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Somatic Mutation Is a Function of Clone Size and Depth in Orbicella Reef-Building Corals.

Introduction

The unit of biological organization where genetic variation occurs is a critical component of evolutionary processes and the conceptualization of individuality (Buss, 1983; Okasha, 2006). Individuals are often considered the principal unit of natural selection and are defined by entailing genetic uniqueness, homogeneity, and physiological autonomy (Santelices, 1999). However, numerous organisms violate one or more of these attributes and consequently may highlight levels of selection and ecological interactions not often considered in classical biology (Okasha, 2006).

Genetic variability within an organism can come about due to fusion between distinct individuals (chimerism) and/or through the accumulation of mutations, resulting in genetic mosaicism (Gill et al., 1995; Barki et at., 2002). Generally, only mutations in the germ line are considered to have evolutionary significance, because somatic cells do not typically contribute to gametic formation (Weismann, 1892). However, this may not apply to many organisms, including fungi, plants, and clonal animals, where somatic mutations may be incorporated into asexual daughter cells, gametes, and resulting offspring (Inagaki et al., 1996; Schweinsberg et al., 2014). Hence, in some organisms somatic mutations may alter the unit of organization at which biological processes operate. For example, somatic mutations are known to accumulate in clonal seaweeds and long-lived plants, leading to phenotypic variability within individuals and to intraorganismal selection, which is often referred to as diplontic or somatic selection (Gill, 1986; Santelices et al., 1995). Somatic mutations can reduce organismal fitness, and their accretion is often considered a proponent of extinction in asexual lineages (Gabriel et al., 1993). Alternatively, somatic mutations may be neutral or advantageous, and they have been viewed as a potential mechanism for seaweeds and trees to adapt intragenerationally in response to herbivores and environmental pressures (Gill, 1986; Monro and Poore, 2009; Padovan et al., 2013). While the occurrence and phenotypic consequences of genetic mosaicism have been explored in plants and algae, the implications of intraorganismal genetic variation in clonal animals such as reef corals have only recently become appreciated.

Reef-building corals are largely colonial animals comprised of asexually formed polyps originally derived from a single sexually produced larva. Thus, a genetic "individual" can be characterized as all modules, physiologically connected or not, originating from the same zygote (Jackson and Coates, 1986). Unfortunately, corals have suffered global losses in abundance over the past several decades due to natural and anthropogenic stressors (Pandolfi et al., 2003; Carpenter et al., 2008). As a result, genetic bottlenecks threaten the resiliency of coral reef ecosystems and may constrain the adaptability of reef corals facing global climate change (Baums, 2008). Somatic mutations have been identified as a potential hidden source of genetic variability that may aid the recovery of declining coral populations (van Oppen et al., 2011), but the evolutionary significance of somatic mutations is difficult to assess because few studies have evaluated their prevalence in situ (but see Maier et al., 2012; Schweinsberg et al., 2015; Barfield et al., 2016; Devlin-Durante et al., 2016).

Interspecific differences in the occurrence of somatic mutations have been highlighted in reef corals (Schweinsberg et al., 2015), but identifying biological and/or environmental mechanisms influencing the degree of genetic mosaicism remains elusive. Somatic mutations may be expected to be more abundant in large clonal organisms relative to smaller-statured species because of the increased number of mitotic events required to reach large size (Scofield and Schultz, 2006). However, within closely related species, fragmentation and the degeneration of tissue may muddle the relationship between clonal size and the prevalence of mutations (Babcock, 1991; Ally et al., 2008). Furthermore, correlations between the intensity and spectrum of ultraviolet radiation, water temperature, and ocean depth suggest that individuals occupying shallower habitats may be more susceptible to oxidative stress-induced DNA damage (Lesser, 2010) and perhaps mutation. Although clonal size has been implicated in genetic mosaicism (Gill et al., 1995; van Oppen et al., 2011), and exposure to abiotic stressors is known to incite mutation (Cullis, 1987; Jackson et al., 1998), a relationship between clonal size, oceanic depth, and intraorganismal genetic variation in natural populations has not been previously investigated. Prior studies have identified interspecific differences in the frequency of observed somatic mutations (Schweinsberg et al., 2015), but it is not clear whether this is driven by disparities in the mutational properties of separate markers, environmental effects, or true species variation in the rate of mutation accumulation.

Here, we evaluate the prevalence of intraorganismal genetic variation in two closely related species of scleractinian corals in the Orbicella species complex. Orbicella annularis and Orbicella franksi are considered to be two of the most important framework builders on Caribbean coral reefs, but they have sustained region-wide declines in past decades (Weil and Knowlton, 1994; Hughes and Tanner, 2000; Edmunds and Elahi, 2007). The two species recently diverged between 0.5 and 2.5 million years ago, are gametically compatible and capable of hybridization, and share conserved microsatellite loci (Fukami et al., 2004; Severance et al., 2004; Levitan et al., 2011; Davies et al., 2013). Typically, O. franksi colonies are smaller and occur at greater depths than O. annularis (Weil and Knowlton, 1994), but at the site of this study, these species are in close proximity and overlap in size and depth distributions. These characteristics provide the unique opportunity to evaluate patterns of intraorganismal genetic variation in the same loci across a broad range of coral sizes and depths. Considering the potential evolutionary significance of somatic mutations in clonal organisms, we aim to assess the pervasiveness of intraorganismal genetic variation in these species and to determine whether the occurrence of somatic mutations is related to clonal size and an individual's previous exposure to abiotic stressors via a depth gradient.

Materials and Methods

Sampling

This study was conducted on a permanent transect in Bocas del Toro, Panama (9[degrees]19'38"N, 82[degrees]12'14"W), previously described by Levitan et al. (2011). Briefly, the monitored reef is primarily composed of Orbicella franksi (Gregory, 1895) and Orbicella annularis (Ellis & Solander, 1786), it is about 100 m long by 30 m wide, and it follows a depth contour of 2.5-8 m. In this location, the two species differ in clonal structure: O. franksi individuals are comprised of physically intact colonies, whereas the majority of O. annularis colonies have fragmented into groups of asexually produced but spatially separated daughter colonies. The entirety of ramets derived from the same colony is commonly referred to as a genet or clone (Jackson et al., 1985). The species identity, position, and genotype of O. franksi colonies and O. annularis ramets and clones on this reef have previously been mapped using numerical tags (Levitan et al., 2004, 2011).

To evaluate the prevalence of intraorganismal genetic variation in O. franksi and O. annularis, 29 genetic individuals encompassing a range of depths and coral sizes were sampled multiple times with a metal core (1.5 cm in diameter), resulting in a total of 193 tissue samples (Fig. 1). Specifically, 18 genetically distinct O. franksi colonies were chosen based on depth (3.35-6.71 m) and size (0.05-1.41 [m.sup.2]), and they were sampled 4 times per colony from the north, east, south, and west extents of the coral, resulting in 72 tissue samples. Thus, the distance between cores for a particular coral was dependent on the size of the colony being evaluated. Effort was made to ensure that there was continuous tissue between cores, to avoid the possibility of comparing individuals derived from distinct settlement events.

In O. annularis, we sampled independent ramets of fragmented individuals and used the sampling strategy described for O. franksi for intact clones. In total, we evaluated 11 O. annularis clones based on depth (2.56-4.86 m) and size (0.37-8.76 [m.sup.2]). Due to differences in growth form and number of ramets per genetic individual, O. annularis clones were sampled

unevenly, with 2-19 cores per clone (average = 11.0, SE [+ or -] 1.9), resulting in 121 tissue samples.

Coral size was estimated in O. franksi by using measurements of colony height, longest diameter, and perpendicular diameter. Surface area was approximated using these measurements, estimated as an elliptical hemisphere. In O. annularis, coral size was estimated using the relative position of mapped ramets along the 100 m x 30 m transect. Ramet position was plotted, and the total area encompassed by each clone was approximated in triplicate, using the tracing function in Image J (https://imagej.nih.gov/ij/). Depth was measured in both species to the uppermost extent of each coral, and the average depth for each independent ramet was used in fragmented O. annularis clones.

DNA extraction

Tissue samples were partially digested and fixed in 1.5 mL of CHAOS solution (4 mol [L.sup.-1] guanidine thiocyanate, 0.1% N-lauroyl sarcosin sodium, 10 mmol [L.sup.-1] Tris, pH 8, 0.1 mol [L.sup.-1] 2-mercaptoefhanol) (Fukami et al., 2004). Samples in CHAOS were left at room temperature for 72 h before being frozen and stored at -20 [degrees]C. Digestion was completed by incubating 500 [micro]L of partially digested tissue in 1.5 mL of extraction buffer (440 [micro]L of 60 mmol [L.sup.-1] Tris, pH 8, 500 [micro]L of 100 mmol [L.sup.-1] ethylenediaminetetraacetic acid, 500 [micro]L 0.5% sodium lauryl sulfate, and 60 [micro]L of 1 mg m[L.sup.-1] proteinase K) at 60 [degrees]C for about 12 h.

After incubation, DNA was extracted from 500 [micro]L of digested tissue by using a phenol : chloroform protocol. Digested tissue was combined with 500 [micro]L of 1 : 1 liquefied phenol (J. T. Baker, Center Valley, PA) and chloroform (Sigma-Aldrich, St. Louis, MO), inverted for 2 min, and centrifuged at 12,000 relative centrifugal force (RCF) for 10 min. The aqueous upper phase was transferred to a new tube, combined with 500 [micro]L of chloroform, and the resulting aqueous phase was again transferred to a new tube. DNA was precipitated by the addition of 3 mol [L.sup.-l] of sodium acetate and 95% ethanol, and it was pelleted by centrifugation at 12,000 RCF for 10 min. Pelleted DNA was washed with 75% ethanol and re-suspended in 100 [micro]L of double-distilled water. DNA concentrations were quantified using a NanoDrop ND-1000 spectrophotometer (Thermo Scientific, Wilmington, DE), and samples were diluted to 10 ng [micro][L.sup.-1] for use in polymerase chain reactions (PCRs).

Microsatellite genotyping

Four microsatellite loci were used for genotyping all O. franksi and O. annularis tissue samples (maMS8, maMSl 1, maMS12, and maMS2-8). An additional 6 loci were used to genotype all of the 72 O. franksi tissue samples and 24 of the 121 O. annularis cores (Table 1). The PCR master mixes consisted of 2.4 [micro]L 5X PCR buffer (Promega, Madison, WI), 1.2 [micro]L 1 mmol [L.sup.-1] deoxyribonucleoside triphosphate, 0.15 [micro]L GoTaq polymerase (Promega, Madison, WI), 1.0 [micro]L 10 [micro]mol [L.sub.-1] bovine serum albumin, 1.2-3.5 [micro]L Mg[Cl.sub.2] (depending on primer), 1.0 [micro]L fluorescently labeled forward primer, 1.0 [micro]L reverse primer, 1.5 [micro]L DNA (5-10 ng [micro][L.sup.-1]), and double-distilled water, to bring the total volume to 12 [micro]L. The PCR amplification was conducted under the following cycling parameters: 94 [degrees]C for 5 min, 35 cycles of 94 [degrees]C for 40 s, 53 [degrees]C (primer M_fav3), 55 [degrees]C (primers maMS2-8, maMS8, maMS12, M_fav9, M_fav29, and M_fav30), or 57 [degrees]C (primers maMS11, M_fav5, and M_fav8) for 1 min, 72 [degrees]C for 1 min, and a final extension time of 72 [degrees]C for 10 min. The PCR products from multiple loci were multiplexed (Table 1) with Hi-Di formamide (1 : 20) and 1 [micro]L Genescan 500 ROX (Applied Biosystems, Foster City, CA) and were analyzed with a 3730 Genetic Analyzer with Capillary Electrophoresis (Applied Biosystems, Foster City, CA). Finally, alleles were binned into di-, tri-, tetra- or penta-nucleotide repeats, dependent on locus, and called using Geneious 9 software (Biomatters, Auckland, New Zealand).

Genetic disparities were identified by comparing the genotypic signature of each sample to the most frequent genotype of the coral. Typically, genetic differences represented deviations from the main genotype by a single bin shift (two to five nucleotides, depending on repeat motif) at a single locus, but differences of more than one bin and in more than one locus were detected. Samples containing a genetic deviation were re-amplified with independent PCR reactions and were reanalyzed to exclude allelic differences due to PCR artifacts.

Somatic mutation versus chimerism

To evaluate whether genetic disparities represented somatic mutations or were the result of fusion between distinct individuals, we applied a Bayesian clustering analysis, using the program STRUCTURE 2.3.4 (Pritchard et al., 2000). Potential chimeras were identified if cores differed by more than 60% in their cluster assignment probability from other samples of their genet, as suggested by Schweinsberg et al. (2015). Runs were conducted with a burn-in period of 100,000 and 100,000 Markov chain Monte Carlo repeats with 3 iterations (O. franksi K = 18/0. annularis K = 11), using the default settings without a prior. Results from each iteration were merged using CLUMPAK (Kopelman et al., 2015).

Statistical analysis

For each coral not suspected to be a chimera, we calculated the number of cores with at least one mutation and the number of cores that did not contain a mutation relative to the most common genotype of that individual. Thus, a tissue sample could contain mutations in one or two alleles and/or at multiple loci, but it would still be characterized as a single core with a mutation. We modeled the probability of a coral containing mutations by using a binomial generalized linear model (GLM), with number of cores collected, coral species, size, depth, and their interaction terms as predictor variables, and a two-column matrix of the number of cores with and without a mutation as the response variable. All analyses were performed in the R statistical program (R Core Team, 2016). Additionally, the probability of a core harboring mutations at a particular locus was modeled as a function of the number of alleles observed in that microsatellite, using a binomial GLM. Inferences were made using a combination of hypothesis-testing and model-fitting frameworks with Wald's Z test statistic (glm command in R) and likelihood ratio testing (lrtest command in the "lmtest" package in R), respectively. Multicollinearity between biotic and abiotic predictors (i.e., coral species, size, and depth) was evaluated using variance inflation factors (vif command in the "car" package in R). Given a significant interaction, prediction lines and Wald-type 95% confidence bands were simulated from the model (visreg command in the "visreg" package in R). When applicable, non-significant predictor variables were removed from the GLM via hierarchical model selection to maximize model fit. For all analyses, model assumptions were evaluated as described in Bolker et al. (2009).

Results

In total, 58 of the 193 cores taken from both species harbored some genetic difference relative to the respective reference

genotype. Genetic differences were detected exclusively in 3 (maMS11, maMS12, maMS2-8) of the 10 loci, and these 3 markers were amplified in all tissue samples collected (Table 1). The majority of the disparities (43 of 58) represented deviations at a single locus, where remaining loci were identical to other samples from the same clone. The other 15 cores differed at 2 loci but were also identical at remaining markers. All genetic variants in a clone were successfully confirmed with independent PCR amplifications.

Cores containing genetic deviations were found in 13 of the 29 corals tested (45%). Of these, 2 corals had tissue samples that differed by more than 60% in their cluster assignment probability (Orbicella annularis clones 1 and 7; Fig. 2). Consequently, we cannot rule out the potential contributions of chimerism to the genetic disparities in these corals, and these individuals were not included in assessments of somatic mutations. Unreported analyses that included these genetic differences as somatic mutations did not alter our findings.

Somatic mutations were detected in 38% (11 of 29) of corals evaluated, and the degree of genetic mosaicism was associated positively with clone size and negatively with coral depth (Fig. 3). Coral species, size, and depth demonstrated some multicollinearity, which can negatively affect model parameterization in multiple regression analyses (Graham, 2003). To explore this, we mean-centered continuous predictors to reduce collinearity prior to conducting a binomial GLM (Table Al; Fig. Al). Qualitatively, the results of analyses with mean-centered predictors were the same as those with the raw data (Table A1), and we consequently report statistical results from analyses with the original data set. Furthermore, comparison of the residual deviance and degrees of freedom in all models suggested an appropriate fit for a binomial GLM.

The probability of a coral containing mutations was not significantly affected by the number of cores collected per individual (GLM: Wald Z = -0.063, P = 0.950), and it did not vary significantly between the two species (GLM: Wald Z = 1.050, P = 0.294; Table 2). Also, removing either the number of cores collected (likelihood ratio test [LRT]: df = 1, [chi square] = 0.004, P = 0.950) or species (LRT: df = 4, [chi square] = 1.090, P = 0.300) from the GLM did not reduce model fit, whereas removing coral size (LRT: df = 2, [chi square] = 11.336, P = 0.004) or depth (LRT: df = 2, [chi square] = 11.333, P = 0.004) significantly reduced model fit. Thus, the number of cores collected and coral species were removed from the statistical model, resulting in a binomial GLM comprised of size, depth, and their interaction term as predictor variables (Table 3).

The interaction between size and depth was significant (GLM: Wald Z = 3.639, P < 0.001), and removing the term significantly reduced model fit (LRT: df= 1. [chi square]= 18.761, P< 0.001). To evaluate the interaction, we simulated the mutation frequency predicted by the model by using parameter estimates from the binomial GLM (Table 3) and either fixed values of depth or clone size across the range of observed clone sizes and depths (Fig. 4). Mutation frequency was predicted to increase for both shallow (25th quantile of observed depths) and deep (75th quantile of observed depths) corals with increasing size (Fig. 4A). Moreover, the slopes of each prediction were transverse and highlighted the root of the interaction between shallow and deep corals. Mutation frequency was predicted to decrease for both small (25th quantile of observed coral sizes) and large (75th quantile of observed coral sizes) corals with increasing depth in a fairly parallel fashion (Fig. 4B). While the majority of mutations occurred in a single locus (maMS11), unreported analyses that sequentially removed loci from the statistical model did not alter our findings associated with the prevalence of mutations in relation to coral size and depth. Thus, the described mutational patterns were largely consistent across those loci harboring mutations, and they were not entirely driven by any one locus. Six loci (M_ fav3, M_fav5, M_fav8, M_fav9, M_fav29, and M_fav30) were amplified in a subset of tissue samples and did not harbor mutations. These loci were included in the study to test for a relationship between locus polymorphism and the frequency of mutation.

All loci evaluated were polymorphic, and the number of alleles observed for each microsatellite ranged from 4 to 30 (Table 1). Mutation frequency for a particular locus was plotted as a function of the number of alleles observed for that microsatellite (Fig. 5). The probability of a microsatellite containing a mutation was positively associated with the degree of locus polymorphism (GLM: Wald Z = 8.180, P = 0.001), and removing the predictor variable significantly reduced model fit compared to the intercept alone (LRT: df = 1, [chi square] = 74.223, P < 0.001).

Discussion

The proliferation of genetic variability within a clonal organism has the capacity to alter the unit of biological organization at which ecological and evolutionary processes operate (Buss, 1983; Gill et al., 1995). We found intraorganismal genetic variation to be common in reef-building corals of the Orbicella species complex, with 45% of individuals harboring more than one multilocus genotype. The predominant underlying cause of intraorganismal genetic variation was attributed to somatic mutations, and our results highlight potential mechanisms contributing to this genetic mosaicism. Mutation frequency increased in corals of greater size and in those occupying shallower depths. We ascribe the increased prevalence of mutations to the greater number of somatic DNA duplication events experienced by larger corals and to abiotic factors correlated with depth, such as temperature, which may influence mutation rates and the degree of genetic mosaicism.

The potential for somatic mutations to produce intraorganismal genetic variation and to influence biological processes has been modeled primarily as a function of the number of mitotic cell divisions experienced by an individual (Orive, 2001; van Oppen et al., 2011; Folse and Roughgarden, 2012). Accordingly, these models predict that the frequency and influence of genetic mosaicism will be most pronounced in relatively large and long-lived organisms such as trees and corals (Gill et al., 1995). In corals, colony size and the amount of asexually produced polyps contained within a clone have been used to estimate the number of somatic mutations accrued within an individual (van Oppen et al., 2011). Moreover, somatic mutations in microsatellite loci have been utilized to approximate clonal age in the reef coral Acropora palmata (Devlin-Durante et al., 2016), and the probability of detecting intraorganismal genetic variation in long-lived trees is thought to be primarily dependent on size and/or age (Whitham and Slobodchikoff, 1981; Gill et al., 1995). Our results are consistent with the notion that size, age, and, ultimately, the number of somatic cell divisions accumulated within an individual are important factors influencing the occurrence and degree of genetic mosaicism in clonal organisms. It should be acknowledged that the predictive relationship between size and age can be complicated in modular organisms by indeterminate growth, tissue degeneration, fragmentation, and/or fusion (Babcock, 1991; Ally et al., 2008). However, we maintain that size and number of somatic cell divisions are likely to be correlated, and that, on average, larger clones should experience a greater number of mitotic events relative to smaller ones. Thus, we do not aim to tease apart the contributions of clonal size and age to somatic mutations; rather, we suggest that larger individuals generally undergo an increased amount of somatic cell divisions and have greater potential to harbor genetic mosaicism as a consequence.

Abiotic conditions such as temperature and ultraviolet (UV) radiation are often correlated with depth in coastal habitats and can induce sublethal stress in marine organisms after prolonged exposure (Dahms and Lee, 2010; Lesser, 2010; Olsen et al., 2013, 2014; Ross et al., 2013). Subjection to elevated temperature and/or UV radiation facilitates the excess production of reduced oxygen intermediates that damage DNA and that can promote mutation (Jackson et al., 1998; Lesser, 2006). Moreover, the physiological response to abiotic stress is often more pronounced in individuals of shallower habitats, where temperature and UV radiation are increased (Shick et al., 1995; Lesser, 2006).

In the same locality as this study, Levitan et al (2014) described a consistent negative correlation between depth and temperature across several years. The authors characterized a bleaching event where host corals expelled their photosynthetic symbiont in response to temperature stress. They found that although mortality was rare, individuals occupying shallower parts of the reef were more likely to bleach and suffered severe losses in reproductive output compared to corals at greater depths. The results of the current study indicate that corals at shallower depths also have a greater propensity to accumulate somatic mutations. Although other factors, such as elevated UV radiation, might contribute to the pattern described here, we find elevated temperature stress to be the most parsimonious explanation for the increase in genetic mosaicism detected with decreasing depth, because mutations induced by UV radiation are often associated with the production of thymine dimers (Cadet et al., 2005). In our study, mutations were not related to the nucleotide composition of the markers. Nonetheless, the described patterns suggest that exposure to abiotic stress might contribute to the genetic means necessary for reef corals to adapt to increasing sea surface temperatures by producing beneficial mutations. Alternatively, abiotic stress may accelerate the accumulation of deleterious mutations in corals and could exacerbate the negative effects of warming oceans. Primarily, our results highlight the potential for abiotic mechanisms, and perhaps stress, to generate somatic mutations and genetic variation to allow for intraorganismal selection of asexual modules.

Chimerism and genetic mosaicism have been hypothesized to be potential sources of genetic variation that may influence evolutionary processes in long-lived corals (Puill-Stephen et al., 2009; van Oppen et al., 2011). However, the occurrence and degree of intraorganismal genetic variation in reef-building corals have only recently been investigated (Maier et al., 2012; Schweinsberg et al., 2015). In two corals, we could not exclude the potential contributions of chimerism to detected genetic disparities. We caution that although we cannot exclude chimerism as a possibility, our ability to distinguish between fusion and mutation was limited in these cases as a result of the number and degree of polymorphism in the loci evaluated. Nonetheless, mosaicism was found to be the primary cause of genetic discrepancies, with 38% of corals harboring at least a single somatic mutation. Similarly, intraorganismal genetic variation has been reported to vary between 17% and 46% of individuals in 6 different reef-building corals where mosaicism was the predominant underlying mechanism (Maier et al., 2012; Schweinsberg et al., 2015). Moreover, the degree of genetic mosaicism was pronounced in some corals of this study. The ramets of one particular Orbicella annularis clone represented 9 unique multilocus genotypes and contained deviations from the most common genotypic signature in 79% of the cores sampled. These results suggest that genetic mosaicism can be appreciable in reef corals and that it at least has the potential to influence their evolution through somatic selection (van Oppen et al., 2011).

The notion that intraorganismal genetic variation may alter the units of selection in reef-building corals is supported in seaweeds and long-lived terrestrial plants, where recent work suggests that mosaics exhibit phenotypic variability and somatic selection in response to environmental pressures (Monro and Poore, 2009; Folse and Roughgarden, 2012; Padovan et al., 2013). A similar mechanism has been proposed for reef corals with intraclonal differences in their susceptibility to ocean acidification and thermal stress (van Oppen et al., 2011). If somatic mutations are proliferated within colonies via asexual propagation and if genetically distinct units have differential survival, then modules harboring non-deleterious mutations may be favored (Monro and Poor, 2009). Hence, in heterogenous corals, selection may operate on the scale of genetically distinct modules rather than at the level of the colony. Furthermore, mosaicism has been found to amplify the standing genetic variation in populations of red algae, seagrasses, sponges, and fire corals (Milleporidae) (Santelices et al., 1995; Blanquer and Uriz, 2011; Reusch and Bostrom, 2011; Schweinsberg et al., 2017), which could be critical in reef corals suffering from recent population bottlenecks (Baums, 2008). Although mosaicism may inflate genetic variation, the majority of new mutations are likely to be harmful (Keightley and Lynch, 2003), suggesting that corals may become more susceptible to reductions in fitness as mutations accumulate (Gabriel et al., 1993).

Studies evaluating intraorganismal genetic variation in natural populations have primarily used microsatellite loci to detect genetic disparities (Puill-Stephen et al., 2009; Blanquer and Uriz, 2011; Reusch and Bostrom, 2011; Schweinsberg et al., 2015). Microsatellite loci are largely neutral, typically have elevated rates of mutation compared to other genes, and can be subject to genotyping errors (Li et al., 2002; Pompanon et al., 2005). Our study found consistent genotypic signatures from repeated samplings and independent PCR reactions, indicating that genotyping error was inconsequential. Elevated mutation rates in microsatellites suggest that our estimates of mutation per locus and tissue sample are likely to be inflated relative to other genes in the genome; but they provide a potential marker for how the likelihood of somatic mutations, across other loci, may vary with individual size and depth. However, considering that our methodologies exclusively targeted insertion and deletion events, we caution that our results may not necessarily be representative of patterns associated with other forms of mutation.

The evolutionary significance of somatic mutations in clonal organisms is profoundly influenced by their heritability through both sexual and asexual pathways (Buss, 1983; Gill et al., 1995). Mutations in the meristem of modular plants and in the soma of seaweeds have discernible importance because they are proliferated through clonal growth and sexual reproduction (Inagaki et al., 1996; Monro and Poore, 2009; Padovan et al., 2013). In corals, somatic mutations can be propagated through asexual fragmentation and have been shown to be transferable through the gametes of some species (Schweinsberg et al., 2014), but the presence or absence of a segregated germ line across cnidarians is currently unclear (Barfield et al., 2016). The extent to which somatic mutations are transmissible to future generations of scleractinians will have important implications for coral reef ecosystems.

The prevalence of genetic mosaicism detected in two species of the Orbicella species complex was best explained by disparities in coral size and depth. Characteristically, O. annularis encompasses a greater area and occurs at shallower depths than Orbicella franksi, which may raise thoughts about how species-specific mutation rates could have contributed to our results. In addition, sampling effort per individual was typically greater in O. annularis compared to O. franksi, which also could have biased our detection of somatic mutations. However, this study is unique in that the same loci were targeted across closely related species (Weil and Knowlton, 1994; Levitan et al., 2004, 2011), as opposed to utilizing separate markers for each species with distinct mutational properties. Considering that patterns of mutation in relation to coral size and depth were consistent across multiple loci, it is unlikely that our results were heavily influenced by interspecific differences. Furthermore, our analysis indicates that sampling effort did not significantly influence the probability of a coral harboring mutations, which coincides with other work that found no systematic effect of the number of cores collected per individual and the degree of genetic mosaicism (Schweinsberg et al., 2015). Accordingly, we contend that the detected patterns of genetic mosaicism are a result of the outlined mechanisms rather than artifacts of sampling effort or inherent interspecific differences. Our results also emphasize the importance of marker-specific disparities in detecting somatic mutations. Specifically, insertion and deletion events were more likely to be detected in microsatellite loci that demonstrated a greater degree of polymorphism. We recommend that locus polymorphism should be considered when comparing interspecific differences in the occurrence of genetic mosaicism as a consequence.

Our results suggest that biotic and environmental mechanisms influence the occurrence of somatic mutations and may be important contributors to seemingly interspecific disparities in genetic mosaicism. We hypothesize that larger individuals have a greater potential to accrue somatic mutations because they experience more somatic DNA duplication events. Furthermore, organisms at shallower depths may have a greater propensity to accumulate mutations as a result of increased exposure to abiotic stress. Further research is needed to determine how genetic mosaicism may influence ecological and evolutionary processes in reef-building corals facing global climate change.

Acknowledgments

We thank W. Boudreau, K. Hill, K. Levitan, and A. Schmidt for assistance with sample collection. This work was supported in part by a grant from the American Museum of Natural History Lerner-Gray Memorial Fund awarded to KCO. We also thank the staff at the Smithsonian Tropical Research Station in Bocas del Toro. Research permits were supplied by the Republic of Panama.

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KEVIN C. OLSEN (*), JOSE A. MOSCOSO, AND DON R. LEVITAN

Department of Biological Science, Florida State University, 319 Stadium Drive, Tallahassee, Florida 32306

Received 15 February 2018; Accepted 20 August 2018; Published online 28 November 2018.

(*) To whom correspondence should be addressed. Email: kolsen@bio.fsu.edu.

Abbreviations: GLM, generalized linear model; LRT, likelihood ratio test; PCR. polymerase chain reaction; RCF. relative centrifugal force.

DOI: 10.1086/700261
Table A1
Statistical summary of binomial generalized linear model and Wald Z
test statistic evaluating the probability of a coral containing
mutation(s) as a function of number of cores collected per individual,
species, mean-centered continuous predictors, and their
interaction term

Coefficients           Estimate  SE      Z        P

Intercept              -2.726    1.176   -2.319  0.020
Cores collected        -0.006    0.092   -0.063  0.950
Species                 1.222    1.164    1.050  0.294
Mean-centered size      0.932    0.333    2.796  0.005
Mean-centered depth    -1.487    0.583   -2.550  0.011
Mean-centered size      0.563    0.186    3.033  0.002
x mean-centered depth
Significant P-values are shown in bold.

Table 1
Summary of 10 polymorphic microsatellite loci used to assess
intraorganismal genetic variation in Orbicella franksi and Orbicella
annularis

Locus (repeat)               Observed range (bp)  [n.sub.a]

maMS8 (C) [(CAA)7.sub.]      195-204               4
maMS11 [(GA).sub.7]          308-384              30
GT[(GA).sub.5]
maMS12 (A) [(GA).sub.11]     248-278              16
maMS2-8 (B) [(GTT).sub.6]    172-231              14
ATT[(GTT).sub.3]
GCT[(GTT).sub.4]
M_fav3 (A) [(ATG).sub.25]    143-203               6
M_fav5 (A) [(CGA).sub.17]    320-413              17
M_fav8 (C) [(CAA).sub.38]    295-340              12
M_fav9 (B) [(CAAT).sub.21]   233-299              15
M_fav29 (D) [(CAT).sub.16]   418-477              14
M_fav30 (D) [(TTTTG).sub.8]  227-247               4
Locus (repeat)               Proportion of mutations

maMS8 (C) [(CAA)7.sub.]       0/181 = 0.00
maMS11 [(GA).sub.7]          33/181 = 0.18
GT[(GA).sub.5]
maMS12 (A) [(GA).sub.11]     12/181 = 0.07
maMS2-8 (B) [(GTT).sub.6]    15/181 = 0.08
ATT[(GTT).sub.3]
GCT[(GTT).sub.4]
M_fav3 (A) [(ATG).sub.25]    0/92 =   0.00
M_fav5 (A) [(CGA).sub.17]    0/92 =   0.00
M_fav8 (C) [(CAA).sub.38]    0/92 =   0.00
M_fav9 (B) [(CAAT).sub.21]   0/92 =   0.00
M_fav29 (D) [(CAT).sub.16]   0/92 =   0.00
M_fav30 (D) [(TTTTG).sub.8]  0/92 =   0.00
Locus (repeat)               Reference

maMS8 (C) [(CAA)7.sub.]      Severance el al., 2004
maMS11 [(GA).sub.7]          Severance et al., 2004
GT[(GA).sub.5]
maMS12 (A) [(GA).sub.11]     Severance et al., 2004
maMS2-8 (B) [(GTT).sub.6]    Severance et al., 2004
ATT[(GTT).sub.3]
GCT[(GTT).sub.4]
M_fav3 (A) [(ATG).sub.25]    Davies et al., 2013
M_fav5 (A) [(CGA).sub.17]    Davies et al., 2013
M_fav8 (C) [(CAA).sub.38]    Davies et al., 2013
M_fav9 (B) [(CAAT).sub.21]   Davies et al., 2013
M_fav29 (D) [(CAT).sub.16]   Davies et al., 2013
M_fav30 (D) [(TTTTG).sub.8]  Davies et al., 2013

The top four loci were used on all tissue samples: remaining markers
were used on all O. franksi samples and a subset of O. annularis.
Superscript letters (A), (B), (C), and (D) represent multiplex primer
groups. Proportion of mutations represents number of mutations detected
divided by number of samples analyzed, with potential chimeras
excluded. [n.sub.a], number of observed alleles.

Table 2
Statistical summary of binomial generalized linear model and Wald Z
test statistic evaluating the probability of a coral containing
mutation(s) as a function of number of cores collected per individual,
coral species, size, depth, and their interaction term

Coefficients     Estimate  SE     Z       P

Intercept         6.038    2.669   2.263  0.024
Cores collected  -0.006    0.092  -0.063  0.950
Species           1.222    1.164   1.050  0.294
Size             -1.489    0.531  -2.806  0.005
Depth            -2.385    0.854  -2.793  0.005
Size x depth      0.563    0.186   3.033  0.002

Significant P-values are shown in bold.

Table 3
Statistical summary of binomial generalized linear model and Wald Z
test statistic evaluating the probability of a coral containing
mutation(s) as a function of coral size, depth, and their interaction
term

Coefficients   Estimate   SE     Z        P
Intercept      4.120     1.872   2.244   0.025
Size          -1.102     0.366  -3.015   0.002
Depth         -1.699     0.531  -3.200   0.001
Size x depth   0.416     0.114   3.639  <0.001

Significant P-values are shown in bold.
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Author:Olsen, Kevin C.; Moscoso, Jose A.; Levitan, Don R.
Publication:The Biological Bulletin
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Date:Feb 1, 2019
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