DOI: 10.5061/dryad.p2ngf1vnt
title: Data from: Scat DNA provides important data for effective monitoring of mammal and bird biodiversity

description: Fauna has long been neglected in the monitoring of ecological restoration,
 despite the key role they play in ecosystem function. Vertebrate surveys
 can be time consuming and costly, often requiring multiple methodologies
 and taxonomic expertise, making comprehensive monitoring cost prohibitive.
 Here we evaluate a new method of assessing mammal and bird diversity
 through the genetic identification of scat collections. Using DNA
 metabarcoding of scat collections from three bioregions we generated bird
 and mammalian assemblage data and distinguished between sites with
 different restoration histories. However, scat detectability was affected
 by environmental conditions (e.g. rainfall and soil), suggesting that our
 approach is most applicable at certain times of year or in arid (or
 semi-arid) environments with rocky soils, where conditions are favourable
 for scat preservation. Taken together these data provide a pathway to:
 plan, monitor and establish best-practice when restoring landscapes and
 add to the growing body of literature on the value of DNA metabarcoding in
 biomonitoring applications.
description: <strong>1. Study Sites</strong>
 Samples were collected at three locations. The Swan Coastal Plain
 (SCP) and Jarrah Forest (<i>Eucalyptus marginata</i>) (JF)
 Bioregions are both within the Mediterranean-climate Southwest Australian
 Global Biodiversity Hotspot (Myers et al. 2007), while the third is in the
 semi-arid Pilbara Bioregion (PB). At each location, we sampled two
 restoration sites under nine years old, two restoration sites over 9 years
 old, and two reference sites (Figure 1). Therefore, a total of 18 sites
 were targeted for scat collection.  The Banksia
 Woodland of the Coastal Plain is a biodiverse ecosystem with a
 Mediterranean climate with cool, wet winters and hot dry summer. The area
 has a mean annual minimum temperature of 12.8°C, a mean annual maximum of
 24.7°C, and a mean annual rainfall of 757 mm (Australian Bureau of
 Meteorology). The dominant tree species are <i>Banksia
 attenuata</i> and <i>B. menziesii</i>, with less
 dominant <i>Eucalyptus todtiana</i> and <i>Nuytsia
 floribunda. </i>The understory consists of woody species of
 Myrtaceae, Fabaceae, Proteaceae, and Ericaceae, and non-woody species in
 Anthericaceae, Stylidaceae, Cyperaceae, and Haemodoraceae (Trudgen 1977).
 In October 2018, we sampled six sites at a Hanson Construction Materials
 sand quarry in Lexia (-31.76°, 115.95°); two reference sites and
 restoration sites 1, 3, 14, 22 years old. The four restoration sites were
 established to restore mined areas to surrounding native Banksia
 woodlands. All restoration was done by Hanson and previous mine owners and
 included seeding with native species and planting of dominant tree species
 (<i>Banksia attenuata</i>, <i>B. menziesii</i>,
 and <i>Eucalyptus todtiana</i>). Plant species richness and
 density tends to be higher in restoration sites, and percent cover
 increases with restoration age and is highest in reference sites (Benigno
 et al. 2013). According to a previous study looking at banksia woodland
 restoration, birds colonize rapidly and there is little difference in
 richness or assemblages between restoration and reference (Comer and
 Wooller 2002). There is less data available of mammalian recovery; some of
 the more common animals include the Western Grey Kangaroo
 (<i>Macropus fugilinosus</i>) and the Australian Raven
 (<i>Corvus coronoides</i>) (www.ala.org.au).
 The Jarrah location has a similar Mediterranean climate to the
 Coastal Plain site. The area has a mean annual minimum temperatures of
 8.6°C, a mean annual maximum of 23.7°C, and mean annual rainfall of 668.9
 mm (Australian Bureau of Meteorology). The overstory vegetation is
 dominated by <i>E. marginata, </i>with <i>E. patens,
 </i>and <i>E. wandoo </i>also<i> </i>common.
 The understory is sclerophyllous and floristically diverse, dominated by
 several families (i.e. Fabaceae, Asteraceae, Proteaceae, Dasypogonaceae,
 Myrtaceae) (Havel 1975). We sampled six sites from the bauxite mine
 South32 (-32.96°, 116.48°) in October 2018; two reference sites and
 restoration sites 2, 6, 11, 20 years old (Figure 1). All restoration was
 undertaken by South32 or the previous mine owners and included seeding
 with over 100 native species. Reference and restoration sites are
 dominated by species of Myrtaceae and Fabaceae. Total cover increases with
 age of restoration and reaches similar cover percentages to reference
 sites. There are uncontrolled feral animals including <i>Sus scrofa
 </i>(pig), and <i>Vulpes vulpes </i>(fox) (Williams and
 Mitchell 2002). Fauna surveys indicate bird diversity increases with age
 of restoration and a greater abundance of Carnaby’s black-cockatoo
 (<i>Calyptorhynchus latirostris) </i>and new-holland
 honeyeater (<i>Phylidonyris novaehollandiae</i>) in
 restoration sites (Data from South32). Mammalian richness remains similar
 throughout restoration, although the restoration sites have a higher
 abundance of house mouse (<i>mus musculus</i>) and Western
 Grey Kangaroo (<i>M. fugilinosus</i>) (Data from
 South32). The open woodlands of the Pilbara occur in a
 hot, arid location of north-western Australia with most rainfall occurring
 in the summer, associated with cyclonic activity (McKenzie et al. 2009).
 Temperatures have a mean annual minimum of 15°C and mean annual maximum of
 30.6 °C, with a mean annual rainfall of 263.8 mm (Australian Bureau of
 Meteorology). The open snappy gum (<i>E. leucophloia</i>)
 woodland occurs over hummock grasses (<i>Triodia wiseana, T.
 basedowii, T. lanigera</i>) and low acacia shrubs (McKenzie et al.
 2009). This site is located in a region that includes globally significant
 iron ore resources and accounts for up to 39% of global iron ore
 production (Government of Western Australia 2019). We sampled 6 sites at a
 BHP iron ore mine (-22.84°, 118.95°) in September 2018, two reference
 sites and restoration sites 4, 7, 11, and 15 years old (Figure 1). These
 sites are within 1 km of several ephemeral creeks. Restoration was
 conducted by the mine owners and restoration areas tended to have higher
 coverage of woody shrubs (<i>Acacia</i>), while reference
 sites and older restoration areas has more hummock grasses
 (<i>Triodia</i>) and mature gum (<i>E.
 leucophloia</i>) (Data from BHP). There is limited data in the
 Pilbara, none of the 31 studies examining vertebrate recovery in
 Australasia used sites restored post iron-ore extraction (Cross et al.
 2019a). Some of the more common animals in the area, according to the
 Atlas of Living Australia (www.ala.org.au) include the Pebble-mound mouse
 (<i>Pseudomys chapmani</i>) and the Galah (<i>Eolophus
 raseicapilla</i>) <strong>2. Sample
 Collection</strong> Scat samples were collected
 from 4 randomly chosen points at each restoration/reference site. At each
 point, scat was collected over an 8-minute period along a 200 m transect,
 at least 5 m from the edge of the site. A portion of all scats seen during
 this transect were collected; herbivore, carnivore, and bird, no matter
 how degraded. Latex gloves were used in collection to minimize
 contamination and new gloves were used for each sample.  A total of 72
 scat samples were collected, 24 from each location. Scat samples were
 frozen as soon as possible in a mobile freezer and transported to the
 Trace and Environmental DNA lab where they were stored at -20°C until
 processed.   <strong>3. Sample
 processing</strong> A portion of each scat in the
 sample was put in a 50 mL falcon tube. Scats that could be easily
 identified was noted for each sample, to provide a morphological
 comparison to sequencing results. These included identifying Emu scat
 because it is very distinctive, bird scat (not including Emu), and
 kangaroo and wallaby scat. Scat was homogenized in 50mL falcon tubes using
 a TissueLyser (Qiagen) with 4 steel balls (5mm) for 3 minutes in 30 second
 intervals.  DNA was extracted from 250uL scat homogenate, using the DNeasy
 PowerLyzer PowerSoil kit (Qiagen) on the QiaCube Connect automated
 platform (Qiagen). A final elution volume of 100 μL (Tris buffer) was
 used, and extraction controls (blanks) were included in every set of
 extractions.  Quantitative PCR (qPCR) was run on neat extracts and a 1/10
 dilution to see if samples exhibited inhibition, and to determine optimal
 DNA input for each sample (Murray et al. 2015). The qPCR assays were run
 with primers that target the 12S gene <i>12SV5</i>-F/R (Riaz
 et al. 2011). This assay is both short enough (98 bp), to pick up degraded
 DNA and broad enough to amplify mammalian, avian, and reptilian
 DNA. The PCR mix for quantitation contained: 2.5 mM
 MgCl2 (Applied Biosystems, USA), 1× PCR Gold buffer (Applied Biosystems),
 0.25 mM dNTPs (Astral Scientific, Australia), 0.4 mg/ml bovine serum
 albumin (Fisher Biotec, Australia), 0.4 μmol/L forward and reverse primer,
 1 U AmpliTaq Gold DNA polymerase (Applied Biosystems) and 0.6 μl of a
 1:10,000 solution of SYBR Green dye (Life Technologies, USA). The qPCRs
 were run on a StepOne Plus (Applied BioSystems) real-time qPCR instrument
 with the following conditions: 5 min at 95°C, 40 cycles of 95°C for 30s,
 30s at the 60°C and 45s at 72°C, a melt curve stage of 15s at 95°C, 1 min
 at 60°C and 15s at 95°C, ending with 10 min elongation at 72°C. Extraction
 controls were included in qPCR assays. DNA dilutions that showed
 uninhibited amplification (Murray et al. 2015) were selected for further
 analysis. The DNA extracts that were deemed of
 sufficient template number and free of inhibition (qPCR described above)
 were assigned a unique combination of multiplex identifier (MID) tags.
 These MID tags were incorporated into fusion tagged primers, and, to
 prevent cross contamination, none of the primer-MID tag combinations had
 been used previously in the lab. Fusion PCRs for scat samples were done in
 duplicate and the PCR master mixes were prepared in a dedicated
 ultra-clean lab before DNA was added to minimize the potential for
 contamination. The PCR cycling conditions were as stated above for the
 quantification qPCRs. Samples that amplified successfully were then pooled
 into approximately equimolar concentrations to produce a final
 metabarcoding library that was size-selected (150-450bp) using a
 PippinPrep 2% ethidium bromide cassette (Sage Science, Beverly, MA,
 U.S.A).  Libraries were cleaned using a QIAquick PCR Purification Kit
 (Qiagen, Germany) and final library quantification with a Qubit
 Fluorometric Quantitation (Thermo Fisher Scientific). Sequencing was
 performed on the Illumina MiSeq platform using the 300 cycle V2 kit
 following the manufacturer’s protocol. <strong>4.
 Sequencing analysis</strong> Bioinformatics and
 taxonomic assignments were performed on the Pawsey Supercomputer.
 Sequences were demultiplexed using OBITools (Boyer et al. 2016). Further
 sequence processing carried out in R using the “DADA2” package (Callahan
 et al. 2016) where sequences were quality filtered, the error rates were
 estimated, and the sequences were dereplicated. The error rates are then
 used in the sample inference stage to remove sequences likely to be errors
 and leave Amplicon Sequence Variants (ASV). These ASVs are equivalent to
 zero radius operational taxonomic units (ZOTUs) in usearch (Edgar 2016).
 The sequence table was constructed, chimeras removed, and curated using
 LULU (Frøslev et al. 2017). Taxonomy was assigned using BLASTN to search
 against the GenBank reference database (https://www.ncbi.nlm.nih.gov/genba
 nk/). Minimum percent coverage was set at 97%, minimum percent
 identification at 95%, and taxonomic identification was dropped to the
 lowest common ancestor if sequences matched with more than one taxa. Any
 identifications with less than 97% identity were dropped to family level.
 Taxonomic identifications were validated against the Atlas of Living
 Australia database (www.ala.org.au) to verify the organisms occurred in
 the area. If an identification did not occur in the area, the
 identification was dropped to the lowest common ancestor that occurred in
 the area or removed from the dataset (See Supplementary table 1 for
 details).