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Experimental design and sample collection
This study was carried out in accordance with the provisions in the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes (7th edition, 2004) and all protocols were approved by the Animal Ethics Committee of La Trobe University. Twelve Angus × Friesian cattle foetuses at 5, 6 and 7 months gestation (n = 4 per age) were collected from Radford Warragul Abattoir, Victoria, Australia. Approximately 35–45 min after cows were slaughtered by abattoir staff, the intact uterus (containing the placenta and foetus) was removed. All sampling was conducted at the abattoir using sterile equipment and procedures. The outside surface of the amniotic sac was rinsed three times with sterilised phosphate-buffered saline (PBS; pH 7.0) to remove excess blood. The amnion was cut using sterile scalpels and amniotic fluid was sampled. The amniotic fluid was suctioned using sterile 50-mL syringes with tubing and the amniotic fluid was transferred immediately into 50-mL tubes. Then, the amniotic sac was opened further, the umbilical cord was cut, and the foetus was removed. The abdomen of the foetus was opened using sterilised equipment and the rostral and caudal ends of each GIT compartment were tied with sterile surgical thread to avoid mixing of the contents. The compartments were then separated between the ties. Each compartment was longitudinally incised along the dorsal line. Tissue samples (~ 2 cm2) of the rumen were taken from the dorsal area and caecal tissue samples were taken from the region 5 cm after the ileocaecal valve. Meconium pellets (~ 100 g) were taken by severing the rectum 5 cm from the anus. The fluid, tissue and meconium samples were collected into sterile 15-mL or 50-mL polypropylene centrifuge tubes. All samples were immediately placed into dry ice for transport. All samples were processed within 6 h of collection to extract gDNA.
DNA extraction
Genomic DNA was extracted from 250 mg of ruminal tissue, ruminal fluid, caecal tissue, caecal fluid and meconium. An 8-mL aliquot of amniotic fluid was centrifuged (11,000g, 5 min) to produce sufficient material in the pellet for extraction. DNA was extracted using an Isolate II Genomic DNA kit following the manufacturer’s instructions. Final DNA concentrations and purity were estimated using a P330-Class NanoPhotometer (Implen, München, Germany). All samples were stored at − 80 °C for later analysis.
16S rRNA library preparation and sequencing
Libraries were prepared for sequencing on an Illumina MiSeq following the protocol ‘16S Metagenomic Sequencing Library Preparation’ (Part # 15044223 Rev. B; Illumina, San Diego, CA, USA). The locus-specific primers were the universal 16S rRNA primer pairs S-D-Bact-0341-b-S-17 (5′-CCTACGGGNGGCWGCAG-3′) and S-D-Bact-0785-a-A-21 (5′-GACTACHVGGGTATCTAATCC-3′), Archaea349F (5′-GYGCASCAGKCGMGAAW-3′), and Archaea806R (5′-GGACTACVSGGGTATCTAAT-3′), which target the V3–V4 region of the bacterial and archaeal 16S rRNA genes, respectively. Primers had forward (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3′) and reverse (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3′) Illumina overhang adaptors merged to the 5′ ends.
PCR was performed in 25-µL reactions using 5 µL of each forward and reverse primer (10 µM), 12.5 µL 2 × KAPA HiFi HotStart ReadyMix (Kapa Biosystems, Boston, MA, USA) and 2.5 µL of genomic DNA template (5 µL/ng). PCR cycle settings for the amplification of the bacterial and archaeal V3–V4 region were as follows: denaturation at 95 °C for 3 min, followed by 28 (bacterial) or 30 (archaeal) cycles of 30 s at 95 °C, 30 s at 55 °C and 30 s at 72 °C, followed by an extension step at 72 °C for 5 min. To normalise libraries prior to pooling, the DNA content of PCR reactions was quantified using an Agilent D1000 ScreenTape System (Agilent Technologies, CA, USA). Samples were adjusted to the same molarity (4 nM), pooled, and paired-end sequenced (2 × 300 bp) on an Illumina MiSeq platform. The MiSeq run was performed at La Trobe University Genomics Platform (Melbourne, Australia).
Analysis of sequence data
Raw, de-multiplexed, fastq files were re-barcoded, joined and quality filtered using the UPARSE clustering pipeline (USEARCH version 9.2.64; https://drive5.com/uparse)46. Paired-end reads were merged such that alignments with > 20 bp difference (i.e. approximately more than 10–14% mismatched) were discarded, and merged reads less than 300 bp in length were discarded. Reads that could not be assembled were discarded. Merged reads were quality filtered by discarding reads with total expected errors > 1.0. ESVs were generated with the “unoise3” command47. Taxonomic assignments were performed using the UTAX algorithm. Reference databases were created using the RDP_trainset_15 dataset, available from the UTAX downloads page (https://drive5.com/usearch/manual/utax_downloads.html). A minimum percentage identity of 90% was required for an ESV to be considered a database match hit. ESVs identified as chloroplasts and mitochondrial DNA were removed from the data. After filtering, the average read number (± SD) for each git compartment was: 4342 ± 1594 reads for the AM , 17,276 ± 17,376 reads for the CF, 6182 ± 3918 reads for the CT, 5732 ± 3262 reads for the Mec, 5630 ± 2234 reads for the RF and 5847 ± 4052 reads for the RT. The rarefying threshold of 1000 reads was chosen to maximise the amount of reads included in the analysis whilst minimize the number of samples excluded from the analysis. A total of three bacterial samples resulted in reads below the rarefication threshold (1000 reads) and were excluded from downstream alpha- and beta-diversity analyses. The samples were: Month_5_Mec-2, Month_6_CF-3 and Month_6_Mec-1. DNA extraction or library preparation was unsuccessful for the following samples: n = 0 (5 months cecum fluid), n = 1 (6 and 7 months rumen tissue, 7 months amniotic fluid), n = 2 (5 months rumen tissue, 6 months amniotic fluid), n = 3 (5 and 6 months cecum tissue, 5 months meconium), the remaining GIT compartments and months were n = 4. Raw fastq files for this project and metadata have been deposited with the NCBI SRA database and can be accessed using Bioproject ID: PRJNA421384 or SRA study ID: SRP126299.
Bacterial culture from ruminal fluid samples and identification of bacterial isolates
A 50-mL sample of ruminal fluid was taken from each foetus and maintained under anaerobic conditions (Oxoid AnaeroJar with an AnaeroGen™). A 1-mL aliquot of the ruminal fluid was transferred to anaerobic solid medium and cultured at 37 °C for 48–72 h. The anaerobic solid medium had the following composition (per litre of distilled water): 15 g agar (Oxoid), 10 g peptone (Oxoid), 10 g yeast extract, 8.8 g Oxoid Lab-Lemco beef extract powder, 10 g proteose peptone (Oxoid), 12 g dextrose, 10 g KH2PO4, 12 g NaCl, 20 g soluble starch, 1.2 g l-cysteine hydrochloride and 0.3 g sodium thioglycollate with a pH (at 25 °C) of 7.3 ± 0.1. Colonies were isolated and subcultured 5 times onto new agar media plates, except for the control plates (n = 3) which showed no microbial growth. Colonies were subcultured on fresh media and DNA extracted. The extracts for 5, 6 and 7 months were combined prior to next-generation sequencing of the 16S rRNA genes to characterise the taxonomic structure.
Quantitative PCR
Quantitative PCR (qPCR) was used to enumerate total bacterial and archaeal DNA copy number in each sample type (GIT component and amniotic fluid) as an indicator of abundance. The primer pairs bacF (5′-CCATTGTAGCACGTGTGTAGCC-3′) and bacR (5′-CGGCAACGAGCGCAACCC-3′) were used to amplify bacterial 16S rRNA, and Archaea364F (5′-CCTACGGGRBGCAGCAGG-3′) and Archaea1386R (5′-GCGGTGTGTGCAAGGAGC-3′) were used to amplify archaeal 16S rRNA. PCR reactions were run in triplicate on a CFX Connect Real-Time PCR Detection System (Bio-Rad, CA, USA). The total volume of each reaction mix was 20 μL, comprising 10 μL of SensiFAST SYBR Green Master Mix (Bioline), 0.4 μL of each forward and reverse primer (10 µM), sterile DNA-free water, and 7 ng of DNA. Triplicate control samples (no-DNA templates) were included to verify that no contaminating nucleic acid was introduced into the master mix or into samples. Positive controls contained gDNA extracted from laboratory cultured bacteria (E. coli strain DH5α) and archaea (Methanobrevibacter smithii), respectively. Thermocycling conditions were as follows: initial denaturation for 3 min at 94 °C, followed by 40 cycles of 10 s at 94 °C, 30 s at 60 °C. This was followed by a dissociation protocol (increasing 1 °C every 30 s from 60 °C to 98 °C).
A standard curve was constructed using serial tenfold dilutions from 10−1 to 10−11 of DNA from the bacterium E. coli strain DH5α (Stratagene, CA, USA) or the archaeon M. smithii. Real-time PCR efficiency ranged from 97 to 102%. Copy numbers for each standard curve were calculated based on the following equation: (NA × A × 10−9)/(660 × n), where NA is the Avogadro constant (6.02 × 1023 mol−1), A is the molecular weight of DNA molecules (ng/mol) and n is the length of amplicon (bp).
Control procedures for sample contamination
Potential airborne bacteria were passively sampled to determine if there was a detectable contribution of environmental bacteria contaminating the foetal samples. Sampling tubes containing aerobic or anaerobic medium were opened and exposed to the dissection area in the abattoir for the duration of sampling from each foetus. The exposed media were incubated at 37 °C and samples taken at 72 h and at 2, 3 and 4 weeks. DNA was extracted using an Isolate II Genomic DNA kit. Final DNA concentrations and purity were estimated using a P330-Class NanoPhotometer.
The dissection table and external surfaces of the amniotic sac and intestinal compartments were swabbed to determine if there was a detectable contribution of bacteria contaminating the foetal samples. For each foetus, 9 swabs were taken at six locations using sterile Fisherbrand synthetic-tipped applicator swabs (Thermo Fisher Scientific, MA, USA). The surface of the dissection table (first location) was washed with 70% ethanol and then swabbed prior to dissecting each foetus. The external surface of the amniotic sac (second) was rinsed three times with sterilised PBS to remove excess blood and then swabbed prior to opening the sac. The skin of the foetal abdomen (third) was rinsed three times with sterilised PBS to remove amniotic fluid and then swabbed prior to opening. The external (mesenteric) surfaces of the rumen (fourth), caecum (fifth) and rectum (sixth location) were separately swabbed prior to opening. The 9 swabs for each foetus were tested for the presence of microorganisms, using three swabs for each of the three methods: qPCR, anaerobic culture and aerobic culture. DNA was extracted from three of the swabs using an Isolate II Genomic DNA kit. Final DNA concentrations and purity were estimated using a P330-Class NanoPhotometer. Quantitative PCR was used to more accurately quantify the presence of DNA.
Anaerobic and aerobic liquid mediums were inoculated from three swabs each. Three swabs were placed into a 2.5-L anaerobic Oxoid AnaeroJar with an AnaeroGen sachet (Thermo Fisher Scientific) and three swabs were placed into aerobic Oxoid Nutrient Broth (Thermo Fisher Scientific). All mediums were incubated at 37 °C for 48–72 h. Monitoring for growth during storage at 4 °C was continued for up to one month. The anaerobic liquid medium had the following composition (per litre of distilled water): 10 g peptone (Oxoid), 10 g yeast extract, 8.8 g Oxoid Lab-Lemco beef extract powder, 10 g proteose peptone (Oxoid), 12 g dextrose, 10 g KH2PO4, 12 g NaCl, 20 g soluble starch, 1.2 g L-cysteine hydrochloride and 0.3 g sodium thioglycollate with a pH (at 25 °C) of 7.3 ± 0.1. The aerobic nutrient broth had the following composition (per litre of distilled water): 10 g Lab-Lemco powder, 10 g peptone and 5 g NaCl, with a pH (at 25 °C) of 7.5 ± 0.2.
Controls for kit contamination
Two blank DNA spin columns from two different Isolate II Genomic DNA kits were used as no-template controls to determine if the kits were a source of contamination during DNA extraction and library preparation of samples. The no-template controls were tested using qPCR and Illumina MiSeq next-generation sequencing.
Statistical analysis of the total community
The adequacy of the sampling effort to capture the microbial community richness was examined by generating species rarefaction curves and species accumulation plots using the ‘rarecurve’ and ‘specaccum’ functions in the R library vegan (v. 2.3-4)48 using R 3.2.049. Alpha- and beta-diversity analyses were performed on archaeal and bacterial OTU-matrices rarefied to depths of 2000 and 1000 reads, respectively, using the ‘phyloseq’ (v. 1.16-2)50 and ‘vegan’ (v. 2.4-0)48,51 packages in the R programming language (v. 3.3.1)49. Normality and variance homogeneity of the data were tested using the ‘shapiro.test’ and ‘bartlett.test’ functions. As normality and homogeneity of variance assumptions were not met, Kruskal–Wallis tests were carried out using the ‘kruskal.test’ function with Dunn’s test of multiple comparisons used for post hoc testing. The NMDS ordinations were used to visualise differences between communities from different sample types (GIT components and amniotic fluid). Dissimilarity matrices were generated using the weighted and unweighted UniFrac metrics52. Analysis of similarity (ANOSIM) procedures were implemented to test for significant differences in the mean group centroids53. Differential abundance testing of ESVs was performed using the DESeq2 extension available within the ‘phyloseq’ package50,54. Tests were performed by applying model-fitting normalisation to unrarefied ESV tables as recommended by McMurdie and Holmes for each taxonomic rank50. For differential abundance tests only ESVs with high ( > 90%) confidence values for phylum-level taxonomic assignments were considered. All low confidence taxonomic assignment we re-classified as ‘unknown’. Differences were considered significant if Benjamin-Hochberg adjusted p  More

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Psyllids or jumping plant-lice are a group of small, generally host-specific plant-sap sucking insects with around 4000 described species1. A few species are major pests on fruits or vegetables, mostly by transmitting plant pathogens. Others damage forest plantations or ornamental plants by removal of plant-sap, stunting new growth, inducing galls or secreting honeydew and wax, an ideal substrate for sooty mould which reduces photosynthesis2. Modern psyllids, defined by the enlarged and immobile metacoxae in adults allowing them to jump, display a wide range of morphological diversity regarding the head, antennae, legs, forewings, terminalia, etc. in adults and body shape, antennal structure and the type of setae or wax pores in immatures. Modern psyllids are documented in the fossil record since the Eocene (Lutetian)3 (Fig. 1). The stem-group of modern psyllids constitutes, according to Burckhardt & Poinar, 20194, the paraphyletic Liadopsyllidae Martynov, 19265 with 17 species and six genera (Liadopsylla Handlirsch, 19256, Gracilinervia Becker-Migdisova, 19857, Malmopsylla Becker-Migdisova, 19857, Mirala Burckhardt & Poinar, 20194, Neopsylloides Becker-Migdisova, 19857 and Pauropsylloides Becker-Migdisova, 19857) from early Jurassic to late Cretaceous4,8. Shcherbakov9 added three species from the Lower Cretaceous for one of which he erected the genus Stigmapsylla and for the other two the subgenus Liadopsylla (Basicella). He also transferred two previously described species from Liadopsylla to Cretapsylla Shcherbakov9. Further he resurrected the Malmopsyllidae Becker-Migdisova, 19857 splitting it into Malmopsyllinae (for Gracilinervia, Malmopsylla, Neopsylloides and Pauropsylloides) and Miralinae Shcherbakov9 (for Mirala). Apart from three species described from amber fossils, all Mesozoic psyllids are poorly preserved impression fossils of which usually only the forewing is preserved. The current classification of Mesozoic psyllids (Liadopsyllidae and Malmopsyllidae) is based almost exclusively upon forewing characters7,9, despite that several phylogenetically significant characters from other body parts have been described from amber inclusions4,8. Judging from the impression fossils, Liadopsyllidae and Malmopsyllidae appear morphologically quite homogeneous but this may be a result of the surprisingly scarce fossil record of psyllids compared to other insect groups. The discoveries of Cretaceous amber fossils radically alter this picture, e.g. the recently described Mirala burmanica Burckhardt & Poinar, 2019 from Myanmar amber4.
Figure 1

Relationships and stratigraphic distribution of Liadopsyllidae and its subunits within Sternorrhyncha according to Drohojowska & Szwedo10, Hakim et al.11 and Drohojowska et al.12, modified. Numbers denote described taxa of fossil Liadopsyllidae—1: Liadopsylla geinitzi Handlirsch, 1925—Lower Jurassic, Mecklenburg, Germany, 2: Liadopsylla obtusa Ansorge, 1996—Lower Jurassic, Mecklenburg-Vorpommern, Germany, 3: Liadopsylla asiatica Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 4: Liadopsylla brevifurcata Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 5: Liadopsylla grandis Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 6. Liadopsylla karatavica Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 7. Liadopsylla longiforceps Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 8. Liadopsylla tenuicornis Martynov, 1926—Upper Jurassic, Karatau, Kazakhstan, 9. Liadopsylla turkestanica Becker-Migdisova, 1949—Upper Jurassic, Karatau, Kazakhstan, 10. Gracilinervia mastimatoides Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 11. Malmopsylla karatavica Becker- Migdisova, 1985 – Upper Jurassic, Karatau, Kazakhstan, 12. Neopsylloides turutanovae Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 13. Pauropsylloides jurassica Becker-Migdisova, 1985—Upper Jurassic, Karatau, Kazakhstan, 14. Liadopsylla mongolica Shcherbakov, 1988—Lower Cretaceous, Bon Tsagaan, Mongolia 15. Liadopsylla apedetica Ouvrard, Burckhardt et Azar, 2010—Lower Cretaceous, Lebanon, 16. Liadopsylla lautereri (Shcherbakov, 2020)—Lower Cretaceous, Buryatia, Russia 17. Liadopsylla loginovae (Shcherbakov, 2020)—Lower Cretaceous, Buryatia, Russia 18. Stigmapsylla klimaszewskii Shcherbakov, 2020—Lower Cretaceous, Buryatia, Russia 19. Mirala burmanica Burckhardt et Poinar, 2019—mid-Cretaceous, Kachin amber, 20. Amecephala pusilla gen. et sp. nov.—mid-Cretaceous, Kachin amber, 21. Liadopsylla hesperia Ouvrard et Burckhardt, 2010—Upper Cretaceous, Raritan amber, U.S.A.

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Here we describe a second taxon of Mesozoic psyllids from Kachin amber, Amecephala pusilla gen. et sp. nov., possessing a series of characters unique within Mesozoic psyllids, discuss the phylogenetic relationships within the group, and provide an updated key to genera as well a checklist of recognised species (Table 1).
To satisfy a requirement by Article 8.5.3 of the International Code of Zoological Nomenclature this publication has been registered in ZooBank with the LSID: urn:lsid:zoobank.org:act:D3AF7597-47BF-4D6C-9020-982F4C20315E.
Table 1 Annotated checklist of known species of Liadopsyllidae Martynov, 19265.
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Systematic palaeontology
Order Hemiptera Linnaeus, 175817
Suborder Sternorrhyncha Amyot et Audinet-Serville, 184318
Superfamily Psylloidea Latreille, 180719
Family Liadopsyllidae Martynov, 19265
Genus †Amecephala gen. nov
urn:lsid:zoobank.org:act:9DABC236-FFB9-4305-82EC-4E293212849B
Type species
† Amecephala pusilla sp. nov., by present designation and monotypy.
Etymology From ancient Greek ἡ άμε [ē áme] = shovel and ἡ κεφαλή [ē kefalé] = head for its shovel-shaped head. Gender: feminine.
Diagnosis
Vertex rectangular; coronal suture developed in apical half; median ocellus on ventral side of head, situated at the apex of frons which is large, triangular; genae not produced into processes; toruli oval, medium sized, situated in front of eyes below vertex. Eyes hemispheric, relatively small (Fig. 2a,b,e,g). Antenna with pedicel about as long as flagellar segments 1 and 8, longer than remainder of segments. Pronotum ribbon-shaped, relatively long, laterally of equal length as medially. Forewing (Fig. 2a,b,f,g) elongate, widest in the middle, narrowly rounded at apex; pterostigma short and broad, triangular, not delimited at base by a vein thus vein R1 not developed; veins R and M + Cu subequal in length; vein Rs relatively short, slightly curved towards fore margin; vein M shorter than its branches which are of subequal length; cell cu1 low and very long. Female terminalia short, cuneate.
Figure 2

(a‒i) Amecephala pusilla gen. et sp. nov. imago. Drawing of body in dorsal view (a), Body in dorsal view (b), Metatarsus (c), Drawing of hind leg (d), Head in dorsal view (e), Forewing (f), Body in ventral view (g), Basal part of claval suture (h), Distal part of claval suture (i); Scale bars: 0.5 mm (a,b); 0.2 mm (f,g); 0.1 mm (c,d,e,h,i).

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Description
Head weakly inclined from longitudinal body axis; about as wide as pronotum and mesoscutum, dorso-ventrally compressed. Vertex rectangular; anterior margin weakly curved, indented in the middle; posterior margin slightly concavely curved; coronal suture developed in apical half, basal half not visible; lateral ocelli near posterior angles of vertex, hardly raised; median ocellus on ventral side of head, situated at the apex of frons which is large, triangular; genae not produced into processes; preocular sclerites lacking; toruli oval, medium sized, situated in front of eyes below vertex; clypeus partly covered by gas bubble, appearing flattened, pear-shaped. Eyes hemispheric, relatively small (Fig. 2a,b,e,g). Antenna 10-segmented, filiform, moderately long, flagellum 1.6 times as long as head width; pedicel very long, about as long as flagellar segments 1 and 8; rhinaria not visible (Fig. 2a,b). Thorax (ventrally not visible) with pronotum wider than mesopraescutum as wide as mesoscutum, laterally of the same length as medially. Mesothorax large; mesopraescutum triangular, with arcuate anterior margin, almost twice wider than long in the middle; mesopraescutum slightly longer than pronotum in the middle; mesoscutum subtrapezoid with slightly arched anterior margin, about 3.0 times wider than long in the middle; delimitation between mesoscutum and mesoscutellum clearly visible. Metascutellum trapezoid, narrower than mesoscutellum with a submedian longitudinal low ridge on either side. Parapterum and tegula forming small oval structures of about the same size; the former slightly in front of the latter. Forewing (Fig. 2a,b,f) membranous, elongate, narrow at base, widest in the middle, narrowly rounded at apex which lies in cell m1 near the apex of vein M3+4; vein C + Sc narrow; cell c + sc long, widening toward apex; costal break not visible, perhaps absent; pterostigma short and broad, triangular, not delimited at base by a vein thus vein R1 not developed; vein R + M + Cu relatively short; veins R and M + Cu subequal in length; vein R2 relatively short and straight; vein Rs relatively short, slightly curved towards fore margin; vein M shorter than its branches which are of subequal length; vein Cu short, splitting into very long Cu1a and short Cu1b, hence cell cu1 low and very long; claval suture visible (Fig. 2h,i); anal break near to apex of vein Cu1b (Fig. 2f,i). Hindwing (Fig. 2a) shorter than forewing, more than twice as long as wide, membranous; venation indistinct. Legs similar in shape and size, long, slender (Fig. 2c,d,g); femora slightly enlarged distally, tibiae long and slightly enlarged distally; metatibia lacking genual spine and apical sclerotized spurs, but bearing several apical bristles and, in distal quarter, a row of short bristles (Fig. 2d); tarsi two-segmented, tubular of similar length though basal segment slightly thicker than apical one, claws large, one-segmented, pulvilli absent (Fig. 2c–d). Abdomen appearing flattened, tergites and sternites not clearly visible. Female terminalia short, slightly shorter than head width, cuneate (Fig. 2a,b,g).
Revised key to Mesozoic psylloid genera (after Burckhardt & Poinar4 , modified)
1.
Forewing lacking pterostigma………………………………………………………………………………………………………………Liadopsylla Handlirsch, 1921 (= Cretapsylla Shcherbakov, 2020 syn. nov.; = Basicella Shcherbakov, 2020 syn. nov.)
-Forewing bearing pterostigma………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….2

2.
Vein Rs in forewing straight, veins Rs and M subparallel; vein M not branched; vein R shorter than M + Cu; vein Cu1b almost straight, directed toward wing base………………………………………………….Mirala Burckhardt et Poinar, 2020
-Combination of characters different. Vein Rs in forewing concavely curved towards fore margin (not visible in Stigmapsylla), veins Rs and M from base to apex first converging then diverging; vein M branched; vein Cu1b straight or curved, directed toward hind margin or apex of wing………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………3

3.
Vein R of forewing distinctly shorter than M + Cu……………………………………………………………………………………………………………………………………………………………………………………………………………….Stigmapsylla Shcherbakov, 2020
-Vein R of forewing distinctly longer than M + Cu, or veins R and M + Cu subequal in length…………………………………………………………………………………………………………………………………………………………………………………………….4

4.
Vein R of forewing distinctly longer than M + Cu; vein Cu1a almost straight…………………………………………………………………………………………………………………………………………………………………..Malmopsylla Becker-Migdisova, 1985
-Veins R and M + Cu of forewing subequal in length; vein Cu1a distinctly curved……………………………………………………………………………………………………………………………………………………………………………………………………………..5

5.
Forewing with cell cu1 low and very long, around 6.0 times as long high……………………………………………………………………………………………………………………………………………………………………………………………..Amecephala gen. nov.
-Forewing with cell cu1 higher and shorter, less than 2.5 times as long high…………………………………………………………………………………………………………………………………………………………………………………………………………………….6

6.
Forewing with long pterostigma, vein R2 straight……………………………………………………………………………………………………………………………………………………………………………………………………..Neopsylloides Becker-Migdisova, 1985
-Forewing with short pterostigma, vein R2 curved……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….7

7.
Vein R + M + Cu of forewing ending at basal quarter of wing……………………………………………………………………………………………………………………………………………………………………………………..Gracilinervia Becker-Migdisova, 1985
-Vein R + M + Cu of forewing ending at basal third of wing…………………………………………………………………………………………………………………………………………………………………………………..Pauropsylloides Becker-Migdisova, 1985

†Amecephala pusilla sp. nov
urn:lsid:zoobank.org:act:6B20A4F4-57DB-4F06-A43C-5DE3653D76E3 (Fig. 2a–i)
Etymology
From Latin pusillus = tiny, very small—for its small body size.
Holotype
Female, specimen number MAIG 6686; deposited in the Museum of Amber Inclusion, University of Gdańsk, Gdańsk, Poland. Complete and well-preserved (Fig. 2b,g), probably slightly compressed dorso-ventrally; the wings appear slightly detached from thorax and have been probably forced away from the thorax by the compression. Several gas bubbles on the ventral body side obscure parts of the head, thorax, abdomen, legs and the right forewing (Fig. 2g). Syniclusions: Aleyrodidae (part; second part in broken piece).
Locality and stratum
Myanmar, Kachin State, Hukawng Valley, SW of Maingkhwan, former Noije Bum 2001 Summit Site amber mine (closed). Lowermost Cenomanian, Upper Cretaceous.
Species diagnosis
As for the genus.
Description
Female; male unknown. Body minute, 1.20 mm long including forewing when folded over body. Head (ventrally partly covered by gas bubble) 0.28 mm wide, 0.10 mm long; vertex width 0.20 mm wide, 0.09 mm long; microsculpture or setae not visible. Antenna (Fig. 2a,b) with globular scape and cylindrical pedicel, thinner and longer than scape; flagellum 0.40 mm long; 1.6 times as long as head width; flagellar segments slightly more slender than pedicel, relative lengths as 1.0:0.7:0.6:0.6:0.6:0.6:0.7:1.0; flagellar segment 8 bearing two subequal terminal setae shorter that the segment. Clypeus and rostrum not visible, covered by gas bubble. Forewing (Fig. 2a,b,f,g) 0.90 mm long, 0.30 mm wide, 3.0 times as long as wide; membrane transparent, colourless, veins pale; anterior margin curved basally, posterior margin almost straight; vein R + M + Cu ending in basal fifth of wing; vein R slightly shorter that M + Cu; bifurcation of vein R proximal to middle of wing; cell r1 relatively narrow; vein R2 distinctly shorter than Rs; vein Rs relatively short, strongly curved towards fore margin; vein M slightly longer than veins R and M + Cu; M branching proximal to Rs–Cu1a line; cell m1 value more than 2.6, cell cu1 value more than 6.0; surface spinules not visible. Hindwing (Fig. 2b,f) membranous, transparent and colourless. Female terminalia (Fig. 2a,b,g) with apically pointed proctiger; circumanal ring irregularly oval, about half as long as proctiger. More

Comparison between old and new deciduousness metrics
At first, to check the reliability of the proposed metric, we estimated the deciduousness from the equation proposed by Cuba et al.14 (Eq. 1; referred as ‘old’) and the new metric proposed in this study (Eq. 2; referred as ‘new’) during the extreme and normal rainfall years. The results of dry and moist deciduous samples and 4 pheno-classes revealed an over-estimation and under-estimation of deciduousness with the old-metric, whereas the new metric revealed the accurate relative variability (Fig. 2b,c, Table S1). Table 1 provides the estimated deciduousness values from the old and new metrics for 22 homogeneous sample pixels representing four major vegetation types in the study area (refer Fig. 1 for their spatial locations and Fig. S1 for their annual growth profile). The litter fall information collected from literature revealed a higher litter fall quantity of 10–14.4 Mg Ha−1 year−1 for the moist deciduous forest39,40,41,42 and lower litter fall quantities of 1–8.65 Mg Ha−1 year−1 and 5.63–7.84 Mg Ha−1 year−1 for the dry deciduous forest42,43,44 and the semi-evergreen and evergreen forest44 , respectively. The new metric showed a relatively similar variability in deciduousness to ground observations especially for the moist and dry-deciduous forests than the old metric (Table 1).
Figure 2

Graphical illustration of deciduousness estimation: (a) Theoretical phenology curves from high and low deciduous vegetation and the parameters of deciduousness, (b) Actual RS derived annual growth profiles of moist and dry deciduous vegetation and their deciduousness estimation using the old and new metric, and (c) Annual growth profile of four theoretical pheno-classes for depicting the different magnitude of deciduousness.

Full size image

Table 1 Performance of old and new deciduous metric in a normal rainfall year (2011) using 22 samples from different vegetation types (spatial locations of these samples can be seen in Fig. 1).
Full size table

Further, the difference between the old and new metric was spatially checked and is shown at the center of Fig. 3, and the actual values are presented in the surrounding in eight different sub-set locations. The difference image denotes the under-estimated (70.76% of forest area) and the over-estimated (29.23% of forest area) deciduousness obtained by the old metric (Fig. 3). The under-estimated area observed was mainly in the moist forested regions of states- Chhattisgarh, Odisha, and Jharkhand states, whereas, the over-estimated area observed was mainly in the dry forested region of states—Madhya Pradesh, Maharashtra, Northern Chhattisgarh and some parts of Jharkhand (Fig. 3). The over- and under-estimations are with respect to the new metric, and not with the real in-situ measurements. However, the new metric is in good agreement with annual growth profiles of different vegetation types, and have positive relation with ground litter fall observations39,40,41,42,43,44.
Figure 3

Difference in the spatial distribution of deciduousness (central figure) and the actual deciduousness (subset boxes) derived from the new and old metric for the year 2011. (These maps were created using ESRI’s ArcMap 10.3—https://desktop.arcgis.com/en/arcmap/, and MS-Office PowerPoint 2007 software).

Full size image

The deciduousness derived from these two metrics were also tested for their statistical significance using ANOVA (Table 2). In this test 800 stratified random samples belonging to different deciduous forests of different density classes for dry (2002), normal (2011) and wet (2013) years were used. It was found that the mean deciduousness values from the old metric were similar in the majority of the cases and different rainfall conditions. Hence, it could not be used for understanding rainfall impact on the deciduousness. On the other hand, the new metric performed better than the old metric in terms of its variability under (a) different rainfall conditions (p  More

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A time-resolved meta-omics dataset
To characterize the niche space of lipid-accumulating populations as well as resistance and resilience of the microbial community, we sampled a municipal BWWTP weekly over a 14-months period (from 2011-03-21 to 2012-05-03). Additionally, two preliminary time-points outside of the time-series were included13,26. Samples were split into intracellular and extracellular fractions, followed by concomitant biomolecular extractions27 and high-throughput measurements (Fig. 1). MG, MT, and MP data were obtained on the intracellular fractions and MM data was generated on both the intracellular and extracellular fractions.
Fig. 1: Overview of the study design.

Samples are derived from in situ sampling of an anoxic tank of a municipal biological wastewater treatment plant. Metagenomic (MG), metatranscriptomic (MT), metaproteomic (MP), and meta-metabolomic (MM) data is generated. Physicochemical data is also collected. Additionally, MG and MT data is generated for ex situ experiments using biomass from the same system and fed with oleic acid under different oxic conditions to evaluate short-term responses to pulse disturbance. The time-series meta-omics data is integrated to define metagenome-assembled genomes (MAGs) over all time points. Representative MAGs (rMAGs) across time are selected for further analysis. The rMAGs’ functional potential is used to infer the fundamental niches. Abundance and activity data are derived from the functional omics and substrate usage is inferred per population. The variability of gene expression is used to assess the phenotypic plasticity of the individual populations.

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After quality filtering, the per-sample averages of MG and MT reads were 5.3 × 107 (±7.7 × 106 s.d.) and 3.3 × 107 reads (±1.2 × 107 s.d.), respectively (Supplementary Data 1). We performed sample-specific genome assemblies (average of 4.1 × 105 contigs per sample) followed by binning28 yielding a total of 1364 MAGs passing our quality filtering criteria (see “Methods” section). To track the abundance, gene expression, and activity of individual microbial populations over time, we dereplicated29 the MAGs across samples to generate 220 representative MAGs (rMAGs). From these, we further selected those with the highest completeness resulting in 78 rMAGs (76.2% mean completeness, 2.2% mean contamination) (Supplementary Data 2). These genomes represent the major populations across the time-series, with an average mapping percentage of 26% ± 3% (s.d.) and 27% ± 3% (s.d.) of total MG reads and total MT reads per time-point, respectively, and are corroborated by a previous study based on 16S rRNA amplicon sequencing13. For the MP measurements, we obtained a per-sample average of 1.5 × 105 ± 8.2 × 103 (s.d.) MS2 spectra and a total of 7.6 × 106 MS2 spectra. Of 7.8 × 105 identified peptides, 3.3 × 105 (43%) could be matched to 2.1 × 105 predicted coding sequences of the 78 rMAGS. Per time-point, on average 1.5 × 104 ± 4.5 × 103 (s.d.) spectral matches, i.e., on average 94% of all rMAG-associated matches could be assigned to genes with predicted functions, i.e., assigned KEGG ortholog groups (KOs). To study the community-wide resource space and metabolite turnover, we measured metabolite levels by an untargeted approach using gas chromatography (GC) coupled with mass spectrometry (MS) (Supplementary Data 3). In total, 89% (58 of 65) of the identified metabolites could be linked to enzymes encoded by the rMAGs. We estimated resource uptake by calculating intracellular vs. extracellular metabolite ratios for 42 metabolites detected in both fractions (Supplementary Data 3). Additionally, six abiotic parameters were measured during sampling, as well as 34 parameters recorded continuously as part of the BWWTP online monitoring (Supplementary Data 4).
We also generated MG and MT data for the ex situ experiments. These simulated the fluctuating conditions within the BWWTP, namely the short-term response to pulse disturbances of oleic acid influx under shifting dissolved oxygen conditions. We sequenced DNA and RNA fractions obtained at 0, 5, and 8 h after addition of oleic acid, yielding on average 1.02 × 108 MG and 9.33 × 107 MT reads per sample. The increased sequencing depth compared to the in situ time-series was important to obtain a fine-grained view on short-term responses to oleic acid. Mapping of the sequencing reads to the selected set of rMAGs revealed mapping percentages comparable to the in situ time-series (mean: 21% ± s.d.: 1% for both MG reads and MT reads).
Overall, our meta-omics dataset comprehensively describes mixed microbial communities underlying lipid-accumulation processes in BWWTPs, and in particular their functional potential, composition, activity, as well as substrate availability and assimilation.
Distinct niche types
To resolve the fundamental niches of the pertinent bacterial populations through their functional genomic potential, we assigned KOs to the rMAGs’ predicted coding sequences. We hypothesized that individual populations would form clusters based on the similarity/dissimilarity of their functional potential. We found four distinct clusters of rMAGs by projecting pairwise Jaccard distances of KO presence (Fig. 2a and Supplementary Fig. 1). These functional clusters (FunCs) represent differences of known, overall metabolic capabilities of the rMAGs and reflect their fundamental niches. FunC-1 consisted of Actinobacteria, and FunC-2 was primarily comprised of members of the Bacteroidetes phylum, mainly of the Sphingobacteriia class (Fig. 2a). FunC-3 contained Betaproteobacteria and Gammaproteobacteria whereas FunC-4 appeared more diverse, containing Spirochaetia as a subcluster, Deltaproteobacteria, and taxonomically unclassified rMAGs. We found mash-based genomic distance30 to be strongly linked to FunC assignment (PROCRUSTES sum of squares: 0.399, correlation 0.775, PROTEST p-value 0.001, Supplementary Fig. 2a), highlighting that phylogeny is a strong determinant for FunC assignment. However, some distantly linked subgroups were defined by their shared functional complement, i.e., assigned to a different FunC than their neighbors in a corresponding phylogenetic tree (Supplementary Fig. 2b). This shows that KO profile similarity-based analyses provide important information in addition to phylogeny-based approaches31.
Fig. 2: Fundamental niche types.

a Multidimensional scaling (MDS) of Jaccard distances for the functional repertoire (presence of KEGG ortholog groups [KOs]) for each rMAG. Ellipses containing 95% (inner) or 99% (outer) of cluster-assigned data points are shown resulting in four distinct functional clusters (FunCs). Colors indicate the class-level taxonomy of the rMAGs. b Numbers of shared and unique KO assignments between the FunCs. Colored bars show the total number of nonredundant KO assignments within the individual FunCs. Overlaps between different sets of FunCs and their unique KOs are represented by the central black bars with the points below defining the members of the respective sets. c Presence of key functions within the four FunCs. Bars next to metabolic conversions show the proportion of rMAGs encoding characteristic enzymes for the respective reaction or pathway adjusted for mean rMAG completeness. Pathways ubiquitously present across rMAGs are shown in gray color. Source data are provided as a Source Data file. red. reductase, GLN syn. glutamine synthetase, GLU dhg. glutmatate dehydrogenase, glyox. cyc. glyoxylate cycle, ethylm.-CoA ethylmalonyl-CoA pathway, PHA depolym. PHA depolymerase.

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A total of 1857 KOs was shared between all FunCs and we found that FunCs 1, 3, and 4 contained comparable numbers of nonredundant KOs with 4276, 4177, and 4129 KOs, respectively (Fig. 2b). FunC-2 exhibited a reduced number of KOs (3550), however it also represented the least taxonomically diverse FunC as it almost exclusively consisted of Haliscomenobacter spp. and Chitinophaga spp. (Supplementary Data 2). We tested for the molecular functions that were significantly enriched in individual FunCs and found, among others, functions related to lipid metabolism for FunC-1, amino sugar, and nucleotide sugar metabolism for FunC-2, and biofilm and secretion systems for FunC-3 to be enriched (Fig. 2c and Supplementary Data 5; one-sided Fisher′s exact test, adjusted p-values < 0.05). While lipid-accumulating organisms hold great potential for the recovery of high-value molecules5, interactions between these organisms as well as the community at large are understudied in situ. We found that diacylglycerol O-acyltransferase (DGAT/WS), which is involved in lipid storage32, was encoded in 23 out of 24 rMAGs of FunC-1, pointing to the importance of TAG accumulation in this cluster. Most FunC-3 members also encoded DGAT/WS (14 of 19). Moreover, PHA synthase was enriched in this cluster (15 of 19). All rMAGs encoded lipases, functions involved in fatty acid synthesis, or beta-oxidation. However, several acyl-CoA and acyl-ACP dehydrogenases were overrepresented in FunC-1 and FunC-3. Additionally, acetyl-CoA acetyltransferases involved in the degradation and biosynthesis of fatty acids were prevalent throughout all FunCs. The enrichment in FunC-1 and FunC-3 for genes involved in lipid accumulation are consistent with previous metabolic characterizations, with FunC-1 consisting mainly of Actinobacteria for which TAG accumulation has been described33. FunC-3 contains Betaproteobacteria and Gammaproteobacteria that have been characterized as TAG, WE, and/or PHA accumulators, e.g., Thauera spp., Albidiferax spp., or Acinetobacter spp.33,34. Importantly, we observed a difference between these FunCs in the utilization of acetyl-CoA. Specifically, FunC-1 members showed an enrichment in functions related to the ethylmalonyl-CoA pathway (crotonyl-CoA reductase and enoyl ACP reductase), while FunC-3 members encoded key enzymes involved in the glyoxylate cycle (malate synthase and isocitrate lyase). We further determined specific functional enrichment for the four FunCs in relation to the breakdown of other macromolecules (including CAZymes and proteases), nitrogen cycling, stress response, and motility (Supplementary Data 5). The discriminating functions point towards interdependencies between the different FunCs, e.g., in terms of denitrification (Fig. 2c). We found that the separation into taxonomically consistent groups is accompanied by specific conserved functions, e.g., strong enrichment in FunC-1 for WhiB transcriptional regulators characteristic of the Actinobacteria35. Overall, we observed a widely distributed set of core functions in foaming sludge microbiomes and identified groups of populations characterized by distinct functional potential in lipid metabolism, amino sugar, and nucleotide sugar metabolism as well as biofilm and secretion systems. Community dynamics and stability To understand whether population dynamics can be related to substrate availability and other abiotic factors36, we used MG depth-of-coverage to infer rMAG population abundance across the time-series. We computed distances between the rMAGs’ abundance profiles (based on their pairwise correlations) and found that the dynamics of rMAGs can be partially explained by the FunC assignment (PERMANOVA R2 = 0.12, Pr > F = 0.002; no significant difference in dispersion; Supplementary Fig. 3), thereby linking FunC membership to temporal abundance shifts. The most abundant taxa (Supplementary Data 2) included Candidatus Microthrix (26.0% relative abundance across the time-series; referred to as Microthrix in the remaining text), Acinetobacter (8.1%), Haliscomenobacter (8.0%), Intrasporangium (7.2%), Leptospira (6.3%), Albidiferax (5.7%), and Dechloromonas (2.4%) (Fig. 3a). Several of the recovered rMAGs belonged to filamentous taxa according to the MiDAS field guide database for organisms in activated sludge37, such as the highly abundant Microthrix, and Haliscomenobacter, as well as the less abundant Anaerolinea (1.1 %) and Gordonia (0.2 %).
Fig. 3: Community structure and function dynamics.

a, b Relative abundance and expression levels of recovered populations represented by rMAGs over time based on MG depth (a) and MT depth (b) of coverage, respectively, representing mapping percentages of MG [26% ± 3% (s.d.)] and MT [27% ± 3% (s.d.)]. The relative abundance of individual rMAGs is grouped based on genus-level taxonomic assignment with rMAGs of unresolved taxonomy grouped in “Other”. Recovered genera with mean abundance below 2% are summarized as a single group (light gray). c, d Ordination of Bray–Curtis dissimilarity of relative abundances, MG (c) and MT (d), of individual rMAGs constrained by selected abiotic factors (metabolite levels, metabolite-ratios, and physico-chemical parameters shown as black arrows with arrow lengths indicating environmental scores as predictors for each factor). Points are colored by month of sampling and point-shape reflects the year of sampling. Thin black lines connecting the points visualize the time course of sampling. Source data are provided as a Source Data file.

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Variations during the operation of BWWTPs occur largely due to changes in the influent wastewater composition and climatic conditions38. We observed gradual changes in the community structure with the seasons (Fig. 3a). In October 2011 (month seven of the timeseries), the community composition began to shift, with a markedly altered composition in late November 2011. This shift was characterized by spikes in the relative abundance of Leptospira (peak at 2011-11-23) and Acinetobacter (peak at 2011-11-29) (Fig. 3a), and co-occurred with a pronounced shift in substrates (Fig. 4 and Supplementary Fig. 4). The substrates included mainly nonpolar metabolites, including long-chain fatty acids (LCFAs) and glycerides, as well as polar metabolites mannose, glucose, disaccharides, ethanolamine, and putrescine. We found that the intersample distances of MG-based abundances could partially be explained by a subset of the abiotic factors (Fig. 3c). Summer samples were characterized by higher temperatures, phosphate levels and higher intracellular vs. extracellular oleic acid ratios. Higher extracellular mannose levels and a slight increase in conductivity marked the beginning of the autumn shift. During November, intracellular and extracellular levels for LCFAs increased, indicating a higher availability or turnover of LCFAs, but not necessarily an equivalent conversion to neutral storage lipids. In the subsequent winter time-points, substrate levels normalized and the community transitioned back to the predisturbance state.
Fig. 4: Levels of metabolites and physico-chemical parameters.

Z-score transformed metabolite intensities, metabolite ratios, and physico-chemical parameter levels over time are shown. Row annotations highlight classes of metabolites and parameters, measurement types (bnp: intracellular nonpolar metabolites, bp: intracellular polar m., pcparams: physico-chemical parameters, ratio: metabolite intrac./extrac. ratio, snp: extracellular nonpolar m., sp: extracellular polar m.), and the subtype or fraction of the measurement (manual: measured during sampling, online: measured during WWTP operation). Selected rows are shown (comprehensive heatmap shown in Supplementary Fig. 6). Source data are provided as a Source Data file.

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The dominance of Microthrix was re-established within approximately ten generations, given estimates for in situ growth rates of 0.12–0.3 growth cycles per day7,8. The stability39 of the individual rMAGs was heavily affected by the November shift (mean population stability: 1.43 ± 0.69 s.d.), compared to the stability when excluding the respective time-points (mean population stability: 2.39 ± 1.28 s.d.; 2011-11-02 to 2011-11-29; Supplementary Data 2). The observed population dynamics indicate that the community composition is resilient, i.e., recovers after pronounced changes in available substrates, and resistant to small-scale environment fluctuations over time.
While MG depth was used as a proxy for population abundance, MT depth enabled the analysis of transcriptional activity within the community and of individual populations (Fig. 3b). The comparison of intersample distances based on mean, relative MT depth showed similar patterns to MG-based results (Fig. 3c), albeit with a higher degree of variability indicated by increased inter-sample distances (Fig. 3d). A comparison of relative MP counts showed a more even distribution between populations with comparable overall trends (Supplementary Fig. 5). Samples collected in April 2011 and 2012 appeared to represent transition states between seasons. Additionally, a set of late winter and early spring samples in 2011 and 2012 showed higher similarities at the expression level than at the abundance level. Interestingly, the high abundance of individual genera, such as Microthrix or Chitinophaga was not necessarily reflected in their mean expression levels (Fig. 3b and Supplementary Fig. 5): populations assigned to Leptospira, Haliscomenobacter, Anaerolinea, and Acinetobacter showed higher mean expression overall. Spikes in relative MT depth as for Acinetobacter rMAGs (Fig. 3b; 2011-04-14, 2011-05-08, and 2012-04-25) point towards increased activity around these time-points, which however did not lead to major shifts in community structure. Notably, higher expression levels of Acinetobacter were succeeded by increased expression levels of Haliscomenobacter (2011-04-14 to 2011-05-20) or Anaerolinea (2011-05-08 to 2011-09-19). On average, MT-based stability values were less affected by the community shift than MG-based stability values (Supplementary Table 2). We also observed adaptation of metabolic pathway activity to environmental conditions (Fig. 5). Pentose to EMP pathway intermediates exhibited the highest correlation between MT and MP abundances, followed by Hydrogen metabolism and Fatty acid oxidation. Several pathways exhibited a characteristic drop during the November shift, e.g., hydrogen metabolism, hydrocarbon degradation, and TCA cycle, while fatty acid oxidation showed a marked peak. This highlights the transition from dominance by generalist, lipid-accumulating populations towards a lipolytic community.
Fig. 5: Gene levels over time grouped by functional categories.

Metatranscriptomic and metaproteomic levels (normalized relative expression for MT data and normalized relative spectral counts for MP data) of rMAG-derived genes assigned to FOAM ontology-based functional categories. Pearson correlation coefficients (r) of MT and MP values are shown in the title of each panel. Panels are ordered from highest to lowest mean MP relative count in row-major order. Source data are provided as a Source Data file.

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With each of the four FunCs comprising multiple organisms encoding similar KOs and, hence, metabolic capabilities, we studied how individual populations adapt to their environment. To this end, we linked changes in community structure and in the expression levels of individual populations to the influence of environmental parameters. While rMAG abundance patterns could be linked to FunC assignment (Supplementary Fig. 3), we could not identify an analogous categorization when correlating rMAG abundances to abiotic factors. Instead, correlation patterns indicating similar preferences to environmental conditions emerged for subgroups of rMAGs across different FunCs (Supplementary Fig. 7). This shows that populations with a similar fundamental niche type responded differently to the environmental conditions pointing towards functional plasticity and, thus, adaptations of their realized niches
Niche characteristics of in situ and ex situ time-series
While we identified four fundamental niche types, it may be assumed that cohabiting species cannot occupy the same realized niches, leading to realized niche segregation within and between types. We hypothesized that different degrees of niche overlap, leading to variable levels of competition, must exist40,41. To better understand the complementarity of realized niches, we used the functional omics data to study how rMAGs overlapped in relation to their encoded genes and how rMAGs varied in their expression profiles. While the former represents competition between populations with overlapping profiles, the latter is an important factor for the adaptability and overall survival strategy of individual populations. We distinguished between expressed KOs and nonexpressed KOs based on MT/MG ratios as well as MP data and computed distances between the resulting time-point-specific expression profiles. While the separation based on the functional potential was preserved in a clustering of expression profiles (in particular for FunC-2), the expression profiles of FunCs-1, FunCs-3, and FunCs-4 overlapped to a greater extent than those of FunC-2 (Fig. 6a). Two Anaerolinea populations assigned to FunC-1 appeared to express similar functions compared to the rMAGs of FunC-3 and FunC-4 and were found in a subgroup of rMAGs that showed a higher overall activity in terms of MT/MG ratios also when clustering expression profiles per time-point (Supplementary Fig. 8). Overall, the clusters based on KO expression status per time-point did not exhibit a separation according to the grouping into FunCs (Supplementary Fig. 8). This indicates a propensity of the respective rMAGs to more frequently express shared KOs than discriminatory KOs and, consequently, increased the competition for specific substrates.
Fig. 6: Realized niches.

a MDS of time-point specific expression profiles based on MT/MG ratios or evidence at the MP level. Colors indicate FunC assignment of the individual rMAGs. Point shape represents cluster assignment based on automated clustering of the embedded points. Ellipses containing 95% of cluster-assigned data points are shown. Points size represents the average MT/MG depth ratios of the individual rMAGs. The amounts of variance explained by the first two dimensions are shown on the respective axes. b Mean MT/MG depth ratios over all time-points are shown per condition for 78 rMAGs (boxplots show: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; Each group of boxplots corresponds to a group of rMAGs (FunC-1 n = 24, FunC-2 n = 23, FunC-3 n = 19, FunC-4 n = 12), each boxplot represents an independent experiment.). c Mean MT/MG depth ratios grouped according to class-level taxonomic assignment of the rMAGs with the number of rMAGs for each group are shown in the top of the plot (n). Source data are provided as a Source Data file.

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To investigate the importance of individual, discriminatory functions, we selected rMAG clusters, based on gene expression and MP counts, to which the two most abundant rMAGs (D51_G1.1.2, A01_O1.2.4) had been assigned. We observed that clusters into which rMAG D51_G1.1.2 (Microthrix) was consistently categorized showed expression of few KOs with the majority being ribosomal proteins, TCA cycle-related enzymes such as pyruvate, malate, and glyceraldehyde 3-phosphate dehydrogenases, chaperones, and most frequently the WhiB family transcriptional regulator (19 time-points; Supplementary Data 6).
Clusters containing rMAG A01_O1.2.4 (Acinetobacter) frequently exhibited expression of genes related to motility and chemotaxis as well as stress response, but also functions related to phosphate accumulation, such as K08311 and K00937 (Supplementary Data 6). KOs related to lipid metabolism were also frequently expressed in these clusters e.g. acylglycerol lipase (in 35 time-points) or diacylglycerol O-acyltransferase (25 time-points). This indicates that high expression of key functionalities is an integral part of the strategies of the populations within these clusters even though they differed with respect to their encoded functions.
We next studied how the observed distinction between populations with high activity is linked to phenotypic plasticity. As alternating oxygen levels in BWWTPs play an important role in selecting for lipid accumulating populations7,42, we added oleic acid, the preferred carbon source for Microthrix43, in lab-scale experiments under different oxygen fluctuation conditions8 (see “Methods” section; Fig. 1). These ex situ conditions involved aerobic, anoxic, aerobically preconditioned biomass followed by hourly anoxic alternations, and anoxically preconditioned followed by hourly aerobic alternations. The MT/MG ratios for FunC-1 and FunC-3 were higher ex situ when compared to the in situ samples, and vice versa for FunC-2 and FunC-4 rMAGs (Fig. 6b). Furthermore, especially for FunC-3, average MT/MG ratios were highest in the aerobic conditions and lowest in the anaerobic conditions. This is in line with FunC-3 being comprised mainly of Betaproteobacteria and Gammaproteobacteria, which include mostly aerobic genera44. A more fine-grained view on differences in specific activity was obtained, when grouping rMAGs based on taxonomic assignment (Fig. 6c). While rMAGs of the classes Acidimicrobia and Actinobacteria (FunC-1) showed the lowest mean MT/MG ratios across the in situ time-series (0.5), the ratio was twice as high in the ex situ experiments across all conditions which can be attributed to the oleic acid pulse. Betaproteobacteria (FunC-3) behaved similarly, while Gammaproteobacteria (FunC-3) showed a tendency towards higher activity with increased oxygen levels. We observed high activity for rMAGs assigned to Anaerolineae and Spirochaetia in the in situ time-series. Interestingly, this was not the case for Spirochaetia in the ex situ experiments, which points towards the necessity for additional substrates. The Anaerolineae rMAGs, with taxonomically related species being mainly anaerobic45, showed the lowest MT/MG ratio under the alternating conditions, while Deltaproteobacteria rMAGs showed high MT/MG ratios. Overall, the differentiated responses under alternating conditions point to distinct short-term and long-term adaptation strategies.
To study how fast the adaptations in response to the influx of oleic acid occur, we compared the baseline (0 h time-points, before oleic acid addition) against the 5 and 8 h time-points (after oleic acid addition). At 5 h, lipases, involved in TAG hydrolysis, for which high expression in the in situ samples was observed, were downregulated in the ex situ response to the addition of oleic acid (Supplementary Fig. 9a). An increased number of genes related to beta-oxidation were upregulated at 5 h, particularly in rMAGs assigned to FunC-3 (Supplementary Fig. 9b). Similar effects were observed when comparing the 0 h and 8 h time-points (Supplementary Fig. 10a, b). This suggests that responses in gene expression happen within the 5 h timeframe but on distinct time scales for different populations. In-depth analyses of the populations exhibiting the highest expression levels for TAG lipases, DGAT/WS, and PHB synthases (Supplementary Note 1 and Supplementary Figs. 11–14) underline the previously determined role of Microthrix as a key lipid accumulator in BWWTPs13,46. The results also indicate that populations such as Anaerolinea, Leptospira and Acinetobacter overlap with Microthrix in terms of their capacity to assimilate LCFAs and available neutral lipids. Niche complementarity and plasticity, i.e., overlapping fundamental and realized niches, as well as gene expression variability, impart population-independent processing of lipids in situ. From an ecosystem perspective, this community-wide trait confers functional resistance and resilience. More