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<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:oasis="http://docs.oasis-open.org/ns/oasis-exchange/table" dtd-version="3.0">
  <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-17-19-2017</article-id><title-group><article-title>Sand quarry wetlands provide high-quality habitat <?xmltex \hack{\break}?> for native amphibians</article-title>
      </title-group><?xmltex \runningtitle{Sand quarry wetlands provide high-quality habitat for native amphibians}?><?xmltex \runningauthor{M.~Sievers et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1 aff2">
          <name><surname>Sievers</surname><given-names>Michael</given-names></name>
          <email>msievers@student.unimelb.edu.au</email>
        <ext-link>https://orcid.org/0000-0001-7162-1830</ext-link></contrib>
        <aff id="aff1"><label>1</label><institution>School of BioSciences, University of Melbourne, Parkville, Victoria, 3010, Australia</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>School of Ecosystem and Forest Sciences, University of Melbourne, Parkville, Victoria,  3010, Australia</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Michael Sievers (msievers@student.unimelb.edu.au)</corresp></author-notes><pub-date><day>19</day><month>May</month><year>2017</year></pub-date>
      
      <volume>17</volume>
      <issue>1</issue>
      <fpage>19</fpage><lpage>27</lpage>
      <history>
        <date date-type="received"><day>21</day><month>March</month><year>2017</year></date>
           <date date-type="rev-recd"><day>21</day><month>April</month><year>2017</year></date>
           <date date-type="accepted"><day>26</day><month>April</month><year>2017</year></date>
      </history>
      <permissions>
<license license-type="open-access">
<license-p>This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/3.0/">http://creativecommons.org/licenses/by/3.0/</ext-link></license-p>
</license>
</permissions><self-uri xlink:href="https://we.copernicus.org/articles/17/19/2017/we-17-19-2017.html">This article is available from https://we.copernicus.org/articles/17/19/2017/we-17-19-2017.html</self-uri>
<self-uri xlink:href="https://we.copernicus.org/articles/17/19/2017/we-17-19-2017.pdf">The full text article is available as a PDF file from https://we.copernicus.org/articles/17/19/2017/we-17-19-2017.pdf</self-uri>


      <abstract>
    <p>Anthropogenic disturbances to habitats influence the fitness of
individual animals, the abundance of their populations, and the composition
of their communities. Wetlands in particular are frequently degraded and
destroyed, impacting the animals that inhabit these important ecosystems. The
creation of wetlands during and following sand extraction processes is
inevitable, and thus, sand quarries have the potential to support aquatic
animals. To determine how amphibians utilise these wetlands, I conducted
nocturnal call surveys at wetlands within the Kables Sands quarry, New South
Wales, Australia, and within surrounding reference wetlands, and quantified
levels of developmental instability (DI) as a proxy for fitness. Whilst
quarry and reference wetlands were largely similar in terms of environmental
characteristics, quarry wetlands consistently harboured more amphibian
species and individuals. Using unsigned asymmetry as a measure of DI, frogs
from the quarry sites exhibited significantly lower levels of DI compared to
reference wetlands, indicating that quarry wetlands may be comparatively
higher quality. Levels of DI within quarry wetlands also compared favourably
to data from healthy frog populations extracted from the literature. Further
enhancing the suitability of quarry wetlands would require minimal effort,
with potentially significant increases in local and regional biodiversity.
Documenting species presence and quantifying individual fitness by measuring
limb lengths is an economically and logistically feasible method to assess
the health of quarry wetlands. Overall, the methods outlined here provide a
powerful, yet simple, tool to assess the overall health and suitability of
quarry wetlands that could be easily adopted at quarries throughout the
world.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <title>Introduction</title>
      <p>Humans are altering natural environments at unprecedented rates, with
significant negative impacts for animals (MEA, 2005; Pereira et al., 2010).
Wetlands in particular harbour highly diverse biological communities and
provide multiple ecosystem services, yet are frequently degraded and
destroyed, with over 50 % of global wetland surface area lost during the
last century (Mitsch and Gosselink, 2007; Zedler and Kercher, 2005).
Consequently, declines of wetland-dependent species are some of the greatest
recorded (MEA, 2005). Concurrent with these losses, wetlands are being
created to harness the ecosystem services they provide, such as those
constructed in urban areas to treat storm water, and settlement dams created
in mining areas to store and treat processed materials (Hammer, 1989; Odum,
2016).</p>
      <p>Although typically not designed to support and conserve wildlife, these
artificial wetlands regularly attract animals, as they superficially resemble
natural wetlands and contain the cues used by animals when selecting habitats
(often termed secondary wetlands; Dolny and Harabis, 2012). Therefore,
secondary wetlands may provide critical habitat, enhancing landscape-level
connectivity and promoting biodiversity and population persistence
(Bendell-Young et al., 2000; Scheffers and Paszkowski, 2013; Thiere et al.,
2009). For some organisms, properly managed secondary wetlands may provide
habitat superior to that of natural wetlands. For example, Dolny and
Harabis (2012) observed more than twice as many dragonfly species in mine
subsidence pools than in reference wetlands, suggesting that this was due to
enhanced environmental heterogeneity resulting from abiotic succession
processes occurring as a direct consequence of mining. Given this potential,
there has been considerable interest in simultaneously satisfying industrial
needs and promoting wildlife within secondary wetlands (i.e. multi-objective
management; Benyamine et al., 2004).</p>
      <p>However, since secondary wetlands are often designed to store contaminated
water, or are in areas prone to contamination and human interference, animal
inhabitants may experience compromised fitness (e.g. reduced survival and
reproduction; Dods et al., 2005; Laposata and Dunson, 2000). Therefore,
despite being conceptually appealing, multi-objective management may prove
problematic if individual fitness within secondary wetlands is impacted, and
particularly if individual fitness impacts scale up to affect population
persistence.</p>
      <p>Quantifying community composition, population sizes and individual fitness
will assist management efforts to maintain and enhance the conservation
potential of secondary wetlands. In particular, utilising fast, cheap and
non-lethal methods of estimating individual fitness is preferable under
certain circumstances. Quantifying developmental instability (DI) is a
promising method for estimating the fitness of individuals and provides an
indirect proxy for habitat quality (Alford et al., 1999; Burghelea et al.,
2013; Tracy et al., 1995). Developmental stability occurs when genotypes
repeatedly produce the same phenotype under the same environmental conditions
during development (Tracy, et al., 1995; Zakharov, 1992), so levels of
deviation from stability (i.e. DI) provide information on environmental
quality and individual health. DI is calculated by measuring bilateral
structures such as the length of limbs and quantifying disparity from
symmetry. Studies have shown that DI increases as health decreases, and that
amphibian population declines have preceded periods of increasing DI (Alford
et al., 2007). Therefore, DI may be a valuable, non-lethal early warning
indicator of the impact of poor-quality wetlands.</p>
      <p>Determining the suitability of secondary wetlands for currently threatened
taxa in particular should be at the forefront of conservation science.
Amphibian populations are currently experiencing dramatic declines around the
world, with approximately 40 % of species facing the threat of extinction
(Monastersky, 2014; Whitfield et al., 2007). Amphibians are often considered
particularly sensitive to environmental contamination due to their physiology
and biphasic life cycle (Wake and Vredenburg, 2008). Although numerous
factors have been implicated in current population declines, such as climate
change, disease and pollution, habitat loss clearly stands out as one of the
main threats facing amphibians (Cushman, 2006). Consequently, secondary
wetlands such those within sand quarries may provide vitally important
habitat. Relative to other mining industries which may release materials into
wetlands that are highly toxic to wildlife, such as fossil fuel mining, sand
quarry wetlands may be more suitable due to the comparably lower
environmental impact of this industry (Anderson and Arruda, 2006; Rowe et
al., 1996). However, there are few empirical studies investigating how sand
quarry wetlands function as habitat for amphibians or indeed any other animal
groups (although see Catchpole and Tydeman, 1975; Eversham et al., 1996;
Spencer and Griffith, 2012).</p>
      <p>Here, I determine how quarry wetlands within the Kables Sands Quarry,
Australia, function as habitat for local amphibians, and investigate the
factors that may be influencing amphibian populations and communities. To do
this, I measured environmental variables and quantified amphibian population
density, species richness and levels of developmental instability (DI) within
sand quarry wetlands, and compared findings to reference wetlands and
previously published studies.</p>
</sec>
<sec id="Ch1.S2">
  <title>Methods</title>
<sec id="Ch1.S2.SS1">
  <title>Study site selection and description</title>
      <p>The Kables Sands Quarry (33<inline-formula><mml:math id="M1" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>27<inline-formula><mml:math id="M2" display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula>27.51<inline-formula><mml:math id="M3" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>′</mml:mo><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> S,
150<inline-formula><mml:math id="M4" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>14<inline-formula><mml:math id="M5" display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula>26.04<inline-formula><mml:math id="M6" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>′</mml:mo><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> E) is located in Clarence, New South Wales,
Australia (Fig. 1). The extraction of sandstone and production of sand began
here 60 years ago, with current production at approximately 350 000 tonnes per
annum. Mining activities have inadvertently led to the formation of wetlands
filled with groundwater, and other wetlands have been intentionally created
for sand washing and other production purposes. The four accessible wetlands
within the quarry were surveyed: the settlement dam (Q1), quarry swamp (Q2),
tailings storage cell (Q3) and the reservoir (Q4; Fig. 1). Quarry wetlands
are surrounded by terrestrial areas currently undergoing rehabilitation, and
are between 10 and 50 years old (see Table 1). The adjacent suburb, Lithgow
(33<inline-formula><mml:math id="M7" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>28<inline-formula><mml:math id="M8" display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula>51.19<inline-formula><mml:math id="M9" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>′</mml:mo><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> S, 150<inline-formula><mml:math id="M10" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>09<inline-formula><mml:math id="M11" display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula>26.67<inline-formula><mml:math id="M12" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>′</mml:mo><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> E), was searched
using high-resolution aerial imagery to locate reference wetlands. Those that
were accessible were selected for surveying, with highly isolated sites within
densely forested or fenced areas excluded due to logistical and safety
issues. This limited reference sites to five wetlands within suburban areas:
Geordie St Wetland (R1), Laidley St Wetland (R2), Vale of Clwydd
Wetland (R3), Lake Pillans Wetland 1 (R4) and Lake Pillans Wetland 2 (R5;
Fig. 1).</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><caption><p>Site characteristics including wetland area, perimeter and age,
water quality data and the percentage cover of vegetation in various
regions within wetlands. Reference wetlands (ref) – R1 is Geordie St Wetland;
R2 is Laidley St Wetland; R3 is Vale of Clwydd Wetland; R4 is Lake Pillans Wetland
1; R5 is Lake Pillans Wetland 2. Quarry wetlands (quarry) – Q1 is settlement
dam; Q2 is quarry swamp; Q3 is tailings storage cell; Q4 is reservoir.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="10">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:colspec colnum="6" colname="col6" align="right"/>
     <oasis:colspec colnum="7" colname="col7" align="right"/>
     <oasis:colspec colnum="8" colname="col8" align="right"/>
     <oasis:colspec colnum="9" colname="col9" align="right"/>
     <oasis:colspec colnum="10" colname="col10" align="right"/>
     <oasis:thead>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1"/>  
         <oasis:entry colname="col2">R1</oasis:entry>  
         <oasis:entry colname="col3">R2</oasis:entry>  
         <oasis:entry colname="col4">R3</oasis:entry>  
         <oasis:entry colname="col5">R4</oasis:entry>  
         <oasis:entry colname="col6">R5</oasis:entry>  
         <oasis:entry colname="col7">Q1</oasis:entry>  
         <oasis:entry colname="col8">Q2</oasis:entry>  
         <oasis:entry colname="col9">Q3</oasis:entry>  
         <oasis:entry colname="col10">Q4</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>  
         <oasis:entry colname="col1">Site type</oasis:entry>  
         <oasis:entry colname="col2">Ref</oasis:entry>  
         <oasis:entry colname="col3">Ref</oasis:entry>  
         <oasis:entry colname="col4">Ref</oasis:entry>  
         <oasis:entry colname="col5">Ref</oasis:entry>  
         <oasis:entry colname="col6">Ref</oasis:entry>  
         <oasis:entry colname="col7">Quarry</oasis:entry>  
         <oasis:entry colname="col8">Quarry</oasis:entry>  
         <oasis:entry colname="col9">Quarry</oasis:entry>  
         <oasis:entry colname="col10">Quarry</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Area (m<inline-formula><mml:math id="M14" 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></oasis:entry>  
         <oasis:entry colname="col2">1667</oasis:entry>  
         <oasis:entry colname="col3">461</oasis:entry>  
         <oasis:entry colname="col4">2678</oasis:entry>  
         <oasis:entry colname="col5">3888</oasis:entry>  
         <oasis:entry colname="col6">2305</oasis:entry>  
         <oasis:entry colname="col7">12 420</oasis:entry>  
         <oasis:entry colname="col8">1831</oasis:entry>  
         <oasis:entry colname="col9">8588</oasis:entry>  
         <oasis:entry colname="col10">3760</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Perimeter (m)</oasis:entry>  
         <oasis:entry colname="col2">272</oasis:entry>  
         <oasis:entry colname="col3">134</oasis:entry>  
         <oasis:entry colname="col4">514</oasis:entry>  
         <oasis:entry colname="col5">369</oasis:entry>  
         <oasis:entry colname="col6">199</oasis:entry>  
         <oasis:entry colname="col7">453</oasis:entry>  
         <oasis:entry colname="col8">225</oasis:entry>  
         <oasis:entry colname="col9">391</oasis:entry>  
         <oasis:entry colname="col10">507</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Approx. age (yr)</oasis:entry>  
         <oasis:entry colname="col2">100<inline-formula><mml:math id="M15" display="inline"><mml:mrow><mml:msup><mml:mo>+</mml:mo><mml:mo>*</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col3">100<inline-formula><mml:math id="M16" display="inline"><mml:mrow><mml:msup><mml:mo>+</mml:mo><mml:mo>*</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col4">100<inline-formula><mml:math id="M17" display="inline"><mml:mrow><mml:msup><mml:mo>+</mml:mo><mml:mo>*</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col5">105</oasis:entry>  
         <oasis:entry colname="col6">105</oasis:entry>  
         <oasis:entry colname="col7">10</oasis:entry>  
         <oasis:entry colname="col8">10</oasis:entry>  
         <oasis:entry colname="col9">30</oasis:entry>  
         <oasis:entry colname="col10">50</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Survey period 1 (30/03/16–01/04/16)</oasis:entry>  
         <oasis:entry colname="col2"/>  
         <oasis:entry colname="col3"/>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5"/>  
         <oasis:entry colname="col6"/>  
         <oasis:entry colname="col7"/>  
         <oasis:entry colname="col8"/>  
         <oasis:entry colname="col9"/>  
         <oasis:entry colname="col10"/>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Water temp (<inline-formula><mml:math id="M18" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C)</oasis:entry>  
         <oasis:entry colname="col2">15.7</oasis:entry>  
         <oasis:entry colname="col3">15.8</oasis:entry>  
         <oasis:entry colname="col4">15.0</oasis:entry>  
         <oasis:entry colname="col5">17.9</oasis:entry>  
         <oasis:entry colname="col6">17.2</oasis:entry>  
         <oasis:entry colname="col7">16.5</oasis:entry>  
         <oasis:entry colname="col8">–</oasis:entry>  
         <oasis:entry colname="col9">17.5</oasis:entry>  
         <oasis:entry colname="col10">17.0</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">pH</oasis:entry>  
         <oasis:entry colname="col2">7.6</oasis:entry>  
         <oasis:entry colname="col3">7.8</oasis:entry>  
         <oasis:entry colname="col4">7.2</oasis:entry>  
         <oasis:entry colname="col5">7.6</oasis:entry>  
         <oasis:entry colname="col6">7.4</oasis:entry>  
         <oasis:entry colname="col7">7.3</oasis:entry>  
         <oasis:entry colname="col8">–</oasis:entry>  
         <oasis:entry colname="col9">6.1</oasis:entry>  
         <oasis:entry colname="col10">6.2</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Conductivity (<inline-formula><mml:math id="M19" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>S)</oasis:entry>  
         <oasis:entry colname="col2">133.2</oasis:entry>  
         <oasis:entry colname="col3">88.1</oasis:entry>  
         <oasis:entry colname="col4">157.0</oasis:entry>  
         <oasis:entry colname="col5">147.2</oasis:entry>  
         <oasis:entry colname="col6">166.6</oasis:entry>  
         <oasis:entry colname="col7">30.1</oasis:entry>  
         <oasis:entry colname="col8">–</oasis:entry>  
         <oasis:entry colname="col9">25.4</oasis:entry>  
         <oasis:entry colname="col10">20.8</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Survey period 2 (20/07/16–21/07/16)</oasis:entry>  
         <oasis:entry colname="col2"/>  
         <oasis:entry colname="col3"/>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5"/>  
         <oasis:entry colname="col6"/>  
         <oasis:entry colname="col7"/>  
         <oasis:entry colname="col8"/>  
         <oasis:entry colname="col9"/>  
         <oasis:entry colname="col10"/>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Water temp (<inline-formula><mml:math id="M20" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C)</oasis:entry>  
         <oasis:entry colname="col2">10.1</oasis:entry>  
         <oasis:entry colname="col3">10.4</oasis:entry>  
         <oasis:entry colname="col4">10.9</oasis:entry>  
         <oasis:entry colname="col5">11.0</oasis:entry>  
         <oasis:entry colname="col6">11.7</oasis:entry>  
         <oasis:entry colname="col7">10.8</oasis:entry>  
         <oasis:entry colname="col8">–</oasis:entry>  
         <oasis:entry colname="col9">10.1</oasis:entry>  
         <oasis:entry colname="col10">10.1</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">pH</oasis:entry>  
         <oasis:entry colname="col2">7.0</oasis:entry>  
         <oasis:entry colname="col3">7.5</oasis:entry>  
         <oasis:entry colname="col4">7.1</oasis:entry>  
         <oasis:entry colname="col5">7.6</oasis:entry>  
         <oasis:entry colname="col6">7.5</oasis:entry>  
         <oasis:entry colname="col7">6.0</oasis:entry>  
         <oasis:entry colname="col8">–</oasis:entry>  
         <oasis:entry colname="col9">5.8</oasis:entry>  
         <oasis:entry colname="col10">5.1</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Conductivity (<inline-formula><mml:math id="M21" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>S)</oasis:entry>  
         <oasis:entry colname="col2">176.4</oasis:entry>  
         <oasis:entry colname="col3">188.8</oasis:entry>  
         <oasis:entry colname="col4">199.2</oasis:entry>  
         <oasis:entry colname="col5">138.5</oasis:entry>  
         <oasis:entry colname="col6">148.7</oasis:entry>  
         <oasis:entry colname="col7">20.4</oasis:entry>  
         <oasis:entry colname="col8">–</oasis:entry>  
         <oasis:entry colname="col9">16.5</oasis:entry>  
         <oasis:entry colname="col10">23.8</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Percentage vegetation</oasis:entry>  
         <oasis:entry colname="col2"/>  
         <oasis:entry colname="col3"/>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5"/>  
         <oasis:entry colname="col6"/>  
         <oasis:entry colname="col7"/>  
         <oasis:entry colname="col8"/>  
         <oasis:entry colname="col9"/>  
         <oasis:entry colname="col10"/>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Submerged (%)</oasis:entry>  
         <oasis:entry colname="col2">0</oasis:entry>  
         <oasis:entry colname="col3">20</oasis:entry>  
         <oasis:entry colname="col4">60</oasis:entry>  
         <oasis:entry colname="col5">10</oasis:entry>  
         <oasis:entry colname="col6">0</oasis:entry>  
         <oasis:entry colname="col7">0</oasis:entry>  
         <oasis:entry colname="col8">–</oasis:entry>  
         <oasis:entry colname="col9">0</oasis:entry>  
         <oasis:entry colname="col10">0</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Emergent (%)</oasis:entry>  
         <oasis:entry colname="col2">10</oasis:entry>  
         <oasis:entry colname="col3">20</oasis:entry>  
         <oasis:entry colname="col4">90</oasis:entry>  
         <oasis:entry colname="col5">20</oasis:entry>  
         <oasis:entry colname="col6">80</oasis:entry>  
         <oasis:entry colname="col7">10</oasis:entry>  
         <oasis:entry colname="col8">100</oasis:entry>  
         <oasis:entry colname="col9">3</oasis:entry>  
         <oasis:entry colname="col10">30</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Fringing (%)</oasis:entry>  
         <oasis:entry colname="col2">80</oasis:entry>  
         <oasis:entry colname="col3">100</oasis:entry>  
         <oasis:entry colname="col4">100</oasis:entry>  
         <oasis:entry colname="col5">100</oasis:entry>  
         <oasis:entry colname="col6">100</oasis:entry>  
         <oasis:entry colname="col7">5</oasis:entry>  
         <oasis:entry colname="col8">100</oasis:entry>  
         <oasis:entry colname="col9">30</oasis:entry>  
         <oasis:entry colname="col10">90</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Floating (%)</oasis:entry>  
         <oasis:entry colname="col2">0</oasis:entry>  
         <oasis:entry colname="col3">0</oasis:entry>  
         <oasis:entry colname="col4">20</oasis:entry>  
         <oasis:entry colname="col5">0</oasis:entry>  
         <oasis:entry colname="col6">0</oasis:entry>  
         <oasis:entry colname="col7">0</oasis:entry>  
         <oasis:entry colname="col8">–</oasis:entry>  
         <oasis:entry colname="col9">0</oasis:entry>  
         <oasis:entry colname="col10">0</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table><table-wrap-foot><p><inline-formula><mml:math id="M13" display="inline"><mml:msup><mml:mi/><mml:mo>*</mml:mo></mml:msup></mml:math></inline-formula> Age of wetlands could not be precisely determined. However, all are in-line
wetlands connected to long-established creeks or manmade channels formed or
constructed, respectively, over 100 years ago. Note that water quality variables
could not be measured within the swamp due to safety concerns over entering
this site.</p></table-wrap-foot></table-wrap>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><caption><p>Map of the study area (the suburbs of Lithgow and Clarence, New
South Wales, Australia), with the Kables Sands Quarry expanded inset,
showing site locations, and photographs of the nine wetlands surveyed.
Reference wetlands: R1 is Geordie St Wetland, R2 is Laidley St Wetland, and R3
is Vale of Clwydd Wetland, R4 is Lake Pillans Wetland 1, and R5 is Lake Pillans
Wetland. Quarry wetlands: Q1 is settlement dam, Q2 is quarry swamp, Q3 is
tailings storage cell and Q4 is reservoir. Satellite image credit: Google
Earth©.</p></caption>
          <?xmltex \igopts{width=455.244094pt}?><graphic xlink:href="https://we.copernicus.org/articles/17/19/2017/we-17-19-2017-f01.pdf"/>

        </fig>

</sec>
<sec id="Ch1.S2.SS2">
  <title>Local habitat variables</title>
      <p>Wetland area and perimeter were calculated using online spatial analysis
tools available at <uri>www.nearmap.com.au</uri>. During surveys, I measured variables
that may influence amphibian detection rates: date, time, cloud cover, wind
speed and current rainfall. In addition, approximately 100 mL of wetland
water was collected from three equidistant points around the pond perimeter
(1 m from the shoreline and at a depth of 5–10 cm), mixed and tested for
water temperature, conductivity and pH using a hand-held electronic meter
(WP-81 meter; TPS, Brisbane, Australia). Local air temperature, humidity and
total rainfall during the survey period were extracted from the Bureau of
Meteorology website (<uri>www.bom.gov.au</uri>). I visually estimated the percentage
cover of vegetation in the fringing, emergent, submerged and floating
regions of each wetland  (Parris, 2006).</p>
</sec>
<sec id="Ch1.S2.SS3">
  <title>Amphibian surveys</title>
      <p>Each wetland was surveyed for amphibians 3–5 times over two distinct
temporal periods: during March–April and July 2016. The sequence in which
wetlands were visited was randomised and stratified according to wetland
proximity, since wetlands occurred in clusters (i.e. quarry wetlands).
Adopting standard nocturnal search methods (see Parris et al., 1999), I
listened for the advertisement calls of male frogs at a site for a minimum
of 15 min, and then searched the wetland and surrounding banks and
vegetation with a headlamp for a minimum of 30 min. Frogs were captured,
identified to species level, weighed and measured (snout-vent and limb
lengths). Species-specific abundance was estimated by categorising
advertisement call intensity according to the index provided by Pope and
colleagues (2000): (0) no individuals calling; (1) individual(s) can be
counted with calls not overlapping; (2) calls of &lt; 15 individuals
can be distinguished, but there is some overlapping; and (3) &gt; 15
individuals are calling.</p><?xmltex \hack{\newpage}?>
</sec>
<sec id="Ch1.S2.SS4">
  <title>Developmental instability</title>
      <p>To calculate DI, I examined the total unsigned asymmetry of each collected
frog (i.e. the difference between the left and right forelimbs, plus the
corresponding value for the hindlimbs) using the blind protocol method
developed by Alford et al. (1999). Additionally, informal
comparisons with values extracted from Alford et al. (2007) were
made in order to consider these DI data from a more biologically relevant
perspective. These authors provided temporal trends in DI, so I calculated
the mean DI over this period for control and impact groups. Although this
provides only a simplified quantification of levels of DI in the different
populations, it nonetheless provides a relevant comparator with which to
compare measurements here.</p>
</sec>
<sec id="Ch1.S2.SS5">
  <title>Data analysis</title>
      <p>I used two general linear multivariate models to examine the influence of
environmental and local habitat characteristics on species richness and the
indexed abundance of <italic>C. signifera</italic>. The first model consisted of more temporally stable
factors: treatment (quarry or reference wetlands), wetland perimeter
(log-transformed) and a vegetation index (summed proportion of emergent and
fringing vegetation) were all fitted as fixed effects. The second model
included water temperature, pH and conductivity (all log-transformed) as
well as the sampling period as fixed effects. The addition of the sampling
period removes inherent variability in the other factors (i.e. water
temperature) between dates, providing more accurate estimates for the
influence of the other factors on the response variable.</p>
      <p>To analyse levels of DI, I used the methods outlined by Alford et al. (1999).
I first partitioned measurement error and the variation representing the
different kinds of asymmetry – directional asymmetry occurs when the mean of
the left/right differences in sizes of bilateral structures is not ideally
equal to zero (e.g. mammalian hearts; Graham et al., 1993); antisymmetry is
in a structure that is less than perfectly symmetrical in the majority of
individuals, but in any one individual, either side is equally likely to be
larger (e.g. lobster claws; Palmer and Strobeck, 1986); and fluctuating
asymmetry is found in structures that are ideally perfectly symmetrical, with
left-right differences normally distributed with a mean of zero (i.e. a
useful measure of DI). By calculating <inline-formula><mml:math id="M22" display="inline"><mml:mi>F</mml:mi></mml:math></inline-formula>-statistics from the appropriate
mean square estimates, I determined (1) whether the degree of asymmetry can
be used as an index of DI (i.e. if directional asymmetry is not present;
<inline-formula><mml:math id="M23" display="inline"><mml:mi>F</mml:mi></mml:math></inline-formula> <inline-formula><mml:math id="M24" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> MS<inline-formula><mml:math id="M25" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">SIDE</mml:mi></mml:msub></mml:math></inline-formula>/MS<inline-formula><mml:math id="M26" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mrow><mml:mi mathvariant="normal">SIDE</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant="normal">IND</mml:mi></mml:mrow></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>. If
<inline-formula><mml:math id="M27" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> &lt; 0.05, the degree of asymmetry cannot be used as an index of
DI, as the ideal degree of symmetry cannot be known (i.e. structures appear
to not be ideally bilateral). (2) Whether measurement error is small relative
to developmental instability (fluctuating asymmetry and antisymmetry: <inline-formula><mml:math id="M28" display="inline"><mml:mrow><mml:mi>F</mml:mi><mml:mo>=</mml:mo></mml:mrow></mml:math></inline-formula> MS<inline-formula><mml:math id="M29" display="inline"><mml:msub><mml:mi/><mml:mrow><mml:mi mathvariant="normal">SIDE</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant="normal">IND</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>/MS<inline-formula><mml:math id="M30" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mrow><mml:mi mathvariant="normal">SIDE</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant="normal">IND</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant="normal">REP</mml:mi></mml:mrow></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>.
If <inline-formula><mml:math id="M31" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> &lt; 0.05, measurement error is small
relative to the levels of fluctuating asymmetry and antisymmetry in the
data, and thus, DI can be compared between populations. (3) How levels
of DI differ between quarry and reference frog populations (quarry vs.
reference: <inline-formula><mml:math id="M32" display="inline"><mml:mrow><mml:mi>F</mml:mi><mml:mo>=</mml:mo></mml:mrow></mml:math></inline-formula> MS<inline-formula><mml:math id="M33" display="inline"><mml:msub><mml:mi/><mml:mrow><mml:mi mathvariant="normal">SAMPLE</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant="normal">IND</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant="normal">SIDE</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>/MS<inline-formula><mml:math id="M34" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mrow><mml:mi mathvariant="normal">SAMPLE</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant="normal">IND</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant="normal">SIDE</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant="normal">REP</mml:mi></mml:mrow></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>.
If <inline-formula><mml:math id="M35" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> &lt; 0.05, levels of DI differ between
populations. Here, SAMPLE is the quarry or reference wetland, SIDE is the left or right
limbs, REP is replicate measurement number and IND is individual frog number.</p>
      <p>Finally, I investigated the relationship between the abundance index for
<italic>C. signifera</italic> and levels of DI using a general linear model with site
as a blocking factor. Abundance index and DI were log-transformed. For all
analyses, I assessed normality and homogeneity of variances using Q–Q and
residual plots, respectively. All analyses were performed on R, Version 3.3.1
(R Development Core Team, 2016) using the lm function (Chambers, 1993). Data
are available online (Sievers, 2017).</p>
</sec>
</sec>
<sec id="Ch1.S3">
  <title>Results</title>
<sec id="Ch1.S3.SS1">
  <title>Site characteristics</title>
      <p>Quarry wetlands (mean <inline-formula><mml:math id="M36" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> SE; 6650 <inline-formula><mml:math id="M37" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 2391 m) were on average
approximately three times the size of reference (2200 <inline-formula><mml:math id="M38" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 566 m)
wetlands, whilst wetland perimeter was largely similar between quarry (394 <inline-formula><mml:math id="M39" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 61 m)
and reference (298 <inline-formula><mml:math id="M40" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 67 m) wetlands. Although the precise
age of several reference wetlands was unknown, all quarry wetlands were
younger than reference wetlands (Table 1). During the two survey periods,
water temperature was similar between quarry and reference wetlands, whereas
pH and salinity varied considerably; Quarry wetlands were consistently more
acidic and less saline than reference wetlands during both survey periods
(Table 1). Vegetation cover within each spatial region (i.e. submerged,
emergent, fringing and floating) was generally greater at reference
wetlands.</p>
</sec>
<sec id="Ch1.S3.SS2">
  <title>Amphibian populations</title>
      <p>Three amphibian species were calling and captured during the two survey
periods: the common eastern froglet <italic>Crinia signifera</italic>, the southern brown tree frog <italic>Litoria ewingii</italic> and the
striped marsh frog <italic>Limnodynastes peronii</italic>. All quarry wetlands contained at least one species,
whereas only two of the five reference wetlands contained frogs (Fig. 2).
Rarefaction curves quickly flattened out, suggesting that additional
sampling would not considerably increase the number of species identified
(Supplement Fig. S1). Multivariate analyses revealed little influence of
the length of wetland perimeter or the proportion of emergent and fringing
vegetation on the richness of amphibian communities, but quarry wetlands in
general were significantly richer than reference wetlands (Table 2).
Further, the pH of wetlands was a significant driver of species richness
(Table 2). In terms of <italic>C. signifera</italic> abundance, wetland perimeter and the proportion of
emergent and fringing vegetation were not significant predictors, but pH,
treatment and the survey period were (Table 2), again with quarry wetlands
harbouring more individuals than reference wetlands (Fig. 2).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><caption><p>Abundance index for the common eastern froglet <italic>Crinia signifera</italic>, the eastern brown
tree frog <italic>Litoria ewingii</italic> and the spotted marsh frog <italic>Limnodynastes peronii</italic> at   quarry and reference wetlands.
Abundance was estimated by categorising advertisement call intensity
according to the index provided by Pope et al. (2000): (0) no
individuals calling; (1) individual(s) can be counted with calls not
overlapping; (2) calls of &lt; 15 individuals can be distinguished, but
there is some overlapping; and (3) &gt; 15 individuals are calling.</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://we.copernicus.org/articles/17/19/2017/we-17-19-2017-f02.pdf"/>

        </fig>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T2" specific-use="star"><caption><p>Output from ANOVA for species richness and <italic>Crinia signifera</italic>
abundance index with treatment (quarry or reference wetlands), wetland
perimeter and vegetation (summed proportion of emergent and fringing
vegetation) fitted as factors (Model 1). Treatment, water temperature, pH and
conductivity, and date fitted as a blocking factor were included in Model 2.
Asterisks indicate factors that were log-transformed to meet the assumptions
of normality and homogeneity of variance. Boldface values represent
significance at <inline-formula><mml:math id="M41" display="inline"><mml:mi mathvariant="italic">α</mml:mi></mml:math></inline-formula> <inline-formula><mml:math id="M42" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.05.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="9">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right" colsep="1"/>
     <oasis:colspec colnum="6" colname="col6" align="right"/>
     <oasis:colspec colnum="7" colname="col7" align="right"/>
     <oasis:colspec colnum="8" colname="col8" align="right"/>
     <oasis:colspec colnum="9" colname="col9" align="right"/>
     <oasis:thead>
       <oasis:row>  
         <oasis:entry colname="col1"/>  
         <oasis:entry rowsep="1" namest="col2" nameend="col5" align="center" colsep="1">Species richness </oasis:entry>  
         <oasis:entry rowsep="1" namest="col6" nameend="col9" align="center">Crinia signifera abundance index </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Factor</oasis:entry>  
         <oasis:entry colname="col2">df</oasis:entry>  
         <oasis:entry colname="col3">MS</oasis:entry>  
         <oasis:entry colname="col4"><inline-formula><mml:math id="M43" display="inline"><mml:mi>F</mml:mi></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M44" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col6">df</oasis:entry>  
         <oasis:entry colname="col7">MS</oasis:entry>  
         <oasis:entry colname="col8"><inline-formula><mml:math id="M45" display="inline"><mml:mi>F</mml:mi></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col9"><inline-formula><mml:math id="M46" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula></oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Model 1</oasis:entry>  
         <oasis:entry colname="col2"/>  
         <oasis:entry colname="col3"/>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5"/>  
         <oasis:entry colname="col6"/>  
         <oasis:entry colname="col7"/>  
         <oasis:entry colname="col8"/>  
         <oasis:entry colname="col9"/>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Treatment</oasis:entry>  
         <oasis:entry colname="col2">1</oasis:entry>  
         <oasis:entry colname="col3">4.050</oasis:entry>  
         <oasis:entry colname="col4">7.451</oasis:entry>  
         <oasis:entry colname="col5"><bold>0.041</bold></oasis:entry>  
         <oasis:entry colname="col6">1</oasis:entry>  
         <oasis:entry colname="col7">1.606</oasis:entry>  
         <oasis:entry colname="col8">1.579</oasis:entry>  
         <oasis:entry colname="col9">0.265</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Perimeter<inline-formula><mml:math id="M47" display="inline"><mml:msup><mml:mi/><mml:mo>*</mml:mo></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">1</oasis:entry>  
         <oasis:entry colname="col3">0.007</oasis:entry>  
         <oasis:entry colname="col4">0.013</oasis:entry>  
         <oasis:entry colname="col5">0.915</oasis:entry>  
         <oasis:entry colname="col6">1</oasis:entry>  
         <oasis:entry colname="col7">0.864</oasis:entry>  
         <oasis:entry colname="col8">0.850</oasis:entry>  
         <oasis:entry colname="col9">0.399</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Vegetation</oasis:entry>  
         <oasis:entry colname="col2">1</oasis:entry>  
         <oasis:entry colname="col3">1.226</oasis:entry>  
         <oasis:entry colname="col4">2.255</oasis:entry>  
         <oasis:entry colname="col5">0.194</oasis:entry>  
         <oasis:entry colname="col6">1</oasis:entry>  
         <oasis:entry colname="col7">0.001</oasis:entry>  
         <oasis:entry colname="col8">0.001</oasis:entry>  
         <oasis:entry colname="col9">0.981</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Residuals</oasis:entry>  
         <oasis:entry colname="col2">5</oasis:entry>  
         <oasis:entry colname="col3">0.544</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5"/>  
         <oasis:entry colname="col6">5</oasis:entry>  
         <oasis:entry colname="col7">1.017</oasis:entry>  
         <oasis:entry colname="col8"/>  
         <oasis:entry colname="col9"/>
       </oasis:row>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Model 2</oasis:entry>  
         <oasis:entry colname="col2"/>  
         <oasis:entry colname="col3"/>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5"/>  
         <oasis:entry colname="col6"/>  
         <oasis:entry colname="col7"/>  
         <oasis:entry colname="col8"/>  
         <oasis:entry colname="col9"/>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Treatment</oasis:entry>  
         <oasis:entry colname="col2">1</oasis:entry>  
         <oasis:entry colname="col3">7.004</oasis:entry>  
         <oasis:entry colname="col4">19.834</oasis:entry>  
         <oasis:entry colname="col5"><bold>0.001</bold></oasis:entry>  
         <oasis:entry colname="col6">1</oasis:entry>  
         <oasis:entry colname="col7">12.604</oasis:entry>  
         <oasis:entry colname="col8">24.828</oasis:entry>  
         <oasis:entry colname="col9"><bold>0.001</bold></oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Temperature<inline-formula><mml:math id="M48" display="inline"><mml:msup><mml:mi/><mml:mo>*</mml:mo></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">1</oasis:entry>  
         <oasis:entry colname="col3">0.000</oasis:entry>  
         <oasis:entry colname="col4">0.001</oasis:entry>  
         <oasis:entry colname="col5">0.976</oasis:entry>  
         <oasis:entry colname="col6">1</oasis:entry>  
         <oasis:entry colname="col7">0.096</oasis:entry>  
         <oasis:entry colname="col8">0.189</oasis:entry>  
         <oasis:entry colname="col9">0.673</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">pH<inline-formula><mml:math id="M49" display="inline"><mml:msup><mml:mi/><mml:mo>*</mml:mo></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">1</oasis:entry>  
         <oasis:entry colname="col3">3.168</oasis:entry>  
         <oasis:entry colname="col4">8.970</oasis:entry>  
         <oasis:entry colname="col5"><bold>0.013</bold></oasis:entry>  
         <oasis:entry colname="col6">1</oasis:entry>  
         <oasis:entry colname="col7">3.423</oasis:entry>  
         <oasis:entry colname="col8">6.742</oasis:entry>  
         <oasis:entry colname="col9"><bold>0.027</bold></oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Conductivity<inline-formula><mml:math id="M50" display="inline"><mml:msup><mml:mi/><mml:mo>*</mml:mo></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">1</oasis:entry>  
         <oasis:entry colname="col3">0.036</oasis:entry>  
         <oasis:entry colname="col4">0.103</oasis:entry>  
         <oasis:entry colname="col5">0.755</oasis:entry>  
         <oasis:entry colname="col6">1</oasis:entry>  
         <oasis:entry colname="col7">0.166</oasis:entry>  
         <oasis:entry colname="col8">0.326</oasis:entry>  
         <oasis:entry colname="col9">0.581</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Date</oasis:entry>  
         <oasis:entry colname="col2">1</oasis:entry>  
         <oasis:entry colname="col3">0.698</oasis:entry>  
         <oasis:entry colname="col4">1.975</oasis:entry>  
         <oasis:entry colname="col5">0.190</oasis:entry>  
         <oasis:entry colname="col6">1</oasis:entry>  
         <oasis:entry colname="col7">3.073</oasis:entry>  
         <oasis:entry colname="col8">6.053</oasis:entry>  
         <oasis:entry colname="col9"><bold>0.034</bold></oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Residuals</oasis:entry>  
         <oasis:entry colname="col2">10</oasis:entry>  
         <oasis:entry colname="col3">0.353</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5"/>  
         <oasis:entry colname="col6">10</oasis:entry>  
         <oasis:entry colname="col7">0.508</oasis:entry>  
         <oasis:entry colname="col8"/>  
         <oasis:entry colname="col9"/>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

</sec>
<sec id="Ch1.S3.SS3">
  <title>Developmental instability</title>
      <p>Only DI data for <italic>Crinia signifera</italic> were formally analysed (see
Supllment Sect. S1 for all raw data). Analyses revealed no evidence of
directional asymmetry in the frog population measured (forelimb: <inline-formula><mml:math id="M51" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo></mml:mrow></mml:math></inline-formula> 0.35;
hindlimb: <inline-formula><mml:math id="M52" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo></mml:mrow></mml:math></inline-formula> 0.58; Table S1 in the Supplement), indicating that ideally,
frog limbs should be symmetrical. Furthermore, measurement error was low
relative to levels of fluctuating asymmetry and antisymmetry (forelimb:
<inline-formula><mml:math id="M53" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> &lt; 0.001; hindlimb: <inline-formula><mml:math id="M54" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> &lt; 0.001; Table S1), indicating
that a comparison between populations is permissible. Levels of DI were lower
within quarry wetlands relative to reference wetlands (Fig. 3), with both
forelimb (<inline-formula><mml:math id="M55" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> &lt; 0.001) and hindlimb asymmetry (<inline-formula><mml:math id="M56" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo></mml:mrow></mml:math></inline-formula> 0.027) that
are significantly different between populations (Table S1). Informal
comparisons with values extracted from Alford et al. (2007) revealed that
unsigned asymmetry in the quarry wetlands was similar to their control
groups, whilst unsigned asymmetry in reference wetlands was similar to the
impact group from Alford which suffered population decline following a period
of increasing DI (Fig. 3). There was also a significant relationship between
the abundance index for <italic>C. signifera</italic> and levels of DI, whereby
indexed abundance was higher at sites with lower DI (<inline-formula><mml:math id="M57" display="inline"><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>,</mml:mo><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mn mathvariant="normal">11</mml:mn></mml:mrow></mml:msub><mml:mo>=</mml:mo></mml:mrow></mml:math></inline-formula> 17.9,
<inline-formula><mml:math id="M58" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo></mml:mrow></mml:math></inline-formula> 0.001).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3"><caption><p>Levels of developmental instability (DI; quantified as unsigned
asymmetry) for the three frog species occurring at quarry and reference
wetlands. Mean DI values are also shown from Alford et al. (2007) for informal
comparison. Frog populations from Alford control sites remained stable,
whilst those from impact sites exhibited substantial decline following a
period of increasing DI.</p></caption>
          <?xmltex \igopts{width=113.811024pt}?><graphic xlink:href="https://we.copernicus.org/articles/17/19/2017/we-17-19-2017-f03.pdf"/>

        </fig>

<?xmltex \hack{\newpage}?>
</sec>
</sec>
<sec id="Ch1.S4">
  <title>Discussion</title>
      <p>Wetlands within the Kables Sands quarry were inhabited by more frog species
than nearby urban wetlands, and the individuals within quarries had lower
levels of developmental instability. Of the species present at the quarry,
only <italic>Crinia signifera</italic> were present within reference wetlands, and these frogs had comparable
rates of DI with frogs from populations that underwent crashes (Alford,
et al., 2007). I also present evidence that there is a negative relationship
between the abundance of <italic>C. signifera</italic> and unsigned asymmetry of their limbs at particular
wetlands. Species occurrence and individual fitness proxies may be used to
predict population persistence   (Aldridge and Boyce, 2007). Therefore,
quarry wetlands may provide more suitable habitat conditions than urban
wetlands in the longer term.</p>
      <p>Importantly, reference wetlands selected for this study do not necessarily
represent pristine natural sites, but rather were artificial wetlands in
suburban areas. Therefore, there may be additional pressures on these
wetlands that quarry wetlands are not exposed to, such as elevated
contaminants from storm water run-off, which influence amphibian richness,
abundance and fitness  (Hale et al., 2015). Despite not being pristine,
amphibians frequently inhabit urban wetlands and these provide essential
habitat in many urban areas  (Brand and Snodgrass 2010; Le Viol et al.,
2012). Since these reference wetlands are omnipresent in human-dominated
landscapes, they serve as relevant and useful comparators to determine the
suitability and ecological importance of quarry wetlands.</p>
      <p>Typically, the mean abundance of <italic>Crinia signifera</italic> is greater at larger wetlands and at those
with greater connectivity (Hamer et al., 2012). Although the site
with the highest abundance of <italic>Crinia signifera</italic> – Q4 – was the second smallest quarry wetland
by area, importantly, it also had the largest perimeter. Although there was
no overall effect of wetland perimeter, given that this species calls from
the waters edge, a greater perimeter may permit more individuals to be
present and calling at any one time (Antis, 2013). Whilst salinity and pH were
significantly higher within reference sites, and pH may have influenced
frogs within this study, the levels observed within all surveyed wetlands
were well within the range deemed suitable for the majority of amphibian
species native to this region  (Kearney et al., 2012; Sadinski and Dunson,
1992; Smith et al., 2007).</p>
      <p>Our study and others conducted within Australian wetlands may shed light onto
the factors driving community- and population-level patterns, and also
provide information on how to best manage quarry wetlands. The occurrence of
both <italic>Limnodynastes peronii</italic> and <italic>Litoria ewingii</italic> correlate
with the proportion of vegetation in the emergent and submerged zones
(Hamer et al., 2012). Although we found little influence of
vegetation, at wetlands with &gt; 80 % cover, these species have
a &gt; 0.8 probability of being found, and yet were not heard calling
at the reference wetlands that contained high levels of vegetation. In
addition, <italic>L. peronii</italic> occurrence is correlated to the depth of
wetland shores, where gently sloping shores are preferred over steep drops
(Hamer et al. 2012). The shoreline of one of the reference wetlands,
R4, was made up of a concrete wall, largely inappropriate for successful
breeding by ground frog species  (Parris, 2006). Within the quarry,
there was considerable inter- and intra-wetland variability in the steepness
of the shoreline, and given other species within the region (e.g.
<italic>Limnodynastes dumerlii</italic>) prefer steep shores. This variability likely
enhances year-round biodiversity in quarries. I provide considerable evidence
that for sand quarry wetlands, multi-objective management may be highly
beneficial for local amphibian populations  (Bendell-Young,
et al., 2000).</p>
      <p>In addition to quantifying trends in species presence and population size
within quarry wetlands, effective management and conservation also require
knowledge of individual fitness. Studies have shown that DI increases as
health decreases, and amphibian population declines have preceded periods of
increasing DI  (Alford, et al., 2007). I provide three lines of evidence
suggesting quarry wetlands are providing a comparatively high-quality
resource. Firstly, relative to reference wetlands, frogs within quarry
wetlands had significantly lower levels of DI. Secondly, DI rates within
quarry wetlands were comparable to the control populations from Alford et al. (2007). Frog populations within these control sites remained
stable, whilst populations from impact sites exhibited substantial declines
following a period of increasing DI. Finally, there was a significant
relationship between levels of DI and the abundance of <italic>C. signifera</italic> populations. Although
this suggests that population persistence and/or growth may be enhanced when
DI is lower, quantifying population dynamics was beyond the scope of this
study. Furthermore, whilst the incidence of amphibian abnormalities has been
shown to be higher in wetlands impacted by other forms of mining
(Anderson and Arruda, 2006; Rowe  et al., 1996), I identified no
abnormalities in any of the frogs captured in this study.</p>
      <p>Empirical studies that monitor the relationship amongst population size,
community composition, individual fitness and the environmental factors
driving these responses are imperative to the long-term persistence of
amphibians in human-impacted environments. Given that the quarry wetlands
surveyed here provide a seemingly good habitat for amphibians, they – and
similar wetlands elsewhere – should be managed to enhance their suitability
and attractiveness. Gallagher et al. (2014) suggest ensuring that
high-quality wetlands are connected to other wetlands, with their hydroperiod
and vegetation managed to promote amphibian survival and reproductive
success. Measures to remove and exclude exotic predators, such as
mosquitofish and carp, will also greatly enhance amphibian populations and
communities, with considerable improvements to individual fitness  (Maezono
and Miyashita, 2004; Tsunoda et al., 2010). To further promote and enhance
amphibian biodiversity within quarries, it is important to provide
substantial environmental heterogeneity; a mosaic of both wetland types and
terrestrial refuges. Creating a diverse mosaic of wetlands that vary in size
and depth will help ensure amphibian breeding at a proportion of quarry
wetlands each season, thereby enhancing overall population and community
persistence at any one quarry  (Rannap et al., 2009). Despite little influence of vegetation on richness or abundance found here, creating
wetlands with a minimum of 80 % emergent vegetation will help ensure a
high probability of many native species occurring, such as
<italic>Limnodynastes peronii</italic> and <italic>Litoria ewingii</italic> (Hamer
et al., 2012). All species identified here oviposit their eggs within and
around aquatic and semi-aquatic vegetation, which in turn provide refuge and
protection to tadpoles  (Anstis, 2013). Therefore, planting native
vegetation around and within these wetlands would greatly increase the
probability of colonisation. In quarries with existing wetlands, such as
Kables Sands, implementing topographical modifications will ensure sufficient
heterogeneous habitat to cater for as many species as possible.</p>
      <p>In terms of monitoring, identifying amphibian species presence and
population size and quantifying individual fitness by measuring
limb lengths are economically and logistically feasible methods that can be used to assess
the health of quarry wetlands. Measuring DI is a relatively simple process
that could be employed periodically throughout the year at mining sites
around the world. Coupled with the water quality data that are often already
collected on site, levels of DI could provide an early warning indicator of
potential issues, allowing mitigation strategies to be implemented prior to
amphibian population declines. As researchers elsewhere adopt DI as a
fitness proxy, these data can be used to compare levels observed in quarry
wetlands, and ultimately, used to evaluate population health. Overall, the
methods outlined here provide a powerful, yet simple, tool to assess the
overall health of quarry wetlands that could be easily adopted and
implemented at quarries anywhere in the world.</p>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <title>Conclusion</title>
      <p>The creation of wetlands during and following mining activities is
inevitable. Given the rate at which natural wetlands are being lost and
degraded, the ecological importance of these wetlands has never been more
important. Here, I show that quarry wetlands within the Kables Sands plant
are not only attracting amphibians, but also providing conditions conducive
to high individual fitness. Relative to other wetlands in this region,
quarry wetlands consistently had greater species richness, as well as
individuals with lower levels of developmental instability. Several
management practices could further enhance the role quarry wetlands play in
promoting amphibian biodiversity, such as planting out emergent zones with
native vegetation, and creating a mosaic of wetland types. Implementing
these basic survey techniques at quarries is an economically and
logistically feasible strategy to ensure quarry wetlands are and remain high-quality systems capable of enhancing local and regional
biodiversity and conservation goals.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability">

      <p>Data for DI analysis is in Sect. S1.</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p><bold>The Supplement related to this article is available online at <inline-supplementary-material xlink:href="http://dx.doi.org/10.5194/we-17-19-2017-supplement" xlink:title="pdf">doi:10.5194/we-17-19-2017-supplement</inline-supplementary-material>.</bold></p></supplementary-material>
        </app-group><notes notes-type="competinginterests">

      <p>The author declares that he has no conflict of interest.</p>
  </notes><ack><title>Acknowledgements</title><p>The financial and logistical aspects of this project were made possible by
the Quarry Life Award initiative run by HeidelbergCement Group and Hanson
Australia Pty Ltd.<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?>
Edited by: D. Montesinos<?xmltex \hack{\newline}?>
Reviewed by: two anonymous referees</p></ack><ref-list>
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    <!--<article-title-html>Sand quarry wetlands provide high-quality habitat  for native amphibians</article-title-html>
<abstract-html><p class="p">Anthropogenic disturbances to habitats influence the fitness of
individual animals, the abundance of their populations, and the composition
of their communities. Wetlands in particular are frequently degraded and
destroyed, impacting the animals that inhabit these important ecosystems. The
creation of wetlands during and following sand extraction processes is
inevitable, and thus, sand quarries have the potential to support aquatic
animals. To determine how amphibians utilise these wetlands, I conducted
nocturnal call surveys at wetlands within the Kables Sands quarry, New South
Wales, Australia, and within surrounding reference wetlands, and quantified
levels of developmental instability (DI) as a proxy for fitness. Whilst
quarry and reference wetlands were largely similar in terms of environmental
characteristics, quarry wetlands consistently harboured more amphibian
species and individuals. Using unsigned asymmetry as a measure of DI, frogs
from the quarry sites exhibited significantly lower levels of DI compared to
reference wetlands, indicating that quarry wetlands may be comparatively
higher quality. Levels of DI within quarry wetlands also compared favourably
to data from healthy frog populations extracted from the literature. Further
enhancing the suitability of quarry wetlands would require minimal effort,
with potentially significant increases in local and regional biodiversity.
Documenting species presence and quantifying individual fitness by measuring
limb lengths is an economically and logistically feasible method to assess
the health of quarry wetlands. Overall, the methods outlined here provide a
powerful, yet simple, tool to assess the overall health and suitability of
quarry wetlands that could be easily adopted at quarries throughout the
world.</p></abstract-html>
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</mixed-citation></ref-html>--></article>
