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\begin{document}

\title{Evaluating restoration trajectories using DNA metabarcoding of
invertebrates and their associated plant communities}



\author[1]{Mieke van der Heyde}%
\author[1]{Michael Bunce}%
\author[1]{Kingsley Dixon}%
\author[1]{Kristen Fernandes}%
\author[1]{Jonathan Majer}%
\author[1]{Grant Wardell-Johnson}%
\author[1]{Nicole White}%
\author[1]{Paul Nevill}%
\affil[1]{Curtin University Bentley Campus}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Invertebrate communities provide many critical ecosystem functions (e.g.
pollination, decomposition, herbivory and soil formation), and have been
identified as indicators of ecological restoration. Unfortunately,
invertebrates are often overlooked in restoration monitoring because
they are time-consuming to survey, often require rare taxonomic
expertise, and there are many undescribed species. DNA metabarcoding is
a tool to rapidly survey invertebrates and can also provide information
about plants with which those invertebrates are interacting. Here we
evaluate how invertebrate communities may be used to determine ecosystem
trajectories during restoration. We collected ground-dwelling and
airborne invertebrates across chronosequences of mine-site restoration
in three ecologically different locations in Western Australia, and
identified invertebrate and plant communities using DNA metabarcoding.
Ground-dwelling invertebrates showed the clearest restoration signals,
with communities becoming more similar to reference communities over
time. These patterns were weaker in airborne invertebrates, which have
higher dispersal abilities and therefore less local fidelity to
environmental conditions. Invertebrate community recovery was most
evident in ecosystems with relatively stable climax communities, while
the trajectory in the Pilbara, with its harsh climate and unpredictable
monsoonal flooding, was unclear. Plant assay results indicate
invertebrates are foraging locally, providing data about interactions
between invertebrates and their environment. Thus, we show how DNA
metabarcoding of invertebrate communities can be used to evaluate likely
trajectories for restoration. Testing and incorporating new monitoring
techniques such as DNA metabarcoding is critical to improving
restoration outcomes, and is now particularly salient given the
ambitious global restoration targets associated with the UN decade on
Ecosystem Restoration.%
\end{abstract}%



\sloppy


Evaluating restoration trajectories using DNA metabarcoding of
invertebrates and their associated plant communities

van der Heyde, M.\textsuperscript{1,2*}, Bunce, M.\textsuperscript{2,3},
Dixon, K.W.\textsuperscript{1}, Fernandes, K.\textsuperscript{2}, Majer,
J.\textsuperscript{1}, Wardell-Johnson, G.\textsuperscript{1}, White,
N.E. \textsuperscript{2}, Nevill, P.\textsuperscript{1, 2}

\textsuperscript{1}ARC Centre for Mine Site Restoration, School of
Molecular and Life Sciences, Curtin University, Bentley, GPP Box U1987,
Perth, Western Australia, 6845

\textsuperscript{2}Trace and Environmental DNA Laboratory, School of
Life and Molecular Sciences, Curtin University, GPP Box U1987, Perth,
Western Australia, 6845

\textsuperscript{3}Environmental Protection Authority, 215 Lambton Quay,
Wellington 6011, New Zealand.

*Corresponding author

Mieke.vanderheyde@curtin.edu.au

\subsection*{Abstract}

{\label{abstract}}

Invertebrate communities provide many critical ecosystem functions (e.g.
pollination, decomposition, herbivory and soil formation), and have been
identified as indicators of ecological restoration. Unfortunately,
invertebrates are often overlooked in restoration monitoring because
they are time-consuming to survey, often require rare taxonomic
expertise, and there are many undescribed species. DNA metabarcoding is
a tool to rapidly survey invertebrates and can also provide information
about plants with which those invertebrates are interacting. Here we
evaluate how invertebrate communities may be used to determine ecosystem
trajectories during restoration. We collected ground-dwelling and
airborne invertebrates across chronosequences of mine-site restoration
in three ecologically different locations in Western Australia, and
identified invertebrate and plant communities using DNA metabarcoding.
Ground-dwelling invertebrates showed the clearest restoration signals,
with communities becoming more similar to reference communities over
time. These patterns were weaker in airborne invertebrates, which have
higher dispersal abilities and therefore less local fidelity to
environmental conditions. Invertebrate community recovery was most
evident in ecosystems with relatively stable climax communities, while
the trajectory in the Pilbara, with its harsh climate and unpredictable
monsoonal flooding, was unclear. Plant assay results indicate
invertebrates are foraging locally, providing data about interactions
between invertebrates and their environment. Thus, we show how DNA
metabarcoding of invertebrate communities can be used to evaluate likely
trajectories for restoration. Testing and incorporating new monitoring
techniques such as DNA metabarcoding is critical to improving
restoration outcomes, and is now particularly salient given the
ambitious global restoration targets associated with the UN decade on
Ecosystem Restoration.

\subsection*{Introduction}

{\label{introduction}}

Fauna are often overlooked in restoration monitoring in favor of
vegetation (Cross, Tomlinson, Craig, Dixon, et al. 2019; Ruiz-jaen and
Aide 2005; Gomes Borges et al. in press), with the general assumption
that they will naturally recolonize an area with the return of plant
communities (Palmer, Ambrose, \& Poff, 1997). However, this is not
always the case (Cristescu, Rhodes, Fr\selectlanguage{ngerman}ére, \& Banks, 2013), and
understanding the recovery of fauna is important because they play a
vital role in many ecosystem function including pedogenesis, seed
dispersal, pollination and nutrient cycling (Bronstein, Alarcón, \&
Geber, 2006; Catterall, 2018; Hunter, 2001; Ness, Bronstein, Andersen,
\& Holland, 2004). Recently, greater attention has been paid to fauna to
both assess and facilitate ecological restoration (Catterall 2018;
Cross, Bateman, and Cross 2020; Majer 2009).

Invertebrates are of particular interest as they have long been used as
indicators of ecosystem recovery in both aquatic and terrestrial systems
(Andersen et al. 2002; Andersen \& Sparling, 1997; Folgarait, 1998;
Majer, 2009). They are sensitive to disturbances and are essential for
ecosystem function (Folgarait, 1998; Rosenberg, Danks, \& Lehmkuhl,
1986), not to mention being numerous, easy to capture, and incredibly
diverse (Gaston, 1991). Because studies tend to target particular groups
of arthropods, responses to restoration are mixed, depending on the
target taxa (Cristescu, Frère, \& Banks, 2012). Some of the variation in
responses to restoration among different arthropod classes may be
attributed to dispersal ability. For example, beetles with high
dispersal abilities are able to recolonize more quickly than millipedes
in a regenerating forest (Magura et al., 2015). Along with dispersal
ability, changes in community composition during restoration (Andersen
et al. 2002; Majer 2009) have been attributed to a shift from generalist
r-strategist species, which thrive in disturbed and unpredictable
environments, to K-selected species, which require predictable, and
favorable environments (Majer 1989). As such, invertebrate communities
may be used to evaluate restoration trajectories of recovery or
convergence, where the objective and expectation is
\emph{directional}change in composition towards a reference community
(Mcdonald et al. 2016; Suding and Gross 2006). However, in harsher
ecosystems that are often naturally unpredictable, the lower diversity
and selection of A (adversity)-strategists (Southwood 1977; Dunlop et
al. 1985; Majer 1989) may make directional changes during restoration
less likely.

Despite being excellent indicators of ecosystem change, the high
diversity within invertebrate communities makes it particularly
difficult to identify invertebrate specimens, often requiring many
expert person-hours from multiple taxonomists specializing in different
invertebrate taxa (Majer 1983). This process is costly and time
consuming, and dependent on taxonomic expertise that is dwindling
worldwide (Pearson, Hamilton, \& Erwin 2011; Majer et al. 2013).
Additionally, many invertebrate taxa are cryptic (Smith, Fisher, \&
Hebert, 2005) or have yet to be identified, especially in Australia with
its high degree of endemism (Austin et al., 2004; Rix et al., 2015) and
where as much as 75\% of arthropod diversity is undescribed (Austin et
al., 2004; Yeates, Harvey, \& Austin, 2003). Consequently, most studies
looking at invertebrate responses to restoration have targeted
particular taxa either because they have previously shown to be good
bioindicators (Andersen et al. 2002), or they are threatened and of
legal and conservation value (i.e. Lepidoptera) (Majer, 2009).

Some of the difficulties associated with invertebrate monitoring can be
reduced using DNA metabarcoding to provide taxonomic assignments. This
process uses high-throughput sequencing to determine invertebrate
diversity from small barcoding regions of the genome (Beng et al., 2016;
Ji et al., 2013; Yu et al., 2012). Compared to morphological
identification, where each specimen has to be identified individually,
DNA metabarcoding has been shown to be accurate, reliable, and faster
than conventional morphological methods (Beng et al., 2016; Ji et al.,
2013). As an added benefit, the sequencing data can be readily stored
and analyzed by a third party, such as regulators (Fernandes et al.,
2018). Although abundance estimates using DNA metabarcoding are often
skewed by primer bias (Elbrecht \& Leese, 2015) or DNA extraction method
(Majaneva et al., 2018), presence/absence data has been used to
demonstrate arthropod responses to restoration post mining (Fernandes et
al., 2019) and land-use change (Beng et al., 2016).

One of the advantages of DNA metabarcoding is its ability to detect not
only invertebrate diversity and composition but also provide functional
data by identifying the organisms they have been interacting with
(Jurado-Rivera et al., 2009; Pornon et al., 2016). In the case of
arthropods, previous studies suggest that DNA from arthropod samples
should be able to identify which plant species pollinators have visited
(Pornon et al., 2016) and which plant species they have consumed
(Jurado-Rivera et al., 2009). However, these studies have hitherto not
been undertaken in a restoration context, so the utility of such
approaches for restoration monitoring is unknown. Presumably, assessing
these communities can illustrate the interaction between invertebrates
and plants during restoration. However, since the invertebrates may
carry plant DNA from outside the restoration area (van der Heyde et al.,
2020a), they may not necessarily have high fidelity to local conditions.

Our earlier work has explored the use of DNA metabarcoding of
ground-dwelling invertebrates to monitor mine site restoration
(Fernandes et al., 2019); however, this study used a single reference
site per mine and the results were spatially auto-correlated as older
sites were closest to the reference sites. Here we use two spatially
separated reference sites per mine, two trap types that capture ground
dwelling and airborne invertebrates, and study sites in multiple
locations with different climates and ecosystems. This study evaluates
whether we can use DNA metabarcoding of invertebrates to evaluate
restoration trajectories (convergence to reference communities) in
restored sites. We have four hypotheses:

i) Ground dwelling invertebrates will show recovery trajectories better
than airborne invertebrates because with lower dispersal abilities they
better reflect local environmental conditions.

ii) Ecosystems with stable climax communities demonstrate trajectories
of recovery more clearly than less diverse, climatically harsher
unpredictable ecosystems.

iii) The plants associated with invertebrates will not show trajectories
of recovery as well as invertebrates because plant DNA may be sourced
from outside the site area.

iv) Metabarcoding provides functional information by indicating how
invertebrate communities are interacting with the plants in and around
restoration sites

\subsection*{Materials and Methods}

{\label{materials-and-methods}}

\subsubsection*{Study Sites}

{\label{study-sites}}

Restoration and reference sites were sampled from three locations up to
1000 km apart in Western Australia, namely: Swan Coastal Plain (SCP);
Jarrah Forest (JF); and Pilbara (PB). There was consistency in
restoration approaches, soil type, climate and site aspect of the sites
within each location. At each location, sites of different restoration
age were sampled along with two spatially separated reference sites
(Figure 1, see Figure S1 for maps). At all three locations, we sampled
at least two sites less than 9 years old (Young), and at least two sites
older than 9 years (Older). These sites are previously described in van
der Heyde et al. (2020b), and briefly below. At all locations two
reference sites were selected on the basis of the following criteria:
similarity to ecosystems that are the target of restoration efforts,
proximity to restoration sites, similarity in slope and aspect, and
spatially separate from each other to account for variation in reference
communities.

The Coastal Plain has a warm-summer Mediterranean climate with mild cool
wet winters; mean minimum temperature 12.8°C, mean maximum 24.7°C, and
with 757 mm mean annual rainfall (Australian Bureau of Meteorology).
This location is part of the broader region of south-western Australia,
a globally recognized biodiversity hotspot (Myers et al., 2007). The
mine is located on the silicaceous Bassendean dunes, with high acidity
and low water-holding capacity (Dodd \& Heddle, 1989; McArthur, 1991).
The ecosystem is referred to as Banksia woodland after the dominant tree
species, \emph{Banksia attenuata} and \emph{B. menziesii} . Other trees
include less dominant \emph{Eucalyptus todtiana} and \emph{Nuytsia
floribunda.} The understory consists of woody species of Myrtaceae,
Ericaceae, Proteaceae, and non-woody species in Anthericaceae,
Stylidiaceae, Cyperaceae, and Haemodoraceae (Trudgen, 1977). In October
2018, we sampled eight sites at a Hanson Construction Materials sand
quarry in Lexia (31.76 °S, 115.95 °E), with two reference sites and
restoration sites 1, 3, 7, 11,14, 22 years old. The sites have been
restored with the aim of returning mined areas to the surrounding native
Banksia woodlands. All restoration was done by Hanson and previous mine
owners and included direct transfer of fresh topsoil, ripping, and
seeding with native plant species. Plant species richness and density
tended to be higher in restoration than reference sites, and percent
cover has increased with restoration age and is highest in reference
sites (Benigno, Dixon, \& Stevens, 2013).

The second location in the Jarrah (\emph{Ecalyptus marginata} ) forest
is also part of the Southwest Australia Global Biodiversity hotspot
(Myers et al., 2007) and has a similar hot-summer Mediterranean climate;
mean minimum temperature of 8.6°C, mean maximum of 23.7°C, and 668.9 mm
annual mean rainfall (Australian Bureau of Meteorology). The lateritic
soils are nutrient poor and high in gravel with surfaces rich in iron
and aluminum (McArthur, 1991). The vegetation is dominated by \emph{E.
marginata;} other common trees are \emph{E. patens,} and \emph{E.
wandoo} . The understory consists of sclerophyllous shrubs from several
families including Anthericaceae, Fabaceae, Asteraceae, Proteaceae,
Dasypogonaceae, and Myrtaceae (Havel, 1975). We sampled six sites from
the bauxite mine which is now run by South32 (32.96°S, 116.48°E) in
October 2018; two reference sites and restoration sites 2, 6, 11, and 20
years old. All restoration was undertaken by South32 or the previous
mine owners. After mining the landscape was shaped using waste material
and gravel. Fresh topsoil was directly transferred from newly mined
areas to the restoration area and supplemented with stockpiled topsoil
as needed. The sites were then ripped, seeded with over 100 native
species, recalcitrant plants (mostly grasses) were planted, and a
one-time treatment of superphosphate is applied (Data from South32).
Reference and restoration sites are dominated by Myrtaceae and Fabaceae
species. Total cover has increased with age of restoration to similar
cover percentages of reference sites (Data from South32).

The third location, the Pilbara, is in north-western Australia. The
Pilbara has a hot, arid climate with most rainfall occurring in summer,
and associated with cyclonic activity (McKenzie, van Leeuwen, \& Pinder,
2009) causing unpredictable flooding. Temperatures have a mean minimum
of 15°C and mean maximum of 30.6 °C, with 263.8 mm mean rainfall
(Australian Bureau of Meteorology). The unfavourable conditions and
large variation in yearly rainfall are thought to select for a wide
range of r-and A-strategist invertebrates (Majer, 1989). Soils are
acidic stony loams with low fertility, which support open woodlands of
snappy gum (\emph{E. leucophloia} ) over hummock grasses (\emph{Triodia
wiseana, T. basedowii, T. lanigera} ) and low \emph{Acacia} shrubs
(McKenzie et al., 2009). The Pilbara is a significant mining region and
accounts for 39\% of global iron ore production (Government of Western
Australia, 2019). We sampled six sites at a BHP iron ore mine (22.84 °S,
118.95 °E) in September 2018, with two reference sites and restoration
sites 4, 7, 11, and 15 years old. Restoration was conducted by the mine
owners; landscapes were reformed and stockpiled topsoil (average age 10
years) was applied and then ripped. Restoration areas tended to have
higher coverage of woody shrubs (\emph{Acacia} ), while reference sites
and older restoration areas have more hummock grasses (\emph{Triodia} ).
Restoration areas also had invasive species such as buffel grass
(\emph{Cenchrus ciliaris} ) and kapok bush (\emph{Aerva javanica} ),
which were absent in reference sites (Data from BHP).

\subsubsection*{Sample Collection}

{\label{sample-collection}}

At each site we collected 10 invertebrate samples, five from vane traps
and five from pitfall traps (n=200). Each vane trap sample included the
contents of a yellow and blue vane trap with 150 mL of ethylene glycol
and was left on the site for 7 days. Each pitfall trap sample included
the contents of four pitfall traps (4 cm diameter, 12 cm deep with
ethylene glycol as a capture fluid), and was also left in the field for
7 days Pitfall traps were spaced 10 m apart in a square around the vane
traps in the center for each sample point.

\subsubsection*{Sample Processing}

{\label{sample-processing}}

For DNA extraction, we first rinsed off the ethylene glycol with
de-ionized water using 20-\selectlanguage{greek}µ\selectlanguage{english}m sieves that were sterilized in bleach and
under UV light between every sample. Samples were then homogenized using
a TissueLyser (Qiagen) for 2 min in 30 sec increments at 30/s in 50mL
falcon tubes with 4 steel balls (4 mm diameter). 400 \selectlanguage{greek}μ\selectlanguage{english}L of the
homogenate was digested overnight and the DNA extracted using the DNeasy
Blood and Tissue kit (Qiagen) on the QiaCube Connect automated platform
(Qiagen). The final elution volume was \selectlanguage{english}200 \selectlanguage{greek}μ\selectlanguage{english}L, and extraction controls
(blanks) were carried out for 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 PCR for
each sample to maximize input relative to any inhibitors (Murray,
Coghlan, \& Bunce, 2015). Two assays were used in this study to target
invertebrate and plant diversity. The invertebrate assay used the
primers fwhF2/fwhR2n (Vamos, Elbrecht, \& Leese, 2017) to amplify a 205
bp section of the cytochrome c oxidase I (COI) region. For plants we
used the trnlc/h primers (Taberlet et al., 2007) which targets the
chloroplast trnL (UAA) intron

The qPCRs were run on a StepOne Plus (Applied BioSystems) real-time qPCR
instrument with the following conditions: 5 min at 95\selectlanguage{ngerman}°C, 40 cycles of
95°C for 30s, 30s at the annealing temperature (50°C for invertebrates,
52°C for plants) 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. The
PCR mix for quantitation contained: \selectlanguage{english}2.5 mM MgCl2 (Applied Biosystems,
USA), 1\selectlanguage{ngerman}× PCR Gold buffer (Applied Biosystems), 0.25 mM dNTPs (Astral
Scientific, Australia), 0.4 mg/ml bovine serum albumin (Fisher Biotec,
Australia), 0.4 \selectlanguage{greek}μ\selectlanguage{english}mol/L forward and reverse primer, 1 U AmpliTaq Gold DNA
polymerase (Applied Biosystems) and 0.6 \selectlanguage{greek}μ\selectlanguage{english}l of a 1:10,000 solution of
SYBR Green dye (Life Technologies, USA). Extraction control and
non-template controls were included in qPCR assays.

After optimal DNA input was determined by qPCR, each sample was assigned
a unique combination of multiplex identifier (MID) tags for each primer
assay. These MID tags were incorporated into fusion tagged primers, and
none of the primer-MID tag combinations had been used previously in the
lab to prevent cross contamination. Fusion PCRs were done in duplicate
and to minimize PCR stochasticity, the mixes were prepared in a
dedicated clean room before DNA was added. The PCRs were done with the
same conditions as the standard qPCRs described above. Samples were then
pooled into approximately equimolar concentrations to produce a PCR
amplicon library that was size-selected to remove any primer-dimer that
may have accumulated during fusion PCR. Size selection was performed
(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 quantified using Qubit
Fluorometric Quantitation (Thermo Fisher Scientific). Sequencing was
performed on the Illumina MiSeq platform using the 300 cycle V2 as per
manufacturer's instructions.

\subsubsection*{Sequencing analysis}

{\label{sequencing-analysis}}

Sequences were demultiplexed using a demultiplex function in the
``insect'' package (Wilkinson et al., 2018) on the R 3.5.3 platform (R
Core Team, 2018). Further sequence processing was performed 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 were 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 then constructed and chimeras removed. Taxonomy was
determined using the Basic Local Alignment Search Tool (blastn) on a
high-performance cluster computer (Pawsey Supercomputing Centre) to
search against the online reference database GenBank
(\emph{https://www.ncbi.nlm.nih.gov/genbank/}). Invertebrate sequences
were also searched against and arthropod COI reference sequences
extracted from the Barcode of Life Database
(BOLD:\emph{https://www.barcodeoflife.org}), because there are reference
sequences that are found uniquely on one of the two databases. We used
MEGAN (Huson et al., 2007) to assign taxonomy with a minimum support of
205.

\subsubsection*{Statistics}

{\label{statistics}}

All statistics were run using R 3.5.3 (R Core Team, 2018). Samples with
low sequencing depth were removed and ASVs that were present in the
extraction controls were removed from the dataset (Figure S2). We
selected ASVs in the phylum `Arthropoda' for the invertebrate assay and
`Plantae' for the plant assay. Copy numbers in each sample were filtered
to a minimum of 0.05\% within sample abundance. We verified there was no
correlation between sequencing depth and ASV richness before continuing.
Read counts were transformed to presence/absence to reduce the effects
of biases (Elbrecht \& Leese, 2015; Majaneva et al., 2018). Spatial
autocorrelation was tested using the Mantel test in the `ade4' package
in R (Mantel, 1967). Three criteria were examined to determine if
communities showed a trajectory of recovery or convergence to the
reference community. First, community composition should be different
between younger restoration, older restoration, and reference sites.
This was visualized using Non metric multidimensional scaling (NMDS),
based on presence/absence ASV table and with Bray-Curtis dissimilarity.
The `ordiellipse' function from the `vegan' R package was used to draw
ellipses showing the 95\% confidence interval of the group (Oksanen et
al., 2018). Differences between restoration ages were tested using
permutational multivariate analysis of variance (PERMANOVA). Second,
establishing a restoration trajectory requires \emph{directional}change;
we expect that restored communities become more similar to reference
communities over time. Replicates at each site were pooled and the
similarity between each site and the reference sites was calculated.
This relationship was tested using linear models separately for each
assay and location. Third, we expect that the proportion of `reference'
ASVs, that is, ASVs that were found in reference sites, would increase
over time. This relationship was tested using a simple linear model. For
all three, we tested the SCP data with and without the extra two sites
(7 years and 11 years) to ensure that any comparisons of trajectory
between the locations were fair. This analysis is based on the
prediction of changing composition from r- or A- to K-strategists and
provides additional information about whether the patterns in community
similarity to reference communities is driven by compositional changes,
or richness. Finally, to understand the taxa associated with restoration
and reference sites, we ran a multipattern analysis for each site using
the R package `indicspecies' (De Caceres \& Legendre, 2009).

\subsection*{Results}

{\label{results}}

In total, we generated 14,780,759 quality-filtered invertebrate
sequences from 196 samples with a minimum of 3,000 reads/sample. Out of
5862 initial ASVs, 2635 belonged to the phylum Arthropoda. The remaining
ASVs were either unidentified or fungi, and only made up 23.7\% of the
read count. In the plant assay, we generated 13,441,527 filtered plant
sequences from 197 samples with a minimum of 5600 sequences/sample. From
the initial 511 plant ASVs, 381 remained post filtering and these
accounted for 87.8\% of the sequences. Overall, there were fewer ASVs in
the Pilbara compared to the Coastal Plain or Jarrah, especially in the
pitfall traps where the Pilbara had 17-32\% fewer invertebrate ASVs
(Table S1)

\subsubsection*{Community Composition}

{\label{community-composition}}

Invertebrate diversity in the vane traps was dominated by Hymenoptera,
Coleoptera, Diptera, Hemiptera, and Lepidoptera. Some of these
(Hymenoptera, Coleoptera, and Hemiptera) also made up most of the
diversity in the pitfall traps, along with Collembola and Araneae.
Collembola were largely absent from the Pilbara, which had more
Orthoptera ASVs. The majority (67\%) of invertebrate ASVs could not be
identified beyond order level. However, 99\% of plant ASVs could be
identified to family level. Plant diversity in the SCP and Jarrah forest
was dominated by Myrtaceae, Fabaceae, Dilleniaceae, and Proteaceae,
while in the Pilbara the richest families were Fabaceae, Poaceae and
Malvaceae (Figure 2). Because of the poor taxonomic assignments, we
confined our considerations to ASVs for our subsequent analyses.

There were significant differences in community composition between
younger restoration, older restoration and reference sites in all
locations for both trap types and assays (Figure 3, PERMANOVA,
alpha=0.05). Similarly, the pairwise analysis showed all restoration
ages were significantly different from one another (alpha=0.05), with
the exception of the plant community in the Pilbara samples, where the
reference samples were not significantly different from the younger
(vane) or the older (pitfall) restoration samples (Table S2). The Mantel
tests showed significant spatial autocorrelation in the invertebrate
communities from pitfall traps but not the vane traps (alpha=0.05, Table
1). Similarly, the spatial correlation with community dissimilarity was
lower in the plant sequences compared to the invertebrate assay (Table
1).

\subsubsection*{Similarity to reference
communities}

{\label{similarity-to-reference-communities}}

The invertebrate communities showed clear directional changes
(increasing similarity to reference over time) in the pitfall traps from
the Coastal Plain and the Jarrah forest (Figure 4). This trajectory was
less evident (in the SCP) or entirely absent in the vane traps (in the
JF, PB). There were no observed directional changes in invertebrate
community composition in the Pilbara. The results from the plant
communities were different. In the Coastal Plain, there was a
significant relationship between similarity to reference communities and
age of restoration in the vane traps, but not the pitfall traps. The
directional change in plant communities occurred in both the pitfall and
vane trap samples in the Jarrah, but was significant only in the pitfall
traps. Similarly, the plant communities became more similar to reference
communities in the Pilbara pitfall traps, while there was not a
relationship in the vane traps (Figure 4).

\subsubsection*{\texorpdfstring{Proportion of ``reference'' associated
ASVs}{Proportion of reference associated ASVs}}

{\label{proportion-of-reference-associated-asvs}}

Only the invertebrate communities from the pitfall trap samples from the
Coastal Plain and the Jarrah forest showed significant increases in the
proportion of `reference' ASVs over time. For plant sequences, only
pitfall traps in the Pilbara showed increasing `reference' ASVs over
time (Figure 5). Overall, the vane traps had a higher proportion of ASVs
that were shared with reference samples than pitfall traps. This was
true for both the invertebrate assay (49.4\% vs 22.2\% `reference' ASVs)
and the plant assay (78.6\% vs 59.7\% `reference' ASVs). There was also
a higher proportion of shared ASVs in the plant assay compared to the
invertebrate assay (Figure 5). Between the two reference sites, there
was variation in the number of ASVs shared with each other. The pitfall
traps in the Pilbara only had 3 ASVs shared between the two reference
sites (average of 1.2 \selectlanguage{ngerman}±0.4ASVs per sample). The amount of shared ASVs
was higher between the Coastal Plain and Jarrah pitfall traps (10 and 8
respectively).

\subsubsection*{Multipattern Analysis}

{\label{multipattern-analysis}}

Across the three locations, there were 82 invertebrate ASVs with
significant association (alpha =0.05) with younger restoration
(\textless{}9 years), older (\textgreater{}9 years), reference sites, or
a combination (Table S3). Of these, 44 were assigned to family, 16 to
genus, and only 3 to species level. This includes the ant
\emph{Iridomyrmex sanguineus} , which was associated with younger
restoration in the Pilbara and the ant\emph{Monomorium rothsteini} ,
associated with reference sites in the Pilbara. Most Coleoptera (12/16)
were associated with older restoration or reference sites and 13 of
those were from vane trap samples. Apidae ASVs were found mainly in the
younger restoration vane traps. For the plant assay, there were 59 ASVs
with significant association (Table S4), 52 of which were assigned to
family, 21 to genus, and 8 to species level. Species included
\emph{Petrophile squamata} (Proteaceae)\emph{,}found in older
restoration in the Jarrah, and \emph{Duperreya
commixta}(Convulvulaceae)\emph{,} found in vane traps of reference sites
in the Pilbara (Table 2). Most Fabaceae ASVs (13/14) were associated
with younger restoration in the Coastal Plain and Jarrah forest.

\subsection*{Discussion}

{\label{discussion}}

Terrestrial invertebrate fauna are key indicators of ecosystem change
(Andersen et al., 2002; Majer, 2009; Majer, Brennan, \& Moir, 2007), and
in this study, we show that even with limited taxonomic identification,
DNA metabarcoding of invertebrate samples can be used to rapidly assess
complex biological interactions and establish restoration trajectories.
These trajectories of community recovery were more evident in stable
climax ecosystems and in ground-dwelling invertebrates with lower
dispersal ability than airborne invertebrates. Examining plant diversity
associated with invertebrate samples also showed some indications of
directional changes in community composition and indicates that
invertebrates are likely foraging locally.

\subsubsection*{Ground-dwelling vs airborne
invertebrate}

{\label{ground-dwelling-vs-airborne-invertebrate}}

Vane traps do not show the same local fidelity as pitfall traps and, as
expected, tend to have weaker indications of community recovery (Figure
3, Figure 4). Vane traps capture airborne invertebrates, often
pollinators (Hall, 2018), and can trap organisms that may come from more
than 1.8 km away (Jha \& Dick, 2010) while species caught by pitfall
traps have more limited dispersal (Majer, 1980; Ness et al., 2004; Ward,
New, \& Yen, 2001). This would also explain the greater proportion of
shared taxa in the vane traps compared to the pitfall traps (Figure 5),
and the greater spatial correlation in pitfall trap samples (Table 1).
Beyond the differences in attraction distance of the traps, our results
also suggest quicker recolonization of airborne invertebrates as
evidenced by the number of `reference' associated taxa is similar to
reference sites within a few years (Figure 5, SCP, PB). Variation in
dispersal abilities is important as those with more mobility are able to
recolonize areas more quickly (Magura et al., 2015) and from greater
distance (Knop, Herzog, \& Schmid, 2011). Fortunately, there is no sign
of thermophilic or other barriers (Cranmer, McCollin, \& Ollerton, 2012;
Tomlinson et al., 2018) preventing invertebrates from accessing and
using restoration sites. Because of their more sedentary nature,
ground-dwelling invertebrates are good indicators of organisms that are
likely reproducing in situ, while airborne invertebrates can indicate
the forage support and attractiveness of a site. Our findings indicate
that invertebrate communities are demonstrating an ability to recover
without intervention following the establishment of plant communities.
This conforms with the `Field of Dreams' hypothesis which posits that if
suitable habitat can be re-established, species will colonize it,
leading to the restoration of function (Palmer et al., 1997). However,
this is dependent on the presence of source populations. In this study,
all sites were near remnant vegetation that could act as a taxa pool; in
cases of isolated restoration sites, it may be more difficult to
evaluate restoration trajectories using invertebrate communities.

\subsubsection*{Stable vs unpredictable
ecosystems}

{\label{stable-vs-unpredictable-ecosystems}}

The r/K selection theory is a predictive model for life history
strategies that vary from r selected (high fecundity, short lifespan,
small bodies, opportunistic, high dispersal) in unpredictable
environments, to K-selected (low fecundity, long lifespan, large bodies,
low dispersal) in unpredictable environments (Pianka 1970). In
ecological succession and restoration, it is expected that systems are
dominated by r-selected species initially as they take advantage of the
disturbance, followed by a shift to K-selected species as the system
develops towards a stable climax community (Majer, 1989). This concept
is developed further by Southwood (1977) and Greenslade and Greenslade
(1983) as a `habitat template', which condenses the variety of habitats
onto two axes equivalent to their favourableness and predictability. As
well as explaining the conditions for r- and K-strategists, this
template introduces a third adversity or A-selection strategy, which is
selected for in environments that are very unfavourable and not always
predicable. Such environments, including the Pilbara, support lower
diversities of organisms with lower interaction between species
(Greenslade and Greenslade 1983). In this study, we classified taxa
based on whether they were found in reference sites as a proxy for
selection strategy, since there was inadequate information to classify
them based on taxonomic identification. As expected, in older restored
sites we recorded significant increases in the proportion of `reference'
taxa with time in both the Jarrah and Coastal Plain (Figure 5), which
shows a directional change in community composition toward that of the
reference community.

The Pilbara location, which has a more unpredictable and harsher
climate, did not show a similar trajectory of community recovery. Dunlop
et al. (1985), and to a lesser extent, Fletcher (1990), observed that
ant richness rapidly recovered in young Pilbara rehabilitation, but,
similar to our results, the species composition remained different
between natural and restored sites. In the Pilbara, the main factors
driving compositional turnover in terrestrial fauna are regolith/soil
and landform/hydrogeologic, as well as climate (Gibson et al. 2015). All
were factors that were shared between Pilbara restored and reference
sites. Here, the structure of the revegetation rapidly came to resemble
the structure of the original predominantly grassland habitat (see
Figure 1), which is in marked contrast to the situation at the other two
locations. In that regard, the reference areas may provide conditions
that are as unpredictable and unfavorable as the areas under
restoration; and compared with the other two regions, they are also less
rich in species. Thus, recolonization of Pilbara sites may be more
stochastic and less influenced by selection pressures than in the
Coastal Plain and Jarrah forest. However, there was a particularly low
proportion of shared `reference' taxa overall in the Pilbara pitfall
traps (Figure 5), so ecosystem recovery is far from complete.

\subsubsection*{Plants associated with invertebrate
samples}

{\label{plants-associated-with-invertebrate-samples}}

Generally, directional changes in community composition were less
evident in the plant diversity associated with invertebrate samples
(Figure 3, Figure 4). This was expected, as we hypothesized that the
signal would be diluted because invertebrates can carry plant DNA from
outside the study area (van der Heyde et al., 2020a). The lack of
abundance or behavior data is a commonly acknowledged limitation of DNA
metabarcoding (Elbrecht \& Leese, 2015; Elbrecht, Peinert, \& Leese,
2017; Fernandes et al., 2018; Lim et al., 2016). However, the clear
difference in plant assay community composition between restoration
sites (Figure 3) indicates that the invertebrates are interacting with
plants locally on the restoration site, rather than only passing
through. Plant sequences reflected some site characteristics, generating
a greater richness of Fabaceae ASVs in younger restoration sites
observed to have high cover of \emph{Acacia} shrubs (Data from BHP,
South32). While some plant DNA may originate from debris falling into
traps, there is also evidence that these are plants that were ingested
or otherwise visited by invertebrates. For example, plants in the family
Goodeniaceae require insect pollination (Jabaily et al., 2012; Keighery,
1980). While there are virtually no Goodeniaceae ASVs in the pitfall
traps (PB and JF), they are present in most sites in vane traps (PB and
JF, Figure 2). Unfortunately, we cannot identify which invertebrates are
interacting with which plants. This would require isolating
invertebrates and extracting DNA from each species separately, for
example by extracting DNA from the pollen loads (Bell et al., 2017;
Pornon et al., 2016). Alternatively, DNA from flowers has also been used
to identify probable pollinators (Thomsen \& Sigsgaard, 2019). However,
these methods require species-specific sampling and therefore far more
samples and greater costs. We argue that using bulk arthropod samples is
a cost, time and resource efficient method that allows researchers to
gain an informative snapshot of the invertebrate community and the
plants they are interacting with.

Importantly, as this study was conducted in the spring/early summer, we
cannot confirm whether the same patterns exist throughout the year.
Seasonality affects invertebrate communities (Santorufo, Van Gestel, \&
Maisto, 2014; Shimazaki \& Miyashita, 2005), plant communities, and
especially the interaction between the two (CaraDonna et al., 2017;
Rico-Gray et al., 1998). A previous study conducted during autumn
(April) in the Coastal Plain sites using pitfall traps also detected
directional changes in invertebrate communities (Fernandes et al.,
2019), but no differences in plant communities generated from pitfall
traps (Unpublished). In the spring there is more new plant growth and
flowering, resulting in more invertebrates that use those resources
(Clark \& Dallwitz, 1974; Herrera, 1988). This study offers preliminary
testing of consistency in restoration patterns across space, but not
temporally within or between years.

\subsection*{Conclusion}

{\label{conclusion}}

We have demonstrated the use of high throughput sequencing of
invertebrate samples to establish restoration trajectories. Defining the
likely trajectory of a restored site is important as it enables the
definition of success criteria, and the required time scales for
restoration monitoring. We show that trajectories towards reference
ecosystems were more evident in ground dwelling invertebrates in stable
climax ecosystems. Despite the lack of abundance data, metabarcoding can
indicate functional ecosystem recovery by showing how the invertebrates
are interacting with the plant community. Understanding restoration
trajectories using DNA metabarcoding will require additional research to
determine the effects of seasonal variation, and consistency of patterns
across multiple years and different ecosystems. It is important to
remember that ecosystems are dynamic, so determining whether sites have
been fully restored depends heavily on the selection of appropriate
reference sites to capture the natural variation in the reference
ecosystem. The Bonn Challenge goal to restore 350 million
km\textsuperscript{2} of degraded terrestrial ecosystems by 2030 (Suding
et al., 2015) means we must ensure that we get the best value from the
considerable financial investment required to meet these ambitious
global restoration targets. Testing new monitoring techniques such as
DNA metabarcoding and evaluating where they are beneficial is critical
to potentially incorporating them in restoration projects and improving
restoration outcomes.

\subsection*{Acknowledgements}

{\label{acknowledgements}}

This work was supported by the Australian Research Council Industrial
Transformation Training Centre for Mine Site Restoration (ICI150100041)
and the Pawsey Supercomputing Centre with funding from the Australian
Government and the Government of Western Australia. We thank the mining
companies BHP, Hanson Construction Material, and South32 for
facilitating access to sites for sampling. We would also like to thank
Sheree Walters for help with sample collection and the members of the
Trace and Environmental DNA (TrEnD) Laboratory for support with
metabarcoding workflows and bioinformatics.

\subsection*{Data Accessibility}

{\label{data-accessibility}}

Sequencing and sample data and is available at the Dryad Digital
Repository:\emph{https://doi.org/10.5061/dryad.q573n5tgw}

\subsection*{Author Contributions}

{\label{author-contributions}}

MvH conducted the study and wrote the manuscript. MvH, PN, MB, NW, and
GW-J were involved in the experimental design. Samples were collected
and processed by MvH; molecular and bioinformatics work was performed by
MvH; all data was analyzed and processed by MvH; statistical analysis
was done by MvH; the manuscript was edited by all authors.

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\textbf{Tables and Figures}

Table Results of the Mantel test showing the correlation between spatial
distances and community dissimilarity. Results for the samples
separately, and pooled (sites) are shown. SCP-Swan Coastal Plain,
JF-Jarrah Forest, PB-Pilbara.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
& & & Samples & Samples & Sites & Sites\tabularnewline
\midrule
\endhead
Trap & \textbf{Assay} & \textbf{Location} & r & p & r & p\tabularnewline
Pitfall & Invertebrate & JF & 0.161 & 0.001 & 0.189 &
0.205\tabularnewline
& & PB & 0.308 & 0.002 & 0.566 & 0.018\tabularnewline
& & SCP & 0.375 & 0.001 & 0.349 & 0.116\tabularnewline
& Plant & JF & 0.069 & 0.064 & -0.1858 & 0.706\tabularnewline
& & PB & -0.0118 & 0.508 & -0.203 & 0.838\tabularnewline
& & SCP & -0.077 & 0.785 & -0.2365 & 0.789\tabularnewline
Vane & Invertebrate & JF & 0.131 & 0.011 & 0.237 & 0.104\tabularnewline
& & PB & 0.114 & 0.082 & -0.084 & 0.548\tabularnewline
& & SCP & -0.117 & 0.851 & -0.074 & 0.555\tabularnewline
& Plant & JF & 0.0466 & 0.147 & -0.314 & 0.916\tabularnewline
& & PB & 0.233 & 0.007 & 0.079 & 0.319\tabularnewline
& & SCP & 0.115 & 0.125 & -0.359 & 0.874\tabularnewline
\bottomrule
\end{longtable}

\par\null

Table Taxa of interest, based on general observations of the data and
indicator species analysis\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Taxa of interest & Name & Reason\tabularnewline
\midrule
\endhead
\emph{Melophorus} & Australian genus of ant & Associated with younger
restoration sites in both the SCP and PB. Species in this genus are
known as 'sun-loving' (Andersen et al. 2002) and are often found in
restoration sites with large areas of bare ground (Andersen, Hoffmann,
and Somes 2003).\tabularnewline
\emph{Iridomyrmex sanguineus} & Northern meat ant & Associated with
younger restoration sites in PB. \emph{Iridomyrmex} species are among
the first to colonize revegetated sites (Andersen 1993). Much like
\emph{Melophorus}, \emph{Iridomyrmex} species are attracted to areas of
bare ground in newly revegetated sites (Andersen, Hoffman, and Somes
2003).\tabularnewline
Hemiptera & Order of sucking insects & Higher richness in younger
restoration in the JF pitfall traps. Hemipteran species composition is
linked to the presence of host plants, vegetation structure, and soil pH
(Orabi, Moir, and Majer, 2010). In newly restored sites, there is
generally a higher abundance of generalist Hemiptera species, with a
slower recolonization of specialist Hemiptera species dependant on
vegetation structure reassembly (Moir et al. 2005).\tabularnewline
Apidae & Family of bees & Found primarily in the younger restoration
sites in JF. Newly restored sites with less ground cover offer optimal
nesting area for ground nesting bees (Seitz and Leonhardt, 2019). Apidae
have also been used as important indicators of pollution and stress in
ecosystems (Rabea et al. 2010; Schindler et al. 2013).\tabularnewline
Julida - \emph{Ommatoiulus} & Portugese millipede & Invasive detritivore
species found in great abundance in the SCP, particularly in older
restoration and reference sites. Feeds on litter, which is more
available in those sites. The Portuguese millipede is known to be
widespread throughout southern Australia (Baker et al.
2013).\tabularnewline
Fabaceae & Legume family & ASVs in this family are strongly associated
with younger restoration in JF and PB. Acacia shrubs tend to establish
rapidly at restored sites in these locations (Data from BHP, Data from
South32).\tabularnewline
\emph{Goodenia microptera} & Narrow-winged Goodenia & An insect
pollinated species found predominantly in the vane traps of PB reference
sites. Common Pilbara arid plant species, known to flower between
February-October (Barrett and Barrett, 2015).\tabularnewline
\emph{Anigozanthos} & Kangaroo paw & Associated with younger restoration
in SCP pitfall traps. These grow quickly (within a year) in SCP
restoration. \emph{Anigozanthos} are known to be predominantly
pollinated by nectar-feeding birds (Ayre et al. 2020) and could be
encouraging of faunal recolonization of restored sites.\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure Chronosequences of mining restoration where invertebrate samples
were collected. Restoration sites shown with the number of years
restoration from 1 to 22 years. Reference sites shown below. SCP-Swan
Coastal Plain, JF-Jarrah Forest, PB-Pilbara.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure Composition of invertebrates (above) and plant (below)
communities detected from pitfall and vane traps. Shows the number of
ASVs in each order (invertebrates) or family (plants) at all restoration
and reference sites.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure NMDS ordinations or Invertebrate and plant communities in
restoration and reference sites. Ellipses were drawn using `Ordiellipse'
in the vegan R package and indicate 95\% confidence interval of the
group. PERMANOVAs were significant for all facets (alpha=0.05).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure Similarity (Bray-Curtis) of restoration sites of different ages
(years) to communities in reference sites. Lines indicate linear models
with 95\% confidence interval shown with shading. P-values for the
linear models shown for each plot. Removing the two extra sites in the
SCP (7,11 years) did not change the relationships or the significance of
the models, with the exception of invertebrate communities from vane
traps (from p=0.036 to p=0.053)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure ASV richness in the different sites, separated into ASVs that
were present in reference sites and those not found in reference sites.
Separated into a) invertebrate and b) plant communities. P-values
indicate the significance of the relationship between the proportion of
reference ASVs and age of restoration (years).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1-v2/Figure-1-v2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure2-Invert-Barplot/Figure2-Invert-Barplot}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure3-Invert-NMDS-v2/Figure3-Invert-NMDS-v2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure4-Invert-sim-to-ref/Figure4-Invert-sim-to-ref}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure5ref-ASVs/Figure5ref-ASVs}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

