Host filtering and biogeography structure island bird gut microbiomes

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Karina Montero , Mark A.F. Gillingham , View ORCID Profile Mark Ravinet , View ORCID Profile Juan Carlos Illera doi: https://doi.org/10.1101/2025.08.20.671302 B. Karina Montero 1 Biodiversity Research Institute, Consejo Superior de Investigaciones Científicas (CSIC) and Oviedo University–Principality of Asturias, University of Oviedo , Campus of Mieres, Mieres E-33600, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for B. Karina Montero For correspondence: b.karina.montero{at}gmail.com jcillera{at}csic.es Mark A.F. Gillingham 1 Biodiversity Research Institute, Consejo Superior de Investigaciones Científicas (CSIC) and Oviedo University–Principality of Asturias, University of Oviedo , Campus of Mieres, Mieres E-33600, Spain 2 Department of Ornithology, Max Planck Institute for Biological Intelligence , Eberhard Gwinner Straße, 82319 Seewiesen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mark Ravinet 3 Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo , Oslo 0316, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mark Ravinet Juan Carlos Illera 1 Biodiversity Research Institute, Consejo Superior de Investigaciones Científicas (CSIC) and Oviedo University–Principality of Asturias, University of Oviedo , Campus of Mieres, Mieres E-33600, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Juan Carlos Illera For correspondence: b.karina.montero{at}gmail.com jcillera{at}csic.es Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Gut microbiomes are central to host ecology and evolution, yet the mechanisms driv-ing their diversification remain elusive, partly because host evolution and biogeography are often confounded. The recent radiation of chaffinches in Macaronesia, coupled with their broadly similar ecologies across islands, makes them an ideal replicated natural experiment to separate spatial from host-driven effects on microbiome structure. Us-ing long-read 16S rRNA gene sequencing, diet metabarcoding, and whole-genome host data, we show that gut microbiome diversity aligns with predictions of island biogeog-raphy theory rather than host colonization history, revealing that microbial dispersal limitation independent of the host is a dominant mechanism of community assembly. Geography and diet primarily explain bacterial presence–absence patterns, whereas host genetic differentiation and heterozygosity influence abundant, potentially resident taxa. Challenging previous assumptions about birds, we detect clear signals of phylosymbiosis (i.e. similar microbiome composition among closely related hosts), however phylogenetic reconciliation reveals these associations result from convergent host filtering of environ-mentally acquired bacteria, likely via shared diets or physiological traits, rather than de-tectable co-diversification. Our study reframes our understanding of avian host-microbe relationships, revealing that birds maintain host-specific microbiome associations within biogeographic constraints, but these arise from ecological filtering processes rather than co-evolutionary partnerships. Introduction The host microbiome underpins a wide range of biological functions, from improving nu-trient uptake ([ 1 – 3 ]) to modulating behaviour [ 4 , 5 ], profoundly impacting host health [ 6 – 8 ] and fitness [ 9 ]. These roles suggest microbiomes may facilitate adaptation to novel en-vironments [ 10 – 12 ], with composition reflected in proxies of habitat use, such as diet [ 13 ]. Over evolutionary timescales, reciprocal host-microbe interactions can generate patterns whereby microbiome composition mirrors host phylogeny [ 14 – 18 ], a phenomenon known as phylosymbiosis [ 19 ]. While phylosymbiosis may reflect co-diversification through mu-tual selective forces [ 20 ], it can also arise via environmental filtering, whereby local abiotic and biotic conditions favour certain microbial taxa, through shared ecological niches or through parallel selective pressures [ 21 – 23 ]. Alternatively, neutral processes, such as stochastic dispersal, the colonisation-extinction dynamics of island biogeography theory [ 24 ] and unified neutral models [ 25 , 26 ], can also structure microbiomes independently of host selection. Ultimately, stochasticity and host-mediated selection interact to shape mi-crobiome structure [ 27 , 28 ]; a central challenge is to disentangle the relative contributions of these mechanisms. We address this issue using a multi-genomic dataset from an avian host system that diversified in the North Atlantic Macaronesian archipelagos: the genus Fringilla . This passerine lineage colonised the region in two independent events, giving rise to two blue chaffinch and three common chaffinches species [ 29 – 32 ] ( Figure 1A ). Blue chaffinches arrived in the Canary Islands about 3 mya [ 33 ] and are now restricted to the pine forests of Tenerife and Gran Canaria. In contrast, Macaronesian common chaffinches diverged from their mainland counterparts ∼ 0.8 mya [ 34 ] and occupy 15 islands across the Azores, Download figure Open in new tab Figure 1: Overview of the study system and dataset A . Phylogenetic rela-tionships, geographic distributions, and genetic diversity patterns across Macaronesian finches (genus Fringilla ).We studied six species: four common chaffinches and two blue chaffinches. Three subspecies are currently recognised for F. canariensis , the common chaffinch in the Canary Islands [ 65 ]. Genetic diversity follows a gradient consistent with stepwise colonization: Azorean and mainland chaffinches maintain high genetic diversity and dispersal rates, F. maderensis and F. canariensis show intermediate reductions due to founder effects [ 31 ], while blue chaffinches exhibit the lowest diversity with no inter-island dispersal [ 66 ] (see Supplementary Table S3). B . Colonization route of common chaffinches across Macaronesia (black arrows) [ 31 ] and sampling locations. Numbers in brackets indicate island age (mya). Dot colors correspond to populations shown in A . The genus Fringilla is naturally absent from Fuerteventura and Lanzarote. C . Sample sizes for microbiome profiling (16S rRNA), diet metabarcoding (rbcL and COI mark-ers), and whole-genome sequencing (WGS). D–E . Non-metric multidimensional scaling (NMDS) ordination based on Bray-Curtis distances for ( D ) gut microbiomes (species-level or lowest taxonomic assignment) and ( E ) diet composition (family-level or lowest taxonomic assignment) from merged rbcL and COI marker data. F . Host population genetic structure from principal component analysis of WGS data. Inset shows PC1 vs. PC3. Madeira, and Canary archipelagos, where they primarily inhabit “Monteverde” forests and, to a lesser extent, mixed woodland habitats [ 35 ]. Notably, their colonisation followed a counter-intuitive route from the mainland to the remote Azores, then southward via stepping stone dispersal to Madeira and finally the Canary Islands [ 31 , 32 ] ( Figure 1B ). The recent radiation of chaffinches, combined with their ecological similarity across Macaronesia, offers a natural experiment for studying microbiome diversification mecha-nisms. Colonization of new environments, such as oceanic islands, often triggers adaptive radiations driven by ecological opportunity [ 36 – 40 ], leading to pronounced phenotypic divergence [ 41 – 46 ]. However, many island radiations exhibit minimal ecological niche disparity [ 47 ], a pattern known as non-adaptive radiation, which typically occurs un-der limited geographic overlap [ 48 , 49 ] and is mainly driven by neutral forces such as genetic drift [ 46 , 50 , 51 ]. Importantly, the limited niche disparity observed in insular non-adaptive radiations, as seen in Macaronesian finches [ 46 ], makes them particularly well suited for disentangling the relative contributions of spatial and evolutionary pro-cesses to diversification. To disassociate between spatial and host-related factors shaping microbiome diver-sity, we first assess whether gut microbiome diversity in Macaronesian finches mirrors the sequence of colonisation events, predicting that successive founder events would re-duce both host genetic and microbial diversity if dispersal is primarily host-mediated. Alternatively, if microbial dispersal occurs independently of host movement, patterns of microbiome diversity should align with the predictions of island biogeography theory [ 24 , 52 , 53 ], with island area, age, and isolation contributing to diversity irrespective of the hosts’ colonisation history. We then leverage host genomic, dietary, and microbiome data to evaluate the relative contribution of environmental filtering versus host-mediated selection in shaping micro-bial communities. We test whether microbiome composition is more strongly associated with diet, host phylogenetic relationships, or genomic differentiation, while controlling for geographic effects. We also examine host-microbe phylogenetic relationships at two lev-els: testing for general co-phylogenetic signal (where closely related hosts associate with closely related microbes) and phylogenetic congruence (matching branching patterns in-dicating true co-diversification) [ 54 , 55 ]. We predict that taxa under host selection, identified using neutral theory models [ 56 – 58 ], will exhibit stronger co-phylogenetic pat-terns than neutral taxa, and that phylogenetic congruence may be restricted to specific microbial lineages. The relatively recent divergence of Macaronesian chaffinches (∼ 0.8-3 mya) may limit our ability to detect co-diversification signals using the slow-evolving 16S rRNA marker. Nevertheless, this system offers an opportunity to address the ongoing debate over host genetic control of microbiome structure. This question has been widely investigated in mammals [ 14 – 17 , 59 – 61 ], but remains contentious in birds, where tran-sient microbial taxa are thought to dominate and host control is generally considered minimal [ 62 , 63 ]. By integrating host genomics, long-read 16S microbiome profiling, and fine-grained dietary data within a well-characterized island radiation, we disentangle spatial and evo-lutionary drivers of gut microbiome structure across replicated island systems. Our find-ings clarify mechanisms underlying phylosymbiosis patterns and advance understanding of how host filtering and neutral assembly processes shape avian microbiomes in natural populations. Results We sampled 9-28 birds per species or subspecies of Fringilla across Macaronesia and the Iberian mainland ( Figure 1B , Supplementary Table S1). From each bird, we collected faeces for microbiome taxonomic profiling using synthetic long-read (SLR) full-length se-quencing of the 16S rRNA gene and diet characterisation through rbcL and COI amplicon sequencing. We analysed bacterial features from phylum to species level, and to inves-tigate diversity at finer taxonomic scales, we used operational taxonomic units (OTUs) clustered at 99% similarity. Unless otherwise specified, we report results based on the finest taxonomic assignment available (hereafter species but see Supplementary Materials for results at higher taxonomic ranks and at the OTU level). Microbiome composition differed between finch species (Bray-Curtis PERMANOVA, R 2 = 0.12, p = 0.0001; Jaccard PERMANOVA, R 2 = 0.06, p = 0.0001) and popula-tions (Bray-Curtis: PERMANOVA, R 2 = 0.18, p = 0.0001; Jaccard: PERMANOVA, R 2 = 0.11, p = 0.0001) ( Figure 1D ; Supplementary Table S2). Neither age nor sex in-fluenced microbiome composition (all p > 0.09), allowing us to pool samples across these demographic categories for subsequent analyses. Similarly, diet composition varied sig-nificantly between finch species (Bray-Curtis PERMANOVA, R 2 = 0.14, p = 0.0001; Jaccard: PERMANOVA, R 2 = 0.11, p = 0.0001;) and populations (Bray-Curtis: PER-MANOVA, R 2 = 0.23, p = 0.0001; Jaccard: PERMANOVA, R 2 = 0.21, p = 0.0001; Figure 1E ). We conducted whole-genome-sequencing (WGS) on blood samples from a subset of individuals ( Figure 1C ), generating a dataset of ∼ 51.7 million genotyped SNPs. Genetic diversity of Macaronesian chaffinches exhibits a gradient consistent with founder effects during sequential island colonisation events (e.g. [ 64 ]). Blue chaffinches, in particular, show high levels of inbreeding and low genetic diversity ([ 30 ], Supplementary Table S1). Principal component analysis (PCA) of the WGS data recapitulated previously described patterns of population structure and clustering patterns among Macaronesian finches [ 31 ] ( Figure 1F ). Island biogeography but not colonisation history explains microbiome diver-sity We predicted that if gut microbes primarily disperse via their hosts, microbial richness should decline sequentially with each colonisation event, mirroring the founder effects observed in host genetic diversity. Under this scenario, microbial community composi-tion would also progressively diverge from the mainland source population [ 67 ]. Con-trary to these predictions, most island populations exhibited microbial richness similar or greater than mainland finches ( Figure 2A ). In fact, two of five populations of Canary Island chaffinches and both blue chaffinch species showed higher microbial richness than mainland populations ( Figure 2A ). Furthermore, dissimilarity in microbiome composition relative to the mainland was high across all populations ( Figure 2B ). To further test the influence of colonisation history on microbial communities, we partitioned beta diversity into nestedness (reflecting species loss where communities are subsets of richer sites), and turnover (species replacement along spatial or environmental gradients)[ 67 ]. If founder effects shaped microbial diversity through sequential colonisation, beta diversity should be primarily driven by nestedness [ 67 ]. Instead, turnover dominated among-island varia-tion (90.01% for Bray-Curtis, 84.74% for Jaccard; Figure 2C , Supplementary Figures S1 and S2), indicating that hosts acquire most gut microbes from local environments rather than through host-mediated dispersal or vertical transmission. Together, these findings indicate that neither microbial richness nor community composition follows the host’s sequential colonisation history. Download figure Open in new tab Figure 2: Microbiome diversity patterns according to colonisation history and biogeography A . Species-level microbial richness across populations of the common chaffinch complex and blue chaffinches. For the common chaffinch, richness estimates are displayed in the order of the colonisation route: mainland, Azores, Madeira, and Canary Islands. Asterisks indicate significance levels (*P < 0.05, **P < 0.01. B . Microbiome dis-similarity (Bray–Curtis index) between Macaronesian and mainland populations. Grey bars show mean values and 95% confidence intervals. C Ternary plots representing the additive components of beta diversity. The top plot shows results based on abundance data (Bray–Curtis), and the bottom on presence–absence data (Jaccard). Each black dot corresponds to a pair of common chaffinch individuals from different populations. Co-ordinates reflect the relative contributions of richness difference, replacement (turnover), and similarity in pairwise comparisons. Red dots at plot edges indicate the mean values of nestedness, replacement, and similarity; the central red dot marks the centroid. D–F . Relationships between microbiome richness and (D) island area, (E) distance to main-land, and (F) island age. We then disentangled evolutionary history from geography by examining sympatric species pairs on Gran Canaria and Tenerife. Blue chaffinches share evolutionary his-tory but occur on different islands, while each occurs in sympatry with the genetically divergent and reproductively isolated common chaffinches ( Figure 1 ). We found that individuals from different islands but the same species complex shared only 11.2% of microbial species, a comparable proportion to individuals from different islands and dif-ferent species complexes (11.4%). In contrast, individuals from the same island shared significantly more species regardless of host relatedness (13.1-13.3%, p-value < 0.05; Sup-plementary Figure S3), confirming the dominant role of geography over host evolutionary history in shaping microbial communities. To investigate the role of microbial dispersal limitation [ 52 , 53 ] on gut microbial diversity, we tested predictions derived from the equilibrium theory of island biogeogra-phy [ 24 ]. We found that chaffinches on older, larger islands harboured richer microbial communities than those on younger, smaller, and more isolated islands ( Figure 2 D-F , Supplementary Table S4). Additionally, microbiome dissimilarity increased significantly with geographic distance (Mantel tests: Bray-Curtis r = 0.16, Jaccard r = 0.37, both p < 0.001; Supplementary Table S5) supporting isolation-by-distance effects. The absence of a colonisation history effect combined with richness patterns aligning with island bio-geography theory provides strong evidence that host-independent dispersal limitation is a primary driver of microbiome structure in Macaronesian chaffinches. Diet composition influences gut microbiome structure Sampling occurred primarily outside the breeding season when plant material domi-nated chaffinch diets ( Figure 3A ). Blue chaffinches showed pronounced dietary specializa-tion ( Figure 3B-D ), feeding mainly on endemic Canary Island pine ( Pinus canariensis ), with Tenerife populations exhibiting the highest individual-level specialization. Common chaffinches displayed more generalized diets, ( Figure 3B and C ) except on El Hierro where individuals consumed primarily larvae of an endemic moth ( Episauris kiliani ) found in western Canary Island forests ( Figure 3C ). Diet richness showed no sex or age differences, and resembled mainland patterns in Azores and Madeira but was reduced across most Canary Island populations (Supplementary Figure S4A). Shannon diversity, accounting for both taxon number and relative abundance, showed no significant geographical dif-ferences (Supplementary Figure S4B). Download figure Open in new tab Figure 3: Chaffinch diet and its relationship with microbiome richness A . Rel-ative proportions of plant versus animal content in chaffinch diets across populations, determined by metabarcoding of rbcL (plants) and COI (animals) markers. B–C .Dot plots showing the relative abundance of the top 10% plant taxa (B) and animal taxa (C) in the diet. Dot size and colour intensity reflect taxon prevalence across populations. D . Individual diet specialization index (1-E) measuring among-individual dietary variation within populations, where 0 indicates complete diet overlap and 1 indicates each individ-ual has a unique diet. E–G . Relationship between diet richness (E) , Shannon diversity (F) , and individual diet specialisation (G) with microbiome richness across populations. We found that hosts with higher diet diversity (measured by Shannon diversity, but not richness) had richer gut microbiomes (Shannon GLMM: β = 0.091, p = 0.034; richness GLMM: β = 0.044, SE = 0.045, z-value = 0.98, p = 0.329; Figure 3E-F , Supplementary Table S6). Contrary to expectations, dietary specialization was associated with higher microbiome richness (GLMM, β = 0.193, p = 0.001, Supplementary Table S6). This counter-intuitive pattern may reflect antimicrobial compounds in diverse plant diets that limit microbial growth [ 68 – 70 ], whereas specialist diets, such as those rich in fibre (e.g. pine seeds), may provide resources supporting greater bacterial diversity [ 71 , 72 ]. Analysis of specific dietary items revealed that plant consumption had limited effects on microbial taxa, with only pine and geranium significantly altering the abundance of three and one bacterial taxa, respectively (Supplementary Figure S5). In contrast, arthro-pod consumption correlated with increased abundance of a broad range of bacterial taxa commonly associated with soil and plant material (e.g. Bradyrhizobium , Streptomyces , and Rahnella ) and with known arthropod pathogens (e.g. Aquirickettsiella and Serratia ; Supplementary Figure S5). Although plants dominated the diet, the stronger associa-tion between arthropod consumption and bacterial diversity suggests that arthropods contribute disproportionately to environmental microbial acquisition in finches. When considering presence-absence data, birds with a similar diet tended to have a more similar microbiome (Jaccard Mantel r = 0.29, p-value = 0.0001, Supplementary Table S7) a pattern that remained significant after controlling for geographic distance (Partial Mantel r = 0.15, p-value = 0.0001). However, abundance based similarity showed weaker correlation with diet (Bray-Curtis, Mantel r = 0.06, p-value = 0.016, Supplemen-tary Table S7) which disappeared when accounting for geography (Partial Mantel r = 0.005, p-value = 0.368), suggesting that dietary influences on microbiome composition are primarily qualitative rather than quantitative. Host genetic effects are stronger for abundant than rare microbial taxa Host phylogenetic distance significantly predicted microbiome similarity, with stronger effects on abundance-weighted community patterns (Bray-Curtis Mantel r = 0.18, p-value = 0.0001, Supplementary Table S8) than for absence-presence data (Jaccard Mantel r = 0.10, p-value = 0.04, Supplementary Table S8). After controlling for geography, the phylogenetic signal remained for abundance (partial Mantel r = 0.15, p-value = 0.0001) but not presence-absence data (partial Mantel r = 0.016, p-value = 0.38), suggesting that geographic effects primarily influence which taxa are present rather than their relative abundances. Using multiple regression on distance matrices (MRM), we assessed the joint effects of host diet, phylogeny, and geographic distance on microbiome dissimilarity. The explana-tory power and relative importance of these factors varied with the microbiome distance metric and taxonomic resolution ( Figure 4a ). This analysis revealed three key patterns. First, presence-absence based models explained more variation than abundance-based metric ( Figure 4a ), though substantial unexplained variation remained ( > 78%), likely reflecting high inter-individual variability characteristic of avian microbiomes with many transient taxa. Second, host phylogeny significantly predicted abundance patterns at finer taxonomic scales (family-OTU: p-value < 0.001; Figure 4a ) but not presence-absence data. Third, geographical distance and diet were dominant predictors of presence-absence but had less influence on abundance-based patterns. Population genetic differentiation ( F ST ) mirrored phylogenetic patterns, correlating with abundance-based microbiome dissimi-larity (family-species: partial Mantel r = 0.28 −0.44, p-value < 0.05) but not presence-absence data ( Figure 4b ). These contrasting results suggest that rare taxa respond pri-marily to environmental factors (geography, diet), while abundant taxa show stronger host genetic control. Download figure Open in new tab Figure 4: Host genetics impacts microbiome abundance but not occupancy pat-terns A . Variance partitioning of microbiome composition explained by host phylogeny, diet, and geography, based on multiple regression models (MRM) using two distance ma-trices: Bray–Curtis (left; abundance-based) and Jaccard (right; presence–absence). Euler diagrams display explained and unexplained variance for species-level data. B Correlation between population genetic divergence ( F ST ) and microbiome distance at the species level (top). The lower panel shows Mantel r coefficients and test p-values for the relationship across taxonomic ranks. C . Inbreeding effects on microbiome stability. Top: relationship between individual inbreeding coefficient ( f ) and among-individual microbiome diver-gence (species level). Bottom: GLMM effect sizes and p-values across taxonomic ranks. Finally, we tested whether reduced host genetic diversity influences microbiome struc-ture. Birds with lower heterozygosity showed greater among-individual microbiome vari-ation, but again only for abundance-based metrics ( Figure 4c ). This suggests that in-breeding primarily weakens host control over microbial abundance rather than having an effect on prevalence. Evidence of co-phylogeny but limited phylogenetic congruence Building on our observation of phylosymbiosis, we then investigated the nature of host-microbe phylogenetic relationships by differentiating between co-phylogenetic signal and phylogenetic congruence. Co-phylogenetic signal denotes a general association where closely related hosts tend to associate with closely related microbial lineages [ 54 , 55 , 73 ]. Our earlier analyses, including Mantel tests, supported the presence of such co-phylogenetic signals. However, detecting this signal alone does not imply that host and microbe phylogenies mirror each other. Phylogenetic congruence, characterized by a close matching of branching patterns between host and symbiont phylogenies, is the pattern expected from true co-diversification events [ 54 , 55 ]. To assess this more stringent pattern, we utilized event-based reconciliation methods. Specifically, we tested whether microbial taxa inferred to be under host selection exhibit stronger co-phylogenetic patterns than neutral taxa (see below), and whether phylogenetic congruence is restricted to particular microbial lineages, as has been shown in primates and humans [ 15 , 74 ]. We first distinguished neutral and non-neutral (putatively host-selected) microbial taxa by fitting the prokaryote neutral model (PNM) [ 56 – 58 ] across taxonomic levels. Model fits (goodness-of-fit, R2) were poor at the OTU level (R2 = −0.18; Supplementary Figure S6) but improved at coarser ranks (R2 = 0.31–0.79, species to phylum; Figure 5A ), consistent with most fine-scale microbial variants being unique to individual hosts. These results suggest limited community equilibrium at fine taxonomic resolution and indicate that short-read data, which effectively collapse taxa at coarser taxonomic levels, may overestimate the proportion of the microbiome structured by neutral processes. However, resolving this conclusively will require broader, longitudinal studies across host clades [ 75 ]. Download figure Open in new tab Figure 5: Evidence of host-microbiome co-phylogeny but not of co-diversification A . Phylogenetic relationships of prevalent microbial families found in at least two individuals per population. Tip symbols and branch colors denote core families present on all islands. Symbols indicate whether each family’s abundance de-viates above (upward triangle), below (downward triangle), or fits neutral expectations according to the procaryote neutral model (PNM) at the family level. The inner ring displays phylum assignments, and the outer ring shows prevalence of families across all samples. The inset shows the PNM model fit at the family level. B . PACo goodness of fit estimates ( R 2 ) for 27 core families with at least five microbial species (details in Meth-ods). Bold family labels mark significant co-phylogeny signals after Bonferroni correction (p < 0.05). C-E . Phylogenies of microbial lineages with significant PACo signals. Tip symbols indicate host populations. The inner and outer strips correspond to archipelago and species complex origins, respectively. Using PACo (Procrustean Approach to Cophylogeny) [ 76 , 77 ], we quantified co-phylogeny between hosts and microbes in both global and neutrality-partitioned datasets. At low taxonomic resolution (OTU–species), PACo detected weak but statistically sig-nificant associations for both empirical and randomized host trees (e.g., OTU: R2 = 0.07, p = 0.003, Supplementary Table S9), indicating potential false positives and sug-gesting that this approach is not reliable to assess co-phylogeny signals when the ratio of host–symbiont links is high, as observed in other systems [ 78 , 79 ]. In contrast, at higher-level bacterial groupings (genus and family), associations with randomized trees disappeared, and only weak signals of co-phylogeny remained for empirical host trees (e.g., R2 = 0.07–0.12, Supplementary Table S9). This pattern indicates that PACo re-sults become more robust at coarser taxonomic resolutions, (e.g. species level), where randomized tree tests do not yield significant signals. Moreover, the observed weak as-sociations (R2 0.25) and congruent branching patterns [ 54 ]. To investigate whether stronger patterns exist among particular microbial groups, we assessed co-phylogeny at the species level (where PACo results are robust; see pre-vious paragraph) for core bacterial families found across all populations ( Figure 5 A ). Among 27 core bacterial families, only three showed significant co-phylogenetic signals (p < 0.05): Burkholderiaceae (R2 = 0.45), Lactobacillaceae (R2 = 0.30), and Microbacte-riaceae (R2 = 0.15). To test whether these signals reflect phylogenetic congruence rather than mere co-phylogenetic association [ 54 , 55 ], we used eMPRess, an event-based phy-logenetic reconciliation method [ 80 ]. Results showed high uncertainty ( > 1000 equally parsimonious solutions) with no evidence of phylogenetic congruence across cost param-eters (co-speciation/transfer ratio > 1, p > 0.05; Supplementary Table S10), suggesting symbiont duplication and host switching dominate over co-speciation events. Direct comparison of host and microbe trees for PACo-significant families revealed no consistent congruence corresponding to host species splits or colonization history ( Figures 5C–E ). Instead, symbiont diversification within these families corresponded primarily to island geography and dispersal limitation. For example, Burkholderiaceae diversified pre-dominantly within the Canary Islands, spanning both chaffinch and blue chaffinch com-plexes. This is consistent with incomplete host-switching or host filtering by conserved traits, rather than co-diversification. Lactobacillaceae diversification occurred mainly within chaffinches and showed no clear link to host phylogeny. Microbacteriaceae showed weak cophylogenetic structure and included many taxa shared across host species, sev-eral of which were linked to dietary items (Supplementary Fig. S4), further supporting environmental acquisition. Discussion The non-adaptive radiation of chaffinch species in Macaronesia provided a natural ex-periment to test competing hypotheses of microbiome diversification that have proven challenging to disentangle [ 22 , 23 ]. Here, we present pioneering evidence that host-microbiomes display diversity patterns consistent with island biogeography theory [ 24 , 53 , 81 ]. These results demonstrate that in isolated host populations, microbial disper-sal limitation significantly shapes gut microbiomes independently of host colonisation history. Our results reveal a fundamental distinction in how environmental versus host factors influence different components of the microbiome. Strong associations between geogra-phy and gut microbiome structure, and to a lesser extent diet, were most pronounced in presence-absence (Jaccard) analyses, which are particularly sensitive to the detec-tion of rare taxa. In contrast, abundance-based metrics (Bray-Curtis) highlighted more subtle, yet consistent, effects of host genetics. This suggests that presence-absence pat-terns capture variation of the environmentally acquired, transient fraction of the mi-crobiome, whereas abundance-weighted analyses emphasize the established, potentially resident “core” gut microbiota [ 23 ]. Thus, fully understanding the interplay between host genetics and environmental drivers requires careful distinction between transient and stable members of the community [ 28 , 75 ]. Presence–absence–based models explained more variance than abundance-weighted models ( Fig. 4a ), yet a substantial proportion of variation remained unexplained ( > 78%), consistent with the high inter-individual variability typical of avian gut microbiomes, which often harbour large numbers of transient microbial taxa [ 16 , 82 , 83 ]. Additional unmeasured factors, such as temporal shifts in community composition across breeding seasons or years, may also contribute to the residual variance; in this study, however, year and season were confounded with island and could not be tested independently. Such unexplained variance likely reflects a combination of individual-level stochasticity, the rapid turnover of transient taxa, and any potential temporal dynamics [ 82 – 86 ]. Population genetic analyses reinforced this pattern: both host genetic differentiation ( F ST ) and heterozygosity correlated positively with abundance-based microbiome dissim-ilarity but showed no relationship with presence-absence patterns. Inbred finches, in par-ticular vulnerable blue chaffinch populations with characteristically low genetic diversity, exhibited greater among-individual microbiome variation, but only for abundance-based metrics. These results extend findings from experimental systems like Drosophila [ 87 ] to an avian system, and suggests that reduced heterozygosity compromises host control over microbial abundance and stability, likely through effects on immune gene diversity [ 88 – 92 ] and gut microbiome regulation [ 75 , 93 ], rather than affecting which taxa can initially colonize the gut. Our study also addresses the longstanding assumption that birds lack strong host-microbe associations due to adaptations linked to flight [ 16 , 62 , 63 , 94 ]. Contrary to these predictions, we detect signals of phylosymbiosis in Macaronesian finches independent of geography and diet, and that primarily emerge in abundance-based microbiome patterns. These findings align with recent evidence for pervasive co-phylogenetic signals in birds [ 18 ] and suggest that methodological limitations, including short-read sequencing with limited taxonomic resolution, coarse dietary characterization, and difficulty controlling confounding factors, may have obscured host genetic effects in previous studies. Finally, our results reinforce the notion that co-phylogenetic signals do not necessar-ily imply co-diversification [ 22 , 23 , 54 ]. While we detected significant co-phylogenetic patterns among select bacterial lineages, event-based analyses found no support for phy-logenetic congruence, and host-microbe phylogenies lacked matching cladogenesis. One plausible explanation is the temporal mismatch resulting from the relative recent diver-gence of Macaronesian chaffinches (0.8-3 mya) [ 31 ] together with the slow evolutionary rate of the 16S rRNA gene (∼ 1-2% per million years) [ 95 , 96 ]. The weak co-phylogenetic signals we observed and high uncertainty in phylogenetic reconciliation are consistent with either ecological filtering of environmentally-acquired microbes through conserved host traits such as gut physiology and dietary preferences or undetectable recent co-diversification. Future metagenomic studies with higher resolution may be able to clarify whether these patterns are indeed masked by marker limitations [ 15 , 74 , 96 ]. Nonetheless, given our observed weak co-phylogenetic signals and stronger influence of biogeography over host phylogeny, we expect that even with improved resolution, only a restricted number of lineages, if any, would show true co-diversification patterns. In conclusion, our findings challenge the assumption that host genetic control plays a minimal role in shaping gut microbiome associations in birds. Instead, we show that island birds maintain host-specific microbiomes within biogeographic constraints that primarily arise via ecological filtering processes. Methods Sample collection We captured birds between 2018 and 2021 using 2–4 mist nets (12 m each) aided by song playbacks. Individuals were marked with unique numbered metal rings authorized by Spanish or Portuguese authorities. We determined sex and age based on plumage coloration and molt patterns [ 97 ], with a few birds sexed molecularly [ 98 ]. To collect fecal samples, we placed each bird in a clean ringing bag for up to 10 minutes, transferring fecal material with sterile swabs into screw-cap microtubes containing 1 ml of NAP buffer [ 99 ]. All ringing bags were bleach-cleaned before field use. We collected approximately 50 µl of blood from the brachial vein, preserved in absolute ethanol at room temperature until transfer –25 °C at IMIB facilities. Microbial library preparation and processing We isolated microbial DNA in a cleanroom at IMIB’s Ecological Molecular Laboratory (University of Oviedo, Mieres). Libraries were generated using LoopSeq kits (Loop Ge-nomics) and sequenced by LoopGenomics on the NovaSeq S4 platform. Synthetic long reads obtained from five sequencing runs totaled 18,237,257 reads, which we processed with the DADA2 (v1.32.0) pipeline [ 100 ] for long-read data in R v4.4.2 [ 101 ]. None of the PCR and extraction blanks (n = 12) yielded long-read sequences. After primer removal, we applied quality filters (maxEE = 1, truncQ = 8, maxN = 0, minQ = 3) and trimmed sequences to lengths between 1400 and 1600 bp. We per-formed denoising separately for each run using run-specific error models optimized for NovaSeq data ( https://github.com/benjjneb/dada2/issues/1307 ) and the pooled option in DADA2. We merged abundance tables for chimera removal and assigned taxonomy to amplicon sequence variants (ASVs) using the RDP Naive Bayesian Classifier [ 102 ] against the GTDB v4.4 database ( https://zenodo.org/records/10403693 ). We excluded non-bacterial sequences and filtered ASVs with fewer than 10 total reads across all sam-ples. To standardize sequencing depth, we rarefied samples to 2000 sequences (threshold chosen based on rarefaction curves) using the mirl function [ 103 ] over 1000 iterations, excluding samples below this threshold. Long-read 16S sequencing assigned 36.22% and 77.23% of sequences to species and genus levels respectively, with 99.96% reaching at least family-level classification. To analyze diversity below genus level, we clustered ASVs into operational taxonomic units (OTUs) at 99% similarity, yielding 14,772 OTUs (1890 with species-level and 1412 with genus-level assignments). We constructed a phylogenetic tree of OTU sequences using the align-to-tree-mafft-iqtree pipeline in QIIME2 [ 104 ] with a maximum gap frequency of 0.9 and optimal substi-tution model via IQ-TREE [ 105 ]. Subsequent analyses used phyloseq [ 106 ] for alpha diver-sity (richness) estimation and adespatial [ 107 ] for beta diversity using presence–absence (Jaccard) and abundance (Bray-Curtis) distance matrices. Diet metabarcoding library preparation and processing We characterized finch diets using DNA metabarcoding of the same fecal samples with two marker genes: rbcL for plant material (primers rbcL1 f: “TTGGCAGCATTYCGAG-TAACTCC”, rbcLB r: “AACCYTCTTCAAAAAGGTC” [ 108 ]) and COI for arthropod material (primers Lep f: “ATTCAACCAATCATAAAGATATTGG” [ 109 ], ZBJ-ArtR2c-deg r: “WACTAATCAATTWCCAAAHCCHCC” [ 110 ]). Library preparation and se-quencing were performed by AllGenetics & Biology SL using a two-step PCR protocol. First, triplicate PCRs were conducted for each marker per sample, then PCR products were mixed and a second amplification step attached indices. Equimolar libraries were pooled and sequenced on a NovaSeq PE250 platform. Raw data were demultiplexed by region using cutadapt [ 111 ] and separately for each marker using the DADA2 workflow [ 100 ]. We trimmed primers, filtered low-quality reads (minLen = 175, maxLen = 184, maxN = 0, maxEE = c(2,2), truncQ = 2), and performed denoising. For taxonomy assignment, we built custom reference databases for rbcL and COI markers using the DB4Q2 pipeline [ 112 ], which generates curated baselines imported into QIIME2 to create RDP-trained classifier files. We clustered ASVs into 99% similarity OTUs and binned data to the lowest taxonomic level (73.8% and 87.4% of OTUs reached genus and family levels, respectively). After merging rbcL and COI abundance tables, we rarefied samples to 1000 sequences per sample using the mirl function [ 103 ] over 1000 iterations, excluding samples below this sequencing depth. Diet diversity metrics We calculated diet richness and evenness (Shannon index) as measures of dietary diversity. For each population, we quantified individual specialization using the E index [ 113 , 114 ] with the RInSp package [ 115 ]. The E index measures among-individual diet differences, ranging from 0 (complete diet overlap) to 1 (each individual has a unique diet) [ 113 , 114 ]. Whole-genome sequencing and population genomics We extracted DNA from blood samples using the ammonium acetate method ( https://dx.doi.org/10.17504/protocols.io.knycvfw ) and conducted library preparation to a mean depth of 11.7x (range: 9.4 −16.9X) using 150-bp paired-end reads on a NovaSeq 6000 platform (Illumina). We processed raw reads using a custom nextflow pipeline consisting of three main steps ( https://github.com/EcoEvoGenomics/genotyping_pipeline/tree/main ). First,we mapped adapter-trimmed reads to the Fringilla coelebs v2 [ 34 ] eference genome using bwa-mem and flagged duplicates with picard MarkDuplicates ( http://broadinstitute.github.io/picard/ ). Second, we generated 10 Mb windows across genomes for paral-lelized genotyping and variant calling using bcftools mpileup, call, norm, and concat tools. Finally, we filtered the resulting VCF files using vcftools (v0.1.16) to remove genotypes with quality 16X, and sites with > 50% missing data. We excluded one individual with call rates < 90%, yielding a final dataset of 51,694,530 autosomal SNPs. We estimated genome-wide differentiation ( F ST ) between populations using a filtered biallelic dataset of 5,060,316 SNPs (minor allele count ≥ 3) with vcftools weir-fst-pop function. Principal component analysis was performed using PLINK2 (v 2.00a2.3) [ 116 ]. For phylogenetic inference, we generated mitochondrial consensus genomes with bcftools (v1.19; call -m) under haploid ploidy settings and extracted fasta sequences with sam-tools (v1.19). Consensus sequences were concatenated, aligned in MAFFT (v7.49, 1000 iterations), and used to reconstruct maximum likelihood trees in IQ-TREE (v2.1.1) [ 105 ] with optimal site model selection and 1000 ultrafast bootstraps [ 117 ]. Statistical analysis Community composition and diversity patterns We characterized microbiome and diet composition using Bray-Curtis (abundance-weighted) and Jaccard (presence-absence) indices. Population and species-level differ-ences were evaluated using PERMANOVA [ 118 ] with age and sex as covariates. Tem-poral effects (sampling year and breeding season) could not be included as covariates because these factors were completely confounded with island of origin: each island was sampled during a single field season, and logistical constraints prevented repeated annual or seasonal sampling within islands. Consequently, variation associated with year/season is inseparable from island effects in our dataset. To understand ecological processes underlying microbiome compositional differences, we partitioned beta diversity into replacement (turnover) and richness difference (nest-edness) components [ 67 , 119 – 121 ] using the beta.div.comp function [ 107 ]. We applied this partitioning to both abundance data (Bray-Curtis-derived percentage difference) and presence-absence data (Jaccard index) from the Baselga family of indices. Island biogeography analyses We tested predictions of island biogeography theory [ 81 ] using generalized linear mixed models (GLMMs) with microbiome richness as the response variable, island area (km2), distance from mainland, and island age (mya) as predictors, and population as a random factor. Models were fitted using glmmTMB [ 122 ] with negative binomial distribution. We assessed whether individuals from the same species complex (i.e. common vs blue chaffinces) shared more microbial taxa than those occupying the same island by focusing on sympatric populations in Gran Canaria and Tenerife. We calculated shared species proportions using the Sørensen similarity index and evaluated differences with two-sided permutation t-tests (Bonferroni-corrected p-values). Geographic distance effects on microbiome dissimilarity were evaluated using Mantel tests (9999 permutations) [ 118 ] with pairwise geographic distances calculated using the geosphere package [ 123 ]. Diet-microbiome relationships We explored relationships between diet richness, specialization, and microbiome di-versity using GLMMs with negative binomial distribution and population as a random factor. To examine effects of specific dietary items on microbial taxa abundance, we used ANCOM-BC (Analysis of Composition of Microbiomes with Bias Correction) [ 124 ]) focusing on the top 10% most common dietary items with high taxonomic resolution. ANCOM-BC was run with default parameters using a prevalence threshold of 0.1, with dietary item relative abundance and population as predictors. Host genetic effects We assessed relationships between host phylogenetic distance (calculated from mi-tochondrial trees using cophenetic.phylo [ 125 ].), genetic differentiation ( F ST ), and mi-crobiome dissimilarity using Mantel tests and partial Mantel tests [ 118 ] controlling for geographic distance. Multiple regression on distance matrices (MRM) [ 126 ] and variance partitioning [ 127 ] estimated variation explained uniquely and jointly by host phylogeny, diet, and geographic distance. To test relationships between individual heterozygosity (inbreeding coefficient F) and microbiome divergence, we used GLMMs [ 128 ]) with Gaussian distribution and popula-tion as a random effect. Neutral community assembly We applied the Prokaryote Neutral Model (PNM) as done in Burns et. al. [ 129 ] to distinguish taxa following neutral expectations from those potentially under host selection using the fit sncm() function in the tyRa package. The PNM uses abundance-occupancy distributions under the null hypothesis of unlimited dispersal and equal fitness [ 56 – 58 ], identifying taxa above (host-selected) or below (dispersal-limited or selected against) neutral expectations. Co-phylogeny and phylogenetic congruence analyses We tested for host-microbiome co-phylogenetic signals using PACo (Procrustes Ap-proach to Cophylogeny) [ 76 , 77 ]) applying it to paired individual host mitochondrial and microbial OTU-family phylogenies and presence-absence abundance matrices (Cailliez-corrected), with quasiswap null models and 9999 permutations. We compared global and neutrality-partitioned datasets by residual sum of squares goodness-of-fit. For core microbial families (present on all islands and populations, prevalence ≥ 2 individuals per population), PACo was run separately on OTU and species-level clusters. We repeated analyses using random host trees to test the robustness of PACo to false positive co-phylogeny results. Significant co-phylogeny signals were Bonferroni-corrected. To discern phylogenetic congruence from co-phylogeny [ 54 ], we performed event-based reconciliation analyses with eMPRess [ 80 ], subsampling one host population per microbial variant and testing various cost parameters. Phylogenetic congruence was supported if reconciliations showed significantly low costs and co-speciation/transfer event ratios above one [ 54 ]. Data availability All fastq files have been deposited in ENA sequence read archive under the accession number ERP150413. All the scripts and data for performing the analyses are available through the following link: https://github.com/bkmontero/ChaffinchMicrobiome Ethics statement All fieldwork and specimen sampling were conducted in compliance with institutional an-imal care and use policies. Capture and ringing of birds were authorized by the Regional Government of the Canary Islands (Exp. 2019/11937 and 2020/16669), the Regional Government of the Azores (Licença N° 5/2021/DRAAC), the Regional Government of Madeira (N° 04/IFCN/2020), and the Principality of Asturias (DECO/2021/19003). Ad-ditional permissions to conduct research within protected areas were obtained from the Cabildo of Gran Canaria (Exp. FA 16/2020), La Palma (Ref: A/EST-012/2019), Tener-ife (Exp. AFF 141/19), and the National Park of Garajonay (Exp. 190.673, PTSS/2.206 and 3.129). Ethical approval for blood sampling was granted by the Regional Government of Asturias (PROAE 45/2019). Author contributions B.K.M., M.G., and J.C.I. conceived and designed the study. J.C.I. coordinated field-work and collected samples. B.K.M. and M.R. performed whole-genome data analy-ses. B.K.M., M.G., and J.C.I. performed DNA extractions. B.K.M. and M.G. con-ducted metabarcoding data processing and statistical analyses. B.K.M. wrote the first manuscript draft with input from M.G. All authors discussed the results and contributed to the final manuscript. Conflict of interest The authors declare no conflicts of interest. Acknowledgements We are very grateful for helpful discussions and feedback provided by Hanna Prüter, Bart Kampenaers, and Sarah Worsley. Sara Alberdi assisted with DNA extraction with the modern samples. Elena Nicolás, Eduardo González, Félix Medina, Ángel C. Moreno, Felipe Rodríguez, David P. Padilla, Domingo Trujillo, Alejandro Delgado, and Tiago Ro-drigues contributed to the fieldwork. Francisco Camacho provided faecal samples from the Iberian Peninsula. Abelardo Margolles provided access to the high-speed benchtop homogenizer (FastPrep) at IPLA-CSIC. We are grateful to CESGA (Centro de Super-computación de Galicia) for providing the computational resources used to perform the genomic analyses. This project was funded by two research grants from the Spanish Min-istry of Science, Innovation and Universities and the European Regional Development Fund (PGC2018-097575-B-I00; PID2022-140091NB-I00) and a EECG research award granted to B.K.M. and M.A.F.G by the American Genetic Association. Funder Information Declared Consejo Superior de Investigaciones Científicas, https://ror.org/02gfc7t72 , PGC2018-097575-B-I00 Footnotes mark.gillingham{at}bi.mpg.de , mark.ravinet{at}ibv.uio.no References 1. ↵ Sommer , F. et al. The Gut Microbiota Modulates Energy Metabolism in the Hi-bernating Brown Bear Ursus arctos . Cell Reports 14 , 1655 – 1661 ( 2016 ). OpenUrl PubMed 2. Semova , I. et al. Microbiota Regulate Intestinal Absorption and Metabolism of Fatty Acids in the Zebrafish . Cell Host & Microbe 12 , 277 – 288 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 3. ↵ Pope , P. B. et al. 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