Host-microbiome associations of native and invasive small mammals across a tropical urban-rural ecotone

Host-microbiome associations of native and invasive small mammals across a tropical urban-rural ecotone | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Molecular Ecology This is a preprint and has not been peer reviewed. Data may be preliminary. 18 January 2025 V1 Latest version Share on Host-microbiome associations of native and invasive small mammals across a tropical urban-rural ecotone Authors : Alessandra Giacomini 0000-0001-9121-1660 [email protected] , Maklarin Lakim , Fred Tuh , Matthew Hitchings , Sofia Consuegra 0000-0003-4403-2509 , Tamsyn Uren Webster 0000-0002-0072-9745 , and Konstans Wells 0000-0003-0377-2463 Authors Info & Affiliations https://doi.org/10.22541/au.173720389.93870617/v1 Published Molecular Ecology Version of record Peer review timeline 404 views 189 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Global change and urbanisation profoundly alter wildlife habitats, driving native animals into novel habitats while increasing the co-occurrence between native and invasive species. Host-microbiome associations are shaped by host traits and environmental features, but little is known about their plasticity in co-occurring native and invasive species across urban-rural gradients. Here, we explored gut microbiomes of four sympatric small mammal species along an urban-rural ecotone in Borneo, one of the planet’s oldest rainforest regions experiencing recent urban expansion. Host species identity was the strongest determinant of microbiome composition, while land use and spatial proximity shaped microbiome similarity within and among the three rat species. The urban-dwelling rat Rattus rattus had a microbiome composition more similar to that of the native, urban-adapted rat Sundamys muelleri (R. rattus’ strongest environmental niche overlap), than to the closely related urban-dwelling R. norvegicus. The urban-dwelling shrew Suncus murinus presented the most distinct microbiome. The microbiome of R. norvegicus was the most sensitive to land use intensity, exhibiting significant alterations in composition and bacterial abundance across the ecotone. Our findings suggest that environmental niche overlap among native and invasive species promotes similar gut microbiomes. Even for omnivorous urban-dwellers with a worldwide distribution like R. norvegicus, gut microbiomes may change across fine-scale environmental gradients. Future research needs to confirm whether land use intensity can be a strong selective force on mammalian gut microbiomes, influencing the way in which native and invasive species are able to exploit novel environments. Introduction Human-induced habitat alteration and encroachment present ever increasing threats to wild animals, affecting both physical and biotic environments and compromising habitat suitability and availability (Alberti 2015; McKinney 2002; Otto 2018). To avoid extinction, native species are often forced to constrain their distribution into the remaining habitat or adapt to new anthropized environments (here, defined as urban adapters) (McKinney 2002; Otto 2018). Urban sprawl also facilitates the introduction and establishment of urban-dwelling species (Bradley and Altizer 2007; Hassell et al. 2017). These include commensal, non-native and invasive species capable of thriving in heavily modified landscapes and highly adaptable to changing environmental conditions (Borden and Flory 2021; Hulme-Beaman et al. 2016). Consequently, habitat encroachment and urban sprawl likely result in the co-occurrence of urban-adapted native species and urban-dwelling non-native species, promoting changes in community composition and creating new contact opportunities within and between species. Urban-rural ecotones offer fascinating ‘natural laboratories’ to gain insights into the co-occurrence of species with different life histories and levels of adaptation to anthropized environments, including their associations with symbiotic species such as parasites and microbial communities, which significantly impact their health and survival. Both environmental conditions as well as the interactions among coexisting species may exert selective pressures or offer novel opportunities for the formation of associations of host species with parasites and microbes (Clark et al. 2018; Raulo et al. 2024; Teng et al. 2022). Therefore, exploring the plasticity in microbiome associations of native and invasive species that co-occur across habitat ecotones offers a multi-host perspective of how selective forces (i.e. environmental constraint and host traits) and ecological opportunities arising from new contact opportunities among host species may shape host-microbiome associations. Animal gut microbiomes play a critical role in maintaining the health and homeostasis of their hosts (Mcfall-Ngai et al. 2013). While significant disruption in microbiome structure and diversity, known as dysbiosis, can have negative effects on animal health and fitness, metagenomic plasticity can also contribute to the host’s adaptive plasticity to environmental change (Alberdi et al. 2016). Understanding the factors that can alter the host-associated microbiome, and how they do so, can therefore provide valuable insights into the impact of different environmental conditions on animals and their capacity for adaptation. Generally, the host’s evolutionary history and diet are considered to be the main drivers of gut microbiome composition and diversity, although the debate around their respective roles and degree of influence is still open (Mallott and Amato 2021; Youngblut et al. 2019). Host phylogeny has been widely reported as a major modulator of gut microbiome composition in mammals (Brooks et al. 2016; de Jonge et al. 2022; Mazel et al. 2023). In addition, major diet shifts during the evolution and radiation of mammals have likely shaped gut microbiomes, and it has been suggested that host phylogeny relates to the acquisition of more recently diverged microbial lineage, while host diet relates to the association with large groups of more ancient microbial lineages (Groussin et al. 2017). Strong diet specialisation could enhance host-microbiome coadaptation (Ley et al. 2008), while opportunistic feeding on certain diet types may increase or reduce microbiome diversity and favour certain bacterial taxa (Davidson et al. 2020; Li et al. 2023; Youngblut et al. 2019). More broadly, a host species realised ecological niche, including preferred habitat, diet, as well as intra- and interspecific interactions among host individuals, can be expected to shape the variation in microbial associations found in different host species in any given environment. For example, social behaviours increase the microbial dispersion between individuals, while sharing a common environment can indirectly facilitate the transfer of gut microorganisms among both conspecifics and heterospecifics (Raulo et al. 2024; Sarkar et al. 2020; Stothart et al. 2021; Teng et al. 2022). Anthropogenic pressures that dramatically alter local habitats and community compositions can be expected to have fundamental effects on the diversity and composition of the gut microbiome. Studies focusing on single host species, for example, have demonstrated that human-induced land-use changes, such as habitat loss, fragmentation, and urbanisation are altering gut microbiomes across a diversity of vertebrate species (Berlow et al. 2021; Fackelmann et al. 2021; Maraci et al. 2022; Stothart et al. 2019; Teyssier et al. 2018). Recent research exploring microbiome variation of multiple small mammal hosts has further shown that environmental conditions may impose different selective forces on gut microbiomes according to host habitat requirements and environmental niche breadth (Anders et al. 2022; Bouilloud et al. 2024; Heni et al. 2023). However, the diversity and composition of gut microbiomes where native and invasive host species assemble into novel communities remains poorly understood. Here, we address the variation in gut microbiome diversity and composition within and among sympatric native and invasive host species that co-occur across a continuous urban-rural ecotone. For this, we targeted four sympatric small mammal species in Borneo. These species were: one rat endemic to the forest environments of Sundaland (the Mueller’s giant Sunda rat Sundamys muelleri ) and three key invasive/commensal species that have originated elsewhere (most likely the Indian Peninsular, Aplin et al. 2011; Hutterer and Tranier 1990) and successfully spread in South-East Asian anthropogenic environments, namely the black rat Rattus rattus (species complex), the Norwegian rat Rattus norvegicus , and the Asian house shrew Suncus murinus . The relatively recent onset of large-scale logging in Borneo in the 1970’s (Gaveau et al. 2014) has led to environmental encroachment that offers a unique opportunity to study possible shifts in species occurrences across ecotones and the potential consequences for species association and biotic interactions (Bordes et al. 2017; Wells et al. 2014; Wilcove et al. 2013). Sund. muelleri has been recently described as urban adapter, preferring pristine natural landscapes but also frequently found in suburban green spaces. The three invasive species, in turn, are considered urban dwellers that differ in their capacity to exploit natural and non-anthropogenically shaped environments (Blasdell et al. 2022; Wells et al. 2014). We therefore aimed to explore the extent to which host phylogenetic relatedness, land use intensity (LUI), and spatial proximity of hosts individuals across an ecotone from forest to highly urbanised areas influence gut microbiome composition and diversity in the four sympatric species. We expected that host species identity would play a primary role in shaping the host gut microbiome, but changes in LUI, that represent variable gradients of habitat suitability for the studied species (Wells et al. 2014), would also drive significant microbiome alterations. We also expected the microbiome of the native and urban adapted rat Sund. muelleri to be distinct from the two congeneric invasive Rattus rats, if the historical and phylogenetic background was a strong driver. In contrast, if ecological opportunity arising from the co-occurrence of the different host species plays a role in microbiome associations, we expect this to result in some overlap in the microbiome compositions among the different host species. Materials and Methods Study system and sample collection We analysed 245 individual faecal samples of four sympatric small mammals captured on a continuous urban-rural gradient in Kota Kinabalu (lat. 6.0° long. 116.1°), northern Borneo (Sabah, Malaysia), between March 2012 and May 2013 (Wells et al. 2014). The city, which has expanded towards the nearby tropical rainforest in the last 50 years, is surrounded by suburban and villages areas interlaced with production forest and nearby old-growth forest of the Crocker Range biosphere reserve. The captured animals belonged to four species from two families: the native Muller’s giant Sunda rat Sundamys muelleri , the commensal Asian black rat Rattus rattus (species complex), and the commensal Norway rat Rattus norvegicus , all three belonging to the tribe Rattini within the family Muridae (Pagès et al. 2016) and the commensal Asian house shrew Suncus murinus , from the family Soricidae. For each trapping location, geographical coordinates and elevation were recorded with a handheld GPS device (Garmin GPSmap62st, Olathe, USA). We recorded the proportional coverage with different landcover types within 20m radii around trapping locations, categorising and scoring land cover types according to increasing human impact and urbanisation as ‘forest’ = 1, ‘fallow tree’ = 2, ‘fallow grass/agriculture/garden’ = 3, ‘housing soil’ = 4 and ‘housing compound’ = 5 (Wells et al. 2014). The landcover types ‘soil’, ‘sealed’, and ‘water’ were scored as 3 due to the difficulty to categorise their land-use intensity. We then computed a land use index (LUI) by summarizing the products of proportional coverage and land cover scores for each trapping location and scaling the resulting vector between 0 and 1. Captured animals were then transferred to nearby mobile field laboratories for subsequent anaesthesia via diethyl ether inhalation (anaesthetic grade) and then sacrificed by cervical dislocation (according to guidelines by the American Veterinary Medical Association, https://www.avma.org). Species were identified based on morphological characters (Aplin et al. 2003; Carleton and Musser 2005; Musser and Carleton 2005). Faecal samples were collected from dissected colons, stored in ethanol and frozen at -20 °C. Biological resource access and export permits were issued by the Sabah Biodiversity Centre (JKM-MBS.1000-2/2[35], JKM-MBS.1000-2/2[63]); access to forest field sites were approved by Sabah Parks and individual landowners. DNA extraction, library preparation, and high-throughput sequencing DNA was extracted from faecal samples (~50 mg) using the prepGEM Bacteria kit (MicroGEM, Charlottesville, VA, USA), according to the manufacturer’s instructions, with an additional preliminary wash step using the prepGEM wash buffer to minimise potential surface contamination. Samples were homogenised using bead beating (1.4 mm ceramic beads, 3 x 30 sec using a Precellys 24 homogeniser). We performed a two-step polymerase chain reaction (PCR) targeting the V4 region of 16S rRNA gene using the updated sequences of the 515F and 806R primers (Apprill et al. 2015; Parada et al. 2016). The first PCR reaction of 20 µl consisted of 2 µL of DNA, 10 µL of Platinum™ II Hot-Start PCR Master Mix (2X) (Thermo Fisher Scientific, Waltham, MA, USA), 0.4 µL of forward and 0.4 µL of reverse primers and 7.2 µL of ultra-pure water. Reaction conditions consisted of an initial denaturation at 95 °C for 3 min, followed by 28 cycles of 30 sec at 95 °C, 30 sec at 55 °C, and 30 sec at 72 °C, and finally 72 °C for 5 minutes. During the second PCR, sample-indexing was performed using the Nextera® XT Index Kit (Illumina, Inc., San Diego, CA, USA), in a 25 µL reaction consisting of 2.5 µL of amplified DNA from the previous PCR, 12.5 µL of Platinum™ II Hot-Start PCR Master Mix (Thermo Fisher Scientific, Waltham, MA, USA), 1.25 µL of each index, and 10 µL of ultra-pure water. Reaction conditions were as above, but with 8 cycles instead of 28. PCR products were pooled based on agarose gel band relative intensity, cleaned using AMPure XP beads kit (NEBNext® sample purification beads, USA) according to manufacturer’s instructions, and quantified via qPCR using the NEBNext® Library Quant Kit for Illumina® (Ipswich, MA, USA). Final libraries were normalised to 4 nM and sequenced on an Illumina MiSeq platform (paired 300 bp reads). Bioinformatics The raw sequence reads were assigned into amplicon sequence variants (ASVs) using DADA2 (Callahan et al. 2016) within QIIME2 (version 2023.2, Bolyen et al. 2019). After quality checking, raw sequences were truncated at 240 bp (forward) and 220 bp (reverse) and trimmed (leading 19 bp) to avoid potential adaptor contamination, denoised and filtered to remove chimeras. ASVs were taxonomically classified using the naive Bayes classifiers trained on Silva database (v138, (Pruesse et al. 2007)). ASVs that were not assigned as bacterial sequences were removed, as well as mitochondrial and chloroplast DNA sequences. A phylogenetic tree was then constructed using qiime2 – phylogeny plugin. We excluded all singleton ASVs and individual samples with less than n = 6,526 reads (minimum library size determined based on acceptable levels of saturation in alpha diversity rarefaction curves in QIIME2). This resulted in a dataset of 236 samples (55 Sund. muelleri , 74 R. rattus , 49 R. norvegicus, 58 Sunc. murinus ), which we analysed in R version 4.2.3 (R Core Team 2023) for further analysis. Raw sequence reads have been deposited in the European Nucleotide Archive (Accession number PRJEB81284). Statistical analyses To characterise the relative microbiome alpha diversity for each host individual, we computed Chao1 richness as an estimate of relative species richness (Chao 1984) and Shannon entropy as an estimate of relative species diversity (Shannon 1948). We used Gaussian Generalised Linear Models (GLMs) to explore variation in individual alpha diversity in relation to LUI for each host species (with separate models for each species) and for estimating the expected average individual species richness and diversity (using a single model with all species). We characterised the pairwise similarity in microbial assemblages among host individuals by computing Bray-Curtis (Bray and Curtis 1957) and weighted UniFrac (Lozupone and Knight 2005) beta diversity metrics. We then used a Generalised Dissimilarity Model (GDM) as a matrix-based regression approach (Ferrier et al. 2007; Mokany et al. 2022) to explore changes in these beta diversity metrics in relation to host phylogenetic relatedness (computed as the pairwise phylogenetic distance between host individuals based on a consensus phylogenetic tree generated from the vertlife.org project, accessed in June 2023 (Upham et al. 2019)), LUI (to be transformed into dissimilarity metrics during model fit) and spatial proximity (geodesic distance between trapping locations). GDM uses non-linear splines to describe how changes in predictor variables (i.e. host phylogenetic relatedness, LUI, spatial proximity) influence similarity (i.e. Bray-Curtis and weighted UniFrac) between samples. Since the utilised GDMs with an underlying negative exponential link function assume a monotonic increase in dissimilarity in relation to predictor variables, the height of the splines indicate the relative importance of the predictor variable (i.e. the total amount of compositional turnover associated with the predictor variable being evaluated), while its shape indicates the rate of change in the beta diversity measures across the predictor’s values with a steeper slope indicating higher compositional turnover at a given data point (Mokany et al. 2022). GDMs were performed using the R package ‘gdm’ (Fitzpatrick et al. 2022), whereby we run models considering gdm1 ) all host individuals together, gdm2 ) the three rat species only ( S. muelleri , R. rattus , R. norvegicus ), and gdm3 ) each host species separately. For the above-mentioned analysis, we normalized the ASV data by rarefaction, iteratively sampling 100 random subsets of n = 6,526 reads (according to minimum library size) for each host individual sample in order to account for heterogeneous sequencing effort (Schloss 2024). For inference on alpha diversity variation, we iteratively computed the alpha diversity metrics (Chao1, Shannon entropy) from these normalised subsets, ran the GLMs, and extracted the mean and SE of the intercept and coefficient estimates. We then drew a total of 10,000 ’posterior’ values from these estimates and the corresponding SEs (from an underlying normal distribution) and reported the mode and 95% credible intervals (CI) as results that account for both the uncertainty arising from the rarefaction and the GLM likelihood procedures. We considered CIs not overlapping zero to represent ‘significant effects’. For analysis of beta diversity, we iteratively calculated Bray-Curtis and weighted UniFrac diversity metrics for the 100 subsets, ran the GDMs, extracted the partition of deviance estimates, and calculated the ’posterior’ mode and 95% CI from the resulting distribution of estimates. From each GDM, we also extracted the estimated spline for each predictor variable. We performed analysis of similarity (ANOSIM) across all host species and specific pairwise combinations to investigate the similarity in microbiome composition among the studied species, using the function ‘anosim’ from the package ‘vegan’ (Martinez 2020). We investigated beta diversity dispersion of microbial assemblages in different host species (homogeneity/variance of distance-to-centroid dispersion for each host species) using the function ‘betadisper’ from the package ‘vegan’ (Dixon 2003), and the Tukey honestly significant difference (HSD) to test for pairwise differences in the beta dispersion among host species. In addition, we tested the relationship between beta diversity dispersion (distance-to-centroid scores) and LUI separately for each host species using Gaussian GLM. To visualise the variation in microbiome composition within and among host species, we used non-metric multidimensional scaling (NMDS) based on averaged Bray-Curtis and weighted UniFrac measures. Finally, we performed ASV differential abundance analysis using Analysis of Composition of Microbiomes with Bias Corrections 2 (ANCOM-BC2) (Lin and Peddada 2020; 2024) to investigate how shifts in microbial composition allow to trace changes in the relative abundance of ASVs in relation to the LUI within each host species. This method includes p-value adjustments to control for multiple testing and reduce the false discovery rate, as well as a sensitivity analysis to minimize false positives due to the pseudo-counts added to handle zero values. We performed ANCOM-BC2 separately for each host species for all ASVs with > 10% prevalence in the respective species. ASVs were defined as significantly differentially abundant if their adjusted p-value was < 0.05. Occurrence of host species across the urban-rural ecotone In order to generate an index of habitat suitability based on whether a species is more likely to occur in a location with a certain land use intensity than others across the urban-rural ecotone, we quantified the relative occurrence probability of each focal species along the urban-rural gradient based on capture success; for this we regressed for each species the presence-absence records for 3,538 trap locations (from Wells et al. 2014) against the LUI metric and with a spatial smoothing term of paired geographical coordinates (in order to account for spatial autocorrelation) in a binomial generalised additive models (GAM) using the package ‘mgcv’ (Wood 2017). The results of this analysis showed that the three rat species occurred in distinct pattern across the gradient of LUI, with Sund. muelleri predominantly present in more natural environments characterised by low to moderate LUI, R. rattus occurring along the entire gradient but most frequently in areas of moderate LUI, and R. norvegicus mostly found in semi-urban to strongly urbanised areas, characterised by average to high LUI. The shrew Sunc. murinus occurred most often in areas with moderate to high LUI, while it was unlikely to occur in natural or strongly urbanised areas. In summary – as outlined in our previous work (Wells et al. 2014) – the four sympatric species exhibited clearly distinct habitat preferences, while also exhibiting some overlap in habitat use and occurring in close proximity to each other (Fig. 1). Since metrics of individual habitat suitability and associated LUI values were correlated for R. norvegicus and Sund. muelleri , we only considered LUI in the analyses. Faecal microbiome composition A total of 5,984 unique bacterial ASVs were identified after quality checking ( Sundamys muelleri = 2,934 ASVs, Rattus rattus = 2,943 ASVs, R. norvegicus = 1691 ASVs, Suncus murinus = 935 ASVs; Fig. S1), of which 66 were found in all four host species and none was found in all 236 individuals. At species level, Sund. muelleri had 1,431 ASVs in common with R. rattus (> 48% of the number of ASVs found in any of these two species) and 598 ASVs in common with R. norvegicus , while 729 ASVs were found in both focal Rattus species (25% of ASVs found in R. rattus and 43% of ASVs found in R. norvegicus ). The shrew Sunc. murinus had 142 ASVs in common with Sund. muelleri , 160 ASVs with R. rattus , and 152 ASVs with R. norvegicus . The three most abundant bacterial families in all three rat species were Lachnospiraceae , Prevotellaceae , and Lactobacillaceae , with the first family being the most abundant in R. rattus and Sund. muelleri , and the second being most abundant in R. norvegicus , respectively (Fig. 2, Fig. S2). The families Muribaculaceae and Helicobacteriaceae were mainly detected in R. rattus and Sund. muelleri , while Peptostreptococcaceae and Enterobacteriaceae were mostly found in R. norvegicus and Sund. muelleri . Of the less frequent bacterial families, Selemonadaceae , Fusobacteriaceae , and Succinivibrionaceae were mostly detected in R. norvegicus , while Rikenellaceae , and the taxa Clostridia UCG-014 were mainly found in Sund. muelleri . The faecal microbiome composition of the shrew Sunc. murinus was clearly distinct from those of the three rat species with the most abundant families Clostridiaceae , Leuconostocaceae , and Peptostreptococcaceae of the shrew microbiome rarely found in the rat species. The families Enterobacteriaceae and Dermabacteraceae , but also less frequent Staphyloccoccaceae and Pasteurellaceae , were found in Sunc. murinus only. These clear differences in microbiome composition of the rat species and Sunc. murinus at family level also translated into distinct microbiomes at the phylum level, with Bacteroidota predominantly found in the rat species and Proteobacteria most abundant in Sunc. murinus (Fig. 2, Fig. S2). Sunc. murinus also exhibited higher species-specific variance in microbiome composition compared to the three rat species, with lower consistency in individual microbiome composition (Fig. 2). Faecal microbiota alpha and beta diversity The average individual-level richness estimates of microbial assemblages were highest in Sund. muelleri (Chao1: 168, 95% CI of 154 – 179) and R. rattus (Chao1: 174, 95% CI of 160 – 188), slightly lower in R. norvegicus (Chao1: 124, 95% CI of 110 – 139) and lowest in individuals of the shrew Sunc. murinus (Chao1: 46, 95% CI of 40 – 52) (Fig. S3, Table S1). Likewise, we found the highest average Shannon diversity in Sund. muelleri and R. rattus and the lowest in Sunc. murinus (Fig. S3, Table S1). We found no evidence that variation in alpha diversity among individuals from any of the host species correlated with land use intensity (LUI) (according to zero-overlapping CIs from GLMs; Fig. S3, Table S1). Exploring the variation in microbial assemblage composition within and among all host species, assemblage similarity was mostly explained by host phylogenetic relatedness (34-35% of deviance explained for both Bray-Curtis and weighted UniFrac metrics in all-species GDMs ( gdm1 ) (Fig. 3, Table 1). In all-species GDMs ( gdm1 ), LUI explained only a small fraction of the deviance in assemblage similarity for both Bray-Curtis (1.19%) and weighted UniFrac (0.26%) metrics as response variables. In these models, the variation of Bray Curtis was mostly constant along the LUI gradient with a plateau towards large LUI values in highly urbanised environments, whereas variation in weighted UniFrac in microbial assemblages was mostly associated with relatively high LUI values according to the fitted functional splines (Fig. 3, Table 1). This suggests that microbial community turnover associated with higher LUI in more urbanised environments exhibit the most pronounced phylogenetic compositional turnover as captured by the weighted UniFrac metric. In the GDMs for the three rat species only ( gdm2 ), the overall deviance explained by covariates was lower compared to gdm1 (14% for Bray-Curtis and 3% for weighted UniFrac). However, in these models, the total amount of compositional turnover in microbiome assemblages associated with LUI (for Bray-Curtis and weighted UniFrac; Fig. 3 H, K) and spatial proximity (for weighted UniFrac; Fig. 3 L) was of similar magnitude than compositional turnover explained by host phylogenetic relatedness (Fig. 3, Table 1). In fact, the LUI explained more deviance in gdm2 compared to gdm1 models (2.38% vs 1.19% for Bray-Curtis and 1.44% vs 0.26% for weighted UniFrac). The functional relationships between LUI and compositional turnover in GDM models for rat species only ( gdm2 ) were similar to those in models of for all species (gdm1 ) in that the variation of Bray Curtis was mostly constant along the LUI gradient with a plateau towards large LUI values and variation in weighted UniFrac mostly associated with relatively high LUI values (Fig. 3). Species-specific GDMs ( gdm3 ) revealed a notable impact of land use contrast on assemblage similarity in R. norvegicus individuals only (7% and 3% of deviance explained for Bray-Curtis and weighted UniFrac as response variables; Table S2). While analysis of similarity (ANOSIM) and multidimensional scaling (NMDS) visualisation (Fig. 4 A, B) confirmed that microbial assemblage composition was most similar for host individuals from the same species for both Bray-Curtis and weighted UniFrac measures (Bray-Curtis: ANOSIM: R value = 0.69, p <0.001; weighted UniFrac: ANOSIM: R value = 0.49, p <0.001), there was a clear overlap in the assemblage compositions in Sund. muelleri and R. rattus individuals and to a lesser extent also in assemblages from other pairs of host species. The similarity in terms of microbiome composition between these two species was corroborated by the pairwise ANOSIM, which showed the smallest R value between Sund. muelleri and R. rattus compared to the other host species pairs (Table S3). For both beta diversity measures, Sunc. murinus microbiome assemblages were clearly distinct from the three rat species, with R. norvegicus as the closest rat species in both cases according to ANOSIM results (Fig. 4 A, B, Table S3). The beta diversity dispersion of microbial assemblages for Bray-Curtis revealed significantly lower average distance from the centroid in R. norvegicus compared to the other three host species (PERMDISP: df: 3, F value: 11.94, p : 0.001; Fig. S4, Table S4). However, the beta diversity dispersion of R. norvegicus was also characterised by a number of individuals dispersing most strongly from the centroid compared to more homogeneously distributed assemblage dispersion among individuals of the other host species (Fig. S4). This dispersion (Bray-Curtis of assemblages R. norvegicus ) decreased with increasing LUI (GLM on PERMDISP distance to centroid and LUI, β = -0.26, SE = 0.08, p < 0.01), suggesting that host individuals of R. norvegicus in less urbanised environments harboured the most distinct microbial assemblages (Fig. 4C, Table S5). These pattern was not observed for weighted UniFrac, as we found no association between the dispersion from centroid and LUI and the weighted UniFrac dispersion of assemblage compositions in R. norvegicus was not significantly different from that measured for assemblages in the other three host species for this metric (Fig. 4D, Table S5). Shifts in ASV relative abundances across the urban-rural ecotone LUI significantly influenced the relative abundance of 83 ASVs (out of 941 ASVs with a species-specific prevalence > 10%) in the four small mammal species, with the ASVs assigned to 30 different bacterial families (ANCOM-BC2, adj p < 0.05; Table S6). The largest number of differentially abundant ASVs in association with LUI were found in R. norvegicus with 73 ASVs, compared to 8 ASVs in Sunc. murinus and 3 in Sund. muelleri . We found no evidence of such ASV variations in R. rattus . In R. norvegicus , the highest number of differentially abundant ASVs were reported for the bacterial families Lachnospiraceae (11 ASVs), Bacteroidaceae (9 ASVs), and Prevotellaceae (8 ASVs). Out of these 73 differentially abundant ASVs, 43 (59%) exhibited a decrease in their relative abundance with higher LUI (ANCOM-BC2, adj p < 0.05; Table S6). Most of these ASVs were from the families Lachnospiraceae (9/11), Oscillospiraceae (4/5), Prevotellaceae (6/8), Muribaculaceae (5/5), and Bacteroidaceae (6/9) (Fig. 5) . In Sund. muelleri , Clostroidium perfringens was reported higher in abundance in more urban environments, while the other two differentially abundant ASVs found in this species were significantly less abundant with higher LUI (ANCOM-BC2, adj p < 0.05; Fig. 5, Table S6). In Sunc. murinus , Clostridium baratii and three other ASVs were more abundant in more urban environments, while 4 ASVs decreased in their relative abundance with higher LUI (ANCOM-BC2, adj p < 0.05; Fig. 5, Table S6). Rombutsia spp. was the only ASV found differentially abundant in two different host species, R. norvegicus and Sunc. murinus, reflecting in both cases a positive effect of LUI. Of these 83 differentially abundant ASVs, 13 passed the sensitivity test for the pseudo-count addition, all belonging to R. norvegicus : Anaerostipes spp. , Eubacterium halii group spp. , Lactobacillus spp. , Clostridium sensu stricto 1 spp. , Romboutsia spp. , Subdoligranulum spp. , Bacteroides spp. , Sutterella spp. , and Collinsella spp . were positively associated with LUI (higher abundance with higher LUI), whereas Terrisporobacter spp. , Eubacterium cospostanoligenes group spp. , Bacteroides sartorii and Bacteroides spp. were negatively associated with LUI (lower abundance with higher LUI) (ANCOM-BC2, adj p < 0.05, ss = TRUE; Table S6). Discussion Understanding the patterns and processes that shape the microbial communities associated with animal species that exploit modified and urbanised landscapes could fill some important knowledge gaps of how native species are resilient to habitat changes and commensal species encroach into modified habitats. Here, we explore the gut microbiome composition of four sympatric small mammal species across an urban-rural ecotone in Borneo and show that despite distinct microbiomes among all host species, the native forest rat Sundamys muelleri, which occupies both natural and suburban habitats as an urban adapter and the commensal rat Rattus rattus , which is an urban dweller but capable to exploit suburban and semi-natural environments, exhibited the highest similarity in microbiota. We further show that the relative abundance of specific bacterial taxa correlates with land use intensity (LUI), whereby the strongest changes in microbiota in relation to LUI were found in the urban-dwelling commensal rat R. norvegicus , which has limited capacity to explore suburban and rural areas of moderate to low land use intensity in our study area. Our findings reveal a strong phylogenetic signal when comparing individuals from distinct host species, while underlying habitat conditions play an important role among closely related species. These results provide some first evidence that ecological opportunity, possibly arising from the co-occurrence and environmental niche overlap of native and invasive species may facilitate similarities in gut microbiomes, amid environmental forcing as reflected in the impact of land use intensity on specific ASV abundances. Host biological features and niche overlap correlate with host-microbiome associations Host species identity was the strongest factor shaping the microbiome of the four studied small mammal species. In particular, we found that the bacterial community of the shrew Suncus murinus was clearly distinct from those of the three rat species ( Sund. muelleri , R. rattus , and R. norvegicus ), but among these latter species, we found the strongest similarity in microbial communities between Sund. muelleri and R. rattus rather than the two congeneric Rattus species. Our results, therefore, contrast with the commonly found pattern of closely related mammalian species harbouring the most similar gut microbiomes (Amato et al. 2019; Heni et al. 2023; Kartzinel et al. 2019), while they are consistent with those of other studies on small mammals that reported the strongest effects of host phylogeny on microbiome composition when comparing distantly related host species and much weaker effects near the tips of phylogenies (Brown et al. 2023; Knowles et al. 2019). Phylogeny impacts the host microbiome by shaping host physiology, including immune system function and gut morphophysiology. These factors influence microbial selection and diversification, which are further modulated by diet, as well as vertical and horizontal transmission of microbes (Mallott and Amato 2021; Maritan et al. 2024). The clearly distinct faecal microbiota between the studied shrew and rats as well as the lower species richness in shrew matched our expectations with regards to taxonomy, but also diet and host gut morphology. The shrew Sunc. murinus is an insectivorous species, typically having a shorter and simpler gastrointestinal tract compared to omnivorous species (Boonzaier et al. 2013; Shinohara et al. 2019). Differences between the gut microbiome of insectivorous versus omnivorous mammals have been scarcely investigated in comparative studies to date. Lower microbiome diversity in insectivores compared to sympatric omnivores has been also observed in primates from the family Strepsirrhines (Bornbusch et al. 2019; Bornbusch et al. 2022) and in the insectivores shrew Crocidura russula compared to the omnivore mouse Apodemus sylvaticus (Koziol et al. 2023), whereas such differences were not found among sympatric desert rodents (Kohl et al. 2022). More generally, if we consider insectivores as specialised towards a narrower diet range than omnivorous rodents, our results align with studies showing that carnivores have less diverse and more unstable microbiome compared to omnivores and herbivores (Ley et al. 2008; Zoelzer et al. 2021). The bacterial families Lachnospiraceae , Prevotellaceae , and Bacteroidaceae which were dominant in the rats in this study, were not only reported to dominate in other rodents (Gu et al. 2013) but also to comprise core microbial members of large hindgut fermenters (O’ Donnell et al. 2017). On the other hand, the shrew’s dominant symbiotic bacterial families Clostridiaceae , Enterobacteriaceae , and Peptostreptococcaceae were reported as more prevalent in carnivores compared to omnivores and herbivores (de Jonge et al. 2022; Zoelzer et al. 2021). Given the limited influence of phylogeny on rat species’ microbiomes and the strong impact of diet on microbiome composition observed in many comparative studies (Kartzinel et al. 2019; Ley et al. 2008), it would be of interest to analyse how the diet of our focal rat species varied among species and also the urban-rural ecotone. Unfortunately, we currently lack details about the diet and dietary variation of these three rat species. As omnivore species, one can expect some opportunistic feeding on different food items for all the three rat species, while across an urban-suburban gradients, available food sources likely shift from more natural products to human-derived processed food and organic waste items, including those from local restaurants and markets. Alternatively, the exchange of microbes with the environment is another pathway through which gut microorganisms can colonise hosts, and overlaps of habitat use among host species has been associated with more similar gut microbiome in humans and other mammals (Knowles et al. 2019; Raulo et al. 2024; Rothschild et al. 2018; Teng et al. 2022). Moreover, social interactions, both within and between host species, can further enhance microbial dispersal between individuals, influencing host-associated microbiome composition and increasing or decreasing microbiome similarity among more or less connected host individuals (Raulo et al. 2024; Sarkar et al. 2020; Stothart et al. 2021). Across the studied urban-rural ecotone, Sund. muelleri and R. rattus were both found to exploit habitats of intermediate land use intensity, which corresponded to suburban vegetation patches with some tree or shrub cover and nearby housing. In Borneo and elsewhere within its geographic range, Sund. muelleri is frequently recorded in pristine and logged forest environments (Wells et al. 2007), while it was only recently found to also strive in suburban vegetation as an urban adaptor (Blasdell et al. 2022; Wells et al. 2014). R. rattus as a commensal species, in turn, is highly adapted to urban conditions where it potentially also overlaps in habitat use with R. norvegicus ; however it is also capable to exploit natural vegetation and even forests near human infrastructures (Loveridge et al. 2016; Wells et al. 2006). R. norvegicus, one the other hand, was predominantly reported in urban areas or suburban vegetated patches near streams or sewages and urban infrastructure in our study area (Wells et al. 2014). Elsewhere, R. norvegicus has been also reported to benefit from sewage systems or habitat and soil conditions that allow excavating sufficiently large burrowing systems for their colonies (Feng and Himsworth 2014), suggesting that habitat features linked to sheltering colonies might be a constraining factor in exploiting novel environments as much as diet. Apparently, with more similar microbiomes found in the more distantly related rat species Sund. muelleri and R. rattus , strong co-evolution and co-speciation among hosts and associated microbiota cannot be the sole driving force at work (Groussin et al. 2020; Mazel et al. 2023). Our findings suggest that host biological features and some aspects of niche overlap (perhaps diet sharing or environmental exposure) may synergistically drive the observed patterns of microbiome sharing. Land use intensity influences the microbiome most strongly in Rattus norvegicus Exploring changes in the microbiome across a continuous land use gradient from urban to forest habitats, we found some evidence that land use intensity (LUI) was associated with changes in microbiome composition and ASV relative abundance in the analysed small mammal species. Surprisingly, despite R. norvegicus being an urban dweller with an almost worldwide distribution, its microbiome composition revealed the strongest relationship with land use intensity, with relatively homogeneous microbiome composition in individuals exploiting urban environments and more distinct (dispersed from the ‘average’ according to Bray-Curtis compositional turnover) microbiomes in those individuals exploiting less urbanised environments. The underlying food landscape that might be of relevance for R. norvegicus in our study area is complex and currently incompletely understood. The urban environments where this species was trapped include regional food markets as well as nearby restaurants and urban canalisation and drainage. Since this rat species is unlikely to occur in natural habitats in Borneo, we expect that individuals in less urbanised environments will still exploit similar food items, though in reduced quantities due to fewer food vendors and housing compounds. Additionally, they may need to increase their intake of food sourced from nature resulting in a more heterogeneous diet. Such scenario could explain the stronger divergence in microbiome composition of the individuals captured in less urbanised areas and the more homogenised microbiomes in individuals captured in the most urbanised areas. More homogeneous microbiomes in more urbanised areas were also found in bird nests (Maraci et al. 2022), the skin microbiome of amphibians (Zhou et al. 2023) and even urban soil microbial community (Delgado-Baquerizo et al. 2021) suggesting that urban homogenisation of microbial communities is a common phenomenon. R. norvegicus also displayed the highest number of differentially abundant ASVs in relation to LUI. In particular, the relative abundance of various ASVs from the phylum Bacteroidota , particularly the family Prevotellaceae , and the family Lachnospiraceae was significantly decreased in individuals captured in more urban environments. These fibre degrading taxa have previously been reported as significantly less abundant in humans and mice consuming high-fat low-fibre diets (Bailén et al. 2020; De Filippo et al. 2010; Pasolli et al. 2019; Velázquez et al. 2019) and in non-human primates exposed to anthropogenic activities and human food waste (Moy et al. 2023; Wasimuddin et al. 2022). Additionally, we found the relative abundance of Treponema berlinense and Treponema succinifaciens from the family Spirochetaceae to be significantly depleted in individuals trapped in urban areas. While taxa from this family are generally assumed to be reduced in their abundance in industrialised human populations (De Filippo et al. 2010; Pasolli et al. 2019), the two Treponema species we found in commensal R. norvegicus have been previously reported in non-industrialised, rural (Bedouin) human populations in Arabia (Angelakis et al. 2016) and several non-human primates not exposed to urban environments (Manara et al. 2019). While these results may be ‘snapshot observations’ and warrant future research to explore in more depth microbiome associations in response to dietary changes across urban-rural gradients, they lead to the hypothesis that in less urbanised areas, R. norvegicus may need to rely on different, perhaps more natural food resources compared to those in urban environments and such dietary shift are associated with changes in gut microbiomes. Whether the alteration of R. norvegicus gut microbiome in less urbanised environment would compromise host health or is linked to demographic features remain unknown. An interesting avenue of research could be more detailed comparisons of microbiome changes in R. norvegicus and R. rattus at larger biogeographical scale, given their joint and worldwide occurrence in urban environments but differing capacities to exploit natural habitats. Notably, microbial abundance in R. rattus was not correlated with LUI in this study, being the only small mammal species without observed differentially abundant ASVs. Contrarily to our expectations, we found little evidence that the microbiome composition and diversity of the native species Sund. muelleri is strongly affected by land use change across the studied ecotone. As an urban adapter species that appears to have expanded its habitat range from forests to sub-urban woody vegetation and garden areas with the onset of rainforest logging in Borneo (Blasdell et al. 2022; Wells et al. 2014), we would have expected it to encounter novel environmental conditions across the studied ecotone that have been mostly absent in the original natural habitat of this species. Although we did not find a strong effect of LUI on the microbiome of Sund. muelleri , it is worth noting that Clostridium perfringens , a well-known potential pathogen for humans and other animals (Kiu and Hall 2018), was significantly more abundant in individuals found in more urban environments. This bacterium is commonly found in the urban environment as well as in the microbiota of humans and other vertebrates (Kiu and Hall 2018), therefore its higher abundance in rats captured in more urbanised areas is not necessarily a direct threat for people and other animals. However, in context of our findings of considerable microbial similarity between Sund. muelleri and R. rattus , which is a well-known vector of zoonotic pathogens (Blasdell et al. 2022; Kosoy et al. 2015; Panti-May et al. 2017), the detection of a zoonotic bacterium species in Sund. muelleri leads to (mostly unanswered) questions around whether native-invasive small mammal species interfaces could facilitate the host shifting and spillover of parasites from wildlife to humans and vice versa, and whether urban adapted species like Sund. muelleri could amplify the risk of zoonotic disease outbreaks (Roberts et al. 2021). Conclusion Our study uncovers multi-host microbiome associations across a continuous urban-rural ecotone that should motivate future research into how such patterns and the possible underlying mechanisms play a role in the constraints of how native and invasive species exploit habitats in times of global change. We highlight how host phylogeny shapes microbiome differences between distantly related host species, while environmental factors such as co-occurrence and niche overlap across land use gradients are likely to converge microbiomes among closely related and sympatric species. We suggest that the ecological niche overlap between the native urban adapter Sundamys muelleri and the invasive Rattus rattus offer ecological opportunity that shape microbial associations, while even for generalist host species such as the cosmopolitan R. norvegicus , changes in environmental conditions encountered across fine-scale land use gradients can drive variation in microbiome composition and bacterial abundance. We found first evidence that such shifts in microbial associations across urban-rural ecotones can be of relevance for the abundance of zoonotic gastrointestinal pathogens associated with rodents, but future research is necessary to understand how shifts in microbial associations across land use gradients are linked to changes in habitat suitability, diet, and altered host health and whether plasticity and selective forces on gut microbiomes could limit the way native and invasive species are able to exploit novel environments. Acknowledgements We thank the Sabah Biodiversity Centre for research permits (JKM-MBS.1000-2/2(35), JKM-MBS.1000-2/2(63). The Sabah Parks research department team, Jorimia Molubi, Stella Schee, the late Brigitte Fiala, and K. Eduard Linsenmair provided technical and logistic support during field work. We thank landowners and citizens in Sabah for provided access to their properties or were in other ways helpful during field work. 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Unraveling differences in fecal microbiota stability in mammals: From high variable carnivores and consistently stable herbivores. Animal Microbiome. 3(1). Data accessibility The dataset (raw sequence reads) analysed during the current study is available in the European Nucleotide Archive (Accession number PRJEB81284) (awaiting data validation). R code for data analysis and display are available from the Zenodo repository: https://doi.org/10.5281/zenodo.14617337. Benefit-Sharing statement Benefits Generated: This research involved the local National Parks authorities (Sabah Parks), with the research motivated to collaboratively study the potential interaction of native and invasive small mammal species found in Borneo’s rainforests and anthropogenic environments. Benefits from this research accrue from findings, the sharing of our data and results in public databases and institutional capacity building. Author Contributions AG: designed research (equal); performed molecular analysis and bioinformatics (equal); performed formal data analysis (lead); wrote the paper – original draft (lead), review and editing (equal). MBL: performed field work (support); wrote the paper – review and editing (equal). FYYT: performed field work (support); wrote the paper – review and editing (equal); MH: performed molecular analysis (equal); wrote the paper – review and editing (equal). SC: Wrote the paper – review and editing (equal). TUW: designed research (equal); supervised the work (equal); supervised molecular analysis and bioinformatics; wrote the paper – review and editing (equal). KW: designed research (equal); supervised the work (equal); performed field work (lead); supervised formal analysis; wrote the paper – review and editing (equal). Figure 1 : Illustration of the occurrence/ co-occurrence patterns of the four focal small mammal species across the urban-rural gradient in Borneo. Dotted lines show the estimated relative occurrence probabilities of species across the underlying gradient of land use intensity (LUI), stacked bars show the number of individuals for which microbiomes have been analysed in this study matched to assigned LUI values. Bar colours match the colours of the dotted lines but are displayed with increased transparency. Figure 2 : Compositional plot of the bacterial families found in the fecal microbiomes of host individuals from four small mammal species studied in Borneo. Families with less than 2% relative abundance were clustered together as ‘Other’. Figure 3 : Relative influence of host phylogenetic relatedness, land use intensity (LUI) and spatial proximity on microbial community turnover within and among individuals from all four small mammal species (A, B, C for Bray Curtis and D, E, F for weighted UniFrac) and the three rat species only ( Sund. muelleri , R. rattus , R. norvegicus ) (G, H, I for Bray Curtis and J, K, L for weighted UniFrac). The effects are plotted as fitted I-splines from Generalized Dissimilarity Models exploring the relationship between Bray Curtis and weighted UniFrac beta diversity as response variables and the three predictor variables (host relatedness, LUI, spatial proximity). Spline height indicates the beta diversity variation explained by each predictor, while slope indicates the rate of change in microbiome assemblage along each predictor’s range. Splines are plotted for iterative model fitting to rarified data subsets (see methods). Figure 4 : A, B Non-metric multidimensional scaling (NMDS) plots of the faecal microbiome of the four studied species based on Bray-Curtis (A) and weighted UniFrac (B) dissimilarity metrics. Ellipses represent 95% confidence intervals. C, D Scattered plot of individual sample distance to host species centroid based on Bray-Curtis (C) and weighted UniFrac (D) plotted against underlying land use intensity (LUI) (0 = natural environment, 1 = urbanised environment); linear regression lines are shown for each host species. (Sample sizes: n = 55 for Sund. muelleri, n = 74 for R. rattus, n = 49 for R. norvegicus, n = 58 for Sunc. murinus). Figure 5 : Differentially abundant ASVs in relation to land use intensity (LUI) found in the four sympatric small mammal species. The log fold change represents the effect size of a one-unit increase in LUI on the abundance of the ASV as determined by compositional analysis with bias correction (ANCOM-BC2). ASVs are grouped and coloured by taxonomic assignment. Points represent the average log fold change and bars the 95% CI. Asterisks identify the differentially abundant ASVs that passed the sensitivity test for pseudo-count addition. Table 1 : Deviance explained by host phylogenetic relatedness, land use intensity (LUI), and spatial proximity for Bray Curtis and weighted UniFrac beta diversity calculated by generalised dissimilarity models fitted to all species (on the left) and rat species only (on the right). Predictor variable Bray Curtis Weighted Unifrac Bray Curtis Weighted Unifrac Host phylogenetic relatedness 34.68 [34.54 – 34.81] 35.20 [35.96 – 36.31] 10.25 [10.16 – 10.33] 0.56 [0.54 – 0.57] LUI 1.19 [1.17 – 1.21] 0.26 [0.24 – 0.28] 2.38 [2.33 – 2.42] 1.44 [1.36 – 1.51] Spatial proximity 0 0.19 [0.18 – 0.20] 0.07 [0.07 – 0.08] 0.81 [0.76 – 0.85] Host phylogenetic relatedness ꓵ LUI 0 0 1.34 [1.32 – 1.36] 0 Host phylogenetic relatedness ꓵ Spatial proximity 0 0 0.05 [0.04 – 0.05] 0 LUI ꓵ Spatial proximity 0 0.06 [0.06 – 0.06] 0.03 [0.03 – 0.03] 0.26 [0.25 – 0.27] Host phylogenetic relatedness ꓵ LUI ꓵ Spatial proximity 0 0 0.04 [0.04 – 0.04] 0 Unexplained 64.76 [64.60 – 64.88] 63.83 [63.69 – 64.04] 85.86 [85.74 – 85.95] 97.03 [96.94 – 97.10] Information & Authors Information Version history V1 Version 1 18 January 2025 Peer review timeline Published Molecular Ecology Version of Record 28 Apr 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Molecular Ecology Keywords bacterial gut microbiota invasive rats microbial community ecology native-invasive species interactions phylosymbiosis urban adaptation Authors Affiliations Alessandra Giacomini 0000-0001-9121-1660 [email protected] Swansea University Faculty of Science and Engineering View all articles by this author Maklarin Lakim Sabah Parks View all articles by this author Fred Tuh Sabah Parks View all articles by this author Matthew Hitchings Swansea University Faculty of Medicine Health and Life Science View all articles by this author Sofia Consuegra 0000-0003-4403-2509 Swansea University Faculty of Science and Engineering View all articles by this author Tamsyn Uren Webster 0000-0002-0072-9745 Swansea University Faculty of Science and Engineering View all articles by this author Konstans Wells 0000-0003-0377-2463 Swansea University Faculty of Science and Engineering View all articles by this author Metrics & Citations Metrics Article Usage 404 views 189 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Alessandra Giacomini, Maklarin Lakim, Fred Tuh, et al. 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