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

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

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

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