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/lp/springer-journals/no-evidence-for-associations-between-brood-size-gut-microbiome-vAjZYBuQA4
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- Springer Journals
- Copyright
- Copyright © The Author(s) 2023
- eISSN
- 2524-4671
- DOI
- 10.1186/s42523-023-00241-z
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- See Article on Publisher Site
Abstract
Background The gut microbiome forms at an early stage, yet data on the environmental factors influencing the development of wild avian microbiomes is limited. As the gut microbiome is a vital part of organismal health, it is important to understand how it may connect to host performance. The early studies with wild gut microbiome have shown that the rearing environment may be of importance in gut microbiome formation, yet the results vary across taxa, and the effects of specific environmental factors have not been characterized. Here, wild great tit (Parus major) broods were manipulated to either reduce or enlarge the original brood soon after hatching. We investigated if brood size was associated with nestling bacterial gut microbiome, and whether gut microbiome diversity predicted survival. Fecal samples were collected at mid‑nestling stage and sequenced with the 16S rRNA gene amplicon sequencing, and nestling growth and survival were measured. Results Gut microbiome diversity showed high variation between individuals, but this variation was not significantly explained by brood size or body mass. Additionally, we did not find a significant effect of brood size on body mass or gut microbiome composition. We also demonstrated that early handling had no impact on nestling performance or gut microbiome. Furthermore, we found no significant association between gut microbiome diversity and short ‑term (survival to fledging) or mid‑term (apparent juvenile) survival. Conclusions We found no clear association between early‑life environment, offspring condition and gut microbi‑ ome. This suggests that brood size is not a significantly contributing factor to great tit nestling condition, and that other environmental and genetic factors may be more strongly linked to offspring condition and gut microbiome. Future studies should expand into other early‑life environmental factors e.g., diet composition and quality, and paren‑ tal influences. Keywords Avian microbiome, Brood size, Gut microbiome, Parus major, 16S rRNA gene Introduction powerful proximate mechanism affecting host perfor - The digestive tract hosts a large community of differ - mance [1, 2]. The gut microbiome has been studied ent microorganisms (i.e., gut microbiome) and is known across a wide range of animal taxa e.g., humans [3–5], to be a fundamental part of organismal health and a fish [ 6], and economically important species such as poultry [7], and data from wild populations is slowly increasing [8]. Generally, a more diverse gut microbi- *Correspondence: ome is considered beneficial for individual health [9], but Martta Liukkonen martta.a.liukkonen@jyu.fi there are also community structure effects that define Full list of author information is available at the end of the article the functionality [10]. For example, laboratory-bred mice © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Liukkonen et al. Animal Microbiome (2023) 5:19 Page 2 of 16 with a less diverse gut microbiome have a substantially parents’ performance and ability to feed their young [53], lower chance of surviving an influenza infection com - and the trade-off between offspring quality and quantity pared to their wild counterparts unless receiving a gut has been studied widely [54, 55]. Food quantity per nest- microbiota transplant from their wild counterparts [11, ling can decrease in enlarged broods, as parents may not 12]. Moreover, gut microbiome had been linked to host be able to fully compensate for the additional amount of fitness and survival in the Seychelles warbler (Acrocepha - food an enlarged brood requires [56, 57]. For example, lus sechellensis). Individuals that harbored opportunistic in great tits (Parus major) it has been shown that nest- pathogens, i.e., microbes that usually do not cause dis- lings from reduced broods may have a higher body mass ease in healthy individuals, but may become harmful in [58] and tend to survive better [59]. Importantly, great individuals that are immunocompromised, in their gut tit nestling body mass has been connected to gut micro- microbiome showed higher mortality [13, 14]. Therefore, biome diversity and composition: body mass positively understanding how gut microbiome affects fitness within correlates with gut microbiome richness [52]. This could and between individuals is necessary for not only under- imply that good physiological condition and high food standing species survival but also evolution [15–17]. availability would allow the host to have a diverse gut Gut microbiome forms at a young age and remains microbiome that promotes a healthy gut. somewhat stable in adulthood as found for example in Alterations in early-life gut microbiome could have laboratory bred mice [18–20]. Disruption in the gut long-term consequences on individual performance [60], microbiome that leads to a microbiome imbalance at yet such effects have rarely been studied in wild organ - a young age could result in both short-term and long- isms. In wild birds, some bacterial taxa have been linked term changes in the gut microbiome [21, 22]. Of the to better survival. For example, a high abundance of bac- environmental effects, diet [23], including e.g., macro - teria in the order Lactobacillales of the phylum Firmi- nutrient balance (carbohydrates, fats, amino acids) [3, cutes is related to higher individual fitness in Seychelles 24] has been concluded to be major determinants of rat warblers [14] and great tits [61]. These bacteria are also and mouse gut microbiome, and this effect has recently known for the benefits for bird health in economically been seen in avian models as well [25–28]. Moreover, important species such as poultry, in which Lactobacilli macronutrient balance has been linked to intestinal are used as probiotics to boost immune functioning [62]. microbiome composition [3, 24] and the functioning of Besides Lactobacillales, gut bacteria belonging to other individual immune response [29, 30]. However, as a large genera such as Clostridium and Streptococcus are impor- part of the prior research has focused strictly on humans tant for the degradation of non-starch polysaccharides or species living in controlled environments in which and for the synthesis of essential molecules such as the environmental effects on both the microbiome and host short-chain fatty acids [63, 64]. Short-chain fatty acids are sidelined [31, 32], many species, including most birds are important in host energy metabolism [65] and there- [8], have only started to attract attention [33]. fore crucial for performance. Changes in nestling’s early- The mechanisms of bacterial colonization of the bird life gut microbiome could affect such key physiological gut are somewhat unique as avian life-histories differ sig - processes that could influence for example nestling body nificantly from those of e.g., mammals [34]. In mammals, mass, which is tightly linked to survival to fledging [58, the offspring are exposed to bacterial colonization dur - 59]. Because the gut microbiome establishes at a young ing vaginal birth [35] and lactation [36, 37], whereas bird age and is less plastic later in life [18–20], gut microbi- hatchlings are exposed to bacteria first upon hatching ome and changes to its richness can have long-term [20, 38]. Few studies have investigated the possibility of effects on juvenile and adult survival [21, 22]. For exam- bacterial colonization in ovo, but results are still lacking ple, antibiotic treatment at infancy can affect the expres - [39]. Genetics [40–42] as well as the post-hatch environ- sion of genes involved in immune system functioning and ment [20, 43–46] have a significant effect on the forma - lead to long-term effects on host metabolism [20]. More - tion of the avian gut microbiome. Once hatched, most over, changes in the rearing environment can affect indi - altricial birds feed their young, which exposes the hatch- vidual physiology and these effects can carry over to later lings to various bacteria that originate from the parents stages of an individual’s life such as survival to fledging i.e., via vertical transmission [47]. It has also been shown and lifetime reproductive success [66]. that environmental factors are major contributors in the Here, we use an experimental approach to investi- formation of gut microbiome [48–51], one of these being gate whether brood size manipulation influenced wild the rearing environment in the nest [44]. great tit nestlings’ bacterial gut microbiome diver- As early-life environment is connected to the estab- sity on day 7 post-hatch. We also investigated whether lishment of gut microbiome, brood size may affect gut brood size influenced nestling body mass on day 7 or on microbiome [52]. Brood size is often associated with day 14 post-hatch, and if the gut microbiome predicts Liuk konen et al. Animal Microbiome (2023) 5:19 Page 3 of 16 short-term (i.e., survival to fledging) and mid-term (i.e., Brood size manipulation experiment apparent juvenile) survival. The great tit is a well-studied Nest boxes were first monitored weekly and later daily species in the fields of ecology and evolution, and it is when clutches were close to the estimated hatching date. easy to monitor in the wild due to its habit of breeding Brood size manipulation took place on day 2 after hatch- in nest boxes. Great tit nestlings’ gut microbiome under- ing. Increases in great tit brood size can lead to lowered goes profound shifts during early life [52], and it has been weight in both the nestlings and adults [70–75], and our linked to nestling natal body mass and body size [52, 61], decision on the number (i.e., + 2 or − 2) of manipulated yet studies focusing on gut microbiome associations with nestlings (i.e, + 2 or − 2) followed the cited studies. We survival are still scarce. Here, we manipulated wild great had four treatment groups (see Fig. 1): in the ‘enlarged tit broods by reducing or enlarging the original brood group (henceforward called E)’, we increased the brood size in order to analyze if this affected the gut microbi - size by two individuals that were taken from a ‘reduced ome. In large broods, nestlings need to compete for their brood’. Correspondingly, in the ‘reduced group (hence- food more [67, 68], and the lower food availability could forward called R)’, we decreased the brood size by two result in a lower gut microbiome diversity. This might individuals, that were added to the enlarged broods. In impair nestling body mass and fitness prospects [13, 52]. the ‘control group (henceforward called C)’, we swapped We used a partial cross-fostering design that enabled us nestlings between nests but did not change the brood to disentangle the relative contributions of genetic back- size. And lastly, in the ‘unmanipulated control group ground, early maternal effects, and rearing environment (henceforward called COU)’, we only weighed and col- such as parents, nest and nestmates on gut microbiome. lected fecal samples on day 7 but did not move the nest- Furthermore, we used an unmanipulated control group lings between nests. We also moved nestlings between in which no nestling was cross-fostered to control for the the reduced nests to ensure that all nests except for COU possible effects of moving the nestlings between nests. had both original and fostered nestlings. Control nests For example, early human handling such as marking and were used to control for potential cross-fostering effects weighing at day 2 post-hatch could influence gut microbi - unrelated to brood size. Additionally, in the unmanipu- ome later on. We hypothesized that (1) in reduced broods lated control group nestlings were not moved or weighed nestlings would have a higher body mass, (2) in reduced on day 2 in order to control for any handling effects per broods nestling gut microbiome would be more diverse se. This study design enabled us to test the potential than in enlarged broods, and (3) higher gut microbiome impacts of handling nestlings and swapping the nest early diversity on day 7 post-hatch would increase survival to after hatching. We aimed to move approximately half of fledging and potentially reflect apparent juvenile sur - the chicks in the manipulated nests, so that the number vival. Such knowledge could provide new information of original and the fostered nestlings would be the same about gut microbiome in wild passerine bird population in each nest after manipulation. and how the early-life environment may associate with Before they were moved, nestlings were weighed using nestling gut microbiome, body mass, and short-term and a digital scale with a precision of 0.1 g and identified mid-term survival. by clipping selected toenails. We aimed to add/remove nestlings that were of similar weight to avoid changing Methods the sibling hierarchy in the brood. The moving proce - Study area and species dure was performed as quickly as possible to minimize The great tit is a small passerine bird, which breeds in the risk of stress and the nestlings were kept in a warmed secondary holes and artificial nest-boxes, making it a box during transportation. For each pair of nests in the suitable model species. Great tits breed throughout brood size manipulation experiment, we selected nests Europe and inhabit parts of Northern Africa and Asia as that had a similar hatching date. In case of uneven num- well, and the breeding areas differ in environment and ber of nests hatching within a day, one or three nest(s) diet [69]. In Finland the great tit is a common species was/were allocated to the COU group. To avoid poten- with an estimate of 1.5 to 2 million breeding pairs. They tial bias from hatching date, we allocated nests in any lay 6 to 12 eggs between April and May and the female given day evenly to each treatment. We also checked incubates the eggs for 12–15 days. The nestlings fledge that the treatments had an equal brood size on average approximately 16 to 21 days after hatching. The study was i.e., we did not want to only reduce the larger clutches conducted during the breeding season (May–July 2020) and enlarge the smaller clutches. These is also a signifi - on Ruissalo island (60°25′59.99″ N 22°09′60.00″ E). Ruis- cant bias towards COU nests being later in the season on salo island habitat is a mostly temperate deciduous for- average (Table 1). est and meadows, and some areas have small patches of coniferous trees. Liukkonen et al. Animal Microbiome (2023) 5:19 Page 4 of 16 After manipulation Before manipulation Enlarged nest gained four nestlings from reduced nest. Additionally, two nestlings from the original enlarged nest were moved to the reduced nest. Reduced nest lost two nestlings when four of the nestlings were moved to the enlarged nests and only two nestlings were moved back to the reduced nest. Moved across broods but original and manipulated brood Control C sizes were identical. Not moved at all, but only sampled for a fecal sample at day 7 post-hatch. Unmanipulated control COU Fig. 1 Brood size manipulation experiment schematic diagram. 2‑ day‑ old nestlings were moved between nestboxes to enlarge or to reduce original brood size (an example with brood size of seven is given). Some nests were kept as control nests (nestlings were moved but brood size remained the same) and some were kept as unmanipulated control nests (nestlings were not moved at all to test whether early‑life handling affects gut microbiome). The original brood size varied between nests Fecal sample collection nestlings were weighed, and wing-length was measured To study the effects that brood size may have on the nest - to detect if the manipulation had any effects on nestling ling gut microbiome and its links to individual nestling growth. Nests were subsequently monitored for fledging body mass, survival to fledging and apparent juvenile success. Additionally, we monitored our study population survival, we used a subset of data from a larger experi- for apparent juvenile survival (i.e., mid-term survival) ment (Cossin-Sevrin et al., unpublished data). In this after the breeding season (i.e., approximately 3 months subset, we use individuals from which fecal samples were after fledging) to assess the association between gut collected on day 7 after hatching and analyzed for micro- microbiome and post-fledging survival. We captured biome diversity and composition (C = 23 nestlings/15 juvenile great tits by mist netting during the autumn– nests, COU = 22/13, E = 23/15, R = 24/16) We aimed to winter 2020 at six different feeding stations that had a collect two samples (one from original and one from fos- continuous supply of sunflower seeds and suet blocks. ter nestlings) per nest. Fecal samples from the nestlings Feeding stations were located within the previously men- were collected gently by stimulating the cloaca with the tioned nest box population areas. For each site mist net- collection tube. Samples were collected straight into a ting with playback was conducted on three separate days sterile 1.5 ml Eppendorf tube to avoid possible contami- during October–November 2020 for three hours at a nation of the sample. At time of sampling, each nest- time, leading to a total of 69 h of mist netting. A total of ling was weighed (0.1 g), and the nestlings were ringed 88 individuals from the brood size manipulation experi- for individual identification using aluminum bands. ment were caught, and the caught juvenile great tits were The samples were stored in cool bags onsite and after - weighed, and wing length was measured. Our catching wards moved to a -80 °C freezer for storage until DNA method provides an estimate of post-fledging survival extraction. yet, it could be slightly biased based by dispersal. In a previous study in our population [76], none of the birds Apparent juvenile survival ringed as nestlings were recaptured outside the study We monitored all study nests until fledging to measure area, suggesting that dispersal is likely limited. short-term survival. On day 14 post-hatch, the sampled Liuk konen et al. Animal Microbiome (2023) 5:19 Page 5 of 16 Table 1 (A) Brood size before and after manipulation, (B) hatching date across treatments (A) Brood size Before manipulation (mean ± SD) After manipulation (mean ± SD) Enlarged broods (E) 7.700 ± 1.61 9.650 ± 1.309 Reduced broods (R) 8.375 ± 1.637 6.375 ± 1.637 Control broods (C) 7.565 ± 1.805 7.565 ± 1.805 Unmanipulated broods 7.810 ± 2.112 na ANOVA F3 = 0.987, p = 0.403 (B) Hatching date Mean ± SD Enlarged broods (E) 58.60 ± 5.77 Reduced broods (R) 59.83 ± 6.41 Control broods (C) 58.74 ± 5.34 Unmanipulated broods 63.81 ± 4.79 (B) Tukey’s post-hoc for between-group comparisons Average hatching date ANOVA F3 = 3.964, p = 0.011* Contrasts Estimate SE t.ratio p COU‑ C 5.070 1.70 2.983 0.019* COU‑E 5.210 1.76 2.961 0.020* COU‑R 3.976 1.68 2.363 0.092 C‑E 0.139 1.72 0.081 0.100 C‑R − 1.094 1.64 − 0.666 0.910 E‑R − 1.233 1.70 − 0.723 0.888 (E) enlarged brood size, (R) reduced brood size, (C) control brood size, (COU) unmanipulated control brood size. Brood size was successfully either reduced or enlarged by two chicks DNA extraction and sequencing identification [79]. For this, PCR cycling conditions were We chose two samples per nest for DNA extraction, yet as follows: first, an initial denaturation at 95 °C for 4 min in such a way that both fledged and not-fledged nestlings followed by 18 cycles of 98 °C for 20 s, 60 °C for 15 s, would be included in the dataset. DNA was extracted and 72 °C for 30 s, and finished with a 3-min extension from nestling fecal samples using the Qiagen QIAamp at 72 °C. We performed replicate PCR reactions to con- PowerFecal Pro DNA Kit (Qiagen; Germany) following trol for errors during the amplification. Further on, the the manufacturer’s protocols. Additionally, we included PCR products were measured for DNA concentration negative (RNAse and DNAse free ddH O) controls to with Quant-IT PicoGreen dsDNA Assay Kit (ThermoFis - control for contamination during DNA extraction and cher Scientific; Waltham, MA, USA) and for quality with additional controls to confirm successful amplification TapeStation 4200 (Agilent; Santa Clara, CA, USA). The during PCR. A short fragment of hypervariable V4 region samples from each of the PCR replicates were pooled in the 16S rRNA gene was amplified using the purified equimolarly creating two separate pools and purified DNA samples as template with the following primers: using NucleoMag NGS Clean-up and Size Select beads 515F_Parada (5’-GTG YCA GCMGCC GCG GTAA-3’) and (Macherey–Nagel; Düren, Germany). Finally, pooled 806R_Apprill (5’-GGA CTA CNVGGG TWT CTAAT-3’) samples were sequenced (2 × 300 bp) on the Illumina [77, 78]. PCRs were performed in a total volume of 12 µL MiSeq platform (San Diego, CA, USA) at the Finnish using MyTaq RedMix DNA polymerase (Meridian Bio- Functional Genomic Center at the University of Turku science; Cincinnati, OH, USA). The PCR cycling condi - (Turku, Finland). tions were as follows: first, an initial denaturation at 95 °C for 3 min followed by 30 cycles of 95 °C for 45 s, 55 °C Sequence processing for 60 s, and 72 °C for 90 s, and finished with a 10-min All statistical analyses were performed with R (v. 4.11.0; extension at 72 °C. After the first round of PCR, a sec - R Development Core Team 2021) unless otherwise ond round was conducted to apply barcodes for sample stated. The demultiplexed Illumina sequence data was Liukkonen et al. Animal Microbiome (2023) 5:19 Page 6 of 16 first processed with Cutadapt version 2.7 [80] to remove either body mass on day 7 or 14 as the dependent vari- locus-specific primers from both R1 and R2 reads. Then, able and brood size manipulation treatment, hatching the DADA2 pipeline (v. 1.24.0; [81]) was used to filter the date, body mass on day 2 post-hatch and original brood reads based on quality, merge the paired-end (R1 and R2) size as predicting variables. Hatching date is used as a reads, to define the single DNA sequences i.e., Amplicon predicting variable because it is known to affect nestling Sequence Variants (henceforward ASV), and to construct body mass during the breeding season [92] and there a ‘seqtab’. Seqtab is a matrix also known as otutable or were differences in hatching date between the COU and readtable: ASVs in columns, samples in rows, number other treatment groups (see Table 1). We included the of reads in each cell, using default parameter settings. interaction between original brood size and brood size In total, our seqtab consisted of 6,929,537 high-quality manipulation treatment in both models as the effect of reads. Reads were assigned to taxa against the SILVA manipulation may depend on the original brood size. For v132 reference database [82] resulting in 8658 ASVs. To example, there could be stronger effect of enlargement in control for contamination, negative DNA extraction and already large broods. Nest of origin and nest of rearing PCR controls were used to identify contaminants (60 were used as random intercepts to control for the non- ASVs) using the decontam package (v. 1.12; [83]) and all independence of nestlings sharing the same original or were removed from the dataset. Sequencing runs (rep- foster nests. Here, we did not include the COU group in licate PCR’s) were merged using the phyloseq package the analysis because we wanted to measure the effects (v. 1.32.0) and non-bacterial sequences (mainly Chloro- of treatment on nestling body mass, and only enlarged, phyta) were removed from the data as they were not of reduced or control broods’ nestlings were moved interest in this study resulting in a total of 6566 ASVs between nests. (a total of 4,107,028 high-quality reads in all samples; Second, to analyze whether the actual brood size mean per sample: 15,155.085; mean range per sample: affected nestling body mass, we ran two models where 0–97,264). Singleton reads were removed from the data- we used it as a continuous dependent variable to explain set by the DADA2 pipeline. Data was further analyzed body mass either on day 7 or on day 14 post-hatch. with the phyloseq package (v. 1.32.0; [84]), and the micro- Hatching date and body mass on day 2 post-hatch were biome package (v. 1.18.0; [85]) and visualized with the used as predicting variables and nest of origin and nest ggplot2 package (v. 3.3.6; [86]). of rearing as random intercepts to control for the non- The final dataset contained 92 samples from great tit independency of samples. We included the interaction nestlings resulting in a total of 3,161,696 reads (mean between manipulated brood size and hatching date in the per sample: 34,366.261; mean range per sample 108 models because the effect of brood size may depend on – 189,300 reads), which belonged to 6,505 ASVs. The the hatching date. For example, hatching date can reflect dataset was then rarefied for alpha diversity analyses at environmental conditions and large broods may perform a depth of 5000, as this was where the rarefaction curves poorly late in the season due to poorer food availability. plateaued (see Additional file 2). The rarefied dataset The COU group was initially excluded from this model contained 4,791 ASVs in 88 samples. For beta diversity, to see which of the two random effects, nest of origin or the unrarefied dataset was used after confirming that the nest of rearing, explained a larger portion of variation beta diversity statistics were quantitatively similar for the in the treatment groups. In the COU group, nest of ori- rarefied and unrarefied datasets. Bacterial relative abun - gin and nest or rearing were the same, which meant we dances were summarized at the phylum and genus level could not include both random effects in models where and plotted based on relative abundance for all phyla all treatment groups were present due to the model fail- and genera. A Newick format phylogenetic tree with the ing to converge. Nest of origin explained more of the UPGMA algorithm to cluster treatment groups together variation in the first model (see Additional file 4) and was used to visualize sample relatedness (see Additional therefore, we used it in the full models with all treat- file 3) and was constructed using the DECIPHER (v. ment groups: C, COU, E and R. In these models, nestling 2.24.0; [87]), phangorn (v. 2.8.1; [88]), and visualized with body mass either on day 7 and or on day 14 post-hatch ape (v. 5.6-2; [89]), and ggtree (v. 3.4.0; [90]) packages. was used as a dependent variable and manipulated brood size as the explanatory variable. Hatching date and body Statistical analyses mass on day 2 post-hatch were set as predicting variables. Nestling body mass Nest of rearing was used as a random intercept to control First, to analyze whether brood size manipulation for the non-independence of nestlings sharing the same affected nestling body mass in the C, E, and R treatment foster nests. The significance of factors included in the groups, we ran two linear mixed-effects models with the models were tested using the F-test ratios in analysis of lme4 package (v. 1.1-29; [91]). In these models we used variance (Type III ANOVA). Liuk konen et al. Animal Microbiome (2023) 5:19 Page 7 of 16 Alpha diversity the binomial response variable (yes–no) in each model. For alpha diversity analyses, which measures within- Alpha diversity (Shannon Diversity Index and Chao1 sample species diversity, we ran two linear mixed-effects Richness) was the main predicting variable, and weight models with the lme4 package (v. 1.1-29; [91]) to measure on day 7 post-hatch (same time as sampling the fecal gut if either brood size manipulation or manipulated brood microbiome), hatching date and manipulated brood size size as a continuous variable were associated with gut were included as covariates in the model. We did not microbiome diversity. We used two alpha diversity met- include brood size manipulation treatment in the sur- rics: the Shannon Diversity Index, which measures the vival models as not enough birds from each treatment number of bacterial ASVs and their abundance even- group were recorded for fledging and juvenile survival. ness within a sample, and Chao1 Richness, which is an Moreover, we excluded random effects from this model estimation of the number of different bacterial ASVs in a as the model failed to converge. 65 nestlings fledged suc - sample. Both metrics were used to check if alpha diver- cessfully, while 8 nestlings were found dead in nest boxes. sity results were consistent across different metrics. Each For 15 nestlings we had no fledging record, so these were diversity index was used as the dependent variable at a excluded from the survival to fledging analysis. In appar - time and either brood size manipulation treatment or ent juvenile survival, 19 birds out of 92 (with data on manipulated brood size as a predicting variable. In both microbiome diversity) were recaptured as juveniles. For models we included original brood size, weight on day 7 all analyses, the R package car (v. 3.0-13; [93]) was used post-hatch and hatching date as covariates. We included to test Variance Inflation Factors (VIFs) and the package interaction between brood size manipulation treatment DHARMa (v. 0.4.5; [94]) to test model diagnostics for lin- and original brood size as there could be a stronger effect ear mixed-effects and generalized linear models. of enlargement in initially large broods. We also included interaction between manipulated brood size and weight on day 7 post-hatch because effect of brood size on Beta diversity microbiome may depend on nestling weight. We also For visualizing beta diversity, i.e., the similarity or dis- tested whether alpha diversity predicted weight on day 7 similarity between the treatment group gut microbiomes, post-hatch, as weight and gut microbiome diversity have non-metric multidimensional scaling (NMDS) was used been connected in previous studies. In this analysis we with three distance matrices: Bray–Curtis [95], weighted used weight on day 7 post-hatch as the dependent vari- UniFrac, and unweighted UniFrac [96]. Permutational able and alpha diversity (Shannon Diversity Index and multivariate analysis of variance (PERMANOVA) using Chao1 Richness), treatment and hatching date as predict- the Euclidean distance matrix and 9999 permutations ing variables and nest of rearing as the random effect. In was tested with the R package vegan (adonis2 function; these sets of models, we first excluded the COU group v. 2.6-2; [97]) to investigate if any variables affected to the to see which of the two random effects, nest of origin or variation in gut microbiome composition. Nest of rearing nest of rearing, explained a larger proportion of variation was set as a blocking factor in the PERMANOVA to con- in the treatment groups. Nest of rearing explained more trol for the non-desirable effects of the repeated sampling of the variation in this model (see Additional file 4) and of foster siblings. The test for homogeneity of multivari - therefore, we used it in the full model with all treatment ate dispersions was used to measure the homogeneity of groups: C, COU, E and R. The significance of factors group dispersion values. We used the phyloseq package included in the models were tested using the F-test ratios (v. 1.32.0; [84]) to run a differential abundance analysis in analysis of variance (ANOVA). with a significance cut-off p < 0.01 to test the differential abundance of ASVs between the treatment groups. Short-term survival To explore whether alpha diversity associated with sur- Results vival to fledging (i.e., short-term survival) and with The effects of brood size manipulation on nestling body apparent juvenile survival in Autumn 2020 (i.e., mid- mass term survival), we used generalized linear models with Brood size manipulation did not significantly affect binomial model (v. 1.1-29; lme4 package, [91]), and then nestling body mass on day 7 post-hatch (ANOVA: F 2, tested the significance of factors with type 2 ANOVA = 0.441, p = 0.648; see Additional file 5). Moreo- 25.832 from the car package (v. 3.0-13; [93]). Type 2 ANOVA ver, there was no significant interaction between brood was used because the model did not contain interac- size manipulation and original brood size (ANOVA: F 2, tion between predicting and there was no order between = 0.678, p = 0.517; see Additional file 5). On day 14 24.610 covariates, as they could not be ranked. Survival to post-hatch, brood size manipulation did not significantly fledging and recapture in Autumn 2020 were used as affect nestling body mass (ANOVA: F = 0.831, 2, 24.335 Liukkonen et al. Animal Microbiome (2023) 5:19 Page 8 of 16 p = 0.448; see Additional file 5). However, body mass p = 0.959; see Additional file 7), and hatching date increased with increasing hatching date (ANOVA: F (ANOVA: F = 1.073, p = 0.305; see Additional 1 1, 50.276 = 13.367, p = 0.001; see Additional file 5). Next, we file 7) did not significantly associate with alpha diver - 24.070 did not find any significant associations between manip - sity. There was no significant interaction between ulated brood size and nestling body mass (ANOVA for brood size manipulation and original brood size weight on day 7: F = 1.777, p = 0.191; ANOVA for (ANOVA: F = 0.126, p = 0.944; see Additional 1, 35.149 3, 48.053 weight on day 14: F = 2.156, p = 0.153; see Addi- file 7). Results for Chao1 Richness were quantitatively 1, 29.491 tional file 6). Nest of origin explained a larger proportion similar: brood size manipulation did not affect alpha of the variation in weight than the nest of rearing on both diversity (ANOVA: F = 0.358 p = 0.784, Fig . 3; 3, 45.936 day 7 (nest of origin 41.1% and nest of rearing 24.4%) and see Additional file 7). Nest of rearing explained a larger day 14 (nest of origin 65.5% and nest of rearing 21.9%) proportion of the observed variance in alpha diversity post-hatch, but this result was not statistically significant (27.7%) than nest of origin (10.8%), but the result was (Pr > χ2 = 1) (see Additional file 4). not statistically significant (Pr > χ2 = 1) (see Additional file 4 ). Alpha diversity Next, we tested whether the manipulated brood size As 7-day-old nestlings, most bacterial taxa belonged to as a continuous variable was associated with alpha the phyla Proteobacteria, Firmicutes, and Actinobacte- diversity (Shannon Diversity Index), but found no sig- ria (Fig. 2). nificant association (ANOVA: F < 0.001, p = 0.984; 1, 63.001 Brood size manipulation did not significantly influ - see Additional file 8) in this analysis either. Weight on ence alpha diversity (Shannon Diversity Index) day 7 post-hatch (ANOVA: F = 0.015, p = 0.903; 1, 82.840 (ANOVA: F = 1.026, p = 0.390, Fig . 3; see Addi- see Additional file 8) and hatching date (ANOVA: F 3, 47.488 1, tional file 7). Moreover, original brood size (ANOVA: = 0.137, p = 0.713; see Additional file 8) did not 59.734 F = 0.388, p = 0.536; see Additional file 7), correlate with alpha diversity in this model either. There 1, 50.269 weight on day 7 post-hatch (ANOVA: F = 0.003, was no significant interaction between manipulated 1, 80.551 Fig. 2 Bacterial relative abundances on Phylum level across the four treatment groups. Each bar represents an individual sample. Treatment groups are control (C), unmanipulated control (COU), enlarged (E), and reduced (R). N = 88 samples divided into treatment groups as follows: C = 23, COU = 21, E = 20, R = 24. Phyla with less that 10% in relative abundance is collapsed into the category “< 10% abundance” Liuk konen et al. Animal Microbiome (2023) 5:19 Page 9 of 16 interaction between alpha diversity and manipulated brood size (χ2 = 1.268, df = 1, p = 0.260; see Additional file 9). However, apparent juvenile survival was nega- tively associated with hatching date (χ2 = 4.654, df = 1, p = 0.031; see Additional file 9). Additional analyses to check for the consistency of results were tested the fol- lowing way: survival to fledging with nestlings from the COU group removed and apparent juvenile survival without the nestlings with no recorded survival for fledg - ing (see methods). These results were quantitatively simi - lar as in the whole dataset for both Shannon Diversity Index (survival to fledging: χ2 = 2.285, df = 1, p = 0.131; apparent juvenile survival: χ2 = 1.515, df = 1, p = 0.218; see Additional file 11) and Chao1 Richness (survival to fledging: χ2 = 0.665, df = 1, p = 0.415; apparent juve- nile survival: χ2 = 2.654, df = 1, p = 0.103; see Additional file 11). Fig. 3 The gut microbiome alpha diversity of 7‑ day‑ old great tit Beta diversity nestlings across the four treatment groups visualized with two Non-metric multidimensional scaling (NMDS) using diversity metrics: A Shannon Diversity Index and B Chao1 Richness. The black dots represent each observation within a treatment group. weighted and unweighted UniFrac and Bray–Curtis dis- The whiskers represent 95% confidence intervals. Treatment groups similarity did not show clear clustering of samples based are control (C), unmanipulated control (COU), enlarged (E), and on brood size manipulation treatment (see Additional reduced (R). N = 88 samples divided into treatment groups as follows: file 3). The test for homogeneity of multivariate disper - C = 23, COU = 21, E = 20, R = 24 sions supported the visual assessment of the NMDS (Betadispersion: F = 0.650, p < 0.001; 9999 permutations 3, 0.069 see Additional file 12). Pairwise PERMANOVA further indicated that the treatment (PERMANOVA: R = 0.061, brood size and weight on day 7 post-hatch (ANOVA: F 1, F = 1.951, p = 0.278; see Additional file 12), weight on < 0.000, p = 0.998; see Additional file 8). Results for 82.702 day 7 post-hatch (PERMANOVA: R = 0.015, F = 1.387, Chao1 Richness were quantitatively similar (ANOVA: p = 0.091) or hatching date (PERMANOVA: R = 0.0232, F = 0.246, p = 0.622; see Additional file 8): manip- 1, 65.064 F = 2.214, p = 0.993) did not significantly contribute to ulated brood size did not affect alpha diversity, and the variation in gut microbiome composition between neither did weight on day 7 post-hatch (ANOVA: F the treatment groups. Differential analysis of ASV abun - = 0.690, p = 0.409; see Additional file 8) nor hatch- 83.513 dance between the treatment groups showed that there is ing date (ANOVA: F = 1.133, p = 0.292; see Addi- 1 57.110 variation in taxa abundance. E group showed higher taxa tional file 8). abundance when compared to COU and C groups and was slightly higher than the R group. C and COU groups Alpha diversity and short/mid-term survival were generally lower in taxa abundance than R and E Next, we explored whether alpha diversity (Shannon groups, and COU group showed lower abundance than Diversity Index and Chao1 Richness) contributed to pre- the other groups in each comparison (see Additional dicting short/mid-term survival (survival to fledging and file 13). apparent juvenile survival). Survival to fledging was not predicted by alpha diversity (Shannon Diversity Index: Discussion χ2 = 0.010, df = 1, p = 0.923; see Additional files 9 and 10), In this study, we investigated the associations between manipulated brood size (χ2 = 0.090, df = 1, p = 0.764; see great tit nestling gut microbiome, brood size, and nestling Additional file 9), weight on day 7 post-hatch (χ2 = 0.388, body mass by experimentally manipulating wild great tit df = 1, p = 0.533; see Additional file 9) or hatching date broods to either reduce or enlarge the original brood size. (χ2 = 0.438, df = 1, p = 0.508; see Additional file 9). The results show that even though there was individual Apparent juvenile survival was not significantly asso - variation in the nestling gut microbiome (Fig. 2), brood ciated with alpha diversity (Shannon Diversity Index: size did not significantly contribute to gut microbiome χ2 = 1.916, df = 1, p = 0.166; see Additional file 9 and diversity. Neither did gut microbiome diversity explain Additional file 10). Moreover, there was no significant Liukkonen et al. Animal Microbiome (2023) 5:19 Page 10 of 16 short-term (survival to nestling) nor mid-term (apparent could be a result of changes in the food items that great juvenile) survival. Body mass was also not significantly tits use, changes in temperature conditions or in paren- affected by brood size manipulation. The COU group that tal investment during the breeding season. As the season functioned as a control for moving and handling effects, progresses, the abundance of insect taxa varies, and this did not differ in this respect from the other groups. This can result in changes in nutrient rich food [103, 107]. For suggests that human contact or handling nestlings 2 days example, great tits can select certain lepidopteran larvae post-hatch did not influence nestling gut microbiome that vary in their abundance during the great tit breeding or body mass. The partial cross-fostering design ena - season [108]. Thirdly, it could be that the change in brood bled us to disentangle the relative contributions of rear- size was influencing the parents’ condition instead of ing environment (i.e., parents, nest and nestmates) from the nestlings [109, 110]. In enlarged broods, parents are genetic, prenatal such as maternal allocation to egg, and required to forage more which can lead to higher energy early post-natal effects such as feeding up to day 2. Nest expenditure and increased stress levels in parents [72, 73, of rearing seemed to explain more of the variation in 109]. nestling gut microbiome diversity than the nest of origin (although not statistically significant), which follows pre - Brood size manipulation and gut microbiome vious studies. Contrastingly, nest of origin seemed to be a We found large inter-individual differences in gut micro - stronger contributor than the nest of rearing on nestling biome diversity, yet this variation was not explained body mass on day 7 and day 14 post-hatch. This result by brood size or nestling body mass. It is possible that was also not statistically significant. brood size did not result in differences in food intake. For example, parents were likely able to provide an Brood size manipulation and nestling body mass equivalent amount of food, given that body mass was First, we explored whether brood size was associated not significantly affected by the brood size manipula - with nestling body mass, as such changes may explain the tion. Therefore, brood size manipulation did not affect underlying patterns in gut microbiome [52]. Against our gut microbiome diversity through differences in nutrient hypothesis, we found no significant association between uptake. Alternatively, in this study, fecal sampling took nestling body mass and brood size: neither reduction nor place 5 days after the initial brood size manipulation (day enlargement of the broods resulted in significant body 2 post-hatch). It could be that sampling on a later date or mass differences in the nestlings on day 7 and day 14 at multiple timepoints [61, 111] would have led to differ - post-hatch. While the result is supported by some stud- ent results. Firstly, the time interval may not have been ies in which associations between nestling body mass and long enough to detect effects of the brood size manipula - brood size have been tested [61, 98], the majority of the tion. Secondly, it has been shown in previous studies that literature shows that brood size negatively correlates with the nestling gut microbiome undergoes profound shifts nestling body mass: in larger broods nestlings are gener- at the nestling stage: overall gut microbiome diversity ally of lower mass [52, 53, 57, 67, 99–104]. decreases but relative abundance in some taxa increases There are a few possible explanations why brood size [52]. We suggest that fecal samples could be collected on manipulation did not affect nestling body mass. Firstly, if multiple days post-hatch to understand the potential day environmental conditions were good, parents may have to day changes in the nestling gut microbiome. been able to provide enough food even for the enlarged Our results suggest that the variance in gut micro- nests and thus, variance in brood size may not result in biome is a result of other factors than those linked to differences in nestling body mass between reduced and brood size. Firstly, one of these factors could be diet (i.e., enlarged nests. In that case the number of nestlings food quality) which has gained attention in gut microbi- transferred between enlarged and reduced nests should ome studies during the past years [25, 27, 112–115]. The probably have been larger to create differences in nestling overall diversity in gut microbiome could be explained body mass between the two treatments. Still, we think by adaptive phenotypic plasticity because it is sensi- that the decision to transfer + 2/− 2 was reasonable since tive to changes in the environment e.g., changes in diet it was based on extensive evidence from previous stud- [116, 117]. The food provided by the parents can vary ies [103]. Secondly, it could be that the enlarged brood between broods in different environments [118], and this size negatively influences some other physiological traits variation in diet can lead to differences in gut microbi - while body mass was retained at the expense of these ome diversity [114–119]. For example, abundance in cer- other traits e.g., immune system functioning [105, 106]. tain dietary items such as insects or larvae can result in Moreover, our analysis showed that hatching date had a lower gut microbiome diversity than other dietary items significant effect on nestling body mass: nestlings that [113–116]. As great tits have been reported to adapt their hatched later in the season were of lower weight. This diet along the breeding season due to changes in insect Liuk konen et al. Animal Microbiome (2023) 5:19 Page 11 of 16 taxa frequency [103, 107] this could affect the between- even though their diet and housing conditions were nestling and between-nest gut microbiome diversity. standardized. The study suggested that individual home - However, using wild bird populations in gut microbiome ostatic mechanisms linking to naturally occurring dif- studies limits the ability to control the consumed dietary ferences in individual gut microbiome could be why gut items because parents may use variable food resources microbiome composition varied even with standardized and there can be variance in dietary between sexes and housing conditions [41]. Secondly, gut microbiome com- even individuals. Visual assessment of dietary items [116] position could have been affected by the same environ - and metabarcoding could be of use here as they enable mental effects that may have linked to the variation in the identification of food items on genus and even spe - gut microbiome diversity: diet and feeding behavior [115, cies level from e.g., fecal samples [119]. 116]. Secondly, breeding habitat may lead to differences Differential analysis of ASV abundance showed vari - in gut microbiome diversity [120]: adult birds living in ation in differential abundance of taxa between the deciduous forests have shown to harbor different gut treatment groups. However, several ASVs were not tax- microbiome diversity than their counterparts living in onomically assigned beyond family level making it diffi - open forested hay meadows. Here, we used a cross-fos- cult to draw conclusions about the significance of these tering design to study if the rearing environment contrib- results. All treatment groups had taxa belonging to the uted to the variation in gut microbiome diversity: Our order Firmicutes, Proteobacteria and Actinobacteria, study indicated that the nest of rearing seemed to explain which was to be expected because they are usually the more of the gut microbiome variation than the nest of most core phyla in passerine gut microbiomes [33]. Nest- origin (although not significant), which follows some pre - lings belonging to E, R or C group showed higher taxa vious results [43, 44, 52]. For example, a study with great abundance than the COU group in each comparison. and blue tit (Cyanistes caerulaeus) nestlings showed that This result could be a result of the COU nestlings gen - the nest of rearing contributed more to the gut microbi- erally hatching later in the season and potentially having ome than the nest of origin [43], and another study with a less diverse diet [103, 107]. Of the E, R and C groups, the brown-headed cowbird (Molothrus ater) concluded C group was less abundant than E and R groups. Both E that the sampling locality had a significant contribution and R group showed high taxa abundance, which is inter- to the gut microbiome [44]. Teyssier et al. [52] conducted esting because we hypothesized that nestlings belonging cross-fostering at day 8 post-hatch in great tits and found to the E group would potentially experience less parental that the nest of rearing influenced the gut microbiome investment per nestling and have lower gut microbiome more than the nest of origin. Additionally, parents can diversity and therefore, be less abundant [56, 57, 67, 68]. pass down their bill and feather microbiome through We did not observe differential abundance in e.g., the vertical transmission, which could influence nestling gut order Lactobacillales which would have been of interest, microbiome [20]. because the order hosts taxa that are beneficial for gut Results from beta diversity analysis were similar to microbiome health [14, 62]. The genus Staphylococcus that of alpha diversity: brood size manipulation did not was differentially abundant in the E group, but not in the contribute to the variation in gut microbiome composi- other groups. Staphylococcus is a gram-positive genus of tion. Overall, variation in gut microbiome composition bacteria and known to cause infections in its host species could be a result of different genetic and environmental [122]. Curiously, the COU group was differentially abun - contributors. Firstly, great tit nestling gut microbiome dant in the genus Dietzia, which is a human pathogen composition could be explained by underlying genetic [123]. effects that we did not measure in this study. Phylosym - biosis i.e., the matching of gut microbiome composition Gut microbiome and short-term and mid-term survival to host genetic structure, could be explained by underly- Our results showed that gut microbiome diversity ing genetics that may translate into physiological differ - and brood size were not significantly associated with ences that affect the gut microbiome e.g., founder effects short-term (survival to fledging) or mid-term (appar - or genetic drift [121]. Davies et al. [14] found that MHC ent juvenile) survival. However, while a more diverse genes correlate with gut microbiome composition: the gut microbiome is considered a possible indicator of a expression of specific alleles in the MHC genes was con - healthy gut microbiome, the effects of the gut microbi - nected to the abundance of specific bacterial taxa such ome on the host health may often be more complex and as Lactobacillales and Bacteroidales that influenced host related to specific taxa [9, 10]. For example, Worsley et al. health. In a study by Benskin et al. [41] captive zebra [13] did not find a correlation between body condition finches (Taeniopygia guttata) showed significant varia - and gut microbiome diversity, yet they found that spe- tion in gut microbiome composition between individuals cific taxa in the gut microbiome linked with individual Liukkonen et al. Animal Microbiome (2023) 5:19 Page 12 of 16 body condition and survival. Not only environment, but association in our study, but instead found a significant also genetic background of the individual may contrib- association between hatching date and apparent juvenile ute to gut microbiome and survival. In a study by Davies survival. et al. [14], Ase-ua4 allele of the MHC genes was linked to lower gut microbiome diversity and it was suspected that the variation in the MHC genes could affect the sensitiv - Conclusions ity to pathogens that could lead to variation in gut micro- Offspring condition can be affected by the early-life envi - biome diversity and eventually, host survival. ronment and early-life gut microbiome, thus highlight- To gain a better understanding of gut microbiome ing the importance of understanding how changes in the diversity and the contribution of different taxa to host rearing environment affect individual body mass and sur - survival, functional analyses of the gut microbiome vival. Even though our results showed between-individ- should be included in gut microbiome studies. Differ - ual variation in nestling gut microbiome diversity, we did ent bacterial taxa can have similar functions in the gut not find a significant link between brood size and nestling microbiome [5, 124] and therefore, the absence of some gut microbiome. Moreover, we did not find a significant taxa may be covered by other functionally similar taxa, association between nestling gut microbiome diversity resulting in a gut microbiome that is functionally more and short-term or mid-term survival. This suggests that stable [125]. Similarity in functions may also contribute other environmental factors (e.g., diet quality) may con- to host’s local adaptation e.g., to the changes in the host’s tribute more to variation in nestling gut microbiome. early-life environment [124]: changes in brood size or Further research is needed to uncover the environmen- dietary items could result in variation in the gut micro- tal factors that contribute to nestling gut microbiome in biome diversity, yet there may be no effects on host body wild bird populations, and how gut microbiome may be condition. linked to nestling survival. Gut microbiome can adapt The lack of association between brood size, nestling size faster to environmental changes than the host, which and survival contrasts with previous studies, but it should makes it important to understand the causes of inter- be noted that the majority of previous studies have been individual variation in microbiome, and how variation in done with adult birds and not nestlings. Because nestling microbiome possibly mediate adaptation to environmen- gut microbiome is still quite flexible compared to that of tal changes. the adults [20], it is possible that our experiment did not result in a strong enough effect on the gut microbiome. In Supplementary Information future studies, it would be important to study the parents The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s42523‑ 023‑ 00241‑z. as well as it could be more likely to find an association between adult microbiome and fitness than with nestling Additional file 1: Brood size before and after manipulation: brood sizes gut microbiome and survival. Also, our sample size in the between treatment groups were tested with a linear model to see if the survival analyses was small, and it is hard to determine differences were statistically significant. if the result was affected by the sample size. Firstly, nest - Additional file 2: Rarefaction curves for the unrarefied dataset. Species (ASVs) plateaued at about 5000 reads which was used as the rarefying ling survival is often found to correlate with brood size depth. and more specifically, with fledging mass and in particu - Additional file 3: Phylogenetic tree using the Newick ‑format. The tree lar, the ability to forage for food [61, 126]. Intra-brood describes the dissimilarity among the treatment groups. Each tip repre‑ competition may explain survival to fledging, as compe - sents an individual sample, and each tip is colored and shaped based on treatment. Treatment groups are clustered using the UPGMA algorithm. tition between nestlings can limit food availability and thus, leading to lower nestling body condition [68, 127]. Additional file 4: (A) Linear mixed effects model for gut microbiome diversity (Shannon Diversity Index and Chao1 Richness) and brood size A study with blackbirds (Turdus merula) showed that manipulation treatment. (B) Linear mixed effects model for GM diversity nestling body mass explained juvenile survival [128], and (Shannon Diversity Index and Chao1 Richness) and manipulated brood similar results have been shown with great tits and col- size. lared flycatchers (Ficedula albicollis; [31]). Contrastingly, Additional file 5: A linear mixed effects model investigating the effects of brood size manipulation on nestling body mass on day 7 and day 14 Ringsby et al. [129] observed that in house sparrows post‑hatch. (Passer domesticus) juvenile survival was independent of Additional file 6: A linear mixed effects model investigating the effects nestling mass and brood size. Moreover, natal body mass of manipulated brood size on nestling body mass on day 7 and day 14 is often positively correlated with survival to fledging and post‑hatch. juvenile survival as heavier nestlings are more likely to be Additional file 7: A linear mixed effects model investigating the associa‑ recruited [92, 130, 131], yet we failed to demonstrate this tions between alpha diversity (Shannon Diversity Index and Chao1 Rich‑ ness) and brood size manipulation. in our study. Hatching date is also often positively corre- lated with fledging success [132] yet we did not find this Liuk konen et al. Animal Microbiome (2023) 5:19 Page 13 of 16 of Biological Sciences, University of Alaska Anchorage, Anchorage, AK 99508, Additional file 8: A linear mixed effects model investigating the associa‑ USA. tion between alpha diversity (Shannon Diversity Index and Chao1 Rich‑ ness) and manipulated brood size. Received: 20 September 2022 Accepted: 13 March 2023 Additional file 9: A generalized linear model exploration into alpha diversity’s (Shannon Diversity Index and Chao1 Richness) association with short‑term (survival to fledging) and mid‑term (apparent juvenile) survival. Additional file 10: The gut microbiome alpha diversity (Shannon Diver ‑ References sity Index and Chao1 Richness) and short‑term survival. 1. Kinross JM, Darzi AW, Nicholson JK. Gut microbiome‑host interactions Additional file 11: Generalized linear model to measure the association in health and disease. Genome Med. 2011;3(3):1–12. between alpha diversity (Shannon Diversity Index and Chao1 Richness) 2. van Dongen WF, White J, Brandl HB, Moodley Y, Merkling T, Leclaire S, survival to fledging and apparent juvenile survival. Wagner RH. Age‑related differences in the cloacal microbiota of a wild bird species. BMC Ecol. 2013;2013(13):1–12. Additional file 12: Ordination of the gut microbial communities. 3. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe Additional file 13: Differential analysis of abundance (DESeq2) to assess BE, Turnbaugh PJ, et al. Diet rapidly and reproducibly alters the human the ASV abundance between the treatment groups gut microbiome. Nature. 2014;505(7484):559–63. 4. Sánchez B, Delgado S, Blanco‑Míguez A, Lourenço A, Gueimonde M, Margolles A. Probiotics, gut microbiota, and their influence on host Acknowledgements health and disease. Mol Nutr Food Res. 2017;61(1):1600240. We would like to thank Charli Davies, Petri Papponen, Sari Viinikainen and Elina 5. Heintz‑Buschart A, Wilmes P. Human gut microbiome: function matters. Virtanen at the University of Jyväskylä for their help with bioinformatics (CD) Trends Microbiol. 2018;26(7):563–74. laboratory optimizations (PP, SV and EV ), as well as Jorma Nurmi, Robin Cristo‑ 6. Egerton S, Culloty S, Whooley J, Stanton C, Ross RP. The gut microbiota fari, Natacha Garcin, Toni Laaksonen at the University of Turku and volunteer of marine fish. Front Microbiol. 2018;9:873. bird ringers for their help in the field. 7. Pan D, Yu Z. Intestinal microbiome of poultry and its interaction with host and diet. Gut microbes. 2014;5(1):108–19. Author contributions 8. Hird SM. Evolutionary biology needs wild microbiomes. Front Microbiol. The idea for this study was by AS, SR and NCS. Sample collection was done 2017;8:725. by NCS, MH, AS and SR. Laboratory analyses were done by ML with assistance 9. Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diver‑ from SR, KG and EV. Sequence processing and statistical analyses were sity, stability and resilience of the human gut microbiota. Nature. done by ML with assistance from KG, EV (bioinformatics) and SR (statistics). 2012;489(7415):220–30. Manuscript was written by ML and all authors commented and approved the 10. Zaneveld JR, McMinds R, Vega TR. Stress and stability: applying the manuscript. Anna Karenina principle to animal microbiomes. Nat Microbiol. 2017;2(9):1–8. Funding 11. Rosshart SP, Vassallo BG, Angeletti D, Hutchinson DS, Morgan AP, Takeda Open Access funding provided by University of Jyväskylä (JYU). This study K, Rehermann B. Wild mouse gut microbiota promotes host fitness and was funded by the Emil Aaltonen Foundation. NCS was supported by EDUFI improves disease resistance. Cell. 2017;171(5):1015–28. Fellowship (Opetushallitus). AS was funded by the Turku Collegium for Science 12. Rosshart SP, Vassallo BG, Angeletti D, Hutchinson DS, Morgan AP, Takeda and Medicine, who contributed to fund the field study. AS acknowledges K, Rehermann B. Wild mouse gut microbiome protects laboratory mice funding from the European Commission Marie Sklodowska‑ Curie Postdoctoral against lethal influenza virus infection and colorectal cancer. J Immu‑ Fellowship (#894963) at the time of writing. nol. 2017;198:68–5. 13. 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Round JL, Mazmanian SK. The gut microbiota shapes intestinal immune guidelines and legislation. The animal work was licensed by the environmental responses during health and disease. Nat Rev Immunol. 2009;9:313–23. and ethical committee of Varsinais‑Suomi (environmental permit license num‑ 17. Kopac SM, Klassen JL. Can they make it on their own? Hosts, microbi‑ ber VARELY/890/2020; animal ethics permit number ESAVI/5454/2020). The omes, and the holobiont niche. Front Microbiol. 2016;7:6. birds were ringed from their nest‑sites by permission from the Finnish Ringing 18. Hansen CHF, Nielsen DS, Kverka M, Zakostelska Z, Klimesova K, Centre to SR (Finnish Museum of Natural History). Hudcovic T, Hansen AK. Patterns of early gut colonization shape future immune responses of the host. PLoS ONE. 2012;7(3): e34043. Consent for publication 19. Cox LM, Yamanishi S, Sohn J, Alekseyenko AV, Leung JM, Cho I, Not applicable. Blaser MJ, et al. 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Journal
Animal Microbiome – Springer Journals
Published: Mar 22, 2023
Keywords: Avian microbiome; Brood size; Gut microbiome; Parus major; 16S rRNA gene