrast, fish harvest quantity in Rwamagana increased from 211 kg/year to 825 kg/year (<italic>p</italic> &lt; 0.001). Similarly, fish price increased significantly across all sites, rising from 1,694 to 2,300 Rwandan francs per kilogram (FRw/kg) in Bugesera (<italic>p</italic> &lt; 0.001), from 1,581 to 2,254 FRw/kg in Ngoma (<italic>p</italic> &lt; 0.001), and from 1,638 to 2,501 FRw/kg in Rwamagana (<italic>p</italic> &lt; 0.001). The Kruskal–Wallis tests further confirmed significant differences among the three study sites for both fish harvest quantities (<italic>H</italic> = 118.7, <italic>p</italic> &lt; 0.001) and fish prices (<italic>H</italic> = 29.4, <italic>p</italic> &lt; 0.001).</p>
          <table-wrap id="T4" position="float" orientation="portrait">
            <label>Table 4.</label>
            <caption>
              <p>Comparison of changes in fish harvest quantity and price before and after water hyacinth invasion in the Rweru-Mugesera wetland complex.</p>
            </caption>
            <table>
              <tbody>
                <tr>
                  <th rowspan="1" colspan="1">
                    <bold>Site</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Mean income before (kg/year)</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Mean income after (kg/year)</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Statistical test</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Mean price before (FRw/year)</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Mean price after (FRw/year)</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Statistical test</bold>
                  </th>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Bugesera</td>
                  <td rowspan="1" colspan="1">2,147</td>
                  <td rowspan="1" colspan="1">39</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 4,051***</td>
                  <td rowspan="1" colspan="1">1,694</td>
                  <td rowspan="1" colspan="1">2,300</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 785***</td>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Ngoma</td>
                  <td rowspan="1" colspan="1">1,200</td>
                  <td rowspan="1" colspan="1">419</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 789***</td>
                  <td rowspan="1" colspan="1">1,581</td>
                  <td rowspan="1" colspan="1">2,254</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 681***</td>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Rwamagana</td>
                  <td rowspan="1" colspan="1">211</td>
                  <td rowspan="1" colspan="1">825</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 966***</td>
                  <td rowspan="1" colspan="1">1,638</td>
                  <td rowspan="1" colspan="1">2,501</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 16,201***</td>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Among Sites</td>
                  <td rowspan="1" colspan="1">—</td>
                  <td rowspan="1" colspan="1">—</td>
                  <td rowspan="1" colspan="1"><italic>H</italic> = 118.7***</td>
                  <td rowspan="1" colspan="1">—</td>
                  <td rowspan="1" colspan="1">—</td>
                  <td rowspan="1" colspan="1"><italic>H</italic> = 29.4***</td>
                </tr>
              </tbody>
            </table>
            <table-wrap-foot>
              <fn>
                <p>Source: Field Survey (September 2025). Note: *** indicates significance at <italic>p</italic> &lt; 0.05; <italic>H</italic> = Kruskal–Wallis test statistic; <italic>W</italic> = Wilcoxon rank-sum test statistic; FRw = Rwandan francs.</p>
              </fn>
            </table-wrap-foot>
          </table-wrap>
        </sec>
        <sec sec-type="Effect of water hyacinth on agricultural activities" id="sec17">
          <title>Effect of water hyacinth on agricultural activities</title>
          <p>Respondents reported that water hyacinth invasion negatively affected farming, primarily by reducing irrigation water availability, blocking irrigation channels, and lowering crop yields. People in invaded areas reported these problems more often, while most respondents from uninvaded areas did not report any impact of water hyacinth invasion on agricultural activities.</p>
          <p>As highlighted in Table <xref ref-type="table" rid="T5">5</xref>, water hyacinth invasion also had a significant negative impact on crop yield quantity across all study sites, with a substantial reduction observed in Bugesera. In fact, the annual crop yield declined from 730 kg/year before invasion to 162 kg/year after invasion (<italic>p</italic> &lt; 0.001). In Ngoma District, crop yield dropped from 1,143 kg/year to 497 kg/year (<italic>p</italic> &lt; 0.001), while in Rwamagana District, crop yield showed a smaller but still significant reduction from 554 kg/year before invasion to 264 kg/year after invasion (<italic>p</italic> &lt; 0.001). In contrast to declining yields, crop prices increased significantly across all sites following the invasion. In Bugesera, prices rose from 647 FRw/year to 988 FRw/year (<italic>p</italic> &lt; 0.001), while in Ngoma, prices increased from 691 FRw/year to 1,044 FRw/year (<italic>p</italic> &lt; 0.001). In Rwamagana District, a similar trend was recorded, with prices rising from 680 FRw/year to 1,041 FRw/year (<italic>p</italic> &lt; 0.001). The Kruskal–Wallis tests confirmed significant differences in both crop yield quantity (<italic>H</italic> = 23.8, <italic>p</italic> &lt; 0.001) and crop prices (<italic>H</italic> = 7.5, <italic>p</italic> &lt; 0.001) among the three study sites.</p>
          <table-wrap id="T5" position="float" orientation="portrait">
            <label>Table 5.</label>
            <caption>
              <p>Perceived changes in crop yield quantity and price before and after water hyacinth i nvasion.</p>
            </caption>
            <table>
              <tbody>
                <tr>
                  <th rowspan="1" colspan="1">
                    <bold>Site</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Mean crop yield before (kg/year)</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Mean crop yield after (kg/year)</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Statistical test</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Mean price before (FRw/year)</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Mean price after (FRw/year)</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Statistical test</bold>
                  </th>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Bugesera</td>
                  <td rowspan="1" colspan="1">730</td>
                  <td rowspan="1" colspan="1">162</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 2,438***</td>
                  <td rowspan="1" colspan="1">647</td>
                  <td rowspan="1" colspan="1">988</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 15,279***</td>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Ngoma</td>
                  <td rowspan="1" colspan="1">1,143</td>
                  <td rowspan="1" colspan="1">497</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 2,731***</td>
                  <td rowspan="1" colspan="1">691</td>
                  <td rowspan="1" colspan="1">1044</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 16,184***</td>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Rwamagana</td>
                  <td rowspan="1" colspan="1">554</td>
                  <td rowspan="1" colspan="1">264</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 5,970***</td>
                  <td rowspan="1" colspan="1">680</td>
                  <td rowspan="1" colspan="1">1041</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 16,201***</td>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Among sites</td>
                  <td rowspan="1" colspan="1"/>
                  <td rowspan="1" colspan="1"/>
                  <td rowspan="1" colspan="1"><italic>H</italic> = 23.8***</td>
                  <td rowspan="1" colspan="1"/>
                  <td rowspan="1" colspan="1"/>
                  <td rowspan="1" colspan="1"><italic>H</italic> = 7.5***</td>
                </tr>
              </tbody>
            </table>
            <table-wrap-foot>
              <fn>
                <p>Source: Field Survey (September 2025). Note: *** indicates significance at <italic>p</italic> &lt; 0.05; <italic>H</italic> = Kruskal–Wallis test statistic; <italic>W</italic> = Wilcoxon rank-sum test statistic; FRw = Rwandan francs.</p>
              </fn>
            </table-wrap-foot>
          </table-wrap>
        </sec>
        <sec sec-type="Impacts of water hyacinth on transport" id="sec18">
          <title>Impacts of water hyacinth on transport</title>
          <p>When respondents were asked to indicate the major effects of water hyacinth proliferation on local transport, they reported that the spread of water hyacinth worsened local transportation by causing longer travel times, higher fuel use, lower income, and even boat abandonment. These problems were reported more often in areas with high levels of water hyacinth invasion, while people in less affected areas usually did not notice any impact on local transport.</p>
          <p>These perceptions are supported by quantitative data on transport income, which demonstrate that water hyacinth invasion significantly reduced earnings for local transport operators. As presented in Table <xref ref-type="table" rid="T6">6</xref>, substantial reductions were observed in Bugesera District, where the mean annual transport income decreased from 68,705 FRw/year to 30,119 FRw/year (<italic>p</italic> = 0.006). There was also a significant decline in Ngoma District, with income decreasing from 111,974 FRw/year to 70,970 FRw/year (<italic>p</italic> &lt; 0.001). A similar decline was observed in Rwamagana District, where transport income decreased from 278,371 FRw/year to 167,447 FRw/year (<italic>p</italic> &lt; 0.001). The Kruskal–Wallis test confirmed significant differences in transport income among the three study sites (<italic>H</italic> = 40.9, <italic>p</italic> &lt; 0.001).</p>
          <table-wrap id="T6" position="float" orientation="portrait">
            <label>Table 6.</label>
            <caption>
              <p>Comparison of transport income before and after water hyacinth invasion in the study area.</p>
            </caption>
            <table>
              <tbody>
                <tr>
                  <th rowspan="1" colspan="1">
                    <bold>Site</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Mean income before (FRw/year)</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Mean income after (FRw/year)</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold>Statistical test</bold>
                  </th>
                  <th rowspan="1" colspan="1">
                    <bold><italic>p</italic>-value</bold>
                  </th>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Bugesera</td>
                  <td rowspan="1" colspan="1">68,705</td>
                  <td rowspan="1" colspan="1">30,119</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 96.5</td>
                  <td rowspan="1" colspan="1">0.006 ***</td>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Ngoma</td>
                  <td rowspan="1" colspan="1">111,974</td>
                  <td rowspan="1" colspan="1">70,970</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 2,888</td>
                  <td rowspan="1" colspan="1">&lt;0.001 ***</td>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Rwamagana</td>
                  <td rowspan="1" colspan="1">278,371</td>
                  <td rowspan="1" colspan="1">167,447</td>
                  <td rowspan="1" colspan="1"><italic>W</italic> = 2,765</td>
                  <td rowspan="1" colspan="1">&lt;0.001 ***</td>
                </tr>
                <tr>
                  <td rowspan="1" colspan="1">Among sites</td>
                  <td rowspan="1" colspan="1"/>
                  <td rowspan="1" colspan="1"/>
                  <td rowspan="1" colspan="1"><italic>H</italic> = 40.9</td>
                  <td rowspan="1" colspan="1">&lt; 0.001 ***</td>
                </tr>
              </tbody>
            </table>
            <table-wrap-foot>
              <fn>
                <p>Source: Field Survey (September 2025). Note: *** indicates significance at <italic>p</italic> &lt; 0.05; <italic>H</italic> = Kruskal–Wallis test statistic; <italic>W</italic> = Wilcoxon rank-sum test statistic; FRw = Rwandan francs.</p>
              </fn>
            </table-wrap-foot>
          </table-wrap>
        </sec>
      </sec>
    </sec>
    <sec sec-type="Discussion" id="sec19">
      <title>Discussion</title>
      <p>The study found that the local riparian communities around the Rweru-Mugesera wetland complex were generally knowledgeable about water hyacinth, although knowledge levels varied across the three study sites. This variation reflects differences in infestation severity and the extent of community exposure to the invasive species. In areas where infestation was most severe, 92.5% of respondents reported being knowledgeable about the weed. This high awareness likely results from frequent interaction with water hyacinth, which directly affects fishing, water access, agriculture, and other livelihood activities. Communities facing persistent biological invasions tend to develop greater species recognition and practical knowledge due to daily exposure to the invasive species and its socio-economic consequences (<xref ref-type="bibr" rid="B27">Ligeve et al. 2012</xref>; <xref ref-type="bibr" rid="B1">Abba et al. 2025</xref>), whereas in the absence of visible infestation and associated impacts, community members are less likely to recognize invasive species as an environmental concern (<xref ref-type="bibr" rid="B39">Mwangi and Swallow 2005</xref>).</p>
      <p>Neither gender nor education level had any influence on respondents’ knowledge of water hyacinth across the study sites, except in Ngoma District, where an association between education and knowledge was observed. This implies that awareness of water hyacinth proliferation is widespread through lived experiences rather than through formal education (<xref ref-type="bibr" rid="B43">Ndimele et al. 2011</xref>). This is in line with findings from a study conducted in a semi-arid area of Zimbabwe on farmers’ local knowledge, perceptions, and attitudes toward water hyacinth, where both educated and less-educated community members exhibited a similar understanding of water hyacinth ecology (<xref ref-type="bibr" rid="B18">Hungwe et al. 2016</xref>). Similarly, a study conducted by <xref ref-type="bibr" rid="B13">Gudina et al. (2024)</xref> in the Central Rift Valley of Ethiopia reported that the possession of local knowledge depends not only on formal education but also on direct experience and interaction with the environment.</p>
      <p>With regard to the status of water hyacinth in the wetland complex, most respondents (62.9%) associated its increase with agricultural runoff, which increased nutrient enrichment in the wetland system. Such nutrient loading creates favorable conditions for rapid water hyacinth spread, as nutrients from fertilizers, livestock waste, and eroded soil often enter wetlands, creating eutrophic conditions that favor water hyacinth propagation (<xref ref-type="bibr" rid="B56">Villamagna and Murphy 2010</xref>; <xref ref-type="bibr" rid="B14">Harun et al. 2021</xref>). Poor land-use practices, such as unsustainable waste disposal, were also mentioned as contributing factors. A study by <xref ref-type="bibr" rid="B43">Ndimele et al. (2011)</xref> reported that agricultural runoff and urban wastewater inflows significantly fuel water hyacinth expansion by enriching aquatic environments with excess nutrients.</p>
      <p>The perceived decline in water hyacinth coverage in the study area was mostly due to mechanical harvesting carried out by fishing cooperatives. Manual and mechanical water hyacinth removal efforts have the capacity to reduce its biomass in East African lakes for a period of time (<xref ref-type="bibr" rid="B9">Essl et al. 2011</xref>; <xref ref-type="bibr" rid="B53">Shackleton et al. 2017</xref>). In addition to mechanical removal, respondents mentioned the use of water hyacinth for animal feed and handicraft production as factors contributing to the reduction of water hyacinth spread. Water hyacinth is suitable for use as livestock feed due to its nutrient content, and its use is considered as one of the more effective and sustainable approaches to managing its growth (<xref ref-type="bibr" rid="B11">Gichuki et al. 2012</xref>; <xref ref-type="bibr" rid="B35">Moyo et al. 2013</xref>; <xref ref-type="bibr" rid="B15">Heuzé et al. 2015</xref>; <xref ref-type="bibr" rid="B47">Rajan et al. 2022</xref>).</p>
      <p>Beyond its value as animal feed, water hyacinth was also used as a raw material for handicrafts such as mats, carpets, tissue boxes, trays, hats, sandals, shopping bags, and handbags (<xref ref-type="bibr" rid="B48">Rakotoarisoa et al. 2016</xref>; <xref ref-type="bibr" rid="B29">Mbaka 2023</xref>).</p>
      <p>Fishing activities are vital to the livelihoods of riparian communities and were among the socio-economic activities severely affected by water hyacinth infestation in the Rweru-Mugesera wetland complex. This impact is particularly severe because the riparian community in the area depends on fishing as a primary source of income and livelihood. Despite its economic significance, the fishing sector encounters considerable challenges due to environmental degradation, especially the proliferation of aquatic invasive species such as water hyacinth, which disrupts fishing operations and reduces productivity (<xref ref-type="bibr" rid="B14">Harun et al. 2021</xref>; <xref ref-type="bibr" rid="B29">Mbaka 2023</xref>). Water hyacinth damages fishing hooks and nets, increases fish prices, and reduces fish weight and catchability (<xref ref-type="bibr" rid="B30">Mbula 2016</xref>). This weed affects fishing activities by reducing the quantities of fish caught, destroying fishing gear, causing the disappearance of some important fish species, limiting access to fishing sites, and reducing the size of some fishing areas (<xref ref-type="bibr" rid="B36">Mujingni 2012</xref>). For instance, a study conducted in the Wouri River Basin, Cameroon, by <xref ref-type="bibr" rid="B25">Kenfack et al. (2019)</xref> found that water hyacinth destroyed fishing gear and reduced open-water areas, leading to lower fish catches and higher fish prices. In Lake Victoria, <xref ref-type="bibr" rid="B24">Kateregga and Sterner (2009)</xref> reported that water hyacinth reduced fish stocks of tilapia and young Nile perch by limiting access to breeding, nursing, and feeding grounds. It has also been reported that fish catch rates decreased because water hyacinth mats blocked access to fishing grounds, delayed access to markets, and increased fishing costs.</p>
      <p>Crop production is one of the rural livelihoods impacted by water hyacinth. The expansion of this weed on agricultural land causes a number of interrelated impacts, such as reductions in household crop production, especially for crops cultivated around the lake, and reduced crop costs. These effects are mainly due to the fact that water hyacinth promotes waterlogging in low-lying farmlands and interferes with land preparation and harvesting operations (<xref ref-type="bibr" rid="B26">Khotsa et al. 2025</xref>; <xref ref-type="bibr" rid="B62">Zewdie et al. 2025</xref>). In addition, it often forms dense floating mats that obstruct irrigation canals, drainage systems, and water flow, thereby reducing water availability for crops (<xref ref-type="bibr" rid="B6">Dersseh et al. 2019</xref>; <xref ref-type="bibr" rid="B12">Gosal et al. 2022</xref>). A study conducted in Lake Tana, Ethiopia, reported that water hyacinth reduced crop production by 75% and 100% in riparian agricultural systems (<xref ref-type="bibr" rid="B8">Enyew et al. 2020</xref>). Similarly, <xref ref-type="bibr" rid="B4">Damtie et al. (2022)</xref> found that household crop production, especially crops cultivated around the lake such as rice, was reduced due to the expansion of water hyacinth on agricultural land.</p>
      <p>The most commonly reported impacts of water hyacinth infestation on transport in this study included longer travel times, reflecting how dense water hyacinth mats physically obstruct waterways and make navigation slower and riskier, forcing boat operators to take longer routes, travel at reduced speeds, or sometimes wait for the weed to be cleared (<xref ref-type="bibr" rid="B24">Kateregga and Sterner 2009</xref>; <xref ref-type="bibr" rid="B54">Tewabe et al. 2017</xref>). The reported increases in fuel use and reductions in income are consistent with higher operating costs and lost working hours because boat operators must spend more time cutting or avoiding water hyacinth, fuel consumption rises, and the number of profitable trips is reduced, which in turn leads to reduction in household revenues (<xref ref-type="bibr" rid="B25">Kenfack et al. 2019</xref>). In the Tano River and Tano Lagoon in Ghana, <xref ref-type="bibr" rid="B17">Honlah et al. (2019)</xref> reported that water hyacinth invasion increased the length of time farmers required to access their farms, mainly because they had to use their hands to remove water hyacinth from their designated routes to pave the way for their canoes. Similarly, in the Wouri River Basin in Douala, Cameroon, water hyacinth invasion significantly increased the time required to travel between villages, mainly because waterways were blocked by dense mats of the weed, making certain routes completely inaccessible. Therefore, the blockade of river transport led to a decline in passenger numbers, which contributed to reduced income for transport operators. Average monthly income from water transport fell sharply from approximately US$200 before the spread of water hyacinth to approximately US$50 after the spread of water hyacinth (<xref ref-type="bibr" rid="B25">Kenfack et al. 2019</xref>).</p>
    </sec>
    <sec sec-type="Conclusion" id="sec20">
      <title>Conclusion</title>
      <p>This study examined local riparian communities’ perceptions of water hyacinth and its socio-economic impacts in three districts surrounding the Rweru-Mugesera wetland complex in Eastern Rwanda. These communities showed extensive awareness of water hyacinth invasion, regardless of their gender, occupation, or educational background. Most respondents indicated that water hyacinth had increased over time, mainly due to fertilizer runoff from agricultural activities and increased temperatures associated with climate change. The common management strategies they highlighted included manual removal, utilization as animal feed, and production of handicrafts. The findings further indicate that water hyacinth has significantly disturbed local riparian livelihoods. This affected the fishing sector because fishing equipment was frequently damaged and access to fishing grounds became more difficult, leading to decreased fish production and rising fish prices. Agricultural activities were also negatively impacted due to reduced water availability for crops, decreased yields, and rising crop prices. Dense mats of water hyacinth slowed water transport, increased transportation costs, reduced transport-related income, and, in some cases, made boat operations impossible.</p>
      <p>Although this study provides valuable insights into community perceptions of water hyacinth and its effects, the findings are primarily based on local perception data and are limited to three districts surrounding the Rweru-Mugesera wetland complex. Future research should incorporate long-term environmental monitoring, economic assessments, and scientific measurements to more accurately evaluate the severity of these impacts and determine the most effective management strategies over time. To address this challenge, a coordinated management plan for water hyacinth is required. National and local stakeholders, as well as community organizations, should work in close collaboration to reduce nutrient runoff by promoting improved agricultural practices, establish regular monitoring, and support manual removal efforts. Policy measures should also facilitate the conversion of harvested water hyacinth into value-added products such as handicrafts, compost, animal feed, and bioenergy through training, financial support, and market linkages. Such collaborative efforts can mitigate the spread of water hyacinth, enhance local incomes, strengthen wetland resilience, and support sustainable management of the Rweru-Mugesera wetland complex.</p>
    </sec>
  </body>
  <back>
    <ack>
      <title>Acknowledgments</title>
      <p>The authors acknowledge the Partnership for Skills in Applied Sciences, Engineering and Technology-Regional Scholarship and Innovation Fund (PASET-RSIF) for the scholarship and financial support. They thank the local administrative authorities for authorizing this study and are grateful to all participants who agreed to share their insights about water hyacinth infestation, its impacts, and control strategies.</p>
    </ack>
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    <sec sec-type="Additional information" id="sec21">
      <title>Additional information</title>
      <sec sec-type="Conflict of interest" id="sec22">
        <title>Conflict of interest</title>
        <p>The authors have declared that no competing interests exist.</p>
      </sec>
      <sec sec-type="Ethical statement" id="sec23">
        <title>Ethical statement</title>
        <p>No ethical statement was reported.</p>
      </sec>
      <sec sec-type="Artificial Intelligence (AI) use" id="sec24">
        <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>No AI tools were used in the preparation of this manuscript.</p>
      </sec>
      <sec sec-type="Funding" id="sec25">
        <title>Funding</title>
        <p>This work is supported by the Partnership for Skills in Applied Sciences, Engineering and Technology-Regional Scholarship and Innovation Fund (PASET-RSIF).</p>
      </sec>
      <sec sec-type="Author contributions" id="sec26">
        <title>Author contributions</title>
        <p>Conceptualization: JT, DS, GR. Data curation: DS, GR, JT. Formal analysis: JT. Funding acquisition: JT. Methodology: DS, GR, JT. Resources: JT. Software: JT. Supervision: DS, GR. Validation: JT. Visualization: GR, DS, JT. Writing - original draft: GR, DS, JT. Writing - review and editing: GR, DS, JT.</p>
      </sec>
      <sec sec-type="Author ORCIDs" id="sec27">
        <title>Author ORCIDs</title>
        <p>J. Tuyishime <ext-link xlink:href="https://orcid.org/0009-0009-4260-5087" ext-link-type="uri">https://orcid.org/0009-0009-4260-5087</ext-link></p>
        <p>G. Rwanyiziri <ext-link xlink:href="https://orcid.org/0000-0001-8138-2538" ext-link-type="uri">https://orcid.org/0000-0001-8138-2538</ext-link></p>
        <p>D. Soro <ext-link xlink:href="https://orcid.org/0000-0003-2260-6030" ext-link-type="uri">https://orcid.org/0000-0003-2260-6030</ext-link></p>
      </sec>
      <sec sec-type="Data availability" id="sec28">
        <title>Data availability</title>
        <p>The data supporting the findings of this study are available in the main text and Supplementary Information. Any additional datasets used during the study are available from the first author, Josine Tuyishime (<ext-link xlink:href="mailto:josinetuyi@gmail.com" ext-link-type="uri">josinetuyi@gmail.com</ext-link>), upon reasonable request.</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="arpha">BCFA6F04-DBFE-52FB-A158-B2BDF9454BA3</object-id>
        <label>Supplementary material 1</label>
        <caption>
          <p>Questionnaire</p>
        </caption>
        <statement content-type="dataType">
          <label>Data type</label>
          <p>docx</p>
        </statement>
        <statement content-type="notes">
          <label>Explanation note</label>
          <p>The structured questionnaire used to assess riparian community perceptions of water hyacinth (<italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Eichhornia">Eichhornia</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="crassipes">crassipes</tp:taxon-name-part></tp:taxon-name></italic>) infestation and its socio-economic impacts in the Rweru-Mugesera wetland complex, Eastern Rwanda.</p>
        </statement>
        <media xlink:href="nature_conservation-64-163_article-193189__-s001.docx" mimetype="application" mime-subtype="vnd.openxmlformats-officedocument.wordprocessingml.document" position="float" orientation="portrait" id="oo_1753566.docx">
          <uri content-type="original_file">https://binary.pensoft.net/file/1753566</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"> Josine Tuyishime, Gaspard Rwanyiziri, Dodiomon Soro</attrib>
      </supplementary-material>
    </sec>
  </back>
</article>
