Direct measurement of the mutation rate and its evolutionary consequences in a critically endangered mollusk

15 The rate at which mutations arise is a fundamental parameter of biology. Despite recent progress 16 in measuring germline mutation rates across diverse taxa, such estimates are missing for much 17 of Earth’s biodiversity. We present the first estimate of a germline mutation rate from the phylum 18 Mollusca, which is diverged by more than 1200 Ma years from the closest relative for which a 19 mutation rate estimate exists. We sequenced three pedigreed families of the white abalone 20 Haliotis sorenseni, a long-lived, large-bodied, and critically endangered mollusk, and estimated a 21 de novo mutation rate of 8.60e -09 single nucleotide mutations per site per generation. This 22 mutation rate is similar to rates measured in vertebrates with similar generation times and 23 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint longevity to abalone, and higher than mutation rates measured in faster -reproducing 24 invertebrates. We use our estimated rate to infer baseline effective population sizes (N e) across 25 multiple Pacific abalone and find that abalone persisted over most of their evolutionary history as 26 large and stable populations, in contrast to extreme fluctuations over recent history and small 27 census sizes in the present day. We then use our mutation rate to infer the timing and pattern of 28 evolution of the abalone genus Haliotis, which was previously unknown due to few fossil 29 calibrations. Our results are an important step toward understanding mutation rate evolution and 30 establish a key parameter for conservation and evolutionary genomics research in mollusks. 31

32 Mutations are the ultimate source from which variation arises. Although mutation is a fundamental 33 feature of all life on Earth, the rate at which new mutations arise can vary considerably between 34 and within species. Consequently, the mutation rate and the extent to which it is fine -tuned by 35 natural selection has long held the interest of biologists (Sturtevant 1937; Lynch et al. 2016). 36 Mutations may occur in any cell type but only mutations that occur in an organism’s 37 germline contribute to subsequent generations and drive evolutionary innovation (Bergeron et al. 38 2023). Within multicellular eukaryotes, germline mutation rates (GMRs) vary by at least three 39 orders of magnitude, and the etiology of this variation is not completely understood (Lynch 2010). 40 Mutation rates per generation are generally highest in large -bodied, long-lived organisms with 41 modest effective population sizes, and several mechanisms (which are not mutually exclusive) 42 have been proposed to explain this trend. Long generation times could increase GMRs by 43 allowing more time for mutations to accumulate in spermatocytes and oocytes prior to 44 reproduction. Consequently, the length of time elapsed between puberty and reproduction has 45 been proposed to explain observed mutation rate variation among vertebrates (Thomas 2018). 46 However, small effective population sizes could also explain this variation, since weakly 47 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint deleterious alleles may drift to high frequencies in small populations, increasing observed 48 mutation rates (Lynch 2010) . Both generation time and effective population size are similarly 49 correlated with mutation rate variation in empirical datasets, making it difficult to disentangle the 50 relative etiological contributions of reproductive longevity and the drift -barrier effect (Wang and 51 Obbard 2023). 52 Current understanding of the causes and extent of GMR variation is shaped by available 53 direct estimates of germline mutation rates. However, these data are not representative of Earth’s 54 biodiversity. Roughly 83% of animals with an estimated GMR are vertebrates (Wang and Obbard 55 2023), although that vertebrates represent only 4.6% of animal diversity (Bánki et al. 2024). Some 56 animal phyla are entirely unrepresented among available data for GMRs, including Mollusca. 57 Mollusks encompass a broad diversity of form and function, spanning terrestrial species like the 58 common garden snail to the deep ocean dwelling giant squid, as well as considerable variation 59 among lineages in population size, gamete production, parental investment, and longevity 60 (Ponder and Lindberg 2008) , and may therefore provide useful data to better understand the 61 evolution of variability among GMRs. The closest relative of Mollusca with an estimated GMR, 62 however, shares a common ancestor with mollusks over 600 million years ago, a distance that 63 represents more than 1200 Ma of independent evolution (Fig. 1). 64 The rate at which mollusks accumulate de novo mutations remains unknown, although the 65 high genetic diversity in many mollusk populations has led to speculation of a fast GRM (Zhang 66 et al. 2012; Hoeh et al. 1996; Cutter, Jovelin, and Dey 2013; Launey and Hedgecock 2001) . 67 Previous work, for example, estimated a GMR for the Pacific oyster Crassostrea gigas that was 68 90 times faster than that of Drosophila, based on the oyster’s anomalously high deleterious 69 mutation load (Plough, Shin, and Hedgecock 2016) . This estimate was predicated on the 70 theoretical relationship between the frequency of lethal mutations and mutation rates (Nei 1968), 71 however, and not a direct estimate of the GMR (Plough, Shin, and Hedgecock 2016) . Although 72 fast mutation rates can lead to high levels of genetic diversity, genetic diversity ( 𝜋) can also be 73 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint maintained in populations with large effective sizes (Ne) without requiring faster rates of mutation 74 (μ) (𝜋 = 4Neμ (Nei and Tajima 1981) . Many marine mollusks do have large census population 75 sizes (N c) but effective size may be much smaller due to processes like ‘sweepstakes 76 reproduction’, in which a handful of highly fecund individuals reproduce each generation 77 (Hedgecock and Pudovkin 2011; Hedrick 2005) . Therefore, uncertainty surrounding N e makes 78 inference of a GMR based on population genetic diversity difficult (Harrang et al. 2013). 79 In contrast to inferences based on population genetic diversity, phylogenetic analyses of 80 mollusks have suggested lower GMRs. Based on rates of nucleotide substitution between mollusk 81 lineages calibrated by fossil ages, these estimates are on the order of 3 x 10 -9 82 mutations/bp/generation (A. Li et al. 2021; Allio et al. 2017) .Phylogenetic approaches to 83 estimating mutation rates are, however, error -prone due to uncertainty in fossil calibration, 84 generation time, mutation saturation over long timescales, and difficulty in identifying truly neutral 85 sites for estimation (Wang and Obbard 2023; Scally and Durbin 2012) . Because indirect 86 estimates of mutation rates in mollusks vary to such an extent, a direct estimate based on 87 pedigree sampling is needed. 88 Accurate estimates of germline mutation rates have practical applications for conservation 89 and evolutionary genomics research. Effective population size, which describes the size of an 90 idealized Wright-Fisher population that would exhibit the magnitude of genetic drift and inbreeding 91 as seen in a real-world wild population (Wright 1931), is used as a conservation metric and often 92 requires a germline mutation rate to estimate. Minimum ‘healthy’ Ne thresholds of 50 or 500 often 93 being used to guide wildlife management decisions (I. G. Jamieson and Allendorf 2012) . 94 Outgrowths of this have incorporated the census population size Nc, with high Ne/Nc ratios serving 95 as an accurate indicator of high extinction risk (Wilder et al. 2023; Palstra and Fraser 2012) . 96 Knowing the germline mutation rate also has direct implications for evolutionary genomics, and 97 can help to date the origin of a clade (Bergeron et al. 2021; Besenbacher et al. 2019) or the age 98 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint of a beneficial allele (Smith et al. 2018), particularly for groups where phylogenetic estimates of μ 99 are absent or problematic. 100 To determine if the germline mutation rate of mollusks differs from that of other animals, 101 we measured de novo mutation rates in three families of the white abalone, Haliotis sorenseni 102 (Fig. S1). Individuals for this study were provided by the White Abalone Captive Breeding 103 Program, which aims to restock wild populations of this critically endangered species with 104 aquaculture-raised individuals. H. sorenseni gastropod mollusk typically found in 20-60m of water 105 along the coast of California and Mexico (T. C. Tutschulte 1976). Like many broadcast-spawning 106 invertebrates, white abalone are highly fecund; one individual can release millions of eggs or 107 sperm into the water column in a single spawning event, and several spawning events can occur 108 in one year (Hobday, Tegner, and Haaker 2000) . After fertilization, white abalone larvae will 109 disperse for ~10 days before settling on rocky substrate, after which point they will move very 110 little, if at all, over the course of their adult lives (Leighton 1972; Lafferty et al. 2004) . Growth in 111 white abalone is slow, as sexual maturity in wild individuals occurs around four years of age with 112 most individuals likely reproducing by six years (T. Tutschulte and Connell 1981). Individuals may 113 live up to 30 years of age and grow to more than 20cm (Hobday, Tegner, and Haaker 2000; 114 Andrews et al. 2013). While much is still unknown about the biology of this species, white abalone 115 represent a unique combination of mollusk-like and vertebrate-like life history traits. 116 We then use our estimated GMR to resolve both recent and long -term questions in 117 abalone conservation and evolutionary history. This species’ high fecundity in a sea of dispersive 118 currents was once thought to buffer white abalone - and organisms with similar life histories - 119 against overexploitation (Laura Rogers-Bennett et al. 2016) (G. Jamieson 1993). However, slow 120 growth and overharvesting of mature adults can reduce gamete abundance and concentration to 121 the point of total recruitment failure (Hobday, Tegner, and Haaker 2000; Stephens, Sutherland, 122 and Freckleton 1999) . Despite an estimated historical population size of 360,000 in California, 123 white abalone populations declined precipitously during the 20th century, due to a combination of 124 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint intensive fishing, disease, and this overly optimistic view of the species’ ability to recover (Laura 125 Rogers-Bennett 2002) . Given the extent of decline, white abalone was the first marine 126 invertebrate to be listed under the U.S. Federal Endangered Species Act. A better understanding 127 of how current population sizes stack up against historical baselines (e.g. long -term Ne) will help 128 guide management criteria. Additionally, the evolutionary history of white abalone and its 129 relationship to other congeners, some of which are better studied or more robust to environmental 130 stressors (Crosson and Friedman 2018) , is largely unknown. Outlining the timescale of 131 diversification in abalone can help set expectations for how much one species’ traits might be true 132 of another species in this understudied group. 133 134

135 We observed 107 unique de novo mutations across the three families: nine offspring and five 136 parents (Table 1). Only 13 (12.1%) of these mutations were inherited by two or more offspring 137 when they had parents in common (Fig S2). After incorporating estimates of the false discovery 138 rate (FDR) and false negative rate (FNR), 0.05 and 0.139 respectively, and the size of the callable 139 genome (Table S1), we observe a median mutation rate of 7.99 x 10 -9 mutations/bp/generation 140 and a mean mutation rate of 8.60 x 10 -9 (95% CI: 6.10 - 11.11 x 10-9; Fig. S3). The mean rate is 141 faster than most arthropod per-generation rates but falls within the distribution of per -generation 142 rates estimated for most vertebrates, plants, and the one echinoderm (Fig. 1; Wang and Obbard 143 2023, Popovic et al. 2024). When our rate is plotted as a function of life history traits, including 144 approximations of generation time (~6 years), age at sexual maturity (~4 years), and lifespan in 145 the wild (~20-30 years), it remains consistent with vertebrate distributions (Fig. S4A-C; Bergeron 146 et al. 2023, Hobday et al. 2000). 147 148 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint 149 150 151 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint Figure 1. Distribution of all SNP germline mutation rate estimates for multicellular eukaryotes, adapted from Wang and 152 Obbard et al. (2023). Time -scaled phylogeny to the left retrieved from TimeTree. White abalone ( Haliotis sorenseni), 153 the sole mollusk and focal species of this study, is highlighted in blue. 154 155 Using read-based haplotype phasing, we were able to attribute 29.2% of these mutations 156 to either a mother or father. One of these families showed significant paternal bias (α), with the 157 father contributing 21 mutations and the mother contributing only 6 (α=3.5; Table 1). The two 158 other families, which happened to share a mother, showed roughly equal contributions from both 159 parents (α = 1.33, 0.83). As neither shell size, weight, nor age at time of reproduction is known 160 for all parents, it’s not possible to relate the age of each parent at spawning and to the relative 161 contribution of mutations. We do know that two of the individuals born in captivity, the shared 162 mother of Families 2 & 3 and the father of Family 1, were at least 19 years old at spawning. The 163 three remaining parents were wild-caught and greater than 19 years old. 164 165 166 Table 1. Counts of observed mutations by family. Num. mutations = count of unique mutations 167 across the set of offspring. Families 2 and 3, which share a mother, share two of these mutations. 168 Num. multi-sib mutations = count of unique mutations found in two or more offspring of a family. 169 Mean rate = Mean of the individual -level rates for each family. Contribution = Mutations from 170 the paternal line (P), maternal line (M), or unknown (?) as determined by read -backed phasing 171 only. ***Significant parental bias; p < 0.005, chi-squared test. 172 173 Family Num. offspring Num. mutations Num. multi-sib mutations Mean rate (x 10-9) Contribution (P / M / ? ) .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint #1 3 49 4 10.9 21 / 6 / 22 *** #2 4 43 9 8.6 8 / 6 / 29 #3 2 17 0 5.1 5 / 6 / 6 174 Given our estimate of a germline mutation rate for Haliotis sorenseni, we aimed to infer 175 effective population size through time across Haliotis spp. and gather a sense of long -term 176 baselines that could inform management of these species. To do this, we performed demographic 177 inference and all available high coverage (>20X) whole genome sequencing data for Haliotis spp. 178 Demographic analysis of five species distributed throughout the Pacific Ocean points towards 179 large and stable effective population sizes (N e) over long timescales (Fig. 2A). The harmonic 180 mean of Ne, summarized across roughly 1 million generations (1e4-1e6), is between 100,000 and 181 300,000 for all species. When we compare this value for our focal species H. sorenseni against 182 the mean mutation rate, we see that values for white abalone are consistent with GMR~N e 183 relationships in vertebrates (Fig. S4D; (Bergeron et al. 2023). H. sorenseni does exhibit a gradual 184 decline over the most recent 100,000 generations, rarely surpassing an Ne of 50,000. The blacklip 185 abalone - H. rubra - exhibits a more abrupt decline, dropping by roughly ~90% in the past 10,000 186 generations. However, inferences of population size within this most recent time interval should 187 be interpreted with caution when based on a single individual (Wilder et al. 2023). Therefore, the 188 growth inferred for H. cracherodii, H. rufescens, and H. laevigata within this time interval suffers 189 from similar limitations. 190 These historical estimates of Ne based on haplotype coalescence are supported by direct 191 calculations of N e from population -level sequence diversity ( 𝜋). We called genotypes for 11 192 sequenced individuals each of both H. sorenseni and H. cracherodii, the only abalone species 193 which had sufficient population -level sequencing data. With these genotype calls, we observed 194 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint intraspecific diversity (𝜋) of 0.0018 and 0.0103 for H. sorenseni and H. cracherodii, respectively 195 (Fig. 2B). By applying these values and our mean estimate of the mutation rate μ to the 196 relationship 𝜋 = 4Neμ (Nei and Tajima 1981), we obtained Ne values of 52,236 and 299,419. These 197 estimates, based on population -level nucleotide diversity (Hare et al. 2011; Nadachowska -198 Brzyska, Konczal, and Babik 2022), closely match the historical values of Ne for both species (Fig. 199 2A). 200 201 202 Figure 2. A) Historical effective population size (N e) for five abalone species as estimated by MSMC2 203 (Schiffels and Wang 2020). Both original data and twenty bootstrap replicates per species are shown. B) 204 Distribution of intraspecific nucleotide diversity ( 𝜋) for species where multiple sequenced individuals were 205 available. Effective population size is calculated from 𝜋 = 4Neμ using the mean observed 𝜋. 206 207 We next applied our mutation rate to examine Haliotis evolution on deeper evolutionary 208 timescales. To do this, we used genes present in the reference genomes for the above set of 209 Haliotis species, the tropical abalone Haliotis asinina (for which a reference was also available), 210 and the sea snail Gibbula magus, which served as an outgroup. We find a single highly-supported 211 topology based on the coding sequences of 2,525 coding genes (Figure 3A). We then used a 212 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint multi-species coalescent (MSC) approach to date this topology according to our germline 213 mutation rate estimate (Tiley et al. 2020). Specifically, we used a subset of 150 clock-like genes, 214 a mean and standard generation time of 6 and 2 years respectively (L. Rogers -Bennett, 215 Dondanville, and Kashiwada 2004), and a mutation rate standard deviation of 3.26e -9. With this 216 approach, we date the split between Western and Eastern Pacific abalone at 36.4 million years 217 before present (95% CI 33.6-39.1 x 106 Ma; Figure 3). Within the Western Pacific clade, the lone 218 tropical abalone representative in this analysis, H. asinina, splits off from H. rubra and H. laevigata 219 early on at 19.9 Ma. In contrast, all three Eastern Pacific abalone share a relatively recent 220 common ancestor at 4.3 Ma (95% CI 3.9 -4.7 x 10 6 Ma), with white ( H. sorenseni) and red ( H. 221 rufescens) abalone diverging only 2.2 Ma (95% CI 1.9-2.5 x 106 Ma). 222 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint Figure 3. A) Multi-species-coalescent (MSC) phylogeny inferred with BPP (Flouri et al. 2018) and calibrated 223 assuming μ.mean = 8.60e-9, μ.sd = 3.26e-9, g.mean = 6 and g.sd = 2, where μ is the mutation rate and g 224 is the generation time. The tree is rooted with the outgroup G. magus, not shown. B) Distributions of the six 225 abalone species presented in 3A. Range maps obtained from IUCN Red List. 226

227 We estimated the germline mutation rate of white abalone to be 8.60e -9 228 mutations/bp/generation. Although H. sorenseni is diverged from vertebrates by over 1200 Ma 229 years of evolutionary history, the rate we estimate is similar to rates estimated previously for 230 vertebrates with similar generation time, age at sexual maturity, and effective population size (Fig 231 1, Fig. S4; (Bergeron et al. 2023). Our rate is also similar to the previously estimated GMR for 232 crown-of-thorns sea star, the only member of the phylum Echinodermata with a GMR estimate 233 and the only other marine invertebrate and broadcast spawner for which such an analysis has 234 been performed (9.13e -09; (Popovic et al. 2024) . Our direct estimate based on pedigreed 235 samples is more technically reliable than previous indirect estimates of mollusk mutation rates, 236 some of which were much higher (Hoeh et al. 1996; Plough, Shin, and Hedgecock 2016) and 237 some of which were much lower (Allio et al. 2017; A. Li et al. 2021) than ours. Altogether, these 238

suggest that germline mutation rates in mollusks do not deviate from the established 239 distributions and relationships observed in other branches of the tree of life. 240 We observed high variance in the degree of paternal bias (α) in the contribution of de novo 241 mutations to offspring. Of the three families in our analysis, two show near equal contributions 242 from both parents (α = 1.33; 0.83), while the third family shows a clear paternal skew (α = 3.5) 243 (Table 1). A male mutation rate bias is not unexpected in GMR studies, and the magnitude of α 244 is known to increase with longevity (Thomas et al. 2018). The larger number of germ-cell divisions 245 in males of many species is thought to drive a greater contribution of de novo mutations from 246 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint fathers (Venn et al. 2014; Jónsson et al. 2017) , although evidence of male bias independent of 247 cell division number points to contributions from other processes, such as sex-specific differences 248 in DNA damage and repair (de Manuel, Wu, and Przeworski 2022) . Male and female white 249 abalone appear to invest roughly the same amount of energy towards gonad development (T. 250 Tutschulte and Connell 1981), but the number of sperm produced will far exceed the number of 251 eggs produced for most abalone species (Babcock and Keesing 1999) . Therefore, the greater 252 number of germ cell divisions in male abalone could be driving the paternal bias we observe in 253 one of our three families (α = 3.5). However, it does not explain the lack of bias in the other two 254 families, which exhibit values of α closer to species with similar male and female reproductive 255 input, including the crown -of-thorns sea stars (α = 0.96; (Popovic et al. 2024) and several fish 256 species (α = 0.8; (Bergeron et al. 2023) . Because the extent of male bias scales with age, it is 257 possible that age differences between males and females in two of the three families are 258 modifying the parental contributions. However we are unable to examine this relationship because 259 we lack all parent ages at the time of spawning, and the wild origin of three of the five parents 260 complicates age-size relationships measured in captivity (Mccormick et al. 2016). 261 Reproductive life history affects not only the degree of paternal mutation bias, but also the 262 proportion of mutations that are shared among siblings. In our families we see that 12.1% of 263 mutations were transmitted to multiple offspring (Table 1; Figure S2), which implies that at least 264 12% of the mutations we observe in the parents prior to primordial germ cell specification. 265 Previous work has found that the sharing of germline mutations among siblings is most 266 widespread in species with short generation times, and our results are consistent with that trend–267 the abalone generation time is intermediate between the generation times of mice and humans, 268 and the proportion of mutations shared among siblings is similarly intermediate. In a previous 269 study of mouse germline mutations, 23.9% were shared among siblings, while in humans the 270 corresponding rate is just 4% (Lindsay et al. 2019). In the guppy Poecilia reticulata, which has a 271 very short generation time of 3 months, the majority of de novo mutations are shared (Lin et al. 272 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint 2023). Given the roughly 6 year generation time of wild white abalone, it is perhaps unsurprising 273 that we observe a mutation rate closer to humans than that of mice or guppies. 274 Our inference of effective population size showed large and stable abalone populations 275 over evolutionary timescales, including our focal species H. sorenseni. Over roughly 1 million 276 generations (103-106), Ne for all five species varied between 1 x 10 5 and 5 x 105 (Fig. 2). At face 277 value, these absolute values of Ne are encouraging for the future of abalone conservation. Large 278 Ne populations are thought to be less susceptible to genetic drift and inbreeding depression, and 279 Ne values of 50 or 500 are often cited as desirable conservation thresholds (I. G. Jamieson and 280 Allendorf 2012). Ne/Nc , another metric of population vulnerability (Palstra and Fraser 2012; Wilder 281 et al. 2023) further indicates population stability when considering historical abalone N c. For 282 example, estimates of pre -collapse N c for H. sorenseni and H. cracherodii in California are 283 360,000 and 3,500,000, respectively (Laura Rogers-Bennett 2002). When considering our long-284 term summary estimates of Ne (Fig. 2B), Ne/Nc ratios are 0.145 for H. sorenseni and 0.085 for H. 285 cracherodii, close to the 0.1 metric typical of healthy wild populations (Palstra and Ruzzante 2008; 286 Frankham 1995). However, natural populations of species that have high juvenile mortality and 287 fecundity, such as abalone, often exhibit N e/Nc ratios much lower than 0.1 (Hoban et al. 2020; 288 Popovic et al. 2024). Uncertainties regarding life history, for example the influence of sweepstakes 289 reproduction, make it difficult to generate clear expectations for N e/Nc in healthy populations of 290 abalone (Hedrick 2005). The historical census size is also a source of uncertainty for abalone, 291 but fisheries landings data (Laura Rogers-Bennett 2002), written accounts (Vileisis 2020), and 292 population genomic data (Wooldridge et al. 2024) all depict populations of H. sorenseni and H. 293 cracherodii as large and continuous prior to collapse. 294 When we compare N e to contemporary population sizes, we see the degree to which 295 recent population collapses have rendered H. sorenseni and H. cracherodii vulnerable to 296 extinction. Both H. sorenseni and H. cracherodii are thought to exist at less than 1% of their pre-297 collapse abundances (Laura Rogers-Bennett 2002), meaning extreme declines in Nc and inflation 298 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint of Ne/Nc. For white abalone, contemporary populations number as few as 500 and no more than 299 5000 (Stierhoff, Neuman, and Butler 2012; Stierhoff et al. 2014), resulting in Ne/Nc as high as 100, 300 greater than that estimated for even the most threatened mammals (Wilder et al. 2023) . Black 301 abalone show greater variation in population size. They were completely extirpated in many 302 southern California sites (VanBlaricom et al. 2009) and are showing incipient recovery at some 303 locations (Nc = 2,341 at San Nicolas Island (Kenner 2021), while at the northern end of their range 304 declines have been less severe (Neuman, Tissot, and VanBlaricom 2010) . The range in N e/Nc 305 resulting from this variation further emphasizes the need for population -specific approaches to 306 management. Even recovering sites like San Nicolas Island still fall in the ‘highly vulnerable’ range 307 (Ne/Nc > 100) while northern sites exhibiting minor decline are of less concern. While over-reliance 308 of these values is not recommended, especially when decades of census data sufficiently 309 demonstrate a species’ vulnerability, having some sense of Ne/Nc - now enabled by our knowledge 310 of the rate of input mutations - can shed light on the magnitude of vulnerability and provide 311 important guidance for species’ recovery metrics (J. A. Robinson et al. 2022). 312 Our estimate of a germline mutation rate for H. sorenseni permitted the first time-calibrated 313 phylogeny for the abalone genus Haliotis and resolved the timing of diversification of Pacific 314 species. Despite their historical abundance, abalone are poorly represented in the fossil record 315 (Geiger and Groves 1999) . For the abalone fossils that do exist, morphological ambiguity in the 316 fossilized shells makes it difficult to place these specimens in the context of present day diversity 317 (Geiger and Groves 1999). Therefore, a mutation rate-based approach to phylogenetic dating is 318 particularly suited to this system, which has yet to see an attempt at divergence dating. Our 319 inferred topology agrees with previously reported relationships (Gruenthal and Burton 2006; 320 Streit, Geiger, and Lieb 2006; Masonbrink et al. 2019) , and we identify a common ancestor for 321 the analyzed species at 36.4 Ma, during the late Eocene (Fig. 3). These species represent the 322 major lineages of Pacific abalone diversity and the date is consistent with a late Cretaceous 323 specimen found in California and an Eocene specimen from New Zealand (Estes, Lindberg, and 324 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint Wray 2005; Geiger and Groves 1999) . Furthermore, the estimated 4.3 Ma common ancestor of 325 California abalone (Fig. 3) agrees with the handful of Pliocene (5.3 -2.6 Ma) and much larger 326 number of Pleistocene (2.6 Ma-11.7 ka) fossils of H. cracherodii and H. rufescens from California 327 (Geiger and Groves 1999). Similarly, single Pleistocene fossils of H. laevigata and H. rubra from 328 Australia are consistent with the 4.0 Ma common ancestor of these species. While the agreement 329 between our rate -based estimates and the limited fossil record are encouraging, it should of 330 course be recognized that these figures are subject to change as a better understanding of life 331 history (i.e. generation time) and mutation rate variation emerges (Tiley et al. 2020). Nevertheless, 332 these results demonstrate the utility of such an approach for closely related clades with similar 333 constraints on fossil information. 334 Deriving an estimate of a species’ germline mutation rate drives at fundamental questions 335 in biology but also contributes an essential resource for conservation and evolutionary genomics 336 research. Here we have added to the growing understanding of how GMRs vary, finding that a 337 significant branch of Earth’s biodiversity previously absent from the literature - in this case 338 mollusks - exhibits mutation rate characteristics that match both empirical distributions and 339 theoretical predictions. 340 341 342

343 We thank the White Abalone Captive Breeding Program at the Bodega Marine Lab of UC Davis 344 for their work in conserving this species. Brock Wooldridge was supported by the National Science 345 Foundation Ocean Science Department (NSF–OCE) (No. 2307479). 346 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint Author Contributions 347 TBW conceptualized the study with assistance from KH. TBW performed all bioinformatics 348 analyses. SF and HC performed quality control of DNA extracts and generated all sequencing 349 libraries. JH tracked down white abalone families and provided DNA extracts. BS provided funding 350 for sequencing and analysis. TBW wrote the manuscript with input and approval from all authors. 351 352

353 354 Sample collection and DNA extraction 355 Samples for this study derive from the White Abalone Captive Breeding Program at the Bodega 356 Marine Lab of UC Davis, offspring were sampled at the NOAA Southwest Fisheries Science 357 Center in La Jolla, CA. White abalone at these facilities are bred and reared in captivity under the 358 National Marine Fisheries Service (NMFS) Endangered Species Act (ESA) Section 10(a)(1)(A) 359 Research Permit 14344-3R. 360 DNA extractions were prepared from epipodial tissue sampled from each individual. 361 Samples from parents were obtained by excising a single epipodial tentacle from a live animal, 362 while samples from offspring required both tentacle and epipodial fringe due to their small size 363 and all samples came from fresh mortalities. Extractions were performed following the protocol 364 of Gemmel and Akiyama (1996). 365 366 367 Sequencing library preparation 368 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint DNA extract concentration was quantified using the Qubit dsDNA HS Assay Kit (Invitrogen) and 369 fragment length was found with the Fragment Analyzer Genomic 50 kb DNA Kit (Agilent). 370 Sequencing libraries were prepared following the NEBNext Ultra II FS DNA Library Prep Kit for 371 Illumina (NEB) standard recommendations, using Y-Adapters rather than the NEBNext Adapters. 372 All samples were diluted with 1X TE (10 mM Tris pH 8.0, 1 mM EDTA) to reach ≤100 ng inputs 373 and incubated for 6 minutes during the enzymatic fragmentation step. Libraries were amplified for 374 7-8 cycles using dual unique indexes, and eluted in a final volume of 21 μL of 0.1 × TE. DNA 375 concentration was quantified using the Qubit dsDNA HS Assay Kit (Invitrogen) and fragment 376 length was determined using the Fragment Analyzer High Sensitivity NGS Kit (Agilent). Each 377 library was then screened via low-coverage sequencing on an Illumina Nextseq 200 (2 x 150 bp). 378 Libraries were then sequenced on an Illumina NovaSeq X (2 x 150 bp) with a target depth of 50x 379 genome-wide coverage at Duke University School of Medicine’s Sequencing and Genomics 380 Technologies Core Facility. 381 382 383 Alignment and variant calling 384 385 With our raw sequencing reads, we performed initial quality control and trimmed Illumina adapters 386 by running fastp (v0.23.4) with default parameters on each lane x sample combination of paired-387 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint end reads (Chen et al. 2018). We then merged the post-fastp reads for each sample. We aligned 388 these reads to the white abalone reference genome ( https://abalone.dbgenome.org/) via bwa-389 mem with –p to indicate interleaved paired -end fastq input, –M to mark short split hits as 390 secondary for compatibility with Picard, and -a to output alignments of unpaired reads (H. Li and 391 Durbin 2009). Following mapping, we marked duplicate reads in two steps using Sentieon: 1) 392 driver --algo LocusCollector --fun score_info, then 2) driver --algo Dedup providing the output of 393 step 1) with --score_info (Kendig et al. 2019). 394 To begin variant calling, we used Sentieon driver –algo Haplotyper --emit_mode gvcf to 395 create gvcf files for each individual sample. We then performed joint genotyping on this set of 396 gvcfs using Sentieon driver –algo GVCFtyper, which produced cohort level variant sites across 397 the white abalone genome. Finally, we filtered these variant sites using GATK VariantFiltration. 398 We performed initial filtering on SNPs and INDELs independently, excluding SNPs with QUAL < 399 30.0, QD 60.0, MQ < 40.0, MQRankSum < -12.5, ReadPosRankSum 3.0 and excluding INDELs with QUAL < 30.0, QD 200.0, ReadPosRankSum 10.0. These filtering parameters were based on a combination of GATK 402 recommendations for datasets without truth/training sets, and visual inspection of the distributions 403 for each metric. 404 To validate Sentieon variant calls, we also called and filtered variants in parallel with 405 bcftools v1.13 (Danecek et al. 2021) . First, we generated ‘pileup’ files for the set of all samples 406 by running bcftools mpileup --annotate FORMAT/AD, FORMAT/ADF, FORMAT/ADR, 407 FORMAT/DP, FORMAT/SP, INFO/AD, INFO/ADF, INFO/ADR --min-MQ 20 --min-BQ 20 --max-408 depth 500 on all input bam files. We then piped this output into bcftools call -m --ploidy 2 to 409 generate vcfs for the whole set of samples. Filtering was performed using the same criteria as 410 stated above for the GATK variants, with the exception of all FisherStrand (‘FS’) filters, which 411 were not available via the bcftools method. 412 413 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint Kinship matrix 414 To validate our sample pedigrees, we estimated a kinship matrix with plink2 v2.00a4.4LM 415 --make-king square –allow-extra-chr using the set of all genome -wide biallelic SNPs (Chang et 416 al. 2015). We visualized the resulting kinship matrix in R 4.3.3 and confirmed that families showed 417 the expected degree of relatedness (Fig. S1). 418 419 420 Masking and determining the callable genome 421 Accurate estimation of germline mutation rates requires dividing the number of observed 422 mutations by the proportion of the genome where such mutations could potentially be observed 423 given a) genome quality and b) sequencing effort. We combined several masking approaches to 424 determine this denominator. 425 We quantified mappability of the white abalone reference genome with genmap v1.3.0 426 (Pockrandt et al. 2020) . First, we indexed the genome with genmap index, then we determined 427 the mappability of 150 length kmers with up to 2 mismatches using genmap map -K 150 -E 2. We 428 then retained all regions where the 150x2 mappability score was less than 1.0 to create a 429 ‘negative mappability mask’, or a list of regions with poor mappability to exclude from downstream 430 analyses. 431 In addition to the above mappability mask, we also generated masks based on sequencing 432 depth for each individual. First, we generated base-pair level resolution depth files with samtools 433 depth -a for each sample, and determined the mean, median, and standard deviation of 434 sequencing depth based on the first (largest) chromosome in the white abalone reference 435 genome. Given these parameters, we then identified regions for each individual where read depth 436 was either less than 20 or greater than the mean read depth plus two standard deviations. Such 437 regions, either too low to reliably call heterozygotes or outside the standard coverage distribution 438 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint for each individual, were designated as regions to mask (‘negative per -sample mask’) from 439 subsequent analyses. 440 Finally, for each family, we combined the above negative mappability mask and negative 441 per-sample masks to create a conservative set of regions to exclude from mutation rate 442 estimation. The genome remaining after this exclusion is referred to as the ‘callable genome’, and 443 averaged around 75-80% (Table S1). 444 445 Table S1. Sequencing coverage and proportion of the callable genome for each family 446 Family Avg. seq. coverage Pct. callable genome 1 78.48 76.8 2 62.60 77.4 3 91.95 79.1 447 448 Mutation rate estimation 449 Provided variant calls from GATK and bcftools as well as callable regions of the genome, 450 we then proceeded with mutation rate estimation. First, for each potential trio (two parents + 1 451 offspring), we selected variants within the callable regions that were heterozygous in the offspring 452 and homozygous in the parents. We referred to these as candidate de novo mutations, and further 453 filtered this set following the stringent criteria outlined in (Bergeron et al. 2022) . Specifically, we 454 retained mutations with a) genotype quality (GQ) greater than 60 in each member of the trio, b) 455 no reads containing the mutation present in either parent, c) allelic balance > 0.30, and d) no 456 occurrence in other offspring except those sharing one of the parents. 457 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint We applied this same filtering pipeline to both GATK and bcftools variant calls, and 458 intersected those that appeared via both methods under the assumption that those appearing in 459 only one approach were more likely to be spurious (Sendell-Price et al. 2023; Bergeron et al. 460 2022). Of the 2,817 de novo mutations detected via GATK and 1,023 detected via bcftools, 107 461 were shared. 462 We adjusted these raw rates for false positives and false negatives. To estimate the false 463 discovery rate (FDR), we manually inspected the read alignments of each trio for 20 de novo 464 mutations in IGV (P. Robinson and Others 2017) . Only one of the 20 mutations did not display 465 convincing alignment -based evidence with low alternate allelic depth and map quality < 40. 466 Despite this result, the corresponding variant call passed our strict filters because of read 467 realignment during GATK/bcftools variant calling. Nevertheless, we set the FDR to 5%. 468 To calculate the false negative rate (FNR), we first set out to identify high quality variants 469 that would have to be heterozygous in offspring (0/1) based on the parent genotypes (e.g. 0/0 470 and 1/1). We then calculated what proportion of such variants, assumed to be true heterozygotes, 471 would not pass the allelic balance, genotype quality, and depth filters listed above. This ratio was 472 calculated on a per-child basis, and across all children exhibited a median of 0.139 and mean of 473 0.181. Given that this approach may overestimate the FNR because of overconfidence in parent 474 genotypes, we opted to implement the median value of 0.139 in our FNR correction. 475 Finally, our reported mutation rates were calculated as: 476 477 𝜇 = 𝑛𝑏𝑐𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒𝐷𝑁𝑀𝑠 × (1 − 𝐹𝐷𝑅) 2 × 𝐶𝐺 × (1 − 𝐹𝑁𝑅) 478 479 where nbcandidateDNMs was the count of observed de-novo mutations, and CG was the size, 480 in base pairs, of the callable genome. We determined the confidence interval for the mutation 481 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint rate based on the confidence interval reported by the R function t.test on the reported values of 482 all nine offspring with estimates. 483 484 Parent-of-origin tracing 485 We used read phasing in order to determine which parents contributed de novo mutations. 486 To do this, we applied POOHA (https://github.com/besenbacher/POOHA/) to each mother-father-487 offspring trio of bam alignments with the options --min-parents-GQ 60 --min-child-GQ 60 --max-488 marker-distance 10000 --output_variants germline. Due to insufficient haplotype information, we 489 were only able to trace 33 of the 107 total mutations to a parent. 490 491 Mutation rate comparisons 492 In order to compare our estimated mutation rate to other published rates in multicellular 493 eukaryotes (Fig. 1), we retrieved the set of estimates compiled by (Wang and Obbard 2023). We 494 also obtained the corresponding phylogeny for this list of species using TimeTree (Kumar et al. 495 2017), omitting Amphilophus and Marasmius oreades when they were not located in the 496 database. All visualizations were executed in R with the packages ggtree (Yu et al. 2017) and 497 aplot (Yu 2023). 498 499 Analysis of polymorphisms in wild-caught H. sorenseni and H. cracherodii 500 To examine sequence diversity in wild populations, we first downloaded whole genome 501 shotgun data from NCBI’s SRA database for H. sorenseni (n=11) and H. cracherodii (n=11), the 502 only species which had multiple wild caught individuals with such data (Extended Data Table 1). 503 We then generated variant calls for these data following the pipeline detailed in ‘Alignment and 504 variant calling’. For both species we used their respective reference genomes (Extended Data 505 Table 1). At the ‘GVCFtyper’ stage, in which cohort level VCFs are generated, we specified ‘ --506 emit_mode ALL’ in order to produce VCFs containing both invariant and variant sites. We filtered 507 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint variant and invariant sites separately. For variant sites, we used the exact criteria specified for 508 GATK filtering in ‘Alignment and variant calling’. For invariant sites, we filtered based on site 509 quality (‘QUAL>30’) and the fraction of missing genotypes at a site (‘F_MISSING<0.25’). 510 We then proceeded to analyze the variant + invariant site VCFs with pixy (Korunes and 511 Samuk 2021), which estimates sequence diversity while accounting for the pitfalls in generating 512 such estimates from heterogeneous data with high rates of missingness. We ran pixy --stats pi --513 window_size 10000 , then examined the distribution of site missingness in 10kb windows to 514 determine filtering heuristics for downstream analysis. We reported values of 𝜋 after retaining 515 windows with more than 8,000 sites for H. sorenseni and more than 6,000 sites for H. cracherodii. 516 517 Demographic inference 518 We reconstructed demographic histories of multiple Haliotis species with MSMC2 519 (Schiffels and Wang 2020) using our new mutation rate. First, we downloaded whole genome 520 shotgun data from NCBI’s SRA database for these additional Haliotis species, as well as their 521 respective reference genomes (Extended Data Table 1). We then analyzed these data following 522 the exact pipeline detailed above in ‘Alignment and variant calling.’ Following the production of 523 filtered variant calls, we generated two data masks: 1) reference genome mappability masks 524 following the pipeline detailed above in ‘Masking and determining the callable genome’, and 2) 525 sequencing depth masks following msmc-tools recommendations. For the latter, we used the 526 msmc-tools script bamCaller.py on the output of samtools mpileup -q 20 -Q 20 -C 50 -u | bcftools 527 call -c -V indels --ploidy 2. 528 With our input variant calls, reference genome mask, and sample sequencing depth mask, 529 we then generated the MSMC2 input with the msmc-tools script generate_multihetsep.py. We 530 created bootstrap replicates of this input data using the multihetsep_bootstrap.py, specifying -n 531 20 -s 5000000 --chunks_per_chromosome 10 --nr_chromosomes 20 for all species except H. 532 laevigata, which had a highly fragmented genome assembly. For H. laevigata, we specified -n 20 533 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint -s 100000 --chunks_per_chromosome 10 --nr_chromosomes 1000 to generate bootstrap 534 replicates with similar characteristics as the input data. Finally, we ran MSMC2 on the original 535 data and all bootstrap replicates with the time segment pattern specified as -p 25*1+1*2+1*3. All 536 demographic histories were then visualized in R and scaled by the newly estimated mutation rate 537 of 8.60e-09 and generation time of 1. 538 539 540 Phylogenomic inference 541 542 To supplement the H. sorenseni genome for phylogenomic analysis, we downloaded reference 543 genome assemblies for five Haliotis species as well as the outgroup G. magus from NCBI’s 544 RefSeq Database. (Extended Data Table 1). We subsequently annotated these genomes for 545 BUSCO gene content using compleasm (v.02.6) with the mollusca_odb10 database (Huang and 546 Li 2023). Following this, we identified all genes that were present only as complete single copies 547 in each of the seven taxa (six Haliotis + G. magus), and extracted the corresponding spliced CDS 548 nucleotide sequences from each taxon using gffread -x ( v.0.12.8) (Pertea and Pertea 2020) . 549 Then, for each gene, we aligned the seven sequences with mafft (v7.526) (Katoh and Standley 550 2013) and quality trimmed the resulting alignments with trimal -gt 0.50 -cons 50 (v.1.4.rev15) 551 (Capella-Gutiérrez, Silla-Martínez, and Gabaldón 2009). From this set of trimmed alignments, we 552 selected only those greater than 900bp in length, resulting in 2,525 total genes. We concatenated 553 these genes into a single alignment with seqkit concat (v.0.16.1) (Shen et al. 2016) and inferred 554 a phylogeny with iqtree -bb 1000 -bnni -m MFP (v.2.3.4) (Minh et al. 2020) . We plotted the G. 555 magus-rooted tree in R using the package ggtree (v.3.10.1) (Yu et al. 2017). 556 Having observed 100% bootstrap support at all nodes in this initial tree, we then 557 proceeded with inference of species divergence times under this topology. Motivated by our 558 estimate of the germline mutation rate and a lack of obvious fossil calibrations for Haliotis, we 559 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint opted for a fossil -free approach under the multi -species-coalescent (MSC) following principles 560 outlined in (Tiley et al. 2020). To do this, we first estimated the extent to which each of the 2,525 561 genes used for tree inference evolved in clock-like fashion along the seven lineages. Specifically, 562 we estimated the parameter ‘rate.coefficientOfVariation’ (CoV) for each gene alignment 563 individually in BEAST (v.2.6.6) (Bouckaert et al. 2019) . Following each BEAST analysis, we a) 564 filtered for genes which reached an Effective Sample Size (ESS) greater than 200 for the posterior 565 and CoV, and b) mean CoV < 0.50 and the upper and lower limits of the 95% HPD for CoV less 566 than 1 and 0.1, respectively. This filtering resulted in 393 clock-like genes for further analysis. 567 With these genes and our inferred topology, we performed Bayesian estimation of 568 divergence times under the multi -species-coalescent with BPP (Flouri et al. 2018) . Specifically, 569 we applied the A00 model to a partitioned alignment of 150 randomly selected genes from the 570 original set of 393 genes and provided the fixed topology estimated by IQTREE. We also set the 571 following parameters in the BPP control file: 572 573 cleandata = 0 574 575 thetamodel = linked-all 576 thetaprior = 3 0.01 577 tauprior = 3 0.01 578 579 locusrate = 1 0 0 5 iid 580 clock = 1 581 582 # Step proposals 583 finetune = 1: .01 .02 .003 .004 .05 .01 .01 # auto (0 or 1): MCMC step lengths 584 585 # MCMC samples, locusrate, heredityscalars, Genetrees 586 print = 1 0 0 0 * 587 burnin = 16000 588 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint sampfreq = 2 589 nsample = 200000 590 591 592 We then recalibrated branch lengths in the inferred tree to real time in R using the msc2time.r 593 function from the R package bppr (). To do this, we specified the mean mutation rate u.mean as 594 8.60e-09 and the standard deviation u.sd as 3.26e-09 based on our findings. For lack of a precise 595 generation time for any abalone species, we used data from growth -reproduction curves in 596 Eastern pacific abalone like H. sorenseni and H. rufescens as a crude proxy. Specifically, we set 597 the mean generation time g.mean as 6 and the standard deviation g.sd as 2, as most of these 598 abalone start reproducing by at least 4 years of age and continue on into adulthood, with some 599 evidence for reproductive senescence with age (L. Rogers-Bennett, Dondanville, and Kashiwada 600 2004). After using these parameters to obtain a rate for the conversion of substitution rates to real 601 time, we rescaled all nodes, edges, and 95% confidence intervals. Finally, we reran BPP to 602 confirm convergence on these parameters across independent runs. 603 604 Species range maps 605 The world map was obtained from the R package rnaturalearth v0.3.2 (Massicotte and South 606 2023) with the function ne_countries and transformed to a Pacific -centered Robinson projection 607 with the function st_transform(st_crs("+proj=robin +lon_0=0 +x_0=0 +y_0=0 "+datum=WGS84 608 +units=m +pm=180 +no_defs")) from the R package sf v1.0.8 (Pebesma 2018). Species range 609 shapefile were downloaded from the IUCN Red List of Species. We visualized the world map and 610 range maps together with ggplot2 v3.3.6. 611 612 613 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint Supplement 614 615 Caption for Extended Data Table 1. Breakdown of species and data used in this study, as well 616 as the analysis for which they were used. Species: binomial nomenclature. Sample ID: sample 617 shorthand label. SRA ID: NCBI SRA accession number, if applicable. Reference genome: 618 source of genome assembly. Mutation rate analysis, MSMC2, and PIXY: 0 = data not used for 619 this particular analysis, 1 = data was used for this analysis, NA = does not apply (i.e. only analyzed 620

genome for said species). Notes: any additional sample/run information. 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint 639 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint Figure S1. Pedigree and kinship matrix for samples included in this study. Kinship calculated with 640 plink2 (Chang et al. 2015) , values are scaled so that 0.5 corresponds to perfect identity (same 641 individual). Sexes are annotated for parents (F0), but unknown for offspring (F1). 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint 666 Figure S2. Venn diagrams showing counts of mutations unique to each individual, and shared 667 across two or more offspring in a family. Two mutations are shared between multiple individuals 668 of Fam2 and one member of Fam3. Thus, they are represented in the Fam2 diagram, but we do 669 not plot all Fam2 + Fam3 intersections together for simplicity’s sake. 670 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint 671 Figure S3. Individual-level mutation rates for all offspring included in the study. 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint 693 Figure S4. The germline mutation rate of white abalone as a function of life history traits (A -C) 694 and demography (D). Figures reproduced from Bergeron et al. 2023. The harmonic mean N e in 695 plotted in panel D) is calculated from 5000-166,666 generations (30,000-1,000,000 abalone years 696 assuming g = 6), following the timeframe used in Bergeron et al.. 697 698 699 700 701 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted September 20, 2024. ; https://doi.org/10.1101/2024.09.16.613283doi: bioRxiv preprint

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