Philippa Kaur

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We found that operculate snails were more abundant than were non-operculate snails: 34 and 22 individuals were encountered, respectively. Among snails of moderate size (shell width, 10–20 mm), which were often consumed by A. boa, operculate snails were more than 10 times as abundant as were non-operculate snails (25 and 2 individuals were encountered, respectively).
Individuals of A. boa (n = 8) readily preyed upon Leptopoma sp. (n = 30) in our feeding trials. Upon capture, the snakes immediately inserted their mandibles into the aperture of the shell and then extracted the operculate soft body using the mandibles. After extraction, the snakes regurgitated the extracted snail and precisely repositioned it so that the operculum protruded out of the mouth and the junction of the operculum and the soft body came to lie along the mandible on the right (n = 24, 80%) or the left (n = 6, 20%) side. From this position, the snakes moved the side of the relevant mandible backward and forward, while the snail was held in the stable position by the upper jaws and the mandible on the other side. These mandibular movements were especially vigorous when the mandible was being retracted. Six to 51 (median, 14) strokes of these sliding movements resulted in the removal of the operculum (Fig. 1, Supplementary Information, Videos S1, S2). Snakes consumed solely the soft part of the snails, while discarding the shell and the operculum (n = 30, 100%). Discarded opercula retained little soft tissue. The duration of extraction, repositioning, and sawing processes ranged 36–274 (median, 96), 9–459 (median, 66), and 15–428 (median, 30) seconds, respectively (Supplementary Information, Table S1). Thus, the snakes spent considerable time for handling of the extracted snail (reposition and sawing). The sequence of feeding was highly consistent among all cases, and the routine operculum-removing behaviour presumably allows A. boa to regularly consume the otherwise hazardous prey, which is abundant in its habitat.
Figure 1

Infrared images illustrating mandibular sawing by the blunt-headed snail-eating snake, Aplopeltura boa. (a) The extracted operculate snail in the snake’s mouth. (b) The snail has been accurately repositioned. (c) The operculum being sliced off. (d) The soft parts of the snail being ingested while the operculum is discarded. A white and black arrow indicates the position of the operculum and the tip of the mandible, respectively. Videos showing this behavior are available with supplementary information of this article (Videos S1, S2).

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The feeding apparatus of A. boa is illustrated in Fig. 2. Aplopeltura boa exhibits a set of derived morphological features known in other pareids and dipsadines, including short snout, short pterygoids, reduced supratemporals, long mandibles, and comb-like mandibular teeth. The skull of A. boa is short and tall, in which the snout is very short, and the orbits are exceptionally large. The pterygoids are greatly shortened, and their posterior ends are completely detached from the quadrato-mandibular joint. The quadrates are remarkably long and stout, extending ventrally rather than ventrolaterally. The mandibles are long and carry dense teeth, which are more robust than the maxillary teeth. The size and interval of mandibular teeth gradually change along the mandible; the anterior teeth are larger and sparser. The lower jaw unit (the mandible and the quadrate) is L-shaped, and the mandible travels anteroposteriorly when the quadrate swings backward and forward, as anticipated or observed in other pareids or dipsadines. This mechanism enables independent, substantial anteroposterior excursions of the mandible (Fig. 3), which is used for the extraction and the sawing processes during feeding on the operculate snails. The elongated quadrates are likely to contribute to long mandibular excursion18.
Figure 2

Feeding apparatus of the blunt-headed snail-eating snake, Aplopeltura boa. CT images of the skull and the jaws from left lateral (a), posterior (b), ventral (c), and dorsal (d) views and the quadrates and the mandibles on the left (e) and right (f) sides. m, mandible; p, pterygoid; q, quadrate. These images are from the specimen KUHE59285.

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Figure 3

Movements of the lower jaw in the blunt-headed snail-eating snake, Aplopeltura boa. CT images of the skull and the jaws from left lateral view with the mandible protracted (a) and slightly retracted (b). These images and the videos on feeding behaviour demonstrate that the mandibles slide about half the length of the skull. These mandibular movements can be performed unilaterally. Images (a) and (b) represent the specimen SRC01008 and SRC01009, respectively.

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Most of  > 3,700 species of snakes swallow their prey whole, and prey-breaking behaviours are known only from a few species that feed either on crabs20,21,22,23 or termites24,25,26, which may be relatively easily broken into segments. These snakes grasp their arthropod prey with their jaws and break it apart usually using the movements of the head or the trunk. In contrast, A. boa cuts its mollusk prey using independent movements of the lower jaw. The mandibular sawing is, therefore, a surprising evolutionary solution for the limbless animals to utilize new food. This dexterous behaviour is especially surprising given that few other vertebrates, if any, are able to sever food in the mouth using unilateral sliding movements of a jaw element like A. boa. The evolutionary invention of sawing was evidently made possible by the unique feeding mechanism in the snail-eating snakes. Extensive mobility of the mandibles is a convergent trait in the two distinct lineages of snakes (pareids and dipsadines) that feed on slugs or snails, suggesting it is an adaptation to feeding on their slippery prey4,5,6,7,8,9. It is likely that acquisition of the free mandibular apparatus promoted the subsequent evolution of the novel behaviour and has resulted in functional versatility of the free-moving jaw elements.
Most pareids and some dipsadines have a larger number of teeth on the right mandible than on the left as feeding specialization to extract dextral (clockwise-coiled) snails13,15. There is a cline in the degree of the dentitional asymmetry in correlation with diets, where snail-specialist species have highly asymmetrical mandibles, whereas slug-specialist species have symmetrical mandibles13,15. However, A. boa is an exception of this pattern because individuals exhibit only weak mandibular asymmetry despite its snail diet13. In the phylogeny, A. boa is nested within the derived clade with mandibular asymmetry27, suggesting the presence of additional selective forces toward the mandibular symmetry in this species. By showing the additional role of its mandibles (cutting the prey), our results suggest functional trade-offs in A. boa (typical comb-like teeth in snail-eating snakes are expected to facilitate extraction by providing a firm grip on the prey but probably are not optimal to cut the prey tissue), highlighting the importance of behavioural studies to understand selective forces on functional units. More

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To explore whether ICABs are associated with patterns of microbial colonization in young children, stool samples (reflecting the distal gut) and swab samples from the dorsum of the tongue were collected from 47 Tsimane families living in six villages located along the Maniqui River in the Bolivian lowlands of the Amazon basin. Samples were collected from infants (0–2 years of age) and mothers (14 or more years of age) using a mixed longitudinal design (Supplementary Table 1, Fig. 1a).
Fig. 1: Infant microbiota dynamics and dietary factors associated with maturation.

a Timeline denoting fecal sample and tongue swab collections for each mother–child dyad relative to the infant’s birth date. For maternal samples, time refers to the immediate postpartum period. Infant stool samples are circles, adult stool samples are squares, infant and adult tongue swabs are triangles that point up or down, respectively. Shapes are colored by sample type (red = stool, blue = tongue swabs), and the color darkens as the subject’s age increases. Shapes and colors are consistent across a–e. b Shannon diversity index regressed against the time since the infant’s birth for stool samples (adult stool: P = .065, R2 = 0.277; infant stool: P = 1.08 × 10−11, R2 = 0.481). Lines indicate the linear mixed-effects regression of diversity on time since delivery, while treating subject as a random effect. The shading indicates the 95% confidence interval. The conditional R2 describes the proportion of variation explained by both the fixed and random factors, and was calculated using the R package, “piecewiseSEM”. c Shannon diversity index regressed against the time since the infant’s birth for tongue swab samples (adult tongue swabs: P = .099, R2 = 0.298; infant tongue swabs: P = .013, R2 = 0.397). Figure details are the same as in (b). d Principal coordinate analysis (PCoA) using a distance matrix calculated using the Jaccard similarity index of microbiota taxa composition in stool samples and tongue swabs from Tsimane dyads. e A partial canonical correspondence analysis (CCA) of ASV abundances, constrained against a matrix of diet survey data. The effects of infant age and village were controlled using a conditioning matrix. Significance was assessed using an ANOVA-like permutation test with 1000 permutations. Source data are provided in the Source Data file.

Full size image

Infant and maternal microbiota dynamics following birth
Overall, infant stool samples had high relative abundances of Bifidobacteriaceae, Enterobacteriaceae, and Veillonellaceae, while adult stool samples were dominated by Ruminococcaceae, Prevotellaceae, and Lachnospiraceae (Supplementary Fig. 1). On the tongue, both infants and adults had high relative abundances of Streptococcaceae, Veillonellaceae, and Micrococcaceae, although adult tongue samples also exhibited high relative abundances of Pasteurellaceae and Neisseriaceae (Supplementary Fig. 2). In addition, bacterial diversity increased with age in both stool and on the tongue of infants over the first 18 months of life (Fig. 1b, c). We used a linear mixed-effects model that accounted for the longitudinal structure of these data by treating the subject as a random effect. It demonstrated that diversity was positively correlated with age in both stool (P = 1.08 x 10−11, conditional R2 = 0.481) and tongue communities (P = .013, conditional R2 = 0.397), although diversity increased at a faster rate in the stool samples (Fig. 1b, c; ANOVA, P = .004). The diversity of the maternal stool and dorsal tongue communities was stable during this time period (Fig. 1b, c, P = .065 and .099, respectively), as has been observed previously28.
Variation in bacterial community composition among samples from mothers and infants was primarily explained by body site (13.7%, PERMANOVA, 1000 permutations, P  More

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Bacterial community composition
From 36 soil specimens, 3,370,643 high-quality 16S rDNA reads were obtained [(CK1, T1, CK2, and T2 treatments) × 9 replicates] with 74,955–103,931 sequencing reads (mean = 91,908) per sample. The maximum read length was 478 bp and the minimum average length was 341 bp for the 16S rDNA genes. All rarefaction curves for the bacterial samples revealed that the amount of recorded OTUs was generally 7,000 reads per plateau, indicating the assessment adequately covered the microbial variety (see Supplementary Fig. S2a). The bacterium richness (Chao1 and ACE), evenness indexes (Shannon and Simpson), and number of OTUs between CK1 and T1 as well as between CK2 and T2 were not significantly different (see Supplementary Table S1a).
The soil bacterial composition of the two treatment groups at the two growth periods were compared at the level of the phylum. A total of 26 bacterial phyla were identified, with the exception of 1.03% of unclassified sequencing reads. The main phyla of the sequenced bacteria were Proteobacteria, Actinobacteria, and Gemmatimonadetes, which occupied over 64.5% of the total bacterial populations in the sample sequences. Chloroflexi, Acidobacteria, Bacteroidetes, Parcubacteria, Verrucomicrobia, and Firmicutes were also identified at relatively elevated richness (average relative abundance > 1%) (Fig. 1a). Wheat straw addition significantly increased the relative abundances of Actinobacteria, Chloroflexi, and Saccharibacteria, while significantly decreasing the relative abundance of Parcubacteria at both moments of sampling (P  0.05) were detected (see Fig. 1a and Supplementary Table S2a).
Figure 1

Major bacterial (a) and fungal phylum (b) relative abundance in the soil with (T1 and T2) and without (CK1 and CK2) wheat straw addition. Bacterial phyla with > 1% and fungal phyla with > 0.1% average relative abundances. Others included bacterial phyla below 1% relative abundance and unidentified bacterial and fungal phyla. According to the Student’s t-test (n = 9), * and ** represented P  0.1% bacterial reads in T1 soil (only 0.04% in CK1 soil) (see Supplementary Table S3a). In addition, 14 bacterial genera with relative abundance > 0.1% were more prevalent in CK2 samples, including Spororosarcina, Chryseollinea, Nitrososporia, Truepera, Actinomadura, two Planctomycetes (SM1A02 and I-8), and some Ptoteobacteria (Sulfurifustis, Polycyclovorans, Woodsholes, and H16) (Fig. 2b). In contrast, the Actinobacteria (Aeromicrobium, Nonomured, Nocardioides, Dactylosporangium, and Ilumatobacter) group and Proteobacetia (brachysporum_group, Ramlibacter, Dongia, Hyphomicrobium, Rhizobium, Sphingobium, Parablastomonas, Pseudoxanthononas, Dokdonella, and Pseudohoniella) group were more abundant in the T2 soil than in CK2 (see Fig. 2b and Supplementary Table S3b).
Figure 2

LDA histogram scores for bacterial genera with different abundance for the flowering stage (a) and fruiting stage (b) in the watermelon monoculture system.

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With respect to the fungal genera, LEfSe analysis showed that there was higher relative abundance of Schizothecium, Entoloma, Preussia, Lecanicillium, and Bipolairs in T1, whereas there was a higher relative abundance of Thanatephorus, Scopulariopsis, Fusarium, and Conocybe in CK1 (Fig. 3a) for the flowering stage. In the fruiting stage, Psathyrella, Filobasidium, Aphanoascus, Cladosporium, Microascus, and Scopulariopsis were found in CK2, while only Schizothecium was found in T2 (Fig. 3b). Furthermore, the second prevailing genus, Fusarium, accounted for 9.70% of all fungal genera in CK1 (only 0.64% in T1). The relative abundance of Fusarium was higher in CK2 than in T2 (see Supplementary Table S4).
Figure 3

LDA histogram scores for fungal genera with different abundance for the flowering stage (a) and fruiting stage (b) in the watermelon monoculture system.

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Microbial community variety and the link between genera abundance and environmental conditions
Non-metric multidimensional scaling (NMDS) clearly indicated that there were considerable variations in the composition of the soil bacterial populations between the samples with (T1 and T2) and without (CK1 and CK2) wheat straw addition in the consecutive watermelon monoculture system in the two growth stages evaluated (Fig. 4a). In the NMDS plot, the nine replicates in the same groups were not closely located for the fungal communities in all the samples, which indicated that there was no distinct difference in fungal community composition between two treatments for the two growth stages (Fig. 4b). RDA revealed that the relative abundances of Planctomyces, Pirellula, and Exiguobacterium were positively correlated with the DI for bacteria (Fig. 5a and Supplementary Table S5a). The relative abundances of Aspergillus, Fusarium, Sopulariopsis, Cladosporium, and Aphanoascus were positively correlated with the DI for fungi (Fig. 5b and Supplementary Table S5b).
Figure 4

Non-metric multidimensional scaling (NMDS) according to the Euclidean distance plot of bacterial (a) and fungal (b) microbiota in the flowering stage (CK1 and T1) and fruiting stage (CK2 and T2).

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Figure 5

Plots of redundancy analysis (RDA) ordination displaying the interactions between the top 10 bacterial (a) and fungal genera (b) and soil environmental variables. AP denotes available phosphorus; pH denotes the solar pH; EC denotes electrolyte conductivity; the disease index (DI) denotes healthy plants as “0”and Fusarium wilt plants as “1”. CK1 represents the soil without wheat straw addition at the watermelon flowering stage while T1 represents the soil with wheat straw addition at the watermelon flowering stage; CK2 represents the soil without wheat straw addition at the watermelon fruiting stage and T2 represents the soil with wheat straw addition at the watermelon fruiting stage.

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Fungal community network analysis
Soil microbial network analysis is widely performed to understand the taxonomic and functional relations within complex microbial communities13. With respect to fungi, the top 300 OTUs of T1 and CK1 soil at the watermelon flowering stage were chosen for pMEN analysis (Fig. 6). The T1 network consisted of 180 nodes (OTUs), 1,036 connections, and 12 modules, with an average connectivity of 11.511, average path length of 2.999, and clustering coefficient of 0.278. The CK1 network consisted of 166 nodes, 741 connections, and 18 modules, with an average connectivity of 8.927, average path length of 2.920, and clustering coefficient of 0.155. The modularity proportion was higher in the T1 network, although fewer total modules were recognized (Table 1). Strikingly, there were more links in T1 soil (1,036 links) than in CK1 soil (741 links). The positive link/negative link ratio (P/N) was higher in T1 soil (P/N = 0.333) than in CK1 soil (P/N = 0.211), demonstrating that the T1 soil had more complex and positive co-occurrence correlations than the CK1 soil.
Figure 6

Network plots of fungal community at the order level from soil without (CK1) (a) and with (T1) (b) wheat straw addition at the watermelon flowering stage. The size of the node corresponds to the relative abundance of the OTUs. The node colors show various phylogenetic associations. Node (edge) connection lines represent co-occurrence with positive (blue) and negative (red) correlations.

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Table 1 Major topological properties of the empirical phylogenetic Molecular Ecological Networks (pMENs) of fungal communities for soil with (T1 and T2) and without (CK1 and CK2) wheat straw addition and their associated random pMENs.
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The top 300 OTUs of T2 and CK2 soil at the watermelon fruiting stage were also chosen for pMEN analysis (see Supplementary Fig. S3). The T2 network consisted of 202 OTU nodes, 1,040 connections, and 14 modules, with an average connectivity of 12.545, average path length of 2.727, and clustering coefficient of 0.214. The CK2 network consisted of 181 nodes, 739 links and 15 modules, with an average connectivity of 8.166, average path length of 3.186, and clustering coefficient of 0.264 (Table 1). Strikingly, there were more links in T2 soil (1,040 links) than in CK2 soil (739 links), which indicated that the T2 soil had more complex and stable microbial networks than the CK2 soil. In T2 soil, the P/N (P/N = 0.218) was lower than that in CK2 soil (P/N = 0.451), indicating that the T2 soil had more negative co-occurrence relationships in the microbial community than those in CK2 soil.
In addition, CK1 and T1 networks shared 49 nodes (Fig. 7). Nodes of the Sordariales, Onygenales, Microascales, Hypocreales, Eurotiales, Agaricales, and Arachnomycetales genera dominated in both networks. The relative abundance of Sordariales and Hypocreales was higher in the two networks. Furthermore, there was a higher proportion of Sordariales-affiliated OTUs and a lower proportion of Hypocreales-affiliated OTUs in T1 compared to CK1 (Fig. 7a). However, 72 nodes were shared between CK2 and T2 networks. Nodes belonging to the Sordariales, Pleosporales, Onygenales, Microascales, Hypocreales, Eurotiales, and Agaricales genera dominated in both networks. Sordariales, Hypocreales, and Agaricales were relatively more abundant in these two networks. However, the relative abundance of Sordariales and Hypocreales had more significant differences in CK2 compared to T2. There was also a higher proportion of Sordariales-affiliated OTUs and a lower proportion of Hypocreales-affiliated OTUs in T2 compared to CK2 (Fig. 7b). These network analysis results suggested that Sordariales dominated in the T1 and T2 soils, which were treated with wheat straw, while Hypocreales dominated in the soil (CK1 and CK2) without wheat straw addition.
Figure 7

Relative abundance of nodes at the order level in modules inside the fungal network created from the flowering stage (a) and fruiting stage (b). Venn diagrams display the amount of shared and unshared network nodes in the soil sample with and without wheat straw addition.

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Bacterial community network analysis
T1 and CK1 soil bacterial community analysis revealed similar sized networks with 224 and 221 nodes, respectively (see Supplementary Fig. S4 and Table S6). The average connectivity for the T1 and CK1 networks was 7.482 and 7.493, with an average path length of 4.273 and 3.557, respectively. The average clustering coefficient value (0.323 or 0.326) was comparable in the T1 and CK1 soil networks, while modularity was somewhat lower in the T1 network (0.403) than in the CK1 network (0.440). However, the number of modules in T1 (27) was higher than that in CK1 (22). In T1 and CK1 soil, the total number of links was 828 and 838 (P/N = 2.33 for T1 soil and P/N = 2.37 for CK1 soil), respectively. However, T2 and CK2 soil had different nodes (228 and 201, respectively) at the watermelon fruiting stage. Furthermore, the average connectivity was higher in T2 (4.263) than in CK2 (3.562) networks. The average path length, average clustering coefficient value, and modularity were higher in CK2 than in T2 networks (see Supplementary Fig. S5 and Table S6). In T2 and CK2 soils, the total number of links was 486 and 358 (P/N = 1.612 for T2 soil and P/N = 1.732 for CK2 soil), respectively. The data suggested that wheat straw addition did not affect the bacterial co-occurrence relationship in both the watermelon flowering and fruiting stages.
Inside the T1 versus the CK1 network, a greater percentage of OTUs associated with Proteobacteria, and a reduced OTU ratio for Chloroflexi, Bacteroidetes, and Acidobacteria were found. (see Supplementary Fig. S6a). However, a higher proportion of Proteobacteria and Actinobacteria- affiliated OTUs and a lower proportion of Gemmatimonadetes, Euryarchaeota, Chloroflexi, and Bacteroidetes-affiliated OTUs inside the modules were identified in the T2 versus CK2 network (see Supplementary Fig. S6b). More