Philippa Kaur

ScopeWe compared disposable face masks that were used once with face masks that were sterilized and used five more times (six times in total). Sterilisation and PFE test data of the Aura 1862+ (3M, Saint Paul, Minnesota, USA) face mask indicate that this type of face mask shows good performance after multiple sterilisation cycles10,11,12. In a previous pilot study, the company CSA Services (Utrecht, the Netherlands), a sterilization facility for cleaning, disinfection and sterilization of medical instruments, was rebuild to process FFP2 face masks. In total, 18,166 single use FFP2 masks were sterilised after use in a medical autoclave. As the majority (n = 7993) were Aura 1862+ (3M, Saint Paul, Minnesota, USA), this particular type of face mask was chosen for the LCA.The total weight of the face masks and packaging together during end-of-life consists of incineration for the face masks (97%) and landfill for the carton box packaging of new face masks (3%). There is no recycling potential used in our model since the materials coming from the operating room and its packaging is commonly disposed as medical waste. In the Netherlands, no energy recovery takes place at the incineration of regulated medical waste. Therefore, no co-function was applicable for the end-of-life scenario.Recycling is often a multi-functional process that produces two or more goods. To deal with the multi-functionality in the background processes, the cut-off approach was applied to exclude the allocation of the greenhouse gas emissions to additional goods. This means that potential rest materials such as energy gained during incineration are cut-off and that the greenhouse gas emissions are fully allocated to the waste treatment processes itself.In the LCA, the ‘functional unit’ defines the primary function that is fulfilled by the investigational products and indicates how much of this function is considered18. In this study, we pragmatically chose as a definition for the protection of 100 health care workers against airborne viruses, using one FFP2 certified face mask, each during one working shift of an average of 2 h in a hospital in the Netherlands.Table 1 shows the differences between the two scenarios:

1.

100 masks including packaging, transported from production to the hospital, used and disposed.

2.

100 times use of reprocessed masks. We calculated that 27.1 masks are being produced and transported from production to the hospital. The 27.1 are being reprocessed five times, taking into account that 20% of the batch cannot be reprocessed. Therefore 80% of the batch could be used for reprocessing after each step resulting in: 27.1 (new) + 21.7 (repro 1) + 17.3 (repro 2) + 13.9 (repro 3) + 11.1 (repro 4) + 8.9 (repro 5) = 100 times of use. For each time of reprocessing the batch is transported from the hospital to the (hospital) Central Sterilization Services Department (CSSD) and disposed after five times of reprocessing.

Table 1 Comparison between reference flow 1 and 2.Full size tableCombining the functional unit with the two alternative scenarios results in the reference flows for the protection of 100 health care workers against airborne viruses, either using a face mask one single time (100 virgin masks produced for the 1st scenario), or reusing a face mask for five additional times (27.1 virgin masks produced for the 2nd scenario). For both reference flows, only FFP2 certified face masks are considered. For the calculations each mask is used for a single two hours working shift in an average hospital in the Netherlands.Life cycle inventory (LCI) analysisThe inventory data includes all phases from production (including material production and part production), transport, sterilisation to end-of-life of the life cycle of the single use and reprocessed face masks. We disassembled one face mask to obtain the weight of each individual component on a precision scale (Fit Evolve, Bangosa Digital, Groningen, the Netherlands) with a calibrated inaccuracy of 1.5%. Component information and materials were obtained from the data fact sheet provided by the manufacturer. We conducted a separate validation experiment to establish the material composition in the filtering fabric (Supplement file).This LCA with the Aura 3M masks was based on steam sterilization by means of a hospital autoclave and therefore part of this study. Therefore, face masks were placed in a sterilization bag that contained up to five masks. A total of 1000 masks were placed into an autoclave (Getinge, GSS6713H-E, Sweden) per cycle. After sterilization, the masks were transported to the hospital. Masks were reprocessed for a maximum of five times before final disposal10,11.The assessment of climate change impact is done following as closely as possible the internationally accepted Life Cycle Assessment (LCA) method following the ISO 14040 and 14044 standards19,20. The LCA examines all the phases of the product’s life cycle from raw material extraction to production, packaging, transport, use and reprocessing until final disposal19. The LCA was modelled using SimaPro 9.1.0.7 (PRé Sustainability, Amersfoort, The Netherlands). The background life cycle inventory data were retrieved from the ecoinvent database (Ecoinvent version 3.6, Zürich, Switzerland)21.To make a valid comparison between the disposable and reprocessing face masks, the system boundaries should be equal in both scenarios. The system boundaries in this study consisted of the production, the use and the disposal and waste treatment of the masks. For the reprocessed face masks, the lifecycle is extended due to the sterilisation process (Fig. 1). Therefore, the additional PPE’s and materials needed to safely process the masks (e.q. masks, gloves and protective sheets) are included in the production phase. The production of machinery for the manufacturing of the face masks and the autoclave were not included in this study.Figure 1System boundary overview of new and reprocessed face masks including waste treatment by incineration.Full size imageThe production facility for the face masks is located in Shanghai, China22,23. Further distribution took place from Bracknell, UK to Neuss, Germany and the final destination was set in Rotterdam, the Netherlands.The packaging materials were disposed in the hospital where the face masks are used primarily. After first use, face masks were transported to the sterilisation department. All masks were manually checked before reprocessing by personnel wearing PPE. Of all used Aura 1862+ facemasks that entered the CSA, approximately 10% was discarded. To remain conservative, the LCA was conducted based on a 20% rejection rate as a result of face masks which could not be reused anymore due to deformities, lipstick, and broken elastic bands.A full overview of the life cycle inventory table for the two scenarios and details on model assumptions are added in the Supplemental file (Supplemental file, Part B).Life cycle impact assessmentThe carbon footprint (kg CO2 eq) was chosen as the primary unit in the impact category. ReCiPe was applied at midpoint level and used to translate greenhouse gas emissions into climate change impact16.Uncertainty analysisThe final LCA model contains several uncertainties based on assumptions and measurement inaccuracies24. The included uncertainties were based on weighted components of the masks as well as the packaging which were measured with 1.5% inaccuracy of the precision scale apparatus. A Monte Carlo sampling25 was conducted for both alternatives (disposable and reprocessing) where input parameters for the LCA were sampled randomly from their respective statistical distributions in for 10,000 ‘runs’. Because input parameters between scenarios were partly overlapping, we compared these two scenarios directly using a discernibility analysis. This technique, establishes which scenario is beneficial for each of 10,000 Monte Carlo runs. We report the percentage of instances where the reprocessing scenario has a lower carbon footprint than the disposable scenario.Sensitivity analysisA sensitivity analysis was conducted to check the sensitivity of the outcome measures to variation in the input parameters. To determine which parameters are interesting to investigate, three aspects were considered: the variations in number of face masks per sterilization cycle (autoclave capacity), rejection rate (number of losses per cycle) and transport distance to the CSSD. Finally, we included the relative contribution of these variations. The following three parameter variations were chosen for the sensitivity analysis:

1.

Rejection percentage. The rejection rate was defined based on experiences from the participating sterilisation department and studies that show that sterilisation of the face masks up to 5 times is possible. Masks were re-used for 5 times, approximately 10% was discarded during the total life cycle. Out of this experience and to remain conservative, the total rejection rate was set on 20%. Therefore it is interesting to investigate whether variation in PFE testing outcomes or differences in user protocols influence the outcomes. This should indicate if masks from higher or lower quality can also be suitable candidates for reprocessing.

2.

Autoclave capacity, which largely depends on the loading of the autoclave. To mimic different loads of the autoclave, it is interesting to know the influence of sterilizing fewer masks per run on the model.

3.

Transport. As it is likely that many hospitals have a Central Sterilisation Services Department (CSSD) it is interesting to know the effect of having zero transportation. Moreover, in case hospitals are not willing to change the routing in their CSSD it is interesting to observe how outcomes are influenced if transportation is set on the maximal realistic value of 200 km.

The parameters have been varied with 250 and 500 face masks per sterilisation batch. A rate varying with 10% and 30% of the face masks being rejected due to quality reasons and variation in transport kilometres of 0–200 km.There is a small difference between the baselines of the sensitivity, LCIA and contribution analyses because all these are performed using separate Monte-Carlo simulations. The output of the different simulations may show minor differences due to statistical distribution.Cost price comparisonA cost analysis was made to give insight in costing from a procurement perspective. The cost analysis is conducted with five face masks that were steam sterilized per batch in a permeable laminate bag, Halyard type CLFP150X300WI-S20 and includes the expenses of energy, depreciation, water consumption, cost of personnel, overhead and compared to the prices for a new disposable 3M Aura face mask during the first and second Corona waves. Five pieces per bag were chosen in order to have enough space between the masks to sterilise each mask properly. The cost analysis is based on actual sterilization as well as associated costs compared to the prices of new disposable face masks. The costs were then related to the functional unit of protecting 100 health care workers by calculating the difference in the amount of Euros per 100 face masks. More

Study regions and farmsTen European regions from boreal to Mediterranean were selected (Supplementary Table 1). They represented major agricultural land uses such as arable crops including horticulture, mixed farming, grassland and perennial crops (vineyards and olives). Within each region, a pool of ~20–40 farms was selected from which 12–20 farms were randomly selected (169 in total) that belonged to the same farm type, produced under homogeneous climatic and environmental circumstances and fulfilled specific criteria regarding their main production branch. In case the selected farms were not willing to participate, we asked other farms from the pool till the sufficient number has been reached. The selected organic farms had all been certified for at least five years. Farmers were asked if they were willing to participate in the study. If they refused, additional random sampling was conducted. In the region NL, 11 organic farms agreed to participate but only three non-organic farms, whereas seven organic farms and 11 non-organic farms were available in the region HU. During the study, one non-organic farmer in the region CH ceased participation.Habitat maps and farm interviewsThe complete area of all selected farms was mapped, using the BioHab method36. Excluded from the farm area were woody and aquatic habitats larger than 800 m2 and summer pastures. Within the farm area, areal and linear habitats were recorded. For an areal habitat, the minimal mapping unit was 400 m2 with a width of at least 5 m. More narrow habitats, between 0.5 and 5 m wide and at least 30 m long, were mapped as linear habitats. Habitats were distinguished in habitat types according to Raunkiær life forms, environmental conditions and management evidence28. Further, a farmland class was assigned to each habitat that described whether the habitat was managed for agricultural production or other objectives such as e.g. nature conservation. In face-to-face interviews following a standardized questionnaire, farmers provided detailed information on field management and yield.Categorization as production fields and semi-natural habitatsBased on the habitat maps and available information about management intensity, we categorized all habitats as either semi-natural habitats or production fields. In agricultural landscapes, these two categories are often not clearly distinguishable. There is a gradient from more intensively managed production fields to less intensively used semi-natural habitats. In addition, a categorization at the local scale can be different from an approach at a European scale (29 and see p. 45 of37). Here, we applied the same criteria for all ten study regions.In all cases, we categorized as production fields: arable crops, intensively managed grasslands (following main plant species observed, management evidence and objectives, with fertilization and/or two or more cuts a year), horticultural crops, and vineyards.We categorized as semi-natural habitats: linear habitats, habitats that were managed for nature conservation objectives, habitats where mainly geophytes, helophytes or hydrophytes were growing, grasslands with woody vegetation (shrubs and/or trees), and extensively managed grasslands (no fertilization, no or one cut a year).Species samplingVascular plant, earthworm, spider and bee species were sampled in all different habitat types of a farm. One plot per habitat type was randomly selected per farm for species sampling. This resulted in 1402 selected habitat plots on 169 farms (Supplementary Table 2). In the selected habitats, species were sampled during one growing season, using standardized protocols19,38. Plant species were identified in squares of 10 × 10 m in areal habitats and in rectangular strips of 1 × 10 m in linear habitats. Earthworms were collected at three random locations of 30 × 30 cm per habitat. First, a solution of allyl isothiocyanate (AITC) was poured out to extract earthworms from the soil. Afterwards, a 20-cm-deep soil core from the same location was hand sorted to find additional specimens. Identification took place in the lab. Spiders were sampled on three dates at five random locations per habitat within a circle of 0.1 m2. Using a modified vacuum shredder, spiders were taken from the soil surface, transferred to a cool box, frozen, or put in ethanol, sorted and identified in the lab. Bees (wild bees and bumble bees) were sampled on three dates, during dry, sunny and warm weather conditions. They were captured with an entomological aerial net along a 100 m long and 2 m wide transect, transferred to a killing jar and identified in the lab.Grouping of species dataSpecies data were pooled per taxa, habitat and region, and three sub-communities were formed with species (1) exclusively found in semi-natural habitats, i.e. unique to semi-natural habitats, (2) exclusively found in production fields, i.e. unique to production fields, and (3) found in both habitat categories i.e. shared by production fields and semi-natural habitats. For calculations of effects over all four taxa, species richness was the sum of the individual taxa species richnesses.Estimating species richnessSpecies richness was estimated using coverage- and sample-size-based rarefaction and extrapolation curves31,39,40. Rarefaction and extrapolation, including confidence intervals (bootstrap method) and sampling coverage, were calculated in R 3.4.041 using package iNEXT42. Detailed information is provided below for each topic.Estimating richness of unique species to compare semi-natural habitats and production fieldsTo legitimately compare the richness of species unique to semi-natural habitats and to production fields, we used the coverage-based method, i.e. we standardized the samples by their completeness30. The point of comparison was determined by the so-called ‘base coverage’ identified by the following procedure31: (1) select the maximum sample coverage at reference sample size (number of sampling units) of the sub-communities under comparison, (2) select the minimum sample coverage at twice the reference sample size of the sub-communities under comparison, (3) identify the maximum of the results from step (1) and step (2) as ‘base coverage’. The species richness estimates were then read off from the species sample-size-based rarefaction and extrapolation curves at the ‘base coverage’ for each sub-community being compared. If zero or exactly one species was unique to a sub-community at the reference sample size, no sample coverage could be calculated. In this case, we set the species richness at 0 or 1, respectively. The species richness estimate of the other sub-community under comparison was then read off at twice the reference sample size on the curve.The ‘base coverage’ was individually defined for each region and each taxonomic group since the mixed effects models used to analyze the data took into account the variation among regions and taxonomic groups.Differences in species richness unique to semi-natural habitats and production fieldsThe difference between the species richness unique to semi-natural habitats and unique to production fields was tested with mixed effects models using package lme4 (Version 1.1-12) in R43. The data were (Sij | β, b, x) ~ Poisson(µij) from i = 1, …, 10 regions. The model is:$${{{rm{ln}}}}left({mu }_{{ij}}right)={beta }_{0}+{beta }_{1}{x}_{1i}+{b}_{1i}$$
(1)
$${b}_{1} sim N(0,sigma 2)$$where ({beta }_{0}) is a fixed intercept, ({beta }_{1}) a fixed effect sub-community ({x}_{1{ij}}) (species unique to semi-natural habitats versus species unique to production fields), b1i are random intercepts for region i. Random effects are normally distributed with mean 0 and variance σ2. The significance of term ({beta }_{1}) was calculated by log-likelihood ratio tests with one degree of freedom. For the models over all four taxa, an additional random intercept was included, i.e. b2j with mean 0 and variance σ2 for j = 1, …, 4 taxa (Fig. 1b).Differences in species richness between organic and non-organic systemsThe comparison between organic and non-organic systems of species unique to semi-natural habitats and to production fields, and of species shared by the two habitat categories, relied on coverage-based extrapolation as described above. Differences between management systems were tested for significance using mixed-effects models with management system ({beta }_{1}) ({x}_{1{ij}}) as fixed effect in (1).Estimating species loss due to conversion of semi-natural habitats to production fieldsTo predict the species loss due to conversion of semi-natural habitats to production fields, we relied on sample-size-based extrapolations31 with species incidence frequencies. We estimated the richness of the species pool for the total number of mapped habitats including the extrapolated species richness unique to semi-natural habitats and unique to production fields, and the observed richness of shared species for each of the four taxa. This species pool provided the basis for the calculation of the species loss or gain (Table 1 and Supplementary Table 7). To model the species richness decrease for any amount of semi-natural habitats converted to production fields, we calculated and drew backward the curve composed of the accumulation curve for species unique to semi-natural habitats, to which the estimated total species richness unique to production fields (constant) and the corresponding gain of species unique to production fields (increases with increasing area of production fields as semi-natural habitats are converted), and the richness of observed shared species (constant) were added. This is the species decrease curve (Supplementary Fig. 2). If started at the observed species richness, this curve corresponds exactly to a species richness curve calculated by a cumulative random removal of semi-natural habitats one by one from the pool of all habitats. The four taxa decrease curves were added for the curve in Fig. 2. Confidence intervals (CI, 95%) shown in Figs. 2 and 3 are calculated by bootstrapping within the calculation of the species accumulation curves (iNEXT42), upper and lower bounds of the 95% CI of the four taxa being added. From the species decrease curve, we read off the predicted species richness for a conversion of 50% and 90% of the semi-natural habitats, and a conversion required to increase production by 10%.As species were sampled in 20% of all mapped habitats on average per region (min. 8%, max. 35%), extrapolated species accumulation curves used to build the species decrease curve were calculated for more than two to three times the reference sample size, which is the suggested range for reliable extrapolation of the species richness estimator31,44. Obviously, the confidence intervals (CI) of the species richness extrapolations here became wide (Supplementary Fig. 4). As we still wanted to show the impact of a conversion of the whole semi-natural area into production fields on the production gain in the ten regions, we used the uncertainty (upper and lower bounds of the 95% CI of the four taxa added) to define two situations in addition to the average case to predict species richness for a 50% and a 90% semi-natural habitat conversion, and a conversion required to increase production by 10%: (1) a worst case situation with the upper bound of the CI of the expected species richness unique to semi-natural habitats, the lower bound of the CI of the expected species richness unique to production fields, and shared species assumed not to be able to survive without semi-natural habitats and considered like species unique to semi-natural habitats (i.e. upper bound); and (2) a best case situation with the lower bound of the CI of the expected species richness unique to semi-natural habitats, the upper bound of the CI of the expected species richness unique to production fields, and the lower bound of the CI of the expected shared species richness.Estimating production gainFarmer interviews delivered an average yield per crop type per farm for the years 2008–2010 (Supplementary Data45 shows details for organic and non-organic systems separately). Farmers indicated yield in kilograms or tons per hectare. This was transformed into energy units, i.e. mega joules per hectare (MJ ha−1) using standard values46. From this, for each region, the average yield (MJ ha−1) was calculated by first multiplying individual crop type yields by the corresponding crop type areas to obtain the production per crop type, then summing up the production of all crop types, and finally dividing this sum by the total area of the crop types. For livestock farms, the fodder production of grasslands was estimated based on the average requirements per livestock unit, accounting for the amount of feed grain, legumes, silage maize and of imported feedstuff. All yields relate to plant biomass production and do not comprise livestock products. The average yield takes into account the relative cover of the different crop types in the regions. Therefore, the conversion of the semi-natural area to production fields was region-specific. The production of certain semi-natural habitats as e.g. olive groves in Spain was not part of the production calculation. The reason is that data on production for semi-natural habitats were mainly not available and/or negligible, e.g. extensively used grassland in CH or in HU, and we decided to apply the same treatment to all the regions. Consequently, in case of olive groves in Spain the effective increase in production is overestimated. To calculate the production gain per region, the production field area added by the conversion of semi-natural habitat area was multiplied by the average yield. In practice, in many regions it may be impossible to convert semi-natural habitat to productive land due to geomorphological constraints and poor soils, and even if land were converted, yields would be much lower than these averages. The results presented here, especially the 90% scenario, are therefore over-optimistic. On the other hand, our calculations are based on the area of semi-natural habitat available for conversion on existing farms, but in some regions other sources of semi-natural land may be available for conversion, e.g. former agricultural land that has been abandoned.Species loss and production gain for three scenariosWe calculated the change of species richness and the production gain under current day production efficiency for two scenarios: (1) a conversion of 90% of the semi-natural area into production fields. The 10% of semi-natural area remaining is considered unsuitable for agricultural use or even impossible to cultivate; (2) a conversion of 50% of the semi-natural area into production fields, and (3) a necessary conversion of the semi-natural area into production fields to achieve a 10% production increase per region.Standardization for organic and non-organic systemsAlthough the overall mapped area, the number of semi-natural habitats, the number of production fields and the average habitat size did not significantly differ between the two management systems (Supplementary Table 5), we standardized the number and size of habitats to the average across both systems per region to compare the species loss and production gain at current day production efficiency in the organic and non-organic systems. The total production in organic and non-organic systems per region was calculated based on the respective yield and the average mapped area of the production fields across both systems as described in section “Estimation of production gain”. The impact on biodiversity was analyzed for the scenario that organic systems should achieve the same level of production as non-organic systems by converting semi-natural habitats to production fields. We calculated the amount of the required area to be converted into production fields and the corresponding species change.Differences between management systems were again tested for significance using mixed-effects models with management system ({{{{rm{beta }}}}}_{1}) ({{{{rm{x}}}}}_{1{{{rm{ij}}}}}) as fixed effect in (1).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Africa’s population is rapidly growing, with its share of the global population projected to increase from 17% in 2020 to 39% by 2100 (ref. 8). The continent is already grappling with low agricultural productivity and food security challenges. Tremendous efforts are needed to increase food production; however, arable land continues to undergo widespread degradation due to issues such as nutrient mining, erosion, overgrazing and pollution. Climate change and more frequent weather extremes, such as floods and droughts, further degrade land and reduce agricultural productivity.Some efforts to counteract low productivity, however, can increase greenhouse gas emissions and derail efforts to meet global climate targets. Poor water management, fertilizer application and residue burning in rice production are, for example, major sources of potent greenhouse gases such as methane and nitrous oxide9,10. To ensure that the United Nations sustainable development goals and the African Union’s Agenda 2063 for food and water security are realized at minimal environmental cost, science-based land management practices are needed to decouple agricultural productivity from greenhouse gas emissions.
Credit: majimazuri21/PixabayThe Agriculture, Forestry and Other Land Uses (AFOLU) sector contributes the largest share of greenhouse gas emissions in Africa11. Thus, developing large-scale agronomic, livestock and forest management practices that increase productivity and reduce emissions is key to achieving enhanced production and environmental sustainability. However, it is impossible to effectively manage greenhouse gas emissions if there is limited capacity to quantify them in Africa.Improved data infrastructure and research are needed to quantify emissions associated with specific land management practices under different land uses. Similarly, land use mitigation strategies should be informed by existing and potential future land use changes and their impact on greenhouse gas emissions under different climate scenarios. However, past studies that examined land use changes at various temporal scales mainly used coarse resolution satellite imagery and suffered from limited availability or poor-quality of data, partly due to cost. Such challenges have resulted in limited knowledge of land management practices that reduce greenhouse gas emissions while increasing agricultural productivity.Improved greenhouse gas observation networks and in situ measurements12 will enable the development of country-specific emission factors (IPCC tier 2/3)13 and quantification and management of land use specific greenhouse emissions. It will reduce uncertainties in emissions inventory data on Agriculture, Forestry and Other Land Uses14, which are currently estimated using emission factors extracted from default value databases (tier 1 methodologies).Free earth observation data, such as those from the European Space Agency and United States Geological Surveys, are becoming increasingly available. Together with improvements in cloud-based computing infrastructure, this presents an opportunity to advance research into current and future land use and vegetation dynamics. Coupled with accurately quantified greenhouse gas emissions, this can support current and future land management practices that contribute to mitigation and adaptation objectives of countries. More

Sample collection of Liolophura japonica and the genetic diversity of COI barcoding regionTo examine genetic lineage divergence within L. japonica on the northwestern Pacific coast, we newly collected a total of 342 L. japonica samples from 12 sampling localities in the intertidal coasts of the Korean Peninsula and Japanese Archipelago (Fig. 1; Table S1). From the collected L. japonica samples, we amplified the COI barcoding region using PCR, and then sequenced the 635-bp PCR products. As a result, a total of 75 COI haplotypes based on COI sequences obtained from 342 individuals of L. japonica were detected via the present study (Table S2). In addition to this, we extracted 31 COI haplotypes based on COI sequences from 127 individuals of L. japonica (also known as Acanthopleura japonica) previously reported in the NCBI GenBank database, consisting of two Japanese and 29 Chinese COI haplotypes. Finally, we gathered 106 COI haplotypes from 469 L. japonica individuals collected in 15 localities of South Korea, Japan, and southern China (Tables S1, S2). The average haplotype (h) and nucleotide diversities (π) were 0.808 and 0.04936, respectively; the highest haplotype diversity was observed in Tsushima (TS; h = 0.963), and the highest nucleotide diversity was found in Wando (WD; π = 0.04581), located in the South Sea of the Korean Peninsula. As shown in Table S3, the population distribution pattern of COI haplotypes revealed that all collection sites had site-specific haplotype(s) except for Busan (BS), Wando (WD), Sinan (SA), and Jeju Island (JJ). The most abundant haplotype was A1, which was found in 170 (39.4%) out of the COI sequences obtained from 469 L. japonica individuals.Figure 1A map showing sampling localities and photos of a habitat landscape and wild samples of Liolophura japonica inhabiting coastal areas of the Korean Peninsula (N = 249), the Japanese Archipelago (N = 57), and southern China (N = 125) in the northwestern Pacific Ocean. (a) A map showing twelve direct sampling localities for L. japonica in coastal areas of the northwestern Pacific Ocean. The sampling localities of one southern Chinese (ZJ) and two Japanese (EH and MY) previously catalogued haplotype sequencing studies retrieved from NCBI are also depicted. Table S1 and S2 contain more accurate information on the populations and individuals. The basic map is from a free map providing site (https://d-maps.com), which is modified with Adobe Illustrator v.25.2. (https://www.adobe.com). (b) Photos of a habitat landscape and wild samples of L. japonica, taken from Seogwipo-si, Jeju Island, South Korea, photographed by Mi Young Yeo, Bia Park, and Cho Rong Shin. The photos were edited using Adobe Photoshop v.22.2 (https://www.adobe.com).Full size imagePhylogenetic and population genetic analyses based on COI
We constructed a nucleotide sequence alignment set with 106 COI haplotypes of L. japonica (Data S1), and identified 95 polymorphic sites (15.0%, Table S4) and 68 parsimoniously informative sites (10.7%). To elucidate phylogenetic relationships among the populations of L. japonica, we performed molecular phylogenetic analyses, including maximum likelihood (ML), Bayesian inference (BI), and neighbor-joining (NJ) analyses, based on these 106 COI haplotypes with the outgroup Acanthopleura spinosa (Fig. 2a, Figs. S1, S2). The resultant phylogenetic trees clearly revealed the existence of three distinct genetic lineages within the monophyletic group of L. japonica (100 BP in ML, 1.00 BPP in BI, and 100 BP in NJ): Lineage N (91 BP, 1.00 BPP, and 100 BP), Lineage S1 (79 BP, 0.82 BPP, and 98 BP), and Lineage S2 (98 BP, 1.00 BPP, and 100 BP). Among these three genetic lineages, Lineages S1 and S2 were grouped with high node confidence values (94 BP, 1.00 BPP, and 95 BP). We additionally conducted a phylogenetic network analysis using a neighbor net algorithm without an outgroup (Fig. 2b), which confirmed that these sequences were distinctly divided into three genetic lineages, in agreement with the topology of the rooted phylogenetic trees (Fig. 2a, Fig. S1).Figure 2Phylogenetic, TCS network, and PCoA analyses based on 106 COI haplotypes from 469 individuals of Liolophura japonica inhabiting coastal areas of the northwestern Pacific Ocean, suggesting the existence of the three different genetic lineages: Lineage N, Lineage S1, and Lineage S2. (a) Maximum likelihood tree showing the three different genetic lineages for L. japonica: Lineage N members are most likely from the populations inhabiting a wide range of South Korea and Japan, Lineage S1 members from the populations inhabiting southern coastal areas of South Korea and Japan only, and Lineage S2 members from the southern Chinese population. As shown in Fig. S1, Acanthopleura spinosa was used as an outgroup. Numbers on branches indicate node confidence values: BP in ML, BPP in BI, and BP in NJ in order. (b) A phylogenetic network reconstructed using the neighbor net algorithm without an outgroup, showing three different genetic lineages for L. japonica inhabiting the northwestern Pacific coast: Lineages N, S1, and S2. The COI sequence alignment set used is shown in Data S1. Detailed information of the 106 COI haplotypes used in this phylogenetic analysis is summarized in Table S1 and S2. (c) An unrooted TCS network showing three distinct genetic clusters, corresponding to Lineages N, S1, and S2. Three different genetic groups correspond to the three genetic lineages shown in the phylogenetic tree (a), respectively. The haplotype frequency is displayed by the circle size. (d) A two-dimensional PCoA plot showing the three distinct genetic groups corresponding to Lineages N, S1, and S2 shown in the phylogenetic tree (a). The score on the first two axes (Axis 1 = 79.05% and Axis 2 = 15.32%) from the matrix of genetic distances estimated with the 106 COI haplotypes are indicated.Full size imageConsistently, the TCS network analysis (Fig. 2c) and principal coordinate analysis (PCoA) (Fig. 2d) showed the existence of three distinguished genetic groups among L. japonica, in accordance with the phylogenetic analyses (Fig. 2a − b). The TCS network (Fig. 2c) revealed that Lineages S1 and S2 were separated by 18 mutation steps, which is far shorter than the distance between Lineages N and S1 (37 mutation steps) or between Lineages N and S2 (60 mutation steps), indicating that Lineages S1 and S2 have a close affinity and had only recently diverged from each other. The overwhelming dominancy of the A1 haplotype implies a recent and rapid population expansion of Lineage N. In addition to A1, it was found that haplotypes B2 for Lineage S1 and C21 for Lineage S2 were dominant. In the PCoA plot (Fig. 2d), the three genetic groups of L. japonica were also observed, as in the phylogenetic (Fig. 2a,b) and TCS network (Fig. 2c) analyses. Lineage N was distantly located from Lineages S1 and S2, while Lineages S1 and S2 were spatially much closer.Sample collection of L. japonica and the genetic diversity of 16S rRNA
The 342 individuals of L. japonica from 12 localities in the intertidal coasts on the Korean Peninsula and Japanese Archipelago (Fig. 1) were subjected to PCR amplification of a partial region of 16S rRNA (506 bp) (Tables S5, S6). Of these, only 299 samples were successfully amplified and sequenced. Based on 299 individual 16S rRNA sequences, a total of 23 16S rRNA haplotypes of L. japonica were detected (Tables S5, S6). Combined with 11 haplotypes extracted from 16S rRNA sequences of 125 L. japonica individuals known previously in southern China, we totaled 34 16S rRNA haplotypes from 425 L. japonica individuals in 13 collection localities. The average haplotype (h) and nucleotide (π) diversities were 0.702 and 0.02093, respectively; the highest haplotype diversity was found in Geojedo (GJ; h = 0.833), and the highest nucleotide diversity in Wando (WD; π = 0.02244), located in the South Sea of the Korean Peninsula. Overall, the average haplotype and nucleotide diversities of 16S rRNA were lower than those of COI (Table S1). As shown in Table S7, the population distribution pattern of 16S rRNA haplotypes revealed that most of the collection sites had site-specific haplotype(s), except for BS, GJ, WD, SA, JJ, and TT. The most abundant haplotype was RA1, which was found in 186 (48.1%) out of the 16S rRNA sequences obtained from 425 L. japonica individuals.Phylogenetic and population genetic analyses based on 16S rRNA
We constructed a nucleotide sequence alignment set with 34 16S rRNA haplotypes of L. japonica (Data S2), and identified 35 polymorphic sites (6.9%; Table S8) and 24 parsimoniously informative sites (4.7%). Phylogenetic analyses, including ML, BI, and NJ analyses, were conducted with the outgroup Acanthopleura echinata (Table S6). The resultant phylogenetic trees (Fig. S2) and unrooted phylogenetic network (Fig. 3a) consistently supported the three distinct genetic lineages of L. japonica, with the phylogenetic relationship between Lineages S1 and S2 being much closer than those inferred from the results of COI (Fig. 2a,b; Fig. S1). The TCS network (Fig. 3b) revealed that Lineages S1 and S2 were closely connected with only with 4–5 mutation steps between them, while Lineages N and S1 or Lineages N and S2 were remotely distanced by 18 mutation steps. Also, the overwhelming dominance of the RA1 haplotype implied a recent and rapid population expansion of Lineage N. In addition to RA1, haplotypes RB1 for Lineage S1 and RC1 and RC2 for Lineage S2 were dominant (Fig. 3b; Table S7). Consistent with this, in the PCoA plot (Fig. 3c), the three genetic groups of L. japonica were spatially separated. Lineage N was distantly located apart from Lineages S1 and S2, while Lineages S1 and S2 were spatially much closer.Figure 3The results of phylogenetic and population genetic analyses based on 34 16S rRNA haplotypes from 425 individuals of Liolophura japonica inhabiting coastal areas of the northwestern Pacific Ocean. (a) Phylogenetic network reconstructed using the neighbor net algorithm, showing three different genetic lineages for L. japonica: Lineage N, Lineage S1, and Lineage S2. The 16S rRNA sequence alignment set used is shown in Data S2. Detailed information of 34 16S rRNA haplotypes used in these analyses is summarized in Table S5 and S6. (b) An unrooted TCS network. There are distinctly observed three different genetic groups, corresponding to the three genetic lineages shown in the phylogenetic network (a). The haplotype frequency is displayed by the circle size. (c) A two-dimensional PCoA plot showing the three distinct genetic groups, corresponding to Lineage N, Lineage S1, and Lineage S2. The score on the first two axes (Axis 1 = 87.77% and Axis 2 = 4.4%) from the matrix of genetic distances estimated with the 34 16S rRNA haplotypes are indicated.Full size imageExamination of species discrimination of L. japonica based on COI and 16S rRNA
Using the Automatic Barcode Gap Discovery (ABGD), we performed distribution of pairwise genetic divergences, ranked pairwise difference, and automatic partition analyses based on COI and 16S rRNA of L. japonica, respectively (Fig. 4a–c), which confirmed that there were distinct barcoding gaps between intraspecific and interspecific variations, strongly supporting the possibility of species discrimination of L. japonica. the COI-based analysis yielded two different barcoding gaps, while the 16S rRNA-based analysis revealed only a single barcoding gap (Fig. 4a–c). The results of automatic partition at each value of the prior intraspecific divergence (P) divided L. japonica into three groups by COI and two groups by 16S rRNA, respectively (Fig. 4a–c). We also implemented two DNA taxonomy approaches to evaluate the possibility of species discrimination based on COI: the general mixed Yule coalescent (GMYC) approach (Fig. S3) and a Bayesian implementation of a Poisson Tree Processes model (bPTP) (Fig. S4). The results consistently and robustly supported the possibility that L. japonica can be divided into three different species, as shown in the results of ABDG (Fig. 4a–c).Figure 4Distribution of pairwise genetic divergences, ranked pairwise difference, and automatic partition based on COI and 16S rRNA haplotypes of Liolophura japonica and a COI-based NJ tree showing the phylogenetic relationship with a congeneric species L. tenuispinosa. (a) Distribution patterns of pairwise genetic divergences observed in COI and 16S rRNA for L. japonica. The horizontal axis represents intervals of pairwise Kimura-2-parameter (K2P) genetic distance in percentage, and the vertical axis represents the number of individuals associated with each distance interval. (b) The results of ranked pairwise differences based on COI and 16S rRNA, ranked by ordered value, which is similar to the distribution of pairwise genetic divergence in (a). The horizontal axis indicates a ranked ordered value based on K2P genetic distance, and the vertical axis represents the K2P genetic distance in percentage. (c) The results of automatic partition analyses based on COI and 16S rRNA. The horizontal axis represents the prior maximum intraspecific divergence (P), and the vertical axis represents the number of groups inside the partitions (primary and recursive). (d) A COI-based NJ tree with L. tenuispinosa. Refer to Fig. S3 and Data S3.Full size imageThe molecular variance analyses using analysis of molecular variance (AMOVA), based on COI and 16S rRNA, were conducted to evaluate the degree of genetic differentiation among Lineages N, S1, and S2 (Tables S9, S10). According to the results, supposing that there are three genetic lineages (N, S1, and S2) or two genetic lineages (N and S1/S2), almost all variation in both cases is attributed to variation among groups (= among lineages), whereas variations within populations (within lineages) exhibit negative values in common. We confirmed that there was a high degree of genetic differentiation among Lineages N, S1, and S2, which supports the results of the COI barcoding gap analysis shown in Fig. 4a–c, although this was not statistically significant (P  > 0.05; Table S9). When we assumed only two genetic groups, Lineages N and S1/S2, the genetic differentiation between the two groups was statistically significant (P  0.05) (Tables S9 and S10). The discrepancy between the number of barcoding gaps inferred from COI and 16S rRNA may have been affected by different gene evolutionary rates of the molecular markers11; nucleotide substitution rate of 16S rRNA is known to be generally slower than that of COI (which is especially fast in the third codon positions: 105 out of 127 polymorphic sites). When an ML tree was constructed based on 22 polymorphic sites, which are found only in the first and second codon positions of COI that are much more conserved than the third codon position, the three genetic lineages were retained in the resultant tree (Fig. S5), but Lineage S2 was nested within Lineage S1, as in the trees inferred from 16S rRNA (Fig. S2). Reflecting the powerful resolution of the COI barcoding marker well known from animals12 and the high degree of variation among the three genetic lineages (Fig. 4, Figs. S3, S4), we suggested that L. japonica could be categorized into three different species: L. koreana, Yeo and Hwang, sp. nov. for Lineage N, L. japonica for Lineage S1, and L. sinensis Choi, Park, and Hwang, sp. nov. for Lineage S2. To examine whether it is reasonable to give these a species-level taxonomic status, as shown in Fig. 4d, we reconstructed a COI-based NJ tree with one congeneric species L. tenuispinosa13, which was originally described as a subspecies-level taxon of L. japonica14,15 and was then revised as an independent species closely related to L. japonica by Saito & Yoshioka16 in 1993. The resultant tree (Fig. S6 and Data S3) showed that L. tenuispinosa forms a sister group with L. japonica (Lineage S1). This likely indicates that L. koreana and L. sinensis have taxonomic status as independent species.Morphological comparison and geographical distribution of the three Liolophura speciesWe compared morphological characteristics among Liolophura koreana, sp. nov. (Lineage N), L. japonica (Lineage S1), and L. sinensis, sp. nov. (Lineage S2). Their morphological appearances are shown in Fig. 5a–c, which indicated that black spots on the tegmentum (Fig. 5d–e) and shapes of spicules on the perinotum (Figs. 5f–k, 6e–f) represent key morphological characteristics to distinguish them from each other. Although black dots in pleural areas, which are between the middle and lateral areas of the tegmentum on valves II–VII (or VIII), are commonly shared in all three lineages (Fig. 5a–c), other black spots on the valves exhibit a high degree of variation in morphology (Fig. 5a–c, Fig. S7). Herein, we described a new species of genus Liolophura, that is, L. koreana Yeo and Hwang from South Korea and Japan, with detailed descriptions of morphological characteristics observed by light microscopy (M205, Leica Camera AG, Germany) and FE-SEM (SU8220, Hitachi, Japan). In addition, we suggested the divergence of a new species, L. sinensis Choi, Park, and Hwang from southern China, with simple remarks based on distinct genetic difference (mainly COI barcoding gaps), with possible unique morphological characteristics as follows.Figure 5Morphological comparison of Liolophura koreana, sp. nov., L. japonica, and L. sinensis, sp. nov. (a–c) Photos of dorsal views of the individuals belonging to L. koreana (Lineage N), L. japonica (Lineage S1), and L. sinensis (Lineage S2) in order. (d,e) Morphological comparison of pleural and lateral black spots on valves III and IV of the tegmentum of L. koreana (d; holotype) and L. japonica (e). (f,g) Morphological comparison of spicules on the perinotum of L. koreana (f; holotype) and L. japonica (g). (h–k) Morphological comparison of the spicule of L. koreana (h,i; paratype) and L. japonica (j,k) in lateral and dorsal views. The scale bar marks 2.0 mm (d,e), 1.0 mm (f,g), and 0.5 mm (h–k). The photos were edited using Adobe Photoshop v.22.2 (https://www.adobe.com).Full size imageFigure 6Microstructural comparison of Liolophura koreana, sp. nov. and L. japonica using field emission scanning electron microscopy (FE-SEM). (a,b) Middle and lateral areas on the tegmentum of the holotype of L. koreana. (c,d) Middle and lateral areas on the tegmentum of L. japonica. Arrows indicate that morphological difference of the posterior valve margin of the valve II between two species. The scale bar marks 1.0 mm. (e,f) The occurrence frequency, and shape and structure differences of the spicules on the perinotum between the holotype of L. koreana (e) and L. japonica (f). The scale bar marks 1.0 mm and 0.2 mm, respectively. The photos were edited using Adobe Photoshop v.22.2 (https://www.adobe.com).Full size image
Liolophura koreana Yeo and Hwang, sp. nov. (Figs. 5, 6; Figs. S7, S8)(urn:lsid:zoobank.org:act:4418355E-F55C-44FA-B4CE-585589FDCD23).Type specimens examined[Holotype] SOUTH KOREA: 1 specimen, Jeju-do, Seogwipo-si, Seongsan-eup, Seopjikoji, 3.XI.2020, UW Hwang, B Park & CR Shin (LEGOM040501); [Paratypes] SOUTH KOREA: 1 specimen, Gyeongsangbuk-do, Pohang-si, Guryongpo-eup, Janggil-ri, 27.VII.2008, UW Hwang (LEGOM040502); 3 specimens, Gyeongsangbuk-do, Ulleung-gun, Seo-myeon, Namyang-ri, Ulleungdo Island, Namtong tunnel, 12.VI.2007, UW Hwang (LEGOM040503–0505); 1 specimen, Gyeongsangbuk-do, Ulleung-gun, Namyang-ri, Ulleungdo Island, Namyang tunnel, 5.X.2007, UW Hwang (LEGOM040506); 2 specimens, Gyeongsangnam-do, Geoje-si, Nambu-myeon, Dapo-ri, 28.IV.2009, MY Yeo (LEGOM040507,0508); 4 specimens, same data as the holotype (LEGOM040509–0512); 2 specimens, Jeollanam-do, Yeosu-si, Hwajeong-myeon, Sado-ri, Sado Island, 8.IV.2008, MY Yeo (LEGOM040513,0514); 4 specimens, same data as the holotype (LEGOM040515–0518); JAPAN: 6 specimens, Tottori Prefecture, Hakuto, 24.V.2009, UW Hwang (LEGOM040519–0524); 1 specimen, Tottori Prefecture, Iwato, 25.V.2009, UW Hwang (LEGOM040525).DescriptionBody small-sized and broad oval- to oval-shaped (Fig. 5a; Fig. S7); length 3.9 (1.9–12.3) mm and width 2.4 (1.2–7.1) mm. Tegmentum entirely brown (dark brown or black entirely, or each valve with black line anteriorly or white line laterally), with black dots on the pleural areas of valves II–VII (or VIII) (Fig. 5a,d, Fig. S7); articulamentum entirely black (dark brown); whitish and blackish spicules on the perinotum scattered irregularly, sometimes forming a band besides each valve (Fig. 5a, Fig. S7). Surface of the tegmentum in middle and lateral areas as in Fig. 6a,b and Fig. S8; posterior margin of the head valve nearly straight; dorsal shape of intermediate valves round-backed and side slopes slightly convex; the posterior valve margin with a distinct central apex, its shape subtriangular to triangular (rounded or linear), particularly valve II, mainly with a strong projection (Fig. 6a). Perinotum covered with large, solid, slightly curved, and obtusely pointed spicules (rarely with smooth and radial ribbed spicules apically), its density relatively lower than that of L. japonica (Figs. 5f,h,i, 6e).DistributionSouth Korea, Japan; avobe 33°24′ N (Seogwipo, JJ) in South Korea and TT and MY in Japan (Fig. 7).Figure 7Geographical distribution of Liolophura koreana, sp. nov., L. japonica, and L. sinensis, sp. nov. inhabiting coastal areas of the northwestern Pacific Ocean. A COI-based map showing geographical distribution of L. koreana, L. japonica, and L. sinensis on the northwestern Pacific coast. L. koreana are found in a wide range of South Korea and Japan above ca. 33°24′ N (JJ), L. japonica in mainly southern coastal areas of South Korea and Japan below ca. 35°53′ N (TT), and L. sinensis in ZJ of southern China around ca. 27°02′ N–28°00′ N. The sympatric distribution of L. koreana and L. japonica is shown between 33°24′ and 35°53′ N. Table S1–S3 contain the full names of localities and detailed haplotype information. The question mark indicates that collection of Liolophura samples from such coastal areas in Japan is required to clarify distribution patterns of L. koreana and L. japonica in the East Sea (= Sea of Japan). The basic map was obtained from a free map-providing site (https://d-maps.com), which was modified using Adobe Illustrator v.25.2. (https://www.adobe.com).Full size imageHabitatThis new species appears to be attached to rocks in coastal areas with strong waves, or a calm inner shore in the northwestern Pacific Ocean (Fig. S9).EtymologyThe species is named per the locality of the new species.RemarksWe found that Liolophura koreana, sp. nov. has no black spots on lateral areas of the tegmentum (Fig. 5d), and large, slightly curved, and obtusely pointed spicules on the perinotum compared to those of L. japonica (Figs. 5f,h,i, 6e). On the other hand, L. japonica from southern South Korea and southern Japan has two black spots on the lateral areas of valves II–VII (or VIII) (Fig. 5e), and small, almost straight, and cylindrical spicules compared to those of L. koreana (Figs. 5g,j,k, 6f).As shown in Fig. 7, L. koreana (Lineage N) was observed in all the South Korean and Japanese populations examined here, except for the EH population in Japan (refer to Tables S1, S2), which were found from JJ at 33°24′ N to MY at 38°32′ N. On the other hand, L. japonica (Lineage S1) was found from the southern coastal areas of South Korea and Japan, which were found only between JJ at 33°24′ N and TT at 35°53′ N. Interestingly, we found only L. koreana north from the latitude of 35°10′ N (BS) such as UL/DD (37°24′ N) and PH (36°02′ N) in South Korea. In Japan, there was found only L. koreana at MY (38°32′ N) too, but it remains to be explored to clarify its distribution range in Japan with much more sample collections covering northern Japanese coastal areas through further study. It was also confirmed that L. koreana and L. japonica show a sympatric distribution pattern between JJ at 33°24′ N and TT at 35°53′ N on the southern coastal area of South Korea and Japan.
Liolophura sinensis Choi, Park, and Hwang, sp. nov. (Fig. 5c).(urn:lsid:zoobank.org:act:72DF7E75-1853-4F23-AC12-3AB8CD054187).Type specimens examined[Holotype] CHINA: 1 specimen, Zhejiang Province, Dongtou Island, 27°49′57.44″ N, 121°10′19.13″ E, 2017; [Paratypes] CHINA: 1 specimen, Beiji Island, 27°37′08.82″ N, 121°11′47.82″ E, 2017; 1 specimen, Beilongshan Island, 27°40′08.56″ N, 121°58′51.56″ E, 2017; 1 specimen, Chaishi Island, 27°25′40.36″ N, 121°04′54.45″ E, 2017; 1 specimen, Daleishan Island, 27°29′39.48″ N, 121°05′24.50″ E, 2017; 1 specimen, Daqu Island, 27°47′29.92″ N, 121°05′23.97″ E, 2017; 1 specimen, Dazhushi Island, 27°49′12.87″ N, 121°12′48.74″ E, 2017; 1 specimen, Dongce Island, 27°45′32.04″ N, 121°09′01.38″ E, 2017; 1 specimen, Dongxingzai Island, 27°02′40.36″ N, 121°02′47.98″ E, 2017; 1 specimen, Houjishan Island, 27°28′26.96″ N, 121°07′40.71″ E, 2017; 1 specimen, Luxi Island, 27°59′33.43″ N, 121°12′50.70″ E, 2017; 1 specimen, Nanji Island, 27°27′30.57″ N, 121°03′06.28″ E, 2017; 1 specimen, Nanpanshan Island, 28°00′15.29″ N, 121°15′33.62″ E, 2017.DistributionSouthern China; Zhejiang Province around ca. 27°02′–28°00′ N (Fig. 7).HabitatThis new species appears to be attached to rocks in coastal areas of the northwestern Pacific Ocean.EtymologyThe species is named after its locality.RemarksL. sinensis, sp. nov. was examined and established mainly by molecular data such as the COI barcoding gap (Fig. 4a–c) presented in this study and photos provided from Prof. Yong-Pu Zhang (Wenzhou University, Zhejiang Province, China) without any direct real sample observation. The morphology of this new species is very similar to that of a previously known species, L. japonica. L. sinensis from southern China has black spots on the lateral areas of valves II–VII similar to L. japonica, but black bowtie-shaped spots anterior to valves II–VII (Fig. 5c) are a unique characteristic for L. sinensis. L. sinensis (Lineage S2) was found in Zhejiang Province (ZJ) in southern China, around ca. 27°02′–28°00′ N (Fig. 7; Tables S1, S5).Demographic history and divergence time estimation analysesMismatch distribution analyses (MDA) based on COI were performed for L. koreana, L. japonica, and L. sinensis, respectively. The MDA results (Fig. 8a) showed a unimodal curve for each of the three lineages. In addition, when neutrality tests were performed with COI and 16S rRNA (Table S11), all three showed statistically significant negative values in both Tajima’s D and Fu’s Fs, except for the Tajima’s D values in COI (L. sinensis) and 16S rRNA (L. japonensis and L. sinensis), implying that these had experienced population expansions. Bayesian skyline plot (BSP) analyses with COI (Fig. 8b) were performed to examine the fluctuation patterns in effective population sizes for L. koreana, L. japonica, and L. sinensis, respectively. The effective population sizes of L. japonica and L. sinensis had gradually grown between ca. 100 Ka and ca. 50 Ka, while those of L. japonica had grown between ca. 80–50 Ka, L. sinensis had grown between 100–60 Ka, and that of L. koreana had begun to rapidly expand ca. 85 Ka and ceased ca. 75 Ka. This indicated that population expansion had occurred more dramatic in L. koreana than in L. japonica and L. sinensis, following the last interglacial age, called the Eemian (129–116 Ka). As shown in Fig. 8c and Fig. S10, according to the molecular clock analysis by the BEAST program, it was estimated that L. japonica and L. koreana shared their most recent common ancestor about 3.37 Ma, around the mid-Pliocene warm period (3.30–3.00 Ma), before the extensive glaciation in the late Pliocene (ca. 3.00 mya). L. japonica and L. sinensis likely diverged around 1.84 Ma, around the beginning stage of the Early Pleistocene Transition (EPT; 1.85–1.66 mya). The augmentation of haplotype diversity in L. japonica, L. sinensis, and L. koreana might have intensified in the interglacial stages during the late-middle (0.35–0.126 Ma) and late Pleistocene (0.126–0.012 Ma), before the last glacial maximum (LGM: 0.026–0.019 Ma).Figure 8The results of mismatch distribution analyses (MDA), Bayesian skyline plots (BSPs), and molecular clock analysis performed with COI haplotypes for Liolophura koreana, sp. nov., L. japonica, and L. sinensis, sp. nov. (a) MDA plots resulting in a unimodal curve for L. koreana, L. japonica, and L. sinensis. Dotted lines indicate the observed distribution of mismatches, and solid lines represent the expected distribution under a demographic expansion model. (b) BSP results showing the demographic history of population expansions of L. koreana, L. japonica, and L. sinensis. The graph in gray depicts sea level changes during the last 330 Ka. (c) Time-calibrated Bayesian tree reconstructed using BEAST with the inference of ancestral areas under the Bayesian binary MCMC (BBM) model implemented in RASP ver 3.2. Ancestral areas were hypothesized based on the distribution range of the fossil records of Mopalia and the contemporary distribution of L. koreana, L. japonica, and L. sinensis. LGM indicates the last glacial maximum (0.026–0.019 Ma; blue vertical bar) and three interglacial periods are indicated by light green boxes during the late-middle and late Pleistocene. The pictures were edited using Adobe Illustrator v.25.2. (https://www.adobe.com).Full size image More

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