Philippa Kaur

Population sampling and sequencingWe generated whole genome sequence data from 302 wild-caught individual An. minimus female mosquitoes collected from five different field sites in Cambodia using the Illumina HiSeq 2000 platform with 150 bp paired-end reads with a target coverage of 30X for each. Mosquito collections in Thmar Da, in Eastern Cambodia, were done in 2010. Longitudinal monthly collections were performed from February 2014 to January 2015 in two sites in each of the Preah Vihear, and Ratanakiri provinces. Quarterly collections were also done in 2016 in one site in Preah Vihear province, Cambodia.Variant discoveryThe methods for sequencing and variant calling closely follow those of the Anopheles gambiae 1000 Genomes project phase 2 (Ag1000G)27. Sequence reads were aligned to the An. minimus reference genome AminM128. We restricted our analysis to the largest 40 contigs, which cover 96.6% of the AminM1 reference genome, as many smaller-sized contigs can confound diversity and divergence calculations. We found that 138,161,075 (75.4%) of sites within these 40 largest contigs pass our site filters and thus were accessible to SNP calling. Of these, we discovered 38,000,285 segregating single nucleotide polymorphisms (SNPs) that passed all of our quality control filters of 55,307,039 total segregating SNPs. 13.4% of these SNPs were multiallelic, with 32,906,471 biallelic SNPs. There were 4,807,355 triallelic and 286,459 quadriallelic SNPs. A total of 100,160,790 sites were invariant. The median genome-wide coverage was 35X.Population structureA principal component analysis (PCA) over biallelic SNPs distributed over the genome of 302 individual field-collected mosquitoes showed that there is clear population structure of An. minimus in Cambodia. Samples collected from five sites in three provinces split into three distinct clusters; here, we report on 283 individuals that could be clearly assigned to these clusters (Fig. 1), excluding 9 anomalous and 10 outlying individuals. One cluster includes all samples from the western collection site Thmar Da and the northern collection sites in Preah Vihear province, with two further clusters with samples from Ratanakiri province in the northeast. These clusters split primarily along the first and second principal components. This was a surprising finding because this population structure did not correlate to the geographic sampling of these mosquitoes. Individuals collected from the western and northern sites cluster tightly together despite being hundreds of kilometers apart.Fig. 1: Population structure of An. minimus in Cambodia.The map indicates the five Cambodian collection sites. Principal component analysis (PCA) of whole genome sequences of 283 individual An. minimus s.s. collected in five villages in Cambodia shows that there is a distinct population structure and three populations. When performing the same PCA on a large X-chromosomal contig (KB664054), these individuals break into four populations: TD from the West, PV from the northern province in Preah Vihear, and RK1 and RK2, both collected in two sites in Ratanakiri province in the Northeast.Full size imageTo further explore this population structure, we performed the same PCA over individual contigs from different regions of the genome. Performing PCA over the largest X-chromosomal contig KB664054 resulted in a splitting of the western and northern samples, indicating four distinct populations of An. minimus in Cambodia (Fig. 1). PCA from this contig on a quickly evolving sex chromosome revealed more population structure compared to autosomal contigs. The populations defined by these PCA clusters are designated in this study as TD from Thmar Da, in Western Cambodia (n = 41), on the Thai-Cambodian border, PV from the Northern province Preah Vihear (n = 156), and the two distinct populations collected in Ratanakiri province in the Northeast, each including individuals collected at both collection sites, these are designated as populations RK1 (n = 58) and RK2 (n = 28).To confirm our results from PCA, we also performed an admixture analysis. We ran admixture on each of the largest 10 contigs for values of K between 2 and 6 (Supplemental Fig. 1). At K = 2, the samples from Northeastern Cambodia split from Northern and Western Cambodia samples. At K = 3, the two different groups in Ratanakiri were separated, consistent with the PCA results. At K = 4, there was some evidence for geographical population structure between the Western TD and Northern PV populations, but the admixture results did not perfectly correspond with geographic sampling, with some evidence of mixed ancestry in the PV samples. Again, this is consistent with the PCA groupings, with the generally weaker evidence of geographic population structure between TD and PV. A cross-validation analysis showed the lowest cross-validation error for K = 2 and K = 3, consistent with the strongest evidence for population structure between the two RK groups and other populations. Cross-validation error was higher at K = 4, consistent with the weaker differentiation between TD and PV. At no point was their an indication of admixture between RK1 and RK2.To examine population differentiation, we computed differences in allele frequencies between each population using Pairwise Fst. Pairwise Fst between all 4 populations over the largest contig, KB663610, representing 16% of the An. minimus genome, (Fig. 2) shows that differentiation was relatively low between populations of TD and PV with an average pairwise Fst of 0.003, while the difference between RK2 and the other three populations is tenfold higher, around 0.03. Pairwise Fst estimates comparing these populations over other large An. minimus contigs indicate a similar level of differentiation, with average pairwise Fst values over 0.03 (Supplementary Data 3). The two sympatric populations from the Ratanakiri collection sites are as differentiated from each other as they are from the northern and western clusters.Fig. 2: Population diversity and divergence.Nucleotide diversity (π), Watterson’s Theta (θW), and Tajima’s D statistics were calculated over fourfold degenerate sites on autosomal contigs. The error bars indicate 95% confidence intervals calculated over 100 bootstrap replicates over samples. An average pairwise Fst in the table here was calculated in 20 kb windows over the largest contig KB663610.Full size imageThis level of differentiation of RK2, even from the RK1 population, might indicate an emerging cryptic species within An. minimus A or a newly diverging clade. RK1 and RK2 are sympatric populations, both being collected in the same two sites in Northeastern Cambodia. The differences seen here between RK1 and RK2 populations are consistent with cryptic taxa in other anopheline groups. For example, in the An. gambiae complex, the level of differentiation between recently diverged sibling species An. coluzzii and An. gambiae in West Africa is approximately 0.0319.Population diversity and variationTo characterize population diversity among these populations, nucleotide diversity (π), Watterson’s Theta (θW), and Tajima’s D statistics were calculated over 4-fold degenerate sites on autosomal contigs larger than 2 megabases with 100 bootstrap replicates over samples. These 17 contigs represent 80% of the Anopheles minimus genome (Fig. 2). The populations were downsampled for these calculations to have sizes equal to that of the smallest population RK2 (n = 28).There are small but significant differences in the magnitude of the genetic diversity summary statistics between these four different populations. In particular, there were notable differences between the putatively cryptic taxa RK1 and RK2, two populations that were collected in the same sites in Northeastern Cambodia. RK1 had higher levels of nucleotide diversity and lower levels of Tajima’s D than RK2. These differences are consistent with different population size histories between these sympatric groups. Lower values of Tajima’s D suggest stronger population growth in RK1. Comparing all four populations, higher levels of genetic diversity indicate larger effective population sizes of TD and PV compared to RK1 and RK2.RK2 has a significantly reduced nucleotide diversity and Watterson’s Theta compared to the other three populations. This may indicate a smaller population size and a recent bottleneck of the RK2 population in Cambodia. All four An. minimus populations have a negative Tajima’s D, indicating an excess of rare variants, particularly in RK1. This suggests recent population expansions in all populations.Signals of evolutionary selectionWe used Fst to scan across the Anopheles minimus genome to look for regions of the genome with increased differentiation. When we scanned the genome using pairwise Fst, there were no apparent long signals of differentiation that might indicate a large inversion or other structural variants, known to be major drivers of adaptive evolution in other Anopheles groups. To investigate increased differentiation across large regions of the genome, we performed scans of nucleotide diversity (π), Watterson’s Theta (θW), and Tajima’s D over the largest 14 contigs (representing 80% of the An. minimus genome). As with the Fst scans, there were no large regions of higher differentiation in any of the populations that might indicate major structural variants or inversions (Supplementary Figs. 2–4).Whole-genome sequencing allowed us to identify pointed signals occurring across the entire genome using scans of average pairwise Fst. Isolated points of high differentiation were compared over single contigs with average pairwise Fst calculated over windows of 1000 SNPs each and plotted over whole contigs. The strongest signals, indicated by the highest Fst value at the peak of a strong signal of differentiation, were ranked and compared. The five top signals in each of the six comparisons between the four populations are listed in Table 1. These isolated points of high differentiation are one indication of a signal of evolutionary selection. The most differentiated regions by Fst occurred when comparing the RK2 population to the other three populations, with the highest selection peaks with pairwise Fst over 0.4. RK2 also had more distinct signals of selection when compared to the other populations than RK1. Since these signals of differentiation were highly localized, we could look to known gene annotations and gene predictions across the AminM1 reference genome to see which genes were within 100 kbp of the peaks of these signals. We have noted candidate genes of interest that were near the strongest Fst signal peaks and also had known or predicted gene functions (Table 1, Supplementary Fig. 6, Supplementary Fig. 8).Table 1 The top five Fst signals of high differentiation within each of six population comparisons are reported here.Full size tableThere is almost no indication of selection when comparing the Thmar Da population with Preah Vihear, with all but one signal with Fst values below 0.05. The one strong signal between TD and PV (Fst = 0.125) is near a Carbohydrate sulfotransferase, which is involved in detoxification processes. Comparing TD to RK1 and RK2 reveals multiple strong signals of selection, some which are present in both Northeastern populations, as well as many unique RK2-specific signals (Fig. 3, Supplementary Fig. 5).Fig. 3: Signals of selection over a single autosomal contig.Pairwise Fst was calculated in 1000 SNP windows over autosomal contig KB664266, comparing the Thmar Da population to the three other populations, Ratanakiri 2, Ratanakiri 1, and Preah Vihear. There is almost no indication of selection when comparing Thmar Da with Preah Vihear. There is a strongly supported signal of differentiation in both Ratanakiri 1 and Ratanakiri 2 populations at 7.5 Mbp, which is in the same location as a cluster of GSTe genes, including GSTe2, which are known to be involved in metabolic resistance to DDT and pyrethroids. The signal with the highest Fst peak here in RK2, at 6 Mbp is close to an Ecdysteroid UDP-glucosyltransferase gene, shown to confer pyrethroid insecticide resistance in other anophelines. These are a few of many selection signals identified in this study that may be associated with insecticide pressure on these An. minimus populations.Full size imageMany of the strongest signals identified in this study may be associated with insecticide pressure on these An. minimus populations. The strongest selection signals in every population comparison were close to genes that are involved in detoxification, signal transduction, and adaptations to oxidative stress, or have been functionally validated to have mutations that confer resistance to insecticides (Table 1). Some signals of interest include a strongly supported signal of selection in both RK1 and RK2 populations at 7.5 Mbp on the contig KB664266, which is in the same location as a cluster of glutathione-S-transferases, including GSTe2, which has been shown to be involved in the metabolism of DDT and pyrethroids, mutations in which mediate metabolic insecticide resistance29. The signal with the highest pairwise Fst peak on the same contig KB664266, at 6 Mbp is an RK2-specific signal and close to an Ecdysteroid UDP-glucosyltransferase gene, which has been shown to confer pyrethroid insecticide resistance in An. stephensi30.Another notable signal is between the RK1 and RK2 populations on the contig KB663610, a Peptidase S1 domain-containing protein AMIN002286, which has been shown to be involved in response to parasite pathogens in insects31. The signals of selection observed in this study are mostly distinct from the main selection signals seen in An. gambiae complex mosquitoes19, the primary vectors of Plasmodium falciparum in Africa.Insecticide resistanceWe report here variants at known insecticide resistance-associated alleles for each of the four An. minimus populations. Variants occurring at a frequency of more than 2% in at least one of the four populations are reported in the known insecticide-resistance-associated genes Ace1, Rdl, KDR, and GSTe2 (Supplementary Data 2). GSTe2 mutants are present in multiple populations, at a low rate, and there are a few individuals in Thmar Da and Preah Vihear with the Rdl resistance mutation, which is known to confer resistance to cyclodiene insecticides, despite evidence from other studies that species in this region lack this resistance mutation32. We did not investigate copy number variation, which is one mechanism by which GSTe2 confers insecticide resistance. These SNP variants indicate variation throughout these insecticide-resistance-associated genes, and though most of these populations do not currently have high rates of validated insecticide resistance-associated mutations, this underlying variation provides the potential for structural and transcriptional events resulting in greater levels of insecticide resistance in An. minimus populations. More

Variation in environmental parameters across space and time in Bohai BayThe environmental parameters of samples collected near the Tianjin coastal area from different stations and seasons exhibited high temporal and spatial heterogeneity. The seawater temperature was 28.09 ± 0.53 °C in Aug, 17.48 ± 2.36 °C in May, and 19.55 ± 1.26 °C in Oct (Table 1). The seasonal variation in seawater temperature corresponded to the meteorological characteristics in Bohai Bay, with warm seawater in summer and relatively cool seawater in spring. The salinity was 29.69 ± 2.71‰ in Aug, 33.19 ± 0.33‰ in May, and 30.15 ± 1.63‰ in Oct. Seasonal variations in salinity may be mainly related to freshwater loading. According to the precipitation observed data of Bohai Bay in previous years, the rainfall amount and days in summer are the most19, which may lead to the increase in runoff and the relatively low salinity in summer. Chlorophyll a (Chl a) was highest in May, with lower levels in Aug and Oct. The dissolved inorganic nitrogen (DIN) was significantly higher in May and Aug than in Oct. The higher level of DIN in May and Aug may be related to terrestrial input and supply for the demand of phytoplankton growth. In October, the temperature and DIN content were both not suitable for phytoplankton growth, and Chl_a showed the lowest value. Spatially, the DIN distribution across the three seasons was rather similar, with high values observed in nearshore waters and low values in offshore waters (Dataset S1 & Fig. S1), which suggested that terrestrial input was an important source of DIN. The pH, soluble reactive phosphate (SRP) and chemical oxygen demand (COD) showed relatively higher values in October than in August and May, which may be caused by the dead phytoplankton release and terrestrial loadings through coasts and rivers. The dissolved oxygen (DO), conductivity and depth did not show significant variation among sampling times (Table 1), while the conductivity and depth had relatively higher values at offshore stations (Dataset S1) since the more remote the sampling water was, the greater the depth was in Bohai Bay and the closer it was to the open sea with higher salinity and conductivity. The ordination plot showed distinct partitioning of samples from nearshore and offshore sites along principal component axis 1 (PC1) (Fig. 1). The ordination plot could explain 73.49% of the total variation in the geo-physical–chemical parameters and revealed a linear positive correlation between different parameters (Fig. 1). AN, DIN, nitrate and Chl_a were most crucial in the partitioning of samples from May and the other 2 months; salinity, longitude, depth and conductivity were crucial for the partitioning of samples from offshore and nearshore stations; pH, COD, SRP, nitrite and temperature were crucial for the partitioning of samples from nearshore stations in August and October and samples from offshore stations. Overall, the principal component analysis (PCA) plot clearly showed both the temporal and spatial variation of the measured environmental parameters, indicating that complex biogeochemical processes and hydrodynamic conditions lead to the variation among sites and seasons.Table 1 The independent-samples t test of environmental variables and α-diversity among different months.Full size tableFigure 1Biplot of the principal component analysis (PCA) for environmental parameters in the seawater samples of the Bohai Bay coastal area across different seasons and sites. The two principal components (PC1 and PC2) explained 73.49% of the total variation in the environmental data and showed clear partitioning of offshore samples (in blue font) from other nearshore samples along PC1 and partitioning of May samples from August and October along PC2. The variables transparency and latitude were strongly correlated with PC1, and the variables ammonia nitrogen (AN), COD, pH, soluble reactive phosphate (SRP), and nitrite were strongly correlated with PC2. Chlorophyll a (Chl_a), dissolved inorganic nitrogen (DIN), nitrate and DO were mainly positively correlated with samples from May, while salinity, longitude, depth and conductivity were mainly positively correlated with offshore samples. Blue arrows represent environmental parameters, and circles in color represent sampling points.Full size imageProkaryotic α/β-diversity variationMeasures of α-diversity showed significant differences in shannon, evenness, faith_pd and OTU richness between samples from May/Aug and Oct (Fig. 2, Table 1). Principal coordinates analysis (PCoAs) based on weighted UniFrac (WUF) distance and unweighted UniFrac (UUF) distance showed that the PCC from different sampling months separated across the first and second principal coordinates (Fig. 3A-B). Both the analysis of similarity (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA/ADONIS) results indicated that the prokaryotic communities varied significantly across different sampling months when using a WUF distance metric (ANOSIM, r = 0.709, P = 0.001; ADONIS, R2 = 40.0%, P = 0.001) and UUF distance metric (ANOSIM, r = 0.934, P = 0.001; ADONIS, R2 = 38.7%, P = 0.001). At the same time, the prokaryotic α– and β-diversity both showed high within-month variability in Aug (Figs. 2, 3C–D), which indicated that the community varied greatly among different sites in Aug.Figure 2Alpha diversity of shannon, eveness, faith_pd (phylogenetic diversity) and OTU richness value of the prokaryotic community of all the samples from different stations at different sampling times.Full size imageFigure 3Principal coordinate analysis (PCoA) based on unweighted (A) and weighted (B) UniFrac distances for prokaryotic communities in the surface waters; box plots showing the unweighted (C) and weighted (D) UniFrac distances among each station at different sampling times.Full size imageCorrelation between prokaryotic α/β-diversity and physical, chemical and geographic factorsThe α-diversity measurements exhibited significant positive correlations with temperature, pH, SRP, AN and un_ionN (Dataset S2). The correlation between α-diversity indexes and geo factors (longitude and latitude) was not strong or significant both in samples across the three sampling times or from each sampling time (Dataset S2).The environmental variation significantly correlated with β-diversity among the three seasons (r_weighted = 0.4558, r_unweighted = 0.4631, P = 0.001, Table 2), with pH, AN, temperature, un_ionN, COD, nitrite, SRP, salinity, DO and DIN as the main individual determinants. However, it did not show significant correlations with β-diversity at any sampling time except in Oct (Table S1).Table 2 Spearman’s rank correlation between environmental/spatial variability (Euclidean distance) and prokaryotic β-diversity (weighted/unweighted UniFrac distance) among all samples from different season.Full size tableThe geographic distance was not correlated with prokaryotic β-diversity (variation in community composition; r  0.05; Table 2) among the three sampling times. However, samples from Aug and Oct exhibited a significant correlation between β-diversity and geographic distance (Table S1).Factors driving the PCC variationPERMANOVA using the UUF/WUF distance indicated that temperature variation explained the largest part of community variation among the investigated factors (34.90%/19.83%, P = 0.001, Dataset S3), with AN (31.84%/13.56%, P = 0.001) and salinity (12.91%/6.21%, P = 0.001) as the second and third most significant sources of variation.The variance partitioning analysis (VPA) conducted on both UUF/WUF distances showed that almost 100% percent of the variation in PCC among all three sampling times was explained by the detected environmental factors. In May, no environmental or spatial factors could be selected as significantly explain the PCC variation; in Aug, the joint effects of environmental and spatial factors could explain 49.5% of the variation; in Oct, based on WUF distance, the spatial factors could purely explain 10.5%, environmental factors could purely explain 38.8%, their joint effects could explain 28.2%, and based on UUF distance, the joint effects of environmental factors and trend could explain 13.7% of the PCC variation. These results indicated dramatic shifts in the spatial or environmental factor effects on the PCC variation at different sampling times in Bohai Bay (Table 3).Table 3 Variance partitioning analysis of prokaryotic community in Bohai Bay according to seawater environmental factors and geospatial factors. The spatial factors including linear trend and PCNM variables. Forward selection procedures were used to select the best subset of environmental, trend, and PCNM variables explaining community variation, respectively. The community variation was calculated on the weighted and unweighted UniFrac distance matrix, respectively. Monte Carlo permutation test was performed on each set without the effect of the other by permuting samples freely (999 permutations).Full size tableDistinct seasonal features at the phylum and OTU levelsThere were notable differences in the proportions of various phyla among different seasons (sampling month). In May, there was a greater proportion of Alphaproteobacteria (41.41%), Planctomycetes (6.42%), Actinobacteria (3.86%), Firmicutes (1.48%), Acidobacteria (0.45%), TM7 (0.16%), Tenericutes (0.16%), OD1 (0.13%), and WPS-2 (0.09%) than in Aug and Oct, whereas Gammaproteobacteria (44.23%), GN02 (0.08%) and SAR406 (0.04%) were depleted in May and Aug but enriched in Oct. In Aug, Bacteroidetes (13.98%), Deltaproteobacteria (6.93%), Verrucomicrobia (4.5%), Chloroflexi (0.36%), Lentisphaerae (0.97%), TM6 (0.25%), Nitrospirae (0.08%), Chlamydiae (0.07%), Chlorobi (0.07%), Spirochaetes (0.04%) and OP8 (0.03%) were significantly enriched than in the other two sampling times (Duncan test; Table S2).At the OTU level, OTUs with relative abundance  > 0.01% (1040 OTUs) were used to perform the difference analysis, and 175 OTUs in May, 281 OTUs in Aug, and 210 OTUs in Oct were identified as seasonal specific OTUs (ssOTUs). The cooccurrence network showed that the ssOTUs were clustered separately (Fig. 4A). Furthermore, the separation of the three modules contained most of the ssOTUs specific to different seasons (Fig. 4A-B). All the ssOTUs of different seasons comprised a taxonomically broad set of prokaryotes at the phylum (phylum Proteobacteria is grouped at the class level) level (Fig. 4C) belonging to various phyla with different proportions. Betaproteobacteria, Verrucomicrobia, Gemmatimonadetes, Epsilonproteobacteria, PAUC34f., and Euryarchaeota did not show significant differences among the three sampling times at the phylum level, but features belonging to these phyla showed differences at the OTU level (Fig. 4C, Dataset S4). In addition, the phylum ssOTUs belonging to, such as Alphaproteobacteria, Gammaproteobacteria, Bacteroidetes, Actinobacteria, and Deltaproteobacteria, were not only enriched at one sampling time (Dataset S4) but also enriched at the other two sampling times (Fig. 4C, Dataset S4). These results revealed that different seasons do not strictly select specific microbial lineages at the phylum level, but a finer level analysis could more strictly reflect the seasonal variation.Figure 4Co-occurrence patterns of seasonal sensitive OTUs (A). Co-occurrence network visualizing significant correlations (ρ  > 0.7, P  0.01%. Different colors represent ssOTUs in May (green), Aug (red) and Oct (blue). Cumulative relative abundance (as counts per million, CPM; y-axis in × 1000) of all the sensitive modules in the networks (B). The phylum attribution of ssOTUs in each season (C). The y-axis is the percentage of the number of OTUs that belong to a particular phylum that accounts for the total number of all the OTUs.Full size imageRegression analysis between the relative abundance of modules to which the ssOTUs belonged and the environmental factors was also conducted, and module 1 abundance, to which the Aug-ssOTUs belonged, showed a significant positive correlation with temperature (R2 = 0.77, P = 6.609e−62), AN (R2 = 0.43, P = 7.416e−25), and un_ionN (R2 = 0.75, P = 1.366e−58) and a negative correlation with SRP (R2 = 0.81, P = 6.762e-17). This may be caused by the functional role of the microbes in Aug. In the Aug-ssOTUs, Deltaproteobacteria showed a higher ratio than in the other 2 months (Fig. 4c), and in the following functional analysis, Deltaproteobacteria contributed to the genes related to nitrogen fixation, which may help to explain why there was a positive correlation of Aug-ssOTUs to AN and un_ionN. The module 2 abundance to which the May-ssOTUs belonged showed a significant negative correlation with pH (R2 = 0.65, P = 4.026e−44), temperature (R2 = 0.19, P = 2.325e−10), un_ionN (R2 = 0.025, P = 0.01779), and SRP (R2 = 0.12, P = 4.104e−07) and a positive correlation with AN (R2 = 0.26, P = 5.174e−14). In the May-ssOTUs, the ratio of Alphaproteobacteria was the highest, and Alphaproteobacteria were reported to be pH-sensitive groups in marine environments20, which prefer neutral pH environments21. In this study, the pH in May was 8.04 ± 0.07, in Aug was 8.39 ± 0.09, in Oct was 8.38 ± 0.07, and the pH in May was the closest to neutral, and the ratio decreased with increasing pH in Oct and Aug. The abundance of module 3, to which the Oct-ssOTUs belonged, showed a significant positive correlation with SRP (R2 = 0.81, P = 0.16e-10) and pH (R2 = 0.054, P = 0.00075) and a negative correlation with temperature (R2 = 0.44, P = 2.276e−25), AN (R2 = 0.75, P = 4.51e−58), and un_ionN (R2 = 0.6, P = 3.995e-39) (Fig. S2). Phosphate has been identified to limit primary productivity22, which is of great importance in the structure of dominant bacterial taxa in marine environments23. In the Oct-ssOTUs, the ratio of Gammaproteobacteria was the highest, as reported. Gammaproteobacteria was significantly explained by SRP during the seasonal variation in the Western English Channel, with Rho equal to 0.7523, which suggested the sensitivity of it to SRP, and in that study, it also showed a negative correlation between temperature and Gammaproteobacteria and a positive correlation between SRP and Gammaproteobacteria. Although the correlation was not significant, the variation trend was consistent, which indicates that the phenomenon observed in this study was not unexpected. In addition, most ammonia-oxidizing bacteria belong to the Betaproteobacteria and Gammaproteobacteria classes are chemolithoautotrophs that oxidize ammonia to nitrite24. Gammaproteobacteria and Betaproteobacteria both had higher ratios in Oct-ssOTUs, and the functional prediction results also showed that pmoA/amoA and pmoB/amoB, which encode ammonia monooxygenase, were mainly contributed by OTUs from Gammaproteobacteria and Betaproteobacteria (Dataset S10). The utilization of ammonia may explain the negative correlation between the Oct-ssOTUs and AN.Community assembly processes across different sampling months and sitesBased on the analysis of phylogenetic turnover, unweighted βNTI mostly ranged from -2 to 2 across different sites at a single sampling time in May, Aug and Oct, revealing that PCC variations across different sampling sites at a single time were mostly affected by stochastic processes. The unweighted βNTI was greater than 2 across May–Aug, May–Oct and Aug-Oct (Fig. 5A), which revealed that the variations in PCC across different sampling times were mostly affected by deterministic processes. The RCbray values across any two sampling times were equal to 1, and in each sampling time, the RCbray values ranged from − 1 to 1 (Fig. 5B). Combining the βNTI and RCbray values, the community assembly processes were quantified at each sampling time and at any two sampling times. As shown in Fig. 5C, turning over of the community during different sampling times was mainly governed by selection; among the different sites in May and Oct, it was mainly governed by “undominated” processes; community turn over in Aug was mainly governed by the influence of “Dispersal Limitation”. These results indicated that the shifts in the assembly of prokaryotic communities during different sampling times were caused by strong “heterogeneous selection” (βNTI  > 2), and the community variation at each sampling time was mainly caused by stochastic processes.Figure 5Patterns of distribution of unweighted βNTI (A) and RCbray (B) values across different sampling times. Quantification of the features that impose community assembly processes in and among different sampling times. (C) Pie charts give the percent of turnover in community composition governed primarily by Selection acting alone (white fill), Dispersal Limitation (green line fill), Homogenizing Dispersal (blue line fill) and undominated process (cyan fill).Full size imagePrediction of the metabolic potential at different sampling timesThe NSTI scores of each sample ranged from 0.033 to 0.096, with a mean of 0.058 (Dataset S5). Microbial functions were detected in all the samples from the three sampling times, and it was found that the relative abundances of 242 pathways were significantly changed between samples from May and samples from Aug (Dataset S6). The relative abundances of 321 pathways were significantly changed between samples from May and Oct (Dataset S7), and the relative abundances of 370 pathways were significantly changed between samples from Aug and Oct (Dataset S8).Genes related to energy metabolism were given more attention. For nitrogen metabolism genes relevant with nitrogen fixation (nifD, nifK) were detected only enriched in Aug, while genes relevant with nitrate reduction and denitrification (narG, narZ, nxrA, narH, narY, nxrB, narI, narV, nirD, nasA, nasB) were detected enriched in May, genes related with ammonia oxidation were both detected enriched in Oct and Aug. For sulfur metabolism, genes relevant with thiosulfate oxidation (soxA, soxB, soxC, soxX, soxY and soxZ) were only enriched in Aug, while genes relevant with sulfate and sulfite reduction (cysNC, aprA, aprB, cysJ, cysI, cysK, dsrA) were detected enriched in May and Oct (Fig. 6).Figure 6The LEfSe analysis indicated significantly differential abundances of PICRUSt predicted genes relevant to energy metabolism in different months of samples.Full size imageProkaryotic taxa contributed to the significantly varied functional genes related to nitrogen and sulfur metabolism at different sampling times. At the species level, the taxa contributing to nifK and nifD mainly belonged to Deltaproteobacteria and Firmicutes, and the taxa contributing to the sox-series genes mainly belonged to Alphaproteobacteria and Gammaproteobacteria (Fig. S3). The denitrification-related functional genes that were enriched in May were mainly contributed by members from Alphaproteobacteria and Gammaproteobacteria. The taxa contributing to dsrA, aprA and aprB were mainly from Deltaproteobacteria, including members of Desulfarculaceae, Desulfobacteraceae, Desulfobulbaceae, Desulfovibrionaceae and Syntrophobacteraceae (Fig. S4). More

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Total catch control regulation does not lead to the recovery of fisheries and the maintenance of community functionTo contain the decline of wild capture fisheries by overfishing, a series of management regulations have been in place in China to mitigate the fishing impacts as much as possible and maintain sustainable stocks. The “zero-growth” policy is one of the most outstanding representatives. The results showed certain achievements after the implementation of the policy. Simulating the status without the “zero-growth” policy, B/Bmsy fell below 0.5 by 2010 and close to zero by 2019, indicating the impossibility for recovery. However, the policy is not enough for fishery recovery and community health, failing to stop the degradation of fishery resources. Under the implementation of the “zero-growth” policy, B/Bmsy was in a healthy state in 1998, fell below 1 for the first time in 2003, and dropped to 0.52 in 2019, accompanying by F/Fmsy as 1.60. If fishing pressure were maintained at the level of 2019 (F = 1.56 Fmsy), the resource would decline to the depletion state by 2030 (B/Bmsy close to zero, F/Fmsy = 3.64, catch = 35 T). Therefore, a great degree of negative production growth as well as the strict implementation is extremely important. A rapid reduction in the catch control under 0.5 Fmsy scenario would expect to achieve a quick recovery with B/Bmsy over 1 in 2025. Nevertheless, a significant reduction in production would lead to the decline of fishery economics, livelihood difficulties for fishermen and a series of derivative social problems28. An alternative of 1.0 Fmsy would be feasible, under which B/Bmsy could rise to 1 by 2030 with a production of 11.64 MT, close to MSY.The “zero growth” policy faces some inherent challenges, at least from the point of view of ensuring the sustainable use of individual species stocks. Attention should be also paid at the catch quota control of individual species. Because the variation of the intrinsic growth rate of different species, the B is dynamic, and the F changes with the change of B. In a constant production, r-strategic species could remain a higher B/Bmsy than 1 even at a large proportion in catch, but K-strategic species did not show the same fortune. The control of total catch volume rather than individual species could not prevent the community structure from becoming fragile, with the exhaustion of high-trophic species and the decrease of mean trophic level.Individual species have different responses to overfishing that highly associated with their biological characteristicsHigh trophic level species can be sensitive to overfishing, and difficult to rebuild stocks after collapseHairtails Trichiurus spp. are the largest contribution group to China marine capture fisheries, at 0.90 MT about 8.3% of the total production in 20202. They are carnivorous and aggressive with a mean trophic level of 4.4, mainly feeding on fishes in the adult stage, and Mysidacea and Euphausiacea in the juvenile stage29,30. The spawning seasons of Trichiurus spp. are mainly from April to June, and from September to November in Chinese waters31.China coastal areas are excellent foraging and spawning grounds for Trichiurus spp, sustaining a large stock size. If the “zero-growth” policy was not implemented since 1999, the resources of Trichiurus spp. would be exhausted by 2027, having no possibility to recovery at 1.0 Fmsy. Although the total fisheries production has been controlled, and the fishing moratorium period partly covered the spawning seasons of Trichiurus spp., their resource continuous declined into a “destroying” state in 2007, due to the time-lag effect of fishing on high trophic level predators characterized by long population doubling time-consuming32. Under intensive fishing pressure, Trichiurus spp. have showed astonishing fisheries-induced adaption33 by reducing the age and size of maturity, which effectively alleviates the decline rate of B value, resulting the maintenance of Trichiurus spp. capture production. Under the rebuilding scenario of fishing pressure as 1.0 Fmsy, Trichiurus spp. B/Bmsy rose to 0.87 by 2030, lower than the recovery rate of national total capture fisheries, suggesting the recovery rate of high trophic level species could be slow34. Furthermore, in this study fisheries rebuilding only considers the responses of species to fishing pressure, irrespective of a series of factors sensitive to high trophic level species such as pollution and climate change, which indicated a longer period is needed for resource recovery.Middle trophic level species seems non sensitive to total catch control policyAs a representative of middle trophic level species, L. polyactis performed different from Trichiurus spp. Under high fishing pressure. It forms spawning and over-wintering aggregations between nearshore and offshore waters, as well as vertical migration, rising at dusk and falling at dawn35. The spawning season is from mid-February to early May, prior to the national fishing moratorium, indicating young juveniles are in effective protection rather than spawning stock. In the 1950s, L. polyactis was one of the few important species in domestic marine capture fisheries in Chinese waters, producing more than 100,000 T annually5. The catch volumes then showed a downward trend and fell significantly to less than 50,000 T in the 1960–1980s. After 3 decades low catch volumes, the annual capture production rebounded significantly to more than 200,000 T and maintained at such high levels for 2 decades5, showing high resilience to overfishing.Despite many concerns on the risk of resource exhaustion of L. polyactis stocks5,36, official statistics showed that the annual catch remains high. The L. polyactis production broke through 150,000 tons in 1995, and was above 300,000 tons after 2005. There is likely to have a large offshore stock of L. polyactis, which gradually joined the catch under increasing fishing efforts offshore. Furthermore, the L. polyactis stocks can be resilience to high pressure for several reasons: (1) its miscellaneous diet makes them be able to receive sufficient food sources; (2) size and age at sexual maturity reduced37,38; and (3) the over consumption of top predators relieves the prey pressure on middle trophic level species, such as L. polyactis, snappers, and flatfishes. A good job is the difficulties of artificial propagation and seedling breeding of small yellow croaker were broken for the first time in 201539 and the whole artificial cultivation was successfully realized in 2020 (https://www.chinanews.com.cn/cj/2020/07-02/9227715.shtml), which would effectively alleviate the market demand and wild stock sustain of small yellow croaker.Pelagic small fish stocks may not recovery quickly as early cognitionSmall pelagic fishes enjoy assembling in large schools of tens of thousands of individuals, and are more vulnerable to predators. Species S. sagax mainly filter plankton with a low trophic level about 2.8. It spawns in May–June, with high fecundity (an absolute fecundity of 30,000–100,000 pelagic eggs) and fast growth, and has short generation time of 1.4 years40. S. sagax shows strong phototropy, and can be caught using light purse seine, gill net, and fixed net fishing at night41,42.In 1989, the biomass of S. sagax was about twice of Bmsy. With the decreasing capture production of traditional economic fishes, S. sagax became a target species using specific fishing methods43, resulted in catch increase accompanied with B/Bmsy decline into a state of extremely unhealthy in 2019. Recovery of small pelagic species stocks would be delayed by the total catch control policy, mainly because the removal of large numbers of predator species left more opportunities for their feeding objects44. Resource rebuilding of S. sagax was not as quick as expected, as small pelagic species had to endure increasing predation pressure from the recovery of high-trophic species under the total catch control. At 1.0 Fmsy scenario, B/Bmsy would be only 0.88 by 2030, in need of a longer period to healthy state.Well-planned restocking can enhance resource recoverySwimming crab P. trituberculatus has high reproductive capacity, with a female can release two to three batches of eggs during a breeding season, and a batch contains about 1–6 million eggs45. Under the complementary of existing management measures and restocking programmes, the production of P. trituberculatus was kept in a certain amount close to a healthy state, and there is not an urgent need for its stock rebuilding. Since the 1990s, restocking of hatchery-produced larvae of P. trituberculatus has been promoted in coastal waters of China. Large-scaled restocking programmes were documented: 33 million larvae were released into the Yellow Sea by Shandong Province in June 2013 (http://hyj.shandong.gov.cn/xwzx/sjdt/201311/t20131120_507389.html); 50.3 million larvae with carapace width over 6 mm were released in the northern Yellow Sea by Liaoning Province in June 2020 (http://nync.ln.gov.cn/fwzx/zxdt/202007/t20200707_3902016.html); 16.1 million larvae were released into the East China Sea by Daishan County of Zhejiang Province in June 2021 (http://www.daishan.gov.cn/art/2021/6/8/art_1383064_59012675.html). What should be of concern is when, where, and how many seedlings are released46,47,48, to maximumly utilize the environmental resources without encroaching on the benefits of other species.Short-living species can be resilience to overfishingThe main cephalopod species in Chinese fisheries are Sepiella maindroni, mainly distributes in the East China Sea35 and Sepia esculenta, mainly distributes in the Bohai Sea, the Yellow Sea and the East China Sea49. As a 1-year lifespan species with fast growth rate, S. maindroni forms spawning migration from deep water to shallow nearshore bays in spring, partly within the fishing moratorium period. Due to the positive phototaxis, the cuttlefishes can be captured by light seining. Sepiella esculenta was the most important cephalopod economically in the northern coastal seas and one of the four major fisheries in the Bohai Sea and the Yellow Sea until the 1970s50. The abundance of this species has been greatly reduced with continuous fishing pressures and dwindling spawning grounds51.Total catch control and fishing moratorium showed significant output on the short lifespan cuttlefishes. Without the implementation of the “zero-growth” policy, the cuttlefishes resources would have been exhausted by 2015 and impossible to rebuild. According to the current state of resources, by 2030 the cuttlefish stocks can be recovered under the 1.0 Fmsy scenario. Moreover, the extent of cuttlefishes stock recovery relies on food supply.Ways to sustain fisheriesThe conflict between rising demand for fishery products and declining resources under multiple pressures including overfishing, climate change, and marine pollution has put heavy pressures at a global scale52. Chinese government has undertaken serious reforms to effectively replan the fishery industry.The effective recovery and rational utilization of resources depend on the support by sufficient reliable data. China started fishery statistics right after the foundation of the People’s Republic of China, completed by MOA (1949–2017) and MARA (since 2018). However, the statistical dataset has been questioned internationally53. According to the explanation by FAO54, before 2000s, especially from 1979 to the late 1990s, as the central government raced to meet the increasing demand for seafood and to grow the domestic production, the local governments had frequently overreported their local catch. In addition, fishermen may falsely claim to increase their production for surplus compensation, after the government introduced fishing subsidies. On the contrary, the production might have been underreported since the early 2000s55,56, which could be attributed to the existence of a large number of “black ships” (fishing vessels without relevant legal permits). Moreover, the lack of professionals in the early period and inaccurate knowledge of species identification by fishermen also lead to data uncertainty. Reasonable fisheries data should be consistent with the species functional traits and life history characteristics. However, in the actual fishing activities, the intentional and high-intensity selective fishing of species may greatly deviate the catch data from the data predicted by models. The Chinese government has been trying to improve the statistical system, including data coefficient adjustment, training of fishermen and professional, and supervision of statistical authorities5. In this study, selected objects are inshore species: the species are familiar to fishermen; the fishing vessel supervision is in place; the data collection is relatively rational and complete; all these are conducive to the reliability of the results.The zero-growth policy, which has been implemented since 1999, is an important measure in the history of marine fishery development and management in China. That is, the total catch of marine fisheries in the current year cannot be higher than that of the previous year. However, the “12th Five-year Plan” for national fishery development (2011–2015) issued by the Ministry of Agriculture canceled the mandatory targets of controlling the production but to encourage more catches of marine fisheries (http://www.moa.gov.cn/gk/ghjh_1/201110/t20111017_2357716.htm). In 2013, the State Council published the first state-level marine fishery development document as “Several Advices on Promoting Marine Sustainable and Healthy Development”, incorporating marine fishery development into the strategy of building a maritime power (http://www.gov.cn/zwgk/2013-06/25/content_2433577.htm). This policy shift was clearly reflected in the significant increase in the national annual catch from 12 to 14 MT. Until the “13th Five-year Plan” for national fishery development (2016–2020) issued in 2016, the zero-even negative-growth policy was revalidated, and the volume of annual output control was clearly proposed as 8–10 MT57, which was determined by multiplying the fishing coefficient by the total stock size derived from the assessment of surveys on the zoning of fisheries and the supplementary survey of marine biological resources in the exclusive economic zone and the continental shelf7. To achieve the target of keeping fishing capacity at a high level of sustainability, significant reductions in fishing pressures over a period of time are required, as well as rational updates of control policies.Many policies were introduced together or around the same time as the “zero-growth” policy, such as summer fishing moratorium, fishing license system, and fishing fuel subsides. However, the achievements are far from satisfactory. The fishing fuel subsidy policy together with the license system induced the direct fishing vessel construction boom which resulted in fewer but bigger and more powerful fishing vessels. Fishing moratorium is the most promising policy, by leaving enough time and space for fish to successfully reproduce. However, the truth is that, right after the fishing closure season, almost all fishing vessels immediately rush into the sea and fishermen try their best to fish as much as possible within the gears and engine power permission of their fishing licenses, attempting to earn a year’s income in a short period of 2–4 months. As a result of such high fishing effort, the achievements of seasonal fishing bans were largely offset and resource densities fell to low levels after autumn. The number of legally binding standards for mesh size is not enough, only 6 at present of at least 40 fishing target species and over 10 fishing gears, leaving many fishing gears and fish species outside the regulation of existing standards6,58. Ideally, standards of mesh size should be updated corresponding to the changes of species traits, however, it is a challenge because the main fishing mode is multiple species fishery by bottom trawling. Moreover, species in China seas are diverse, and the spawning period of different species may not fall into the fishing closure season5. The lack of specificity to sufficiently cover all the species may result an unbalance of community composition. Another system “Double Control” aims to limit both the numbers of fishing vessels and the total power. Unfortunately, the inspections of fishing vessels and their power are not very strict, due to the need of developing local economy and guaranteeing the fishermen’s income, e.g., under a nominal power mask the low-power engines have been replaced by high-power engines, some fishing vessels do not have the fishing licenses28. The limitation of the license number and engine power also stimulate the technological improvement for more catch7.The structure adjustment of fisheries composition is the main management measure at present. The high degree of self-sufficiency in fishery products in China has been achieved through overfishing of domestic fishery resources, resulting in the rapid depletion of fisheries in China’s coastal waters59. Aquaculture, accounting for more than 70% of China’s total fisheries production2, is identified as a successful way. Accompanying by aquaculture development, a series of problems also arise, particularly, the demand of low-value/trash fish and fish meal that significantly drives further expansion of capture fisheries60. Cooperation with other countries to promote regional aquaculture may be an alternative way to meeting global growing demand for seafood and combating overfishing61,62. Seeking resources from the high seas and EEZs of other countries is also a choice, of course, on the premise of taking full account of ecology, maritime, and food security of other countries63,64,65.In addition, this study pointed out a new focus for fisheries management, in which differences in species biological traits, including species vulnerability, population multiplication, and resilience to environmental pressures, should be given full consideration. On this basis, more detailed and targeted management schemes are supposed to propose to achieve the dual purpose of recoverable fisheries resource and balanced species composition, so as to become a truly sustainable fishery. In short, the effective implementation of various management measures is an indispensable guarantee. More

Genetic variationThe best fitting substitution models for COI codons 1–3 were identified as TN + F + G4, F81 + F + I, and TN + F, respectively. The maximum likelihood (ML) and Bayesian inference (BI) trees showed similar topologies of the main nodes, although the BI tree displayed greater resolution of the ingroup branches (Fig. 1). Furthermore, the BI tree revealed three monophyletic clades, while two of those clades were merged in the ML tree. Two of the three molecular species delimitation methods (ASAP and TCS) recovered three groups in the BI tree as distinct taxa (Fig. 1), corresponding to the three previously described ESUs27,28. The third method (bPTP) recovered between 8 and 43 groups (mean = 28.03) suggesting that there is evidence of additional genetic differentiation within the three groups identified by ASAP and TCS. The outputs of the three methods are provided in the Supplementary information. The molecular diagnosis uncovered several fixed nucleotide differences COI characters for each taxon (Table 1: “W. carteri” I = 10; “W. carteri” II = 3; “W. carteri” III = 5). There were also 13 fixed nucleotide differences in W. carteri for the 16S gene. The remaining two taxa had no fixed nucleotide differences for the 16S gene.Figure 1Phylogenetic trees obtained by maximum likelihood (left) and Bayesian inference (right) analysis of “Westralunio carteri” mtDNA COI sequences, including support values for the major genetic clades [ultrafast bootstrap values (left) and Bayesian posterior probabilities (right)]. Colour coded bars show support for the three major clades by the species delimitation methods (ASAP = dark shade; TCS = lighter shade). Green = WcI = “W. carteri” I; blue = WcIII = “W. carteri” III; red = WcII = “W. carteri” II. Results of bPTP analysis not shown (see supplementary data). Haplotype names correspond to Benson et al.28. Outgroup taxa are Velesunio ambiguus (Philippi, 1847) (Hyriidae: Velesunioninae) and Cucumerunio novaehollandiae (Gray, 1834) (Hyriidae: Hyriinae: Hyridellini).Full size imageTable 1 Molecular diagnoses of “Westralunio carteri” Evolutionarily Significant Units (ESUs) from southwestern Australia (after Bolotov et al.122 with reanalysis of data from Klunzinger et al.27 and Benson et al.28).Full size tableVariation in shell morphologyBased on results from analyses of variances (ANOVAs), shells of “W. carteri” I were significantly larger (for size metrics total length (TL), maximum height (MH), beak height (BH) and beak length (BL)) and more elongated (i.e., had a lower maximum height index (MHI)) than shells of “W. carteri” II and “W. carteri” II + III combined (Table 2). However, there was no difference in size or shape metrics between “W. carteri” I and “W. carteri” III (Table 2). The lack of significant differences in beak height index (BHI) and beak length index (BLI) among any of the taxa (Table 2) indicates that wing and anterior shell development was not discernibly different between any of the ESUs.Table 2 Shell size metrics [mm], shape indices [%] and scores for the first two principal components (PC) obtained by Principal Component Analysis of shape indices and 18 Fourier coefficients generated by Fourier Shape Analysis for each “Westralunio carteri” species and subspecies-level Evolutionarily Significant Units (ESUs): n, number of specimens measured; minimum (min) to maximum (max) and mean (± standard error (SE)).Full size tableThis pattern was partly confirmed in the principal component analysis (PCA) of these three shell shape indices, where PC1, largely explained by variation in BLI (Fig. 2A), did not differ between the two species (i.e., “W. carteri” I vs. “W. carteri” II + III) or among the three taxa (Table 2). The PC2, largely explained by variation in MHI and BHI (Fig. 2A), differed significantly between “W. carteri” I and “W. carteri” II (Table 2). Accordingly, 70% (70% jack-knifed) of specimens were assigned to the correct species in the corresponding discriminant analysis (DA), whilst this was true for only 55% (54%) at the MOTU-level.Figure 2Scatterplots of the first two PC axes obtained by PCA on (A) calculated shape indices based on shell measurements, and (B) 18 Fourier coefficients for “Westralunio carteri” I, “W. carteri” II and “W. carteri” III. 95% Confidence Intervals are displayed at the species level, i.e., for “W. carteri” I (full line) and “W. carteri” II + III (dashed line). Extreme shell outlines in (B) are depicted to visualise trends in sagittal shell shape, along PC axes.Full size imageThe difference in shell elongation between “W. carteri” I and “W. carteri” II was confirmed by Fourier shape analysis. As visualised by synthetic outlines in Fig. 2B, shell elongation is expressed along the PC1 (explaining 15% of total variation in Fourier coefficients). The PC1 as well as PC2 scores differed significantly between the two species (i.e., “W. carteri” I vs. “W. carteri” II + III) as well as between “W. carteri” I and “W. carteri” II, respectively (Table 2). Combined with synthetic outlines, this indicated a tendency towards a more elongated, somewhat wedge-shaped shell in “W. carteri” I, whilst “W. carteri” II shells tended to be relatively high with a stout anterior margin (Fig. 2B). An analysis of similarities (ANOSIM) analysis on all Fourier coefficients revealed no significant difference between the two species (i.e., “W. carteri” I vs. “W. carteri” II + III; ANOSIM: R = − 0.018, p = 0.097), but did indicate a significant difference between the three ESUs (ANOSIM: R = 0.0625, p = 0.0051). Specifically, “W. carteri” I differed significantly from “W. carteri” II (Bonferroni-corrected p = 0.0009). Only 66% and 65% (62% and 62% jack-knifed) of specimens were assigned to the correct species and taxon in DAs on that dataset, respectively.Taxonomic accountsClass: Bivalvia Linnaeus, 175831.Subclass: Autobranchia Grobben, 189432.Infraclass: Heteroconchia Gray, 185433.Cohort: Palaeoheterodonta Newell, 196534.Order: Unionida Gray, 185433 in Bouchet & Rocroi, 201035.Superfamily: Unionoidea Rafinesque, 182036.Family: Hyriidae Parodiz & Bonetto 196337.Genus: Westralunio Iredale, 19349.Type species: Westralunio ambiguus carteri Iredale, 19349 (by original designation).Redescription: Westralunio carteri (Iredale, 1934)SynonymyUnio australis Lamarck38: Menke39, p. 38, specimen 219. (Non Unio australis Lamarck, 181938).Unio moretonicus Reeve40: Smith41, p. 3, pl. iv, Fig. 2. (misidentified reference to Unio moretonicus Reeve, 186540).Hyridella australis (Lam.38): Cotton & Gabriel42 (in part), p. 156. (misidentified reference to Unio australis Lamarck, 181938).Hyridella ambigua (Philippi26): Cotton & Gabriel42 (in part), p. 157. (misidentified reference to Unio ambiguus Philippi, 184726).Westralunio ambiguus carteri: Iredale, 19349, p. 62.Westralunio ambiguus (Philippi26): Iredale9, p. 62, pl. iii, Fig. 8, pl. iv, Fig. 8. (Non Unio ambiguus Phil. 184726), Iredale43, p. 190.Centralhyria angasi subjecta Iredale, 19349, p. 67 (in part), Iredale43, p. 190.Westralunio carteri Iredale9: McMichael & Hiscock10pl. viii, Figs. 1, 2, 3, 4, 5, 6 and 7, pl. xvii, Figs. 4, 5.Type materialLectotype: AMS C.61724 (Fig. 3A) Westralunio ambiguus carteri Iredale, 19349.Figure 3(A) Westralunio ambiguus carteri Iredale, 1934, Lectotype: Victoria Reservoir, Darling Range, 12 mi E of Perth, AMS C.061724. Detail of fusion in anterior muscle scars from either valve represented by dashed lines and black polygons. Bottom image showing detail of hinge teeth. Photos provided with permission by Dr Mandy Reid, AMS. (B) Valves and detail of sculptured umbo of a juvenile W. carteri from Yule Brook, Western Australia, UMZC 2013.2.9. Photo by Dr Michael W. Klunzinger. (C) Glochidia of W. carteri from Canning River, Western Australia. Photo by Dr Michael W. Klunzinger.Full size imageParalectotypes: AMS C.170635 Westralunio ambiguus carteri Iredale, 19349 (n = 4).Type locality: Victoria Reservoir, Darling Range, 12 miles east of Perth, Western Australia (Fig. 4A).Figure 4(A) Victoria Reservoir, Canning River, near Perth, Western Australia, type locality for W. carteri. Photo by Corey Whisson. (B) Goodga River, Western Australia, type locality for W. inbisi inbisi, at vertical slot fishway where holotype of W. inbisi inbisi was collected from. Photo provided with permission by Dr Stephen J. Beatty. (C) Margaret River, Western Australia, type locality for W. inbisi meridiemus, at Canebreak Pool. Photo by Dr Michael W. Klunzinger.Full size imageLectotype: BMNH 1840–10-21–29 Centralhyria angasi subjecta Iredale (selected by McMichael & Hiscock10).Type locality: Avon River, Western Australia.Material examined for redescription: For W. carteri (= “W. carteri” I), molecular data examined included 52 and 61 individual COI mtDNA and 16S rDNA sequences, respectively, for species delimitation. Additionally, Fourier shell shape outline analysis and traditional shell morphometric measurements were examined from 238 and 290 individuals, respectively. Complete details on all specimens examined are provided in Supplementary Table S1.ZooBank registration: urn:lsid:zoobank.org:act:6B740F4D-40C3-4D6A-8938-B0FD7FD1F6D7.Etymology: The species name carteri is most likely named after the surname of the collector who provided original type specimens to the Australian Museum, although Iredale9 did not specify this as the case. We have applied ICZN Articles 46.1 and 47.144, designating W. carteri as the nominotypical species.Revised diagnosis: Specimens of W. carteri are distinguished from other Australian Westralunio taxa by having shell series that are significantly larger and more elongated than Westralunio inbisi inbisi subsp. nov., but not different from Westralunio inbisi meridiemus subsp. nov. The species has 10 diagnostic nucleotides at COI (57 G, 117 T, 210 G, 249 T, 255 C, 345 G, 423 T, 447 T, 465 A, 499 T) and 13 at 16S (137 T, 155 C, 228 C, 229 T, 260 G, 290 A, 305 G, 307 T, 310 A, 311 C, 321 T, 330 A, 460 A), which differentiate it from its sister taxa, W. inbisi inbisi and W. inbisi meridiemus (each described below) using ASAP and TCS species delimitation models.RedescriptionThis species is of the ESU “W. carteri” I27,28.Shell morphology: Shells of relatively small to medium size, generally less than 70 mm in length, but to a maximum length of approximately 100 mm10,45, MHI 46–89%; anterior portion of shell with moderate development, BLI 22–49%; larger shells with abraded umbos scarcely winged; wing development variable, generally decreasing with size, BHI 76–104% (Table 2). Shell outline oblong-ovate to rounded; posterior end obliquely to squarely truncate, anterior end round; ventral edge slightly curved, nearly straight in larger specimens; hinge line curved, hinge strong. Umbos usually abraded in specimens  > 20 mm in length; unabraded umbos with distinctive v- or w-shaped plicated sculpturing (Fig. 3B and Zieritz et al.46). Shell substance typically thick; shells of medium width with pronounced posterior ridge; periostracum smooth, dark brown to reddish, with fine growth lines. Pallial line less developed in smaller specimens and prominent only in large specimens (e.g.,  > 60 mm TL). Lateral teeth longer and blade-like, slightly serrated to smooth and singular in left valve, fitting into deep groove in right valve; pseudocardinal tooth in right valve coarsely serrated, thick, and erect, fitting into deeply grooved socket in left valve. Anterior muscle scars well impressed and anchored deeply in larger specimens; anterior retractor pedis and protractor pedis scars both small and fused with adductor muscle scar; posterior muscle scars lightly impressed; dorsal muscle scars usually with two or three deep pits anchored to internal umbo region.Anatomy: Supra-anal opening absent, siphons of moderate size, not prominent but protrude beyond shell margin in actively filtering live specimens, pigmented dark brown with mottled lighter brown to orange splotches; inhalant siphon aperture about 1.5 times size of exhalant and bearing 2–4 rows of internal papillae (Fig. 5A); ctenidial diaphragm relatively long and perforated. Outer lamellae of outer ctenidia completely fused to mantle, inner lamellae of inner ctenidia fused to visceral mass then united to form diaphragm; palps relatively small, usually semilunar in shape; marsupium well developed as a distinctive swollen interlamellar space in the middle third of the inner ctenidium of females. Outer ctenidia in both sexes thin, with numerous, short intrafilamentary junctions and few, irregular interlamellar junctions; in females similar, but marsupium has numerous, tightly packed, well-developed interlamellar junctions. Thus, brooding in females is endobranchous.Figure 5Live specimens of actively filtering freshwater mussels in the burrowed position. (A) Westralunio carteri (Iredale, 1934), Canning River at Kelmscott, Western Australia, inhalant siphon with 2–4 rows of papillae oriented toward substrate. Photo by Dr Michael W. Klunzinger. (B) Westralunio inbisi meridiemus subsp. nov., Canebreak Pool, Margaret River, Western Australia; inhalant siphon edges lined with protruding papillae facing towards water surface, away from substrate. Photo by Dr Michael W. Klunzinger.Full size imageLife history: Sexes are separate in W. carteri, and hermaphroditism appears to be rare47,48,49. Males and females both produce gametes year-round but brooding of glochidia appears to be seasonal and ‘tachyticitc’ (i.e., as defined by Bauer & Wächtler19, fertilisation and embryonic development occurring in late winter/early spring and glochidia release in early summer)50. Glochidia are released within vitelline membranes, embedded in mucus which extrude from exhalant siphons of females (i.e., ‘amorphous mucus conglutinates’) during spring/summer. Glochidia attach to host fishes and live parasitically on fins, gills or body surfaces for 3–4 weeks while undergoing metamorphosis to the juvenile stage. Host fishes which have been shown to support glochidia metamorphosis to the juvenile stage in the laboratory include Afurcagobius suppositus (Sauvage, 188051), Gambusia holbrooki (Girard, 185952), Nannoperca vitttata (Castelnau, 187353), Pseudogobius olorum (Sauvage, 188051) and Tandanus bostocki Whitley, 194454 but not Carassisus auratus Linnaeus, 175831 or Geophagus brasiliensis (Quoy & Gaimard, 1824 More

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General description of sequencesAfter the quality filtering step, removal of chimeric fragments, and read merging, a total of 3,378,323 reads with 3007 different features was obtained, with an average of 27,244 sequences per individual sample. After quality filtering, none of the samples was excluded from the analysis of microbial communities.Amoxicillin and thiamphenicol treatments influence microbial diversity and the abundance of specific taxaUsing 16S rRNA NGS, the gut microbial community composition of the chicks in each group was characterized at different time points. At phylum level, microbiota composition varied with age rather than with treatment (Supplementary Fig. S1). Proteobacteria were the most abundant phyla at 1 day of age (d.o.a.), Firmicutes became dominant at later stages, while Bacteroidota were highly abundant in caecum samples collected at 46 d.o.a. Similar dynamics were observed also at family level, since Enterobacteriaceae and Clostridiaceae were significantly more abundant at 1 d.o.a. in all groups, Lactobacillaceae, Lachnospiraceae, and Ruminococcaceae seemed to bloom at 8 d.o.a., and Rikenellaceae were the dominant family in the caecum samples collected at 46 d.o.a. (Fig. 1; Supplementary Fig. S2).Figure 1Heatmap representing the microbial community composition at family level. The heatmap was generated in R (version 4.2.1) (https://www.r-project.org/) using package pheatmap (version 1.0.12).Full size imageEarly-age administrationIn both α-diversity indices (Fig. 2A,B), there was a trend towards increasing diversity from early to late time points in all groups; however, the only significant differences were between the group treated with amoxicillin (AMX1) and the other groups on day 21 post treatment (p.t.), and within AMX1 group between day 21 p.t. and the other time points. PERMANOVA showed that the microbial community was significantly different between the group treated with thiamphenicol (THP1) and the other two groups (i.e. AMX1 and control) on day 1 p.t. (p  More