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  <front>
    <journal-meta><journal-id journal-id-type="publisher">WE</journal-id><journal-title-group>
    <journal-title>Web Ecology</journal-title>
    <abbrev-journal-title abbrev-type="publisher">WE</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Web Ecol.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1399-1183</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/we-23-87-2023</article-id><title-group><article-title>Insights into the habitat associations, phylogeny, and diet of <italic>Pipistrellus maderensis</italic> in Porto Santo, northeastern Macaronesia</article-title><alt-title>Habitat associations, phylogeny, and diet of <italic>Pipistrellus maderensis</italic> in Porto Santo</alt-title>
      </title-group><?xmltex \runningtitle{Habitat associations, phylogeny, and diet of \textit{Pipistrellus maderensis} in Porto Santo}?><?xmltex \runningauthor{E. K. N\'{o}brega et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Nóbrega</surname><given-names>Eva K.</given-names></name>
          <email>eva.keltanen@hotmail.com</email>
        <ext-link>https://orcid.org/0000-0003-4256-5213</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Toshkova</surname><given-names>Nia</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-4217-5653</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Gonçalves</surname><given-names>Angelina</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4 aff5">
          <name><surname>Reis</surname><given-names>André</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff6">
          <name><surname>Soto</surname><given-names>Elena J.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff7">
          <name><surname>Ruiz</surname><given-names>Sergio Puertas</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff8 aff9">
          <name><surname>Mata</surname><given-names>Vanessa A.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff8 aff9">
          <name><surname>Rato</surname><given-names>Catarina</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-8292-2433</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff10">
          <name><surname>Rocha</surname><given-names>Ricardo</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-2757-7347</ext-link></contrib>
        <aff id="aff1"><label>1</label><institution>Department of Animal Biology, Plant Biology and Ecology,<?xmltex \hack{\break}?> Autonomous University of Barcelona, 08193 Catalonia, Spain</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>National Museum of Natural History, Bulgarian Academy of Sciences,
1000 Sofia, Bulgaria</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Department of Biology, Faculty of Sciences of the University of Porto, 4099-002 Porto, Portugal</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Instituto Superior de Agronomia, Universidade de Lisboa, 1349-017
Lisbon, Portugal</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>Escola de Ciências e Tecnologia, Universidade de Évora,
7000-671 Évora, Portugal</institution>
        </aff>
        <aff id="aff6"><label>6</label><institution>Faculty of Biology, University of Murcia, 30003 Murcia, Spain</institution>
        </aff>
        <aff id="aff7"><label>7</label><institution>Pyrenean Institute of Ecology, Spanish National Research Council
(IPE-CSIC), 50059 Zaragoza, Spain</institution>
        </aff>
        <aff id="aff8"><label>8</label><institution>Centro de Investigação em Biodiversidade e Recursos
Genéticos (CIBIO), InBIO Laboratório Associado, Campus de Vairão,
Universidade do Porto, 4485-661 Vairão, Portugal</institution>
        </aff>
        <aff id="aff9"><label>9</label><institution>BIOPOLIS Program in Genomics, Biodiversity and Land Planning,
CIBIO, <?xmltex \hack{\break}?> Campus de Vairão, 4485-661 Vairão, Portugal</institution>
        </aff>
        <aff id="aff10"><label>10</label><institution>Department of Biology, University of Oxford, OX1 3SZ, Oxford, UK</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Eva K. Nóbrega (eva.keltanen@hotmail.com)</corresp></author-notes><pub-date><day>1</day><month>August</month><year>2023</year></pub-date>
      
      <volume>23</volume>
      <issue>2</issue>
      <fpage>87</fpage><lpage>98</lpage>
      <history>
        <date date-type="received"><day>28</day><month>January</month><year>2023</year></date>
           <date date-type="rev-recd"><day>12</day><month>June</month><year>2023</year></date>
           <date date-type="accepted"><day>13</day><month>June</month><year>2023</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2023 Eva K. Nóbrega et al.</copyright-statement>
        <copyright-year>2023</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://we.copernicus.org/articles/23/87/2023/we-23-87-2023.html">This article is available from https://we.copernicus.org/articles/23/87/2023/we-23-87-2023.html</self-uri><self-uri xlink:href="https://we.copernicus.org/articles/23/87/2023/we-23-87-2023.pdf">The full text article is available as a PDF file from https://we.copernicus.org/articles/23/87/2023/we-23-87-2023.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d1e224">Around 60 % of all bat species occur in islands, and nearly one in four is
an insular endemic. Bats are often the only native terrestrial mammals in
oceanic islands, and despite increasing anthropogenic pressures, little is
known about the distribution, natural history, and population status of most
insular bat populations. The sub-tropical archipelago of Madeira is composed
of the volcanic islands of Madeira, Porto Santo, and Desertas and is
home to the Macaronesian endemic <italic>Pipistrellus maderensis</italic>, to the endemic subspecies <italic>Nyctalus leisleri verrucosus</italic>, and to <italic>Plecotus austriacus</italic>. <italic>Pipistrellus maderensis</italic> is known
to both Madeira and Porto Santo, whereas the other two species have only
been recorded in the former. However, no bats have been recorded in Porto Santo
for over 15 years, raising fears that bats are probably extinct in the
island. In July 2021, we conducted an island-wide acoustic survey using
AudioMoth passive acoustic recorders, leading to the detection of
<italic>Pipistrellus maderensis</italic> in 28 out of the 46 sampling sites (60 %). The species' activity was
strongly associated with artificial water sources, and genetic samples from
six captured individuals revealed that the populations of <italic>Pipistrellus maderensis</italic> in Porto Santo and
Madeira have a close phylogenetic affinity. Furthermore, using DNA
metabarcoding, we found that the species feeds on a wide variety of insects,
including several economically important pest species and disease vectors.
These findings emphasise the need to target more conservation and research
efforts towards extant island bat populations and the potential ecosystem
services they provide.</p>
  </abstract>
    
<funding-group>
<award-group id="gs1">
<funding-source>National Geographic Society</funding-source>
<award-id>grant EC-64368R-20</award-id>
</award-group>
<award-group id="gs2">
<funding-source>European Regional Development Fund</funding-source>
<award-id>PTDC/BIA-EVL/27958/2017--POCI-01-0145-FEDER-027958</award-id>
</award-group>
<award-group id="gs3">
<funding-source>Bulgarian Academy of Sciences</funding-source>
<award-id>n/a</award-id>
</award-group>
<award-group id="gs4">
<funding-source>Fundação para a Ciência e a Tecnologia</funding-source>
<award-id>2020.02547.CEECIND</award-id>
<award-id>2020.01129.CEECIND</award-id>
<award-id>DL57/2016/CP1440/CT0005</award-id>
</award-group>
<award-group id="gs5">
<funding-source>Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação</funding-source>
<award-id>M1420-09-5369-FSE-000002</award-id>
</award-group>
</funding-group>
</article-meta>
  </front>
<body>
      

<?pagebreak page88?><sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e255">The unique size, isolation, and microclimatic/geomorphological conditions of
oceanic islands often lead to significant evolutionary divergence and high
rates of endemism (Emerson, 2002; Kier et al., 2009). They often act as
evolutionary reservoirs for lineages that have disappeared elsewhere and, as
such, are crucial for the fate of biodiversity in the Anthropocene (Russell
and Kueffer, 2019; Nori et al., 2022). However, historical and ongoing
human-induced environmental degradation have resulted in acute changes to
island biotas (Nogué et al., 2021). Consequently, ca. 50 % of the
planet's terrestrial vertebrates are island dwellers (Leclerc et al., 2018),
and most recorded extinctions since the expansion of Europeans have occurred
in islands (Blackburn et al., 2004; Fernández-Palacios et al., 2021;
Matthews et al., 2022).</p>
      <p id="d1e258">Of over 1400 currently recognised bat species, ca. 25 % are island
endemics (Conenna et al., 2017). Of these, over 60 % of the bats currently
assessed by the International Union for Conservation of Nature (IUCN) are species formerly classified as “microbats”, i.e.
laryngeal echolocating bats (Conenna et al., 2017; Frick et al., 2020).
Similarly to their mainland counterparts, some island-dwelling microbats
are seed dispersers and pollinators (Ramirez-Francel et al., 2021). However,
most species are insectivores and potentially play pivotal roles in the
suppression of arthropods, including agricultural pests and mosquitoes (Kemp
et al., 2019). More than one-fourth of these laryngeal echolocating
bats are assessed as CR, EN, or VU by the IUCN Red List, largely due to the
additive and often synergistic effects of habitat loss and fragmentation,
as well as the impacts of invasive species (Rodríguez-Durán et al., 2010;
Conenna et al., 2017).</p>
      <p id="d1e261">The Macaronesian biogeographical region, composed of the archipelagos of
the Azores, Madeira, Selvagens Islands, Canary Islands, and Cabo Verde, is home to
at least 15 bat species, including three island-restricted ones, namely the
Azores noctule <italic>Nyctalus azoreum</italic>, the Canary big-eared bat <italic>Plecotus teneriffae</italic>, and the Madeiran pipistrelle
<italic>Pipistrellus maderensis</italic> (Gonzáles-Dionis et al., 2022). The latter is a relatively small bat
(forearm length <inline-formula><mml:math id="M1" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 29.5–34.0  mm) likely derived from African <italic>Pipistrellus kuhlii</italic> that
colonised the Canary Islands and the archipelago of Madeira (Pestano et al.,
2003) or from a common ancestor to both species (Jesus et al., 2013). It is
a synanthropic species with flexible habitat requirements, being found in
the archipelagos of Madeira, the Canary Islands, and possibly Azores
(Trujillo and Gonzalez, 2011; Rainho, 2022; Rocha, 2021; Rainho et al., 2002). The species seems
to be more abundant at lower altitudes, occurring in a variety of natural
and humanised habitats, such as native forests and agricultural and urban
areas (Teixeira and Jesus, 2009; Jesus et al., 2009; Ferreira et al., 2022;
Nouioua, 2022; Rocha, 2021). It feeds on a wide diversity of arthropods
(Gonçalves, 2022) and is prone to roost in cliffs, tree holes, and a
variety of human-made structures such as bridges and tunnels (Rocha, 2021).</p>
      <p id="d1e283"><italic>Pipistrellus maderensis</italic> is listed by the IUCN as vulnerable (Alcalde and Juste, 2016) and is one of
Europe's most threatened bat species. Its geographic isolation and
fragmented populations, typical of insular species, make it particularly
vulnerable to anthropogenic stressors and natural catastrophes (Rocha,
2021). The threats to the conservation of aerial insectivorous bats such as
<italic>Pipistrellus</italic> spp. are numerous, and since they are small, mostly nocturnal, and
inconspicuous, their population declines often go unnoticed. As an extreme
example, the last known Christmas Island pipistrelle <italic>Pipistrellus murrayi</italic> disappeared in 2009,
becoming the first animal to go extinct in Australia in the last 5
decades (Woinarski, 2018). In Madeira, where it is more abundant, the
population was suggested to be smaller than 1000 individuals (Cabral et
al., 2005), whereas Azores might be home to fewer than 300 individuals
(Queiroz et al., 2005).</p>
      <p id="d1e295">Although <italic>Pipistrellus maderensis</italic> were known to occur in Porto Santo (archipelago of Madeira),
previous surveys failed to detect the species (Jesus  et al., 2013). However,
it is unclear if the method and intensity of the surveys were sufficient to
prove the absence of the species in the island. Here, we combine
bioacoustics, phylogenetic analysis, and DNA metabarcoding to investigate
the population status, evolutionary history, and trophic interactions of
<italic>Pipistrellus maderensis</italic> in Porto Santo. Specifically, we address the following questions.
<list list-type="bullet"><list-item>
      <p id="d1e306">Is Porto Santo still home to an extant population of <italic>Pipistrellus maderensis</italic>? We anticipate that
<italic>Pipistrellus maderensis</italic> still persists in Porto Santo, and we predict that considering the xeric
climate of the island, bat activity is likely to be strongly influenced by
artificial water sources.</p></list-item><list-item>
      <p id="d1e316">What is the phylogenetic relationship between the populations of
<italic>Pipistrellus maderensis</italic> from Madeira and Porto Santo? We hypothesise that both populations should
be closely related, and considering the relatively small distance between
Madeira and Porto Santo (ca. 40 km), bat populations in both islands will
likely exhibit low genetic divergence.</p></list-item><list-item>
      <p id="d1e323">Is <italic>Pipistrellus maderensis</italic> preying on agricultural insect pests in Porto Santo? We expect bats to
prey mainly on moths and Diptera, and we predict that some of these will be
agricultural pests.</p></list-item></list></p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Material and methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Study area</title>
      <p id="d1e344">Porto Santo (ca. 42 km<inline-formula><mml:math id="M2" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is the second largest and the oldest
(<inline-formula><mml:math id="M3" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 14 million years) island of the Portuguese archipelago of
Madeira, located in the Atlantic Ocean, near the coast of North Africa (Fig. 1; 38<inline-formula><mml:math id="M4" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>40<inline-formula><mml:math id="M5" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup><mml:msup><mml:mn mathvariant="normal">44</mml:mn><mml:mrow><mml:mo>′</mml:mo><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> N, 27<inline-formula><mml:math id="M6" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>13<inline-formula><mml:math id="M7" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup><mml:msup><mml:mn mathvariant="normal">51</mml:mn><mml:mrow><mml:mo>′</mml:mo><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> W). It has
a<?pagebreak page89?> Mediterranean xeric oceanic bioclimate, influenced by the Azores
anticyclone (Rivas-Martínez, 2009). Despite some steep slopes,
especially in the eastern section, the island is relatively flat, with the
highest peak being Pico do Facho (517 m a.s.l.) (Kratochwil and Schwabe, 2018). Largely due
to these geomorphological features, human intervention has been widespread
throughout the island since the arrival of the Portuguese in the XV century,
and Porto Santo's native vegetation is nowadays restricted to small,
localised patches. Non-native coniferous species (mostly pine and/or
cypress) were planted in several of the island's peaks to prevent soil
erosion (Sparrius et al., 2017). Notwithstanding its small size, the island
harbours a wide diversity of land-use covers – e.g. urban areas,
agricultural fields, grasslands, and forests – and despite considerable
environmental degradation, it is still home to nearly 250 extant endemic
taxa, such as the IUCN critically endangered vetch <italic>Vicia ferreirensis</italic> (Carvalho and Osborne,
2011). As with numerous other oceanic islands, human arrival was accompanied
by the introduction of several exotic vertebrates, among which are the domestic
cat <italic>Felis catus</italic>, the European rabbit <italic>Oryctolagus cuniculus</italic>, and house mice <italic>Mus musculus</italic> (Borges et al., 2008; Rocha et al., 2017).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1"><?xmltex \currentcnt{1}?><?xmltex \def\figurename{Figure}?><label>Figure 1</label><caption><p id="d1e435">Location of the island of Porto Santo, Madeira archipelago,
Portugal. Sampling sites are denoted by red dots.</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://we.copernicus.org/articles/23/87/2023/we-23-87-2023-f01.png"/>

        </fig>

</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Bat surveys</title>
      <p id="d1e452">We conducted an island-wide bioacoustic survey in June 2021. Contingent to
orography, we sampled 55 randomly selected sites (generated using the
<italic>random points tool</italic> in QGIS), spaced ca. 1 km apart throughout Porto Santo. However, only 46
sites were used in the analysis due to recorder failures – see below. In
each sampling site, bats were surveyed for one night, using an AudioMoth
recorder (Hill et al., 2018) placed within an appropriate waterproof box
and attached to a tree trunk or a shrub. Each detector was configured to
record continuously from half an hour before sunset to half an hour after
sunrise, at a sample rate of 250 kHz.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><?xmltex \currentcnt{2}?><?xmltex \def\figurename{Figure}?><label>Figure 2</label><caption><p id="d1e460">Relationship between bat activity (number of bat passes) and
distance to the nearest water source <bold>(a)</bold>; artificial water point where
intense bat activity was detected <bold>(b)</bold>.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://we.copernicus.org/articles/23/87/2023/we-23-87-2023-f02.jpg"/>

        </fig>

      <p id="d1e475">Additionally, we conducted four capture sessions using ground-level mist
nets placed in potential foraging and drinking sites, such as forest trails,
tunnel exits, and water ponds, and undertook targeted searches in a vast
array of potential roosts – e.g. caves, abandoned houses, and underground
pipelines. From each captured individual, a small wing tissue sample
(<inline-formula><mml:math id="M8" display="inline"><mml:mo lspace="0mm">&lt;</mml:mo></mml:math></inline-formula> 2 mm in diameter) was collected using a biopsy punch and later
preserved in 96 % ethanol. The age of each specimen was determined by
examination of the extent of ossification in the epiphyses of the phalanges,
and, in the case of adult females, the reproductive state was recorded by
palpation (pregnant vs. non pregnant) and evidence of hair loss around the mamma
and of milk leftovers/or production (lactating vs. non lactating). Additionally,
whenever possible, guano pellets were collected from clean holding bags and
stored with silica gel. Bat capture and handling was performed following
recommendations approved by the American Society of Mammalogists (Sikes et
al., 2011), and all bats were released at the capture site. No bioacoustic
surveys or captures were conducted on rainy or windy days, and precipitation
and wind speed were fairly constant throughout the sampling period.</p>
</sec>
<sec id="Ch1.S2.SS3">
  <label>2.3</label><title>Bioacoustic analysis</title>
      <p id="d1e493">Using Kaleidoscope v. 5.3 software (Wildlife Acoustics, USA), the AudioMoth
recordings in WAV format were split into 5 s recordings. If two or more bat
pulses were detected in a 5 s recording, this was defined as a
“bat pass”, which we used as our unit of measure for bat activity (see e.g.
López-Bosch et al., 2022; Yoh et al., 2022). Kaleidoscope was programmed
to detect the signals in the recordings with frequency ranges between 8 and
120 kHz and the pulse length between 1 and 500 ms. We ran the automated bat
identification in Kaleidoscope Pro and manually verified the sound pulses in
the sonograms. The identification of the echolocation recordings followed
Teixeira and Jesus (2009).</p>

<?xmltex \floatpos{p}?><table-wrap id="Ch1.T1" orientation="landscape"><?xmltex \currentcnt{1}?><label>Table 1</label><caption><p id="d1e499">List of known or suspected agricultural pest insects and disease
vectors found in the diet of four <italic>Pipistrellus maderensis</italic> captured in Porto Santo, archipelago of
Madeira, Portugal.</p></caption><oasis:table frame="topbot"><?xmltex \begin{scaleboxenv}{.94}[.94]?><oasis:tgroup cols="7">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="left"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="left"/>
     <oasis:colspec colnum="6" colname="col6" align="left"/>
     <oasis:colspec colnum="7" colname="col7" align="left"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Order</oasis:entry>
         <oasis:entry colname="col2">Family</oasis:entry>
         <oasis:entry colname="col3">Species</oasis:entry>
         <oasis:entry colname="col4">%<inline-formula><mml:math id="M10" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">a</mml:mi></mml:msup></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col5">Common name</oasis:entry>
         <oasis:entry colname="col6">Status</oasis:entry>
         <oasis:entry colname="col7">Crop/disease</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">Diptera</oasis:entry>
         <oasis:entry colname="col2">Culicidae</oasis:entry>
         <oasis:entry colname="col3"><italic>Culex pipiens</italic></oasis:entry>
         <oasis:entry colname="col4">25</oasis:entry>
         <oasis:entry colname="col5">common house mosquito</oasis:entry>
         <oasis:entry colname="col6">disease vector</oasis:entry>
         <oasis:entry colname="col7">West Nile virus (Hamer et al., 2008), Usutu virus (Fros et al., 2015),</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6"/>
         <oasis:entry colname="col7">Rift Valley fever (Moutailler et al., 2008), avian malaria</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6"/>
         <oasis:entry colname="col7">(Lalubin et al., 2013) (among others)</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Diptera</oasis:entry>
         <oasis:entry colname="col2">Culicidae</oasis:entry>
         <oasis:entry colname="col3"><italic>Culex theileri</italic></oasis:entry>
         <oasis:entry colname="col4">25</oasis:entry>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6">disease vector</oasis:entry>
         <oasis:entry colname="col7">West Nile virus, Rift Valley fever, Sindbis virus (Demirci et al., 2014),</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4"/>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6"/>
         <oasis:entry colname="col7"><italic>Dirofilaria immitis</italic> (Santa-Ana et al., 2006)</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Diptera</oasis:entry>
         <oasis:entry colname="col2">Chironomidae</oasis:entry>
         <oasis:entry colname="col3"><italic>Polypedilum nubifer</italic></oasis:entry>
         <oasis:entry colname="col4">50</oasis:entry>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6">possible pest</oasis:entry>
         <oasis:entry colname="col7">rice (Wallace et al., 2009)</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Lepidoptera</oasis:entry>
         <oasis:entry colname="col2">Depressariidae</oasis:entry>
         <oasis:entry colname="col3"><italic>Depressaria marcella</italic></oasis:entry>
         <oasis:entry colname="col4">25</oasis:entry>
         <oasis:entry colname="col5"/>
         <oasis:entry colname="col6">possible pest</oasis:entry>
         <oasis:entry colname="col7">carrots (Celli, 2013)</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Lepidoptera</oasis:entry>
         <oasis:entry colname="col2">Tortricidae</oasis:entry>
         <oasis:entry colname="col3"><italic>Epiphyas postvittana</italic></oasis:entry>
         <oasis:entry colname="col4">25</oasis:entry>
         <oasis:entry colname="col5">light brown apple moth</oasis:entry>
         <oasis:entry colname="col6">known pest</oasis:entry>
         <oasis:entry colname="col7">horticultural plants (Thrimawithana et al., 2022), apple (Danthanarayana, 1975)</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Lepidoptera</oasis:entry>
         <oasis:entry colname="col2">Noctuidae</oasis:entry>
         <oasis:entry colname="col3"><italic>Peridroma saucia</italic></oasis:entry>
         <oasis:entry colname="col4">25</oasis:entry>
         <oasis:entry colname="col5">pearly underwing moth</oasis:entry>
         <oasis:entry colname="col6">known pest</oasis:entry>
         <oasis:entry colname="col7">peanuts, sunflowers, soybeans, grapevines (Álvarez et al., 2009) (among others)</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup><?xmltex \end{scaleboxenv}?></oasis:table><table-wrap-foot><p id="d1e505"><inline-formula><mml:math id="M9" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">a</mml:mi></mml:msup></mml:math></inline-formula> Percentage of samples for which each prey was detected.</p></table-wrap-foot><?xmltex \gdef\@currentlabel{1}?></table-wrap>

</sec>
<sec id="Ch1.S2.SS4">
  <label>2.4</label><title>Bat–environment relationships</title>
      <p id="d1e796">Landscape-scale land-use metrics were acquired from 25 ha spatial land-use
maps based on CORINE Land Cover 2018. The major land-use types in
Porto Santo were defined as forest, agriculture, grassland,
non-vegetated areas, and built-up areas (Table S1 in the Supplement). Previous assessments
investigating the effect of landscape-scale land-use metrics on the activity
of <italic>Pipistrellus maderensis</italic> at three different sizes (250, 500, and 1000 m) revealed consistent
responses across scales (Ferreira et al., 2022). Thus, considering the small
extension of Porto Santo (maximum length of ca. 9 km) and so to minimise the
spatial overlap between neighbouring buffers, we used QGIS v. 3.28.0 to
calculate the area of each land-use type inside buffers of 250 m, centred<?pagebreak page90?> in
each sampling site. Additionally, we used Google Earth complemented with
field validation to determine the distance between sampling sites and the
closest water sources (Table S2).</p>
      <p id="d1e802">The effects of land-use type on bat activity (number of bat passes per
night) was assessed using generalised linear mixed models (GLMMs) with a
negative binomial distribution. As severe collinearity between predictor
variables can undermine model inference (Dormann et al., 2013), prior to
GLMMs, explanatory variables were centred and scaled (<inline-formula><mml:math id="M11" display="inline"><mml:mrow><mml:mi>x</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0</mml:mn></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id="M12" display="inline"><mml:mrow><mml:mi mathvariant="italic">σ</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula>). We, therefore, quantified collinearity using variance inflation factors
(VIFs), and variables with VIF <inline-formula><mml:math id="M13" display="inline"><mml:mo>≥</mml:mo></mml:math></inline-formula> 3 were excluded. To account for the
nested spatiotemporal sampling design, sampling day was considered as a
random factor in the GLMMs. A candidate model set was further constructed
using all additive combinations of the four explanatory variables retained,
and models were ranked based on Akaike information criterion adapted for small samples (AICc), using the MuMIn R package (Bartoń,
2020). To account for model uncertainty in multi-model inference,
model averaging was used to obtain parameter estimates from the most
plausible models (i.e. <inline-formula><mml:math id="M14" display="inline"><mml:mrow><mml:mn mathvariant="normal">0</mml:mn><mml:mo>&lt;</mml:mo></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M15" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula>AICc <inline-formula><mml:math id="M16" display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> 2) (Burnham and
Anderson, 2002). All GLMMs were conducted using R v. 4.2.1 software and
the glmmTMB package (Brooks et al., 2017).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><?xmltex \currentcnt{3}?><?xmltex \def\figurename{Figure}?><label>Figure 3</label><caption><p id="d1e863">Bayesian phylogenetic tree for <italic>Pipistrellus</italic> spp. derived from a cytochrome b
(cytb) fragment and using <italic>Hypsugo savii</italic> as the outgroup.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://we.copernicus.org/articles/23/87/2023/we-23-87-2023-f03.png"/>

        </fig>

</sec>
<sec id="Ch1.S2.SS5">
  <label>2.5</label><title>Phylogenetic analyses</title>
      <p id="d1e887">Genomic DNA was extracted from wing tissue samples of six <italic>Pipistrellus</italic> <italic>maderensis</italic> captured during
mist netting, using the E.Z.N.A Tissue DNA Kit (Mag-Bind). A fragment of the
cytochrome b (cytb) gene was amplified by polymerase chain reaction (PCR)
using the primers Molcit-F (Ibáñez et al., 2006) and MVZ-16 (Smith
and Patton, 1993). Amplification of the cytb fragment was carried out in a
10 <inline-formula><mml:math id="M17" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>L volume, comprised of 5 <inline-formula><mml:math id="M18" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>L of QIAGEN Multiplex PCR Master
Mix (Quiagen, Crawley, UK), 0.3 <inline-formula><mml:math id="M19" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>L of each primer, 3.4 <inline-formula><mml:math id="M20" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>L of
ultra-pure water, and 2 <inline-formula><mml:math id="M21" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>L of DNA extract. The PCR cycling procedure
was done according to Ibáñez et al. (2006). All amplified fragments
were sequenced in a Sanger sequencer and deposited to the GenBank database,
with accession numbers from OQ260001 to OQ260006 (Table S3).</p>
      <p id="d1e937">A total of 395 cytb sequences of <italic>Pipistrellus</italic> sp. were retrieved from GenBank and added
to the dataset. The obtained sequences were imported into the software
Geneious Prime® (v. 2022.2.2 Biomatters Ltd.) where the
alignment was performed using MAFFT v. 7.490 (Katoh et al., 2002; Katoh and
Standley, 2013) under the default parameters. Phylogenetic analysis based on
the cytb mitochondrial fragment was performed under a Bayesian inference
(BI) method, using <italic>Hypsugo savii</italic> as the outgroup (following Mayer and Helversen, 2001).
To determine the best-fitting nucleotide model, we used ModelFinder
(Kalyaanamoorthy et al., 2017) from the IQ-TREE web server (Trifinopoulos et
al., 2016). The software BEAST v. 2.6.6 (Bouckaert et al., 2019) was used for
the BI topology. Analyses were run twice for 10<inline-formula><mml:math id="M22" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">6</mml:mn></mml:msup></mml:math></inline-formula> generations with a
sampling frequency of 1000. Models and prior specifications applied were as
follows (otherwise by default): strict clock, coalescent with constant
population size, and the JC69 nucleotide model based on ModelFinder.
Convergence for all model parameters was assessed by examining trace plots
and histograms in Tracer v. 1.7.1 (Rambaut et al., 2018) after obtaining an
effective sample size (ESS) greater than 200. The initial 10 % of samples
were discarded as burn-in. Runs were combined using LogCombiner, and maximum
credibility trees with divergence time means and 95 % highest probability
densities (HPDs) were produced using TreeAnnotator. Trees were visualised
using FigTree v. 1.4.4 (Rambaut, 2009). Calculation of genetic distances was
performed using Mega v. 11 (Tamura et al., 2021) based on Kimura's two-parameter
distance (Kimura, 1980).</p>
</sec>
<sec id="Ch1.S2.SS6">
  <label>2.6</label><title>Diet analyses</title>
      <p id="d1e963">DNA was extracted from pellets collected from four out of the six captured
<italic>Pipistrellus</italic> <italic>maderensis</italic>, using the E.Z.N.A Tissue DNA Kit (Omega Bio-Tek, Norcross, Georgia, USA).
For this, we followed Mata et al. (2021), except that no<?pagebreak page91?> Inhibitex tablets
(Qiagen, Hilden, Germany) were used. DNA was then amplified by PCR using
arthropod general cytochrome oxidase subunit 1 (COI) primers fwhF2-R2n
(Vamos et al., 2017) modified with Illumina overhangs. Libraries were
prepared following Mata et al. (2021) and finally sequenced in a MiSeq
desktop sequencer (Illumina) using a MiSeq Reagent Kit v. 3 (<inline-formula><mml:math id="M23" display="inline"><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">250</mml:mn></mml:mrow></mml:math></inline-formula> bp).
Bioinformatic processing of raw sequencing data was conducted using OBITOOLS
(Boyer et al., 2016) and VSEARCH (Rognes et al., 2016) following Martins et
al. (2022). Operational taxonomic units (OTUs) were taxonomically assigned using the Barcode of Life Data
System (BOLD) public database under BOLDigger (Buchner and Leese, 2020).</p>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results</title>
      <?pagebreak page92?><p id="d1e993">During our bioacoustic survey we detected bats in 28 out of the 46 sampling
sites (60 %; Table S2). In total, we identified 7797 bat passes of
<italic>Pipistrellus maderensis</italic> in our recordings. Furthermore, we mist-netted six individuals (including
three juveniles and one lactating female; Fig. S1; Table S4). All captures
occurred at ca. 260 m a.s.l., in forest trails near <italic>Pico Castelo</italic>. No other bat species
were detected.</p>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>Bat–environment relationships</title>
      <p id="d1e1009">Our GLMM results indicate that the activity of <italic>Pipistrellus maderensis</italic> is not strongly influenced
by any of the considered land-use types (forest, agriculture, grassland,
non-vegetated areas, built-up areas) (<inline-formula><mml:math id="M24" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>; Table S5).
However, the number of recorded bat passes increased with decreasing
distance to water sources (<inline-formula><mml:math id="M25" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.00</mml:mn></mml:mrow></mml:math></inline-formula>1; Table S6 and Fig. 2a). GLMM
residuals were not spatially autocorrelated for most models (<inline-formula><mml:math id="M26" display="inline"><mml:mrow><mml:mi>P</mml:mi><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>). However, for one of the GLMMs (non-vegetated areas), the test showed
spatially structured residuals (<inline-formula><mml:math id="M27" display="inline"><mml:mrow><mml:mi>P</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>; Table S7).</p>
</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Phylogenetic analyses</title>
      <p id="d1e1071">The obtained cytb fragment from the sequenced <italic>Pipistrellus</italic> sp. had a length of 779 bp.
The Bayesian phylogenetic analysis clustered the bats sampled in Porto Santo
with the specimens assigned as <italic>Pipistrellus</italic> <italic>maderensis</italic>, with high posterior probability (Fig. 3).
Specifically, the genealogy suggests that these individuals have a close
phylogenetic affinity with the specimens from Madeira Island, separated by
0.04 % of genetic distance. Moreover, the bats captured in Porto Santo
share the exact same cytb haplotype, differing from the sequence KC520772.1
from Madeira by a single base pair (99.8 % of similarity among sequences).
This highlights the low genetic diversity of the bat populations of the
archipelago of Madeira, in comparison with the Canary Islands (98.7 % of
identical nucleotide sites).</p>
</sec>
<sec id="Ch1.S3.SS3">
  <label>3.3</label><title>Diet analyses</title>
      <p id="d1e1091">DNA material from the faecal samples collected from four individuals led to
a total of 16 OTUs and 39 594 reads. In total, at least five orders, 12
families, 13 genera, and 11 species were identified to be consumed by
<italic>Pipistrellus maderensis</italic>. Diptera was the predominant order detected, followed by Lepidoptera,
Hymenoptera, Hemiptera, and lastly Neuroptera (Fig. 4). Of the identified
prey, several are known agricultural insect pest species and disease vectors
(Table 1).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4" specific-use="star"><?xmltex \currentcnt{4}?><?xmltex \def\figurename{Figure}?><label>Figure 4</label><caption><p id="d1e1099">Frequency of occurrence network displaying the operational
taxonomic units (OTUs) consumed by four Madeiran pipistrelles <italic>Pipistrellus maderensis</italic> in Porto
Santo. Different OTUs are separated by white lines, and different colours
denote different taxonomic orders.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://we.copernicus.org/articles/23/87/2023/we-23-87-2023-f04.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S4" sec-type="conclusions">
  <label>4</label><title>Discussion</title>
      <p id="d1e1120">Lack of baseline knowledge is a key constraint for the development of
management strategies for the conservation of insectivorous bats and for
maximising any ecosystem services they might provide. Here, we show that
Porto Santo is still home to an extant population of <italic>Pipistrellus maderensis</italic> and provide
much-needed information about their habitat affinities, phylogenetic
kinship, and trophic interactions.</p>
      <p id="d1e1126"><?xmltex \hack{\newpage}?>Bats are known to present marked responses to landscape features in both
island and non-island ecosystems (Ancillotto et al., 2023;
López-Baucells et al., 2021). In the nearby island of Madeira,
<italic>Pipistrellus maderensis</italic> was found to be particularly associated with shrubland, agricultural areas,
and Laurisilva (primary) forest (Ferreira et al., 2022). However, in Porto
Santo and probably as an artefact of the small dimensions of the island, in
which multiple land-use covers converge within relatively short distances,
we were unable to detect these associations. Yet, consistent with previous
studies (Ancillotto et al., 2019; Moretto et al., 2023; Torrent et al.,
2018), we have found that bat activity was positively associated with
artificial water reservoirs such as dams and urban ponds. Water limitations
and unpredictability in precipitation, typical of xeric climates such as the
one characterising Porto Santo (Maestre et al., 2016), pose important
challenges to insectivorous bats (Conenna et al., 2021). Our results
indicate that in Porto Santo artificial water sources likely provide
important drinking and foraging resources for <italic>Pipistrellus maderensis</italic>, potentially reducing
spatiotemporal variations in food availability. However, it is important to
note that our bioacoustic survey was conducted over ca. 1 week – too short
of a period to be able to capture complex bat activity variations, which can
be affected by mu<?pagebreak page94?>ltiple factors, including lunar phase and season (Appel et
al., 2021; Gorman et al., 2021).</p>
      <p id="d1e1136">The evolutionary history of insular species is often shaped by the
geographic separation of different island populations (e.g. Recuerda et
al., 2021). Previous molecular phylogenetic analyses suggested the existence
of at least three lineages of <italic>Pipistrellus maderensis</italic> in the Canary Islands (Pestano et al., 2003)
and one in Madeira (Jesus et al., 2013). Indeed, there is no haplotype
sharing between <italic>Pipistrellus maderensis</italic> from both archipelagos, suggesting that they represent
distinct evolutionary lineages, albeit not distinct enough to constitute
different species (Jesus et al., 2013). Our results indicate a close
phylogenetic affinity between the <italic>Pipistrellus maderensis</italic> from Porto Santo and Madeira (only
0.04 % genetic distance; Fig. 3), indicating that the ca. 40 km separating
the two islands has not led to considerable genetic divergence between both
populations.</p>
      <p id="d1e1148">Due to their capacity to fly, geographical barriers, such as large expanses
of ocean, do not necessarily represent effective barriers to gene flow among
bat populations (García-Mudarra et al., 2009). Yet, molecular studies
revealed limited overwater dispersal of <italic>Nyctalus azoreum</italic> between some islands of the
archipelago of the Azores (Salgueiro et al., 2008). Likewise, while dispersal
and gene flow of <italic>Hypsugo savii</italic> among some of the closest islands (<inline-formula><mml:math id="M28" display="inline"><mml:mo lspace="0mm">&gt;</mml:mo></mml:math></inline-formula> 40 km) of
the Canary Islands is frequent, gene flow is rare among Canarian populations
of <italic>Pipistrellus maderensis</italic> inhabiting different islands (Pestano et al., 2003). The low genetic
divergence between the populations of <italic>Pipistrellus maderensis</italic> of Porto Santo and Madeira suggests
gene flow between both islands. However, this might also be due to a
relatively recent (re)colonisation of Porto Santo, with individuals from
Madeira. In fact, Porto Santo was heavily impacted by human-induced habitat
change, which combined with the effects of invasive species might have led
to the decline in the abundance – and possible extinction – of <italic>Pipistrellus maderensis</italic> and possibly
other bat species, which would mimic patterns of anthropogenic extinctions
observed in other vertebrate groups (e.g. Alcover et al., 2015).</p>
      <p id="d1e1175">DNA metabarcoding allows unprecedented resolution in the assessment of the
diet of insectivorous predators. Although we were only able to sample and
analyse faecal samples from four individuals, we detected prey items
belonging to five insect orders, corresponding to at least 11 different
species. In Madeira, a recent study identified Lepidoptera as the most
consumed order by <italic>Pipistrellus maderensis</italic> (Gonçalves, 2022). However, Diptera was the most
represented order in the four samples analysed from Porto Santo (Fig. 4).
These results align with findings for <italic>Pipistrellus kuhlii</italic> (a sister taxa of <italic>P. maderensis</italic>) in the Iberian
Peninsula, where Diptera has been found to make up over half of the species'
diet (Goiti et al., 2003). Hymenoptera, Lepidoptera, Hemiptera, and
Neuroptera were also detected in the diet of <italic>Pipistrellus maderensis</italic> in this study, which in
combination with the identification of Coleoptera, Tipulidae, Chironomidae,
and Aranea in the diet of the species in Madeira (Gonçalves, 2022)
suggests that, similarly to <italic>Pipistrellus kuhlii</italic> (Amichai and Korine, 2020), <italic>Pipistrellus maderensis</italic> is likely a
“selective opportunist” that feeds on a wide variety of prey, according to
its availability. Despite the reduced sample size of our diet analyses, we
detected at least four known or potential economically important insect pest
species (e.g. <italic>Epiphyas postvittana</italic>, which is known to damage the fruits of multiple species;
Danthanarayana et al., 1975; Thrimawithana et al., 2022) and two mosquitoes
known to be vectors of human and non-human diseases (e.g. <italic>Culex pipiens</italic>, which can
spread avian malaria; Hamer et al., 2008; Fros et al., 2015) (Table 1). The
high percentage of known or potential agricultural pest species and of
species of health relevance consumed by <italic>Pipistrellus maderensis</italic> highlights the potential role of
insectivorous bats as suppressors of arthropods with negative economic and
(both human and non-human) disease implications (Montauban et al., 2021;
Ancillotto et al., 2022, 2023; Ferreira et al., 2023).</p>
      <p id="d1e1206">Insectivorous bats are often not considered as charismatic as other island
vertebrates, and their conservation is neglected. The population of
<italic>Pipistrellus maderensis</italic> from Porto Santo, despite being genetically similar to the one inhabiting
Madeira, is of critical importance to the ecological balance, acting as
a predator of a multitude of arthropod species. The bats were detected
throughout most of the island, but the extant population appears to be
small and thus particularly vulnerable to threat factors such as the
destruction of roost and feeding habitats, reduction of prey due to
pesticide use, or the impacts of invasive species (e.g. free-ranging cats
<italic>Felis catus</italic>, a known predator of <italic>Pipistrellus maderensis</italic>; Rocha, 2015). We urge that more research is devoted
to this population, which represents the sole native terrestrial mammal of
Porto Santo.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e1223">All DNA sequences are available on GenBank with accession numbers OQ260001 to OQ260006.</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e1226">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/we-23-87-2023-supplement" xlink:title="pdf">https://doi.org/10.5194/we-23-87-2023-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e1235">Conceptualisation: RR and EKN. Methodology: RR, EKN, NT, AG, AR, EJS, SPR, VAM, and CR. Writing – original draft preparation:
RR and EKN. Writing – review and editing: NT, AG, AR, EJS, SPR,
VAM, and CR. Project administration: RR. Funding acquisition: RR and
CR. All authors have approved the submitted version of the manuscript.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e1241">At least one of the (co-)authors is a member of the editorial board of <italic>Web Ecology</italic>. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d1e1250">Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e1256">The authors would like to thank
Inês Órfão and Luísa Drumond for assisting during fieldwork
and Hugo Rebelo for general support. This research was conducted under
permit 10/IFCN/2019 provided by the Institute of Forests and Nature
Conservation from the Autonomous Region of Madeira. The authors acknowledge Rym Nouioua's support on the acoustic analysis and Diogo Ferreira's support on the statistical analysis.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e1261">This research has been supported by the National Geographic Society (grant no. EC-64368R-20) and the European Regional Development Fund (ERDF) (grant no. PTDC/BIA-EVL/27958/2017–POCI-01-0145-FEDER-027958; CR). Vanessa A. Mata and Ricardo Rocha were supported by the Fundação
para a Ciência e a Tecnologia (FCT) through the programme “Stimulus of Scientific Employment, Individual Support – 3rd Edition” (contract nos. 2020.02547.CEECIND and 2020.01129.CEECIND, respectively). Ricardo Rocha was further supported by a postdoctoral fellowship from ARDITI – Madeira's Regional Agency for the Development of Research, Technology and Innovation (grant no. M1420-09-5369-FSE-000002). Catarina Rato was supported by a FCT postdoctoral contract (grant no. DL57/2016/CP1440/CT0005), and Nia Toshkova was supported by the Bulgarian Academy of Sciences via the Erasmus+ programme.</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e1267">This paper was edited by Daniel Montesinos and reviewed by M. Brock Fenton and three anonymous referees.</p>
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