Metal-induced chances in the early-life gut microbial composition and life-history traits in three insectivorous passerines

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Rainio, Lyydia Leino, Eero Vesterinen, Pablo Sánchez Virosta, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6981542/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Nov, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract Pollution-induced changes in gut microbiota may have serious effects on avian health and physiology. Particularly, early-life exposure to environmental pollutants may influence the gut microbiota development and microbiota-dependent physiological functions. We studied the associations of metal pollution with early-life microbial environment of wild passerines by exploring gut bacterial diversity and community composition in nestlings by 16S rRNA sequencing. Samples were collected from three common model species, great tits ( Parus major ), blue tits ( Cyanistes caeruleus ), and pied flycatchers ( Ficedula hypoleuca ) breeding near a copper-nickel smelter, an area with the highest rates of metal pollution in Finland. Responses of microbiota to pollution and microclimate were measured and connected to ecological measures (brood size, growth, fledging success) of nestling performance. The most abundant bacterial phyla across species were Firmicutes, Proteobacteria and Actinobacteria. Our results showed that the faecal microbial alpha diversity decreased with increasing brood size in great tits and blue tits, while in pied flycatchers the diversity increased with increasing nestling growth. However, alpha diversity did not differ between the polluted and control areas. The faecal microbial communities (beta diversity) differed between the species and study areas, and several bacterial orders were associated with metal concentrations, temperature, body mass, or fledging success. Differential abundance analyses showed species-specific differences in microbial abundances between polluted and control areas, e.g. Catellicoccus and Serratia showing elevated abundance within the polluted area. The results suggest that metal pollution may alter the early-life bacterial communities in small passerine species living in metal-polluted areas. blue tit microbial diversity great tit gut bacteria pollution pied flycatcher Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Microbes play a significant role in terrestrial and aquatic ecosystems by acting as plant, animal, and human pathogens, but several microbes have beneficial and protective functions as well (Kohl 2012 , Moreno et al. 2003 ). The balance in microbial communities is important, since they can help us restore natural ecosystems and maintain the well-being of their hosts (Angulo et al. 2019 ). Significance of microbiota to vertebrate health and fitness is an emerging topic in ecology, but the research has mostly focused on mammals. So far, relatively little is known about the factors shaping the establishment and dynamics of microbial communities in wild bird species and the role of environmental pollutants in the development of early-life microbial flora. The colonization of nestlings by environmental microbes begins soon after hatching (Lucas &Heeb 2005 ) via ingestion of adult saliva, food items or nest materials (Berger et al. 2003 , Kyle &Kyle 1993 , Mills et al. 1999 , Singleton &Harper 1998 ). Therefore, pollution-exposed nest material and food items likely shape the bacterial composition of the nests, potentially affecting the balance between beneficial and harmful bacteria. This may have important repercussions for the development of the chicks and the subsequent fitness of the individuals. Anthropogenic pollution is one potential factor affecting the microbial communities in animals, causing various health disorders and dysregulation of the immune system of animals and affecting negatively to the function of gut microbiota (Arun et al. 2021 , Claus et al. 2016 , Duan et al. 2020 , Li et al. 2019 ). However, it is unclear how gut microbiota and environmental pollution interact and whether these interactions are related to animal health. Various environmental contaminants have been shown to alter the composition and metabolic activities of the gut bacteria, thus possibly shaping the host microbiota (Claus et al. 2016 ). There is also evidence that bacteria-dependent metabolism of toxic compounds modulates the toxicity for the host (Claus et al. 2016 ). Changes in microbial composition may therefore disrupt the detoxification processes of normal microbial flora against pollution, thus affecting the physiology (e.g. stress responses), health and fitness of wild animals. Metals are common and widespread anthropogenic pollutants and well known for their antimicrobial activity (Lemire et al. 2013 ). Metal toxicity affects microbial population size, diversity and activity (Ayangbenro &Babalola 2017 , Zhang et al. 2023 ). On the other hand, certain metals (including iron, cobalt, nickel, copper and zinc) are essential for the life of most organisms (Frei et al., 2023 ). Non-absorbed metals can remain in the gut microenvironment, causing direct effects on the gut microbiota and overall physiology of a developing individual (Breton et al. 2013 ). Therefore, it is important to understand the mechanisms of non-absorbed metals on complex host-microbe interactions and the risk levels of metals affecting the host species via microbiota. Gut microbiota is a diverse ecosystem comprised of bacteria, archaea, fungi and viruses and provides crucial physiological functions that host organisms cannot develop by themselves (Kohl 2012 ). The microbiota has evolved with the host as a mutualistic partner, but microbial imbalance may promote the development of metabolic disorders in host species along with environmental factors (Kogut 2013 ). Therefore, the presence or absence of specific microbial species can be essential for maintaining the balance both inside and outside the intestinal tract (Breton et al. 2013 ). Diet is one of the key factors modulating the composition of gut microbiota, both between and within species (Bodawatta et al. 2022b , Glunder 2002 ). For example, many insectivorous species feed their nestlings with caterpillars, but the actual prey species and their accumulation rate of contaminants differ according to environment, which may affect the bacterial communities of the host (Goodenough &Stallwood 2010 ). Gut microbes affect nutrition intake, immunology, development and detoxification processes (Kohl 2012 ), demonstrating the importance of microbes for the physiological functions. Furthermore, gut microbes are known to correlate with phenotypic conditions of nestlings, such as wing asymmetry (Mills et al. 1999 ), nestling size, and body condition (Moreno et al. 2003 ), indicating a significant role of microbial composition in avian development. Bacteria belonging to phyla Proteobacteria, Firmicutes and Actinobacteria typically dominate avian gut bacterial composition (Bodawatta et al. 2022a , Grond et al. 2018 , Kropáčková et al. 2017 , Liukkonen et al. 2023 ), and the most common cloacal bacteria genera include Enterococcus , Escherichia , and Enterobacter . Enterococci , for example, are part of normal bacteria flora in wild birds, some species being opportunistic pathogens, whereas some are beneficial, acting as growth promoters and components of probiotics in birds (Moreno et al. 2003 ). Even though avian microbiota has broad enzymatic capacity to metabolize several environmental contaminants, pollutants may still disrupt the function of essential microbes with detrimental consequences, especially during early life, which is the most sensitive time for nestlings. In this study, we examine the association of metal pollution on early-life avian gut microbiota to understand the interconnection between pollution and microbiota in wild passerine birds and further to reveal whether changes in microbiota are related to the performance of nestlings. Earlier studies of the pollution effects on wild birds have strongly pointed out the importance of indirect effects on physiology and individual performance (Koivula et al. 2011 ). One yet poorly known secondary effect can be metal-related change in the gut microbiota, but studies related to this topic are limited. Thus, the results of this study will bring a new perspective on the generally inferior nestling growth and lower nestling production, which have been repeatedly observed in bird populations of urban and industrial environments (Eeva et al., unpublished), even when direct toxic effects on physiology and performance have not been observed. In this article, we address the following questions: 1) Does metal pollution affect the species diversity and richness (alpha diversity) and microbial community composition (beta diversity) of nestlings’ gut microbiota? Based on earlier studies, metal pollution is known to alter the diversity and metabolic activities of the microbiota (Liu et al. 2021 , Zhang et al. 2023 ), thus, we expect to see differences in microbial composition and diversity in the gut microbiota between the polluted and control areas. 2) Is there interspecific variation in nestling gut microbiota in relation to metal pollution? Our three study species differ from each other in their phenology, diet, nest material composition and tolerance to pollution exposure, thus we expect to see species-specific differences in their microbiota and in the microbiota-mediated responses. 3) Do gut microbial taxa vary in abundance in relation to faecal metal levels? Some bacteria can tolerate or be resistant to certain metals, thus potentially varying in abundance between the polluted and clean areas. 2. Materials and Methods 2.1. Study area and study species The fieldwork was conducted in an established nest box study area close to a copper-nickel smelter in Harjavalta (61°20´ N, 22°10´E), an area with the highest rates of metal pollution in Finland. A long-term nest box scheme has been running in this area since 1991 and there is ample information on metal exposure levels (e.g. in bird faeces, Berglund et al. 2015 ), genetic and physiological effects (Koivula et al. 2011 ), and fitness effects (Eeva & Lehikoinen, 1996 ) on birds from this area. Nine study sites, each with 20–60 nest boxes, were divided to polluted (4 sites 5 km from the smelter) areas, based on the previous data showing that the metal concentrations decrease exponentially with increasing distance to the pollution source (Koivula et al. 2011 ). The habitat type in all study sites was relatively barren forest dominated by Scots pine ( Pinus sylvestris ), thus minimizing habitat-related variation between the areas. However, vegetation near the smelter has suffered from the long-term pollution and the ground layer vegetation cover is patchy at more heavily polluted locations (Kiikkilä, 2003 ). All nest boxes were carefully cleaned from old nest materials before the breeding season. Three insectivorous cavity-breeding passerine birds, the great tit ( Parus major ), blue tit ( Cyanistes caeruleus ), and pied flycatcher ( Ficedula hypoleuca ) were used as model species. Great tit and blue tit are resident species in Finland, while pied flycatchers winter in Western Africa (Lundberg &Alatalo 1992 ). All three species are abundant in our study areas and breed in the nest boxes, making them ideal species to study the associations between anthropogenic pollution and early-life microbiota of the birds. 2.2. Sampling in the field During the breeding season 2021, we randomly selected 44 nests from polluted sites (great tit n = 16, blue tit n = 13 and pied flycatcher n = 15) and 45 nests from control sites (great tit n = 15, blue tit n = 15 and pied flycatcher n = 15) for sampling. The timing of breeding was standardized by avoiding late or replacement nests. Small temperature and humidity data loggers (iButtons® DS1923-F5# Hygrochron, Maxim Integrated Products) were placed inside the nest boxes to measure ambient growing conditions for microbes every three hours. The nest boxes were inspected from the beginning of the breeding season until the fledging phase to collect data on hatchability, brood size, growth and fledgling success. Any microbial cross-contamination was avoided by wearing sterile gloves when there was a need to touch the nest or nestlings. At the average age of 8 days (range 5–11 days), the chicks were individually ringed with aluminium rings and the body mass (Pesola spring balance, g) and wing length (mm) were measured. In addition, a pooled faecal sample was taken from each brood for gut microbiota sequencing and metal analyses. Fresh faecal samples were stored in sterile polypropylene tubes, placed in a portable cooler in the field and frozen at -20°C until the final storage at -80°C before the DNA extractions. The experiments were conducted under licenses of the Regional State Administrative Agency for Southern Finland (license number ESAVI/3021/04.10.07/2017) and the Centre for Economic Development, Transport and the Environment of Southwest Finland (licence number VARELY/3622/2017). 2.3. Metal analyses The concentrations of five metals (Arsenic As, Cadmium Cd, Copper Cu, Nickel Ni and Zinc Zn) were determined from the faecal samples in the CEBAS-CSIC laboratory (University of Murcia, Spain). Metal concentrations were analysed with an inductively coupled plasma optical emission spectrometer (ICP-OES, Thermo ICAP 6500 Duo) with the quantification limit of 0.01 ppm. Faecal samples (0.1 − 0.2 g, dry weight) were placed in digestion tubes to which a mixture of 4 ml HNO 3 (70%) and 1ml H 2 O 2 (33%) was added. The sample was then submitted to a progressive thermal treatment and, after a microwave procedure, the sample was diluted in ultrapure water before the analysis (see Espín et al. 2016 ). Precision of the method was tested with certified reference material (TORT-2, lobster hepatopancreas, National Research Council Canada) and element recoveries were found to vary between 115–169%. Because of the relatively high recovery values we did not use absolute values in the analyses, but instead principal components calculated from those five metals as an index of metal exposure levels. 2.4. DNA extraction and library preparation All molecular work including DNA extraction, NGS library preparation, and sequencing as well as bioinformatics were carried out as turnkey service by DNA analysis company Bioname (Turku, Finland). In summary, bacterial DNA was extracted from the faecal samples using Quick-DNA Fecal/Soil microbe Miniprep Kit (Zymo Research). Microbial 16S rRNA gene V4 region was amplified using one primer pair: forward primer 515FB (GTGYCAGCMGCCGCGGTAA, see (Parada et al. 2016 , Walters et al. 2016 ) and reverse primer 806RB (GGACTACNVGGGTWTCTAAT, see Apprill et al. 2015 , Caporaso et al. 2011 ). Next-Generation Sequencing libraries for sequencing were prepared by using dual indexing approach in subsequent library PCR. The sequencing was performed on the Illumina NovaSeq6000 SP Flowcell using 2 x 250 bp paired-end read length (Illumina Inc. San Diego, California, USA) in the Finnish Functional Genomics Centre (FFGC, University of Turku, Finland). Detailed molecular workflow is available as Supplement text 1: “Molecular analysis”. 2.5. Bioinformatics Our bioinformatics workflow closely followed Kaunisto et al. ( 2020 ). Shortly, the raw reads were trimmed, merged, the PCR primers were removed using the software CUTADAPT 2.7 (Martin, 2011 ), reads were dereplicated, and then collapsed into sequence variants (ZOTUs) using ‘unoise3’ in USEARCH 11 (Edgar, 2010 ). The number of ZOTUs in each sample was assessed and all ZOTUs were assigned to taxa using USEARCH/VSEARCH SINTAX algorithm using pre-built database ( 16S RDP training set v16 ) downloaded from https://drive5.com/usearch/manual/sintax_downloads.html ; Edgar, 2015 ). Finally, reads were filtered for non-target reads, such as Chloroplast DNA. Low abundance ZOTU’s (= read count less than 2) were removed from the data. After final filtering, majority of the reads were retained in the data, including 20 984 942 reads (~ 177 800 reads per sample). Detailed bioinformatics have been collected in the Supplement text 2: “Bioinformatics”. 2.6. Statistics Most of the statistical analyses were performed either with R (v. 4.2.3; R Core Team ( 2023 )) or SAS 9.4 statistical software (SAS 2013 ). Prior to further statistical analyses, the data was transformed to a phyloseq object for R. Subsequently, the microbial data was rarefied to a depth of 29 000 reads (phyloseq 1.42.0 in R, (McMurdie & Holmes 2013 ) to account for the differences in the sequencing depth. The rarefaction cut off was determined based on the plateauing of rarefaction curves, i.e., the point where no significant number of additional taxa would have been found with a greater sequencing depth (Fig. S1 ). The final data included 6918 taxa and 84 samples in total. Since the faecal metal concentrations correlated with each other, we calculated principal components (PC) from the metal data (Ni, Cu, As, Cd and Zn) with SAS 9.4. The first principal component (PC1 M ; Ni, Cu, As) explained 56.2% (eigenvalue 2.81) of the variation in our metal data and was used in the models as an explanatory variable to describe the general level of metal exposure. The second component (PC2 M ; Zn, Cd) explained 23.4% (eigenvalue 1.17) of the variation in our data. Promax rotation method, which allows components to be correlated, was used in the analysis. 2.6.1. Differential abundance analyses Five differential abundance (DA) estimators ALDEx2 (1.30.0) (Fernandes et al. 2013 ), ANCOM-BC2 (2.0.2) (Lin &Das Peddada 2020 ), Corncob (0.3.1) (Martin et al. 2020 ), DESeq2 (1.38.3) (Love et al. 2014 ) and LinDA (0.1.0) (Zhou et al. 2022 ) were used to identify the bacteria affected by study area (polluted, control) at different taxonomic levels: phyla, class, order, family and genus. Non-rarefied observed abundances were used as input and the estimators queried at a significance level of p < 0.05 after Benjamini-Hochberg adjustment. Nearing et al. ( 2022 ) have shown a high variation between the DA estimators across numerous 16S rRNA datasets, which is why we used the consensus of multiple pre-selected estimators for the analysis. Briefly, different DA estimators showed various amounts of differentially abundant taxa between the study areas. While DESeq2 and Corncob found 151 (63.6% unique to this estimator) and 128 (64.1% unique to this estimator) DA taxa in total (including all bird species and taxa), respectively, ANCOM-BC2 and LinDA found only 27 (18.5% unique to this estimator) and 25 (12.0% unique to this estimator) DA taxa, respectively. ALDEx2 found no differentially abundant taxa in any of the species. Prior to the DA analyses the prevalence threshold was set to 10% by species. 2.6.2. Alpha diversity Shannon diversity index (i.e., a number of bacterial ZOTUs and their abundance evenness within a sample), Chao1 richness (estimate of the total number of different bacterial ZOTUs in a sample) and observed richness (observed number of ZOTUs) were used as measures of alpha diversity of the nestling gut microbiota (microbiome 1.20.0. package in R, (Lahti &Shetty 2022 ). Thereafter, each diversity index was used as a response variable separately in the general (LM) or generalized (GLM) linear model (GLIMMIX procedure in SAS 9.4) using study area, temperature, nestling age and brood size at the time of sampling, and nestling relative body mass (RBM) as explanatory variables. The three species were analysed separately, since they differ in their feeding habits and migratory behaviour from each other. The RBM is a proportional (%) deviation of brood mean body mass from predicted mass based on long-term (years 1991–2022) growth curve from the same area. Using RBMs is reasonable for taking account of the slight variation in among-brood measurement ages. Temperature denotes average daily temperature inside the nest box from hatching to the brood age of 8 days, which was the average day for the microbial sampling. For Shannon index we used beta distribution and logit link function, whereas for Chao1 index and observed richness we used lognormal distribution with identity link function. Non-significant terms were dropped sequentially from the final model, but the study area (polluted, control) was always kept in the model, as this was our main study factor. 2.6.3. Beta diversity Beta diversity was analysed with permutational multivariate analysis of variance (PERMANOVA with Bray-Curtis distances) using 999 permutations (“adonis2” in vegan 2.6.4 , (Oksanen et al. 2022 ) to test the community dissimilarity between the study areas and species. In the model, we used study species, area, temperature (inside the nest box), age of nestlings at sampling, brood size, nestling body mass and study area × species interaction as explanatory factors. Pairwise comparisons between the species were calculated ( pairwise.adonis 0.4.1,(Martinez Arbizu 2017 ) with Bray-Curtis method using 999 permutations and Bonferroni correction to adjust p values. To visualize the dissimilarity among species and study areas, we used principal coordinate analysis (PCoA) ordination based on Bray-Curtis distances (phyloseq 1.42.0, McMurdie and Holmes, 2013 ). The samples were plotted according to the first and second component values. The analysis and visualization were performed for non-rarefied and rarefied data with relative abundance, but since the results did not change, we chose to use the rarefied data like in alpha diversity measurements. 2.6.4. Effects on life-history We performed principal component analysis (PCA, SAS 9.4) from the log-transformed reads of 25 orders (thresholds of 5% prevalence and 1% abundance) to get fewer metrics of bacterial orders for further analyses and to visualize the bacterial data between the species and study areas. Based on the eigenvalues (> 1.5) and proportion (> 6%), only first four components were chosen for the later analyses. The first component (PC1 B ; Acidimicrobiales + , Actinomycetales + , Gaiellales + , Solirubrobacterales + , Bacillales + , Planctomycetales + , Rhodobacterales + ) explained 23.4% (eigenvalue 5.86), the second component (PC2 B ; Coriobacteriales + , Bacteroidales + , Clostridiales + , Erysipelotrichales + , Desulfovibrionales + , Sphingomonadales − ) 12.5% (eigenvalue 3.12), the third component (PC3 B ; Caulobacterales + , Burgholderiales + ) 7.4% (eigenvalue 1.86) and the fourth component (PC4 B ; Chlamydiales + , Legionellales + , Entomoplasmatales + ) 6.6% (eigenvalue 1.64) of the variation in our data. The + and – signs refer to positive and negative loadings of the bacteria in each component. For visualization we used a biplot based on the bacterial PCs (SAS). Those four components were further used in separate models as dependent variables to describe the gut bacterial composition of the nestlings. To investigate the variation in gut bacteria abundances, we performed GLM separately for bacterial principal components PC1 B (GLM with beta distribution and logit link function), PC2 B , PC3 B and PC4 B (LM with Gaussian distribution and identity link function) in SAS. We used PC1 M of the metals, species, brood size at sampling time, temperature (inside the nest box), sampling date, and RBM as explanatory variables in the models. Non-significant terms were dropped sequentially from the final model, but the PC1 M was always kept in the model, as this was our main study factor. Tukey’s test was used for post-hoc pairwise comparisons. We assessed the collinearity of the main predictors by variance inflation factor (VIF) analyses. We further tested the association of metals with fledging success (fledglings/hatchlings) (GLM with binomial distribution and logit link function) and RBM (LM with Gaussian distribution and identity link function). The models were performed separately for each study species, using PC1 M of metals, nestling body mass (fledging success model only), temperature inside the nest box, PC1 B and PC2 B of faecal microbes at order level as explanatory variables in the model. Based on the DA results, the microbes that significantly differed between the study areas were chosen for the further analyses to see whether those microbes show potential association on fledging success (probability of a hatchling to fledge; GLM with binomial distribution and logit link function, SAS) and RBM (LM with Gaussian distribution and identity link function, SAS). 3. Results 3.1. Core gut microbiota Firmicutes (39.6%), Proteobacteria (30.4%) and Actinobacteria (13.5%) were the most abundant bacterial phyla across all species (great tit, blue tit and pied flycatcher) and study areas followed by Tenericutes (7.1%), Clamydiae (5.1%) and Bacteroidetes (2.2%) (Fig. 1 , Fig. S2 ). The three most common phyla were, in varying order, the same for all species. The most common phyla in great tits and pied flycatchers were Firmicutes (great tit: 46.7%, pied flycatcher: 43.7%), Proteobacteria (great tit: 21.5%, pied flycatcher: 33.4%) and Actinobacteria (great tit: 15.1%, pied flycatcher: 10.2%), whereas Proteobacteria (37.9%) dominated in blue tits, followed by Firmicutes (25.6%) and Actinobacteria (15.5%, see table S1 ). The predominant bacterial orders in great tits and blue tits were Lactobacillales (great tit:28.8%, blue tit:15.8%), followed by Clostridiales (13.7%) and Actinomycetales (13.1%) in great tits, and Enterobacteriales (12.1%) and Actinomycetales (12%) in blue tits. In pied flycatchers, the orders Clostridiales (22.5%), Lactobacillales (18.3%) and Legionellales (16.1%) dominated the faecal microbiota (see table S1 ). The species showed some more variation of bacterial taxa at genus level; Catellicoccus (13.0%), Ureaplasma (8.1%) and Clostridium sensu stricto (6.6%) dominated the faecal gut bacteria in great tits, whereas Ureaplasma (8.2%), Clostridium sensu stricto (6.8%) and Buchnera (6.2%) were the most dominant genera in blue tits. The most abundant faecal gut bacteria in pied flycatchers were Diplorickettsia (16.1%), Clostridium sensu stricto (15.7%) and Lactobacillus (5.9%) (table S1 ). 3.2. Differential abundance analyses Five DA estimators were used to identify the bacteria associated with study area (polluted, control) at different taxonomic levels (Fig. 2 ). The results varied among the estimators, thus the responses of bacterial taxa in which three or more of the five estimators indicated a significant response were considered significant (see Table S2 ). In blue tits, the phylum Candidatus Saccharibacteria was more abundant in the polluted area compared to control area. In pied flycatchers, the order Rhodobacterales and family Rhodobacteraceae showed significantly greater abundance in polluted areas compared to control areas, whereas the family Clostridialis Incertae Sedis XIII was more abundant in control areas than in the polluted areas. At genus level, Anaerovorax , Catabacter and Eubacterium were more abundant in control areas compared to polluted areas in pied flycatchers, while in great tits Arthrobacter and Rhodococcus were more abundant, but Catellicoccus and Serratia less abundant in control areas compared to polluted areas. All significant comparisons (log2-fold change, SE, 95% CI and adj. p-value) are indicated in Table S2 . The significant results of all DA estimators (log2-fold change, SE, 95% CI and adj. p-value) are shown as supplementary material (Table S3). 3.3. Alpha diversity We used Shannon index, Chao1 richness (Fig. 3 ) and observed richness as measures of alpha diversity of the nestling gut microbiota. Shannon diversity was negatively associated with brood size at sampling time in great tits and blue tits, and positively with temperature inside the nest box in blue tits, while no associations were found with RBM in the two tit species (Table 1 ). On the other hand, in pied flycatchers, higher RBM was associated with increased Shannon diversity, but not to brood size or temperature. Study area (polluted, control) or nestling age at sampling time were not associated with Shannon index in any of the species (Table 1 ). Observed richness and Chao1 richness index were not significantly associated with study area, nestling age, brood size at sampling time or temperature in any of the species. However, as opposed to tit species, in pied flycatchers, both richness indexes showed a significant positive association with RBM, suggesting higher overall microbial richness in well growing chicks. Table 1 The association of area (polluted, control), temperature (inside the nest box), nestling age, brood size, relative body mass (RBM), and species and area*species interaction (PERMANOVA only) on alpha diversity (Shannon index, Chao1 richness and observed richness) and beta diversity (community composition) measures in the nestlings of great tits (P. major), blue tits (C. caeruleus) and pied flycatchers (F. hypoleuca). Non-significant terms were dropped sequentially from each model, starting from interaction. Terms left in the final model are shown in bold. Great tit Alfa-diversity (GLM) Shannon index* Chao1** Obs. richness** Model F df p Fdf p Fdf p Area 2.97 1, 27 0.096 2.31 1, 28 0.140 2.76 1, 28 0.108 Temperature 2.67 1, 26 0.114 1.40 1, 25 0.247 1.57 1, 24 0.222 Age 0.19 1, 24 0.669 1.19 1, 26 0.286 1.28 1, 25 0.268 Brood size 4.52 1, 27 est. -0.11, SE 0.05 0.043 1.97 1, 27 0.172 2.05 1, 27 0.163 RBM 1.17 1, 25 0.290 1.31 1, 24 0.264 1.60 1, 26 0.217 Blue tit Model F p F p F p Area 1.77 1, 20 0.198 1.50 1, 22 0.233 1.82 1, 22 0.191 Temperature 5.52 1, 20 est. 0.14, SE 0.06 0.029 1.86 1, 21 0.188 2.09 1, 21 0.163 Age 1.80 1, 19 0.196 0.53 1, 19 0.475 0.86 1, 19 0.366 Brood size 5.42 1, 20 est. -0.18, SE 0.08 0.031 3.07 1, 20 0.095 3.26 1, 20 0.086 RBM 0.73 1, 18 0.404 0.18 1, 18 0.677 0.33 1, 18 0.574 Pied flycatcher Model F p F p F p Area 0.45 1, 27 0.508 0.35 1, 27 0.560 0.26 1, 27 0.612 Temperature 0.55 1, 25 0.467 0.01 1, 24 0.939 0.00 1, 24 0.997 Age 0.06 1, 24 0.810 1.57 1, 26 0.222 1.35 1, 26 0.256 Brood size 1.11 1, 26 0.302 0.04 1, 25 0.843 0.03 1, 25 0.854 RBM 5.21 1, 27 est. 0.03, SE 0.02 0.031 6.46 1, 27 est. 0.05, SE 0.02 0.017 6.10 1, 27 est. 0.05, SE 0.02 0.020 Beta-diversity PERMANOVA*** Model F df R 2 p Area 2.32 1, 82 0.025 0.001 Species 2.14 2, 82 0.047 0.001 Temperature 1.69 1, 82 0.018 0.002 Area*Species 1.05 2, 78 0.023 0.321 Age 1.28 1, 81 0.014 0.087 Brood size 1.23 1, 80 0.013 0.117 Body mass 1.33 1, 82 0.015 0.053 * GLM Beta distribution, logit link function. ** GLM Lognormal distribution, identity link function. *** PERMANOVA, reduced model (vegan, adonis2) 3.4. Beta diversity Bray-Curtis dissimilarity-based microbial beta diversity was assessed to illustrate the differences in microbial communities between the study areas and study species (Fig. 4 ). The PCoA ordinations with Bray-Curtis dissimilarity index showed significant differences in microbial composition between the study areas (polluted, control) and study species (great tit, blue tit and pied flycatcher) and had significant association with temperature (PERMANOVA, Table 1 ). The pied flycatchers had a significantly different bacterial composition compared to great tits (F df =2.70 1 , p adj =0.003) and blue tits (F df =2.36 1 , p adj =0.003), while the two tit species did not differ significantly from each other (F df =1.18 1 , p adj =0.552). However, nestling age, brood size at sampling and nestling body mass had no association with the gut microbial composition (Table 1 ). 3.5. Association of metals and life-history parameters on gut bacteria Principal components were calculated from the read counts of the bacterial orders and the first four components were further used in the models to study the association of faecal metal levels, temperature, and life-history parameters with bacterial taxa. The PC1 B (including orders Acidimicrobiales, Actinomycetales, Gaiellales, Solirubrobacterales, Bacillales, Planctomycetales, Rhodobacterales) increased significantly with faecal metal concentrations (PC1 M of metals), temperature, and RBM (used as a measure of growth, Table 2). Species did not differ from each other in relation to PC1 B , and brood size and sampling date had no significant effect on PC1 B (Table 2). In contrast, PC2 B (including orders Coriobacteriales, Bacteroidales, Clostridiales, Erysipelotrichales, Desulfovibrionales, Sphingomonadales) had no significant association with faecal metal concentration but showed significant negative association with temperature and RBM (Table 2). On the other hand, PC2 B increased significantly with brood size and sampling date (Table 2). The PC2 B also differed significantly between the species; pied flycatcher showed higher bacterial abundance compared to great tits (Tukey’s test: t df =3.51 75 , p adj =0.002) and blue tits (Tukey’s test: t df =-4.71 75 , p adj = < 0.0001), which did not differ significantly from each other (Tukey’s test: t df =-1.78 75 , p adj =0.184). The PC3 B , including Caulobacterales and Burkholderiales, did not show any association with faecal metal concentration or any other studied parameters (Table 2). The PC4 B , which included bacterial orders Chlamydiales, Legionellales and Entomoplasmatales, was not affected by faecal metal concentrations, temperature, brood size or RBM, but showed a negative association with sampling date (Table 2). PC4 B differed significantly between the species, pied flycatchers having significantly higher bacterial abundance compared to great tits (t df =3.43 78 , p adj =0.003) and blue tits (t df =-2.49 78 , p adj =0.039), while the two tit species did not differ significantly from each other (t df =0.95 78 , p adj =0.608). 3.6. Fledging success and RBM Both fledging success and RBM were significantly decreased with increased faecal metal concentration (PC1 M of metals) in great tits and blue tits, but not in pied flycatchers (Table 3 ). In blue tits, the fledging success increased significantly with warmer temperature and was positively associated with PC2 B of bacterial taxa, while such effects were not found in the other species (Table 3 ). The fledging success had no association with nestling body mass or PC1 B in any of the three species (Table 3 ). In pied flycatchers, the RBM associated positively with PC1 B , but negatively with PC2 B , whereas such associations were not found in the tit species (Table 3 ). The RBM was not significantly associated with temperature in any of the species (Table 3 ). Table 3 The association of metal levels (PC1M) temperature (inside the nest box), body mass and bacterial components PC1B and PC2B on fledging success and relative body mass in the nestlings of great tits (P. major), blue tits (C. caeruleus) and pied flycatchers (F. hypoleuca). Non-significant terms were dropped sequentially from each model, starting from interaction. Terms left in the final model are shown in bold. Great tit Blue tit Pied flycatcher Fledging success* F df p F df p F df p PC1 M 7.16 1, 29 est. -0.55, SE 0.20 0.012 5.11 1, 19 est. -0.84, SE 0.37 0.036 0.66 1, 28 0.425 Body mass 3.48 1, 28 0.072 1.99 1, 18 0.175 1.56 1, 27 0.223 Temperature 0.09 1, 24 0.770 4.47 1, 19 est. 0.26, SE 0.12 0.048 0.50 1, 26 0.488 PC1 B 0.09 1, 25 0.772 0.02 1, 17 0.888 0.04 1, 24 0.849 PC2 B 0.82 1, 26 0.373 5.95 1, 19 est. 0.71, SE 0.29 0.025 0.03 1, 25 0.859 Relative body mass** F df p F df p F df p PC1 M 16.97 1, 29 est. -12.11, SE 2.94 0.0003 13.19 1, 24 est. -12.71, SE 3.50 0.001 3.12 1, 26 0.089 Temperature 0.10 1, 26 0.759 1.26 1, 23 0.274 0.02 1, 25 0.901 PC1 B 3.25 1, 27 est. 1.97, SE 1.10 0.083 0.23 1, 18 0.635 20.01 1, 26 est. 2.87, SE 0.64 0.0001 PC2 B 0.07 1, 25 0.789 1.68 1, 19 0.211 4.80 1, 26 est. -2.19, SE 1.0 0.038 *GLM (binomial distribution, logit link function) ** LM (Gaussian distribution, identity link function) We further tested whether the bacterial families and genera that differed in their abundance between the study areas (DA analyses) showed any effects on fledging success or RBM. The families Rhodobacteraceae and Clostridiales Incertae Sedis XIII and genera Anaerovorax , Catabacter and Eubacterium had no significant association on fledging success or RBM in pied flycatchers (p > 0.05). In great tits, the genera Catellicoccus and Serratia had a negative association with fledging success ( Catellicoccus : F df =11.65 1, 28 , p = 0.002, Serratia : F df =5.34 1, 28 , p = 0.028) and RBM ( Catellicoccus : F df =8.65 1, 28 , p = 0.007, Serratia : F df =6.52 1, 28 , p = 0.016), while Rhodococcus had no significant effects on fledging success (p > 0.05), but showed positive association on RBM (F df =7.61 1, 28 , p = 0.010). Arthrobacter had no significant effects on fledging success or RBM of great tit nestlings (p > 0.05). 4. Discussion 4.1. Core gut microbiota in three passerine species Firmicutes, Proteobacteria and Actinobacteria were the major gut bacterial phyla across our study species, consistent with earlier avian studies (Bodawatta et al. 2022a , Drobniak et al. 2022 , Fu et al. 2020 , Grond et al. 2018 , Kim et al. 2023 , Kropáčková et al. 2017 , Liukkonen et al. 2023 ). Firmicutes, the most abundant phyla in great tits and pied flycatchers, play a crucial role in fermenting organic molecules to supply energy for the host (Flint et al. 2008 , Turnbaugh et al. 2006 ), influence weight gain and fat storage in chickens (Angelakis &Raoult 2010 ) and mammals (Turnbaugh et al. 2006 ), and aid in metabolism, digestion, and protein absorption within the gut (Grond et al. 2018 ). In contrast, Proteobacteria dominated the blue tit gut microbiota. Despite the higher proportion of Proteobacteria in birds compared to mammals, their functions remain poorly understood. Nevertheless, Proteobacteria include several opportunistic pathogens like Campylobacter , Escherichia , Rickettsia and Salmonella (Diakou et al. 2016 , Keller et al. 2011 , Ryu et al. 2014 , Wallmenius et al. 2014 ). They have been associated with metabolic and immune disorders as well as ecological imbalances within the gut thereby posing potential threats to host health (Colston &Jackson 2016 ). Actinobacteria, the third most abundant phylum across species, inhabit diverse environments, including terrestrial and marine habitats as well as gastrointestinal tracts. They include pathogens like Mycobacterium , but also commensal bacteria utilized as probiotics in animals (Barka et al. 2016 , Grond et al. 2018 , Kailasapathy &Chin 2000 ). Bacterial orders Lactobacillales, Clostridiales, and Actinomycetales were found across all three species, with more species-specific variation observed at lower taxonomic levels. The genus Clostridium sensu stricto , known for containing significant human and animal pathogens (Li et al. 2023 ), was prevalent in all studied species, along with Enterococcus and Diplorickettsia in great tits and pied flycatchers. Enterococci , typically part of the natural gut flora and beneficial due to probiotic effects (Fisher &Phillips 2009 , Kwit et al. 2023 , Stępień-Pyśniak et al. 2018 ), can also exhibit opportunistic pathogenic behavior and resistance to antibiotics. Wild birds serve as important reservoirs of enterococci (Kwit et al. 2023 ), potentially facilitating microbial transfer between wild and domesticated animals (Ben Yahia et al. 2018 , Marrow et al. 2009 , Radhouani et al. 2012 ). Lactobacillus , also common in pied flycatchers, offers protection against various bacterial pathogens (Lee et al. 2020 ) by producing antimicrobial substances and aiding in detoxification processes (Hammes & Hertel 2006 ). It has shown efficacy in mitigating the toxic effects of metals such as lead (Pb) and Cd (George et al. 2021 , Shi et al. 2017 ). Ureaplasma , prevalent in gastrointestinal and urogenital tracts of vertebrates (Kropáčková et al. 2017 ) and occasionally contributed to severe pathogenesis (Sumithra et al. 2013 ), was one of the most abundant bacterial genera in both tit species. Catellicoccus , which is frequently found in the guts of seabirds (Góngora et al. 2021 ) and some passerines (Benskin et al. 2010 , Kreisinger et al. 2017 ) was the most abundant genus in great tit, whereas Pseudomonas , associated with several diseases in animals and plants, was common in blue tits (Abd El-Ghany 2021 , Eraky et al. 2020 ). Pseudomonas tended to be more abundant in polluted areas, which may be due to its high resistance to heavy metals, such as Cd, Pb and As (Al-Ansari et al. 2021 , Pramanik et al. 2021 , Zhou et al. 2023 ), making them an interesting taxonomic group in polluted environments. 4.2. Association between metals and gut bacteria We found 11 taxa that were differentially abundant between the polluted and control areas. Candidatus Saccharibacteria was more abundant in polluted area compared to control area in blue tits. This highly ubiquitous phylum can be found in soils, sediments, wastewater and animals, including earthworms (Rattray et al. 2010 ), mouse (Salzman et al. 2002 ), birds (Ma et al. 2023 ) and canines (Dewhirst et al. 2012 ). In pied flycatchers, Rhodobacteraceae , which has been shown to be more common in the digestive tract of insectivore than omnivore birds (Bodawatta et al. 2018 ), was more abundant in polluted areas, but did not show effects on fledging success or nestling growth (RBM). In contrast, Clostridiales Incertae Sedis XIII , Anaerovorax , Catabacter , and Eubacterium were more abundant in control areas compared to polluted areas. Similarly, these bacteria exhibited no adverse fitness effects on pied flycatchers. In great tits, Serratia and Catellicoccus were more abundant in polluted areas, and both were further associated with reduced fledging success and nestling growth. The finding suggest that these bacterial groups may tolerate metals and/or act as potential opportunistic pathogens. For Serratia , this effect may be linked to its potential pathogenicity in birds and mammals (Saidenberg et al. 2007 ), despite its relatively low abundance found in the gut. Catellicoccus , on the other hand, is also suggested to be advantageous to birds by improving their immune response and facilitating nutrient transport (Benskin et al., 2010 ), thereby aiding in the optimization of nutrition under severe conditions (Góngora et al., 2021 ). Opposite to them, Arthrobacter and Rhodococcus , commonly found in soil, water and plants, were more abundant in control areas. Rhodococcus was positively associated to growth in great tit nestlings, but neither Rhodococcus nor Arthrobacter associated with fledging success. Of these bacteria, most Rhodococcus strains have been found to have very high levels of metal resistance and are potential agent for the bioremediation of pollutants, including heavy metals (Nazari et al. 2022 ). However, the causal link between metal and microbiota interactions with growth is still unclear, but our results could suggest that pollution, through its effects on gut microbiota, may play a role in determining growth conditions. Further, the function of these bacterial taxa in bird gut is poorly known, and future studies should consider combining metabolomics analyses with microbial data. 4.3. Association of metals with gut bacterial diversity and composition The alpha diversity measured as Shannon diversity index, Chao1 richness, and observed richness did not differ significantly between the polluted and control areas in any of our study species, suggesting no direct link to environmental pollution. However, the overall gut bacterial community composition (beta diversity) differed between the study areas. Reduced richness and changes in the composition of plumage bacteria have been previously reported in birds exposed to Pb (Chatelain et al. 2016 ), whereas birds exposed to Zn have exhibited lower bacterial loads and changes in both plumage- (Chatelain et al. 2016 ) and gastrointestinal bacteria community composition (Hojberg et al. 2005 , Vahjen et al. 2010 ). Also, decreased alpha diversity of gut microbiota in tree sparrows ( Passer montanus ) inhabiting metal-polluted areas has been observed by Zhang et al. ( 2023 ). However, the effects of metals on microbiota are likely dose-dependent, which could explain the variation across different studies. For example, the Pb and Zn levels in our study area are relatively low compared to heavily polluted areas. Metal-related variation in bacterial communities was further examined at order level by using the principal components of the bacterial taxa. Only PC1 B showed higher bacterial abundance with increased metal levels. It is possible that these bacterial taxa may tolerate or be resistant to metals and/or may have an increased capacity to detoxify metals (Li et al. 2016 , Mawang et al. 2021 , Nazari et al. 2022 , Presentato et al. 2020 ), thus preventing their harmful effects. However, the causality between the metals and these microbes remains unknown and should be confirmed with experimental studies. 4.4. Species-specific differences in bacterial communities The pied flycatchers had a considerably different overall bacterial community composition compared to great tits and blue tits, while tit species shared a more similar composition. Pied flycatchers further showed clearly higher bacterial abundances of two bacterial components (PC2 B and PC4 B ) compared to both tit species. This may be linked to the different phenology and/or ecology (e.g. different diet and feeding habits or the different migratory behaviour) of the species. High individual variation has been observed in several migratory passerines (Lewis et al. 2016 , 2017 , Skeen et al. 2021 ) and also between migrants and their resident counterparts (Turjeman et al. 2020 ), potentially due to their exposure to new bacteria at their different breeding areas or stopover sites during migration (Turjeman et al. 2020 ). The species-specific differences in bacterial composition, like in PC4 B consisting mainly pathogenic bacteria, may also support the idea that migratory birds, such as pied flycatchers, harbour a higher abundance of pathogens with potentially higher infection intensity compared to resident species (Clark et al. 2016 , Koprivnikar & Leung 2015 , Leung & Koprivnikar 2016 ). 4.5. Association of gut bacteria with life-history traits and temperature The Shannon diversity index was negatively associated with brood size in great tits and blue tits, which could be related to higher within-brood competition, for example in the case of limited food resources and space or food quality leading to higher stress, as suggested by Somers et al. ( 2023 ). The availability of good-quality insect food is inferior in the polluted area (Eeva et al. 2005 ), which may reflect to the lower alpha diversity of larger broods due to higher food competition. Some other studies have not found a similar association between brood size and microbial alpha diversity in great tits (Liukkonen et al. 2023 , Somers et al. 2023 ), although brood size has been shown to correlate negatively with Proteobacteria abundance, and positively with Firmicutes abundance (Somers et al. 2023 ). We further showed that the growth of the pied flycatchers was positively associated with Shannon diversity and bacterial richness. This suggests that heavier nestlings exhibited increased microbial diversity and richness possibly due to their better capacity to gain and/or maintain higher microbial diversity during their developmental stages. Enterococcus , for example, have been shown to correlate positively with nestling growth in pied flycatcher (Moreno et al., 2003 ) and this genus indeed was one of the most common in pied flycatchers in the present study. However, such diversity association was not seen in great tits or blue tits, which may be partly explained by their different diet (mainly caterpillars during the nestling phase) compared to pied flycatchers, which also use flying insects as food source for their nestlings. Heavier nestlings are typically found in control areas with better food availability compared to polluted areas (Eeva et al. 2005 ). However, as we did not see significant diversity differences between the areas, the higher alpha diversity of heavier nestlings may be influenced by some other environmental factors. On the other hand, both fitness measures, fledging success and nestling growth (RBM), decreased with higher metal levels (PC1 M ) in great tits and blue tits, but not in the pied flycatchers, indicating that pied flycatchers may be less sensitive to metal pollution either directly or indirectly via environmental changes compared to tit species. Diverse gut communities may be resistant to pathogens and less susceptible to environmental perturbation (Buffie &Pamer 2013 ), while lower microbial diversity is often considered detrimental to host species (Le Chatelier et al. 2013 ), potentially leading to reduced nutrient assimilation or immunodeficiency (Teyssier et al. 2018 ). Similar to alpha diversity, also the abundances of bacteria belonging to PC1 B were clearly higher in well growing broods, whereas bacterial abundances of PC2 B increased in relation to reduced growth. The results suggest that PC1 B includes some beneficial bacteria for nestling growth, whereas potentially pathogenic bacteria, like Clostridiales in PC2 B , may retard nestling growth. Our results are in accordance with the study of Teyssier et al. ( 2018 ), who showed that the microbial diversity increased with nestling body mass. Body condition of chicks is important for their survival especially after fledging (Monrós et al. 2002 , Rodríguez et al. 2016 , Tinbergen & Boerlijst 1990 ) and the role of microbiota may be highlighted at this point of nestling development (Teyssier et al. 2018 ). The fledging success was enhanced by warmer temperature in blue tits, but not in the other species. Likewise, the Shannon diversity was linked to increased temperature inside the nest box only in blue tits, suggesting some temperature-derived differences in bacterial taxa. Some bacteria may favour warmer growth conditions thus benefiting higher ambient temperatures. Temperature inside the nest box further contributed to the variation in gut bacterial composition, for example warmer temperature was associated with increased abundance of bacteria belonging to PC1 B , but decreased abundance of bacteria belonging to PC2 B . In general, endotherms maintain a relatively constant body temperature, less influenced by environmental condition (Crompton et al. 1978 ). However, the thermoregulation of newly hatched chicks is not fully developed (Dawson & Evans 1960 , Howell 1964 , Price & Dzialowski 2018 ) limiting their ability to conserve heat (Dawson &Evans 1960 , Rodríguez & Barba 2016 ), which could increase the impact of temperature on their gut microbiota. Environmental temperature is known to modify the gut microbiome of animals (Sepulveda & Moeller 2020 , Tian et al. 2020 ). Especially Firmicutes have been shown to be sensitive to temperature variation; their relative abundances and alpha diversity have decreased with increased temperature in both endothermic and ectothermic host species (Bestion et al. 2017 , Fontaine et al. 2018 , Kohl &Yahn 2016 , Zhu et al. 2019 ). The mechanism behind the association of temperature and bacteria are unclear, but may be related to the host metabolism and energetics to cope with thermal stress (Sepulveda & Moeller 2020 , Sokolova et al. 2012 ), potentially affecting the host investment in regulating microbiome composition. 5. Conclusions The gut bacterial taxa exhibited similarities across the bird species, although greater variation was observed at the genus level compared to higher taxa. The gut bacterial community composition of pied flycatchers differed from great tits and blue tits, while tit species were more alike. This suggests that factors such as diet, phenology, breeding habits, and the migratory status of the birds likely influence the distinct gut microbiota of pied flycatchers. Although metal concentrations were not directly associated with bacterial alpha diversity, certain bacterial taxa appeared to better tolerate increased metal concentrations, being more abundant in polluted areas compared to control areas. These observations may be linked to the enhanced metal tolerance or detoxification capacities of these bacterial orders within the host organisms. Notably, Catellicoccus and Serratia that appeared in higher numbers in polluted environments, were further associated with reduced fledging success in great tit nestlings. Additionally, gut bacteria were linked to the brood size, nestling growth, and temperature inside the nest boxes, indicating that gut microbiota is not only affected by environmental pollution but also several other factors, highlighting the complex host-microbe interactions. To understand the complex interactions between the host microbiota and various environmental factors, experimental studies would be necessary. However, our results provide evidence of metal-induced effects on the bacterial community composition of nestlings in a species-specific manner either directly or indirectly via diet or other environmental changes. Declarations Acknowledgements We thank Jorma Nurmi, Siiri Nyrhilä, Oona Poranen, Jari Lehto, Kalle Rainio, Petri Suorsa and Ville Vasko for the help in field work and Tuija Koivisto and Saara Koutaniemi for sample preparation. We thank Finnish Functional Genomics Centre supported by University of Turku, Åbo Akademi University and Biocenter Finland for Illumina sequencing. Funding The research was funded by the Academy of Finland (project of TE no: SA338180), Sakari Alhopuro Foundation (project of MJR) and Varsinais-Suomi Regional Fund (project of MJR). CRediT author statement Miia J. Rainio: Conceptualization, Methodology, Formal analysis, Investigation, Writing -Original Draft preparation, Funding acquisition. Lyydia Leino: Methodology, Formal analysis, Investigation, Reviewing and Editing. Eero Vesterinen: Software, Formal analysis, Reviewing and Editing. Pablo Sanchez Virosta: Reviewing and Editing. Pere Puigbò: Reviewing and Editing. Tapio Eeva: Conceptualization, Methodology, Formal analysis, Reviewing and Editing, Supervision, Funding acquisition. Ethical approval The experiments were conducted under licenses of the Regional State Administrative Agency for Southern Finland (license number ESAVI/3021/04.10.07/2017) and the Centre for Economic Development, Transport and the Environment of Southwest Finland (licence number VARELY/3622/2017). Consent to Participate Not applicable. Consent to Publish Not applicable. Competing interest The authors have no competing interests to declare that are relevant to the content of this article. Data availability Data used in this manuscript can be provided by the authors upon request. References Abd El-Ghany WA (2021): Pseudomonas aeruginosa infection of avian origin: Zoonosis and one health implications. 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Supplementary Files Table2.docx RainioetalSupplementarymaterial250625ESPR.docx Cite Share Download PDF Status: Published Journal Publication published 21 Nov, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Minor Revision 02 Oct, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers invited by journal 24 Jul, 2025 Editor invited by journal 22 Jul, 2025 Editor assigned by journal 07 Jul, 2025 First submitted to journal 03 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6981542","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490406031,"identity":"03cac1ea-16ef-405e-a82c-f2de27d9d2bf","order_by":0,"name":"Miia J. Rainio","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAkUlEQVRIiWNgGAWjYDACZsZmBgYDGzD7QwIJWtIYeBgYGGcQpwWoCYgPQ7QQpV63nbnZ4EPB+cT9EgmMDQ+I0WJ2mLE5cYbB7cQekBaiHAbScpgHooX9ASlazpFoSzKPwQEStRjOMEg27jnzsJFILeePP5b48MdOtr09+WDjD2K0IAHGBhI1jIJRMApGwSjACQC2FTK/2yOhXQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0420-9314","institution":"Department of Biology, University of Turku, FI-20014 Turku, Finland","correspondingAuthor":true,"prefix":"","firstName":"Miia","middleName":"J.","lastName":"Rainio","suffix":""},{"id":490406032,"identity":"d896fbd6-20da-4d7b-a36a-2894af70098f","order_by":1,"name":"Lyydia Leino","email":"","orcid":"","institution":"University of Turku: Turun Yliopisto","correspondingAuthor":false,"prefix":"","firstName":"Lyydia","middleName":"","lastName":"Leino","suffix":""},{"id":490406033,"identity":"848b7c04-165c-44e0-a85d-d8aac7b4dce7","order_by":2,"name":"Eero Vesterinen","email":"","orcid":"","institution":"University of Turku: Turun Yliopisto","correspondingAuthor":false,"prefix":"","firstName":"Eero","middleName":"","lastName":"Vesterinen","suffix":""},{"id":490406034,"identity":"1f54d8d0-e685-41be-b9e9-87b22a65ae68","order_by":3,"name":"Pablo Sánchez Virosta","email":"","orcid":"","institution":"Norwegian University of Science and Technology: Norges teknisk-naturvitenskapelige universitet","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"Sánchez","lastName":"Virosta","suffix":""},{"id":490406035,"identity":"d9482a33-d6c7-4e4f-8654-9d5a80fcba9b","order_by":4,"name":"Pere Puigbò","email":"","orcid":"","institution":"Rovira i Virgili University: Universitat Rovira i Virgili","correspondingAuthor":false,"prefix":"","firstName":"Pere","middleName":"","lastName":"Puigbò","suffix":""},{"id":490406036,"identity":"0765e391-89c5-46cb-a75e-40480bb54ad3","order_by":5,"name":"Tapio Eeva","email":"","orcid":"","institution":"University of Turku: Turun Yliopisto","correspondingAuthor":false,"prefix":"","firstName":"Tapio","middleName":"","lastName":"Eeva","suffix":""}],"badges":[],"createdAt":"2025-06-26 09:03:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6981542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6981542/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-025-37181-x","type":"published","date":"2025-11-21T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87733584,"identity":"388cb5a5-1488-4430-805e-094554599ca4","added_by":"auto","created_at":"2025-07-28 12:07:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":455606,"visible":true,"origin":"","legend":"\u003cp\u003eMean relative abundance of the bacteria at different taxonomic levels A) phylum, B) order, C) family and D) genus in blue tit (\u003cem\u003eCyanistes caeruleus\u003c/em\u003e), great tit (\u003cem\u003eParus major\u003c/em\u003e) and pied flycatcher (\u003cem\u003eFicedula hypoleuca\u003c/em\u003e) nestlings in the polluted (Pol) and control (Co) areas. Analyses were performed with samples rarefied to read depth of 29 000. \u0026nbsp;Taxa with the prevalence threshold of \u0026lt;10% across all samples were assigned to “Other” at phylum level and \u0026lt;50% at other taxonomic levels (order, family, genus).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6981542/v1/fdb50d82dbc9ee930ebe3ccc.png"},{"id":87733581,"identity":"a241ef5b-747e-4d9a-8344-5937387e2dd3","added_by":"auto","created_at":"2025-07-28 12:07:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":424989,"visible":true,"origin":"","legend":"\u003cp\u003eAbundance of the bacterial taxa at different levels (phylum, order, family and genus) showing differences between the polluted and control areas in blue tits, great tits and pied flycatchers (yellow=control area, green=polluted area). The differential abundance results are based on multiple estimators (ALDEx2, ANCOM-BC2, Corncob, DESeq2 and LinDA) on significance p \u0026lt; 0.05 after Benjamini-Hochberg adjustment.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6981542/v1/eb4c043095847697f4171785.png"},{"id":87733582,"identity":"e7e0a1f8-3e3c-4c61-ab83-70d10acbf82c","added_by":"auto","created_at":"2025-07-28 12:07:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":400428,"visible":true,"origin":"","legend":"\u003cp\u003eMean (± SE) microbial alpha diversity based on \u003cstrong\u003eA)\u003c/strong\u003eShannon index and\u003cstrong\u003e B)\u003c/strong\u003e estimated richness (Chao1) based on ZOTUs in blue tit (\u003cem\u003eCyanistes caeruleus\u003c/em\u003e), great tit (\u003cem\u003eParus major\u003c/em\u003e) and pied flycatcher (\u003cem\u003eFicedula hypoleuca\u003c/em\u003e) nestlings in polluted and control areas. The shape of the violins describes the kernel probability density at different diversity values. Analyses were performed on data rarefied to read depth of 29 000.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6981542/v1/865a9ac55408411d5a337b58.png"},{"id":87734440,"identity":"36ef4907-bdbc-434e-8d8b-3e71955bd6fa","added_by":"auto","created_at":"2025-07-28 12:15:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":295175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eBacterial community composition changes between the species (blue tit, great tit and pied flycatcher) and study areas (polluted and control) shown as PCoA (Principal Coordinate Analysis) ordinations with Bray-Curtis dissimilarity index, and \u003cstrong\u003eB) \u003c/strong\u003ePrincipal component biplot for bacterial orders in faecal samples of three bird species. The length and orientation of the vectors describe the impact of each order on individual PCs and their correlation with one another. In the PCoA plot each point represents a sample and closeness of the points indicates high similarity in the microbial community. The ellipses were drawn at 95% confidence interval. Analyses were performed with samples rarefied to read depth of 29 000. \u0026nbsp;(CYACAE = blue tit\u003cem\u003e, \u003c/em\u003en=23, PARMAJ = great tit\u003cem\u003e, \u003c/em\u003en=30, FICHYP = pied flycatcher\u003cem\u003e, \u003c/em\u003en=30).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6981542/v1/a442ceeb900fae29ad212f85.png"},{"id":96650289,"identity":"002fba50-9997-45c3-919c-9fba3daa9d59","added_by":"auto","created_at":"2025-11-24 16:10:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3270092,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6981542/v1/91b3d4ac-d0f1-4a64-b3a5-d7c896c4c63c.pdf"},{"id":87733580,"identity":"91361f96-c61f-4eb8-9cb2-e900433cd908","added_by":"auto","created_at":"2025-07-28 12:07:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17257,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6981542/v1/4f9f354cb05db879c249346a.docx"},{"id":87733591,"identity":"87a04428-bc2f-4987-be85-22b96e9dbf70","added_by":"auto","created_at":"2025-07-28 12:07:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2505878,"visible":true,"origin":"","legend":"","description":"","filename":"RainioetalSupplementarymaterial250625ESPR.docx","url":"https://assets-eu.researchsquare.com/files/rs-6981542/v1/8909dfd8eeaf9ae85642d279.docx"}],"financialInterests":"","formattedTitle":"Metal-induced chances in the early-life gut microbial composition and life-history traits in three insectivorous passerines","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMicrobes play a significant role in terrestrial and aquatic ecosystems by acting as plant, animal, and human pathogens, but several microbes have beneficial and protective functions as well (Kohl \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Moreno et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The balance in microbial communities is important, since they can help us restore natural ecosystems and maintain the well-being of their hosts (Angulo et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Significance of microbiota to vertebrate health and fitness is an emerging topic in ecology, but the research has mostly focused on mammals. So far, relatively little is known about the factors shaping the establishment and dynamics of microbial communities in wild bird species and the role of environmental pollutants in the development of early-life microbial flora. The colonization of nestlings by environmental microbes begins soon after hatching (Lucas \u0026amp;Heeb \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) via ingestion of adult saliva, food items or nest materials (Berger et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Kyle \u0026amp;Kyle \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, Mills et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Singleton \u0026amp;Harper \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Therefore, pollution-exposed nest material and food items likely shape the bacterial composition of the nests, potentially affecting the balance between beneficial and harmful bacteria. This may have important repercussions for the development of the chicks and the subsequent fitness of the individuals.\u003c/p\u003e\u003cp\u003eAnthropogenic pollution is one potential factor affecting the microbial communities in animals, causing various health disorders and dysregulation of the immune system of animals and affecting negatively to the function of gut microbiota (Arun et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Claus et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Duan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, it is unclear how gut microbiota and environmental pollution interact and whether these interactions are related to animal health. Various environmental contaminants have been shown to alter the composition and metabolic activities of the gut bacteria, thus possibly shaping the host microbiota (Claus et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). There is also evidence that bacteria-dependent metabolism of toxic compounds modulates the toxicity for the host (Claus et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Changes in microbial composition may therefore disrupt the detoxification processes of normal microbial flora against pollution, thus affecting the physiology (e.g. stress responses), health and fitness of wild animals.\u003c/p\u003e\u003cp\u003eMetals are common and widespread anthropogenic pollutants and well known for their antimicrobial activity (Lemire et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Metal toxicity affects microbial population size, diversity and activity (Ayangbenro \u0026amp;Babalola \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Zhang et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the other hand, certain metals (including iron, cobalt, nickel, copper and zinc) are essential for the life of most organisms (Frei et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Non-absorbed metals can remain in the gut microenvironment, causing direct effects on the gut microbiota and overall physiology of a developing individual (Breton et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, it is important to understand the mechanisms of non-absorbed metals on complex host-microbe interactions and the risk levels of metals affecting the host species via microbiota.\u003c/p\u003e\u003cp\u003eGut microbiota is a diverse ecosystem comprised of bacteria, archaea, fungi and viruses and provides crucial physiological functions that host organisms cannot develop by themselves (Kohl \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The microbiota has evolved with the host as a mutualistic partner, but microbial imbalance may promote the development of metabolic disorders in host species along with environmental factors (Kogut \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, the presence or absence of specific microbial species can be essential for maintaining the balance both inside and outside the intestinal tract (Breton et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Diet is one of the key factors modulating the composition of gut microbiota, both between and within species (Bodawatta et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e, Glunder \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). For example, many insectivorous species feed their nestlings with caterpillars, but the actual prey species and their accumulation rate of contaminants differ according to environment, which may affect the bacterial communities of the host (Goodenough \u0026amp;Stallwood \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Gut microbes affect nutrition intake, immunology, development and detoxification processes (Kohl \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), demonstrating the importance of microbes for the physiological functions. Furthermore, gut microbes are known to correlate with phenotypic conditions of nestlings, such as wing asymmetry (Mills et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), nestling size, and body condition (Moreno et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), indicating a significant role of microbial composition in avian development.\u003c/p\u003e\u003cp\u003eBacteria belonging to phyla Proteobacteria, Firmicutes and Actinobacteria typically dominate avian gut bacterial composition (Bodawatta et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, Grond et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Krop\u0026aacute;čkov\u0026aacute; et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Liukkonen et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the most common cloacal bacteria genera include \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, and \u003cem\u003eEnterobacter\u003c/em\u003e. \u003cem\u003eEnterococci\u003c/em\u003e, for example, are part of normal bacteria flora in wild birds, some species being opportunistic pathogens, whereas some are beneficial, acting as growth promoters and components of probiotics in birds (Moreno et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Even though avian microbiota has broad enzymatic capacity to metabolize several environmental contaminants, pollutants may still disrupt the function of essential microbes with detrimental consequences, especially during early life, which is the most sensitive time for nestlings.\u003c/p\u003e\u003cp\u003eIn this study, we examine the association of metal pollution on early-life avian gut microbiota to understand the interconnection between pollution and microbiota in wild passerine birds and further to reveal whether changes in microbiota are related to the performance of nestlings. Earlier studies of the pollution effects on wild birds have strongly pointed out the importance of indirect effects on physiology and individual performance (Koivula et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). One yet poorly known secondary effect can be metal-related change in the gut microbiota, but studies related to this topic are limited. Thus, the results of this study will bring a new perspective on the generally inferior nestling growth and lower nestling production, which have been repeatedly observed in bird populations of urban and industrial environments (Eeva et al., unpublished), even when direct toxic effects on physiology and performance have not been observed.\u003c/p\u003e\u003cp\u003eIn this article, we address the following questions: \u003cb\u003e1)\u003c/b\u003e Does metal pollution affect the species diversity and richness (alpha diversity) and microbial community composition (beta diversity) of nestlings\u0026rsquo; gut microbiota? Based on earlier studies, metal pollution is known to alter the diversity and metabolic activities of the microbiota (Liu et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Zhang et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), thus, we expect to see differences in microbial composition and diversity in the gut microbiota between the polluted and control areas. \u003cb\u003e2)\u003c/b\u003e Is there interspecific variation in nestling gut microbiota in relation to metal pollution? Our three study species differ from each other in their phenology, diet, nest material composition and tolerance to pollution exposure, thus we expect to see species-specific differences in their microbiota and in the microbiota-mediated responses. 3) Do gut microbial taxa vary in abundance in relation to faecal metal levels? Some bacteria can tolerate or be resistant to certain metals, thus potentially varying in abundance between the polluted and clean areas.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study area and study species\u003c/h2\u003e\u003cp\u003eThe fieldwork was conducted in an established nest box study area close to a copper-nickel smelter in Harjavalta (61\u0026deg;20\u0026acute; N, 22\u0026deg;10\u0026acute;E), an area with the highest rates of metal pollution in Finland. A long-term nest box scheme has been running in this area since 1991 and there is ample information on metal exposure levels (e.g. in bird faeces, Berglund et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), genetic and physiological effects (Koivula et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and fitness effects (Eeva \u0026amp; Lehikoinen, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) on birds from this area. Nine study sites, each with 20\u0026ndash;60 nest boxes, were divided to polluted (4 sites\u0026thinsp;\u0026lt;\u0026thinsp;2 km from the smelter) and control (5 sites\u0026thinsp;\u0026gt;\u0026thinsp;5 km from the smelter) areas, based on the previous data showing that the metal concentrations decrease exponentially with increasing distance to the pollution source (Koivula et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The habitat type in all study sites was relatively barren forest dominated by Scots pine (\u003cem\u003ePinus sylvestris\u003c/em\u003e), thus minimizing habitat-related variation between the areas. However, vegetation near the smelter has suffered from the long-term pollution and the ground layer vegetation cover is patchy at more heavily polluted locations (Kiikkil\u0026auml;, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). All nest boxes were carefully cleaned from old nest materials before the breeding season.\u003c/p\u003e\u003cp\u003eThree insectivorous cavity-breeding passerine birds, the great tit (\u003cem\u003eParus major\u003c/em\u003e), blue tit (\u003cem\u003eCyanistes caeruleus\u003c/em\u003e), and pied flycatcher (\u003cem\u003eFicedula hypoleuca\u003c/em\u003e) were used as model species. Great tit and blue tit are resident species in Finland, while pied flycatchers winter in Western Africa (Lundberg \u0026amp;Alatalo \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). All three species are abundant in our study areas and breed in the nest boxes, making them ideal species to study the associations between anthropogenic pollution and early-life microbiota of the birds.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Sampling in the field\u003c/h2\u003e\u003cp\u003eDuring the breeding season 2021, we randomly selected 44 nests from polluted sites (great tit n\u0026thinsp;=\u0026thinsp;16, blue tit n\u0026thinsp;=\u0026thinsp;13 and pied flycatcher n\u0026thinsp;=\u0026thinsp;15) and 45 nests from control sites (great tit n\u0026thinsp;=\u0026thinsp;15, blue tit n\u0026thinsp;=\u0026thinsp;15 and pied flycatcher n\u0026thinsp;=\u0026thinsp;15) for sampling. The timing of breeding was standardized by avoiding late or replacement nests. Small temperature and humidity data loggers (iButtons\u0026reg; DS1923-F5# Hygrochron, Maxim Integrated Products) were placed inside the nest boxes to measure ambient growing conditions for microbes every three hours. The nest boxes were inspected from the beginning of the breeding season until the fledging phase to collect data on hatchability, brood size, growth and fledgling success. Any microbial cross-contamination was avoided by wearing sterile gloves when there was a need to touch the nest or nestlings. At the average age of 8 days (range 5\u0026ndash;11 days), the chicks were individually ringed with aluminium rings and the body mass (Pesola spring balance, g) and wing length (mm) were measured. In addition, a pooled faecal sample was taken from each brood for gut microbiota sequencing and metal analyses. Fresh faecal samples were stored in sterile polypropylene tubes, placed in a portable cooler in the field and frozen at -20\u0026deg;C until the final storage at -80\u0026deg;C before the DNA extractions. The experiments were conducted under licenses of the Regional State Administrative Agency for Southern Finland (license number ESAVI/3021/04.10.07/2017) and the Centre for Economic Development, Transport and the Environment of Southwest Finland (licence number VARELY/3622/2017).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Metal analyses\u003c/h2\u003e\u003cp\u003eThe concentrations of five metals (Arsenic As, Cadmium Cd, Copper Cu, Nickel Ni and Zinc Zn) were determined from the faecal samples in the CEBAS-CSIC laboratory (University of Murcia, Spain). Metal concentrations were analysed with an inductively coupled plasma optical emission spectrometer (ICP-OES, Thermo ICAP 6500 Duo) with the quantification limit of 0.01 ppm. Faecal samples (0.1\u0026thinsp;\u0026minus;\u0026thinsp;0.2 g, dry weight) were placed in digestion tubes to which a mixture of 4 ml HNO\u003csub\u003e3\u003c/sub\u003e (70%) and 1ml H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (33%) was added. The sample was then submitted to a progressive thermal treatment and, after a microwave procedure, the sample was diluted in ultrapure water before the analysis (see Esp\u0026iacute;n et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Precision of the method was tested with certified reference material (TORT-2, lobster hepatopancreas, National Research Council Canada) and element recoveries were found to vary between 115\u0026ndash;169%. Because of the relatively high recovery values we did not use absolute values in the analyses, but instead principal components calculated from those five metals as an index of metal exposure levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. DNA extraction and library preparation\u003c/h2\u003e\u003cp\u003eAll molecular work including DNA extraction, NGS library preparation, and sequencing as well as bioinformatics were carried out as turnkey service by DNA analysis company Bioname (Turku, Finland). In summary, bacterial DNA was extracted from the faecal samples using Quick-DNA Fecal/Soil microbe Miniprep Kit (Zymo Research). Microbial 16S rRNA gene V4 region was amplified using one primer pair: forward primer 515FB (GTGYCAGCMGCCGCGGTAA, see (Parada et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Walters et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and reverse primer 806RB (GGACTACNVGGGTWTCTAAT, see Apprill et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Caporaso et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Next-Generation Sequencing libraries for sequencing were prepared by using dual indexing approach in subsequent library PCR. The sequencing was performed on the Illumina NovaSeq6000 SP Flowcell using 2 x 250 bp paired-end read length (Illumina Inc. San Diego, California, USA) in the Finnish Functional Genomics Centre (FFGC, University of Turku, Finland). Detailed molecular workflow is available as Supplement text 1: \u0026ldquo;Molecular analysis\u0026rdquo;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Bioinformatics\u003c/h2\u003e\u003cp\u003eOur bioinformatics workflow closely followed Kaunisto et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Shortly, the raw reads were trimmed, merged, the PCR primers were removed using the software CUTADAPT 2.7 (Martin, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), reads were dereplicated, and then collapsed into sequence variants (ZOTUs) using \u0026lsquo;unoise3\u0026rsquo; in USEARCH 11 (Edgar, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The number of ZOTUs in each sample was assessed and all ZOTUs were assigned to taxa using USEARCH/VSEARCH SINTAX algorithm using pre-built database (\u003cem\u003e16S RDP training set v16\u003c/em\u003e) downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://drive5.com/usearch/manual/sintax_downloads.html\u003c/span\u003e\u003cspan address=\"https://drive5.com/usearch/manual/sintax_downloads.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; Edgar, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Finally, reads were filtered for non-target reads, such as Chloroplast DNA. Low abundance ZOTU\u0026rsquo;s (=\u0026thinsp;read count less than 2) were removed from the data. After final filtering, majority of the reads were retained in the data, including 20 984 942 reads (~\u0026thinsp;177 800 reads per sample). Detailed bioinformatics have been collected in the Supplement text 2: \u0026ldquo;Bioinformatics\u0026rdquo;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Statistics\u003c/h2\u003e\u003cp\u003eMost of the statistical analyses were performed either with R (v. 4.2.3; R Core Team (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)) or SAS 9.4 statistical software (SAS \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Prior to further statistical analyses, the data was transformed to a phyloseq object for R. Subsequently, the microbial data was rarefied to a depth of 29 000 reads (phyloseq 1.42.0 in R, (McMurdie \u0026amp; Holmes \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) to account for the differences in the sequencing depth. The rarefaction cut off was determined based on the plateauing of rarefaction curves, i.e., the point where no significant number of additional taxa would have been found with a greater sequencing depth (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The final data included 6918 taxa and 84 samples in total.\u003c/p\u003e\u003cp\u003eSince the faecal metal concentrations correlated with each other, we calculated principal components (PC) from the metal data (Ni, Cu, As, Cd and Zn) with SAS 9.4. The first principal component (PC1\u003csub\u003eM\u003c/sub\u003e; Ni, Cu, As) explained 56.2% (eigenvalue 2.81) of the variation in our metal data and was used in the models as an explanatory variable to describe the general level of metal exposure. The second component (PC2\u003csub\u003eM\u003c/sub\u003e; Zn, Cd) explained 23.4% (eigenvalue 1.17) of the variation in our data. Promax rotation method, which allows components to be correlated, was used in the analysis.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.6.1. Differential abundance analyses\u003c/h2\u003e\u003cp\u003eFive differential abundance (DA) estimators ALDEx2 (1.30.0) (Fernandes et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), ANCOM-BC2 (2.0.2) (Lin \u0026amp;Das Peddada \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Corncob (0.3.1) (Martin et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), DESeq2 (1.38.3) (Love et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and LinDA (0.1.0) (Zhou et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were used to identify the bacteria affected by study area (polluted, control) at different taxonomic levels: phyla, class, order, family and genus. Non-rarefied observed abundances were used as input and the estimators queried at a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after Benjamini-Hochberg adjustment. Nearing et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have shown a high variation between the DA estimators across numerous 16S rRNA datasets, which is why we used the consensus of multiple pre-selected estimators for the analysis. Briefly, different DA estimators showed various amounts of differentially abundant taxa between the study areas. While DESeq2 and Corncob found 151 (63.6% unique to this estimator) and 128 (64.1% unique to this estimator) DA taxa in total (including all bird species and taxa), respectively, ANCOM-BC2 and LinDA found only 27 (18.5% unique to this estimator) and 25 (12.0% unique to this estimator) DA taxa, respectively. ALDEx2 found no differentially abundant taxa in any of the species. Prior to the DA analyses the prevalence threshold was set to 10% by species.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.6.2. Alpha diversity\u003c/h2\u003e\u003cp\u003eShannon diversity index (i.e., a number of bacterial ZOTUs and their abundance evenness within a sample), Chao1 richness (estimate of the total number of different bacterial ZOTUs in a sample) and observed richness (observed number of ZOTUs) were used as measures of alpha diversity of the nestling gut microbiota (microbiome 1.20.0. package in R, (Lahti \u0026amp;Shetty \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thereafter, each diversity index was used as a response variable separately in the general (LM) or generalized (GLM) linear model (GLIMMIX procedure in SAS 9.4) using study area, temperature, nestling age and brood size at the time of sampling, and nestling relative body mass (RBM) as explanatory variables. The three species were analysed separately, since they differ in their feeding habits and migratory behaviour from each other. The RBM is a proportional (%) deviation of brood mean body mass from predicted mass based on long-term (years 1991\u0026ndash;2022) growth curve from the same area. Using RBMs is reasonable for taking account of the slight variation in among-brood measurement ages. Temperature denotes average daily temperature inside the nest box from hatching to the brood age of 8 days, which was the average day for the microbial sampling. For Shannon index we used beta distribution and logit link function, whereas for Chao1 index and observed richness we used lognormal distribution with identity link function. Non-significant terms were dropped sequentially from the final model, but the study area (polluted, control) was always kept in the model, as this was our main study factor.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.6.3. Beta diversity\u003c/h2\u003e\u003cp\u003eBeta diversity was analysed with permutational multivariate analysis of variance (PERMANOVA with Bray-Curtis distances) using 999 permutations (\u0026ldquo;adonis2\u0026rdquo; in \u003cem\u003evegan 2.6.4\u003c/em\u003e, (Oksanen et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to test the community dissimilarity between the study areas and species. In the model, we used study species, area, temperature (inside the nest box), age of nestlings at sampling, brood size, nestling body mass and study area \u0026times; species interaction as explanatory factors. Pairwise comparisons between the species were calculated (\u003cem\u003epairwise.adonis\u003c/em\u003e 0.4.1,(Martinez Arbizu \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) with Bray-Curtis method using 999 permutations and Bonferroni correction to adjust p values. To visualize the dissimilarity among species and study areas, we used principal coordinate analysis (PCoA) ordination based on Bray-Curtis distances (phyloseq 1.42.0, McMurdie and Holmes, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The samples were plotted according to the first and second component values. The analysis and visualization were performed for non-rarefied and rarefied data with relative abundance, but since the results did not change, we chose to use the rarefied data like in alpha diversity measurements.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.6.4. Effects on life-history\u003c/h2\u003e\u003cp\u003eWe performed principal component analysis (PCA, SAS 9.4) from the log-transformed reads of 25 orders (thresholds of 5% prevalence and 1% abundance) to get fewer metrics of bacterial orders for further analyses and to visualize the bacterial data between the species and study areas. Based on the eigenvalues (\u0026gt;\u0026thinsp;1.5) and proportion (\u0026gt;\u0026thinsp;6%), only first four components were chosen for the later analyses. The first component (PC1\u003csub\u003eB\u003c/sub\u003e; Acidimicrobiales\u003csup\u003e+\u003c/sup\u003e, Actinomycetales\u003csup\u003e+\u003c/sup\u003e, Gaiellales\u003csup\u003e+\u003c/sup\u003e, Solirubrobacterales\u003csup\u003e+\u003c/sup\u003e, Bacillales\u003csup\u003e+\u003c/sup\u003e, Planctomycetales\u003csup\u003e+\u003c/sup\u003e, Rhodobacterales\u003csup\u003e+\u003c/sup\u003e) explained 23.4% (eigenvalue 5.86), the second component (PC2\u003csub\u003eB\u003c/sub\u003e; Coriobacteriales\u003csup\u003e+\u003c/sup\u003e, Bacteroidales\u003csup\u003e+\u003c/sup\u003e, Clostridiales\u003csup\u003e+\u003c/sup\u003e, Erysipelotrichales\u003csup\u003e+\u003c/sup\u003e, Desulfovibrionales\u003csup\u003e+\u003c/sup\u003e, Sphingomonadales\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e) 12.5% (eigenvalue 3.12), the third component (PC3\u003csub\u003eB\u003c/sub\u003e; Caulobacterales\u003csup\u003e+\u003c/sup\u003e, Burgholderiales\u003csup\u003e+\u003c/sup\u003e) 7.4% (eigenvalue 1.86) and the fourth component (PC4\u003csub\u003eB\u003c/sub\u003e; Chlamydiales\u003csup\u003e+\u003c/sup\u003e, Legionellales\u003csup\u003e+\u003c/sup\u003e, Entomoplasmatales\u003csup\u003e+\u003c/sup\u003e) 6.6% (eigenvalue 1.64) of the variation in our data. The +\u0026thinsp;and \u0026ndash; signs refer to positive and negative loadings of the bacteria in each component. For visualization we used a biplot based on the bacterial PCs (SAS). Those four components were further used in separate models as dependent variables to describe the gut bacterial composition of the nestlings.\u003c/p\u003e\u003cp\u003eTo investigate the variation in gut bacteria abundances, we performed GLM separately for bacterial principal components PC1\u003csub\u003eB\u003c/sub\u003e (GLM with beta distribution and logit link function), PC2\u003csub\u003eB\u003c/sub\u003e, PC3\u003csub\u003eB\u003c/sub\u003e and PC4\u003csub\u003eB\u003c/sub\u003e (LM with Gaussian distribution and identity link function) in SAS. We used PC1\u003csub\u003eM\u003c/sub\u003e of the metals, species, brood size at sampling time, temperature (inside the nest box), sampling date, and RBM as explanatory variables in the models. Non-significant terms were dropped sequentially from the final model, but the PC1\u003csub\u003eM\u003c/sub\u003e was always kept in the model, as this was our main study factor. Tukey\u0026rsquo;s test was used for post-hoc pairwise comparisons. We assessed the collinearity of the main predictors by variance inflation factor (VIF) analyses.\u003c/p\u003e\u003cp\u003eWe further tested the association of metals with fledging success (fledglings/hatchlings) (GLM with binomial distribution and logit link function) and RBM (LM with Gaussian distribution and identity link function). The models were performed separately for each study species, using PC1\u003csub\u003eM\u003c/sub\u003e of metals, nestling body mass (fledging success model only), temperature inside the nest box, PC1\u003csub\u003eB\u003c/sub\u003e and PC2\u003csub\u003eB\u003c/sub\u003e of faecal microbes at order level as explanatory variables in the model.\u003c/p\u003e\u003cp\u003eBased on the DA results, the microbes that significantly differed between the study areas were chosen for the further analyses to see whether those microbes show potential association on fledging success (probability of a hatchling to fledge; GLM with binomial distribution and logit link function, SAS) and RBM (LM with Gaussian distribution and identity link function, SAS).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Core gut microbiota\u003c/h2\u003e\n \u003cp\u003eFirmicutes (39.6%), Proteobacteria (30.4%) and Actinobacteria (13.5%) were the most abundant bacterial phyla across all species (great tit, blue tit and pied flycatcher) and study areas followed by Tenericutes (7.1%), Clamydiae (5.1%) and Bacteroidetes (2.2%) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). The three most common phyla were, in varying order, the same for all species. The most common phyla in great tits and pied flycatchers were Firmicutes (great tit: 46.7%, pied flycatcher: 43.7%), Proteobacteria (great tit: 21.5%, pied flycatcher: 33.4%) and Actinobacteria (great tit: 15.1%, pied flycatcher: 10.2%), whereas Proteobacteria (37.9%) dominated in blue tits, followed by Firmicutes (25.6%) and Actinobacteria (15.5%, see table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The predominant bacterial orders in great tits and blue tits were Lactobacillales (great tit:28.8%, blue tit:15.8%), followed by Clostridiales (13.7%) and Actinomycetales (13.1%) in great tits, and Enterobacteriales (12.1%) and Actinomycetales (12%) in blue tits. In pied flycatchers, the orders Clostridiales (22.5%), Lactobacillales (18.3%) and Legionellales (16.1%) dominated the faecal microbiota (see table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The species showed some more variation of bacterial taxa at genus level; \u003cem\u003eCatellicoccus\u003c/em\u003e (13.0%), \u003cem\u003eUreaplasma\u003c/em\u003e (8.1%) and \u003cem\u003eClostridium sensu stricto\u003c/em\u003e (6.6%) dominated the faecal gut bacteria in great tits, whereas \u003cem\u003eUreaplasma\u003c/em\u003e (8.2%), \u003cem\u003eClostridium sensu stricto\u003c/em\u003e (6.8%) and \u003cem\u003eBuchnera\u003c/em\u003e (6.2%) were the most dominant genera in blue tits. The most abundant faecal gut bacteria in pied flycatchers were \u003cem\u003eDiplorickettsia\u003c/em\u003e (16.1%), \u003cem\u003eClostridium sensu stricto\u003c/em\u003e (15.7%) and \u003cem\u003eLactobacillus\u003c/em\u003e (5.9%) (table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Differential abundance analyses\u003c/h2\u003e\n \u003cp\u003eFive DA estimators were used to identify the bacteria associated with study area (polluted, control) at different taxonomic levels (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The results varied among the estimators, thus the responses of bacterial taxa in which three or more of the five estimators indicated a significant response were considered significant (see Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). In blue tits, the phylum Candidatus Saccharibacteria was more abundant in the polluted area compared to control area. In pied flycatchers, the order Rhodobacterales and family \u003cem\u003eRhodobacteraceae\u003c/em\u003e showed significantly greater abundance in polluted areas compared to control areas, whereas the family \u003cem\u003eClostridialis Incertae Sedis XIII\u003c/em\u003e was more abundant in control areas than in the polluted areas. At genus level, \u003cem\u003eAnaerovorax\u003c/em\u003e, \u003cem\u003eCatabacter\u003c/em\u003e and \u003cem\u003eEubacterium\u003c/em\u003e were more abundant in control areas compared to polluted areas in pied flycatchers, while in great tits \u003cem\u003eArthrobacter\u003c/em\u003e and \u003cem\u003eRhodococcus\u003c/em\u003e were more abundant, but \u003cem\u003eCatellicoccus\u003c/em\u003e and \u003cem\u003eSerratia\u003c/em\u003e less abundant in control areas compared to polluted areas. All significant comparisons (log2-fold change, SE, 95% CI and adj. p-value) are indicated in Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e. The significant results of all DA estimators (log2-fold change, SE, 95% CI and adj. p-value) are shown as supplementary material (Table S3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Alpha diversity\u003c/h2\u003e\n \u003cp\u003eWe used Shannon index, Chao1 richness (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) and observed richness as measures of alpha diversity of the nestling gut microbiota. Shannon diversity was negatively associated with brood size at sampling time in great tits and blue tits, and positively with temperature inside the nest box in blue tits, while no associations were found with RBM in the two tit species (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). On the other hand, in pied flycatchers, higher RBM was associated with increased Shannon diversity, but not to brood size or temperature. Study area (polluted, control) or nestling age at sampling time were not associated with Shannon index in any of the species (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Observed richness and Chao1 richness index were not significantly associated with study area, nestling age, brood size at sampling time or temperature in any of the species. However, as opposed to tit species, in pied flycatchers, both richness indexes showed a significant positive association with RBM, suggesting higher overall microbial richness in well growing chicks.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe association of area (polluted, control), temperature (inside the nest box), nestling age, brood size, relative body mass (RBM), and species and area*species interaction (PERMANOVA only) on alpha diversity (Shannon index, Chao1 richness and observed richness) and beta diversity (community composition) measures in the nestlings of great tits (P. major), blue tits (C. caeruleus) and pied flycatchers (F. hypoleuca). Non-significant terms were dropped sequentially from each model, starting from interaction. Terms left in the final model are shown in bold.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eGreat tit\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlfa-diversity (GLM)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eShannon index*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChao1**\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eObs. richness**\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003csub\u003edf\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFdf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFdf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.97\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 27\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.096\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.31\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 28\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.140\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.76\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 28\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.108\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67\u003csub\u003e1, 26\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003csub\u003e1, 25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003csub\u003e1, 24\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003csub\u003e1, 24\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003csub\u003e1, 26\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003csub\u003e1, 25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrood size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.52\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 27\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e -0.11, \u003cstrong\u003eSE\u003c/strong\u003e 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97\u003csub\u003e1, 27\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05\u003csub\u003e1, 27\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003csub\u003e1, 25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31\u003csub\u003e1, 24\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60\u003csub\u003e1, 26\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlue tit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.77\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 20\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.198\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.50\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 22\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.233\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.82\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 22\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.191\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.52\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 20\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e 0.14, \u003cstrong\u003eSE\u003c/strong\u003e 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86\u003csub\u003e1, 21\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09\u003csub\u003e1, 21\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80\u003csub\u003e1, 19\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003csub\u003e1, 19\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003csub\u003e1, 19\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrood size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.42\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 20\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e -0.18, \u003cstrong\u003eSE\u003c/strong\u003e 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07\u003csub\u003e1, 20\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.26\u003csub\u003e1, 20\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003csub\u003e1, 18\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003csub\u003e1, 18\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003csub\u003e1, 18\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ePied flycatcher\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.45\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 27\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.508\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.35\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 27\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.560\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.26\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 27\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.612\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003csub\u003e1, 25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003csub\u003e1, 24\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003csub\u003e1, 24\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003csub\u003e1, 24\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003csub\u003e1, 26\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u003csub\u003e1, 26\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrood size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003csub\u003e1, 26\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003csub\u003e1, 25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003csub\u003e1, 25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.21\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 27\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e 0.03, \u003cstrong\u003eSE\u003c/strong\u003e 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.46\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 27\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e 0.05, \u003cstrong\u003eSE\u003c/strong\u003e 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.10\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 27\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e 0.05, \u003cstrong\u003eSE\u003c/strong\u003e 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta-diversity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePERMANOVA***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.32\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 82\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.14\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2, 82\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.69\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 82\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea*Species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003csub\u003e2, 78\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003csub\u003e1, 81\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrood size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003csub\u003e1, 80\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.33\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 82\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.053\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003eGLM Beta distribution, logit link function.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003e**\u003c/sup\u003e GLM Lognormal distribution, identity link function.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003e***\u003c/sup\u003e PERMANOVA, reduced model (vegan, adonis2)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Beta diversity\u003c/h2\u003e\n \u003cp\u003eBray-Curtis dissimilarity-based microbial beta diversity was assessed to illustrate the differences in microbial communities between the study areas and study species (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The PCoA ordinations with Bray-Curtis dissimilarity index showed significant differences in microbial composition between the study areas (polluted, control) and study species (great tit, blue tit and pied flycatcher) and had significant association with temperature (PERMANOVA, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The pied flycatchers had a significantly different bacterial composition compared to great tits (F\u003csub\u003edf\u003c/sub\u003e=2.70\u003csub\u003e1\u003c/sub\u003e, p\u003csub\u003eadj\u003c/sub\u003e=0.003) and blue tits (F\u003csub\u003edf\u003c/sub\u003e=2.36\u003csub\u003e1\u003c/sub\u003e, p\u003csub\u003eadj\u003c/sub\u003e=0.003), while the two tit species did not differ significantly from each other (F\u003csub\u003edf\u003c/sub\u003e=1.18\u003csub\u003e1\u003c/sub\u003e, p\u003csub\u003eadj\u003c/sub\u003e=0.552). However, nestling age, brood size at sampling and nestling body mass had no association with the gut microbial composition (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Association of metals and life-history parameters on gut bacteria\u003c/h2\u003e\n \u003cp\u003ePrincipal components were calculated from the read counts of the bacterial orders and the first four components were further used in the models to study the association of faecal metal levels, temperature, and life-history parameters with bacterial taxa. The PC1\u003csub\u003eB\u003c/sub\u003e (including orders Acidimicrobiales, Actinomycetales, Gaiellales, Solirubrobacterales, Bacillales, Planctomycetales, Rhodobacterales) increased significantly with faecal metal concentrations (PC1\u003csub\u003eM\u003c/sub\u003e of metals), temperature, and RBM (used as a measure of growth, Table 2). Species did not differ from each other in relation to PC1\u003csub\u003eB\u003c/sub\u003e, and brood size and sampling date had no significant effect on PC1\u003csub\u003eB\u003c/sub\u003e (Table 2). In contrast, PC2\u003csub\u003eB\u003c/sub\u003e (including orders Coriobacteriales, Bacteroidales, Clostridiales, Erysipelotrichales, Desulfovibrionales, Sphingomonadales) had no significant association with faecal metal concentration but showed significant negative association with temperature and RBM (Table 2). On the other hand, PC2\u003csub\u003eB\u003c/sub\u003e increased significantly with brood size and sampling date (Table 2). The PC2\u003csub\u003eB\u003c/sub\u003e also differed significantly between the species; pied flycatcher showed higher bacterial abundance compared to great tits (Tukey\u0026rsquo;s test: t\u003csub\u003edf\u003c/sub\u003e=3.51\u003csub\u003e75\u003c/sub\u003e, p\u003csub\u003eadj\u003c/sub\u003e=0.002) and blue tits (Tukey\u0026rsquo;s test: t\u003csub\u003edf\u003c/sub\u003e=-4.71\u003csub\u003e75\u003c/sub\u003e, p\u003csub\u003eadj\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), which did not differ significantly from each other (Tukey\u0026rsquo;s test: t\u003csub\u003edf\u003c/sub\u003e=-1.78\u003csub\u003e75\u003c/sub\u003e, p\u003csub\u003eadj\u003c/sub\u003e=0.184). The PC3\u003csub\u003eB\u003c/sub\u003e, including Caulobacterales and Burkholderiales, did not show any association with faecal metal concentration or any other studied parameters (Table\u0026nbsp;2). The PC4\u003csub\u003eB\u003c/sub\u003e, which included bacterial orders Chlamydiales, Legionellales and Entomoplasmatales, was not affected by faecal metal concentrations, temperature, brood size or RBM, but showed a negative association with sampling date (Table\u0026nbsp;2). PC4\u003csub\u003eB\u003c/sub\u003e differed significantly between the species, pied flycatchers having significantly higher bacterial abundance compared to great tits (t\u003csub\u003edf\u003c/sub\u003e=3.43\u003csub\u003e78\u003c/sub\u003e, p\u003csub\u003eadj\u003c/sub\u003e=0.003) and blue tits (t\u003csub\u003edf\u003c/sub\u003e=-2.49\u003csub\u003e78\u003c/sub\u003e, p\u003csub\u003eadj\u003c/sub\u003e=0.039), while the two tit species did not differ significantly from each other (t\u003csub\u003edf\u003c/sub\u003e=0.95\u003csub\u003e78\u003c/sub\u003e, p\u003csub\u003eadj\u003c/sub\u003e=0.608).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Fledging success and RBM\u003c/h2\u003e\n \u003cp\u003eBoth fledging success and RBM were significantly decreased with increased faecal metal concentration (PC1\u003csub\u003eM\u003c/sub\u003e of metals) in great tits and blue tits, but not in pied flycatchers (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In blue tits, the fledging success increased significantly with warmer temperature and was positively associated with PC2\u003csub\u003eB\u003c/sub\u003e of bacterial taxa, while such effects were not found in the other species (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The fledging success had no association with nestling body mass or PC1\u003csub\u003eB\u003c/sub\u003e in any of the three species (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In pied flycatchers, the RBM associated positively with PC1\u003csub\u003eB\u003c/sub\u003e, but negatively with PC2\u003csub\u003eB\u003c/sub\u003e, whereas such associations were not found in the tit species (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The RBM was not significantly associated with temperature in any of the species (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe association of metal levels (PC1M) temperature (inside the nest box), body mass and bacterial components PC1B and PC2B on fledging success and relative body mass in the nestlings of great tits (P. major), blue tits (C. caeruleus) and pied flycatchers (F. hypoleuca). Non-significant terms were dropped sequentially from each model, starting from interaction. Terms left in the final model are shown in bold.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGreat tit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBlue tit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePied flycatcher\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFledging success*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003csub\u003edf\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003csub\u003edf\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003csub\u003edf\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC1\u003csub\u003eM\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.16\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 29\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e -0.55, \u003cstrong\u003eSE\u003c/strong\u003e 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.11\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 19\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e -0.84, \u003cstrong\u003eSE\u003c/strong\u003e 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.66\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 28\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.425\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.48\u003csub\u003e1, 28\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99\u003csub\u003e1, 18\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003csub\u003e1, 27\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003csub\u003e1, 24\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.47\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 19\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e 0.26, \u003cstrong\u003eSE\u003c/strong\u003e 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003csub\u003e1, 26\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC1\u003csub\u003eB\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003csub\u003e1, 25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003csub\u003e1, 17\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003csub\u003e1, 24\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC2\u003csub\u003eB\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003csub\u003e1, 26\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.95\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 19\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e 0.71, \u003cstrong\u003eSE\u003c/strong\u003e 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003csub\u003e1, 25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative body mass**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC1\u003csub\u003eM\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.97\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 29\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e -12.11, \u003cstrong\u003eSE\u003c/strong\u003e 2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.19\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 24\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e -12.71, \u003cstrong\u003eSE\u003c/strong\u003e 3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.12\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 26\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.089\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003csub\u003e1, 26\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u003csub\u003e1, 23\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003csub\u003e1, 25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC1\u003csub\u003eB\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.25\u003csub\u003e1, 27\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003eest. 1.97, SE 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003csub\u003e1, 18\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e20.01\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 26\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e 2.87, \u003cstrong\u003eSE\u003c/strong\u003e 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC2\u003csub\u003eB\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003csub\u003e1, 25\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u003csub\u003e1, 19\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.80\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1, 26\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eest.\u003c/strong\u003e -2.19, \u003cstrong\u003eSE\u003c/strong\u003e 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e*GLM (binomial distribution, logit link function)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e** LM (Gaussian distribution, identity link function)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eWe further tested whether the bacterial families and genera that differed in their abundance between the study areas (DA analyses) showed any effects on fledging success or RBM. The families \u003cem\u003eRhodobacteraceae\u003c/em\u003e and \u003cem\u003eClostridiales Incertae Sedis XIII\u003c/em\u003e and genera \u003cem\u003eAnaerovorax\u003c/em\u003e, \u003cem\u003eCatabacter\u003c/em\u003e and \u003cem\u003eEubacterium\u003c/em\u003e had no significant association on fledging success or RBM in pied flycatchers (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In great tits, the genera \u003cem\u003eCatellicoccus\u003c/em\u003e and \u003cem\u003eSerratia\u003c/em\u003e had a negative association with fledging success (\u003cem\u003eCatellicoccus\u003c/em\u003e: F\u003csub\u003edf\u003c/sub\u003e=11.65\u003csub\u003e1, 28\u003c/sub\u003e, p\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003eSerratia\u003c/em\u003e: F\u003csub\u003edf\u003c/sub\u003e=5.34\u003csub\u003e1, 28\u003c/sub\u003e, p\u0026thinsp;=\u0026thinsp;0.028) and RBM (\u003cem\u003eCatellicoccus\u003c/em\u003e: F\u003csub\u003edf\u003c/sub\u003e=8.65\u003csub\u003e1, 28\u003c/sub\u003e, p\u0026thinsp;=\u0026thinsp;0.007, \u003cem\u003eSerratia\u003c/em\u003e: F\u003csub\u003edf\u003c/sub\u003e=6.52\u003csub\u003e1, 28\u003c/sub\u003e, p\u0026thinsp;=\u0026thinsp;0.016), while \u003cem\u003eRhodococcus\u003c/em\u003e had no significant effects on fledging success (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but showed positive association on RBM (F\u003csub\u003edf\u003c/sub\u003e=7.61\u003csub\u003e1, 28\u003c/sub\u003e, p\u0026thinsp;=\u0026thinsp;0.010). \u003cem\u003eArthrobacter\u003c/em\u003e had no significant effects on fledging success or RBM of great tit nestlings (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Core gut microbiota in three passerine species\u003c/h2\u003e\u003cp\u003eFirmicutes, Proteobacteria and Actinobacteria were the major gut bacterial phyla across our study species, consistent with earlier avian studies (Bodawatta et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, Drobniak et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Fu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Grond et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Kim et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Krop\u0026aacute;čkov\u0026aacute; et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Liukkonen et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Firmicutes, the most abundant phyla in great tits and pied flycatchers, play a crucial role in fermenting organic molecules to supply energy for the host (Flint et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Turnbaugh et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), influence weight gain and fat storage in chickens (Angelakis \u0026amp;Raoult \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and mammals (Turnbaugh et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and aid in metabolism, digestion, and protein absorption within the gut (Grond et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, Proteobacteria dominated the blue tit gut microbiota. Despite the higher proportion of Proteobacteria in birds compared to mammals, their functions remain poorly understood. Nevertheless, Proteobacteria include several opportunistic pathogens like \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eRickettsia\u003c/em\u003e and \u003cem\u003eSalmonella\u003c/em\u003e (Diakou et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Keller et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Ryu et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Wallmenius et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). They have been associated with metabolic and immune disorders as well as ecological imbalances within the gut thereby posing potential threats to host health (Colston \u0026amp;Jackson \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Actinobacteria, the third most abundant phylum across species, inhabit diverse environments, including terrestrial and marine habitats as well as gastrointestinal tracts. They include pathogens like \u003cem\u003eMycobacterium\u003c/em\u003e, but also commensal bacteria utilized as probiotics in animals (Barka et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Grond et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Kailasapathy \u0026amp;Chin \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBacterial orders Lactobacillales, Clostridiales, and Actinomycetales were found across all three species, with more species-specific variation observed at lower taxonomic levels. The genus \u003cem\u003eClostridium sensu stricto\u003c/em\u003e, known for containing significant human and animal pathogens (Li et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), was prevalent in all studied species, along with \u003cem\u003eEnterococcus\u003c/em\u003e and \u003cem\u003eDiplorickettsia\u003c/em\u003e in great tits and pied flycatchers. \u003cem\u003eEnterococci\u003c/em\u003e, typically part of the natural gut flora and beneficial due to probiotic effects (Fisher \u0026amp;Phillips \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Kwit et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Stępień-Pyśniak et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), can also exhibit opportunistic pathogenic behavior and resistance to antibiotics. Wild birds serve as important reservoirs of enterococci (Kwit et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), potentially facilitating microbial transfer between wild and domesticated animals (Ben Yahia et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Marrow et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Radhouani et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). \u003cem\u003eLactobacillus\u003c/em\u003e, also common in pied flycatchers, offers protection against various bacterial pathogens (Lee et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) by producing antimicrobial substances and aiding in detoxification processes (Hammes \u0026amp; Hertel \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). It has shown efficacy in mitigating the toxic effects of metals such as lead (Pb) and Cd (George et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Shi et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). \u003cem\u003eUreaplasma\u003c/em\u003e, prevalent in gastrointestinal and urogenital tracts of vertebrates (Krop\u0026aacute;čkov\u0026aacute; et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and occasionally contributed to severe pathogenesis (Sumithra et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), was one of the most abundant bacterial genera in both tit species. \u003cem\u003eCatellicoccus\u003c/em\u003e, which is frequently found in the guts of seabirds (G\u0026oacute;ngora et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and some passerines (Benskin et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Kreisinger et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) was the most abundant genus in great tit, whereas \u003cem\u003ePseudomonas\u003c/em\u003e, associated with several diseases in animals and plants, was common in blue tits (Abd El-Ghany \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Eraky et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003ePseudomonas\u003c/em\u003e tended to be more abundant in polluted areas, which may be due to its high resistance to heavy metals, such as Cd, Pb and As (Al-Ansari et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Pramanik et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Zhou et al. \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), making them an interesting taxonomic group in polluted environments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Association between metals and gut bacteria\u003c/h2\u003e\u003cp\u003eWe found 11 taxa that were differentially abundant between the polluted and control areas. Candidatus Saccharibacteria was more abundant in polluted area compared to control area in blue tits. This highly ubiquitous phylum can be found in soils, sediments, wastewater and animals, including earthworms (Rattray et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), mouse (Salzman et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), birds (Ma et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and canines (Dewhirst et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In pied flycatchers, \u003cem\u003eRhodobacteraceae\u003c/em\u003e, which has been shown to be more common in the digestive tract of insectivore than omnivore birds (Bodawatta et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), was more abundant in polluted areas, but did not show effects on fledging success or nestling growth (RBM). In contrast, \u003cem\u003eClostridiales Incertae Sedis XIII\u003c/em\u003e, \u003cem\u003eAnaerovorax\u003c/em\u003e, \u003cem\u003eCatabacter\u003c/em\u003e, and \u003cem\u003eEubacterium\u003c/em\u003e were more abundant in control areas compared to polluted areas. Similarly, these bacteria exhibited no adverse fitness effects on pied flycatchers. In great tits, \u003cem\u003eSerratia\u003c/em\u003e and \u003cem\u003eCatellicoccus\u003c/em\u003e were more abundant in polluted areas, and both were further associated with reduced fledging success and nestling growth. The finding suggest that these bacterial groups may tolerate metals and/or act as potential opportunistic pathogens. For \u003cem\u003eSerratia\u003c/em\u003e, this effect may be linked to its potential pathogenicity in birds and mammals (Saidenberg et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), despite its relatively low abundance found in the gut. \u003cem\u003eCatellicoccus\u003c/em\u003e, on the other hand, is also suggested to be advantageous to birds by improving their immune response and facilitating nutrient transport (Benskin et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), thereby aiding in the optimization of nutrition under severe conditions (G\u0026oacute;ngora et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Opposite to them, \u003cem\u003eArthrobacter\u003c/em\u003e and \u003cem\u003eRhodococcus\u003c/em\u003e, commonly found in soil, water and plants, were more abundant in control areas. \u003cem\u003eRhodococcus\u003c/em\u003e was positively associated to growth in great tit nestlings, but neither \u003cem\u003eRhodococcus\u003c/em\u003e nor \u003cem\u003eArthrobacter\u003c/em\u003e associated with fledging success. Of these bacteria, most \u003cem\u003eRhodococcus\u003c/em\u003e strains have been found to have very high levels of metal resistance and are potential agent for the bioremediation of pollutants, including heavy metals (Nazari et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the causal link between metal and microbiota interactions with growth is still unclear, but our results could suggest that pollution, through its effects on gut microbiota, may play a role in determining growth conditions. Further, the function of these bacterial taxa in bird gut is poorly known, and future studies should consider combining metabolomics analyses with microbial data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Association of metals with gut bacterial diversity and composition\u003c/h2\u003e\u003cp\u003eThe alpha diversity measured as Shannon diversity index, Chao1 richness, and observed richness did not differ significantly between the polluted and control areas in any of our study species, suggesting no direct link to environmental pollution. However, the overall gut bacterial community composition (beta diversity) differed between the study areas. Reduced richness and changes in the composition of plumage bacteria have been previously reported in birds exposed to Pb (Chatelain et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), whereas birds exposed to Zn have exhibited lower bacterial loads and changes in both plumage- (Chatelain et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and gastrointestinal bacteria community composition (Hojberg et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Vahjen et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Also, decreased alpha diversity of gut microbiota in tree sparrows (\u003cem\u003ePasser montanus\u003c/em\u003e) inhabiting metal-polluted areas has been observed by Zhang et al. (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the effects of metals on microbiota are likely dose-dependent, which could explain the variation across different studies. For example, the Pb and Zn levels in our study area are relatively low compared to heavily polluted areas. Metal-related variation in bacterial communities was further examined at order level by using the principal components of the bacterial taxa. Only PC1\u003csub\u003eB\u003c/sub\u003e showed higher bacterial abundance with increased metal levels. It is possible that these bacterial taxa may tolerate or be resistant to metals and/or may have an increased capacity to detoxify metals (Li et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Mawang et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Nazari et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Presentato et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), thus preventing their harmful effects. However, the causality between the metals and these microbes remains unknown and should be confirmed with experimental studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Species-specific differences in bacterial communities\u003c/h2\u003e\u003cp\u003eThe pied flycatchers had a considerably different overall bacterial community composition compared to great tits and blue tits, while tit species shared a more similar composition. Pied flycatchers further showed clearly higher bacterial abundances of two bacterial components (PC2\u003csub\u003eB\u003c/sub\u003e and PC4\u003csub\u003eB\u003c/sub\u003e) compared to both tit species. This may be linked to the different phenology and/or ecology (e.g. different diet and feeding habits or the different migratory behaviour) of the species. High individual variation has been observed in several migratory passerines (Lewis et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Skeen et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and also between migrants and their resident counterparts (Turjeman et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), potentially due to their exposure to new bacteria at their different breeding areas or stopover sites during migration (Turjeman et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The species-specific differences in bacterial composition, like in PC4\u003csub\u003eB\u003c/sub\u003e consisting mainly pathogenic bacteria, may also support the idea that migratory birds, such as pied flycatchers, harbour a higher abundance of pathogens with potentially higher infection intensity compared to resident species (Clark et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Koprivnikar \u0026amp; Leung \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Leung \u0026amp; Koprivnikar \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Association of gut bacteria with life-history traits and temperature\u003c/h2\u003e\u003cp\u003eThe Shannon diversity index was negatively associated with brood size in great tits and blue tits, which could be related to higher within-brood competition, for example in the case of limited food resources and space or food quality leading to higher stress, as suggested by Somers et al. (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The availability of good-quality insect food is inferior in the polluted area (Eeva et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), which may reflect to the lower alpha diversity of larger broods due to higher food competition. Some other studies have not found a similar association between brood size and microbial alpha diversity in great tits (Liukkonen et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Somers et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), although brood size has been shown to correlate negatively with Proteobacteria abundance, and positively with Firmicutes abundance (Somers et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe further showed that the growth of the pied flycatchers was positively associated with Shannon diversity and bacterial richness. This suggests that heavier nestlings exhibited increased microbial diversity and richness possibly due to their better capacity to gain and/or maintain higher microbial diversity during their developmental stages. \u003cem\u003eEnterococcus\u003c/em\u003e, for example, have been shown to correlate positively with nestling growth in pied flycatcher (Moreno et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and this genus indeed was one of the most common in pied flycatchers in the present study. However, such diversity association was not seen in great tits or blue tits, which may be partly explained by their different diet (mainly caterpillars during the nestling phase) compared to pied flycatchers, which also use flying insects as food source for their nestlings. Heavier nestlings are typically found in control areas with better food availability compared to polluted areas (Eeva et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, as we did not see significant diversity differences between the areas, the higher alpha diversity of heavier nestlings may be influenced by some other environmental factors. On the other hand, both fitness measures, fledging success and nestling growth (RBM), decreased with higher metal levels (PC1\u003csub\u003eM\u003c/sub\u003e) in great tits and blue tits, but not in the pied flycatchers, indicating that pied flycatchers may be less sensitive to metal pollution either directly or indirectly via environmental changes compared to tit species. Diverse gut communities may be resistant to pathogens and less susceptible to environmental perturbation (Buffie \u0026amp;Pamer \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), while lower microbial diversity is often considered detrimental to host species (Le Chatelier et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), potentially leading to reduced nutrient assimilation or immunodeficiency (Teyssier et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similar to alpha diversity, also the abundances of bacteria belonging to PC1\u003csub\u003eB\u003c/sub\u003e were clearly higher in well growing broods, whereas bacterial abundances of PC2\u003csub\u003eB\u003c/sub\u003e increased in relation to reduced growth. The results suggest that PC1\u003csub\u003eB\u003c/sub\u003e includes some beneficial bacteria for nestling growth, whereas potentially pathogenic bacteria, like Clostridiales in PC2\u003csub\u003eB\u003c/sub\u003e, may retard nestling growth. Our results are in accordance with the study of Teyssier et al. (\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who showed that the microbial diversity increased with nestling body mass. Body condition of chicks is important for their survival especially after fledging (Monr\u0026oacute;s et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Rodr\u0026iacute;guez et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Tinbergen \u0026amp; Boerlijst \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) and the role of microbiota may be highlighted at this point of nestling development (Teyssier et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe fledging success was enhanced by warmer temperature in blue tits, but not in the other species. Likewise, the Shannon diversity was linked to increased temperature inside the nest box only in blue tits, suggesting some temperature-derived differences in bacterial taxa. Some bacteria may favour warmer growth conditions thus benefiting higher ambient temperatures. Temperature inside the nest box further contributed to the variation in gut bacterial composition, for example warmer temperature was associated with increased abundance of bacteria belonging to PC1\u003csub\u003eB\u003c/sub\u003e, but decreased abundance of bacteria belonging to PC2\u003csub\u003eB\u003c/sub\u003e. In general, endotherms maintain a relatively constant body temperature, less influenced by environmental condition (Crompton et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). However, the thermoregulation of newly hatched chicks is not fully developed (Dawson \u0026amp; Evans \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1960\u003c/span\u003e, Howell \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1964\u003c/span\u003e, Price \u0026amp; Dzialowski \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) limiting their ability to conserve heat (Dawson \u0026amp;Evans \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1960\u003c/span\u003e, Rodr\u0026iacute;guez \u0026amp; Barba \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which could increase the impact of temperature on their gut microbiota. Environmental temperature is known to modify the gut microbiome of animals (Sepulveda \u0026amp; Moeller \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Tian et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Especially Firmicutes have been shown to be sensitive to temperature variation; their relative abundances and alpha diversity have decreased with increased temperature in both endothermic and ectothermic host species (Bestion et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Fontaine et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Kohl \u0026amp;Yahn \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Zhu et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The mechanism behind the association of temperature and bacteria are unclear, but may be related to the host metabolism and energetics to cope with thermal stress (Sepulveda \u0026amp; Moeller \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Sokolova et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), potentially affecting the host investment in regulating microbiome composition.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe gut bacterial taxa exhibited similarities across the bird species, although greater variation was observed at the genus level compared to higher taxa. The gut bacterial community composition of pied flycatchers differed from great tits and blue tits, while tit species were more alike. This suggests that factors such as diet, phenology, breeding habits, and the migratory status of the birds likely influence the distinct gut microbiota of pied flycatchers. Although metal concentrations were not directly associated with bacterial alpha diversity, certain bacterial taxa appeared to better tolerate increased metal concentrations, being more abundant in polluted areas compared to control areas. These observations may be linked to the enhanced metal tolerance or detoxification capacities of these bacterial orders within the host organisms. Notably, \u003cem\u003eCatellicoccus\u003c/em\u003e and \u003cem\u003eSerratia\u003c/em\u003e that appeared in higher numbers in polluted environments, were further associated with reduced fledging success in great tit nestlings. Additionally, gut bacteria were linked to the brood size, nestling growth, and temperature inside the nest boxes, indicating that gut microbiota is not only affected by environmental pollution but also several other factors, highlighting the complex host-microbe interactions. To understand the complex interactions between the host microbiota and various environmental factors, experimental studies would be necessary. However, our results provide evidence of metal-induced effects on the bacterial community composition of nestlings in a species-specific manner either directly or indirectly via diet or other environmental changes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Jorma Nurmi, Siiri Nyrhil\u0026auml;, Oona Poranen, Jari Lehto, Kalle Rainio, Petri Suorsa and Ville Vasko for the help in field work and Tuija Koivisto and Saara Koutaniemi for sample preparation. We thank Finnish Functional Genomics Centre supported by University of Turku, \u0026Aring;bo Akademi University and Biocenter Finland for Illumina sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe research was funded by the Academy of Finland (project of TE no: SA338180), Sakari Alhopuro Foundation (project of MJR) and Varsinais-Suomi Regional Fund (project of MJR).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCRediT author statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMiia J. Rainio:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Investigation, Writing -Original Draft preparation, Funding acquisition. \u003cstrong\u003eLyydia Leino:\u003c/strong\u003e Methodology, Formal analysis, Investigation, Reviewing and Editing. \u003cstrong\u003eEero Vesterinen:\u003c/strong\u003e Software, Formal analysis, Reviewing and Editing. \u003cstrong\u003ePablo Sanchez Virosta:\u003c/strong\u003e Reviewing and Editing.\u003cstrong\u003e\u0026nbsp;Pere Puigb\u0026ograve;:\u003c/strong\u003e Reviewing and Editing. \u003cstrong\u003eTapio Eeva:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Reviewing and Editing, Supervision, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe experiments were conducted under licenses of the Regional State Administrative Agency for Southern Finland (license number ESAVI/3021/04.10.07/2017) and the Centre for Economic Development, Transport and the Environment of Southwest Finland (licence number VARELY/3622/2017).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eConsent to Participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eConsent to Publish\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eCompeting interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData used in this manuscript can be provided by the authors upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbd El-Ghany WA (2021): \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e infection of avian origin: Zoonosis and one health implications. 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Appl Microbiol Biotechnol 103, 461-472\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"blue tit, microbial diversity, great tit, gut bacteria, pollution, pied flycatcher","lastPublishedDoi":"10.21203/rs.3.rs-6981542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6981542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePollution-induced changes in gut microbiota may have serious effects on avian health and physiology. Particularly, early-life exposure to environmental pollutants may influence the gut microbiota development and microbiota-dependent physiological functions. We studied the associations of metal pollution with early-life microbial environment of wild passerines by exploring gut bacterial diversity and community composition in nestlings by 16S rRNA sequencing. Samples were collected from three common model species, great tits (\u003cem\u003eParus major\u003c/em\u003e), blue tits (\u003cem\u003eCyanistes caeruleus\u003c/em\u003e), and pied flycatchers (\u003cem\u003eFicedula hypoleuca\u003c/em\u003e) breeding near a copper-nickel smelter, an area with the highest rates of metal pollution in Finland. Responses of microbiota to pollution and microclimate were measured and connected to ecological measures (brood size, growth, fledging success) of nestling performance. The most abundant bacterial phyla across species were Firmicutes, Proteobacteria and Actinobacteria. Our results showed that the faecal microbial alpha diversity decreased with increasing brood size in great tits and blue tits, while in pied flycatchers the diversity increased with increasing nestling growth. However, alpha diversity did not differ between the polluted and control areas. The faecal microbial communities (beta diversity) differed between the species and study areas, and several bacterial orders were associated with metal concentrations, temperature, body mass, or fledging success. Differential abundance analyses showed species-specific differences in microbial abundances between polluted and control areas, e.g. \u003cem\u003eCatellicoccus\u003c/em\u003e and \u003cem\u003eSerratia\u003c/em\u003e showing elevated abundance within the polluted area. The results suggest that metal pollution may alter the early-life bacterial communities in small passerine species living in metal-polluted areas.\u003c/p\u003e","manuscriptTitle":"Metal-induced chances in the early-life gut microbial composition and life-history traits in three insectivorous passerines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 12:07:44","doi":"10.21203/rs.3.rs-6981542/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revision","date":"2025-10-02T10:52:18+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-08-12T19:11:50+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-24T15:02:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2025-07-22T12:31:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-07T04:36:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2025-07-03T07:07:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a923a9aa-f79e-4f2e-9e1b-33cb44735522","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:05:40+00:00","versionOfRecord":{"articleIdentity":"rs-6981542","link":"https://doi.org/10.1007/s11356-025-37181-x","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2025-11-21 15:57:44","publishedOnDateReadable":"November 21st, 2025"},"versionCreatedAt":"2025-07-28 12:07:44","video":"","vorDoi":"10.1007/s11356-025-37181-x","vorDoiUrl":"https://doi.org/10.1007/s11356-025-37181-x","workflowStages":[]},"version":"v1","identity":"rs-6981542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6981542","identity":"rs-6981542","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}