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MINI REVIEW
Incorporating genomics into insect conservation: Butterflies
as a model group
Alena Sucháˇ
cková Bartoˇ
nová
1
| Daniel Linke
1,2
| Irena Kleˇ
cková
1
|
Pedro de G. Ribeiro
1,2
| Pável Matos-Maraví
1
1
Institute of Entomology, Biology Centre CAS
(Czech Academy of Sciences), ˇ
Ceské
Budˇ
ejovice, Czechia
2
Faculty of Science, Department of Zoology,
University of South Bohemia, ˇ
Ceské
Budˇ
ejovice, Czechia
Correspondence
Daniel Linke, Institute of Entomology, Biology
Centre, CAS (Czech Academy of Sciences),
Braniˇ
sovská 31, 37005 ˇ
Ceské Budˇ
ejovice,
Czechia.
Email: daniel.linke@entu.cas.cz
Funding information
Akademie Vˇ
ed ˇ
Ceské Republiky, Grant/Award
Number: L20096195; Grantová Agentura
ˇ
Ceské Republiky, Grant/Award Number:
GJ20-18566Y; Jihoˇ
ceská Univerzita v ˇ
Ceských
Budˇ
ejovicích, Grant/Award Numbers:
014/2022/P, 04-048/2019/P
Editor: Nusha Keyghobadi and Associate
Editor: Kate Bell
Abstract
1. Genomic data are not yet widely used in insect conservation practice. Here, with a
focus on butterflies, we aim to identify the strengths, limitations and remaining gaps
between the fields of population genomics and insect conservation management.
Based on a literature search complemented with expert opinion, we discuss ave-
nues for translating research into practice.
2. We found that current genomic methodologies available for insect management
enhance the assessment of cryptic diversity and facilitate the inference of historical
population trends (temporal monitoring) by using even degraded material from his-
torical collections.
3. Discovering and tracking adaptive genetic variation linked to increased survival and
fitness is a relatively young research field, but we highlight it as a promising tool in
future insect management actions.
4. We highlight recent case studies where population genomics have guided butterfly
conservation. One conclusion from our advice from our non-exhaustive survey of
expert opinion is to establish meaningful partnerships between researchers and
practitioners, starting at the stage of project planning. Genomics is an informative
tool for securing legal protection of unique populations and may offer guidance in
future conservation translocations and captive breeding programmes.
5. Although insect conservation usually targets habitats, genomic guidance focusing
on populations of flagship and umbrella taxa is a straightforward path to connect
species-specific and habitat conservation initiatives. We conclude that there is
urgency in reporting insect conservation actions guided by genomic data, both suc-
cessful and unsuccessful. This will lead to constructive feedback between fields and
the establishment of standardised methodologies.
KEYWORDS
conservation genetics, genome-wide variation, local adaptation, population genetics, temporal
monitoring, umbrella species
Alena Sucháˇ
cková Bartoˇ
nová and Daniel Linke contributed equally to this study.
Received: 12 May 2022 Accepted: 20 March 2023
DOI: 10.1111/icad.12643
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any
medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2023 The Authors. Insect Conservation and Diversity published by John Wiley & Sons Ltd on behalf of Royal Entomological Society.
Insect Conserv Divers. 2023;1–14. wileyonlinelibrary.com/journal/icad 1
INTRODUCTION
Conservation research and conservation practice have long worked on
different paths despite their common overarching goal of protecting
biodiversity (Habel et al., 2013). The translation of science into action
has been mainly hindered by insufficient communication and inter-
disciplinarity (Sunderland et al., 2009), as well as by methodological gaps
(Shafer et al., 2015). While the former is being tackled by initiatives that
disseminate evidence-based conservation actions (e.g., the Conserva-
tion Evidence project, https://www.conservationevidence.com/), the
latter remains a major limitation given the rapid development of scien-
tific tools but the slower pace of standardising methods that can be
applied in real-world conservation issues. For example, insect popula-
tion genomics (i.e., the study of genome-wide variation instead of a few
loci) is a mature research field with a rich toolkit to study genome-wide
variation (Webster et al., 2022), but insect conservation practice has
been rarely guided by genomic techniques.
Insect conservation efforts have been challenging due to method-
ological and practical limitations. First, about 80% of insect species
diversity remains to be described (Stork, 2018) and there is a shortage
of taxonomists and of standardised and efficient methods to delimit
species (Engel et al., 2021). Second, only a few insect groups have
been intensively inventoried and monitored. Overall, there is limited
understanding of insect species’geographical distributions, population
sizes, population connectivity and even scarcer knowledge of their
ecology and behaviour. Third, most taxon-specific funding resources
are allocated towards large vertebrate species (Mammola et al., 2020).
For example, only 7% of EU-LIFE funded projects since 1992 targeted
invertebrates (Mammides, 2019). Moreover, insect conservation
efforts are impossible to perform on an individual basis because they
are usually short-living and small organisms with high annual fluctua-
tions in population sizes (Longcore & Osborne, 2015).
The ecology, distribution and demographic trends of butterflies are
some of the best known among insects (Boggs et al., 2003;
Thomas, 2005). Especially in Europe and North America, the traditional
monitoring and inventorying programmes (e.g., New, 2009;Roy
et al., 2015;Sandersonetal.,2021; Schultz et al., 2017; Thorne
et al., 2006; Vantieghem et al., 2017) have allowed conservation biolo-
gists to investigate whether butterfly populations are declining, inter-
connected with other populations and susceptible to environmental
changes (Halsch et al., 2021;Maesetal.,2019;Warrenetal.,2021).
Such long-term programmes, includingtransect walks, field observations
and mark–release–recapture, have delivered outputs commonly trans-
lated into conservation actions (Pullin, 1995; Warren et al., 2021).
Although these techniques generally require multiple seasons of data
collection and involve high efforts and financial resources, they provide
invaluable temporal resolution for assessing population and abundance
trends. Assessing genetic diversity as part of ongoing monitoring pro-
grammes using high-throughput techniques (Bruford et al., 2017)pro-
vides another dimension to the study of butterfly biodiversity.
Butterfly conservation actions have sometimes been guided by
population genetics research (Czajkowska et al., 2020; Dinc
a
et al., 2018; Joyce & Pullin, 2004; Kadlec et al., 2010; Mattila
et al., 2012). The analyses of single or a few loci for estimating popula-
tion genetics parameters are often limited (e.g., Ackiss et al., 2020;
Dupuis et al., 2018) and neglect other important aspects in conserva-
tion biology, such as the genetic basis of adaptation in natural popula-
tions (Shafer et al., 2015). Genome-wide diversity and fine-scale
population connectivity patterns, on the other hand, are good proxies
of species resilience to environmental change (e.g., Evans et al., 2017;
Hughes & Stachowicz, 2004; Kardos et al., 2021; von Takach
et al., 2021). For example, accurate and rapid estimates of migration
rates, inbreeding depression, selection and genetic drift provide
unparalleled insights for more efficient conservation biology practice
including recovery, translocation and management plans of threatened
populations (e.g., Bergner et al., 2014; Chemnick et al., 2000; Sutton
et al., 2018). By incorporating genomics into traditional butterfly con-
servation efforts, more accurate estimates of genome-wide neutral,
beneficial and deleterious variation, and the inference of population
dynamics in the face of strong environmental changes will better
guide management practice, in line with calls for a multidisciplinary
approach in conservation biology (Habel et al., 2015).
In this mini-review, by focusing on butterflies, we address ave-
nues for translating population genomics research into applicable
insect conservation actions. We highlight that current evidence points
towards the feasibility of genomics to (1) overcome several methodo-
logical limitations in population genetics research, (2) significantly
improve the precision and accuracy of population genetics estimates
typically used in conservation biology, and (3) to provide new answers
not previously tackled by classical population genetics or traditional
monitoring, albeit yet with methodological limitations (e.g., screening
and monitoring of adaptive variation in populations across land-
scapes). Finally, we stress that a major gap for effectively implement-
ing genomics in conservation actions can only be addressed by
stronger communication among researchers working in genomics,
conservation practitioners and stakeholders.
GENOMIC TOOLS AVAILABLE FOR
BUTTERFLY CONSERVATION
We begin this mini-review by briefly outlining the sampling strategies
and genomic tools that are commonly used in butterfly population
genomics research. We aim to provide a glimpse of each technique
and the available butterfly genomic resources, without going into
technical details, which can be found in the cited literature. We focus
on genomic approaches that study genome-wide variation, though
other techniques that rely on high-throughput DNA sequencing of a
few molecular markers exist and are used in inventorying pro-
grammes, such as environmental molecular sampling (Roger et al.,
2022) and metabarcoding using Illumina short reads (Hausmann
et al., 2020; Ji et al., 2013) and Oxford Nanopore MinION long-read
approaches (Srivathsan et al., 2019). For a glossary of terms commonly
used in population genomics research, see Box 1.
The sampling strategy and the number of individuals accessible
for conservation genomic studies depend on the threat level of the
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study taxa. In butterflies, and insects in general, the sampling for
genetic access often removes several individuals from populations,
though protocols have been established for handling endangered
populations (e.g., Kadlec et al., 2010) including non-lethal sampling
(e.g., Hamm et al., 2010; Koscinski et al., 2011;Marschalek
et al., 2016; Storer et al., 2019). Indeed, traditional population
genetics approaches rely on large individual sampling per
population (e.g., 25–30 individuals per population when using
microsatellites; Hale et al., 2012). The use of genomic approaches
drastically reduces the number of individuals per population
needed for estimating population dynamics by significantly
increasing the amount of genome-wide data (e.g., Aguirre-Liguori
et al., 2020; Després et al., 2019; Li et al., 2020; Nazareno
et al., 2017).
BOX 1 Glossary of genomic terms.
Base pair (bp): a complementary pair of nucleotides (A, T, C or G). Usually, base pairs are the size units of genomes (i.e., the size of a
given genome is 1000 base pairs).
Contig: a fraction of a sequenced genome that contains a contiguous sequence of overlapped assembled reads.
Genome: the complete genetic material of an organism organised in chromosomes.
Genotyping: identifying sets of alleles in a given locus and their frequencies.
Genome alignment: a prediction of homology relation between genomic sequences. The main aim is to identify sequence variants and, in
particular cases, gene orthology prediction (Armstrong et al., 2019).
Genome assembly: a bioinformatic procedure by which it is possible to obtain contiguous sequences of base pairs (contigs) from raw
reads. De novo assembly means constructing genomes without a priori knowledge of the correct order of the contigs.
Genome annotation: bioinformatic procedure by which it is possible to identify, classify and name genes or coding regions of the genome
to better understand their function.
Genome coverage: an average estimate of the number of times a genome is sequenced by the reads generated in a sequencing proce
dure. For example, if a genome has a size of 1000 base pairs and the sequencing generates 500 reads of 10 base pairs each, than the
average coverage of the genome is 5 (i.e., coverage =[number of reads read size]/genome size). Low-coverage sequencing usually
refers to an average read coverage of a genome of <30.High-coverage or deep-coverage usually refers to an average read coverage
of a genome of >30.
Genome size: the total number of base pairs contained in a genome.
Genomic partitioning sequencing: different methods that are used to enrich particular regions of the genome prior to sequencing.
Hybrid enrichment: refers to a type of sequencing in which a sequencing probe is designed to hybridise in specific parts of the genome
that will be isolated from the genome and subsequently sequenced by PCR amplification (Lemmon & Lemmon, 2013).
AHE (anchored hybrid enrichment): sequencing of targeted regions or genes of the genome by designing specific probes for these
target regions (Lemmon et al., 2012).
UCEs (ultraconserved elements): ultra-conserved regions of the genome that can be sequenced by using probes designed for the
enrichment of these regions (Faircloth et al., 2012).
RADseq (restriction site-associated genome sequencing): a sequencing technique that can identify thousands of markers that are randomly
distributed across genomes (Davey & Blaxter, 2010). The process involves three main steps: digestion by restriction enzyme, selection
of fragments’sizes and PCR amplification (see Andrews et al., 2016).
GBS (genotype-by-sequencing): uses only one restriction enzyme and short fragments are amplified by PCR.
ddRAD (double-digestion RAD): uses two restriction enzymes and size selection.
Library: the pool of extracted and fragmented DNA from fresh or museum samples that has been processed to go through a given
sequencing technique.
Megabases: one-million base pairs. Usually noted as (Mbp).
Museomics: used to describe methods of next-generation sequencing used specifically in museum samples often presenting old and
degraded DNA.
Read: a sequenced fragment of a genome that has a specific and determined size and has not been processed by any type of
bioinformatic procedures. Short read—sequenced fragments ranging from 50 to 300 bp—usually produced by techniques such as
Illumina HiSeq. Long reads—sequenced fragments ranging from 5 to 30 kbp—usually produced by techniques such as PacBio or
Oxford Nanopore.
SNP (single nucleotide polymorphism): variation of a single nucleotide of DNA present in the genomes of a given population.
Variant calling: process used to identify SNPs in genomic data.
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When targeting species with populations that are either locally
extinct or severely threatened, museum specimens might still be avail-
able, and their genomic information is possible to access with high-
throughput sequencing techniques that do not rely on PCR and that
are amenable with highly fragmented DNA (Call et al., 2021; Twort
et al., 2021). Such data are valuable because they can be used as base-
line for the analyses of additional contemporary samples from sur-
rounding thriving populations (Bakker et al., 2020).
Genomic techniques have become accessible for researchers
working with threatened insect species, which are usually non-
model organisms, that is, taxa that do not have reference genomes
or other genomic resources, such as transcriptomes or draft
genomes, readily available. Prices for whole-genome sequencing
using short-read technologies such as Illumina are currently esti-
mated at 0.01 USD per million bases (https://www.genome.gov/
sequencingcostsdata). Researchers can further increase the cost-
effectiveness of their population genomic projects by selecting
from a range of laboratory protocols commonly referred to as
genomic partitioning (Lemmon & Lemmon, 2013), which include
reduced-representation approaches (e.g., restriction-site associ-
ated DNA sequencing, RADseq) and target sequence capture.
These methodologies aim to sample either random (RADseq) or
specific (target sequence capture) parts of the genome, while still
obtaining high read coverage for hundreds to thousands of loci
across the whole genome.
RADseq uses restriction enzymes to digest the genomic DNA at
sites distributed across the genome (for a review, see Andrews
et al., 2016). Usually, RADseq projects generate data for thousands of
genome-wide markers and have the advantage to enhance single
nucleotide polymorphisms (SNPs) discovery and to deliver reliable
genotyping for organisms lacking reference genomes. Furthermore,
given that RADseq reduces the complexity of the genome into ran-
dom, homologous loci, and the library preparations are relatively
cheap, sequencing can be carried out for tens or even hundreds of
individuals in parallel. However, the efficiency of genomic digestion
by restriction enzymes is severely reduced in degraded samples, which
might further bias the sequencing of DNA fragments (Davey &
Blaxter, 2010; Graham et al., 2015). Therefore, fresh material with
high molecular genomic DNA weight is usually preferred for RADseq
experiments (Figure 1).
Alternatively, target sequence capture uses probe baits designed
to extract and sequence targeted genomic regions in the laboratory,
even from old and degraded samples (Call et al., 2021). However, the
design of probes requires a good genomic knowledge of the study
taxa and synthesising the probe baits can be expensive for small-scale
studies (Andermann et al., 2020). Laboratory protocols to improve the
capture efficiency of divergent probe baits have been described,
including using lower hybridization temperatures (Li et al., 2013). Fur-
thermore, probe baits based on RADseq libraries (hyRAD; Suchan
et al., 2016) do not rely on restriction sites to targeting such loci. Alto-
gether, these might alleviate the limitations of utilising probes
designed using genomic information from highly divergent taxa.
Finally, universal probe kits targeting ultraconserved elements and
coding regions have been described for Lepidoptera (Breinholt
et al., 2018; Faircloth, 2017), including butterflies (Espeland
et al., 2018; Kawahara et al., 2018,2022), whereas taxon-specific
hyRAD probes have been successfully used in the extraction of
genome-wide loci even from archived butterfly specimens (Gauthier
et al., 2020).
Low-coverage whole-genome sequencing (WGS) has become a
feasible alternative to integrate population-level studies of geno-
mic variation and phylogenomics studies in organisms having
small-to-medium genome sizes, such as butterflies (0.2–1.4 Gbp;
Liu et al., 2020). Performing WGS allows researchers to use sim-
pler molecular laboratory protocols than in genomic partitioning
approaches. In addition, WGS offers the possibility to use both
fresh and old degraded samples (e.g., museum specimens, or
museomics;Twortetal.,2021). Data matrices can be further
improved if the sequence reads are mapped against a reference
genome of the same or a closely related species (Díez-del-Molino
et al., 2018) or even a divergent species (Galla et al., 2019), which
might enhance conservation genomics efforts of congeneric
threatened species (e.g., Parnassius butterflies; Podsiadlowski
et al., 2021). Furthermore, new tools have emerged for more effi-
cient comparative genomics, including genome alignments
(Armstrong et al., 2019;Lin&Hsu,2020), the study of intra-
specific genomic variation (Edwards et al., 2022) and sequence-
based estimates of genetic load or the genome-wide variation that
reduces mean population fitness (Bertorelle et al., 2022).
Reference genomes might not be strictly necessary for bioinfor-
matically mining loci of interest (Allen et al., 2017; Ribeiro et al., 2021;
Zhang et al., 2019) and for variant calling and genotyping (Lou
et al., 2021). Nonetheless, efforts to obtain chromosome-level refer-
ence genomes (i.e., high-coverage and high-quality genomes) using
new technologies, such as long-read sequencing, are critical to further
increase the mapping performance of WGS reads and to improve the
reliability of scoring genome-wide SNPs (Formenti et al., 2022).
Butterfly genomic resources (e.g., read sequences,denovo
contig assemblies and annotated reference genomes)areavailablein
public databases such as NCBI, ENSEMBL and the Lepbase (Challi
et al., 2016). There are around 870 low-coverage butterfly
genomes that are all available at public repositories (Ellis
et al., 2021). In addition, there are over 250 chromosome-level
genome assemblies for Lepidoptera (butterflies and moths), 49 of
those representing butterfly reference genomes (NCBI search as of
December 2022; Taxa =Lepidoptera, Filters =Reference Genomes
and Chromosome Assembly Level). However, such numbers are rap-
idly increasing, thanks to international efforts to obtain high-
quality reference genomes for insects and butterflies (e.g., the i5K
insect genomes: http://i5k.github.io/, the Earth Biogenome pro-
ject: https://www.earthbiogenome.org/,DarwinTreeofLife:
https://www.darwintreeoflife.org/). Such an amount of available
genomic resources places the butterflies as one of the best-studied
groups across insects (Hotaling et al., 2021).
Furthermore, functional genomic annotations are the identification
of genes, their functions and physical locations along chromosomes.
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These are essential to confidently associate loci under selection with
genes related to population fitness and local adaptation to the envi-
ronment (e.g., responses to diseases, fecundity and heat tolerance;
Wheat & Hill, 2014). A major limitation in butterfly and insect geno-
mics, nonetheless, is the low number of studies that validated geno-
mic annotations, especially across highly complex regions of the
genome (e.g., immune genes; Keehnen et al., 2018,2021). Thus, both
continuous sequencing efforts of butterfly reference genomes and
the validation of functional annotations are crucial to enhance the
potential applications of butterfly genomics in conservation.
Finally, the bioinformatic processing of genomic data relies on
computer clusters able to cope with the high memory demand of
analysing and storing hundreds of gigabytes of data. These compu-
tational infrastructure and resources are accessible to researchers
working: (1) at universities or research institutes with sufficient
financial resources; (2) in countries where access to national com-
puter clusters is secured; (3) at private institutions able to finance
access to cloud computing; and (4) in collaboration with other
researchers able to access such high-demand computing infra-
structure. Recent efforts released draft genome assemblies of but-
terflies (Ellis et al., 2021), which can be directly used to search for
loci of interest even using desktop computers. However, we recog-
nise that the limited access to computational resources is another
big deterrent to using genomic data in conservation efforts (see
the section Bridging the gap between insect population genomics and
conservation).
POTENTIAL APPLICATIONS OF POPULATION
GENOMICS IN BUTTERFLY CONSERVATION
In this section, we revisit key literature on butterfly population geno-
mics with the aim of highlighting how their results and interpretation
might be applied in future conservation actions (Table 1). The increas-
ing availability of high-quality reference genomes and the reducing
costs of high-throughput DNA sequencing have made genomics a
more efficient tool for conservation biology (Allendorf et al., 2010;
Formenti et al., 2022), which in the context of insect conservation
might facilitate the discovery of species and estimation of genome-
wide diversity, population connectivity and local adaptation (Webster
et al., 2022).
A better understanding of genomic variation and population
dynamics (e.g., estimates of demographic trends, inbreeding, drift,
introgression and genetic load) provides unparalleled insights on
the threat at the genetic diversity level of endangered populations
and the potential discovery of cryptic diversity. These, in turn, pro-
vide more confidence for the genomic assessment of source popu-
lations for translocations and conservation breeding to increase
resilience and survival of threatened taxa with the overall goal of
maintaining adaptive potential and the fitness of populations
(e.g., Chen et al., 2022)(Figure1). Finally, a better understanding
of population connectivity and barriers to dispersal further illumi-
nates the habitat and landscape management of insect (meta)
populations.
FIGURE 1 Opening the Black Box of Genomics, explaining a general genomic approach to tackle biodiversity threats characterised by insect
conservationists. The Black Box represents the bits of this process, which are not common knowledge of insect conservationists with no
molecular biology background. Two parts of the Black Box of Genomics are first, the lab methods showing the selection of the laboratory method
considering DNA quality and the number of processed specimens. Second, genomic data introduce the specific population data gained by
genomics (see examples in the main text), which can be used to define conservation actions.
INSECT CONSERVATION GENOMICS 5
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QUANTIFYING ANTHROPOGENIC IMPACTS
ON BIODIVERSITY
By using genome-wide variation, the resolution of population evolu-
tionary genetics estimates increases, thus, improving the detection of
small differences in viability among populations, and their connectivity
in space (Figure 1). The genetic viability of a population is determined
by the concurrent estimation of two parameters: (a) demographic
inference, informed by declining trends in effective population size
and increased genetic drift, bottleneck and inbreeding as a result of
shrinking abundances, and (b) genetic diversity, whose loss leads to
genomic erosion and increased probability of unmasking genetic load,
with the overall consequence of hindering species resilience to envi-
ronmental changes and reducing fitness of populations. Population
TABLE 1 The innovations that genomics brings into insect conservation based on a literature review of butterfly population genomics
studies.
Area of improvement Innovation of genomics Examples
Efficient sampling of insect populations
1 Degraded sampling material Genomics tools are amenable with highly fragmented
DNA commonly used in archived collections (e.g.,
museomics); potential to discover cryptic diversity
and relationships of extinct populations
Cong et al. (2021)
de-Dios et al. (2021)
Grewe et al. (2021)
Gauthier et al. (2020)
Ryan et al. (2018)
Fountain et al. (2016)
2 Smaller sampling demands Given the large amount of genome-wide data, less
individuals are needed to infer population genetics
estimates
Després et al. (2019)
Anthropogenic impact on biodiversity
3 Temporal baselines Comparing the diversity and demographic trends of
past and present populations
Gauthier et al. (2020)
Ryan et al. (2018)
Fountain et al. (2016)
4 Quantifying anthropogenic impact Identifying fine-scale barriers to gene flow, estimating
population connectivity across landscape and
demographic trends not possible to detect with
legacy or single genetic markers
Jaun et al. (2022)
Sherpa et al. (2022)
Warson et al. (2022)
Gates et al. (2021)
Trense et al. (2021)
Després et al. (2019)
Discover diversity
5 Cryptic species Genome-wide evaluation of cryptic taxa, and
comparison with other lines of evidence, such as
ecology
Ebdon et al. (2021)
Hinojosa et al. (2020)
Hinojosa et al. (2019)
6 Unique lineages Search for evolutionary distinctive populations of high
conservation value (i.e., evolutionary significant
units)
Dupuis et al. (2020)
Dupuis et al. (2018)
7 Hybridization and introgression Identifying hybrid zones, hybrid populations and
species, the level of gene flow among them and
introgressed loci that may be associated with
natural selection
Augustijnen et al. (2022)
Lucek et al. (2020)
Capblancq et al. (2015)
Potential applications yet to be standardised
8 Landscape genomics Genomics offers a powerful association tool between
environmental drivers of genetic diversity, isolation
and adaptations on the landscape level
Trense et al. (2021)
MacDonald et al. (2020)
9 Adaptive markers under selection Functional genome annotations can be used to identify
loci under selection, which can be linked to
environmental pressure; possibility to screening
and monitoring adaptive alleles, and used in
assisted migration and captive breeding
programmes
Lindestad et al. (2022)
Keehnen et al. (2021)
Tan et al. (2021)
Trense et al. (2021)
Green II and Kronforst (2019)
Ahola et al. (2017)
10 Securing habitats Insect conservation is mainly habitat-oriented,
genomics offers the possibility of advancing
butterflies as flagship and umbrella taxa to inform
habitat management
Yet to be studied
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connectivity (for a review, see Kool et al., 2013) is measured by two
further population genetics parameters: (c) differentiation of popula-
tions informed by estimates of population structure, and (d) gene flow
and genetic admixture, which is associated with introgression and
hybridization. Anthropogenic landscape fragmentation induces loss of
connectivity of populations as it restrains gene flow and induces dif-
ferentiation of populations enhanced by founder effects, population
bottlenecks and fixation of alleles in small populations.
A fruitful genomic approach is to set temporal baselines of popu-
lations’viability and connectivity by comparing extant variation to
those of historical samples (Davis et al., 2021; García &
Robinson, 2021; Habel et al., 2014; Jensen et al., 2022; Jensen &
Leigh, 2022). For example, Gauthier et al. (2020) used genomic data
from archived butterfly specimens in Finland and revealed decreasing
genomic diversity over the past 100 years in two species, Lycaena
helle (Denis & Schiffermüller, 1775) and Erebia embla (Thunberg,
1791). The authors attributed such findings partly to increasing habi-
tat fragmentation through time, resulting, for example, in large isola-
tion of E. embla populations by geographical distance. In another case
study, Fountain et al. (2016) aimed to understand how genomic diver-
sity has been lost over 80 years in the face of declining and highly
fragmented populations of Melitaea cinxia (Linnaeus, 1758). They
compared allele frequency changes of candidate loci associated with
dispersal ability and neutral variation among populations under vary-
ing degrees of disturbances, from locally extinct, highly fragmented, to
highly inter-connected. Their results suggested that extant allele fre-
quencies in fragmented butterfly populations closely resemble those
of already extinct populations and that genotypes associated with dis-
persal capacity have likely been selected in highly fragmented land-
scapes before extinctions. Finally, employing genomic data from
historical collections (40-years-old specimens) and extant popula-
tions, Ryan et al. (2018) documented a south-to-north shift of a hybrid
zone in the North American Papilio glaucus (Linnaeus, 1758) and
P. canadensis (Rothschild & Jordan, 1906). In this case, the use of hun-
dreds of RADseq loci facilitated the discovery of fine-scale clinal vari-
ation as consequence of rapid climate changes.
When landscape features are quantified in space and time, and
jointly evaluated with genomic estimates of genetic viability and pop-
ulation connectivity, a more accurate picture of the anthropogenic
impacts on natural populations emerges. For example, by using a land-
scape genomics approach, Trense et al. (2021) assessed the variation
in populations connectivity of the butterfly species Lycaena tityrus
(Poda, 1761) in Alpine valleys across different landscape structures.
They quantified the impacts of both natural and anthropogenic bar-
riers in population differentiation, showing a greater role as gene flow
barriers of roads compared to natural rivers. On the other hand, Jaun
et al. (2022) found a lack of genomic structure among Parnassius phoe-
bus (Fabricius, 1793) populations in three alpine valleys. One of the
valleys is currently used for hydropower production, suggesting that
the existing dam wall height has not significantly affected the species’
population connectivity. However, it is likely that range shifts driven
by climate change combined with further anthropogenic modifications
of the landscape might become a threat to P. phoebus. Finally, the
analyses of thousands of genome-wide SNPs provide more robust
quantifications of the impact of landscape management. For example,
the active suppression of forest wildfires was found to increase the
isolation of populations in two butterfly species, Erynnis propertius
(Scudder & Burgess, 1870) and Icaricia icarioides (Boisduval, 1852), in
contrast to populations found in recently burned areas (Gates
et al., 2021). These results suggested that reintroducing fire in land-
scape management might be highly beneficial for butterflies’genetic
diversity and population connectivity.
The inference of demographic trends using genome-wide data is
another potential contribution of genomics to insect conservation.
For example, Sherpa et al. (2022) evaluated the connectivity of popu-
lations of Coenonympha hero (Linnaeus, 1761) in the French Jura
Mountains using ddRADseq loci. The authors identified severe demo-
graphic declines in pre-historical times but also more recently possibly
because of increased human pressure. Although the remaining C. hero
populations still constitute one large metapopulation maintaining gene
flow, conservation actions are needed to preserve its connectivity
under severe climate change and anthropogenic pressures. Similarly,
Després et al. (2019) using ddRADseq loci identified a strong popula-
tion differentiation among Coenonympha oedippus (Fabricius, 1787)
populations across its entire distributional range. This is in stark con-
trast with the weak population structure estimates based only on
mitochondrial DNA data. The genome-wide analyses also recognised
a strong and recent bottleneck in those populations, which agrees
with the recorded abundances decline across Europe by traditional
long-term monitoring data. Such a congruence between genome-wide
estimates and traditional monitoring data highlights the opportunities
for genomics-based conservation actions in threatened taxa having no
comprehensive measures of local abundances across their distribu-
tional ranges (e.g., in several tropical taxa).
INFERRING POPULATION DISTINCTIVENESS
AND CRYPTIC DIVERSITY
Isolated populations with unique and rare genotypes, morphology and
ecology are often of high conservation priority. By coupling the mea-
sures of population connectivity and their genetic relationships, geno-
mics can further provide insights on (e) genotype–phenotype
interactions (i.e., the interplay between morphology and genes, in rela-
tion to the environment), and (f) phylogenomic relationships, to shed
light on evolutionary distinctiveness of populations (i.e., significant
units for conservation), unrecognised cryptic taxa and the geographi-
cal origin of enigmatic historical individuals (e.g., the skipper butterfly
Hesperia colorado (Scudder, 1874); Cong et al., 2021; Figure 1). For
example, by using genomics, an isolated population of Euphilotes bat-
toides (Behr, 1867) in the California sand dunes was identified as a
highly divergent and unique lineage that has remained unrecognised
even in the light of legacy molecular markers (Dupuis et al., 2020).
Similarly, the population of the ridionid Apodemia mormo (Felder &
Felder, 1859), which shares the habitat with E. battoides, represents
an evolutionary unique lineage, but also other A. mormo populations
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in surrounding areas were identified as distinctive (Dupuis
et al., 2018).
Furthermore, the species status of the extinct butterfly Glaucop-
syche xerces (Boisduval, 1852) was re-assessed using museomic tech-
niques, though still based only on mitogenome data (Grewe
et al., 2021). Such a species had a small historical range near San Fran-
cisco, USA, and succumbed due to urban development in the 1940s.
Demographic inference using genome-wide data corroborated that
G. xerces has likely had a small range as a result of a gradual popula-
tion contraction and putatively deleterious alleles were already pre-
sent prior to its human-driven extinction (de-Dios et al., 2021).
Therefore, by re-assessing its taxonomic status, it became clear that
G. xerces reintroduction is not possible anymore. By contrast, popula-
tion genomics has also confirmed the phenotypic diversity, and not
species status, of Cupido carswelli Stempffer, 1927 from the Iberian
Peninsula, which is likely a differently coloured morph of Cupido lor-
quinii (Herrich-Schäffer, 1847) (Hinojosa et al., 2020).
Hybridization and introgression between species are common
phenomen a in nature that is increasingly recognised using genomic
data (Mallet, 2013; The Heliconius Genome Consortium, 2012). Quan-
tifying gene flow and admixture proportions in genomes among popu-
lations and species are important to discover potential distinctiveness
and cryptic diversity. Furthermore, they allow recognising threats to
population viability (e.g., outbreeding depression) in hybridization and
introgression mediated by anthropogenic activities. For example, the
pre-historical hybridization between two species of co-occurring
Coenonympha Hübner, 1819 butterflies from the Alps resulted in a
new hybrid species that was only possible to detect using genomic
data (Capblancq et al., 2015). Moreover, the alpine Erebia tyndarus
(Esper, 1781) species complex has maintained its post-glacial second-
ary hybrid zones, and gene flow is still possible even between species
with different chromosome numbers (Lucek et al., 2020).
ESTIMATING LOCAL ADAPTATION
Genome-wide variation of populations is a good predictor of adaptive
potential (Kardos et al., 2021; von Takach et al., 2021). Nevertheless,
genomic footprints of selection can be in some specific cases impor-
tant for decision-making in conservation practice. Responses of genes
to the environment have been identified as the expression of multiple
phenotypes under different conditions (Kardos & Shafer, 2018), which
might increase fitness and adaptive potential in natural arthropod
populations (Wheat, 2010; Wheat & Hill, 2014). For instance, allele
variants in the phosphoglucose isomerase gene have been inferred to
be responsive to thermal gradients in Colias butterflies, which might
provide a good proxy for local adaptation (reviewed in Wheat, 2010;
Wheat & Hill, 2014).
Potential local adaptations have been inferred from the analysis of
anonymous loci under selection across space (landscape genomics) or by
functional genome annotations (functional genomics). Landscape geno-
mics searches for adaptive markers across geographically delimited popu-
lations. For example, the divergent south-to-north populations of Papilio
machaon dodi McDunnough, 1939 in Canada was best explained by a
combination of climatic and geographical isolations, suggesting the exis-
tence of locally adapted populations occupying different climatic niches
(MacDonald et al., 2020). If temperature increases, the northern popula-
tion restricted to colder climates would likely be threatened. The adap-
tive markers under selection might be identified by functional genome
annotations and gene expression studies, some of them with potential
conservation outcomes (e.g., Ahola et al., 2017). For example, genomic
analyses have identified local differences in insect immunity (Keehnen
et al., 2021), in loci related to metabolism and immune responses
selected by temperature along altitudinal gradients (Trense et al., 2021),
or in genes responsible for circadian rhythm and variability in voltinism
(Lindestad et al., 2022). Furthermore, hormonal gene expression analyses
in the monarch butterfly Danaus plexippus (Linnaeus, 1758) revealed
genetically determined mechanisms of diapause termination, which has
important implications to understand climate change responses of wild
populations (Green II & Kronforst, 2019).
Adaptive regions of genomes are under selection pressure
exerted by the current human-induced environmental changes
(Figure 1; Fox et al., 2019; Halsch et al., 2021; Kelly, 2019). However,
it is not yet clear how widespread the ability of insects to adapt rap-
idly is (Marshall et al., 2020; Neu & Fischer, 2021; Waldvogel
et al., 2020). Studying local adaptations in insects, compared to verte-
brates, still needs to answer the following questions to successfully
develop standardised methodologies that translate research into
applied conservation initiatives: (1) Have insect populations had
enough time to locally adapt to abrupt human-induced changes in
their environment (Angert et al., 2020; González-Tokman et al., 2020),
or have they responded mainly by shifting geographical ranges and by
epigenetic variation (i.e., reversible chemical changes in the DNA not
involving nucleotide modifications; Rey et al., 2020)? (2) Which genes
and traits expressed in locally adapted populations among species are
associated with climate change or altered landscape structures (Ahola
et al., 2017)? Such adaptive alleles, if found, will be valuable for char-
acterising insect populations (sources and recipients) and for applying
genomic frameworks informing assisted translocations (e.g., Chen
et al., 2022). (3) Is it possible to identify single-locus adaptive markers
or large-effect variants that can predict future responses to climate
change across insect species (e.g., Wheat & Hill, 2014)?
For the time being and until functional genomics continues gather-
ing information on candidate adaptive loci beyond model species (i.e., in
endangered species), we advocate to focus immediate conservation
efforts on genome-wide variation among threatened populations. This
approach is currently the most informative proxy for safeguarding spe-
cies’adaptive potential and resilience to environmental changes.
BRIDGING THE GAP BETWEEN INSECT
POPULATION GENOMICS AND
CONSERVATION
We have summarised genomic approaches used in evolutionary popu-
lation genetics, their current limitations and future potential in guiding
8SUCH ´
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butterfly conservation practice. Although genomic tools have been
proposed for more than 10 years as game changers in conservation
biology (Allendorf et al., 2010; Shafer et al., 2015; Wheat, 2011), they
are still barely used in insect management planning. We informally
contacted researchers working on butterfly population genomics and
conservation practitioners (n=12). We asked both groups of experts
(i) whether they are aware of any conservation action guided by
genetic/genomic data and/or population genetics interpretations, and
(ii) what their opinion on the current inter-disciplinary gap is. Based
on their responses, a clearer pattern emerged that genomics has so
far played a minor role in the development of butterfly conservation
actions. We identified four major gaps: (1) any results of conservation
practices guided by genomics are not readily reported in scientific or
international, public literature; (2) there are no standardised methodol-
ogies to translate genomic data and interpretations into policy and
management plans; (3) there is limited collaboration between genomic
scientists and conservation practitioners; and (4) genomic studies are
mostly targeting species and not habitats, which are usually of more
interest in many insect conservation projects. However, there is
awareness of the opportunity that genomics brings in conservation
and both sides are eager to begin formal ways of collaboration.
Genomics is expected to significantly improve conservation prac-
tices by enhancing the discovery of species diversity and the re-
assessment of taxonomic conventions. For example, legal protection
of threatened species or populations has been granted after taking
note of characterizations of evolutionarily significant units using
genetics (e.g., Zerynthia cassandra [Geyer, 1828], Zinetti et al., 2013;
and Euchloe bazae Fabiano, 1993, Escuer et al., 2022) and corrobo-
rated or newly discovered ones using genomic data (Euphilotes bat-
toides allyni [Shields, 1975], Dupuis et al., 2020; and Z. cassandra,
Ebdon et al., 2021). Currently, population genetics approaches and,
mainly, COI barcoding programmes (e.g., Dinc
a et al., 2021) have con-
tributed to identifying cryptic species complexes, establishing evolu-
tionary units, and assessing the evolutionary uniqueness of
populations. However, such approaches are heavily limited when it
comes to assessing natural populations (e.g., low power or even biases
in detecting hybridization, sex-biased dispersal, and local adaptations)
and do not necessarily reflect real species and genetic diversity
(e.g., Hinojosa et al., 2019). Population genomics, as reviewed here,
offers more robust estimations of population connectivity and
structure.
A major limitation is that the results of conservation practices
guided by genetic data are usually not reported in the scientific litera-
ture but are mainly in national technical reports. Thus, there is gener-
ally limited feedback to academic researchers about the successes and
failures of genetics-based conservation recommendations. A formal
collaboration between practitioners and academics might alleviate this
limitation because researchers usually publish their results in scientific
journals, thus, making the results of genetics or genomics-guided con-
servation practices available to the scientific community. Furthermore,
national technical reports might further disseminate the advantages of
multi-disciplinary approaches and genomics-informed management
practices to stakeholders and policymakers, which in turn, might result
in policies that prioritise collaborative projects.
Butterfly conservation practitioners usually have a notion or
experience with population genetics as a tool guiding management
practice but acknowledge that genetics does not play a major role in
their practice. Global conservation practices urgently need to embrace
the latest technological developments, including population genomics,
to reach the goal of maintaining at least 95% of genetic diversity
within populations of all species by 2050, as recommended by the
post-2020 UN global biodiversity framework (CBD/WG2020/REC/5/
1; 5 December 2022) of the Convention on Biological Diversity.
Methodologies to achieve this have been proposed, such as monitor-
ing within-species genetic diversity (García & Robinson, 2021;
Jensen & Leigh, 2022; Pärli et al., 2021). For example, following a
landscape-level management, Euphydryas aurinia was captive-bred
and reintroduced in northern UK, where, after 9 years, the reintro-
duced population’s genetic diversity measured with microsatellite data
was comparable to natural populations (Davis et al., 2021). However,
standardised genomic methodologies translating observed population
patterns into insect conservation practice are scarce. To develop
these, more case studies should focus on and report monitoring
genome-wide variation before and after conservation actions. Thus, it
is important to establish long-term partnerships between practitioners
and researchers as research funding is usually short-term (3–5 years),
while the impacts of conservation actions need to be measured in a
longer time frame.
Butterfly conservation practitioners are also aware that reintro-
duction or population enforcement programmes can be enhanced by
having population genetics tools to characterise source and recipient
populations, while warning against illegal, amateur and non-data-
driven relocations. For example, the restoration of the last Czech
metapopulation of Chazara briseis, including the captive breeding pro-
gramme, reintroduction and re-establishment of population connec-
tivity, has been guided by evolutionary genetics of natural populations
(Kadlec et al., 2010; Sucháˇ
cková Bartoˇ
nová et al., 2021;D.ˇ
Cíp [per-
sonal communication]). Frameworks to include genomics in assisted
migration have been described, highlighting the importance of under-
standing (1) the local frequencies of adaptive alleles (source
populations > 50%; recipient populations < 10%), (2) the levels of
gene flow between populations [low gene flow potentially leads to
outbreeding; high gene flow means no intervention is necessary]
(Chen et al., 2022), (3) the dynamics of ancestral population sizes and
the likelihood of introducing deleterious variation from large source
populations (Bertorelle et al., 2022), and (4) genomic monitoring of
neutral and adaptive alleles before and after intervention (Chen
et al., 2022).
We stress the importance of formal and engaging collaboration
between practitioners and researchers already in the phase of project
planning to alleviate potential bottlenecks during the project develop-
ment. Such partnerships aiming for multi-disciplinary management prac-
tices can (1) potentially increase the formal or even financial support
from stakeholders aware of the advantages of using more accurate and
rapid approaches, (2) facilitate logistical planning as practitioners are
more aware of legal requirements, have access to sampling permits,
field data and expert knowledge of the study systems, whereas aca-
demic researchers might provide access to wet-lab and computational
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resources, and (3) provide a comprehensive characterisation of manage-
ment criteria including levels of gene flow, population sizes and demo-
graphic trends to plan a conservation action.
Finally, the use of genetics/genomics in insect conservation is still
very limited and usually tackles single species, which is only part of larger
conservation activities aiming to restore and conserve entire habitats.
However, we argue that certain species can also be used as a flagship of
a habitat or umbrella to protect biodiversity, as is the case of many but-
terfly species (e.g., Spitzer et al., 2009). For example, the protection of
Z. cassandra promoted more funding and studies evaluating habitat man-
agement strategies (Cini et al., 2021). Similarly, the enhanced protection
of C. oedippus, guided by genome-wide loci (Després et al., 2019), led to
the restoration of marshes around Grenoble, France (L. Després [per-
sonal communication]). Thus, genomic studies of selected species might
also aid in restoring and safeguarding either the habitat or other species
in the ecosystems by revealing population connectivity across the land-
scape, barriers to dispersal of habitat specialists or refugia of diversity
(Bobo-Pinilla et al., 2022;Kirschneretal.,2020). Altogether, applying
genomic tools in butterfly conservation practice can elevate the role
insects currently play in effective management and the designation of
protected areas (Chowdhury et al., 2022).
CONCLUSION
Genomics is being increasingly adopted in butterfly population
research and has indirectly informed conservation practices. The most
prominent advantage of genomics for the conservation of vulnerable
species is the unprecedented fine-scale resolution for revealing cryp-
tic diversity, genetic viability and population connectivity. Identifying
adaptive variation among populations is another potentially powerful
tool for future guidance of captive breeding and translocation pro-
grammes. Genomics provides a better quantification and characterisa-
tion of anthropogenic impacts on natural populations, thus, enhancing
data-driven conservation plans. Examples include improved landscape
management based on monitoring of genomic variation and establish-
ing temporal baselines, granting legal conservation status to evolu-
tionarily significant units and more effective population enforcement
and assisted reintroductions.
The most essential task in bridging the gap between genomics
and management actions is to establish meaningful partnerships
between conservation practitioners and researchers. Multi-
disciplinary funding schemes promoting applied research and training
programmes connecting conservation organisations and academia are
important to incentivise the next generation of researchers and practi-
tioners to collaborate and to further develop communication between
institutions. In this direction, population genomics research can be
guided by the experience of practitioners and the key data they have
collected, for example, from long-term monitoring programmes
(e.g., severely declining species, newly discovered population). Impor-
tantly, missing reports on the implementation of genomic recommen-
dations in conservation practice, and on the re-evaluation of genomic
diversity after these actions, represent the current major gaps in the
scientific literature. These reports are urgently needed for
disseminating efficient approaches worth funding to stakeholders, and
for establishing methodologies that translate genomic studies to direct
conservation actions.
AUTHOR CONTRIBUTIONS
Alena Sucháˇ
cková Bartoˇ
nová: Conceptualization (lead); visualization
(equal); writing –original draft (lead); writing –review and editing
(lead). Daniel Linke: Conceptualization (lead); visualization (equal);
writing –original draft (lead); writing –review and editing (lead). Irena
Kleckova: Conceptualization (supporting); writing –original draft
(equal); writing –review and editing (equal). Pedro de G. Ribeiro: Con-
ceptualization (supporting); writing –original draft (equal);
writing –review and editing (equal). Pável Matos-Maraví: Conceptual-
ization (lead); funding acquisition (lead); supervision (lead);
writing –original draft (equal); writing –review and editing (lead).
ACKNOWLEDGEMENTS
We thank Marie Drábková, Zdenˇ
ek F. Fric, Jana Lipárová and Pˇ
remysl
Tájek for their comments during early versions of the manuscript. Fur-
thermore, we thank Philip T. Butterill for English correction, Jana Papp
Mareˇ
sová for the graphics and Pavel Skala for the photos of butterfly
captive breeding and reintroduction in the graphical abstract. We
thank all experts who shared with us their experience and opinion on
bridging the gap between butterfly population genomics research and
conservation: Andrew Bladon, David ˇ
Cíp, Leonardo Dapporto, Lau-
rence Després, Matthias Dolek, Julian R. Dupuis, Klaus Fischer, Patrick
Gross, Vladimír Hula, Pavol Littera, Kay Lucek and Roger Vila.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
DATA AVAILABILITY STATEMENT
Data sharing not applicable to this article as no datasets were gener-
ated or analysed during the current study.
ORCID
Alena Sucháˇ
cková Bartoˇ
nová https://orcid.org/0000-0001-6298-
2466
Daniel Linke https://orcid.org/0000-0002-0686-3961
Irena Kleˇ
cková https://orcid.org/0000-0002-9333-213X
Pedro de G. Ribeiro https://orcid.org/0000-0001-5964-1978
Pável Matos-Maraví https://orcid.org/0000-0002-2885-4919
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How to cite this article: Sucháˇ
cková Bartoˇ
nová, A., Linke, D.,
Kleˇ
cková, I., P. de G. Ribeiro & Matos-Maraví, P. (2023)
Incorporating genomics into insect conservation: Butterflies as
a model group. Insect Conservation and Diversity,1–14.
Available from: https://doi.org/10.1111/icad.12643
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