In many ways, dogs are an ideal model for the study of genetic erosion and population recovery, problems of major concern in the field of conservation genetics. Genetic diversity in many dog breeds has been declining systematically since the beginning of the 1800s, when modern breeding practices came into fashion. As such, inbreeding in domestic dog breeds is substantial and widespread and has led to an increase in recessive deleterious mutations of high effect as well as general inbreeding depression. Pedigrees can in theory be used to guide breeding decisions, though are often incomplete and do not reflect the full history of inbreeding. Small microsatellite panels are also used in some cases to choose mating pairs to produce litters with low levels of inbreeding. However, the long‐term impact of such practices has not been thoroughly evaluated. Here, we use forward simulation on a model of the dog genome to examine the impact of using limited marker panels to guide pairwise mating decisions on genome‐wide population‐level genetic diversity. Our results suggest that in unmanaged populations, where breeding decisions are made at the pairwise—rather than population‐level, such panels can lead to accelerated loss of genetic diversity at genome regions unlinked to panel markers, compared to random mating. These results demonstrate the importance of genome‐wide genetic panels for managing and conserving genetic diversity in dogs and other companion animals. However, a key challenge, at least in the United States, is a lack of population‐level management. Rather, individual dog breeders or groups of breeders typically manage small subsets of a breed, often relying on pedigrees or commercially available molecular tests to minimize known genetic health risks and sometimes overall inbreeding in individual litters of dogs. For breeds still managed in such a way, it is critical to long‐term breed health and survival to understand the long‐term impacts on genome‐wide genetic diversity of chosen mating strategies and the molecular tools used to guide those strategies.As a first step in understanding the impact of such population management, we conducted individual‐based forward‐time population genetic simulations of linked genetic diversity on a model of the dog genome using SLiM 3  and is likely more uniform than in humans . Additionally, we modeled 33 microsatellite loci (m2 in the simulation template) spaced across the first 25 chromosomes as such: 3 on chromosome 1, 2 each on chrs 2 — 7, 1 each on chrs 8 — 25, and with no microsatellites on chrs 26–38. This distribution of markers across chromosomes is similar to the 33 STR panel used by Pedersen et al.   to allow founding genetic diversity to recombine sufficiently and experience sufficient genetic drift. This is followed by a short immediate bottleneck of five generations. Finally, the population expands and goes through 40 mate choice generations. Population genetic data are collected at the beginning of the first generation of mate choice, once every five generations, and again at the very end of the simulations .Choose second parent:Subsampling a fraction of the remaining population randomly (according to MATING_POOL_SIZE) from which to choose potential mates. This step is intended to model the fact that within an effective population of purebred dogs, individual dogs only have access to a limited number of other dogs as mates. This sampling is repeated if no eligible (has been mated < MAXIMUM_NUMBER_OF_MATINGS times) dogs are sampled.A mate for the first individual is chosen according to a specific mate choice model (see Mate Choice Models) from this pool.In the event of a layered mate choice model in which two statistics are used to choose mates, a PROPORTION_OF_MATES_FOR_LAYERED_MATE_CHOICE parameter is used to subsample the potential mates based on each statistic used.Generations in the initial burn‐in/drift period and bottleneck follow the standard Wright–Fisher model in SLiM. However, during mate choice, we induce a slightly different model. Creation of offspring in the mate choice schema includes . We choose as the best mate the individual that would produce offspring with the highest heterozygosity and select randomly among ties.2.4.4For this model, as above, we calculate mean internal relatedness  and ran several tests to ensure that our SLiM implementation and the R version produced identical results. We chose as the second parent the individual that produced the lowest AGR value, selecting randomly from ties.Here, we apply a method designed to calculate relatedness between individuals based on small panels of SNPs or microsatellites Wang, . We base2.4.6We calculate whole‐genome relatedness as in , we also calculated richness for alleles >=0.05 frequency (raw data available but not presented here).2.5.3We calculated the coefficient of inbreeding (COI) for individual dogs as the fraction of all mutations of type “m1” that are identical within an individual divided by the total genomic length. Because each mutation of this type represents 0.5 Mb, this method is identical to that presented in . See python script used to generate results for specific uses .For nearly all statistical comparisons, including calculations of 95% confidence intervals used in Figure 33.1For each of eight mate choice models  across all 800 replicates (100 replicates × 8 model types) for parameter set 1 (PS‐1) is approximately 0.17 — 0.21 and across all simulations is ~0.12 — 0.38, well within the range of several common dog breeds today  performed best overall after 40 generations of mate choice. This model limited loss of heterozygosity and increase in coefficient of inbreeding comparably or slightly better than random mating across all parameter sets tested  to 50 (PS10‐12) and 5 (PS13‐15). Reducing from 200 to 50 had no significant impact on diversity metrics Figure . Howeverto the rest of the population will be overrepresented in the next generation. However, while limiting the number of possible mates for each individual decreased the loss of heterozygosity for MS‐based mate choice models, it has no impact on the random and pedigree‐based mate choice models and increases the loss of heterozygosity slightly in the two genome‐wide genetic models  and from 50 to 10 (PS‐19‐21), unexpectedly, led to a notable reduction in the amount of genome‐wide diversity loss  in all MS‐based mate choice schema Figure . Upon fus Figure , support3.4In addition to the MS‐based mate selection models, we also included several models meant to capture the viability of using genome‐wide metrics in general to preserve genetic diversity. These include tracking two‐generation pedigrees (pedigree)—to avoid matings between very close relatives, genome‐wide heterozygosity (GW‐HET)—to select mates which maximize heterozygosity in the offspring, and genome‐wide relatedness (GW‐REL)—which prefers the most distantly related individuals as mates. We find that these three models all lead to greater preservation of genetic diversity than random mating. Perhaps more importantly, these models reduce the rate of genetic drift over time (random model), compared to the MS models which accelerate the rate of genetic drift Figure .4In this study, we used forward population genetic simulations of a model of the canine genome to investigate the efficacy of using a small genetic marker panel (e.g. a microsatellite panel) to guide mating aimed at preserving existing genetic variation in a population, when mating decisions are made at the pairwise, rather than population, level. We ran these simulations across a range of mate choice models and demographic parameterizations using a genomic model that included both genome‐wide genetic markers and a set of 33 markers distributed across the genome . For example, Fernández, Toro, and Caballero  demonstrImportantly, however, we also found that given enough generations using small panels of markers in such a mating scheme does not preserve diversity genome‐wide. In fact, mate choice models using the MS33 marker set over time led to greater loss of genetic variation compared to random mating in the form of reduced heterozygosity and allelic richness measured using the GW marker set. López‐Cortegano et al.  simulateOur results suggest that reducing the number of times that any given individual can contribute offspring to the next generation, either explicitly in the form of the “maximum number of matings” parameter or implicitly by reducing the “mating pool size” parameter, can act to moderate the severity of diversity loss compared to the random mating model. This finding is generally consistent with theory and prior simulation work which has demonstrated that optimal management schemes to preserve genetic diversity include limiting variance in family size, in other words, ensuring that no single individual contributes disproportionately to the next generation , we see that this reduced allowance of multiple matings has a much weaker impact in MS33‐IR mate selection compared to other MS‐based mate selection schemes.Toro, Silió, Rodrigañez, Rodriguez, and Fernández  demonstrGenetic drift comes from two primary sources in a diploid population: variation in genetic contribution between individuals in a population and variation in genetic diversity at a given locus within an individual  configuration of pairwise matings to preserve existing genetic diversity.Most companion animal species remain relatively unmanaged with respect to genetic diversity at the breed level. As such, genetic diversity has rapidly decayed in many breeds over the past century (Jansson & Laikre, Eventually, companion animal breeding may benefit from large‐scale participation in databases and services aimed at tracking breed‐wide whole‐genome genetic diversity, including awareness of adaptive and deleterious variation, to limit variance in family contributions, maximize the inclusion of genetic variation in subsequent generations, and purge deleterious variation over time. Experimentation and optimization of such a system applied to breeds in a large and diverse species such as domestic dogs would provide critical case studies to the conservation genetics community (Shafer et al., Supplementary MaterialClick here for additional data file.


Artemisia rupestris L. has long been used as a traditional herbal medicine owing to its immunomodulatory activity. Aqueous extracts of Artemisia rupestris L. (AEAR) contain the main functional component and can activate the maturation of dendritic cells (DCs) and enhance the adaptive immunity as the adjuvant against infections. To explore the underlying mechanism of immunomodulatory activities of AEAR, DCs were produced from bone-marrow cells of mice and the effects of AEAR on cell viability were assessed by the Cell Counting Kit 8 (CCK8) method and annexin V/propidium iodide staining assays. Then, the effects of AEAR on the morphology, maturation, and function of DCs were detected using a microscope, flow cytometry-based surface receptor characterization, and endocytosis assays. The secretion levels of cytokines were then analyzed with enzyme-linked immunosorbent assay (ELISA). The activation state of DCs was evaluated by the mixed lymphocyte reaction (MLR). The activity of MAPKs and NF-κB pathways, which were involved in the regulation of AEAR on DCs, was further detected by Western blot. AEAR did not have a cytotoxic effect on DCs or mouse splenocytes. AEAR remarkably enhanced the phenotypic maturation of DCs and promoted the expression of costimulatory molecules and the secretion of cytokines in DCs. AEAR also significantly decreased the phagocytic ability of DCs and augmented the abilities of DCs to present antigens and stimulate allogeneic T-cell proliferation. Simultaneously, AEAR potently activated toll-like receptor (TLR)4-/TLR2-related MAPKs and induced the degradation of IκB and the translocation of NF-κB. In short, AEAR can profoundly enhance the immune-modulating activities of DCs via TLR4-/TLR2-mediated activation of MAPKs and NF-κB signaling pathways and is a promising candidate immunopotentiator for vaccines. Astragalus, lycium barbarum, Angelica, and inulin [Vaccination can prevent and control the spread of infectious diseases. It is still needed to explore new vaccines for diseases that continue to threaten human and animal health, such as HIV, malaria, and Foot-and-mouth disease virus (FMDV) . Traditid inulin ,10,11,12Artemisia rupestris L. (Yizhihao in Chinese) is widely distributed in Xinjiang of China, Asia, and Europe, among others, and has been proven to be safe and less toxic in animal experiments and is widely used in the treatment of influenza virus and acute or chronic inflammation [Artemisia rupestris L. are mainly attributed to its polysaccharides. The immunomodulatory activity of Artemisia rupestris L. is good with low toxicity. In recent years, our studies indicated that the administration of aqueous extracts of Artemisia rupestris L. (AEAR), water-extractable crude polysaccharides, could induce the functional and phenotypic maturation, the activation of T lymphocytes, and the upregulation of TNF-α and IL-12 production of dendritic cells (DCs), which was a critical step in adaptive immune responses and inflammation [Artemisia rupestris L. could enhance the adaptive immune responses of FMDV vaccine and influenza vaccine as an adjuvant [As a part of TCM, ammation . It is mammation . The divammation . We alsoadjuvant ,16. HoweDCs as the initiator and modulator of the immune response play a vital role in the induction of immunity. DCs can control the function of B and T lymphocytes by capturing and processing antigens to activate antigen-specific T cells, and secreting immunomodulatory cytokines  to direct the immune responses against infectious agents . Recent In our previous study, we found that TLR4 played an important role in the activation of DCs by AEAR. Up to now, it is unknown whether TLR2 is also the receptor of AEAR. In the study, we explored the roles of TLR2 in AEAR-induced immunomodulation of DCs and related pathways.Artemisia rupestris L. was obtained from Xinjiang Institute of Materia Medica in Urumqi and was identified by Professor Jiang He. A voucher specimen (No. AR13020307) was deposited in the Xinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, China. AEAR was obtained from Artemisia rupestris L. according to the previous report [s report . Brieflys report . Finally−1.The samples were mixed with KBr and pressed into pellets to determine the infrared spectra in the range of 400–4000 cmFemale C57BL/6 mice (6–8 weeks old) were from Xinjiang Medical University . The animal experiments were approved by the Committee on the Ethics of Animal Experiments of Xinjiang Key Laboratory of Biological Resources and Genetic Engineering in Xinjiang University (BRGE-AE001).DCs were generated according to an established method. Briefly, bone marrow of the tibia and femur from Naïve C57BL/6 mice was separated. Bone marrow was flushed in culture medium, spun down , and re-suspended in RPMI-1640 medium  containing 20 ng/mL GM-CSF . After 6 days, immature bone marrow DCs (BM-DCs) were harvested for subsequent assays.5 cells/well) or single-cell suspension of splenocytes (1 × 106 cells/well) were seeded in 96-well plates and then allowed to attach the plates for 48 h after adding AEAR at serial concentrations (10 to 1600 μg/mL) in triplicate. In the positive control, LPS  was added to replace AEAR. After cells were incubated at 37 °C together with CCK-8 working solution for 4 h, the supernatant was removed, followed by the addition of DMSO. Then, the absorbance at 570 nm was recorded with a microplate reader . Cell survival rate was calculated as follows:  × 100%.To explore the influences of AEAR on splenocytes and DCs of mice, cell viability was determined by CCK-8 kit . DCs were acquired as described above. Splenocyte suspensions were prepared and then the lysis of erythrocytes was performed. DCs (1 × 105 cells/well) were cultured on a 24-well plate, and DCs were stimulated with different concentrations of AEAR  or LPS (100 ng/mL) for 24 h. The photos (magnification = 200× and 400×) were captured.The morphology of DCs was observed under an inverted phase-contrast microscope . Immature DCs (5 × 106 cells/well) in RPMI-1640 medium without or with AEAR (10 to 400 μg/mL) or LPS (100 ng/mL) in triplicate and stood for 24 h. DCs were washed and incubated with FITC-Annexin V and PI for 15 min, and washed three times with 5 mL iced PBS. The samples were analyzed immediately on a flow cytometer . The apoptosis rate was calculated as follows: apoptosis rate (%) = (the number of apoptotic cells) /  × 100%.In the apoptosis analysis, an FITC-Annexin V/PI apoptosis kit  was used according to the manufacturer’s instructions. DCs were added in a 24-well plate (1 × 106 cells per 1 mL) were respectively stimulated with different concentrations of AEAR (10 to 400 μg/mL) or LPS for different periods , and then anti-CD11c, anti-CD40, and anti-CD86 (BD) antibodies were added for 30 min staining. Cells were washed to remove excessive antibodies. The expression levels of CD86 and CD40 were measured using a flow cytometer. For Polymyxin B  experiment, different concentrations of AEAR  were pretreated with or without PMB (100 μg/mL) at 37 °C for 1 h, and then used to stimulate DCs (1 × 106 cells per 1 mL) for 12 h; then, after being stained with antibody, DCs were measured by a flow cytometer. The measurement data were analyzed in FlowJo.The phenotypic maturation of DCs was analyzed with a flow cytometer. DCs (1 × 106 cells per 1 mL) were stimulated with different concentrations of AEAR (10 to 400 μg/mL) or LPS (100 ng/mL) for 12 h. IL-6 and IL-1β were generated in the supernatant of DCs’ cultures with ELISA kits . The absorbance at 450 nm was measured using a microplate reader. The concentrations of IL-6 and IL-1β were calculated with the standard curve.DCs  or LPS and then incubated with 25 μg/mL FITC-dextran  for 1 h at 37 °C. Then, the reaction was stopped by PBS with 3% fetal calf serum (FBS) (Hyclone) After washing three times, the cells were stained for 30 min with PE-CD11c antibody. Non-specific binding of FITC-dextran to DCs was determined at 4 °C in parallel experiments. The double-stained DCs were analyzed using a flow cytometer.4: 2 × 105) or 1:10 (DCs/splenocytes = 2 × 104: 2 × 105) in 96-well plates and then stood for 2 days. LPS was added in the positive control. Finally, the absorbances at 490 nm/630 nm were determined with a microtiter plate reader as the index of DCs stimulating the proliferation of T cells.MLR was utilized to evaluate the allogeneic stimulatory activity by the MTT  method. The spleens from BALB/c mice were removed aseptically and then added in RPMI-1640 medium. Mature DCs from C57BL/6 (matured in the presence of AEAR or LPS) were pretreated with 100 ng/mL mitomycin C (Sigma) at 37 °C for 30 min. After carefully washing, DCs were mixed with splenocytes according to a ratio of 1:5  or anti-TLR4 (2 μg/mL)  for 1 h, and then AEAR (50 or 400 μg/mL) was added for 12 h incubation. The expression levels of CD40, CD86, IL-1β, and IL-6 were measured as described above.To clarify the roles of MAPK and NF-κB pathways in AEAR-stimulated DCs, DCs were incubated together with 200 μg/mL AEAR for different periods . After AEAR treatment, DCs were collected and proteins were extracted with the nuclear and cytoplasmic extraction kit . The protein concentration was then determined by BCA Protein Assay kit . After boiling, the samples from different AEAR samples were separated by 12% SDS-PAGE and then transferred to PVDF blotting membranes. After blocking with PBS containing 5% skimmed milk for 1 h, membranes were incubated overnight at 4 °C with appropriately diluted primary antibodies against cytoplasmic non-modified and phosphorylated proteins  . Membranes were incubated with secondary antibodies for 1 h. Signals were captured with the enhanced chemiluminescence (ECL) kit.p < 0.05 was considered to be significant.Experimental data are expressed as mean ± standard deviation (S.D.). Statistical differences between experimental groups were tested with GraphPad Prism 5.0 through one-way analysis of variance (ANOVA) and Tukey’s multiple-comparison test. −1 and at 1605.68 cm−1 were ascribed to the stretching vibration of -OH and aldehyde group (–CHO), respectively. The peak at 1416.25 cm−1 could be ascribed to O-H or C-H bending and C-O stretching. The peak at 1125.1 cm−1 was ascribed to the asymmetric vibration of the C–O–C glycosidic bond.FT-IR spectra of AEAR are shown in LPS, the most common endotoxin, is a known immunomodulator and often a contaminant in biological preparations. PMB is a cationic polypeptide anti-biotic with a high affinity for the lipid A component of LPS, which results in neutralization of endotoxin-like bioactivity of most forms of LPS. To ensure that the effect of AEAR was not due to endotoxin contamination, as shown in p < 0.05) and was nearly increased by 45% in the AEAR concentration range of 400–1600 μg/mL. Similar results were obtained in DCs (p < 0.05). Thus, in the subsequent experiments, the AEAR concentration range was from 10 to 400 μg/mL.With the MTT method, the potential cytotoxicity of AEAR on splenocytes and DCs was determined. The cells were stimulated with AEAR for 48 h and the cell viability was detected through CCK-8 assays C,D. Compd in DCs E,F. AEARTo further observe the inhibitory effect of AEAR on DCs’ growth, the apoptosis analysis was performed. After DCs were stimulated with different concentrations of AEAR and LPS for 24 h, Annexin V/PI staining was conducted G,H. The To observe the effects of AEAR on DCs’ maturation in terms of morphology, immature DCs were cultured for 6 d in the presence of GM-CSF and then stimulated for 24 h with AEAR or LPS. Then, the cells were observed under an inverted microscope . Mature p < 0.05). The expression levels of IL-1β and IL-6 increased significantly in a dose-dependent manner in the groups of AEAR treatment and LPS (100 ng/mL) stimulation compared with the blank control , indicating that AEAR significantly affected DCs’ maturation.The phagocytic capability was decreased after immature DCs captured antigens ,27. The p < 0.05). The difference suggested that AEAR-treated DCs stimulated allogeneic T cell proliferation.Compared with immature DCs, mature DCs could more significantly stimulate the proliferation of allogeneic T cells . The effp < 0.05). Similarly, the yields of IL-1β and IL-6 were also significantly suppressed by AEAR treatment (p < 0.05), suggesting that AEAR induced DCs’ activation and cytokine production via TLR2.To explore the activation mechanism and investigate the membrane receptor involved in DCs’ activation and cytokine production, the interaction between TLR2 and AEAR was blocked with anti-TLR2 antibody and the influence of TLR2 on AEAR-induced DCs’ activation was evaluated in terms of cell surface molecules in DCs A,B and cp < 0.05). The yields of IL-1β and IL-6 were decreased significantly by anti-TLR4 antibody (p < 0.05). The differences suggested that TLR4 might participate in signaling pathways of AEAR-treated DCs.To determine whether TLR4 was also involved in the AEAR-induced activation of DCs, the collected cells and culture supernatants of DCs were analyzed after the treatment with anti-TLR4 antibody . The up-As NF-κB and MAPKs downstream pathways are critical in DCs’ activation in a TLR2/4-dependent mechanism, the activation of MAPK  in AEAR-treated DCs was explored in the study A–D. AEARIt is necessary to develop new vaccines for emerging diseases such as Ebola and coronavirus disease 2019 (COVID-19). Various kinds of adjuvants are extensively used to elicit an enhanced immune response, thus providing long-lasting protective responses ,30. HoweArtemisia rupestris L. could stimulate activation of DCs was unknown. In this study, we firstly prepared the aqueous extracts of Artemisia rupestris L. and then found that the main absorption peaks of AEAR in the FT-IR spectra were ascribed to glycosidic structures. AEAR was free of LPS contamination. AEAR could activate DCs through TLR2/TLR4-mediated activation of MAPKs and NF-κB signaling pathways. The blocking of the TLR2/TLR4 pathway inhibited the expression levels of costimulatory molecules on DCs and cytokines. The phosphorylation of important molecules in MAPKs pathway and the nuclear translocation of NF-κB were largely upregulated in AEAR-induced DCs.Polysaccharides from TCM have been considered as a promising potential immunostimulator as they can stimulate innate and cellular immunity via the interactions with immunological cell membrane receptors ,32. SeveThe safety of adjuvants is also widely concerned. Although TCM is believed to be effective and safe owing to its long-term use , it is sSalvia miltiorrhiza, Ganoderma, and Astragalus [DCs play an important role in the mediation of innate immune response and the induction of adaptive immune response ,37. Thustragalus ,39,40 coWhen DCs are activated, DCs secrete cytokines. It was reported that the enhanced production of IL-1β and IL-6 could promote the immune function of DCs . In the Pleurotus citrinopileatu, Poria cocos, and Anoectochilus formosanus [TCM-derived polysaccharides can be recognized by multiple PRRs at the same time, including polysaccharides from rmosanus ,42,43. PTo further explore the mechanisms of AEAR in the activation of DCs after TLR2/4 activation, the downstream events after DCs’ activation were traced. TLRs’ signaling can activate the MAPK pathway, whereas the differential activation of MAPK signaling  can activate transcription factors such as NF-κB ,45, whicIn conclusion, AEAR promoted the maturation of DCs without toxicity, stimulated the production of IL-1β and IL-6 and T lymphocyte proliferation, and decreased DCs’ phagocytosis, indicating that AEAR could activate DCs-mediated immune response. TLR2 and TLR4 were responsible for the recognition of AEAR by DCs. AEAR triggered TLR2/4-mediated signaling pathways through phosphorylated ERK, JNK, and p38 and the nuclear translocation of NF-κB. Related signaling pathways and activation effects of DCs induced by AEAR might provide novel insights into the mechanisms of immunomodulation for the application of AEAR in improved vaccines.


Plecturocebus cupreus) in Peruvian Amazon using 18 microsatellite loci. Parentage analyses of 18 young revealed no cases of extra-pair paternity, indicating that the study population is mostly genetically monogamous. We did not find evidence for relatedness- or heterozygosity-based mate choice. Despite the lack of evidence for active inbreeding avoidance via mate choice, mating partners were on average not related. We further found that dispersal was not sex-biased, with both sexes dispersing opportunistically over varying distances. Our findings suggest that even opportunistic dispersal, as long as it is not constrained, can generate sufficient genetic diversity to prevent inbreeding. This, in turn, can render active inbreeding avoidance via mate choice and extra-pair copulations less necessary, helping to maintain genetic monogamy.In pair-living mammals, genetic monogamy is extremely rare. One possible reason is that in socially monogamous animals, mate choice can be severely constrained, increasing the risk of inbreeding or pairing with an incompatible or low-quality partner. To escape these constraints, individuals might engage in extra-pair copulations. Alternatively, inbreeding can be avoided by dispersal. However, little is known about the interactions between mating system, mate choice, and dispersal in pair-living mammals. Here we genotyped 41 wild individuals from 14 groups of coppery titi monkeys ( EPP are common in pair-living, or socially monogamous birds and mammals  has been reported for only seven species so far  play an important role in the evolution of mating systems21. The intriguing question is why some individuals engage in mating with multiple partners while others do not. The advantages to males of engaging in extra-pair copulations (EPC) are well recognized, as males are expected to increase their fitness by increasing the number of mating partners as the result of their higher potential reproductive rate22. However, in pair-living species with biparental care, potential reproductive rates and, consequently, levels of intra-sexual competition will be more similar for males and females2. As a result, both sexes might be expected to gain benefits from engaging in EPC23. These benefits might include insurance against social partner’s infertility, maximizing genetic diversity among offspring, or increasing offspring genetic quality by mating with individuals that are more genetically compatible or of higher genetic quality .Rates of EPP vary substantially between species and populations and have been shown to be affected by various factors, such as, for example, intensity of male care, pair-bond strength, and population density23. This strategy has been demonstrated in various bird species25. In mammals, the evidence is much more limited. In Alpine marmots, Marmota marmota, and meerkats, Suricata suricatta, EPP rates were found to be higher in pairs where partners were more closely related27. But, to our knowledge, the positive relationship between partners’ genetic similarity and EPP rates has been only demonstrated in one pair-living mammal species: in fat-tailed dwarf lemur, Cheirogaleus medius, females sharing more major histocompatibility complex (MHC)-supertypes with their social partner were shown to engage in more EPC28.One potential advantage of EPC to both sexes could be related to limitations in mate choice. In pair-living species with biparental care, especially in those with low mobility and low breeding density, mate choice can be highly constrained. First, mates become unavailable once paired. And second, individuals may face a conflict between choice for direct benefits  and indirect genetic benefits . As a result of this constrained mate choice, individuals may end up paired to a genetically incompatible, closely related, or low-quality partner. To escape these constraints, animals might seek EPC that would allow them to gain indirect benefits while still taking advantage of direct benefits provided by the social partner31. Thus, animals are expected to choose mates that are genetically unrelated or dissimilar at some fitness-related genes . An increase in offspring heterozygosity resulting from this disassortative mating is expected to increase offspring fitness, as indicated by links between individual heterozygosity and various fitness proxies, such as survival, reproductive success, and parasite resistance . In addition, irrespective of genetic compatibility, individuals might also benefit from choosing genetically higher-quality mates, i.e., those who carry “good genes” or are more heterozygous29. With regard to heterozygosity, two opposing hypotheses have been suggested 29. First, heterozygosity may be expected to be positively correlated between mates, because less heterozygous individuals might be less successful in finding the best, i.e., more heterozygous partners. Alternatively, the correlation of heterozygosity between partners might be negative, because less heterozygous individuals need to compensate for their lower quality by choosing more heterozygous mates. Additionally, as heterozygous partners are expected to have higher fitness, they should be more likely to provide direct benefits such as increased parental care, fertility or superior territory34.Any mate choice, whether it is a choice for social or extra-pair partner, is expected to maximize not only direct fitness benefits, but also indirect (genetic) benefits, expressed as increased genetic quality of offspring. The closely related hypotheses of genetic compatibility and heterozygosity posit that individuals benefit from choosing a mate that will maximize offspring heterozygosityCyanistes caeruleus, heterozygosity was positively correlated between social mates, indicating that mating preferences were based on partner’s heterozygosity35. In Antarctic fur seals, Arctocephalus gazella, where females exert choice by moving across a breeding colony to visit largely stationary males, females were shown to move further to maximize the balance between male high heterozygosity and low relatedness34. In many species, mate choice was shown to be based on MHC loci dissimilarity  or, conversely, similarity . Finally, relatedness-based mate choice, while demonstrated in some species, such as Antarctic fur seals, was not found in many other studied species, such as fat-tailed dwarf lemurs, blue tits, and great reed warblers, Acrocephalus arundinaceus38.Mate choice based on heterozygosity was demonstrated in various species of birds and mammals. For example, in blue tits, 39. However, pairings with close relatives might be also avoided “passively” by natal dispersal that can disrupt the associations of opposite-sex kin and thus prevent matings between them40. Dispersal was shown to be sufficient to avoid inbreeding or reach a certain level of genetic dissimilarity in many situations41. It remains unclear, however, if dispersal has to be sex-biased to generate enough local genetic dissimilarity between breeding females and males to avoid inbreeding. In most mammals, males are the dispersing sex, because in polygynous mating systems, which are prevailing in mammals, males experience stronger intra-sexual competition for mates than females42. Following the same logic, mammals that mate monogamously or cooperatively with high levels of reproductive monopolization by a dominant pair are expected to have little or no sex bias in dispersal. This was found to be true in some mammals, such as the genetically monogamous Azara’s owl monkey, Aotus azarae, where both sexes disperse, or cooperatively breeding meerkats, where dispersal is only slightly male-biased44. However, in other mammals, e.g., genetically monogamous California mice, Peromyscus californicus, or socially monogamous greater white-toothed shrew, Crocidura russula, dispersal was found to be female-biased46.The absence of relatedness-based mate choice in pair-living species, such as fat-tailed dwarf lemurs or blue tits, might seem particularly surprising, because the risk of inbreeding in such species is expected to be high due to constrained mate choice. In the absence of other options, or as a result of trade-offs between a choice for a good territory vs. a choice for unrelated/compatible partner, individuals might end up paired with close relatives. Therefore, pair-living animals may be expected to use relatedness-based mate choice to “actively” avoid pairings with closely related individualsPlecturocebus cupreus. Titi monkeys  exhibit almost all the elements of the “monogamy package”, such as pair living, strong long-term pair bonds, an exceptionally high level of male care , territoriality, and sexual monomorphism50. The only missing component which has yet to be characterized is the genetic mating system. Titis are one of the very few mammalian taxa that exhibit both high level of male care and strong pair bonds, two characteristics shown to affect the rates of EPP in mammals3. The examination of their mating system and the proximate mechanisms of its maintenance may, therefore, shed light on the evolution of social and genetic monogamy in mammals. In this study, we first examined the mating system of coppery titis using a set of 18 newly developed microsatellite loci that can be universally applied to Neotropical monkeys. Second, we tested for evidence of relatedness- and/or heterozygosity-based mate choice. Finally, to see if dispersal is sex-biased, we compared genetic relatedness and diversity patterns in adult females and males and performed spatial genetic analysis. Given consistent pair living, strong pair bonds, and high levels of male care in coppery titis, we predicted them to be genetically monogamous or have a very low rate of EPP. Since the risk of inbreeding is expected to be especially high for long-lived pair-living species such as titis, we expected to find evidence for active inbreeding avoidance via mate choice and/or for heterozygosity-based mate choice. We predicted both sexes to disperse, as expected from a pair-living territorial mammal with biparental care.Here, we present a comprehensive study of the genetic mating system, mate choice, and dispersal in a wild population of coppery titi monkeys, Our analyses did not indicate any cases of EPP. In all cases of assigned paternity . One inferred case of female replacement was detected, as the adult female of Group 4 was not identified as the genetic mother of the group’s juvenile offspring; they did not share the mtDNA haplotype and had 11 allelic mismatches. No other female in our sample was identified as the most likely mother for this offspring or shared a mtDNA haplotype with it. The social father of this juvenile was indicated as the genetic father.All assignments were made with a 95% confidence level in Cervus software and confirmed with Colony software  between mating partners was generally low, averaging -0.033, and none of the pair mates shared the same mtDNA haplotype (Supplementary Table r = 0.285). The mtDNA haplotype network  obtained by bootstrapping), or mean heterozygosity HL .Our results indicate that both sexes dispersed similar distances. There were no significant differences between adult females and males in mean mtDNA haplotype diversity , mtDNA nucleotide diversity , mean Wang's relatedness r values overlapped zero for all distance classes . In titis, only anecdotal reports of replacements by intruders exist60, but given the difficulty of detecting floaters, it is possible that they are present in titi populations, too. However, given the high levels of proximity and coordination between pair mates, EPC with the floaters are probably not easier to obtain than EPC with the neighboring individuals. Furthermore, EPC, whether with floaters or neighboring animals, might be costly, with the risks including the higher probability of acquiring sexually transmitted diseases and, for females, the retaliatory withholding of parental care by males62.The opportunities to find extra-pair mates are likely limited, too. The home ranges of our study groups have very little overlap (mean 1.4% of pairwise overlap between neighboring groups (0–4.7), unpublished data; Fig. 63. The positive relationship between population density and EPP rates was demonstrated, e.g., in Eurasian beavers, Castor fiber, and in many bird species64. At our study site, population density was estimated at 34 individuals/km2 (unpublished data). This lies within the average range of values reported from behavioral studies for undisturbed populations of titis ; for comparison, reported population density in forest fragments can be as high as 369 individuals/km266. The relatively low density at our study site likely limited the opportunities for EPC. It should be mentioned, however, that for a population of red titi monkeys, Plecturocebus discolor, from undisturbed habitat, a preliminary analysis reported three cases of EPP in a sample size of 16 offspring, although these data has not been published yet67. The density of this population  was slightly higher than that of our study population, the home ranges were on average smaller (7.2 vs. 5.0 ha), and the percentage of home range overlap was larger (1.4% vs. 4.8%)65, possibly accounting for the occurrence of EPP.Opportunities for EPC are also affected by population density, with the lower densities making the encounters between individuals and, consequently, EPC less likely47. Replacements can create groups that do not consist of biological parents and their offspring, leading to the apparent deviations from genetic monogamy even in the absence of EPC. As Group 10 was only habituated shortly before the genetic sample collection and no older offspring were present in it, we could not reconstruct its demographic history. Our data indicates that adult replacements do happen in our study population. The adult female of Group 4 was not identified as the genetic mother of the group’s juvenile offspring, while the adult male was indicated as the genetic father. When we started following this group, the juvenile was estimated to be 7–8 months old based on its body size and the fact that it walked independently . Lactation in titis lasts ca. 6.5 months69, and we did not see the female nursing. Therefore, we assume that the female replacement must have happened within ca. 2 months before we started following the groups, after the juvenile had been weaned.Although in all cases of assigned paternities the social fathers were identified as genetic fathers for the group offspring , we cannot fully exclude the possibility of a low EPP rate in our study population. First, for one juvenile (Group 10), paternity remained unassigned, as neither social father nor any other male from our sample was identified as the most likely father. While this case could be classified as neither extra- nor intra-pair paternity with confidence, it remains possible that this juvenile was sired by an unsampled extra-pair male. In this case, the EPP rate in our study population would be 6%. Alternatively , this case could be the result of a male replacement in a group. Adult replacements are known to happen in titis, with the breeding positions vacated after the disappearance (presumable deaths) of adults being occupied by same-sex immigrants10 as 1 − (1 − x)n = y, where x is the maximum possible EPP level, n is the sample size (17), and y is the probability of producing at least one extra-pair offspring (0.95 for 95% confidence). The value of 16.2% is a product of the sample size and does imply that there is 16.2% EPP rate in our study population. To narrow down the confidence interval to at least 5% of EPP, we would need a sample size of 58 offspring, which is difficult to achieve in a reasonable period in a secretive arboreal primate with slow life history, living in pairs and giving singleton births only once a year.Second, for the sample size of 17 offspring, an upper limit of 95% confidence interval of EPP level  will be 16.2%. This value is calculated following Brotherton et al.r = -0.033) and never shared the same mtDNA haplotype , has not been found in most pair-living species38; reviewed in72. It has been suggested that in most situations, dispersal may be sufficient to avoid inbreeding38. By disrupting close-kin associations, dispersal can make the probability of encountering close kin relatively low, rendering active inbreeding avoidance via mate choice unnecessary72. In such cases, kin discrimination mechanisms might fail to evolve, and low inbreeding levels that will occasionally occur in such systems will be tolerated72.Contrary to our predictions, we did not find evidence for relatedness- or heterozygosity-based mate choice in our study population. Interestingly, despite the absence of evidence for active inbreeding avoidance via mate choice, the pair mates in our study population were on average not related , with whom she also shared the same mtDNA haplotype , moved to an unoccupied area adjacent to his natal group and later formed a pair (Group 11) with an unknown female . The female did not have any close relatives among the sampled animals, indicating that she, unlike her mate, had not dispersed from any of the neighboring groups. The closest relative of this female was the adult male of Group 4 with Propithecus tattersalli, showing that high levels of outbreeding can be maintained in a population by a combination of social structure and unconstrained dispersal but without the need for active inbreeding avoidance mechanisms74. The link between dispersal and inbreeding risk was further indicated by studies demonstrating a correlation between dispersal distances and inbreeding level . At our study site, the habitat was undisturbed, and dispersal was most likely unconstrained, ensuring passive inbreeding avoidance. As indicated by one case where pair mates were related on the level of second-degree kin, occasional inbreeding can still occur in such populations and is presumably tolerated.Our findings on dispersal and mating patterns, although preliminary, are in line with the evidence from other studies indicating that even opportunistic, not sex-biased dispersal can be sufficient to prevent inbreeding, as long as it is unconstrained by habitat fragmentation or other factors. The importance of unconstrained dispersal for inbreeding avoidance was supported in a simulation study based on empirical dataset from golden-crowned sifakas, 25. However, pair-living mammals do not seem to use this strategy often, possibly because mammals are less mobile than birds and it might be harder for them to quickly evade their social mate and sneak EPC. In pair-living mammals, a similar strategy was, to our knowledge, only demonstrated in one species, fat-tailed dwarf lemur, where females sharing more MHC-supertypes with their social partner engaged in more EPC28. In our study population, the absence of evidence for EPP further confirms our suggestion that dispersal in this habitat is unconstrained and the potential for inbreeding is low, rendering EPC less necessary.In addition to dispersal and preferential mating with unrelated individuals, another way to avoid inbreeding is through engaging in EPC. Positive relationship between EPP rates and pair mate relatedness was demonstrated in many bird speciesSumming up, the current study is the first to examine the link between mating system, mate choice, and dispersal in a wild population of a pair-living primate. We showed that coppery titis in our study population are mostly genetically monogamous. This is likely due to a strong pair bond enabling effective mate guarding and relatively low population density limiting the opportunities for extra-pair copulations. We further showed that coppery titis, despite exhibiting no active inbreeding avoidance via mate choice, still had low relatedness between pair mates. Our results suggest that even opportunistic dispersal, as long as it is not constrained, can create sufficient genetic dissimilarity between opposite sexes to render active mate choice and extra-pair copulations less necessary. Alternatively, the absence of relatedness-based mate choice could be a result of constraints on mate choice, where individuals have so few available mates that they cannot afford to be too selective.Future studies with larger sample size will be needed to examine the extent of genetic monogamy in this study population, as well as other populations of coppery titis and to further investigate dispersal patterns. In particular, to examine if titis indeed lack the mechanisms for active inbreeding avoidance via kin discrimination, it will be necessary to compare mating patterns and levels of inbreeding in undisturbed vs. fragmented habitats. Additionally, the absence of relatedness- and heterozygosity-based mate choice in our study population, of course, does not mean that mate choice does not occur in titis at any level. To better understand mating patterns in titis, future studies will have to examine if mate choice is based on other factors, such as, e.g., variation in MHC loci, body condition or the size or quality of the territory. Finally, current data do not allow to conclude whether extra-pair paternity is absent (or rare) in this study population because extra-pair copulations are not advantageous to individuals or because socio-ecological constrains prevent them from engaging in extra-pair copulations. To address this question, future studies on larger samples would need to compare genetic quality and fitness of group vs. extra-pair offspring (should there be any) in this or other population of titi monkeys.76. The spatial gap between Groups 8–9 and the remaining groups  in June 2017–September 2019. The study area consists mainly of undisturbed primary tropical rainforest of the "terra firme” type (not inundated during the rainy season) interspersed with small swampy areas. The home ranges of Groups 2, 3, and 13 also included land strips of secondary growth along the river that can be inundated for a few days during the height of the rainy season. The spatial gap between Groups 1, 6, 14, and 7 .Study individuals belonged to 14 family groups  living in 14 family groups, including 18 putative offspring of 9 family groups (1 to 5 offspring per group). Five other groups either did not have offspring during the study period (or they had disappeared before we could collect samples) or the samples could not be collected because the offspring were still very young and thus their defecations too diminutive to be detected. Also due to differential habituation to the presence of humans, for some groups we could not obtain samples from all group members. For those groups that were habituated in the beginning of the study period, we collected samples from offspring from several consecutive years.Fecal samples were collected immediately after an identified individual was seen defecating. We dried the samples in 15 mL falcon tubes containing silica gel beads  and stored them at ambient temperature, replacing the silica beads when necessary, until shipping to Germany.We extracted DNA . The reactions and PCR conditions for three separate multiplex reactions were the same as for the total multiplex reaction.We amplified all 27 loci in a single multiplex PCR using the Qiagen Multiplex PCR Kit (Qiagen) with a total volume of 25 μL and containing 12.5 μL 2 × Multiplex Master Mix, 1 μL of primer pool (0.2 Mμ of each primer), 1 μL of DNA extract , and 10.5 μL of RNase-free water. Amplifications were performed with initial denaturation at 95 °C for 15 min, 40 cycles of denaturation at 94 °C for 30 s, annealing at 57 °C for 1.5 min, extension at 72 °C for 1 min, and a final extension at 60 °C for 30 min. PCR products were checked on 1.5% agarose gels together with non-template controls. To prevent false homozygosity due to allelic dropout, we repeated each multiplex reaction three timesFollowing amplification, we pooled 5 μL of each multiplex PCR product (or of each PCR product of three separate multiplex reactions), purified the pooled products with the Monarch PCR & DNA Cleanup Kit (New England BioLabs), and quantified them using Qubit Fluorometer (Thermo Fisher). To prepare sequencing libraries, we performed indexing PCRs using Kapa HiFi Hotstart ReadyMix PCR Kit (Roche) with a total volume of 25 μL containing 12.5 μL 2 × Kapa HiFi Hotstart ReadyMix, 1 μL (0.5 Mμ) of each indexing primer  and 100 ng purified PCR product. Indexing PCRs were done with an initial denaturation step at 98 °C for 45 s, followed by 4 cycles of denaturation at 98 °C for 15 s, annealing at 62 °C for 30 s, and extension at 72 °C for 30 s, and a final extension step at 72 °C for 1 min. Full-length libraries were purified with the Monarch PCR & DNA Cleanup Kit (New England BioLabs) and quantified using Qubit Fluorometer (Thermo Fisher). Fragment sizes and molarities were quantified using a Bioanalyzer 2100 (Agilent Technologies). Libraries were diluted to 10 nM and then pooled and sequenced using Miseq Reagent Kit v2 with PhiX DNA (Illumina) added on the MiSeq system (Illumina). Sequencing was performed with 51 forward and 251 reverse read cycles. Only the reverse reads were used for further analysis, while forward reads were only used for MiSeq quality control. To control for laboratory mistakes, we genotyped each sample twice, leading to at least four genotypes per individual.84. The CHIIMP pipeline calls alleles by first producing unique sequences with relevant attributes  for each MiSeq sequence file, querying the sequences for potential PCR artifacts, such as stutter sequences, and then removing all sequences that do not match the locus attributes. All alleles called by CHIIMP were manually checked to validate the results and to correct automated allele calling for those loci that contain “wobble” positions in the primer sequences and are incorrectly processed by CHIIMP. We used a cutoff of 250 reads. Additionally, we accepted alleles if they yielded > 100 reads in at least three genotypes obtained per individual. Alleles with < 100 reads were not called.After sequencing, the samples were demultiplexed using MiSeq Reporter software and then processed using the CHIIMP analysis pipelineOf 27 loci, nine either consistently failed to amplify in our study animals  or proved to be monomorphic  and were excluded from further analysis. The final set consisted of 18 loci, including 17 autosomal and one X-linked locus (chrXa) (Supplementary Table 85. We assessed Hardy–Weinberg equilibrium (HWE) and calculated observed and expected heterozygosity with PopGenReport 2.2.286. Since the presence of family structure can cause deviations from HWE and bias population genetic analyses, especially in monogamous species, we only included adults in this analysis. The analysis indicated that the population was in HWE. Two loci, chr01b and chr21a, departed from HWE, likely due to the presence of relatives in a study group and/or small sample size.We checked all loci for the presence of null alleles, allelic dropout, and stuttering using Micro-Checker 2.2.5One of these two loci, chr01b, also showed evidence of null alleles. As the locus did not show any mismatches for the known mother/offspring dyads (see below), we ran all further analyses using two sets of data, one with the full set of loci and another one with locus chr01b excluded. Since the results from these two sets did not differ substantially, we present all further results only for the reduced data set.https://nucleobytes.com/4peaks/index.html) and manually edited and assembled in SeaView 4.5.487; all haplotypes were 567 bp long.We genotyped all individuals at the hypervariable region I of the mitochondrial control region using primers 5′-TACCTCGGTCTTGTAAACCG-3′ and 5′-AGGTAGGAACCAGATGCCG-3′, newly designed on the basis of mitochondrial genomes of Neotropical primates available in GenBank. PCR reactions with a total volume of 30 µl contained 1 U BiothermTaq 5000 (Genecraft), 1 × reaction buffer, 0.16 mM of each dNTP, 0.33 µM of each primer, and ca. 100 ng total DNA. PCRs were performed with initial denaturation at 95 °C for 2 min, 40 cycles of denaturation at 95 °C for 1 min, annealing at 58 °C for 1 min, extension at 72 °C for 1 min, and a final extension at 72 °C for 5 min. PCR products were run on 1% agarose gels, excised from the gel, and then purified with the Monarch DNA Gel Extraction Kit (New England BioLabs) and sequenced on an ABI 3130xL sequencer using both amplification primers and the BigDye Cycle Sequencing Kit (Applied Biosystems). Sequence electropherograms were checked with 4Peaks 1.8 , a microsatellite-derived measure that weights the contribution of each locus to the homozygosity value depending on their allelic variability, implemented in R function GENHET 3.197; available from https://github.com/laninsky/genetic_diversity_diffs). We included 12 sampled adult females and 12 adult males in this analysis, plus two females that could not be sampled but whose haplotypes were inferred from the haplotypes of their offspring . Relatedness among females and among males was calculated using Wang’s estimator r and then compared using 1000 bootstrapping samples in Coancestry 1.0.1.998. In this analysis, as well as in the tests described below, we included 12 sampled adult females and 12 males. Individual heterozygosity was calculated using HL estimator  and compared between sexes using a paired t-test.To examine if dispersal distances differ between sexes, we compared the diversity of mtDNA haplotypes, relatedness, and heterozygosity among adult females and males. MtDNA haplotype and nucleotide diversity was calculated and compared using a permutation test implemented in R function genetic_diversity_diff 1.0.6 . These distances varied from 215 to 3200 m.To evaluate spatial genetic structure, we conducted a spatial autocorrelation analysis following Smouse and Peakall100.To further evaluate spatial genetic structure in females, we conducted a test similar to spatial autocorrelation analysis using mtDNA haplotype distances, correlating the number of nucleotide differences between haplotypes with spatial distances. For this test, if a spatial genetic structure is present, a positive correlation between haplotype and spatial distances is expected. We used Mantel tests with 10,000 permutations in R package ecodistThis work was conducted under all necessary permits  and ethical guidelines from the relevant authorities of Peru and the German Primate Center.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Sustainable fisheries management requires detailed knowledge of population genetic structure. The European sprat is an important commercial fish distributed from Morocco to the Arctic circle, Baltic, Mediterranean, and Black seas. Prior to 2018, annual catch advice on sprat from the International Council for the Exploration of the Sea (ICES) was based on five putative stocks: (a) North Sea, (b) Kattegat–Skagerrak and Norwegian fjords, (c) Baltic Sea, (d) West of Scotland—southern Celtic Seas, and (e) English Channel. However, there were concerns that the sprat advice on stock size estimates management plan inadequately reflected the underlying biological units. Here, we used ddRAD sequencing to develop 91 SNPs that were thereafter used to genotype approximately 2,500 fish from 40 locations. Three highly distinct and relatively homogenous genetic groups were identified: (a) Norwegian fjords; (b) Northeast Atlantic including the North Sea, Kattegat–Skagerrak, Celtic Sea, and Bay of Biscay; and (c) Baltic Sea. Evidence of genetic admixture and possibly physical mixing was detected in samples collected from the transition zone between the North and Baltic seas, but not between any of the other groups. These results have already been implemented by ICES with the decision to merge the North Sea and the Kattegat–Skagerrak sprat to be assessed as a single unit, thus demonstrating that genetic data can be rapidly absorbed to align harvest regimes and biological units. This is occurring at a time when many of the world's fisheries are either overexploited, depleted, or recovering from earlier depletion FAO, , challen) reviews new approaches for stock identification with genetic techniques as one of its core methodologies. Recommendations on the validity and use of results from the various stock identification techniques are given to the relevant working groups for use in their stock assessments. Genetic and genomic tools have been applied directly to management issues, including “real‐time” regulation of harvest , hereafter referred to as sprat, is a fast‐growing, small, short‐lived pelagic shoaling fish  provides annual catch advice for this species. The management of exploitation, specifically within the majority of the ICES Greater North Sea Ecoregion . The sprat abundance assessment uses a natural mortality estimate derived from a multispecies model including many of its predators, thus partly ensuring a exploitation level, which will not negatively impact populations reliant on sprat as a prey source  North Sea, (b) Kattegat–Skagerrak and Norwegian fjords, (c) Baltic Sea, (d) West of Scotland—southern Celtic Sea, and € English Channel ICES, . However22.1Approximately 2,500 sprat were sampled by commercial fishermen and scientific cruises from forty locations in the NE Atlantic Figure . Part of2.2DNA was extracted from fin clips stored in ethanol using the Qiagen DNeasy 96 Blood & Tissue Kit in 96‐well plates, each of which contained two or more negative controls.SbfI‐ and SphI‐restricted DNA and involving individual‐specific inline barcode adapters. The methodology has been previously described in detail by Manousaki et al.  = 2, n  = 1). Data were then exported to Microsoft Excel for filtering to identify potential SNPs suitable for Sequenom‐based multiplex SNP assay. This involved selecting RAD loci (trimmed length 135 bases) that contained a single diallelic SNP with at least two occurrences of the minor allele among the eight samples and that the SNP was positioned between base 41 and base 95, to allow for enough flanking sequence for PCR primer design. For the final filtered set, SNP locus primer design, amplification, and genotype calling were performed using the Sequenom MassARRAY iPLEX Platform, as described by Gabriel, Ziaugra, and Tabbaa  and unbiased expected heterozygosity (uHe), as well as the inbreeding coefficient (FIS), were computed for each sample with GenAlEx  including all the locations in the same analysis, both excluding and including the southern out‐group samples , and (b) in a pairwise manner between regions. In the pairwise analysis, all the fish sampled within a region , between the northernmost sample  and each of the remaining ones excluding the southern European outliers, was plotted against the corresponding shortest water distance, which was calculated using the path function in GoogleEarth. The southern out‐groups were excluded for clarity as well as to limit this analysis to the samples for which management advice was intended.To examine demographic relationships between geographically explicit samples, the genetic distance, measured as 3The MiSeq run generated over 6 million paired‐end (pe) reads  identifying 5,648 rad loci. Of these, 121 putative SNPs were selected for a Sequenom‐based high‐throughput genotyping assay for screening all samples, and 99 were distributed into four multiplex reactions. After purging those SNPs for which allele discrimination was not reliable  as well as the ones that produced no genotypes or amplified in a very limited number of individuals , 91 loci were retained for analyses. Additionally, individuals with >30% missing genotypes were purged from the dataset. The SNP loci and their corresponding flanking regions, together with the raw data, are available in Supplementary Information_Raw data, Tables S1 and S2, respectively.The final screened data set consisted of 2,425 individuals genotyped for 91 SNPs using the Sequenom MassARRAY iPLEX Platform, 98.4% of which showed <10% of missing data. Deviations from HWE were found in 293 of the 3,640 loci by population tests (8.05%), which dropped to 51 tests (1.4%) after Bonferroni sequential correction , whereas BayeScan flagged ten (9%), all of which overlapped with those from LOSITAN . However, when the Black and Adriatic Seas were included, LOSITAN revealed fifteen candidates of directional selection (16%) conversely to the 12 (13%) found by BayeScan. In this case, the consensus between LOSITAN and BayeScan was met for ten loci , eight of which overlapped with the ones formerly found without the out‐groups. LOSITAN‐pairwise analyses conducted between regions after excluding the southern out‐groups revealed 1–9 loci putatively under directional selection per comparison . However, the consensus set incorporating BayeScan results reduced the number of outliers to three . In the pairwise analyses including the southern out‐groups, no locus flagged as an outlier candidate by LOSITAN was confirmed by BayeScan.FST estimates ranged between 0 and 0.217, with the largest estimates found between either of the southern out‐group samples and any northern collection (ranging 0.125–0.217). The lowest estimates were found among samples within each of the geographical areas: Norwegian fjords, Baltic Sea, and North Sea–Kattegat–Skagerrak, although 9–12 pairwise comparisons within these areas still came out as statistically significant  revealing strongest genetic divergence between Norwegian fjord sprat and all other locations. In contrast, three of the four estimators of StructureSelector reported K = 3 as the most likely number of genetic clusters , Great Belt (GB), and Øresund (ØS) showed admixed North Sea–Baltic Sea genetic profiles with slightly higher admixture with the Baltic Sea cluster than with the North Sea cluster. An analogous pattern was also observed in the North Sea x Baltic Sea in silico‐generated hybrids : R2 = .615, p < .0001; albeit, there was local variation over the studied geographic range , and each of the 37 other locations (excluding the two out‐groups) significantly correlated with the corresponding genetic distance measured as Heat maps of the major allele frequency for neutral and outlier loci can be found in Tables S4 and S5, respectively, in Supplementary Information. For the three loci consistently flagged as outliers, pairwise allele frequency distances were also examined against the geographic distance. Locus Ssp210 Figure  and Ssp24The primary goal of this study was to investigate population genetic structure of sprat in order to advise the ICES management plan for this species in the North Sea and its surrounding areas. To fulfill this aim, a suite of 91 SNP markers identified by ddRAD sequencing was genotyped in approximately 2,500 individuals collected from 40 locations. Three highly distinct genetic groups were identified, corresponding with the geographical regions: (a) Norwegian fjords; (b) the Northeast Atlantic region including the North Sea, Kattegat–Skagerrak, Celtic Sea, and Bay of Biscay; and (c) the Baltic Sea, including its transition zone with the North Sea, the latter exhibiting admixed genetic profiles. As the former ICES catch advice for sprat in Europe was given for stock units that partially mis‐align with the genetic data presented here, data from the present study have now been implemented by introducing a change in the stock units ICES uses for biological assessment of sprat ICES, . Therefo4.1A handful of previous studies have contributed to the current understanding of sprat population genetic structure in spite of certain limitations regarding geographical scope, sampling coverage, and/or the resolution of the genetic tools implemented  showed clear divergence from the Kattegat samples collected offshore less than 40 km away. Likewise, the Norwegian west coast sample from Lysefjord  showed clear divergence from samples from the North Sea, some of which were collected within distances of less than 250 km. This specific observation supports suggestions from an earlier study that there is little, if any, physical mixing and gene flow between sprat in Norwegian fjords and coastal areas, and sprat in the North Sea , endemic to the Mediterranean  , it was shown that the majority of the identified genomic variation exhibited no differentiation among populations that otherwise had strong genetic divergence for candidate genes inferred to be under local adaptation  display a north–south genetic gradient  and ballan wrasse (Labrus bergylta A.) show clear differentiation across a sandy stretch of habitat discontinuity  in Patagonian Chile display two highly differentiated genetic clusters, potentially the result of larval retention via combination of oceanographic mesoscale processes combined with local geographical configuration  , as is the case for another clupeid feeding in the same area, Atlantic herring  Norwegian fjords; (b) the Northeast Atlantic region including the North Sea, Kattegat–Skagerrak, Celtic Sea, and Bay of Biscay; and (c) the Baltic Sea. Therefore, considering the wide‐ranging MAFs between geographic groups that loci displayed, and that population genetic structure showed plausible geographic and biologic resolution, ascertainment bias seemed not to be of major concern. However, although this simple marker‐identification procedure suitably matched our objectives, whole genome‐based approaches specifically looking for outlier loci and signs of adaptation may lift knowledge further in the future by capturing variation that might have eluded our scrutiny so far.A geographically broad and dense sampling design is beneficial for any population genetic study. Here, a denser net of samples in the southernmost part of Norway as well as in the North Sea and Kattegat–Skagerrak areas might help outlining the transition zone with higher precision. Furthermore, including samples from the meridional range of the distribution and increasing the density in areas such as British Isles, western French coast, Mediterranean, and Black Sea would provide a comprehensive picture of the number of populations and level of connectivity between them in this species.None declaredFig S1‐S4Click here for additional data file.Table S1‐S2Click here for additional data file.Table S3Click here for additional data file.Table S4‐5Click here for additional data file.


Background: Mitochondrial DNA (mtDNA) has long been used to date historical demographic events. The idea that it is useful for molecular dating rests on the premise that its evolution is neutral. Even though this idea has long been challenged, the evidence against clock-like evolution of mtDNA is often ignored. Here, we present a particularly clear and simple example to illustrate the implications of violations of the assumption of selective neutrality.Methods: DNA sequences were generated for the mtDNA COI gene and the nuclear 28S rRNA of two closely related rocky shore snails, and species-level variation was compared. Nuclear rRNA is not usually used to study intraspecific variation in species that are not spatially structured, presumably because this marker is assumed to evolve so slowly that it is more suitable for phylogenetics. Results: Even though high inter-specific divergence reflected the faster evolutionary rate of COI, intraspecific genetic variation was similar for both markers. As a result, estimates of population expansion times based on mismatch distributions differed between the two markers by millions of years.Conclusions: Assuming that 28S evolution is more clock-like, these findings can be explained by variation-reducing purifying selection in mtDNA at the species level, and an elevated divergence rate caused by diversifying selection between the two species. Although these two selective forces together make mtDNA suitable as a marker for species identifications by means of DNA barcoding because they create a ‘barcoding gap’, estimates of demographic change based on this marker can be expected to be highly unreliable. Our study contributes to the growing evidence that the utility of mtDNA sequence data beyond DNA barcoding is limited. Over the past decades, much evidence has accumulated that mtDNA can be strongly affected by selective sweeps and background selection6. As a result, the usefulness of the marker in assessing genetic diversity7 and exploring spatial genetic structure in continuously distributed populations8 has been questioned, and corrections of the mitochondrial molecular clock that account for selection have been proposed10.Mitochondrial DNA (mtDNA) has long been a marker of choice for investigating concepts as diverse as estimating genetic diversity and effective population sizes, reconstructing species’ evolutionary histories, exploring spatial genetic subdivisions, and identifying cryptic species. All these methods assume that mtDNA variation conforms to the neutral model of molecular evolution11, molecular dating of historical demographic events by means of evolutionary rate estimates that are typically based on inter-specific divergence13 will result in considerable underestimates. This is particularly likely because divergence between species can be strongly affected by diversifying selection that is driven by different environmental conditions15, resulting in a faster accumulation of mutations characterising each species than is expected under the neutral model.The implications of reduced genetic diversity at the species or population levels due to purifying selection has so far received little attention. When mutations in mitochondrial genes occur at fewer sites than expected under the neutral modelAfrolittorina that show no spatial genetic structure throughout their ranges16. The finding that data from two genetic markers with mutation rates are that assumed to differ by at least an order of magnitude18 have similar levels of intraspecific variation challenges the usefulness of mitochondrial DNA sequences for studying historical demographic changes.Here, we explore this issue using mitochondrial and nuclear DNA sequence data from two common southern African snails of the genusAfrolittorina africana andA. knysnaensis were collected at 34 sites throughout South Africa  using Big Dye Terminator v3.1 chemistry. Sequences were edited using MEGA723, and 28S sequences were phased in PHASE v2.1.124 using default settings. Genealogical relationships between COI haplotypes and 28S alleles were reconstructed using the median-joining algorithm25 in popArt v1.726. To explore the effect of using interspecific evolutionary rates to estimate species-level population size changes, we calculated population expansion time27 using Arlequin v3.528 using each marker’s slowest and fastest published rates for marine gastropods . We understand what the authors explained in the response to each comment. We know many limitations remarked in their responses. However, the readers who read this report in the future should realize that it was prepared and published under limitation conditions.Is the work clearly and accurately presented and does it cite the current literature?NoIf applicable, is the statistical analysis and its interpretation appropriate?Not applicableAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?NoAre the conclusions drawn adequately supported by the results?NoAre sufficient details of methods and analysis provided to allow replication by others?NoReviewer Expertise:Molecular systematics.We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. A. aficana andA. knysnaensis? Finally, what is the main objective of this work? Whether you aimed to study the genetic differentiation between species of the snail based on different kinds of markers or study on population genetic, the content of this study could not support both objectives. For example, if the authors want to present on comparing the genetic variations that came out from the COI gene and 28S rDNA gene, the analysis used in this study was not suitable, the haplotype network analysis may not fit enough for the genetic variations you want to get. If the author wants to analyze the population genetically, the weak point is the numbers of the snails collected? And why the authors decided to collect from the various sites? What is the hypothesis behind your sampling?   Introduction part, the introduction part seems to focus on the selection process in the population to increase/decrease the genetic variation accumulation in the mt genome. The authors did not emphasize on the research questions; for example, Why did you want to study in two snails of the genus Afrolittorina? Why did you use the 28S rDNA as another genetic marker? What is the research question or hypothesis of this study? What was the reason behind deciding to study onI don't understand why the authors have to analyze the molecular clock by comparing different originated markers like COI from one of the protein-coding genes in the mitochondrial genome and LSU (RNA-specifying gene) from the nuclear genome. Of course, the rate of evolution acting on these two genomes is different. A. aficana and A. knysnaensis comparing with the COI gene. Only 2 nucleotide differences between those two snail species, while the nucleotide variation in the intra-specific level ofA. knysnaensis is higher than that.If the authors considered in Figure 1, you would realize that 28S rDNA is not good enough for species discrimination betweenThe authors didn't discuss the evidence in snails with regards to variation, which is the point that showed in the result. There is no analysis to estimate either positive or negative selection, but the authors discussed it as the condition forced on the snail populations. It becomes over speculation. The title of this study should mention snail species because the genetic variation patterns are various depending on the groups of organisms. This study aimed to explore the genetic variation of two common southern African snails of the genus Afrolittorina. The main finding of this study is that intraspecific variations of nuclear 28S rDNA sequences are higher than such value from the mitochondrial gene in the snail populations. However, many points need to concern before acceptance. I listed the major points for revising the manuscript.Is the work clearly and accurately presented and does it cite the current literature?NoIf applicable, is the statistical analysis and its interpretation appropriate?Not applicableAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?NoAre the conclusions drawn adequately supported by the results?NoAre sufficient details of methods and analysis provided to allow replication by others?NoReviewer Expertise:Molecular systematics.We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Reviewer Report: Abigail Hui En Chan and Urusa Thaenkham, Department of Helminthology, Faculty of Tropical Medicine, Mahidol University, Bangkok, ThailandAPPROVED WITH RESERVATIONSCOMMENT: This study aimed to explore the genetic variation of two common southern African snails of the genus Afrolittorina. The main finding of this study is that intraspecific variations of nuclear 28S rDNA sequences are higher than such value from the mitochondrial gene in the snail populations.similar to that of 28S.”RESPONSE: The statement that “intraspecific variations of nuclear 28S rDNA sequences are higher than such value from the mitochondrial gene” is not entirely correct, as in A. africana, COI has more haplotypes, whereas in A. knysnaensis, 28S has more haplotypes. We specifically state that “intra-specific genetic differentiation was comparatively low for this marker , andCOMMENT: However, many points need to concern before acceptance. I listed the major points for revising the manuscript. Introduction part, the introduction part seems to focus on the selection process in the population to increase/decrease the genetic variation accumulation in the mt genome. The authors did not emphasize on the research questions; for example, Why did you want to study in two snails of the genus Afrolittorina? Why did you use the 28S rDNA as another genetic marker? What is the research question or hypothesis of this study? What was the reason behind deciding to study on A. aficana and A. knysnaensis? Finally, what is the main objective of this work?http://vital.seals.ac.za:8080/vital/access/manager/Repository/vital:5588?site_name=GlobalViewRESPONSE: This article is a Brief Report, which has very strict word limits. For that reason, it is not possible to provide lengthy explanations for the issues raised. Briefly, the original purpose of the research was to determine whether each species is divided into regional populations (which was not found). The purpose was not to compare intraspecific variation between two markers with very different evolutionary rates, as that is an unexpected finding, and essentially a novel discovery that is reported in this paper. To answer the additional questions, we would like to refer the reviewers to the original PhD thesis on which this paper is based, which is now included among the references, and which is accessible via the following link:COMMENT: Whether you aimed to study the genetic differentiation between species of the snail based on different kinds of markers or study on population genetic, the content of this study could not support both objectives. For example, if the authors want to present on comparing the genetic variations that came out from the COI gene and 28S rDNA gene, the analysis used in this study was not suitable, the haplotype network analysis may not fit enough for the genetic variations you want to get. If the author wants to analyze the population genetically, the weak point is the numbers of the snails collected?RESPONSE: The aim of the study was not to analyse each population genetically. We assume here that the reviewers equate ‘populations’ with sampling sites, please see the following paper for an explanation why this is problematic: Waples & Gaggiotti (2006) Molecular Ecology 15:1419-1439. As neither species shows regional sub-structure  and each species thus comprises a single ‘population’, it is possible to pool data from all sites for intraspecific genetic analysis. The number of samples collected (93 and 82) is actually large compared to other papers of this nature. For example, in their highly cited paper on the barcoding gap (whose detection depends on both intra- and inter-specific variation), Paulay & Meyer (2005) used an average number of 7.7 samples per cowrie species.  The fact that neither species of Afrolittorina is genetically subdivided throughout its range is clearly important, and was removed from an earlier version of this article so as not to exceed the word count for Brief Reports. We have decided to briefly mention it in the last paragraph of the Introduction: “Here, we explore this issue using mitochondrial and nuclear DNA sequence data from two common southern African snails of the genus Afrolittorina that show no spatial genetic structure throughout their ranges.” We now also cite the PhD thesis for anyone interested in the biology of these snails.COMMENT: And why the authors decided to collect from the various sites? What is the hypothesis behind your sampling?RESPONSE: The original purpose of collecting samples from multiple sites that span much of each species’ range was to determine whether or not these species are genetically subdivided into regional populations, which was not found. However, this is clearly not relevant to the present study, which focuses on intraspecific variation, not genetic structure. Obtaining large numbers of samples from a few sites would be problematic as one cannot rule out that additional, genetically distinct ‘populations’ exist that were not sampled. Please note that Table 1 is not particularly important to understand this paper, and for that reason should not be the focus of a lengthy discussion related to sampling. We originally had it in an Extended Data section, but the editor requested us to move it to the main text.COMMENT: I don't understand why the authors have to analyze the molecular clock by comparing different originated markers like COI from one of the protein-coding genes in the mitochondrial genome and LSU (RNA-specifying gene) from the nuclear genome. Of course, the rate of evolution acting on these two genomes is different.RESPONSE: The difference in evolutionary rates between these two markers is crucial to highlight the potential role of variation-reducing selection in COI. Without understanding this, it is not possible to understand the take-home message of this paper. We essentially point out that the known rate differences between these two markers manifest at the inter-specific level. The novel finding of this study is that intra-specific levels of variation are surprisingly similar. To clarify, the statement “Of course, the rate of evolution acting on these two genomes is different” is only true between species, not within species.COMMENT: If the authors considered in Figure 1, you would realize that 28S rDNA is not good enough for species discrimination between A. aficana and A. knysnaensis comparing with the COI gene. Only 2 nucleotide differences between those two snail species, while the nucleotide variation in the intra-specific level of A. knysnaensis is higher than that.RESPONSE: The purpose of this paper is not to suggest that 28S is a marker that is suitable for species discrimination, particularly when compared to COI. As we state in the first sentence of the Discussion, “The usefulness of the mtDNA COI gene to uncover overlooked biodiversity is undisputed because of the marker’s tendency to have a well-defined barcoding gap, as was also found here.” It is not clear what the reviewers mean by “not good enough”, the two genetic clusters are clearly distinct, although inter-specific divergence is clearly much lower. The likely reason for this is that 28S evolves in a more clock-like fashion while COI is strongly affected by selective forces, and this is another important point to understand, as a marker that does not evolve in a clock-like fashion should not be used for intraspecific molecular dating. Please see the second paragraph in the Discussion: “In the present study, we have highlighted a largely unexplored problem that likely arises from selection effects in mtDNA data: the fact that demographic events using gene regions under purifying selection are dated using molecular clock calibrations affected by variation-increasing inter-specific divergence.” Given the slow evolutionary rate of 28S, minimal differentiation between species is expected. What is unexpected are the similar levels of intraspecific variation (see title).COMMENT: The authors didn't discuss the evidence in snails with regards to variation, which is the point that showed in the result.RESPONSE: The results report the simplest and most intuitive means of describing instraspecific variation: the number of haplotypes, and the maximum number of mutations between them. Several sentences in the Discussion deal with this, e.g. first paragraph: “In contrast, there was comparatively little genetic variation at the intraspecific level for both markers, which is likely due to the commonly reported strong purifying selection acting upon the COI gene.”COMMENT: There is no analysis to estimate either positive or negative selection, but the authors discussed it as the condition forced on the snail populations. It becomes over speculation.RESPONSE: The paper originally included a test for selection, but as it was not informative and exceeded the word limit for Brief Reports, it was removed. We used a McDonald-Kreitman test  in MKT  to test for selection in COI. Out of 58 mutations for the whole dataset, only one was non-synonymous. Although the test had the highest possible proportion of adaptive substitutions  of 1.0, it was non-significant (P = 0.57), supposedly because of the lack of non-synonymous polymorphism.D. melanogaster. PLoS Genet 9(5): e1003527). Again, a detailed discussion of these issues would far exceed the word limit, and would not add much to the study. The simple dataset used here is clearly not sufficiently informative to conclude that purifying selection is present, and for that reason we have changed the earlier title “Purifying selection can reduce intraspecific mitochondrial gene variation to that of nuclear rRNA” (https://www.biorxiv.org/content/10.1101/2020.03.31.017764v1) to the present one, which merely focuses on intraspecific variation rather than selection.Further, recent genomic evidence indicates that tests comparing synonymous and non-synonymous mutations are not actually conclusive about whether or not selection has taken place, because synonymous sites may be affected by strong selection even though no amino acid changes have taken place  Strong Purifying Selection at Synonymous Sites inWe nonetheless do not think that mentioning selection is this context is over-speculative. There is strong evidence that a finding of barcoding gaps in hundreds of studies is the result of selective forces, and we cite several important papers in this context, including two at the end of the following sentence in the Discussion: “In contrast, there was comparatively little genetic variation at the intraspecific level for both markers, which is likely due to the commonly reported strong purifying selection acting upon the COI gene.” In terms of finding a COI barcoding gap, our study is merely an additional example, but the finding that intraspecific variation-reducing selection can be so significant that COI shows levels of variation similar to a marker that evolves much more slowly is novel.COMMENT: The title of this study should mention snail species because the genetic variation patterns are various depending on the groups of organisms.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3351256/). In the present case, the snail species, their geographic structure, their biology etc. are far less important than are the two genetic markers, and anyone who does not work on littorinid snails may consider the paper irrelevant to their own work if species names are included in the title. However, the finding of this study is likely applicable to a wide range of organisms given the mounting evidence for variation-reducing selection in mtDNA, and giving the impression that it is unique to two rocky shore snails would be an obstacle to further investigation into this issue. To provide an example, had Stoeckle & Thaler 2014 mentioned in the title of their article “DNA barcoding works in practice but not in  theory” that these findings are based entirely on birds, we would probably not have cited their paper, even though it is highly relevant. For that reason, we believe that it should be sufficient to mention “rocky shore snails” in the Abstract.RESPONSE: We respectfully disagree – the practice of including taxonomic information in the title is standard procedure in taxonomy journals, but it is undesirable in a multidisciplinary journal such as F1000Research. In general, it is well known that shorter titles that report results are more likely to get accessed . Is there evidence that the mutation rates of this mitochondrial locus is under the same kind of diversifying selection as PCGs? If not, then the authors should consider changing or removing this reference. In the discussion the authors refer to the different thermal environments of the two snail species used in the study. It would be useful if the authors could elaborate on this as very little background about the species being studied is provided. The authors state that they are assuming 28S evolves in a clock-like manner. Can they provide a references/evidence for this as it is quite a bold statement, which has implications on some of the conclusions being drawn.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Molecular biology, mitogenomics, phylogenetics.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This is an interesting manuscript addressing the methodological issues with the continued trend of using mitochondrial DNA sequence data to infer genetic variation and population structure. The results are based on a very limited data set, however, they are only intended to further illustrate the issues highlighted by the authors. Apart from that, the study does not require much improvement. It is well written and structured and the results support the conclusions and therefore suggest indexing the article once the following issues have been considered and adequately resolved;COMMENT: The authors claim that this is the first study to use 28S ribosomal RNA at this taxonomic level. Do they mean in snails, as this gene has been used extensively for phylogenetics and some population level analysis? More info is needed.RESPONSE: We have conducted an extensive literature review on this issue, and have not found any comparable example. Even though intraspecific variation in 18S or 28S rRNA has been reported in various invertebrate taxa, upon closer inspection it becomes evident that this variation is either located in the more rapidly evolving ITS regions rather than in the rRNA, or the species studied have geographically distinct populations that may constitute cryptic species. However, as we cannot rule out the possibility that comparable examples exist, we have changed the sentence to: “Nuclear rRNA is not usually used to study intraspecific variation in species that are not spatially structured, presumably because this marker is assumed to evolve so slowly that it is more suitable for phylogenetics.”COMMENT: Could the authors include a reference when stating that there is an order of magnitude of difference between the mutation rates of the two markers used in the study? RESPONSE: The paper already includes several references that have estimated mutation rates specifically for marine snails. Please see Table 2, where the 28S rates range from 0.01-0.05 %/myr, whereas the COI rates range from 0.26-0.50 %/myr. We now cite both references in the last paragraph of the Introduction.COMMENT: Reference 29 is cited as an example of the continued use of mtDNA for exploring demography, however, that study uses the mitochondrial control region rather than sequence data Protein Coding Genes (PCGs). Is there evidence that the mutation rates of this mitochondrial locus is under the same kind of diversifying selection as PCGs? If not, then the authors should consider changing or removing this reference.RESPONSE: This is a valid point, and while a signature of diversifying selection in the control region is likely because this marker is linked to the PCGs by virtue of being part of the same genome, it is clearly not under selection itself. For that reason, we have decided to remove this reference.COMMENT: In the discussion the authors refer to the different thermal environments of the two snail species used in the study. It would be useful if the authors could elaborate on this as very little background about the species being studied is provided. RESPONSE: To comply with word count requirements for Brief Reports, we have removed more detailed descriptions of the species’ distribution ranges, morphology and physiology. In response to similar queries from the other reviewers, we now cite the PhD thesis on which the present study is based. This thesis not only provides information on the temperature-defined marine biogeographical provinces in which these species occur, but it also shows evidence for adaptive differences assessed using physiological experiments.COMMENT: The authors state that they are assuming 28S evolves in a clock-like manner. Can they provide a references/evidence for this as it is quite a bold statement, which has implications on some of the conclusions being drawn.RESPONSE: We have found no specific information on this, as the issue of selection on the molecular clock has primarily received attention in the context of mtDNA. However, we believe that the considerable difference in terms of genetic differentiation between species justifies such as statement. There is now strong evidence that the barcoding gap in COI is a result of selective forces, and the comparatively low level of genetic divergence based on 28S implies that similar selective forces are not acting on this marker. We have, however, made the statement in the Abstract less bold and have changed it to: “Assuming that 28S evolution is more clock-like…”.