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\begin{document}

\title{Whole genome resequencing reveals signatures of rapid selection in a
virus affected commercial fishery}



\author[1]{Owen Holland}%
\author[1]{Madeline Toomey}%
\author[2]{Collin Ahrens}%
\author[3]{Ary Hoffmann}%
\author[1]{Larry Kroft}%
\author[1]{Craig Sherman}%
\author[4]{Adam Miller}%
\affil[1]{Deakin University}%
\affil[2]{Western Sydney University}%
\affil[3]{The University of Melbourne}%
\affil[4]{Deakin University - Geelong Campus at Waurn Ponds}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Infectious diseases are recognised as one of the greatest global threats
to biodiversity and ecosystem functioning. Consequently, there is a
growing urgency to understand the speed at which adaptive phenotypes can
evolve and spread in natural populations to inform future management.
Here we provide evidence of rapid genomic changes in wild Australian
blacklip abalone (Haliotis rubra) following a major population crash
associated with an infectious disease. A genome wide association study
on H. rubra was conducted using pooled whole genome re-sequencing data
from commercial fishing stocks varying in historical exposure to
haliotid herpesvirus-1 (HaHV-1). Approximately 25,000 SNP loci
associated with virus exposure were identified, many of which mapped to
genes known to contribute to HaHV-1 immunity in the New Zealand p\selectlanguage{polish}ā\selectlanguage{english}ua (H.
iris) and herpesvirus response pathways in haliotids and other animal
systems. These findings indicate genetic changes across a single
generation in H. rubra fishing stocks decimated by HaHV-1, with stock
recovery determined by rapid evolutionary changes leading to virus
resistance. This is a novel example of rapid adaptation in natural
populations of a non-model marine organism, highlighting the pace at
which selection can potentially act to counter disease in wildlife
communities.%
\end{abstract}%



\sloppy


\textbf{Whole genome resequencing reveals signatures of rapid selection
in a virus affected commercial fishery}

Owen J. Holland\textsuperscript{1,2+}, Madeline
Toomey\textsuperscript{1,2+}, Collin Ahrens\textsuperscript{3,4}, Ary A.
Hoffmann\textsuperscript{5}, Larry Croft\textsuperscript{1,2}, Craig D.
H. Sherman\textsuperscript{1}, Adam D. Miller\textsuperscript{1,2*}

\textsuperscript{1} School of Life and Environmental Sciences, Deakin
University, Warrnambool, Victoria, Australia

\textsuperscript{2} Deakin Genomics Centre, Deakin University, Geelong,
Victoria, Australia

\textsuperscript{3} School of Biotechnology and Biomolecular Sciences,
University of New South Wales, Sydney 2052, Australia

\textsuperscript{4} Research Centre for Ecosystem Resilience, Australian
Institute of Botanical Science, Royal Botanic Garden, Sydney, NSW 2000
Australia

\textsuperscript{5} School of BioSciences, Bio21 Institute, The
University of Melbourne, Parkville, Victoria, Australia

\textsuperscript{+} Joint first author

* Corresponding author: Adam D. Miller, Tel: +61- 355633171; E-mail:
a.miller@deakin.edu.au

\textbf{Abstract}

Infectious diseases are recognised as one of the greatest global threats
to biodiversity and ecosystem functioning. Consequently, there is a
growing urgency to understand the speed at which adaptive phenotypes can
evolve and spread in natural populations to inform future management.
Here we provide evidence of rapid genomic changes in wild Australian
blacklip abalone (\emph{Haliotis rubra} ) following a major population
crash associated with an infectious disease. A genome wide association
study on \emph{H. rubra} was conducted using pooled whole genome
re-sequencing data from commercial fishing stocks varying in historical
exposure to haliotid herpesvirus-1 (HaHV-1). Approximately 25,000 SNP
loci associated with virus exposure were identified, many of which
mapped to genes known to contribute to HaHV-1 immunity in the New
Zealand p\selectlanguage{polish}ā\selectlanguage{english}ua (\emph{H. iris} ) and herpesvirus response pathways in
haliotids and other animal systems. These findings indicate genetic
changes across a single generation in \emph{H. rubra} fishing stocks
decimated by HaHV-1, with stock recovery determined by rapid
evolutionary changes leading to virus resistance. This is a novel
example of rapid adaptation in natural populations of a non-model marine
organism, highlighting the pace at which selection can potentially act
to counter disease in wildlife communities.

\textbf{KEYWORDS}

Infectious diseases, genetic adaptation, genome wide association study,
blacklip abalone, haliotid herpesvirus-1, south-eastern Australia

\textbf{1 \textbar{} INTRODUCTION}

The spread of infectious diseases is recognised as one of the most
pressing global threats to biodiversity and ecosystem function
(Daszak\emph{et al.} 2000; Tompkins \emph{et al.} 2015; Cunningham
\emph{et al.} 2017). In recent decades, infectious diseases have
devastated a range of wildlife groups (Berger \emph{et al.} 1998; Kim \&
Harvell 2004; Hansen \emph{et al.} 2005; Lorch \emph{et al.} 2016),
often exacerbating species declines in ecosystems already stressed by
climate change and habitat destruction (Harvell \emph{et al.} 2002;
Brearley\emph{et al.} 2013; Bosch \emph{et al.} 2018). The future
persistence of many species will likely depend on their ability to adapt
to environmental changes associated with increased disease prevalence,
although selection for disease resistance or tolerance may not keep pace
with rates of pathogen evolution and the emergence and turn-over of
novel diseases (Hawley \emph{et al.} 2013; Ujvari \emph{et al.} 2014).

Detecting evolutionary changes in disease affected populations is
challenging but has been greatly assisted by modern genomic technologies
(Blanchong \emph{et al.} 2016; Storfer \emph{et al.} 2020). These
technologies now allow for rapid and cost-effective estimates of genome
wide variation among populations spanning disease infection gradients
and individuals with distinctive phenotypes related to disease response
(Elbers \emph{et al.} 2018; Grogan \emph{et al.} 2018; Margres \emph{et
al.} 2018; Auteri \& Knowles 2020). Importantly, a number of studies
using these technologies have reported rapid evolutionary changes across
several generations in natural populations of non-model organisms
impacted by disease, including Tasmanian devils (\emph{Sarcophilus
harrisii} ) (Epstein \emph{et al.} 2016; Hubert \emph{et al.} 2018;
Margres \emph{et al.} 2018) and North American house finches
(\emph{Carpodacus mexicanus} ) (Bonneaud \emph{et al.} 2011).
Additionally, recent studies have reported evidence of rapid selection
for disease resistant genotypes across a single generation in North
American sea stars (\emph{Pisaster ochraceus} ) and little brown bats
(\emph{Myotis lucifugus} ), following rapid and severe population
crashes due to infectious diseases (Schiebelhut \emph{et al.} 2018;
Auteri \& Knowles 2020). Such studies are pivotal in highlighting the
pace at which selection can act to counter disease in wildlife
communities and opening up opportunities for interventions, such as
deliberate translocations of adaptive phenotypes, that can increase the
adaptability of threatened populations (Hohenlohe \emph{et al.} 2019;
Hoffmann \emph{et al.} 2020). Despite this progress, the number of
studies demonstrating rapid evolutionary responses to infectious
diseases in natural populations remains limited and biased towards
terrestrial systems.

Marine infectious diseases are responsible for incremental and mass
mortalities in a variety of wildlife groups, including keystone and
habitat forming taxa (Harvell \emph{et al.} 2007; Clemente \emph{et
al.}2014; Martin \emph{et al.} 2016; Montecino-Latorre \emph{et al.}
2016; Harvell \& Lamb 2020), and species supporting wild commercial
fisheries (Marty \emph{et al.} 2010; Cawthorn 2011; Lafferty \emph{et
al.} 2015; Crosson \emph{et al.} 2020). The Australian blacklip abalone
(\emph{Haliotis rubra} ), a species targeted by the world's largest wild
abalone fisheries and a rapidly expanding aquaculture industry (FAO
Fishstat 2021), was heavily impacted by disease between 2006 and 2010
(Mayfield \emph{et al.} 2012). Abalone viral ganglioneuritis (AVG)
caused by the haliotid herpesvirus-1 (HaHV-1) spread along the western
coastline of Victoria in south-eastern Australia, causing rapid and
severe population collapses (\textgreater{} 90\% mortality in some
areas) and devastating both wild and farmed abalone stocks
(Hooper\emph{et al.} 2007). Despite the impact of AVG, abalone stocks in
the Western Zone fishery have seen significant recovery, and are
considered sustainable (Mundy \emph{et al.} 2020). Given the short
generation time of the species (\textasciitilde{}4 years; Andrews 1999),
large population sizes, and high levels of genetic variability that
contribute to existing patterns of adaptation across the fishery (Miller
\emph{et al.} 2019), it is possible that rapid evolutionary responses
have contributed to this recovery.

Previous research has demonstrated heritable genetic variation relating
to herpesvirus immunity in Haliotid species. Challenge tests performed
on New Zealand paua (\emph{H. iris} ) and Japanese black abalone
(\emph{H. discus} ), involving controlled exposure to Haliotid
herpesvirus-1 (HaHV-1), indicated complete immunity to AVG
(Chang\emph{et al.} 2005; Corbeil \emph{et al.} 2017), with
complementary transcriptomic analyses helping to characterise the
genetic basis of the resistance (Bai \emph{et al.} 2019b; Neave \emph{et
al.} 2019). Similar tests on \emph{H. rubra} yielded no evidence of
resistance to AVG (Crane\emph{et al.} 2013; Corbeil \emph{et al.} 2016),
however, these experiments were performed on a small number of animals
from a limited number of locations affected by AVG. While complete
immunity may not occur in \emph{H. rubra} , the presence of AVG immunity
in sister taxa hints at the potential for some level of resistance
developing through standing genetic variation following AVG exposure.

In this study, we explore the possibility of a rapid evolutionary
response in recovering \emph{H. rubra} fishing stocks devastated by AVG.
Specifically, we performed a genome wide association study (GWAS) using
pooled whole genome re-sequencing data on \emph{H. rubra} specimens from
fishing stocks varying in disease exposure. Our findings point to rapid
changes in population-level allele frequencies over a single generation
time-scale in virus affected fishing stocks, with stock recovery
determined by rapid evolutionary changes leading to virus resistance.
This study highlights the pace at which adaptive phenotypes can
potentially evolve and spread in wildlife communities to counter threats
from infectious diseases. We discuss these findings in the context of
future biosecurity management of Australian abalone fisheries and
wildlife conservation more generally.

\textbf{2 \textbar{} METHODS}

\textbf{2.1 \textbar{} Sample collection and DNA sequencing}

Tissue biopsies were collected from a total of 343 individual \emph{H.
rubra} from 14 locations spanning the Victorian Western and Central Zone
fisheries. Locations were selected based on their known virus exposure
history according to confidential records held by the Victorian wild
fishing sector and the Victorian Fisheries Authority (10 AVG affected
and 4 AVG unaffected locations; Table 1, Figure 1). Sampling for 5 of
the locations within the Western Zone fishery was coordinated in 2009 by
the Department of Economic Development, Jobs, Transport and Resources
(DEDJTR). It is expected that animals from these locations during this
sampling period survived the virus event and provide a reliable snap
shot of the post-virus allele frequency distributions in AVG affected
and unaffected populations. Sampling of the remaining 9 locations was
performed between 2015 and 2020. To avoid the potential swamping effects
of inter-generational gene flow since the disease outbreak, sampling was
biased towards fishing stocks expected to be largely self-recruiting
based on biophysical connectivity models (Young et al. 2020), and toward
large adult animals (expected to be either direct survivors or first
generation post-virus survivors). This sampling was performed by
contract divers, commercial fisherman, and our research team. At each
location, individual abalone were collected within a 100
m\textsuperscript{2} area, with tissue biopsies consisting of 20 mg of
muscle tissue from the abalone lip obtained using sterile dissection
tools to avoid cross-contamination. Biopsied material was transferred to
2 ml microcentrifuge tubes containing 80-100\% ethanol and stored at 4
\selectlanguage{ngerman}°C until required for genomic analysis.

Total genomic DNA was extracted from 10 mg of tissue using a DNeasy
Blood and Tissue Kit (Qiagen, Valencia, CA) following the manufacturer's
instructions. Resulting DNA extracts were quantified using a Qubit
version 2 fluorometer (Life Technologies, Carlsbad, CA, USA). To obtain
population genomic data, we applied the Pool-Seq approach (Futschik \&
Schlotterer 2010), which involves pooling the DNA of a large number of
individuals from the same population and then sequencing the `population
variability genome'. This was achieved by pooling individual DNA
extracts from each sample location equimolar, splitting the 25
individuals per location into 2 x pools per locations consisting of DNA
from 12 and 13 individuals, respectively, to account for potential
sequencing bias. The resulting 28 pooled libraries were prepared for
sequencing using the Nextera\textsuperscript{TM} DNA Sample Preparation
kit, and sequenced using the Illumina NovaSeq platform (Illumina, San
Diego, CA, USA), with the 150 base pair (bp) paired-end protocol.
Sequencing was performed allowing for 3 x genome coverage per individual
per pool, equating to 80 - 100 x genome coverage per population.

\textbf{2.2 \textbar{} Data preparation}

The Illumina NovaSeq sequencing yielded a total of 25 x
10\textsuperscript{9} assigned 150 bp reads, and a total of 45 -- 100 Gb
of sequence data for each of the 28 pooled DNA libraries. Raw DNA
sequence reads from the two separate pooled libraries per sample
location were pooled for processing purposes. Raw sequences were
processed using the Trimmomatic V0.36 program (Bolger \emph{et al.}2014)
by removing Nextera\textsuperscript{TM} adaptors and discarding all
reads that had a Phred score below 20. All retained reads were
subsequently aligned to the \emph{H.rubra} reference genome (NCBI RefSeq
QXJH00000000.1; Gan \emph{et al.} 2019) using the PPalign package in the
PoolParty pipeline (Micheletti \& Narum 2018) with default parameters.
Single nucleotide polymorphisms (SNPs) were called using PoolFstat
(Hivert \emph{et al.} 2018) where SNPs were required to have a read
depth of 20 -- 200 reads to be called. SNPs with a minor allele
frequency of \selectlanguage{english}[?] 0.05 were used for downstream genomic analysis.

\textbf{2.3 \textbar{} Estimating overall genetic structure}

SNP frequencies over all loci were initially contrasted between all 14
sample locations to determine patterns of overall genetic structure and
population connectivity. The software PoolFstat implemented in R
(Hivert\emph{et al.} 2018) was used to calculate global and pairwise
measures of population differentiation (\emph{F} \textsubscript{ST};
Weir \& Cockerham 1984).

\textbf{2.4 \textbar{} Genome-wide association analysis}

To identify SNPs associated with virus exposure status we performed a
genome\selectlanguage{english}-wide association study (GWAS) using BayPass 2.1 (Gautier 2015).
Analyses were performed under the auxiliary (AUX) covariate mode
(\selectlanguage{english}-covmcmc and \selectlanguage{english}-auxmode flags), after scaling the variables with the
\selectlanguage{english}-scalecov flag. The underlying models explicitly account for the
covariance structure among the population allele frequencies that
originates from the shared history of the populations through the
estimation of the population covariance matrix \selectlanguage{greek}Ω, \selectlanguage{english}which removes the
variation associated with demography (Bonhomme et al., 2010; Gunther \&
Coop, 2013). The auxiliary covariate model specifically involves the
introduction of a binary auxiliary variable to classify each locus as
associated or not associated. This allows computation of posterior
inclusion probabilities (and Bayes Factors) for each locus while
explicitly accounting for multiple testing issues. The auxiliary
covariate model was applied with default parameters, a 5,000 burn-in of
iterations in the Markov chain Monte Carlo (MCMC) simulation, followed
by 25,000 iterations. To reduce artefacts due to potential variability
between runs, we performed 3 independent BayPass simulations. We then
calculated the average Bayes Factor (BF), expressed in deciban units
(dB), for each SNP as a quantitative estimate of the strength of
association with virus exposure and the standardized allele frequency.
For each SNP, the level of effect was assessed based on the Bayes Factor
(BF) models according to Jeffrey's rule (Jeffreys 1961). SNPs with BF
scores [?] 50 were regarded as decisive associations with virus exposure
and were retained as potential candidate loci.

\textbf{2.5 \textbar{} Posthoc analyses including functional
annotations}

An analysis of principal components (PCA) was implemented in the
adegenet package for R (Jombart 2008; Jombart \& Ahmed 2011) to obtain a
graphical depiction of patterns of genetic structure among virus
affected and unaffected stocks based on all candidate SNPs identified by
BayPass (BFs [?] 50).

Total linkage disequilibrium (LD) among all candidate loci was
calculated using LDx, a package which uses an approximate maximum
likelihood approach from pooled resequencing data (Feder \emph{et
al.}2012). Linkage disequilibrium was calculated as\emph{r}
\textsuperscript{2}, the square of the correlation between alleles of
SNP pairs within the paired sequence reads of each population. We
subsequently calculated the average LD for each pairwise SNP comparison
across sample sites. Next, we assessed the distribution of candidate
loci and signatures of selection across the reference\emph{H. rubra}
genome consisting of 2,854 annotated scaffolds varying between 1,830 and
1.1 x 10\textsuperscript{7} bp in length (Gan \emph{et al.} 2019). This
was achieved by regressing the total number of candidate loci against
scaffold length using ggpubr package for R (Kassambara \& Kassambara
2020). Scaffolds with exceptionally large numbers of candidate loci
relative to scaffold length were interrogated further using the package
LDBlockShow (Dong \emph{et al.} 2021) to measure pairwise linkage
disequilibrium and haplotype blocks using the default -SeleVar option to
calculate \emph{D} ' (the ratio of the difference between the observed
and expected frequency of a haplotype, and its maximum value when
considering total allele frequencies).

SnpEff v2.0.3 (Cingolani \emph{et al.} 2012) was used to map candidate
SNP loci to the \emph{H. rubra} genome and predict variant impacts; high
(highly disruptive impact on protein function), moderate (non-synonymous
mutations, possible change in protein effectiveness), low (unlikely to
change protein behaviour) or a modifier (synonymous mutations,
non-coding or intergenic variant). Functional classification of
candidate genes was achieved by aligning the peptide sequences for
mapped candidate \emph{H. rubra} genes with the annotated genomes for
human (NCBI RefSeq IDs NC\_000001 - NC\_000024), pacific oyster (RefSeq
IDs NC\_047559 - NC\_047568), scallop (RefSeq ID NC\_007234.1) and the
blue mussel (RefSeq ID NC\_006161.1) using DIAMOND (Buchfink \emph{et
al.} 2015). A maximum e-value of 1e\textsuperscript{-40} was set to
conservatively estimate the likelihood of similar gene functions between
taxonomies. Protein GI accessions from the top hit of DIAMOND alignments
were imported into the web-based version of the DAVID bioinformatics
tool (Huang \emph{et al.} 2009a; Huang \emph{et al.} 2009b), where
corresponding annotations were generated. Functional annotations were
performed by focussing specifically on gene homologs known to be
associated with virus-host interactions, in particular herpesvirus
response pathways, including those in response to HaHV-1 in the AVG
resistant \emph{H. iris} .

\textbf{3 \textbar{} RESULTS}

\textbf{3.1 \textbar{} Genotyping and overall population structure}

Pooled whole genome re-sequencing of 384 \emph{H. rubra} specimens from
14 locations distributed across the Victorian Western and Central Zone
abalone yielded a total of 7,745,655 SNPs that were used for population
genomic analyses. Estimates of overall genetic structure indicated a
lack of structure and panmixia across the Western and Central Zone
fisheries. Specifically, global \emph{F} \textsubscript{ST} did not
differ significantly from zero (\emph{F} \textsubscript{ST} = 0,
\emph{P}\textgreater{} 0.05), nor did any pairwise measure of\emph{F}
\textsubscript{ST} between sampling locations.

\textbf{3.2 \textbar{} Genome-wide association analysis}

Our genome-wide association study found 25,854 candidate SNPs with
strong associations with virus exposure (BF \textgreater{} 50), with the
PCA based on candidate loci revealing clear patterns of genetic
structuring between locations varying in historical AVG exposure (Figure
2). Estimates of LD were high at all locations (mean\emph{r}
\textsuperscript{2} = 0.61 \selectlanguage{ngerman}± 0.01 \emph{SD} ) indicating non-random
association of alleles, while comparisons of\emph{r} \textsuperscript{2}
between virus affected and unaffected stocks did not significantly
differ (\emph{P} \textgreater{} 0.05). Analyses suggest a genome wide
patterns of selection, with regression analyses indicating strong linear
relationship between number of candidate loci and scaffold length
(\emph{R} \textsuperscript{2} = 0.83; Figure 3a). However, scaffolds
QXJH01000030.1 and QXJH01000212.1 exhibited a higher number of candidate
loci relative to scaffold length (deviating from the linear
distribution), with candidate SNPs comprising 0.0062\% and 0.0108\% of
the total scaffold nucleotides, respectively. Pairwise linkage among
SNPs across the entirety of these scaffolds was high (\emph{D} ' \selectlanguage{english}[?] 1),
with the detection of large haplotype blocks indicating large sections
of the genome linked to virus exposure (Figure 3b).

\textbf{3.3 \textbar{} Functional annotations}

Candidate SNP loci which showed significant associations with virus
exposure were successfully mapped to the annotated \emph{H.
rubra}genome. SNPeff analyses predicted 333 candidate loci to have
moderate effect on protein function (involving non-synonomous
mutations), while 489 candidates were predicted to have low effect, and
24,722 candidates were recognised as non-coding or intergenic variants.
Candidate loci that successfully mapped to \emph{H. rubra} genome
peptides sequences, were found to correspond with gene homologs in other
animal systems including haliotids\emph{,} non-haliotid marine molluscs,
crustaceans, and humans. These include 13 gene homologs linked to HaHV-1
immunity in New Zealand p\selectlanguage{polish}ā\selectlanguage{english}ua (\emph{H. iris} ), and 13 genes associated
with herpes virus response pathways in Japanese disk abalone (\emph{H.
discus hannai} ), decapod crustaceans (\emph{Penaues monodon}
and\emph{Procambarus clarkia} ), and humans (Table 2). An additional 10
peptides mapped to gene homologs associated with host-virus interactions
in various haliotids (\emph{H. discus hannai} , \emph{H. laevigata} ,
and\emph{H. ruscefens} ), decapod crustaceans (\emph{Penaues monodon}
and\emph{Procambarus clarkia} ), and humans (Table 2). All gene homologs
and known functions are provided in Table 2. Notable findings include
several genes linked to chitin-binding peritrophin-A domain, and the
cytochrome P450 (CYP) 3A family, which have recognised associations with
immune responses in aquatic molluscs (Badariotti \emph{et al.} 2007;
Zhao \emph{et al.} 2017) and humans (Fattahi \emph{et al.} 2018),
respectively. Also, the CREB-binding protein (CBP), which is associated
with herpesvirus responses in humans (Gwack \emph{et al.} 2001;
Chen\emph{et al.} 2020), as well as immune pathways for C-type lectins
that are important contributors to innate immune responses in
invertebrates (Nam \emph{et al.} 2016; Zhang \emph{et al.} 2018; Qin
\emph{et al.}2019).

\textbf{4 \textbar{} DISCUSSION}

We provide evidence of rapid genetic changes over a single generation in
wild \emph{H. rubra} populations decimated by HaHV-1. Specifically, our
GWAS identified SNP loci associated with virus exposure, many of which
mapped to genes known to contribute to HaHV-1 immunity, herpes virus
response pathways, and host-virus interactions in haliotids and other
animal systems. These findings require experimental validation but point
to rapid evolutionary changes in \emph{H. rubra} fishing stocks impacted
by disease, with stock recovery influenced by evolved resistance. This
study highlights the pace at which selection can act to counter disease
in wildlife communities by leading to an increased frequency of adaptive
genotypes. The implications of these findings are discussed in the
context of future infectious diseases management in abalone fisheries
and wildlife conservation more generally.

\textbf{4.1 \textbar{} Evidence of rapid evolution in \emph{H. rubra}}

Our analyses identified approximately 25,000 SNP loci associated with
AVG exposure in \emph{H. rubra} , suggesting that selection has driven
advantageous genetic variants to higher frequencies in virus affected
populations. The number of positively associated candidates is a
substantial proportion of the total number of SNPs included in our
analyses (0.4\%). The strong linear relationship between genome scaffold
length and density of candidate SNPs suggests a genome wide response to
selection pressure. However, only some SNPs identified here may
contribute to pathogen resistant phenotypes, given linkage
disequilibrium among loci and the fact that alleles contributing to
resistance will be embedded in large haploblocks. Nevertheless, we did
find a higher incidence of significant SNPs on two scaffolds, which
could be indicative of important regions of adaptation. The genes
annotated within these regions are not known for providing virus
resistance, but further work is needed to test for possible functional
significance of these regions.

Despite the potential for linkage between loci, our analyses indicate
that some candidate loci may be directly involved in disease adaptation
in \emph{H. rubra} . In particular, functional annotations of candidate
loci point to associations with genes and protein domains that
contribute to HaHV-1 immunity in the New Zealand p\selectlanguage{polish}ā\selectlanguage{english}ua (\emph{H. iris} ).
Neave \emph{et al.} (2019) first characterised genes associated with
HaHV-1 immunity in \emph{H. iris} through transcriptome analyses of
animals subject to HaHV-1 immersion challenge tests. This study was the
first to characterise the molecular basis of HaHV-1 immunity in a
haliotid species, and our study complements these findings by
identifying a common set of genes involved in haliotid host response to
HaHV-1 exposure. Functional annotations of candidate loci also point to
associations with genes and protein domains contributing to herpes
simplex virus responses and immune responses in various haliotids and
other animal systems. These findings strongly support the notion that
emergent genomic architectures have resulted in divergent adaptation and
possible selection for disease resistance phenotypes in \emph{H.
rubra}fishing stocks impacted by AVG.

The results of this study point to heritable genetic changes in \emph{H.
rubra} fishing stocks impacted by disease. However, experimental
validation will be needed to link any genetic changes to disease
resistance. Challenge tests involving the exposure of animals with
putatively resistant genotypes to HaHV-1 will help to determine if, and
how much, HaHV-1 resistance is determined by the candidate genotypes
(Crane \emph{et al.} 2013; Corbeil \emph{et al.} 2016). Given that a
large number of loci appear to be responding to the virus, we suspect
that changes in resistance will represent a polygenic response that can
be followed by controlled breeding studies (Guarna \emph{et al.} 2017;
Gutierrez \emph{et al.} 2018). The response to selection in these
studies will depend on levels of heritable variation as well as the
intensity of selection which will determine the impact of the phenotypic
change. Breeding studies and experimental trials will also be essential
to assess trait heritability and genotype-environment interactions,
particularly if industry intend to control for resistance traits in a
culture environment for breeding purposes (discussed below).

\textbf{4.2 \textbar{} Implications for fisheries management}

Re-emergence of AVG remains a significant threat to the economic
viability of \emph{H. rubra} fisheries in south-eastern Australia
(Lafferty \emph{et al.} 2015; Corbeil 2020). Therefore characterising
the spatial distribution and prevalence of disease resistant genotypes
will help managers identify stocks expected to be either resilient or
vulnerable to AVG re-emergence. Previous population genomic research has
indicated a lack of biological stock structure in these fisheries
(Miller \emph{et al.} 2016), suggesting that gene flow could contribute
to the spread of adaptive genotypes and resilience of na\selectlanguage{ngerman}ïve fishing
stocks. However, gene flow from unaffected parts of the fishery could
eventually reduce the frequency of adaptive genotypes over time in the
absence of ongoing selection. Whether selection for resistance will be a
recurring process is still unclear. However, for the first time in a
decade AVG was recorded in 2021, leading to abalone mortalities at a few
proximal fishing locations heavily impacted by AVG in the early 2000s
(Agriculture Victoria 2021). Unlike the first outbreak, animal mortality
and disease spread has been minimal. While environmental and
epidemiological factors may be contributing to the suppression of the
disease (Bai \emph{et al.} 2019a; Corbeil 2020), it is possible that the
presence of adaptive phenotypes has already reduced the number of
susceptible animals and overall viral load within affected fishing
stocks.

Evidence of panmixia in \emph{H. rubra} (Miller \emph{et al.} 2016)
suggests that standing genetic variation is likely to persist within
disease naïve populations allowing for \emph{in situ} adaptation to
HaHV-1. However, strategic stock augmentation activities, involving the
translocations of animals with AVG resistant genotypes, could
potentially assist the spread of genotypes to reduce risks of
vulnerability across wild fisheries. Also, there may be future
opportunities to biosecure farm fisheries through the establishment of
AVG resistant breeding programs, similar to disease related breeding
programs in other farmed mollusc, crustacean and finfish fisheries
around the world (Ragone Calvo \emph{et al.} 2003; Kjøglum \emph{et
al.}2008; Moss \emph{et al.} 2012; Potts \emph{et al.} 2021). Overall,
these results add to those of Miller \emph{et al.} (2019) demonstrating
patterns of genetic adaptation across environmental gradients and the
adaptability of \emph{H. rubra} populations to new environmental
conditions. This is pertinent in south-eastern Australia where rapid
changes in the physical marine climate are threatening commercial
fisheries through shifts in species distributions (Ling 2008;
Johnson\emph{et al.} 2011), changes in habitat and trophic interactions
(Holland \emph{et al.} 2021), and risks of infectious diseases
(Oliver\emph{et al.} 2017).

\textbf{5 \textbar{} Conclusion}

The spread and impact of existing and emerging infectious diseases is
expected to intensify as a result of ongoing land use change, climate
effects and anthropogenic vectors (Daszak \emph{et al.} 2001;
Tompkins\emph{et al.} 2015; Price \emph{et al.} 2019). Disease responses
in individual species have the potential to influence wider patterns of
biodiversity, ecosystem function, human health and agriculture,
particularly those of high functional or economic importance
(Daszak\emph{et al.} 2001; Wiethoelter \emph{et al.} 2015; Cunningham
\emph{et al.} 2017). Consequently, understanding the ability of such
species to adapt to infectious disease is becoming increasingly
important. While it has been proposed that selection in some species may
fail to keep pace with rates of pathogen evolution and the emergence and
turn-over of novel diseases (Hawley \emph{et al.} 2013; Ujvari \emph{et
al.} 2014), our study demonstrates that rapid evolutionary responses
within a single generation are possible. This study highlights the value
of genome scans for identifying signatures of adaptation among natural
populations spanning infection gradients and characterising putative
genes that contribute to disease resistant phenotypes. Future studies of
this nature will be critical for understanding the adaptability of other
species threatened by infectious diseases, informing the management new
outbreaks, and future proofing populations with little or no resistance.

\textbf{Acknowledgements}

This project was funded by the Fisheries Research and Development
Corporation (FRDC) Project 2018-057: `Population genomic assessment of
Australian Blacklip Abalone for Abalone viral ganglioneuritis (AVG)
resistance' on behalf of the Australian Government. We would like to
thank Richard Zavalas, Jess Fish and Emily Armstrong (Deakin University)
and Greg Staples and Harry Gorfine with assistance with site selections
and sample collections. Finally, we thank Yin Peng Lee (Deakin
University) for assistance with the preparation of genomic libraries and
sequencing, and Mun Hua Tan (The University of Melbourne) and Erica
Nielsen (University of California, Davis) for their assistance with
bioinformatic analyses.

\textbf{Author Contributions}

This project was conceived by A.D.M, while O.J.H, M.T, A.D.M, and L.C
were responsible for generating and analysing the data, with assistance
from C.A and A.A.H. Writing of the manuscript was led by O.J.H and A.D.M
with assistance from all authors.

\textbf{Data accessibility}

All genomic data sets are available upon request and will be made
available in the DRYAD archives.

\textbf{Table 1.} Site location details and corresponding codes for 14
collection locations of \emph{Haliotis rubra} from Victorian Western and
Central Zone fisheries. Sample sizes and AVG exposure history are also
provided.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Zone and Location} & \textbf{Code} & \textbf{Year Sampled} &
\textbf{Sample Size} & \textbf{GPS Location} & \textbf{GPS Location} &
\textbf{AVG Status}\tabularnewline
\midrule
\endhead
& & & & \textbf{Latitude} & \textbf{Longitude} &\tabularnewline
Western Zone fishery & Western Zone fishery & & & & &\tabularnewline
Port Macdonell & \textbf{PMC} & 2020 & 25 & --38.054 & 140.881 &
unaffected\tabularnewline
Inside Murrels & \textbf{MUR} & 2009 & 25 & --38.407 & 141.524 &
affected\tabularnewline
Inside Nelson & \textbf{ISN} & 2009 & 25 & --38.409 & 141.558 &
affected\tabularnewline
The Crags & \textbf{CRG} & 2009 & 25 & --38.390 & 142.135 &
affected\tabularnewline
Lady Julia Percy & \textbf{LJP} & 2009 & 23 & --38.422 & 141.993 &
unaffected\tabularnewline
Killarney & \textbf{KIL} & 2015 & 20 & --38.363 & 142.321 &
affected\tabularnewline
Levies & \textbf{LEV} & 2009 & 25 & --38.385 & 142.235 &
affected\tabularnewline
Central Zone fishery & Central Zone fishery & & & & &\tabularnewline
Childers Cove & \textbf{CHC} & 2019 & 25 & --38.490 & 142.672 &
affected\tabularnewline
Bay of Islands & \textbf{BIP} & 2019 & 25 & --38.582 & 142.827 &
affected\tabularnewline
Cat Reef & \textbf{CAT} & 2015 & 25 & --38.741 & 143.188 &
affected\tabularnewline
White Cliffs & \textbf{WCF} & 2015 & 25 & --38.758 & 143.330 &
affected\tabularnewline
Castle Cove & \textbf{CCV} & 2020 & 25 & --38.783 & 143.422 &
unaffected\tabularnewline
Parker River & \textbf{PKR} & 2020 & 25 & --38.855 & 143.538 &
affected\tabularnewline
Blanket Bay & \textbf{BLK} & 2015 & 25 & --38.827 & 143.586 &
unaffected\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} List of predicted genetic variant impacts, and genes
that candidate loci mapped to. Table also includes gene functions, as
well as the species from which these functions have been reported and
their respective references.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\textbf{Number of candidate SNPs} & \textbf{Predicted variant Impact} &
\textbf{Associated gene} & \textbf{Associated gene function} &
\textbf{Associated gene function} & \textbf{Species} & \textbf{Species}
& \textbf{Reference(s)} &\tabularnewline
\midrule
\endhead
\textbf{Genes involved \emph{H. iris} HaHV-1 immune response} &
\textbf{Genes involved \emph{H. iris} HaHV-1 immune response} &
\textbf{Genes involved \emph{H. iris} HaHV-1 immune response} &
\textbf{Genes involved \emph{H. iris} HaHV-1 immune response} & & & &
&\tabularnewline
17 & MODERATE, LOW, MODIFIER & SLC1A2 & SLC1A2 & Excitatory amino acid
transporter 2, response to HaHV-1 exposure & \emph{Haliotis iris} &
Neave et al. 2019 & Neave et al. 2019 & Neave et al. 2019\tabularnewline
35 & MODERATE, LOW, MODIFIER & CYP3A4 & CYP3A4 & Response to HaHV-1
exposure & \emph{Haliotis iris} & Neave et al. 2019 & Neave et al. 2019
& Neave et al. 2019\tabularnewline
6 & MODERATE, MODIFIER & ACE & ACE & Response to HaHV-1 exposure &
\emph{Haliotis iris} & Neave et al. 2019 & Neave et al. 2019 & Neave et
al. 2019\tabularnewline
2 & MODIFIER & POU6F2 & POU6F2 & Response to HaHV-1 exposure &
\emph{Haliotis iris} & Neave et al. 2019 & Neave et al. 2019 & Neave et
al. 2019\tabularnewline
3 & MODIFIER & NLGN4X & NLGN4X & Response to HaHV-1 exposure &
\emph{Haliotis iris} & Neave et al. 2019 & Neave et al. 2019 & Neave et
al. 2019\tabularnewline
2 & MODIFIER & CYP3A7 & CYP3A7 & Response to HaHV-1 exposure &
\emph{Haliotis iris} & Neave et al. 2019 & Neave et al. 2019 & Neave et
al. 2019\tabularnewline
1 & MODIFIER & Peritrophin 44 like (LOC105317660) & Peritrophin 44 like
(LOC105317660) & Chitin-binding peritrophin-A domain, response to HaHV-1
exposure & \emph{Haliotis iris} & Neave et al. 2019 & Neave et al. 2019
& Neave et al. 2019\tabularnewline
1 & MODIFIER & Uncharacterized LOC105326593 (LOC105326593) &
Uncharacterized LOC105326593 (LOC105326593) & Chitin-binding
peritrophin-A domain, response to HaHV-1 exposure & \emph{Haliotis iris}
& Neave et al. 2019 & Neave et al. 2019 & Neave et al.
2019\tabularnewline
17 & MODIFIER & CHIT1 & CHIT1 & Chitin-binding peritrophin-A domain,
response to HaHV-1 exposure & \emph{Haliotis iris} & Neave et al. 2019 &
Neave et al. 2019 & Neave et al. 2019\tabularnewline
15 & MODIFIER & CYP3A5 & CYP3A5 & Response to HaHV-1 exposure &
\emph{Haliotis iris} & Neave et al. 2019 & Neave et al. 2019 & Neave et
al. 2019\tabularnewline
1 & MODIFIER & CYP3A43 & CYP3A43 & Response to HaHV-1 exposure &
\emph{Haliotis iris} & Neave et al. 2019 & Neave et al. 2019 & Neave et
al. 2019\tabularnewline
14 & MODIFIER & Ganglioside GM2 activator like (LOC105346019) &
Ganglioside GM2 activator like (LOC105346019) & Chitin-binding
peritrophin-A domain, response to HaHV-1 exposure & \emph{Haliotis iris}
& Neave et al. 2019 & Neave et al. 2019 & Neave et al.
2019\tabularnewline
1 & MODIFIER & Ganglioside GM2 activator like (LOC105348613) &
Ganglioside GM2 activator like (LOC105348613) & Chitin-binding
peritrophin-A domain, response to HaHV-1 exposure & \emph{Haliotis iris}
& Neave et al. 2019 & Neave et al. 2019 & Neave et al.
2019\tabularnewline
\textbf{Gene homologs associated with herpesvirus response pathways} &
\textbf{Gene homologs associated with herpesvirus response pathways} &
\textbf{Gene homologs associated with herpesvirus response pathways} &
\textbf{Gene homologs associated with herpesvirus response pathways} &
\textbf{Gene homologs associated with herpesvirus response pathways} & &
& &\tabularnewline
2 & MODIFIER & CASP8 & CASP8 & Herpesvirus response pathway &
\emph{Haliotis discus hannai} & Nam et al. 2016 & Nam et al. 2016 & Nam
et al. 2016\tabularnewline
1 & MODIFIER & TAF10 & TAF10 & Herpesvirus response pathway & \emph{Homo
sapiens} & Wagner \& DeLuca, 2013 & Wagner \& DeLuca, 2013 & Wagner \&
DeLuca, 2013\tabularnewline
1 & MODIFIER & ARNTL & ARNTL & Herpesvirus response pathway & \emph{Homo
sapiens} & Edgar et al. 2016 & Edgar et al. 2016 & Edgar et al.
2016\tabularnewline
7 & MODIFIER & EEF1D & EEF1D & Herpesvirus response pathway & \emph{Homo
sapiens} & Boulben et al. 2003 & Boulben et al. 2003 & Boulben et al.
2003\tabularnewline
2 & MODIFIER & SRSF7 & SRSF7 & Herpesvirus response pathway & \emph{Homo
sapiens} & Tang et al. 2019 & Tang et al. 2019 & Tang et al.
2019\tabularnewline
6 & MODIFIER & Ube2r2 & Ube2r2 & Herpesvirus response pathway &
\emph{Homo sapiens} & Beard et al. 2015 & Beard et al. 2015 & Beard et
al. 2015\tabularnewline
2 & MODIFIER & CREBBP & CREBBP & Herpesvirus response pathway, C-type
lectin containing or involved in c-type lectin pathway (invertebrate
immune response) & \emph{Haliotis discus hannai, Procambarus clarkii,
Penaeus monodon} & Nam et al. 2016, Zhang et al. 2018, Qin et al. 2019,
Wang \& Wang, 2013 & Nam et al. 2016, Zhang et al. 2018, Qin et al.
2019, Wang \& Wang, 2013 & Nam et al. 2016, Zhang et al. 2018, Qin et
al. 2019, Wang \& Wang, 2013\tabularnewline
2 & MODIFIER & SRPK1 & SRPK1 & Herpesvirus response pathway & \emph{Homo
sapiens} & Souki \& Sandri-Golden, 2009 & Souki \& Sandri-Golden, 2009 &
Souki \& Sandri-Golden, 2009\tabularnewline
2 & MODIFIER & TAF6 & TAF6 & Herpesvirus response pathway & \emph{Homo
sapiens} & Wagner \& DeLuca, 2013 & Wagner \& DeLuca, 2013 & Wagner \&
DeLuca, 2013\tabularnewline
1 & MODIFIER & TAB1 & TAB1 & Herpesvirus response pathway & \emph{Homo
sapiens} & Jahanban-Esfahlan et al. 2019 & Jahanban-Esfahlan et al. 2019
& Jahanban-Esfahlan et al. 2019\tabularnewline
1 & MODIFIER & Csnk2b & Csnk2b & Herpesvirus response pathway &
\emph{Homo sapiens} & Carter 2011 & Carter 2011 & Carter
2011\tabularnewline
4 & MODIFIER & Hnrnpk & Hnrnpk & Herpesvirus response pathway &
\emph{Homo sapiens} & Schmidt et al. 2010 & Schmidt et al. 2010 &
Schmidt et al. 2010\tabularnewline
1 & MODIFIER & PPP1CA & PPP1CA & Herpesvirus response pathway &
\emph{Homo sapiens} & Silva et al. 2015 & Silva et al. 2015 & Silva et
al. 2015\tabularnewline
\textbf{Gene homologs associated with host-virus interactions} &
\textbf{Gene homologs associated with host-virus interactions} &
\textbf{Gene homologs associated with host-virus interactions} &
\textbf{Gene homologs associated with host-virus interactions} &
\textbf{Gene homologs associated with host-virus interactions} & & &
&\tabularnewline
4 & MODIFIER & HIST1H2AA & HIST1H2AA & Abalone immune response &
\emph{Haliotis discus hannai} & Nam et al. 2016 & Nam et al. 2016 & Nam
et al. 2016\tabularnewline
1 & LOW & COTL1 & COTL1 & Tropomyosin, abalone immune response &
\emph{Haliotis discus hannai} & Nam et al. 2016 & Nam et al. 2016 & Nam
et al. 2016\tabularnewline
1 & LOW, MODIFIER & PDIA3 & PDIA3 & Protein disulfide isomerase
activity, abalone immune response & \emph{Haliotis discus hannai} & Nam
et al. 2016 & Nam et al. 2016 & Nam et al. 2016\tabularnewline
1 & MODIFIER & HSP90AB1 & HSP90AB1 & Abalone immune response &
\emph{Haliotis discus hannai} & Nam et al. 2016 & Nam et al. 2016 & Nam
et al. 2016\tabularnewline
4 & MODIFIER & Histone H2A (LOC105320412) & Histone H2A (LOC105320412) &
Abalone immune response & \emph{Haliotis discus hannai} & Nam et al.
2016 & Nam et al. 2016 & Nam et al. 2016\tabularnewline
8 & LOW, MODIFIER & AVIL & AVIL & Gelsolin domain, abalone immune
response & \emph{Haliotis discus hannai} & Nam et al. 2016 & Nam et al.
2016 & Nam et al. 2016\tabularnewline
1 & MODIFIER & Hsp70 member 12A (HSPA12A) & Hsp70 member 12A (HSPA12A) &
Abalone immune response & \emph{Haliotis laevigata, Haliotis ruscefens,
Haliotis discus hannai} & Shiel et al. 2015, Brokordt et al. 2015, Nam
et al. 2016 & Shiel et al. 2015, Brokordt et al. 2015, Nam et al. 2016 &
Shiel et al. 2015, Brokordt et al. 2015, Nam et al. 2016\tabularnewline
3 & MODIFIER & PSMA8 & PSMA8 & C-type lectin containing or involved in
c-type lectin pathway (invertebrate immune response) & \emph{Haliotis
discus hannai, Procambarus clarkii, Penaeus monodon} & Nam et al. 2016,
Zhang et al. 2018, Qin et al. 2019, Wang \& Wang, 2013 & Nam et al.
2016, Zhang et al. 2018, Qin et al. 2019, Wang \& Wang, 2013 & Nam et
al. 2016, Zhang et al. 2018, Qin et al. 2019, Wang \& Wang,
2013\tabularnewline
1 & MODIFIER & IRAK4 & IRAK4 & Toll-like receptor activity, innate
immunity & \emph{C. gigas, H. diversicolor} & Tang et al. 2017, Ge et
al. 2011 & Tang et al. 2017, Ge et al. 2011 & Tang et al. 2017, Ge et
al. 2011\tabularnewline
2 & MODIFIER & TIA1 & TIA1 & Viral translation inhibition & \emph{Homo
sapiens} & McCormick \& Khaperskyy, 2017 & McCormick \& Khaperskyy, 2017
& McCormick \& Khaperskyy, 2017\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} Sampling sites selected for population genomic
analysis from the south-eastern Australia. Figure legend and colour
coding of mapped sites indicate history of virus exposure. Refer to
Table 1 for sample codes.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Density plot of axis 1 eigenvalues from the Principal
Components Analysis. Plots are based on candidate SNP genotypes from
each of the 28 pooled whole genome resequencing libraries representing
virus affected (red) and unaffected (black) fishing stocks.

\textbf{Figure 3. (a)} Regression analysis indicating a positive linear
relationship between number of candidate SNPs (BF \textgreater{} 50) and
scaffold length. Outlier scaffolds with a greater frequency of candidate
SNPs relative to scaffold length are plotted in red.
\textbf{(b)}Heatmaps of outlier scaffolds generated with the package
LDBlockShow (Dong, et al., 2021) depicting the pairwise linkage
disequilibrium measure of \emph{D} ' (refer to colour key) between each
SNP with a BF [?] 50. In addition, black triangle sections represent
detected haplotype blocks; genomic regions of low recombination.

\textbf{References}

Agriculture Victoria (2021) Abalone Disease.URL:
https://agriculture.vic.gov.au/biosecurity
/animal-diseases/abalone-disease (accessed July 2021)

Auteri GG, and Knowles LL (2020) Decimated little brown bats show
potential for adaptive change. \emph{Scientific Reports}
\textbf{10:}3023.

Badariotti F, Lelong C, Dubos MP and Favrel P (2007) Characterization of
chitinase-like proteins (Cg-Clp1 and Cg-Clp2) involved in immune defence
of the mollusc Crassostrea gigas. \emph{FEBS J} \textbf{274:} 3646-3654.

Bai CM, Li YN, Chang PH, Jiang JZ, Xin LS \emph{et al.} (2019a)
Susceptibility of two abalone species, \emph{Haliotis diversicolor
supertexta} and \emph{Haliotis discus hannai} , to Haliotid herpesvirus
1 infection. \emph{Journal of Invertebrate Patholology}
\textbf{160:}26-32.

Bai CM, Zhang SM, Li YN, Xin LS, Rosani U \emph{et al.} (2019b) Dual
transcriptomic analysis reveals a delayed antiviral response
of\emph{Haliotis diversicolor supertexta} against Haliotid
Herpesvirus-1.\emph{Viruses} \textbf{11:} 383.

Berger L, Speare R, Daszak P, Green DE, Cunningham AA \emph{et
al.}(1998) Chytridiomycosis causes amphibian mortality associated with
population declines in the rain forests of Australia and Central
America. \emph{Proceedings of the National Academy of Sciences of the
United States of America} \textbf{95:} 9031-9036.

Blanchong JA, Robinson SJ, Samuel MD and Foster JT (2016) Application of
genetics and genomics to wildlife epidemiology. \emph{Journal of
Wildlife Management} \textbf{80:} 593-608.

Bolger AM, Lohse M and Usadel B (2014) Trimmomatic: a flexible trimmer
for Illumina sequence data. \emph{Bioinformatics} \textbf{30:}2114-2120.

Bonneaud C, Balenger SL, Russell AF, Zhang JW, Hill GE \emph{et
al.}(2011) Rapid evolution of disease resistance is accompanied by
functional changes in gene expression in a wild bird. \emph{Proceedings
of the National Academy of Sciences of the United States of
America}\textbf{108:} 7866-7871.

Bosch J, Fernandez-Beaskoetxea S, Garner TWJ and Carrascal LM (2018)
Long-term monitoring of an amphibian community after a climate change-
and infectious disease-driven species extirpation. \emph{Global Change
Biology} \textbf{24:} 2622-2632.

Brearley G, Rhodes J, Bradley A, Baxter G, Seabrook L \emph{et
al.}(2013) Wildlife disease prevalence in human-modified
landscapes.\emph{Biological Reviews} \textbf{88:} 427-442.

Buchfink B, Xie C and Huson DH (2015) Fast and sensitive protein
alignment using DIAMOND. \emph{Nature Methods} \textbf{12:} 59-60.

Cawthorn RJ (2011) Diseases of American lobsters (\emph{Homarus
americanus} ): A review. \emph{Journal of Invertebrate
Pathology}\textbf{106:} 71-78.

Chang PH, Kuo ST, Lai SH, Yang HS, Ting YY \emph{et al.} (2005)
Herpes-like virus infection causing mortality of cultured
abalone\emph{Haliotis diversicolor supertexta} in Taiwan. \emph{Diseases
of Aquatic Organisms} \textbf{65:} 23-27.

Chen C, Feng P and Slots J (2020) Herpesvirus-bacteria synergistic
interaction in periodontitis. \emph{Periodontology 2000}
\textbf{82:}42-64.

Cingolani P, Platts A, Wang le L, Coon M, Nguyen T \emph{et al.} (2012)
A program for annotating and predicting the effects of single nucleotide
polymorphisms, SnpEff: SNPs in the genome of \emph{Drosophila
melanogaster} strain w1118; iso-2; iso-3. \emph{Fly (Austin)}\textbf{6:}
80-92.

Clemente S, Lorenzo-Morales J, Mendoza JC, Lopez C, Sangil C \emph{et
al.} (2014) Sea urchin Diadema africanum mass mortality in the
subtropical eastern Atlantic: role of waterborne bacteria in a warming
ocean. \emph{Marine Ecology Progress Series} \textbf{506:} 1-14.

Corbeil S (2020) Abalone viral ganglioneuritis.
\emph{Pathogens}\textbf{9:} 720.

Corbeil S, McColl KA, Williams LM, Slater J and Crane MS (2017) Innate
resistance of New Zealand paua to abalone viral
ganglioneuritis.\emph{Journal of Invertebrate Pathology} \textbf{146:}
31-35.

Corbeil S, Williams L, McColl K and Crane M (2016) Australian abalone
(Haliotis laevigata, H. rubra and H. conicopora) are susceptible to
infection by multiple abalone herpesvirus genotypes. \emph{Diseases of
Aquatic Organisms} \textbf{119:} 101-106.

Crane MSJ, Corbeil S, Williams LM, McColl KA and Gannon V (2013)
Evaluation of abalone viral ganglioneuritis resistance among wild
abalone populations along the Victorian coast of Australia.\emph{Journal
of Shellfish Research} \textbf{32:} 67-72.

Crosson LM, Lottsfeldt NS, Weavil-Abueg ME and Friedman CS (2020)
Abalone withering syndrome disease dynamics: Infectious dose and
temporal stability in seawater. \emph{Journal of Aquatic Animal
Health}\textbf{32:} 83-92.

Cunningham AA, Daszak P and Wood JLN (2017) One Health, emerging
infectious diseases and wildlife: two decades of
progress?\emph{Philosophical Transactions of the Royal Society
B-Biological Sciences} \textbf{372:} 20160167.

Daszak P, Cunningham AA and Hyatt AD (2000) Wildlife ecology - Emerging
infectious diseases of wildlife - Threats to biodiversity and human
health. \emph{Science} \textbf{287:} 443-449.

Daszak P, Cunningham AA and Hyatt AD (2001) Anthropogenic environmental
change and the emergence of infectious diseases in wildlife. \emph{Acta
Tropica} \textbf{78:} 103-116.

Dong SS, He WM, Ji JJ, Zhang C, Guo Y \emph{et al.} (2021) LDBlockShow:
a fast and convenient tool for visualizing linkage disequilibrium and
haplotype blocks based on variant call format files. \emph{Briefings in
Bioinformatics} \textbf{22} .

Elbers JP, Brown MB and Taylor SS (2018) Identifying genome-wide immune
gene variation underlying infectious disease in wildlife populations - a
next generation sequencing approach in the gopher tortoise. \emph{Bmc
Genomics} \textbf{19} .

Epstein B, Jones M, Hamede R, Hendricks S, McCallum H \emph{et
al.}(2016) Rapid evolutionary response to a transmissible cancer in
Tasmanian devils. \emph{Nature Communications} \textbf{7:} 12684.

Fattahi S, Karimi Alivije M, Babamahmoodi F, Bayani M, Sadeghi Haddad
Zavareh M \emph{et al.} (2018) Cytochrome P450 genes (CYP2E1 and CYP1A1)
variants and susceptibility to chronic hepatitis B virus
infection.\emph{Indian Journal of Clinical Biochemistry} \textbf{33:}
467-472.

Feder AF, Petrov DA and Bergland AO (2012) LDx: Estimation of Linkage
Disequilibrium from High-Throughput Pooled Resequencing Data. \emph{PLoS
ONE} \textbf{7:} e48588.

FAO FishStat (2021) Fisheries and Aquaculture Software. FishStat Plus --
Universal Software for Fishery Statistical Time Series. FAO Fisheries
and Aquaculture Department, Rome. URL: http://www.fao.org/fishery
(accessed July 2021).

Futschik A, and Schlotterer C (2010) The next generation of molecular
markers from massively parallel sequencing of pooled DNA
samples.\emph{Genetics} \textbf{186:} 207-218.

Gan HM, Tan MH, Austin CM, Sherman CDH, Wong YT \emph{et al.} (2019)
Best foot forward: Nanopore long reads, hybrid meta-assembly, and
haplotig purging optimizes the first genome assembly for the Southern
Hemisphere blacklip abalone (\emph{Haliotis rubra} ). \emph{Frontiers in
Genetics} \textbf{10:} 889.

Gautier M (2015) Genome-wide scan for adaptive divergence and
association with population-specific covariates.
\emph{Genetics}\textbf{201:} 1555-1579.

Grogan LF, Cashins SD, Skerratt LF, Berger L, McFadden MS \emph{et
al.}(2018) Evolution of resistance to chytridiomycosis is associated
with a robust early immune response. \emph{Molecular Ecology}
\textbf{27:}919-934.

Guarna MM, Hoover SE, Huxter E, Higo H, Moon KM \emph{et al.} (2017)
Peptide biomarkers used for the selective breeding of a complex
polygenic trait in honey bees. \emph{Scientific Reports}
\textbf{7:}8381.

Gutierrez AP, Matika O, Bean TP and Houston RD (2018) Genomic selection
for growth traits in Pacific oyster (\emph{Crassostrea gigas} ):
Potential of low-density marker panels for breeding value
prediction.\emph{Frontiers in Genetics} \textbf{9:} 391.

Gwack Y, Byun H, Hwang S, Lim C and Choe J (2001) CREB-binding protein
and histone deacetylase regulate the transcriptional activity of
Kaposi's sarcoma-associated herpesvirus open reading frame
50.\emph{Journal of Virology} \textbf{75:} 1909-1917.

Hansen EM, Parke JL and Sutton W (2005) Susceptibility of Oregon forest
trees and shrubs to Phytophthora ramorum: A comparison of artificial
inoculation and natural infection. \emph{Plant Disease}
\textbf{89:}63-70.

Harvell CD, and Lamb JB (2020) Disease outbreaks can threaten marine
biodiversity, pp. 141-- 158 in \emph{Marine Disease Ecology} , edited by
Behringer DC, Silliman BR and Lafferty KD. Oxford Scholarship.

Harvell CD, Mitchell CE, Ward JR, Altizer S, Dobson AP \emph{et
al.}(2002) Ecology - Climate warming and disease risks for terrestrial
and marine biota. \emph{Science} \textbf{296:} 2158-2162.

Harvell D, Jordan-Dahlgren E, Merkel S, Rosenberg E, Raymundo L \emph{et
al.} (2007) Coral disease, environmental drivers, and the balance
between coral and microbial associates. \emph{Oceanography}
\textbf{20:}172-195.

Hawley DM, Osnas EE, Dobson AP, Hochachka WM, Ley DH \emph{et al.}(2013)
Parallel Patterns of Increased Virulence in a Recently Emerged Wildlife
Pathogen. \emph{Plos Biology} \textbf{11} .

Hivert V, Leblois R, Petit EJ, Gautier M and Vitalis R (2018) Measuring
Genetic Differentiation from Pool-seq Data. \emph{Genetics}\textbf{210:}
315-330.

Hoffmann AA, Miller AD and Weeks AR (2020) Genetic mixing for population
management: From genetic rescue to provenancing. \emph{Evolutionary
Applications} \textbf{14:} 634-652.

Hohenlohe PA, McCallum HI, Jones ME, Lawrance MF, Hamede RK \emph{et
al.} (2019) Conserving adaptive potential: lessons from Tasmanian devils
and their transmissible cancer. \emph{Conservation Genetics}\textbf{20:}
81-87.

Holland OJ, Young MA, Sherman CDH, Tan MH, Gorfine H \emph{et al.}(2021)
Ocean warming threatens key trophic interactions supporting a commercial
fishery in a climate change hotspot. \emph{Under review} .

Hooper C, Hardy-Smith P and Handlinger J (2007) Ganglioneuritis causing
high mortalities in farmed Australian abalone (\emph{Haliotis
laevigata}and \emph{Haliotis rubra} ). \emph{Australian Veterinary
Journal}\textbf{85:} 188-193.

Huang DW, Sherman BT and Lempicki RA (2009a) Bioinformatics enrichment
tools: paths toward the comprehensive functional analysis of large gene
lists. \emph{Nucleic Acids Research} \textbf{37:} 1-13.

Huang DW, Sherman BT and Lempicki RA (2009b) Systematic and integrative
analysis of large gene lists using DAVID bioinformatics
resources.\emph{Nucleic Acids Research} \textbf{37:} 1-13.

Hubert JN, Zerjal T and Hospital F (2018) Cancer- and behavior-related
genes are targeted by selection in the Tasmanian devil
(\emph{Sarcophilus harrish} ). \emph{Plos One} \textbf{13:} e0201838.

Jeffreys H (1961) \emph{Theory of probability} . Oxford University
Press, Oxford.

Johnson CR, Banks SC, Barrett NS, Cazassus F, Dunstan PK \emph{et
al.}(2011) Climate change cascades: Shifts in oceanography, species'
ranges and subtidal marine community dynamics in eastern
Tasmania.\emph{Journal of Experimental Marine Biology and Ecology}
\textbf{400:}17-32.

Jombart T (2008) adegenet: a R package for the multivariate analysis of
genetic markers. \emph{Bioinformatics} \textbf{24:} 1403-1405.

Jombart T, and Ahmed I (2011) adegenet 1.3-1: new tools for the analysis
of genome-wide SNP data. \emph{Bioinformatics} \textbf{27:} 3070-3071.

Kassambara A, and Kassambara MA (2020) Package `ggpubr', pp.

Kim K, and Harvell CD (2004) The rise and fall of a six-year
coral-fungal epizootic. \emph{American Naturalist} \textbf{164:}S52-S63.

Kj\selectlanguage{ngerman}øglum S, Henryon M, Aasmundstad T and Korsgaard I (2008) Selective
breeding can increase resistance of Atlantic salmon to furunculosis,
infectious salmon anaemia and infectious pancreatic
necrosis.\emph{Aquaculture} \textbf{39:} 498-505.

Lafferty KD, Harvell CD, Conrad JM, Friedman CS, Kent ML \emph{et
al.}(2015) Infectious Diseases Affect Marine Fisheries and Aquaculture
Economics. \emph{Annual Review of Marine Science, Vol 7}
\textbf{7:}471-496.

Ling SD (2008) Range expansion of a habitat-modifying species leads to
loss of taxonomic diversity: a new and impoverished reef
state.\emph{Oecologia} \textbf{156:} 883-894.

Lorch JM, Knowles S, Lankton JS, Michell K, Edwards JL \emph{et
al.}(2016) Snake fungal disease: an emerging threat to wild
snakes.\emph{Philosophical Transactions of the Royal Society
B-Biological Sciences} \textbf{371:} 20150457.

Margres MJ, Jones ME, Epstein B, Kerlin DH, Comte S \emph{et al.} (2018)
Large-effect loci affect survival in Tasmanian devils (\emph{Sarcophilus
harrisii} ) infected with a transmissible cancer. \emph{Molecular
Ecology} \textbf{27:} 4189-4199.

Martin DL, Chiari Y, Boone E, Sherman TD, Ross C \emph{et al.} (2016)
Functional, phylogenetic and host-geographic signatures
of\emph{Labyrinthula} spp. provide for putative species delimitation and
a global-scale view of seagrass wasting disease. \emph{Estuaries and
Coasts} \textbf{39:} 1403-1421.

Marty GD, Hulson PJF, Miller SE, Quinn TJ, Moffitt SD \emph{et
al.}(2010) Failure of population recovery in relation to disease in
Pacific herring. \emph{Diseases of Aquatic Organisms} \textbf{90:} 1-14.

Mayfield S, Mundy C, Gorfine H, Hart A and Worthington D (2012) Fifty
years of sustained production from the Australian abalone
fisheries.\emph{Reviews in Fisheries Science} \textbf{20:} 220-250.

Micheletti SJ, and Narum SR (2018) Utility of pooled sequencing for
association mapping in nonmodel organisms. \emph{Molecular Ecology
Resources} \textbf{18:} 825-837.

Miller AD, Hoffmann AA, Tan MH, Young M, Ahrens C \emph{et al.} (2019)
Local and regional scale habitat heterogeneity contribute to genetic
adaptation in a commercially important marine mollusc (\emph{Haliotis
rubra} ) from southeastern Australia. \emph{Molecular
Ecology}\textbf{28} .

Miller AD, van Rooyen A, Rasic G, Ierodiaconou DA, Gorfine HK \emph{et
al.} (2016) Contrasting patterns of population connectivity between
regions in a commercially important mollusc Haliotis rubra: integrating
population genetics, genomics and marine LiDAR data. \emph{Molecular
Ecology} \textbf{25:} 3845-3864.

Montecino-Latorre D, Eisenlord ME, Turner M, Yoshioka R, Harvell
CD\emph{et al.} (2016) Devastating transboundary impacts of sea star
wasting disease on subtidal asteroids. \emph{PLoS One}
\textbf{11:}e0163190.

Moss SM, Moss DR, Arce SM, Lightner DV and Lotz JM (2012) The role of
selective breeding and biosecurity in the prevention of disease in
penaeid shrimp aquaculture. \emph{J Invertebr Pathol}
\textbf{110:}247-250.

Mundy C, Chick RC, Stobart B, VFA and Strain L (2020) STATUS OF
AUSTRALIAN FISH STOCKS REPORT Blacklip Abalone (2020), pp.

Nam BH, Jung M, Subramaniyam S, Yoo SI, Markkandan K \emph{et al.}(2016)
Transcriptome analysis revealed changes of multiple genes involved in
\emph{Haliotis discus hannai} innate immunity during Vibrio
parahemolyticus Infection. \emph{PLoS One} \textbf{11:} e0153474.

Neave MJ, Corbeil S, McColl KA and Crane MS (2019) Investigating the
natural resistance of blackfoot Paua Haliotis iris to abalone viral
ganglioneuritis using whole transcriptome analysis. \emph{Diseases of
Aquatic Organisms} \textbf{135:} 107-119.

Oliver ECJ, Benthuysen JA, Bindoff NL, Hobday AJ, Holbrook NJ \emph{et
al.} (2017) The unprecedented 2015/16 Tasman Sea marine
heatwave.\emph{Nature Communications} \textbf{8:} 16101.

Potts RWA, Gutierrez AP, Penaloza CS, Regan T, Bean TP \emph{et
al.}(2021) Potential of genomic technologies to improve disease
resistance in molluscan aquaculture. \emph{Philos Trans R Soc Lond B
Biol Sci}\textbf{376:} 20200168.

Price SJ, Leung WTM, Owen CJ, Puschendorf R, Sergeant C \emph{et
al.}(2019) Effects of historic and projected climate change on the range
and impacts of an emerging wildlife disease. \emph{Global Change
Biology}\textbf{25:} 2648-2660.

Qin Y, Jiang S, Huang J, Zhou F, Yang Q \emph{et al.} (2019) C-type
lectin response to bacterial infection and ammonia nitrogen stress in
tiger shrimp (\emph{Penaeus monodon} ). \emph{Fish \& Shellfish
Immunology} \textbf{90:} 188-198.

Ragone Calvo LM, Calvo GW and Burreson EM (2003) Dual disease resistance
in a selectively bred eastern oyster, \emph{Crassostrea virginica} ,
strain tested in Chesapeake Bay. \emph{Aquaculture} \textbf{220:} 69-87.

Schiebelhut LM, Puritz JB and Dawson MN (2018) Decimation by sea star
wasting disease and rapid genetic change in a keystone
species,\emph{Pisaster ochraceus} . \emph{Proceedings of the National
Academy of Sciences of the United States of America} \textbf{115:}
7069-7074.

Storfer A, Kozakiewicz CP, Beer MA and Savage AE (2020) Applications of
population genomics for understanding and mitigating wildlife disease,
pp. 357-383 in \emph{Population Genomics: Wildlife} , edited by
Holenlohe PA and Rajora OP. Springer, Cham.

Tompkins DM, Carver S, Jones ME, Krkosek M and Skerratt LF (2015)
Emerging infectious diseases of wildlife: a critical
perspective.\emph{Trends in Parasitology} \textbf{31:} 149-159.

Ujvari B, Pearse AM, Swift K, Hodson P, Hua B \emph{et al.} (2014)
Anthropogenic selection enhances cancer evolution in Tasmanian devil
tumours. \emph{Evolutionary Applications} \textbf{7:} 260-265.

Weir BS, and Cockerham CC (1984) Estimating F\selectlanguage{english}-statistics for the
analysis of population structure. \emph{evolution}
\textbf{38:}1358-1370.

Wiethoelter AK, Beltran-Alcrudo D, Kock R and Mor SM (2015) Global
trends in infectious diseases at the wildlife-livestock
interface.\emph{Proceedings of the National Academy of Sciences of the
United States of America} \textbf{112:} 9662-9667.

Zhang XW, Man X, Huang X, Wang Y, Song QS \emph{et al.} (2018)
Identification of a C-type lectin possessing both antibacterial and
antiviral activities from red swamp crayfish. \emph{Fish \& Shellfish
Immunology} \textbf{77:} 22-30.

Zhao JS, Wang AY, Zhao HB and Chen YH (2017) Transcriptome sequencing
and differential gene expression analysis of the
schistosome-transmitting snail \emph{Oncomelania hupensis} inhabiting
hilly and marshland regions. \emph{Scientific Reports} \textbf{7:}15809.

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