Introduction
The spatial and temporal processes that generate and maintain marine
biodiversity are not fully understood (Bowen et al. , 2013; Orsiniet al. , 2013; De Meester et al. , 2016; Costello and
Chaudhary, 2017). Marine populations display diverse patterns of genetic
structure, such as isolation-by-distance (Wright, 1943; Chaves-Fonnegraet al. , 2015; Pérez-Portela, Noyer and Becerro, 2015), regional
clustering (Selkoe et al. , 2014; Brown, Davis and Leys, 2017;
Riesgo et al. , 2019), isolation-by-environment (Orsini et
al. , 2013; Giles et al. , 2015), as well as patterns that are not
clearly linked to spatial or environmental structuring (Cornwellet al. , 2016; Miller et al. , 2018; Taboada et al. ,
2018). However, barriers to dispersal and isolating mechanisms over
small spatial scales, such as the range of marine protected areas,
remain elusive especially for sessile marine organisms with a dispersive
larval stage (Liggins, Treml and Riginos, 2013). Sponges, integral but
often underappreciated assets of benthic communities (Diaz and Rützler,
2001; Bell, 2008; Bell et al. , 2013; De Goeij et al. ,
2013; Dunn, Leys and Haddock, 2015; Webster and Thomas, 2016), are
generally considered to be poor dispersers as their larvae have limited
swimming capacity and are short-lived (Maldonado, 2006). Sponges are
therefore excellent candidates to investigate marine population genetic
structure on small scales. However, despite the recognized relevance of
sponges in benthic ecosystems, results are ambiguous on sponge genetic
diversity, degrees of gene flow between populations and drivers of
divergence.
Different studies have explored how geography, oceanography and
environmental factors may influence gene flow within sponge species. Due
to their restricted dispersal, the spatial scale of sponge gene flow
should be limited. The majority of studies investigating genetic
structure in sponges have revealed species complexes with divergence
among morphologically cryptic lineages (Oppen, Wörheide and Takabayashi,
2002; Wörheide, Solé-Cava and Hooper, 2005; Uriz and Turon, 2012;
Pérez-Portela and Riesgo, 2018). However, studies investigating
within-lineage divergence are scarce. This may be a result of many most
studies using relatively small amounts of genetic data and a single type
of genetic marker (Selkoe et al. , 2016; Timm, 2020). An increase
in number of molecular markers is expected to advance inferences on
demography and structure (Felsenstein, 2004; Allendorf, Hohenlohe and
Luikart, 2010; Kelley et al. , 2016; Pérez-Portela and Riesgo,
2018). Molecular markers commonly used to assess sponge phylogeography
and population structure include mitochondrial markers (mtDNA) such as
Cyochrome c oxidase I (COI ) and ATP6, and nuclear markers such as
introns, internal transcribed spacers (ITS ) and microsatellites
(Oppen, Wörheide and Takabayashi, 2002; Wörheide, Solé-Cava and Hooper,
2005; Uriz and Turon, 2012; Pérez-Portela and Riesgo, 2018). Though
widely used in phylogeographic and population genetic studies (Avise,
2000, 2009), mitochondrial markers exhibit low mutation rates in sponges
(Wörheide, Solé-Cava and Hooper, 2005; Huang et al. , 2008). As a
result, the majority of studies using mtDNA find panmixia among sponge
populations across broad geographic ranges (e.g. Duran, Pascual and
Turon, 2004; Whalan et al. , 2008; De Bakker et al. , 2016;
Ekins et al. , 2016). ITS markers can show more structure
(Bentlage and Wörheide, 2007; Becking et al. , 2013; Ekinset al. , 2016), but generally at larger spatial scales and are
hampered by intra-genomic polymorphisms (Frankham, Briscoe and Ballou,
2002). Microsatellites could be reliable and sufficiently variable to
detect population structure, yet are time-consuming to design de
novo for each species (Frankham, Briscoe and Ballou, 2002;
Pérez-Portela and Riesgo, 2018), and generally relatively few markers
have been used per study (<20), again limiting the molecular
marker panel. Furthermore, microsatellites can be confounded by
homogenizing forces of evolution, making them less effective in
detecting genetic divergence (Oppen, Wörheide and Takabayashi, 2002).
Hence, there is a need for increasing genetic resolution in order to
reassess assumptions of panmixia within sponge populations at fine
spatial scales.
Recently, there has been an increase in the use of reduced
representation genomic methods and Single Nucleotide Polymorphisms
(SNPs) for population studies on non-model organisms (Baird et
al. , 2008; Peterson et al. , 2012; Puritz et al. , 2014;
Catchen et al. , 2017). Genome-wide SNP data increases the number
of loci compared to traditional mitochondrial or nuclear markers and a
larger marker panel is expected to small scaled population structure
when compared to single marker studies. The effect of an increased
marker panel has been shown for example in mussels (Becking et
al. , 2016; Maas et al. , 2018; de Leeuw et al. , 2020), and
fish (Bradbury et al. , 2015; Lemopoulos et al. , 2019;
D’Aloia et al. , 2020; Sunde et al. , 2020). However, high
resolution studies on sponges are lagging behind (Pérez-Portela and
Riesgo, 2018), with notable exception of Brown et al. (2014),
Brown et al. (2017) and Leiva et al. (2019). Using the
SNPs generated by Brown et al. (2014), Brown et al. (2017)
genotyped 67 SNPs for the deep-sea glass sponge Aphrocallistes
vastus and found high differentiation between geographic regions
(average FST= 0.25), but no structure at distances
<275km, indicating connectivity at this scale. Leiva et al.
(2019) observed panmixia for the Antarctic sponge Dendrilla
antarctica over 900km when analyzing 389 neutral SNPs. However, 140
SNPs under putative positive selection did show genetic differentiation
(global FST = 0.20) over 100km. Potentially the number
of SNPs used in these studies are still too low to detect small-scaled
population divergence. Using RADseq techniques such as ddRAD (Petersonet al. , 2012) may increase the number of retained SNPs to
thousands and provide the necessary resolution.
Another challenge to unveiling sponge population genetic patterns is
that sponges are considered true holobionts, associations between the
host and its microbes (Webster and Thomas, 2016), and may not evolve as
single units. Due to the propensity of sponges to harbor dense
communities of microbes, there is a potential of including associated
microbial material in extractions, therefore clouding host specific
patterns. Studies into the sponge holobiont have suggested that
microbial communities are highly specific to sponge host identity
(Easson and Thacker, 2014; Reveillaud et al. , 2014), and
communities to be stable across gradients in geography (Taylor et
al. , 2005), time (Hardoim and Costa, 2014), and, for tropical reef
sponges, depth (Steinert et al. , 2016). However, these
expectations do not always hold true (Swierts, Cleary and de Voogd,
2018; Cleary et al. , 2019; Easson et al. , 2020; Ferreiraet al. , 2020). For example, Easson et al. (2020) concluded
that microbe community structure is influenced by the interplay of
geographic, environmental and host factors, with a potential effect of
even small population-level genetic structure. Since patterns of
microbial diversity can differ from sponge host diversity (Noyer and
Becerro, 2012), it is important to understand how microbial community
patterns are related to sponge host population genetics.
Islands, and other insular systems, provide ideal models to test factors
that underlie population structure since they are well-defined and are
of lower complexity than open areas (Warren et al. , 2015). Marine
lakes are insular systems of bodies of seawater surrounded completely by
land that maintaining a connection with the surrounding sea through
caves or porous rock (Holthuis, 1973; Hamner, Gilmer and Hamner, 1982;
Dawson et al. , 2009; Becking et al. , 2011). Clusters of
marine lakes are present in the Caribbean, Vietnam, Palau, and
Indonesia, particularly in East Kalimantan and in West Papua (Dawsonet al. , 2009; Becking et al. , 2011; Becking, de Leeuw and
Vogler, 2015). Marine lakes were formed de novo when depressions
in karstic rock were filled with sea water after the Last Glacial
Maximum (approximately 20,000 years ago) (Tomascik and Mah, 1994), and
house clearly defined populations (Gotoh et al. , 2011; Itescu,
2018). Sponges are usually well-represented in marine lakes, having high
diversity and abundance (Azzini et al. , 2007; Becking et
al. , 2011, 2013; Cleary et al. , 2013). Having originated roughly
at the same time marine lakes represent relatively controlled biotopes
where each lake can be seen as an independent replicate of
eco-evolutionary dynamics over time.
Marine lakes have been used before to study population genetic and
microbial community patterns studies (Becking et al. , 2013;
Cleary et al. , 2013; Cleary, Polónia and de Voogd, 2018; Ferreiraet al. , 2020). The sponge Suberites diversicolor(Porifera, Demospongiae, Suberitidae, Becking and Lim, 2009) has been
found to occur extensively in marine lakes, and also in brackish coastal
areas (Becking and Lim, 2009; Cleary et al. , 2013). UsingCOI and ITS genetic markers, Becking et al. (2013)
studied the phylogeography of S. diversicolor from multiple
marine lakes and lagoon populations in the Indo-Pacific. They identified
two distinct genetic lineages and regional structuring yet did not
observe subtle levels of structuring at smaller spatial scales. The lack
of structure could be explained by recurrent gene flow among lakes, or
by lack of resolution of genetic markers used by Becking et al.(2013), as they recovered a low number of haplotypes (4 for ITSand 3 for COI ). Analyzing the microbes of the same S.
diversicolor populations, Cleary et al. (2013) found that the
associated microbial community did not differ among sponges sampled from
marine lakes and open sea habitats within one region. Between broad
geographic regions (>1,400km) the associated microbial
communities were significantly different (Ferreira et al. , 2020).
Clearly, there is a need to further elucidate population genetic
patterns and see how host and microbe patterns contrast.
Studying a priori defined sponge populations from nine marine
lakes and two lagoon locations in Indonesia (Berau, East-Kalimantan and
Raja Ampat, West-Papua) and Australia, we aim to assess the population
structure in S. diversicolor and associated drivers. Selecting
marine lakes on different spatial scales (1-1,400km), along a gradient
of connection to the surrounding sea and with different environmental
regimes allows the opportunity to assess effects of geographic distance,
permeability of barriers and local environments in shaping genetic
structure. In order to assess the effect of level of genetic resolution,
we compared results of our genome-wide sequencing strategy
(double-digest restriction-site associated DNA sequencing, (ddRAD,
Peterson et al., 2012)) to previously published results on the same
individuals using single markers (COI and ITS ) (Beckinget al. , 2013). Furthermore, we compare host population structure
with previously published structure from associated microbial
communities in S. diversicolor (Cleary et al. , 2013;
Ferreira et al. , 2020) to assess whether sponge host and microbes
are on similar evolutionary tracts.