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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Ecol. Evol.</journal-id>
<journal-title>Frontiers in Ecology and Evolution</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Ecol. Evol.</abbrev-journal-title>
<issn pub-type="epub">2296-701X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fevo.2022.875578</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Ecology and Evolution</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Assessing and Predicting the Distribution of Riparian Invasive Plants in Continental Portugal</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Pabst</surname> <given-names>Rebecca</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1466243/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Dias</surname> <given-names>Filipe S.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/921855/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Borda-de-&#x00C1;gua</surname> <given-names>Lu&#x00ED;s</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/417686/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Rodr&#x00ED;guez-Gonz&#x00E1;lez</surname> <given-names>Patricia Mar&#x00ED;a</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/155688/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Capinha</surname> <given-names>C&#x00E9;sar</given-names></name>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
<xref ref-type="aff" rid="aff6"><sup>6</sup></xref>
<xref ref-type="corresp" rid="c002"><sup>&#x002A;</sup></xref>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>CIBIO/InBio, Centro de Investiga&#x00E7;&#x00E3;o em Biodiversidade e Recursos Gen&#x00E9;ticos, Laborat&#x00F3;rio Associado, Universidade do Porto</institution>, <addr-line>Vair&#x00E3;o</addr-line>, <country>Portugal</country></aff>
<aff id="aff2"><sup>2</sup><institution>CIBIO/InBio, Centro de Investiga&#x00E7;&#x00E3;o em Biodiversidade e Recursos Gen&#x00E9;ticos, Laborat&#x00F3;rio Associado, Instituto Superior de Agronomia, Universidade de Lisboa</institution>, <addr-line>Lisbon</addr-line>, <country>Portugal</country></aff>
<aff id="aff3"><sup>3</sup><institution>BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO</institution>, <addr-line>Vair&#x00E3;o</addr-line>, <country>Portugal</country></aff>
<aff id="aff4"><sup>4</sup><institution>Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa</institution>, <addr-line>Lisbon</addr-line>, <country>Portugal</country></aff>
<aff id="aff5"><sup>5</sup><institution>Laborat&#x00F3;rio Associado Terra</institution>, <country>Portugal</country></aff>
<aff id="aff6"><sup>6</sup><institution>Centro de Estudos Geogr&#x00E1;ficos, Instituto de Geografia e Ordenamento do Territ&#x00F3;rio, Universidade de Lisboa</institution>, <addr-line>Lisbon</addr-line>, <country>Portugal</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Jorge Capelo, University of Porto, Portugal</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: H&#x00E9;lia Marchante, Instituto Polit&#x00E9;cnico de Coimbra, Portugal; Albano Augusto Figueiredo, University of Coimbra, Portugal; S&#x00ED;lvia Ribeiro, Universidade de &#x00C9;vora, Portugal</p></fn>
<corresp id="c001">&#x002A;Correspondence: Rebecca Pabst, <email>pabst.rebecca@gmail.com</email></corresp>
<corresp id="c002">C&#x00E9;sar Capinha, <email>cesarcapinha@campus.ul.pt</email></corresp>
<fn fn-type="other" id="fn004"><p>This article was submitted to Population, Community, and Ecosystem Dynamics, a section of the journal Frontiers in Ecology and Evolution</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>06</day>
<month>05</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>10</volume>
<elocation-id>875578</elocation-id>
<history>
<date date-type="received">
<day>14</day>
<month>02</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>30</day>
<month>03</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2022 Pabst, Dias, Borda-de-&#x00C1;gua, Rodr&#x00ED;guez-Gonz&#x00E1;lez and Capinha.</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Pabst, Dias, Borda-de-&#x00C1;gua, Rodr&#x00ED;guez-Gonz&#x00E1;lez and Capinha</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<p>The number of alien plant species is growing steadily across all world regions. These numbers tend to be exceptionally high in riparian ecosystems, often with substantial negative consequences for native species communities and ecosystem services provision. Here, we map the richness of invasive alien plant species in riparian ecosystems of continental Portugal, assess the relative importance of human and natural factors in shaping the uncovered patterns, and predict richness values along watercourses and at the municipal level for the whole study area. We found a higher richness of invasive alien plants in low altitudes and in downstream areas where human concentration is high. As time progresses, ongoing and increasing levels of socio-economic activity and globalization of plant trade will conceivably lead to a higher number of alien species becoming established. National and sub-national measures aiming to prevent and manage biological invasions in riparian ecosystems require coordinated efforts involving both local entities and those with responsibilities in the management of upstream catchment areas. These efforts must also be targeted to achieve future biodiversity protection goals as part of the EU Biodiversity Strategy for 2030.</p>
</abstract>
<kwd-group>
<kwd>alien invasive plants</kwd>
<kwd>biological invasions</kwd>
<kwd>restoration</kwd>
<kwd>riparian ecosystems</kwd>
<kwd>distribution modeling</kwd>
<kwd>invasion hotspots</kwd>
</kwd-group>
<contract-sponsor id="cn001">Funda&#x00E7;&#x00E3;o para a Ci&#x00EA;ncia e a Tecnologia<named-content content-type="fundref-id">10.13039/501100001871</named-content></contract-sponsor>
<counts>
<fig-count count="4"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="84"/>
<page-count count="10"/>
<word-count count="7100"/>
</counts>
</article-meta>
</front>
<body>
<sec id="S1" sec-type="intro">
<title>Introduction</title>
<p>Global trade and travel activities are leading to an increasing accumulation of alien species across regions of the world (<xref ref-type="bibr" rid="B15">CBD, 2014</xref>; <xref ref-type="bibr" rid="B69">Seebens et al., 2017</xref>; <xref ref-type="bibr" rid="B5">Beaury et al., 2021</xref>). The establishment and subsequent spread of these species in non-native regions have major negative impacts on economic activities and human wellbeing (<xref ref-type="bibr" rid="B40">IPBES, 2019</xref>; <xref ref-type="bibr" rid="B25">Diagne et al., 2021</xref>), and is among the major threats to biodiversity and species extinction worldwide (<xref ref-type="bibr" rid="B19">Clavero and Garcia-Berthou, 2005</xref>; <xref ref-type="bibr" rid="B6">Bellard et al., 2016</xref>). Recovering from ecosystem degradation has been one major motivation of global restoration effort and current policies (e.g., UN Decade on Ecosystem Restoration and EU Green Deal). However, managing and controlling invasive alien plant species (i.e., the subset of alien plant species that become established and cause negative impacts; hereafter &#x201C;IAP&#x201D;) can be expensive, laborious, and often technically difficult to achieve success (<xref ref-type="bibr" rid="B46">Marais et al., 2004</xref>; <xref ref-type="bibr" rid="B34">Haubrock et al., 2021</xref>). In fact, previous actions against IAP have not been sufficient to counteract their increasing globalization. To support these actions, substantial research has been done to understand the main dispersal pathways (<xref ref-type="bibr" rid="B55">Py&#x0161;ek and Richardson, 2010</xref>), and spatial distribution patterns of IAP (<xref ref-type="bibr" rid="B75">Vil&#x00E0; et al., 2010</xref>). However, research on the distribution patterns of IAP or alien species in general has, thus far, focused mostly on large scales, like global or national studies (<xref ref-type="bibr" rid="B18">Chytr&#x00FD; et al., 2009</xref>; <xref ref-type="bibr" rid="B69">Seebens et al., 2017</xref>; <xref ref-type="bibr" rid="B28">Essl et al., 2019</xref>; <xref ref-type="bibr" rid="B11">Capinha et al., 2020</xref>), but detailed knowledge on the patterns and drivers of invasion at local scales&#x2014;which are key for supporting management and restoration efforts&#x2014;remains missing for many regions and taxonomic groups.</p>
<p>Here we use a comprehensive local scale data set to examine and describe the geography of invasions by alien plants in riparian ecosystems of continental Portugal. Continental Portugal harbors at least 3,314 vascular plant species (<xref ref-type="bibr" rid="B23">de Sequeira et al., 2012</xref>), of which at least 772 are alien (<xref ref-type="bibr" rid="B22">de Almeida, 2018</xref>) and 113 are listed as invasive species in the Portuguese law (<xref ref-type="bibr" rid="B44">Law No. 92/2019, 2019</xref>). Previous works have addressed plant invasions in Portuguese riverine systems (<xref ref-type="bibr" rid="B8">Bernez et al., 2006</xref>; <xref ref-type="bibr" rid="B2">Aguiar and Ferreira, 2013</xref>), however, how IAP are distributed in riparian ecosystems across the country remains largely unknown, making it difficult to identify areas under higher pressure from invasions and for which management or restoration efforts would be most needed. This occurs despite riparian ecosystems often being biodiversity hotspots (<xref ref-type="bibr" rid="B27">Duarte et al., 2004</xref>; <xref ref-type="bibr" rid="B71">Stella et al., 2013</xref>) providing a range of ecosystem functions and services related to water quality, microclimate, structural habitat, a bottom-up energy provider for the food web, and riverbank stability (<xref ref-type="bibr" rid="B51">Naiman et al., 2005</xref>), and being one of the most susceptible habitats to invasion (<xref ref-type="bibr" rid="B62">Richardson et al., 2007</xref>), often hosting a high diversity of IAP (<xref ref-type="bibr" rid="B76">Vil&#x00E0; et al., 2007</xref>; <xref ref-type="bibr" rid="B16">Chytr&#x00FD; et al., 2008</xref>).</p>
<p>In light of previous findings for other regions and taxa, a few hypotheses can be made about the drivers of invasion patterns in Portuguese riparian ecosystems. Propagule pressure resulting from proximity to urban areas and human activities may lead to the introduction of cosmopolitan, ornamental, nitrophilous, or cultivated species, originating hotspots of alien plant species (<xref ref-type="bibr" rid="B35">Hruska et al., 2008</xref>; <xref ref-type="bibr" rid="B37">Hulme, 2009</xref>; <xref ref-type="bibr" rid="B58">Py&#x0161;ek et al., 2010</xref>, <xref ref-type="bibr" rid="B56">2020</xref>). Land-use change is also an important driver of biodiversity change (<xref ref-type="bibr" rid="B67">Sala, 2000</xref>), disrupting ecological equilibrium and creating opportunities for alien species to establish (<xref ref-type="bibr" rid="B62">Richardson et al., 2007</xref>). Similarly, natural regular disturbances and the diversity of microhabitats, as well as movement of organisms, nutrients, and sediments create establishment opportunities and make riparian areas vulnerable to biological invasions (<xref ref-type="bibr" rid="B54">Py&#x0161;ek and Prach, 1994</xref>; <xref ref-type="bibr" rid="B13">Catford et al., 2014</xref>). Seasonal patterns of summer droughts and winter floods, typical of Mediterranean rivers, lead to nutrient pulses and extreme natural disturbances, conditions that are also considered to favor invasions (<xref ref-type="bibr" rid="B31">Gasith and Resh, 1999</xref>; <xref ref-type="bibr" rid="B20">Davis et al., 2000</xref>; <xref ref-type="bibr" rid="B71">Stella et al., 2013</xref>). Finally, climatic variation across wide spatial extents is also a likely relevant factor in determining the distribution of alien plant species (<xref ref-type="bibr" rid="B72">Thuiller et al., 2008</xref>).</p>
<p>In this context, the goals of this study are to (<italic>i</italic>) map the richness of invasive alien plant species in riparian ecosystems of continental Portugal, (<italic>ii</italic>) assess the human and natural factors related to spatial variation in the values of this variable, and (<italic>iii</italic>) predict richness values along water courses and at the municipal level for the whole study area.</p>
</sec>
<sec id="S2" sec-type="materials|methods">
<title>Materials and Methods</title>
<sec id="S2.SS1">
<title>Study Area</title>
<p>Continental Portugal is in southwestern Europe, on the Iberian Peninsula. The climate is characterized by mild winters and dry summers and has a strong latitudinal and altitudinal gradient (<xref ref-type="bibr" rid="B71">Stella et al., 2013</xref>). Mean annual temperatures range between 7&#x00B0;C in the mountains and 18&#x00B0;C along the southern coastline. In the northwest region, mean annual precipitation is the highest (&#x003E;3,000 mm/year), decreasing toward the south and amounting to around 500 mm/year (<xref ref-type="bibr" rid="B49">Miranda et al., 2002</xref>). Two bioclimatic regions are distinguished, the Temperate in the north-west and the Mediterranean in the remainder of the territory (<xref ref-type="bibr" rid="B64">Rodr&#x00ED;guez-Gonz&#x00E1;lez et al., 2008</xref>; <xref ref-type="bibr" rid="B63">Rivas-Mart&#x00ED;nez et al., 2011</xref>). While coastal regions are densely populated and heavily affected by agriculture and industry, the southern and eastern regions are characterized by scattered settlements and extensive agricultural fields (<xref ref-type="bibr" rid="B1">Aguiar et al., 2009</xref>).</p>
</sec>
<sec id="S2.SS2">
<title>Data Collection</title>
<p>We used data on vegetation collected between 2003 and 2006 in 404 sites located in 29 river basins in continental Portugal (<xref ref-type="bibr" rid="B39">Instituto da &#x00C1;gua, 2012</xref>) (<xref ref-type="fig" rid="F1">Figure 1</xref>). This work was conducted during the pre-assessment surveys for the implementation of the Water Framework Directive (WFD) (<xref ref-type="bibr" rid="B78">WFD EU/2000/60; European Council, 2000</xref>). Field work was done according to the protocol established for the WFD implementation. In each site, all macrophyte species (excluding macroalgae) occurring in the water channel and banks along a 100 m length of the river were identified and their percentage cover was visually estimated (<xref ref-type="bibr" rid="B1">Aguiar et al., 2009</xref>). The distribution of sampling sites evenly covers all types of rivers in Portugal, providing a very broad coverage for statistical modeling. To ensure a representative analysis we only considered river basins with at least five samples available, which resulted in the use of 15 basins and 382 samples. For each site we measured the total number of IAP. Species were classified as invasive according to the latest listing of invasive species defined in the Portuguese Law (<xref ref-type="bibr" rid="B44">Law No. 92/2019, 2019</xref>) and specifically concerning continental Portugal - as IAP are also listed for the regions of Azores and Madeira. According to it, an invasive species is an alien species whose introduction into the wild or its spread within a given territory threatens or has an adverse impact on biological diversity, on ecosystem services associated with it, or has other adverse impacts. This list of invasive species includes mainly terrestrial species (<xref ref-type="supplementary-material" rid="DS1">Supplementary Table 1</xref>), therefore we refer to riparian habitats throughout our work.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption><p>Map of continental Portugal with the location of the 382 analyzed vegetation plots (red dots) and the 15 river basins that were considered; the black lines delimit the river basins. This figure was created using QGIS Version 3.14 with the coordinate reference system ETRS 89/Portugal TM06 (<xref ref-type="bibr" rid="B60">QGIS Development Team, 2020</xref>).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-10-875578-g001.tif"/>
</fig>
</sec>
<sec id="S2.SS3">
<title>Predictor Variables</title>
<p>Based on relevant literature (<xref ref-type="bibr" rid="B62">Richardson et al., 2007</xref>; <xref ref-type="bibr" rid="B71">Stella et al., 2013</xref>; <xref ref-type="bibr" rid="B56">Py&#x0161;ek et al., 2020</xref>) and on our own expertise, we selected a set of variables that reflect expected relationships between natural and human factors and patterns of invasion by alien plants in riparian ecosystems. These variables reflect topographic and hydrological features as well as land use and socioeconomic settings. The identification, description, source, and spatial resolution of each variable are given in <xref ref-type="table" rid="T1">Table 1</xref>. To extract the value of each variable for each sampled site, we used a circular buffer with a 500 m radius within the centroid of the site. By extracting values within this radius of the sampled site, we aim to capture the effect of local conditions and of the surrounding landscape, which jointly shape the invasion patterns at local scales (<xref ref-type="bibr" rid="B52">Nov&#x00E1;k and Konvic&#x0306;ka, 2006</xref>). The only exception to this procedure was the variable slope, for which we used a 10 m radius, as we expect its effect to be mainly local, and primarily concerning water flow speed at the site. For climate variables, we used data from the 5 years prior to the recording period, as this period seems to have the strongest effect on the ecological conditions (<xref ref-type="bibr" rid="B26">Dias et al., 2015</xref>). The processing of all variables was performed in QGIS Version 3.14 (<xref ref-type="bibr" rid="B60">QGIS Development Team, 2020</xref>).</p>
<table-wrap position="float" id="T1">
<label>TABLE 1</label>
<caption><p>Covariates used in the generalized additive model aiming to explain and predict spatial variation in values of richness of invasive alien plants in riparian ecosystems.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left">Variables</td>
<td valign="top" align="left">Description and units</td>
<td valign="top" align="left">Source</td>
<td valign="top" align="center">Spatial resolution</td>
<td valign="top" align="left">Transformation</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Precipitation</td>
<td valign="top" align="left">Yearly amount (mm) average of 5 years prior to sampling period</td>
<td valign="top" align="left">Chelsa (<xref ref-type="bibr" rid="B41">Karger et al., 2017</xref>)</td>
<td valign="top" align="center">30 arc s</td>
<td valign="top" align="left"/></tr>
<tr>
<td valign="top" align="left">Temperature</td>
<td valign="top" align="left">Mean, max and min Temperature (&#x00B0;C) average of 5 years prior to sampling period</td>
<td valign="top" align="left">Chelsa (<xref ref-type="bibr" rid="B41">Karger et al., 2017</xref>)</td>
<td valign="top" align="center">30 arc s</td>
<td valign="top" align="left"/></tr>
<tr>
<td valign="top" align="left">Slope</td>
<td valign="top" align="left">Mean slope (degrees)</td>
<td valign="top" align="left"><xref ref-type="bibr" rid="B50">Monteiro-Henriques et al., 2016</xref></td>
<td valign="top" align="center">35 m</td>
<td valign="top" align="left">Log(x + 1)</td>
</tr>
<tr>
<td valign="top" align="left">Altitude</td>
<td valign="top" align="left">Mean elevation (m)</td>
<td valign="top" align="left"><xref ref-type="bibr" rid="B50">Monteiro-Henriques et al., 2016</xref></td>
<td valign="top" align="center">35 m</td>
<td valign="top" align="left">Log(x + 1)</td>
</tr>
<tr>
<td valign="top" align="left">Agriculture</td>
<td valign="top" align="left">Area (%)</td>
<td valign="top" align="left">COS, Version 2007 (<xref ref-type="bibr" rid="B24">DGT, 2018</xref>)</td>
<td valign="top" align="center">100 m</td>
<td valign="top" align="left"/></tr>
<tr>
<td valign="top" align="left">Urban</td>
<td valign="top" align="left">Area (%)</td>
<td valign="top" align="left">COS, Version 2007 (<xref ref-type="bibr" rid="B24">DGT, 2018</xref>)</td>
<td valign="top" align="center">100 m</td>
<td valign="top" align="left"/></tr>
<tr>
<td valign="top" align="left">Eucalyptus</td>
<td valign="top" align="left">Area (%)</td>
<td valign="top" align="left">COS, Version 2007 (<xref ref-type="bibr" rid="B24">DGT, 2018</xref>)</td>
<td valign="top" align="center">100 m</td>
<td valign="top" align="left">Log(x + 1)</td>
</tr>
<tr>
<td valign="top" align="left">Fire</td>
<td valign="top" align="left">Yes/no in the in 5 years prior to sampling period</td>
<td valign="top" align="left"><xref ref-type="bibr" rid="B38">ICNF, 2020</xref></td>
<td valign="top" align="center">10 m</td>
<td valign="top" align="left"/></tr>
<tr>
<td valign="top" align="left">Protected Area</td>
<td valign="top" align="left">Yes/no</td>
<td valign="top" align="left"><xref ref-type="bibr" rid="B73">UNEP-WCMC, 2017</xref></td>
<td valign="top" align="center">Not applicable</td>
<td valign="top" align="left"/></tr>
<tr>
<td valign="top" align="left">Contributing catchment</td>
<td valign="top" align="left">Area (ha)</td>
<td valign="top" align="left"><xref ref-type="bibr" rid="B50">Monteiro-Henriques et al., 2016</xref></td>
<td valign="top" align="center">35 m</td>
<td valign="top" align="left">Log(x + 1)</td>
</tr>
<tr>
<td valign="top" align="left">Distance to river source</td>
<td valign="top" align="left">Length (m)</td>
<td valign="top" align="left"><xref ref-type="bibr" rid="B50">Monteiro-Henriques et al., 2016</xref></td>
<td valign="top" align="center">35 m</td>
<td valign="top" align="left">Sqrt(x + 1)</td>
</tr>
<tr>
<td valign="top" align="left">Land Use Change Index</td>
<td valign="top" align="left">0&#x2013;1 scale</td>
<td valign="top" align="left">SEDAC (<xref ref-type="bibr" rid="B42">Kennedy et al., 2020</xref>)</td>
<td valign="top" align="center">1 km</td>
<td valign="top" align="left"/></tr>
<tr>
<td valign="top" align="left">Barrier density</td>
<td valign="top" align="left">barrier km<sup>&#x2013;1</sup></td>
<td valign="top" align="left"><xref ref-type="bibr" rid="B7">Belletti et al., 2020</xref></td>
<td valign="top" align="center">100 m</td>
<td valign="top" align="left"/></tr>
<tr>
<td valign="top" align="left">River basin</td>
<td valign="top" align="left">1&#x2013;29</td>
<td valign="top" align="left">CCM (<xref ref-type="bibr" rid="B77">Vogt and Foisneau, 2007</xref>)</td>
<td valign="top" align="center">100 m</td>
<td valign="top" align="left"/></tr>
<tr>
<td valign="top" align="left">River fragmentation indices (DOF, DOR, SED, USE, RDD; FLD)</td>
<td valign="top" align="left">0&#x2013;100</td>
<td valign="top" align="left">Free-flowing rivers (FFRs) (<xref ref-type="bibr" rid="B32">Grill et al., 2019</xref>)</td>
<td valign="top" align="center">15 arc s</td>
<td valign="top" align="left"/></tr>
</tbody>
</table></table-wrap>
</sec>
<sec id="S2.SS4">
<title>Statistical Analysis</title>
<p>To model the number of invasive alien plant species as a function of covariates, we used a generalized additive model (GAM), which accounts for linear and non-linear relationships between the response variable and the covariates. Since the response variable is discrete (i.e., a count), we considered three distributions, the Poisson distribution, the Tweedie distribution, and the negative binomial distribution. We fitted models with these three distributions and selected the one with the lowest Akaike information criterion (AIC) (<xref ref-type="bibr" rid="B9">Burnham et al., 2011</xref>). We used thin plate regression splines (<xref ref-type="bibr" rid="B80">Wood, 2003</xref>) as the basis for the model&#x2019;s smooth terms. The model was initiated by considering that the fit is highly &#x201C;wiggly&#x201D;, so a &#x201C;wiggliness&#x201D; penalty is added during the model fitting process, determined by the data (<xref ref-type="bibr" rid="B83">Wood, 2017</xref>). To reduce redundancy among covariates we selected a set of those with pairwise Pearson correlation coefficients below |0.7| and variance inflation factor below 3 (<xref ref-type="bibr" rid="B84">Zuur et al., 2009</xref>). During model fitting, we selected covariates by adding an additional penalty that allowed each smooth term to be removed (<xref ref-type="bibr" rid="B47">Marra and Wood, 2011</xref>). We used a significance level (&#x03B1;) of 0.05. To account for spatial autocorrelation, we added a two-dimension smoothing function with the <italic>x</italic> and <italic>y</italic> coordinates of the centroid of sampled sites. River basins were included in the model as a random effect to account for basin-level dependencies among samples (<xref ref-type="bibr" rid="B82">Wood, 2013</xref>). We used the R package &#x201C;mgcv&#x201D; version 1.8-34 (<xref ref-type="bibr" rid="B81">Wood, 2011</xref>) for model fitting. To assess the robustness of the fitted model we analyzed deviance residuals and checked for normal distribution and constant variance using the <italic>gam.check</italic> function from the &#x201C;mgcv&#x201D; package. To assess the amount of deviance explained by the model we also obtained the adjusted-<italic>R</italic><sup>2</sup> (<xref ref-type="bibr" rid="B83">Wood, 2017</xref>).</p>
<p>We also evaluated the predictive accuracy of the model. For this purpose, we used two cross-validation approaches. First, we used a leave one out cross validation (LOOCV), which consists in removing one site at a time, fitting the model with the remaining sites, and then making a prediction for the site that was left out. We repeated this process for each of the 382 sites and recorded the error estimates. Second, we used a repeated <italic>k</italic>-fold cross validation that consisted in randomly choosing 80% of the survey sites as a training set, fitting the model with those data, and making predictions for the remaining 20%. This procedure was repeated 100 times to maximize the representativity of sampled conditions in both calibration and validation partitions. To measure the agreement of predictions and left out observations, we used the Mean Absolute Error (MAE) from the package &#x201C;metrics&#x201D; (<xref ref-type="bibr" rid="B33">Hamner and Frasco, 2018</xref>) and Relative Absolute Error (RAE) from the package &#x201C;caret&#x201D; (<xref ref-type="bibr" rid="B43">Kuhn, 2021</xref>). A valuable property of the latter metric is that it provides a threshold beyond which predictions are considered uninformative. This threshold corresponds to a value of 100, whereby evaluation results higher than this threshold have a predictive accuracy worse than the one obtained by simply using the average of richness values in the evaluation data (<xref ref-type="bibr" rid="B79">Witten et al., 2017</xref>; <xref ref-type="bibr" rid="B10">Capinha et al., 2018</xref>).</p>
<p>Finally, we used the final model to make predictions for unsampled sites. This model used three predictor variables (see below), so we calculated the values of each in the same way we did for model fitting, but for 1 km grid cells along the river network supplied by <xref ref-type="bibr" rid="B77">Vogt and Foisneau (2007)</xref>. This grid cell resolution roughly matches the spatial area represented in the variables used for model calibration (i.e., a 500 m radius). We then applied the model to predict IAP richness in each cell. In addition, we also averaged the predicted values within each municipality, because this is an administrative division commonly considered in the development and implementation of environment-related initiatives, including biological invasion management and ecological restoration actions. All calculations were performed in R version 4.0.2 (<xref ref-type="bibr" rid="B61">R Core Team, 2021</xref>).</p>
</sec>
</sec>
<sec id="S3" sec-type="results">
<title>Results</title>
<p>A total of 960 plant species were found in riparian ecosystems of continental Portugal. In 382 sites 97 alien species were found and of these 34 are also considered as invasive plant species. The highest number of alien species found in a single plot was 15, and the highest number of invasive alien species was 10. Of the 382 sites, IAP were recorded in 297 sites (77.75%). The IAP species recorded more frequently were <italic>Bidens frondosa</italic> (207 sites), <italic>Conyza bonariensis</italic> (131 sites), <italic>Phytolacca americana</italic> (96 sites), and <italic>Arundo donax</italic> (83 sites) (<xref ref-type="supplementary-material" rid="DS1">Supplementary Table 1</xref>).</p>
<p>The model results suggest that richness of invasive plant species is negatively associated with altitude, and positively with the size of upstream catchment and percentage of urban area (<xref ref-type="fig" rid="F2">Figure 2</xref> and <xref ref-type="table" rid="T2">Table 2</xref>). The model explained a substantial amount of variability in richness values, as indicated by an adjusted-<italic>R</italic><sup>2</sup> of 0.63.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption><p>Smooth functions (black lines) for the predictor variables &#x201C;altitude&#x201D;, &#x201C;basin area&#x201D;, and &#x201C;urban area&#x201D;. The gray shading represents the 95% confidence intervals. The number in brackets on the vertical axis labels is the effective degrees of freedom of the smooth term (1 corresponds to a linear term).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-10-875578-g002.tif"/>
</fig>
<table-wrap position="float" id="T2">
<label>TABLE 2</label>
<caption><p>Results of the generalized additive model explaining values of richness of invasive alien plants in riparian ecosystems.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left">Covariates</td>
<td valign="top" align="center">edf</td>
<td valign="top" align="center">Ref.df</td>
<td valign="top" align="center">Chi.sq</td>
<td valign="top" align="center"><italic>p</italic>-value</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Altitude</td>
<td valign="top" align="center">5.94</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">52.06</td>
<td valign="top" align="center">&#x003C;2e-16<xref ref-type="table-fn" rid="t2fns1">&#x002A;&#x002A;&#x002A;</xref></td>
</tr>
<tr>
<td valign="top" align="left">Basin area</td>
<td valign="top" align="center">1.03</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">19.87</td>
<td valign="top" align="center">3.55e-06<xref ref-type="table-fn" rid="t2fns1">&#x002A;&#x002A;&#x002A;</xref></td>
</tr>
<tr>
<td valign="top" align="left">Urban</td>
<td valign="top" align="center">0.90</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">9.46</td>
<td valign="top" align="center">0.000931<xref ref-type="table-fn" rid="t2fns1">&#x002A;&#x002A;&#x002A;</xref></td>
</tr>
<tr>
<td valign="top" align="left">Latitude and longitude</td>
<td valign="top" align="center">11.84</td>
<td valign="top" align="center">29</td>
<td valign="top" align="center">233.91</td>
<td valign="top" align="center">&#x003C;2e-16<xref ref-type="table-fn" rid="t2fns1">&#x002A;&#x002A;&#x002A;</xref></td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn><p><italic>R<sup>2</sup> = 0.634 and Deviance explained = 60.2%.</italic></p></fn>
<fn id="t2fns1"><p><italic>[n.s. not significant; (.)p &#x003C; 0.1; &#x002A;p &#x003C; 0.05; &#x002A;&#x002A;p &#x003C; 0.01; &#x002A;&#x002A;&#x002A;p &#x003C; 0.001].</italic></p></fn>
</table-wrap-foot>
</table-wrap>
<p>The <italic>k</italic>-fold validation procedure recorded a mean absolute error (MAE) of 1.03 &#x00B1; 0.1 species and relative absolute error (RAE) of 0.63 &#x00B1; 0.06 (<xref ref-type="table" rid="T3">Table 3</xref>). The error values of the LOOCV show similar values (<xref ref-type="table" rid="T3">Table 3</xref>). The relatively small deviations between predicted and observed richness values support the robustness of the model and its use for predictive purposes.</p>
<table-wrap position="float" id="T3">
<label>TABLE 3</label>
<caption><p>Average and standard deviation of error measures of generalized additive model predicting values of richness of invasive alien plants in riparian ecosystems.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left">Model</td>
<td valign="top" align="center">Adjusted <italic>R</italic><sup>2</sup></td>
<td valign="top" align="center">MAE</td>
<td valign="top" align="center">RAE</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">LOOCV</td>
<td valign="top" align="center">0.58</td>
<td valign="top" align="center">1.01</td>
<td valign="top" align="center">0.61</td>
</tr>
<tr>
<td valign="top" align="left">Mean <italic>k</italic>-fold validation (<italic>n</italic> = 100)</td>
<td valign="top" align="center">0.57 &#x00B1; 0.09</td>
<td valign="top" align="center">1.03 &#x00B1; 0.10</td>
<td valign="top" align="center">0.63 &#x00B1; 0.06</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn><p><italic>Values are given for the leave-one-out cross-validation and a 20% data validation partition with 100 repetitions.</italic></p></fn>
</table-wrap-foot>
</table-wrap>
<p>The prediction maps show that there is a higher diversity of IAP in the central and northwestern, lowland, regions of continental Portugal (<xref ref-type="fig" rid="F3">Figures 3</xref>, <xref ref-type="fig" rid="F4">4</xref>). Low richness values were predicted for river sections in inland regions, regions south of Lisbon, and for mountainous areas. Our model predicts that for 43.1% of river sections of continental Portugal there are one to two invasive plant species, for 11.5% there are three to five, and for 2.2% there are five or more invasive plant species (<xref ref-type="fig" rid="F3">Figure 3</xref>). At the municipal level, the highest average richness of invasive alien plants occurs around the country&#x2019;s two main cities, Porto and Lisbon (<xref ref-type="fig" rid="F4">Figure 4</xref>). High richness is generally found throughout the central to north-western continental Portugal. Although the highest numbers are concentrated around the larger cities, nearby areas belonging to the same river basins are also considerably affected. River basins with the highest average IAP richness are those of the rivers Lis (3.42 &#x00B1; 0.97), Vouga (3.13 &#x00B1; 0.97), Ave (2.88 &#x00B1; 1.40), and Mondego (2.72 &#x00B1; 1.64). The basins that harbor more IAP overall are Douro (20), Mondego (20), and Vouga (18) (<xref ref-type="supplementary-material" rid="DS1">Supplementary Table 1</xref>).</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption><p>Predictions of invasive alien plant species richness for the Portuguese river network. Richness values are calculated for each 1 km grid cell corresponding to a river section. Gray areas correspond to river basins that did not have sufficient data to be included in the analysis. This figure was created with QGIS Version 3.14 (<xref ref-type="bibr" rid="B60">QGIS Development Team, 2020</xref>).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-10-875578-g003.tif"/>
</fig>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption><p>Predictions of invasive alien plant species richness for municipalities in Portugal. Predictions correspond to mean values of richness obtained for the 1 km grid cells representing river sections located within the municipality. Gray areas correspond to river basins that were not analyzed due to insufficient data. This figure was created with QGIS Version 3.14 (<xref ref-type="bibr" rid="B60">QGIS Development Team, 2020</xref>).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fevo-10-875578-g004.tif"/>
</fig>
</sec>
<sec id="S4" sec-type="discussion">
<title>Discussion</title>
<p>Our results identified a marked distribution pattern in the richness of invasive alien species in riparian environments of continental Portugal. This pattern could be explained, to a large extent, by a few variables, namely altitude, size of the catchment area and the proportion of urban area in a buffer of 500 m around the target site. In addition, a model using these variables was able to predict the distribution of richness values in unsampled sites with good accuracy and at a high spatial resolution, enabling us to identify the river sections most strongly affected by invasions and for which management or restoration actions are more pressing.</p>
<p>Model results show that the richness of invasive alien plants was higher at sites up to 300 meters above sea level, and far from river sources. At higher altitudes, richness values of invasive alien plants decrease quickly. The relationship between increasing altitude and decreasing IAP richness has been often observed (<xref ref-type="bibr" rid="B57">Py&#x0161;ek et al., 2002</xref>; <xref ref-type="bibr" rid="B17">Chytr&#x00FD; et al., 2005</xref>, <xref ref-type="bibr" rid="B18">2009</xref>; <xref ref-type="bibr" rid="B4">An&#x0111;elkovi&#x0107; et al., 2022</xref>), and has been attributed to the generally milder climatic conditions found in low-altitude areas (<xref ref-type="bibr" rid="B17">Chytr&#x00FD; et al., 2005</xref>), benefiting IAP that mostly originate from warm areas (<xref ref-type="bibr" rid="B59">Py&#x0161;ek et al., 2003</xref>). On the other hand, the IAP found at higher altitudes are mostly generalists with broad climate tolerances and able to thrive across a wide range of altitudes (<xref ref-type="bibr" rid="B3">Alexander et al., 2011</xref>; <xref ref-type="bibr" rid="B28">Essl et al., 2019</xref>). In our data set, 8 species were found at elevations above 500 m. The two most common were <italic>Bidens frondosa</italic> and <italic>Phytolacca americana;</italic> both native to North America and invasive in many temperate regions of the world (<xref ref-type="bibr" rid="B36">Huang and Ding, 2015</xref>; <xref ref-type="bibr" rid="B66">Ronzhina, 2017</xref>).</p>
<p>The positive relationship found between the size of the contributing catchment area and IAP richness likely reflects a scaling effect played by the dendritic structure of riparian networks, suggesting a key role of hydrochory in the accumulation of IAP (<xref ref-type="bibr" rid="B65">Rodr&#x00ED;guez-Gonz&#x00E1;lez et al., 2019</xref>). A larger catchment area may result in a higher number of IAP simply because more species accumulate in downstream areas. Additionally, it draws attention to the interconnection of river segments along the same river basin, which therefore should not be considered or treated separately (<xref ref-type="bibr" rid="B30">Fausch et al., 2002</xref>; <xref ref-type="bibr" rid="B65">Rodr&#x00ED;guez-Gonz&#x00E1;lez et al., 2019</xref>). In our study area, the higher Strahler stream segments of the larger river basins Tejo, Mondego, and Douro hold higher IAP numbers, and across all river basins, an increase toward the coast can be observed (<xref ref-type="fig" rid="F3">Figure 3</xref>).</p>
<p>Socioeconomic influences are important drivers of invasive species accumulation, often more important than the physical environment (<xref ref-type="bibr" rid="B21">Dawson et al., 2017</xref>; <xref ref-type="bibr" rid="B28">Essl et al., 2019</xref>). Here, we also identified a significant positive relationship between richness of invasive alien plant species and the percentage of urban area surrounding each sampled site. Urban areas can favor invasions in multiple ways, very often through high colonization and propagule pressures that result from ornamental horticultural activities promoting the plantation of alien species in urban parks and gardens (<xref ref-type="bibr" rid="B74">van Kleunen et al., 2018</xref>), from accidental introductions resulting from trading or tourism activities (<xref ref-type="bibr" rid="B37">Hulme, 2009</xref>), and in general due to global plant trade (<xref ref-type="bibr" rid="B5">Beaury et al., 2021</xref>). At the same time, intense human activity also provides conditions that can favor the establishment of alien species, including disturbances or the creation of novel habitats that provide niche opportunities by reducing natural enemies, such as native plant competitors (<xref ref-type="bibr" rid="B48">McKinney, 2006</xref>). Although it is not possible to distinguish the relative contribution of these and other urban-related mechanisms in shaping the observed patterns of IAP richness, it seems likely that the number of IAP in the vicinity of these areas will continue to grow driven by high levels of socio-economic activity.</p>
<p>Previous research has found important limitations in the capacity of statistical models to predict regional-scale richness of alien species (<xref ref-type="bibr" rid="B10">Capinha et al., 2018</xref>). We found a good accuracy in predicting IAP richness, with error levels substantially below the threshold indicating uninformative predictions. The reasons for the high accuracy achieved here could be related to the lower diversity of human and natural conditions represented in our model compared to those tested in <xref ref-type="bibr" rid="B10">Capinha et al. (2018)</xref>, and which comprised mostly continental to global extents. Furthermore, our data sample most of continental Portugal, ensuring that predictions are within the range of conditions used for model calibration, thus improving their reliability (<xref ref-type="bibr" rid="B10">Capinha et al., 2018</xref>). This provides good support for the use of our predictions to support decision-making in ongoing and future efforts to reduce IAP pressures on riparian ecosystems. In this regard, it is worth mentioning that the model is based on richness data collected between 2003 and 2006&#x2014;the most recent available&#x2014;so although the spatial patterns of variation are unlikely to have changed significantly, the current figures of IAP richness may be even slightly higher than predicted, reflecting the continued accumulation of alien plants worldwide as time progresses (<xref ref-type="bibr" rid="B69">Seebens et al., 2017</xref>, <xref ref-type="bibr" rid="B68">2021</xref>).</p>
<p>According to the <xref ref-type="bibr" rid="B53">OECD (2019)</xref>, in order to reach the goal of the EU Biodiversity Strategy for 2030 to decrease the number of Red List species they threaten by 50% (<xref ref-type="bibr" rid="B29">European Commission, 2020</xref>), invasive species must be controlled or eradicated by 2030 from 80% of the most important areas of plant diversity in Europe. Future work could thus aim at crossing the richness patterns we uncovered with observed or potential distribution maps of endangered species listed in the recently published Red List of Vascular Plants of Mainland Portugal (<xref ref-type="bibr" rid="B12">Carapeto et al., 2020</xref>) to help pinpoint locations where threatened riparian plant species could be under greater pressure from IAP. Similarly, our estimates could also be combined with estimates of richness of native plant species to map the areas where the proportion of alien invasive species on overall species diversity is higher (<xref ref-type="bibr" rid="B14">Catford et al., 2012</xref>). Results from these analyses could then be used to inform national governmental entities in the definition of priority areas for conservation or restoration efforts and by local entities, such as municipalities or environmental NGOs in directing future efforts of eradication or introduction prevention of riparian invasive species. Future work could also consider differing invasion pressures caused by individual species. For example, some areas may still be strongly affected by single invasive species like <italic>Acacia dealbata</italic>, which might propagate very fast and cover wide areas (<xref ref-type="bibr" rid="B45">Lorenzo et al., 2010</xref>; <xref ref-type="bibr" rid="B70">Souza-Alonso et al., 2017</xref>). Our estimates of species richness weighted by the magnitude of species-level impacts could provide a deeper understanding of the impact of invasion pressures on riparian ecosystems.</p>
<p>We provided a basis for understanding the distribution patterns of invasive alien plant species in riparian areas of continental Portugal. We found a consistent geographical clustering of highly invaded areas in central and northern coastal areas, which appears to be driven, to a large extent, by the joint effect of a high human population concentration, low altitude values, and the convergence of species spread in downstream areas. Our results also showed many contiguous municipalities sharing similar levels of invasion, thus recommending cross-municipal cooperation and coordination when addressing the prevention and management of invasive plants species. Given also the role identified for species spread through the hydrographic network, these efforts would also benefit from the involvement of entities with responsibilities over upstream river stretches (<xref ref-type="bibr" rid="B30">Fausch et al., 2002</xref>; <xref ref-type="bibr" rid="B65">Rodr&#x00ED;guez-Gonz&#x00E1;lez et al., 2019</xref>).</p>
</sec>
<sec id="S5" sec-type="data-availability">
<title>Data Availability Statement</title>
<p>Publicly available datasets were analyzed in this study. These data can be found here: <ext-link ext-link-type="uri" xlink:href="https://www.apambiente.pt/dqa/index.html">https://www.apambiente.pt/dqa/index.html</ext-link>.</p>
</sec>
<sec id="S6">
<title>Author Contributions</title>
<p>CC, FSD, LB-de-&#x00C1;, PMR-G, and RP designed the original research. RP did the analytic work with contributions from FSD. All authors contributed to writing up the article.</p>
</sec>
<sec id="conf1" sec-type="COI-statement">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="pudiscl1" sec-type="disclaimer">
<title>Publisher&#x2019;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
</body>
<back>
<sec id="S7" sec-type="funding-information">
<title>Funding</title>
<p>RP, FSD, LB-de-&#x00C1;, and PMR-G were financed by the FEDER Funds through the Operational Competitiveness Factors Program&#x2014;COMPETE and the National Funds through Funda&#x00E7;&#x00E3;o para a Ci&#x00EA;ncia e a Tecnologia (FCT). RP and FSD were supported through project PTDC/BIA-ECO/28729/2017&#x2014;POCI-01-0145-FEDER-028729 (RIVERSCALE project), LB-de-&#x00C1; under the Norma Transit&#x00F3;ria&#x2014;L57/2016/CP1440/CT0022, and CC and PMR-G through FCT-CEEC Individual Programme CEECIND/02037/2017 and 2020/03356/CEECIND, respectively. Forest Research Centre was a research unit funded by FCT (UIDB/00239/2020).</p>
</sec>
<ack><p>We thank contributions from three reviewers who helped improve the final version of the work.</p>
</ack>
<sec id="S9" sec-type="supplementary-material">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fevo.2022.875578/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fevo.2022.875578/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.zip" id="DS1" mimetype="application/zip" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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