University of Groningen

An ecological perspective on microbes and immune defences in avian eggsGrizard, Stephanie

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Chapter 5

EGGSHELL BACTERIAL COMMUNITIES

SUBSTANTIALLY DIFFER AMONG ENVIRONMENTS AND

SHOWED A FEW ASSOCIATIONS WITH ALBUMEN ANTIMICROBIALS

Stéphanie Grizard, Henry K. Ndithia, Muchane Muchai,

Joana Falcão Salles, B. Irene Tieleman

Unpublished manuscript

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ABSTRACT As eggshell-related microorganisms might constitute a source of egg infections and affect embryo viability, one might expect that females adjust, through natural selection, the level of antimicrobial compounds allocated in albumen to adequately counter microbial invasions. Despite the potential effects of ambient conditions on eggshell microbiome and albumen antimicrobials, little is known about their concomitant variations and their relationship with the environment. We addressed this issue by collecting eggs from red-capped larks (Calandrella cinerea) in three Kenyan habitats which exhibited distinct climates likely affecting microbial distribution. Our results pointed out substantial variations in eggshell bacterial communities among habitats: higher bacterial abundance in the cool and wet location but higher α-diversity indices in the two warmest ones. The latter were also phylogenetically more similar to each other and exhibited broader taxonomical distribution when compared with the cooler location. Furthermore, despite the absence of variation in immune properties among habitats, we found correlations between antimicrobials and bacterial community characteristics within habitats: lysozyme concentrations and pH positively correlated with Pelomonas saccharophila and Pantoea sp. affiliated OTUs, two Gram-negative bacteria. Ovotransferrin concentrations and pH varied negatively with bacterial abundance and positively with α-diversity indices. In conclusion, our study brings new insights into the potential mediating effect of the eggshell microbiome upon the female immune investment into eggs. Moreover, as a given environment does not always reflect the nature and amount of antigens females face, our results call for further ecological immunology research integrating a quantification of the microbial exposure.

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INTRODUCTION Birds and microbes live together and interact with each other, forming associations that vary from diseases to beneficial and commensal partnerships (McFall-Ngai et al. 2013; Russell et al. 2014). These associations might be environment-dependent leading to concomitant specific immune responses as shown in adult birds (Horrocks et al. 2014b; Mendes et al. 2005; Piersma 1997). To understand the complexity of the interactions between environment, immunity, and microorganisms, it has been suggested that zooming in on eggs may bring new insights into avian ecological immunology as eggs are immobile, hence facing a limited number of microbes, and receive fixed defences since the amount of antimicrobial proteins cannot be adjusted by the parent after an egg has been laid (Horrocks et al. 2011a). Previous studies have either described eggshell-related microbial communities from wild (Lee et al. 2014; Potter et al. 2013; Shawkey et al. 2009) or semi-captive birds (Giraudeau et al. 2014; Grizard et al. 2014), antimicrobial components present in albumen (D’Alba et al. 2010b; Horrocks et al. 2014a; Saino et al. 2002; Shawkey et al. 2008), or the combination (Grizard et al. 2015). Yet, little is known about the link between albumen antimicrobial proteins and eggshell microbiota and whether environment mediates their relationship. These questions are of great relevance considering that the infection risks imposed by different environments might mediate egg viability (Cook et al. 2003, 2005a, 2005b; Wang et al. 2011b) and may, through natural selection, lead to maternal adaptation in antimicrobial deposition (Shawkey et al. 2008; Wellman-Labadie et al. 2008b).

While the viscous and fibrous nature of albumen represents an efficient hindrance to microbial invasions (Brooks and Hale 1959), albumen plays as well critical bactericidal and bacteriostatic roles through changes in pH (Tranter and Board 1984) and antimicrobial activities, which are mainly held by lysozyme and ovotransferrin proteins (Board and Fuller 1974; Wellman-Labadie et al. 2007). Variations in protective properties have been scantly explored in relation to environmental conditions and led to contrasting results. In worldwide distributed larks, temperature was shown to be positively correlated with albumen lysozyme activity, but not with ovotransferrin (Horrocks et al. 2014a). Similarly, barn swallows (Hirundo rustica) experiencing higher temperatures prior to laying also displayed higher activity (Saino et al. 2004; but see Cucco et al. 2009). Nevertheless, across sixteen European populations of pied flycatchers (Ficedula hypoleuca), Ruuskanen and colleagues (2011) reported no variation. Aside the potential effects of abiotic factors, the risk of microbial infections was also hypothesised to mould the antimicrobial allocation (Cook et al. 2003, 2005b) as shown along the egg laying sequence within a clutch (e.g. Bonisoli-Alquati et al. 2010; Cucco et al. 2007; Saino et al. 2002; but see Shawkey et al. 2008). Considering that the ultimate goal of immune compounds is to protect the embryo, simultaneously investigating antimicrobials and eggshell microbiome, within and among habitats, may yield new insights in whether and to what extent their allocation relates to microorganisms.

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Our knowledge on the driving force that climate exerts on eggshell microbiome is rather sparse. From natural populations, statistical models built on nineteen birds in one location (Peralta-Sánchez et al. 2012) and within a pied flycatcher population (Ruiz-de-Castañeda et al. 2011c) suggested positive effects of cool temperature and high humidity on eggshell bacterial loads. Moreover, experiments on unincubated eggs of chicken (Gallus gallus) and pearly-eyed thrashers (Margarops fuscatus) revealed that eggshells harbored more microbes in a cool humid habitat than in a hot and drier one (Cook et al. 2003, 2005a, 2005b). The extent to which environment affects eggshell microbial composition remains largely unexplored and is much limited by culture-dependent methods.

Here we examine the eggshell-related microbial communities, by means of molecular tools, within a single bird species and across distinct climatic habitats. Specifically, we tested whether the level of antimicrobial compounds concurs with eggshell microbial communities and if this relationship was mediated by the environment, by examining red-capped lark (Calandrella cinerea) eggs in three Kenyan locations. We chose those three sites because they exhibited different climates (i.e. temperature, rainfall, and relative humidity) which may favour eggshell microbiomes to diverge from one habitat to another (Green and Bohannan 2006; Horner-Devine et al. 2004b). Moreover, as red-capped larks are ground-nester passerines, eggs may be more exposed to ambient conditions and more vulnerable to infections than the ones laid in cavity nests (Godard et al. 2007; but see Peralta-Sánchez et al. 2012), enhancing thus the probability to detect any link between microbes and antimicrobials. To test our hypothesis, we first examined eggshell bacterial communities across habitats by specifically describing their abundance and their taxonomic and phylogenetic diversity. Additionally, we quantified albumen antimicrobial defences across habitats by measuring pH and lysozyme and ovotransferrin protein concentrations. We lastly explored the correlations between antimicrobials and bacterial communities through ‘among-’ and ‘within-habitats’ analyses. MATERIALS & METHODS

Study sites and bird species Our study took place in Kenya, in three locations geographically close: South Kinangop and North Kinangop are localized on the Kinangop Plateau and Kedong on the Rift Valley floor. Each site is characterised by its own climate: wet and cool in South Kinangop, warm and wet in North Kinangop, and warm and dry in Kedong (Table S5.1). Climatic data were daily recorded by our own during the complete year 2012.

Red-capped larks (Calandrella cinerea) commonly breed in these locations, at the onset of rains in South Kinangop and year-round in North Kinangop and Kedong. Females typically lay one egg per day and two eggs per clutch in a shallow open-cup nest generally

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lined with grasses and/or rootlets. Incubation is initiated the day of clutch completion and eggs hatch synchronously 12 days later (Del Hoyo et al. 2004; Horrocks et al. 2014a).

Egg collection and processing To follow nest construction and egg laying, we monitored breeding activity daily. When possible, the first laid egg was discreetly marked with an indelible dot (86.7% of the nests in South Kinangop, 50.0% in North Kinangop, and 33.3% in Kedong) (Appendix 5.1). We collected the two eggs per nest on the day of clutch completion (‘day 1’) or during the two following days. We collected thirty eggs in South Kinangop and twenty-four in both North Kinangop and Kedong, between January and April 2012. Appendix 5.1 describes the egg/nest sample size per location and clutch age.

We handled eggs wearing gloves sterilized with 70% ethanol. Eggs were individually stored in sterile bags (Whirl-Pack® Write-On Bags, Nasco, Fort Atkinson, WI), kept on ice during fieldwork (max: 7h), then frozen at -20°C. In the field station, we performed egg dissections following Grizard et al. (2014) and kept part at -20°C. To assess clutch age when egg laying date was unknown (six out of seventy-eight eggs), we used yolk shape as it quickly changes during the first three days of incubation from round to oblong (Grizard et al. 2015). Molecular work and antimicrobial assays were all carried out in the Netherlands.

Assessing bacterial communities associated with eggshells We extracted and quantified microbial DNA from seventy-three eggshells following Grizard et al. (2014). Briefly, after crushing each entire eggshell into liquid nitrogen, we extracted DNA from the eggshell powder using the Fast DNA SPIN kit (MP Biomedicals LLC, Solon, OH). We followed this ‘crush’ protocol except that the final elution step was done in 150µL. The extracted DNA was further used as template to determine the abundance and diversity of bacterial communities. Due to the often low concentration of extracted DNA per sample, not all eggshells were analysed for abundance and diversity, leading to different sample size per method (Appendix 5.1).

We determined the bacterial abundance by quantitative PCR targeting partial region of the 16S rRNA gene using the primer set FP16S/RP16S. The efficiency of the reaction was 102.0% (±1.46) and we carried out quantifications using 1.5ng (±0.31) of DNA template. Details about the overall procedure are described in Grizard et al. (2014). We calculated abundances per gram of eggshell, after correcting for the amount of DNA template per sample, and obtained log copy number of the 16S rRNA gene for fifty eggshells.

We assessed bacterial community composition by 454-Roche multitag pyrosequencing of the V4-V6 region of the 16S rRNA gene, using the primer set 16s-515F (5’-TGYCAGCMGCCGCGGTA-3’) and 16s-1061R (5’-TCACGRCACGAGCTGACG-3’), where each set was coupled with a unique barcode (MID Roche) per sample. Reactions were carried out in 25μL containing 1.25U FastStart High Fidelity Enzyme (Roche Applied Science, Mannheim, Germany), 1x Reaction Buffer without MgCl2, 2.3mM MgCl2 stock solution, 0.20mM PCR

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nucleotide mix, 0.50mg/ml Bovine Serum Albumin (Roche Applied Science), 0.20μM primer/barcode and 1ng DNA template. The thermal cycle started with 5min at 95°C, followed by 35 cycles at 95°C for 40s, 56°C for 45s, 72°C for 40s, and ended with 10min at 72°C. We ran samples in triplicate and checked PCR mixes for the absence of contamination with negative controls of UltraPure Water (Invitrogen, Carlsbad, CA). All samples were consistently amplified. We pooled amplicons to minimize PCR bias, and slowly ran them in a 2.5% (w/v) agarose gel to check their size and integrity. We excised and purified bands with the QIAquick Gel Extraction kit (Qiagen, Hilden, Germany). We pooled purified amplicons from the same sample together and dried them in a vacuum concentrator at 30°C (Concentrator 5301, Eppendorf, Netherlands). We measured their concentrations by fluorescence using Quant-iTTM PicoGreen® dsDNA kit (Molecular Probes Inc., Eugene, OR). Amplicons from forty eggshells were pooled in equimolar concentrations and ran on a Roche GS-FLX 454 automated pyrosequencer (Titanium chemistry) at Macrogen (Korea).

Pyrosequencing raw data were proceeded using the Quantitative Insights Into Microbial Ecology (QIIME) toolkit (version 1.7.0) (Caporaso et al. 2010a). We trimmed sequences for quality by assigning them into Operational Taxonomic Units (OTUs) at 97% nucleotide identity, using ‘close reference’ function and ‘Greengenes’ reference database (http://greengenes.lbl.gov/). Only sequences matching the database were considered for analyses (DeSantis et al. 2006). After quality trimming, 44,346 sequences from the forty eggshell samples were retrieved. We built OTUs using UCLUST (Edgar 2010). One representative sequence per OTU was selected and aligned against ‘Greengenes’ using PyNAST (Caporaso et al. 2010b) and later taxonomically classified using RDP classifier (Wang et al. 2007). All sequencing data have been deposited in the MG-RAST database (http://www.metagenomics.anl.gov/).

To minimize the effects of sampling effort on α-diversity metrics and β-diversity analyses, we rarefied the number of sequences to 240 per sample. In this process, seven samples were discarded, reducing our sample size to thirty-three eggshells (twelve in South Kinangop, eleven in North Kinangop, and ten in Kedong - Appendix 5.1). The cut-off we applied ensured a reasonable coverage of the OTU diversity (South Kinangop: 96.7% ±0.22; North Kinangop: 77.9% ±1.26; Kedong: 84.8% ±0.91). From those eggshells, we assessed four α-diversity metrics: Shannon’s diversity index, species richness (number of OTUs), Faith’s

phylogenetic diversity index, and Chao1 index. We generated β-diversity plots, supported by Principal Coordinate Analysis (PCoA), using weighted and unweighted UniFrac distance matrices (Lozupone et al. 2011). Bacterial communities were discriminated based on the three first axes of the PCoA plots and the percentage of variability reported per axis. All analyses were performed in QIIME.

Antimicrobial assays We recorded albumen pH using a digital pH meter (model 60, Jenco Instruments, San Diego, CA) for seventy-one eggs. We assessed lysozyme concentrations following Horrocks et al.

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(2014a) and ovotransferrin concentrations following Horrocks et al. (2011b), except that we used 10µL of albumen instead of plasma. Lysozyme concentration was determined for sixty-eight eggs and ovotransferrin for sixty-six ones (Appendix 5.1).

We ran assays with a pool of chicken egg albumen within each plate to determine intra- and inter-assay variation (see Horrocks et al. 2011b, 2014a, for details). For lysozyme and ovotransferrin, the intra-assay coefficients of variation were 14.0% (n=9 plates) and 9.3% (n=7 plates), and the inter-assay coefficients were 17.4% and 15.8%, respectively.

Statistical analysis We analysed bacterial abundance and α-diversity metrics, and pH, lysozyme and ovotransferrin concentrations, with linear mixed-effects models (package nlme, Pinheiro et al. 2011). We assigned nests as a random factor, as we frequently had two eggs per nest, and included site, laying order, clutch age, Julian day, pH (when appropriate), and their two-way interactions, as fixed factors. To test the effect of laying order, we assigned the value ‘1’ to the first laid egg of a clutch and ‘2’ to the second one. When laying order was unknown, we gave ‘1.5’ to both eggs. Including or excluding eggs with unknown laying order did not change outputs. As Kedong lacked information, we tested for laying order using a dataset restricted to South Kinangop and North Kinangop. In this subset, interaction site x laying order was not significant for lysozyme (F1,13 = 1.61, P = 0.23) or ovotransferrin (F1,12 = 0.35, P = 0.56). We simplified all models using backward elimination based on log-likelihood ratio tests and using P<0.05 as selection criterion. Site and clutch age were always kept into the model as main effects. Whenever ‘site’ explained significant variation, we used post-hoc Tukey’s test to compare pairs of sites (package multcomp, Hothorn et al. 2008). We tested for the normality of residuals of final models using Shapiro tests; only ovotransferrin concentrations occasionally deviated from Gaussian distribution and were log-transformed. We reported mean values of models, and other averages, with their standard error. We used R 2.13.1 for statistical analyses (R Development Core Team 2011).

To explore the relationships between bacterial communities and antimicrobial compounds, we analysed samples from which both data were available. While correlating bacterial abundance with antimicrobials, we used forty-six samples for lysozyme, forty-seven for ovotransferrin, and forty-eight for pH. While comparing α-diversity metrics with antimicrobials, we used twenty-nine eggs for lysozyme and ovotransferrin, and thirty for pH. For every dataset, the number of samples was evenly distributed among locations (Appendix 5.1). Data associated with South Kinangop communities showed however little variation compared to North Kinangop and to Kedong, often resulting in the non-homogeneity of the variance between South Kinangop and North Kinangop, and South Kinangop and Kedong, whereas North Kinangop and Kedong data always exhibited a similar one (Bartlett's test, Bartlett 1937). We included South Kinangop into models only when its variance did not significantly differ from the one of North Kinangop and of Kedong. We examined correlations between antimicrobials and bacterial community characteristics

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using linear mixed-effect models. Additionally, we determined the correlation between the relative abundance of main OTUs (twenty-one OTUs - defined by their presence in at least eight samples and containing at least fifteen sequences across all samples) and antimicrobials using Pearson correlation (‘otu_category_significance’ in QIIME).

RESULTS

Bacterial communities on eggshells among habitats Bacterial abundance - The log 16S rRNA gene copy number significantly varied among locations (F2,29 = 13.79, P < 0.001) (Table 5.1). South Kinangop, the wettest habitat, had the highest bacterial abundance with on average log 3.6 (±0.18), while North Kinangop and Kedong counted on average log 2.1 (±0.24) and log 2.5 (±0.16) copies, respectively (Figure 5.1A, Table S5.2). Abundance in South Kinangop was significantly larger than the ones observed at North Kinangop (Z = 4.9, P < 0.001) and at Kedong (Z = 4.0, P < 0.001), those two latters not differing from each other (Z = -1.1, P=0.52) (Table S5.2). Table 5.1 - Linear mixed-effect models examining variation in bacterial communities on red-capped lark eggshells among habitats. ‘Sites’ corresponds to the three Kenyan habitats (South Kinangop, North Kinangop, and Kedong). F tests and P-values are reported for each model; P-values are marked up in bold when significant (P<0.05). Explanatory variables df F P

BACTERIAL ABUNDANCE site * Julian day 2,24 0.92 0.41 (log 16S rRNA gene) site * clutch age 2,27 2.36 0.11 laying order 1,16 0.14 0.72 Julian day 1,26 3.99 0.06 site 1,29 13.79 <0.001 clutch age 1,29 0.45 0.51

SHANNON’S DIVERSITY INDEX site * Julian day 2,14 0.64 0.54 site * clutch age 2,16 2.57 0.11 laying order 1,8 0.54 0.49 Julian day 1,18 1.32 0.27 site 2,19 23.39 <0.001 clutch age 1,19 0.02 0.87

OTU RICHNESS site * Julian day 2,14 0.65 0.54 (Number of OTUs) site * clutch age 2,16 2.11 0.15 laying order 1,8 0.55 0.48 Julian day 1,18 6.43 0.02 site 2,18 23.17 <0.001 clutch age 1,18 1.15 0.30

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α-diversity metrics - The cool and wet South Kinangop exhibited the smallest Shannon’s diversity average (1.2 ±0.18), followed by the warm and dry Kedong (3.4 ±0.54), then the warm and wet North Kinangop (5.2 ±0.49) (Figure 5.1B, Table S5.2). It resulted in a significant site effect (F2,19 = 23.39, P < 0.001) (Table 5.1) where all locations differed from each other (Table S5.2). Similarly, OTU richness significantly differed among habitats (F2,18 = 23.17, P < 0.001) (Table 5.1). In addition to its high Shannon’s average, North Kinangop showed the highest number of OTUs (91.7 ±12.43), making it significantly richer than South Kinangop (16.1 OTUs ±2.63; Z = -6.78, P < 0.001) and Kedong (56.8 OTUs ±10.43; Z = 3.82, P < 0.001), those two latter sites not statistically differing from each other (Z = -6.78, P = 0.48) (Table S5.2, Figure S5.1A). Faith’s phylogenetic diversity and Chao1 indices followed similar trends (Tables S5.2-S5.3, Figures S5.1B-S5.1C).

Figure 5.1 - Bacterial community abundance and diversity, and antimicrobial compounds among habitats. Data are plotted per habitat: South Kinangop (SK), North Kinangop (NK), and Kedong (KE). (A) Bacterial abundance corresponds to the log copy number of 16S rRNA gene per gram of eggshell. (B) Shannon’s diversity index data are associated with eggshell bacterial communities. (C) Lysozyme and (D) ovotransferrin concentrations of egg albumen are given in mg/ml. Data are represented by grey open circles and each mean by a black diamond with its standard error.

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Taxonomical composition - We observed substantial differences among habitats in the community composition at several taxonomic levels. Bacterial communities associated with Kedong and North Kinangop eggshells were spread over broader phylum/class affiliation than did the ones from South Kinangop. In more details, South Kinangop eggshells were dominated by Betaproteobacteria which accounted for 86.9% (±2.00) of the overall sequences, while this class represented two-thirds of the communities in Kedong (61.8% ±8.03) and only one-third in North Kinangop (31.2% ±7.61) (Figure 5.2). Zooming in, we noted that South Kinangop was mainly constituted by one Oxalobacteriacea family while North Kinangop and Kedong were essentially represented by Burkholderiacea (Figure S5.2). At the OTU level, South Kinangop was dominated by one OTU affiliated with Herbaspirillum sp. while OTUs affiliated mainly with Ralstonia sp. dominated the two other habitats (Table 5.1). In Kedong and North Kinangop, the second and third most abundant classes were Actinobacteria (14.2% ±3.58, 28.5% ±7.74, respectively) and Bacilli (4.8% ±1.98, 9.0% ±2.47, respectively). Phylogenetic β-diversity - Principal Coordinates Analyses (PCoA) using UniFrac showed clear clustering of the eggshell bacterial communities by habitat. Weighted UniFrac-based PCoA showed that South Kinangop communities shared higher similarity in OTU relative abundance; those communities were closer (0.05 ±0.004) than were the ones from North Kinangop (0.29 ±0.01) or Kedong (0.20 ±0.02). South Kinangop communities were discriminated by the first PCoA axis, which explained 75.4% of the overall variability in eggshell bacterial communities among habitats (Figure 5.3A; Figure S5.3A.B.C). Unweighted UniFrac-based PCoA more clearly distinguished between the eggshell communities from South Kinangop and the ones from North Kinangop and Kedong along the first PCoA axis (32.4% of the variability), while the communities of those two latters were quite variable along the second axis (7.5% of the variability, Figure 5.3B; Figure S5.3D.E.F). Based on OTU presence/absence, and in line with the observed taxonomical distribution, North Kinangop and Kedong communities were phylogenetically closer to each other (0.69 ±0.01) than was South Kinangop with each of the two sites (0.77 ±0.01, 0.75 ±0.01, respectively).

Antimicrobials in albumen among habitats Neither pH (F2,33 = 0.23, P = 0.80), nor lysozyme (F2,34 = 0.60, P = 0.56) or ovotransferrin concentrations (F2,33 = 1.26, P = 0.30) differed between the three locations (Figure 5.1C-1D, Table S5.4).

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Figure 5.2 - Taxonomical composition of eggshell bacterial communities among habitats. The taxonomical affiliation (phylum/class) is based on the assignation of Operational Taxonomic Units (OTUs) to the RDP Classifier. Sequences were assigned to OTUs at 97% nucleotide identity. The relative abundance of each phylum (ph) or class (cl) is plotted per eggshell. Gamma-, Delta-, Beta-, and Alphaproteobacteria (each noted cl*) represent the four classes of the Proteobacteria phylum. Clostridia and Bacilli (each noted cl**) represent the two classes of the Firmicutes phylum. Actinobacteria (noted cl***) was the single representative class of the Actinobacteria phylum. Lanes are ordered by habitats (South Kinangop (SK), North Kinangop (NK), and Kedong (KE)), per nest (in ascending order), and per clutch age (day 1 (d1), day 2 (d2) and day 3 (d3) after clutch completion). When the two eggs of a same nest are present, they are also noted with a or b.

Figure 5.3 - Phylogenetic β-diversity plots of bacterial communities associated with red-capped lark eggshells among three habitats. Each dot represents one eggshell-related community. Eggshell communities are based on UniFrac distance matrices and plotted on a Principal Coordinates Analysis (PCoA) plot. The variability of these communities is based on the two first axes of the PCoA. The percentage of variation explained per axis is mentioned on the graph. (A) Based on weighted UniFrac, PC1 explained 32.40% of the variation among communities and PC2 7.51%; (B) based on unweighted UniFrac, PC1 explained 75.35% of the variation and PC2 7.85%. South Kinangop communities are symbolized by blue circles, North Kinangop by green squares, and Kedong by red triangles.

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Correlation between antimicrobial compounds and bacterial communities within habitats

Within all locations, variation in lysozyme concentrations was not explained either by bacterial abundance or by any α-diversity metric, but showed positive correlations only with two OTUs affiliated with Pelomonas saccharophila and Pantoea sp., both low abundant Gram-negative bacterial types. Conversely, variation in ovotransferrin concentrations was explained by a negative correlation with bacterial abundance (F1,14 = 13.55, P = 0.003) within the three habitats, and a positive one with each α-diversity index (e.g. Shannon’s diversity: F1,3 = 14.46, P = 0.03) within North Kinangop and Kedong. Ovotransferrin was however not linked to any specific OTU present on eggshells. As for pH, within all habitats, it positively correlated with bacterial abundance (F1,15 = 14.33, P = 0.002), did not vary with any α-diversity index, and negatively correlated with the two same OTUs than lysozyme (Table 5.2, Table S5.5). DISCUSSION Microbes growing on shell surfaces might serve as a source of infection. If this microbiota relates to deleterious consequences, one might expect that albumen antimicrobial properties have concomitantly evolved to control microbial growth. Moreover, if ambient conditions alter eggshell microbiota, one might expect the relationship between antimicrobials and microbes to be in relation with environment. Our results pointed out substantial differences in eggshell microbiota associated with red-capped lark eggs among three habitats, but we found no significant variation in antimicrobials. Interestingly however, we found a few correlative evidences of the relationship between antimicrobials and bacterial communities.

Eggshell microbiome substantially vary with climatic and ecological factors In South Kinangop, eggshell-related bacterial communities exhibited a singular phylogenetic composition characterised by low diversity and high abundance compared with those from North Kinangop and Kedong. South Kinangop is a wet habitat, with high rainfall and relative humidity and periods of standing water (up to six consecutive months) on the grasslands where larks breed, possibly influencing the nest microbiome and ultimately the eggshell microbiome (Baggott and Graeme-Cook 2002; Brandl et al. 2014). Considering that high relative humidity at the nest (Ruiz-De-Castañeda et al. 2011a) as well as high ambient temperature after laying (Ruiz-de-Castañeda et al. 2011c) have been associated with increased eggshell bacterial loads, it is likely that the unique combination of temperature and humidity at each site explains the observed differences in process of microbial selection on eggshells. While our study revealed that eggs, at early incubation stages, exhibited distinctive microbiome across habitats, the extent to which climatic

125

Tabl

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n w

ith a

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icro

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com

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ds.

A

ffili

ated

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per s

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(1) O

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(OTU

s) w

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at l

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sequ

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outh

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t th

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clu

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t 99%

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(3)

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at 9

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enus

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(4) T

he re

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was

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data

base

allo

win

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shar

ed (i

n pe

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) with

a re

fere

nce

sequ

ence

.

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parameters drive those characteristics remains unclear. Further studies may characterise bacterial communities immediately upon laying to determine if eggs laid in different locations already harbored unique microbiome, as a possible reflection of the female gut/cloacal microflora (Gantois et al. 2009; Ruiz-de-Castañeda et al. 2011c) through eventual specific diet in a given habitat (e.g. Lozupone et al. 2012). In addition, swapping freshly laid eggs could determine if shifts in microbiomes are possible due to environmental changes or if their characteristics are driven, through the overall incubation, by their original assemblages on eggshells.

Female reproductive/digestive tracts, nests, and feathers of incubating parent(s), may all contribute to the shell colonisation and the development of its microbiome (Potter et al. 2013; Lee et al. 2014). The two most abundant bacterial genera found in our study, Herbaspirillum and Ralstonia, are found in association with grasses (Monteiro et al. 2012) and soil (Coenye et al. 2003), respectively, which are used as lining materials in red-capped lark nests in South Kinangop and North Kinangop. As South Kinangop regularly suffers from flooding, the prevalence of Herbaspirillum may result from characteristic site-specific vegetation. Additionally, Pseudomonas, Bacillus, and Propionibacterium genera, although found in minor proportions, were commonly described in bird plumage (Bisson et al. 2007; Shawkey et al. 2005). Interestingly, as we barely observed bacteria commonly described in avian gut (Salmonella, Staphylococcus, and Enterococcus genera, Godoy-Vitorino et al. 2012; Waite and Taylor 2014), our results suggest that these gut-bacteria are either faintly present in the red-capped lark digestive tract or that there is a rapid turnover of eggshell communities after egg laying, where environment-borne bacteria take over and dominate this microbiome. Examining feces would provide information about the red-capped lark gut microflora. More generally, concurrently targeting bacterial sources, as recently done with nests in free-living reed warblers (Acrocephalus scirpaceus) (Brandl et al. 2014), would improve the comprehension of the eggshell microbiome assemblage.

Antimicrobials do not vary across habitats but show a few associations with eggshell microbiome within habitats

Among habitats, we observed no differences in antimicrobial activities, suggesting that environmental factors did not mediate protein allocation in red-capped lark eggs. In line with our results, constant albumen lysozyme activity was observed among sixteen European populations of pied flycatchers (Ruuskanen et al. 2011). However, among nine worldwide distributed larks - from mesic to arid habitats - lysozyme activity was positively related to ambient temperature, although this was not verified for ovotransferrin (Horrocks et al. 2014a). Nevertheless, significant correlations between antimicrobials and ambient conditions must be interpreted with cautious as confounding factors may influence their relationship (e.g. food availability, Cucco et al. 2009). While antimicrobial defences are well known, in particular lysozyme (Callewaert and Michiels 2010; Lesnierowski and Kijowski 2007) and ovotransferrin (Giansanti et al. 2012; Supernati et al. 2007) bactericidal and

127

bacteriostatic respective roles, as well as the inimical effect of pH on microbes (Fang et al. 2012a; Tranter and Board 1984), factors altering their properties when the ecological context varies are less understood and experimental studies are required to disentangle the effect of abiotic from biotic parameters.

Within-site analyses revealed a few evidences about the link between albumen antimicrobials and eggshell bacterial communities. In more details, we found some correlations between two OTUs affiliated with Gram-negative bacteria and lysozyme, and between bacterial abundance and alpha-diversity metrics and ovotransferrin, supporting the idea that eggshell microbiome may, to a certain extent, relate to antimicrobial allocation. Although our sample size is small, suggesting caution when interpreting data, antimicrobial properties might, at least partially, explain our results. The low presence of Gram-positive bacteria on shells, against which lytic activity is the most effective (Callewaert and Michiels 2010), might limit correlations. However, as lysozyme can bind to bacterial membranes thus leading to abnormal cell functioning (Cegielska-Radziejewska et al. 2008), it may act upon Gram-negative through mechanisms other than enzymatic ones (Lesnierowski and Kijowski 2007; Wellman-Labadie et al. 2007), warranting the few correlations we observed. As for ovotransferrin, its iron-binding property makes it an efficient protein against a broader range of microbes, favouring its correlation at levels different than OTUs, more specifically targeting the overall bacterial diversity or abundance (Giansanti et al. 2012). Although lysozyme and ovotransferrin are two major proteins, the albumen itself also holds crucial antimicrobial properties. It has been experimentally shown that these properties can be altered without changing lysozyme or ovotransferrin activities (Bedrani et al. 2013; Sellier et al. 2007); other proteins and smaller peptides may play extensive bactericidal roles (Baron and Réhault 2007; Réhault et al. 2007; Wellman-Labadie et al. 2007). Investigating the overall albumen activity might therefore be a valuable complementary tool. Additionally, future studies characterising the trans-shell penetration ability of certain microbes, by specifically extracting microbial DNA directly from albumen (Javŭrková et al. 2013), would yield new insights into the relationship between antimicrobials and microbiome.

Compared to the considerable differences observed among eggshell bacterial communities, variations in antimicrobial properties were quite restricted. In one previous study on red-capped larks, we also found limited correlations between eggshell microbiome and antimicrobial concentrations (Grizard et al. 2015). In a recent experiment, eggs of genetically uniform hens, exposed to three extreme microbial environments, did not display differences in lytic or iron-binding properties and only limited albumen antibiotic activities (Bedrani et al. 2013). Furthermore, in another experimental work where nest-associated bacterial growth was chemically promoted or inhibited, great tit (Parus major) females did not modify either lysozyme or ovotransferrin investment into their eggs (Jacob et al. 2015). Contrasting results may arise from unexplored genetic and/or phenotypically plastic mechanisms which likely regulate immune allocation. The roles of microorganisms

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must consequently be interpreted with cautious as both genetic relationships among the three red-capped lark populations and their reactions norms in antimicrobial deposition are unknown. Future studies focusing on teasing apart fixed and plastic responses could shed light onto the variation of antimicrobials in birds experiencing various antigens. CONCLUSIONS Our study stressed the importance of implementing direct measurements of the microorganisms present on eggshell surfaces instead of gambling on a relationship between habitats and microbial abundance/presence. Our study demonstrated that the link between environment and antigens is not necessarily straightforward or predictable. More studies investigating eggshell microbiome, at the taxonomical level but also at the functional one, are essential to improve our comprehension about the role of these particular microbes on antimicrobial allocation in egg white, and more generally on immune responses of adult birds. ACKNOWLEDGEMENTS We are grateful to Peter K. Gachigi, Abrahim M. Kuria, Paul M. Kimani, and Susan V. Cousineau, who contributed to the sampling effort. Maaike A. Versteegh provided advice on the statistical analysis and Francisco Dini-Andreote on the pyrosequencing data analysis. We are grateful to ‘Kedong Ranch’ which provided permission to work on their property. Financial support for this study was provided by a VIDI grant from the Netherlands Organisation for Scientific Research (NWO) (to BIT).

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SUPPLEMENTARY INFORMATION

Table S5.1 - Climatic parameters per location. Weather data were daily recorded at each site over the overall year 2012 in South Kinangop, North Kinangop, and Kedong. Minimum (min) and maximum (max) daily average per parameter are presented with their standard error (±S.E.). GPS coordinates and altitudes were recorded using a GPS data logger.

Habitats South Kinangop North Kinangop Kedong Geographical location 0° 42’S; 36° 36’E 0° 35’S; 36° 29’E 0° 53’S; 36° 23’E Altitude (m amsl) 2556 2428 2077 Cumulative precipitation (mm) 1028 546 331

Temperature (°C) min: 5.5 (±0.11)

max: 24.2 (±0.29) min: 7.8 (±0.20)

max: 26.0 (±0.30) min: 9.7 (±0.11)

max: 29.5 (±1.59)

Relative humidity (%) min: 91.1 (±0.91)

max: 98.9 (±0.81) min: 20.0 (±0.19) max: 51.4 (±0.88)

min: 20.0 (±1.08) max: 52.0 (±2.81)

Table S5.2 - Abundance and α-diversity indices from eggshell bacterial communities and associated post-hoc tests among habitats. An average per metric with its standard error (±S.E.) and the minimum (min) and maximum (max) values are given. Post-hoc Tukey’s tests compare pairs of habitats: South Kinangop (SK), North Kinangop (NK), and Kedong (KE).

BACTERIAL ABUNDANCE South Kinangop North Kinangop Kedong (log 16S rRNA gene copy) 3.6 (±0.18) 2.1 (±0.24) 2.5 (±0.16) min:2.0 - max:5.2 min: 1.1 - max: 3.8 min: 1.4 - max: 4.2

post-hoc test SK vs KE NK vs KE NK vs SK Z=4.0, P<0.001 Z=-1.1, P=0.52 Z=4.9, P<0.001

SHANNON'S DIVERSITY INDEX South Kinangop North Kinangop Kedong

1.2 (±0.18) 5.2 (±0.49) 3.4 (±0.54)

min:0.6 - max:2.8 min: 1.8 - max: 6.7 min: 0.9 - max: 5.9 post-hoc test SK vs KE NK vs KE NK vs SK

Z=0.60, P<0.001 Z=0.62, P=0.007 Z=0.60, P<0.001

OTUs RICHNESS South Kinangop North Kinangop Kedong

16.1 (±2.63) 91.7 (±12.43) 56.8 (±10.43)

min:9.0 - max:42.7 min: 29.9 - max: 142.1 min: 18.1 - max: 113.1 post-hoc test SK vs KE NK vs KE NK vs SK

Z=-6.78, P=0.48 Z=3.82, P<0.001 Z=-6.78, P<0.001

FAITH'S PHYLOGENETIC South Kinangop North Kinangop Kedong DIVERSITY INDEX 1.5 (±0.16) 5.6 (±0.70) 3.9 (±0.59) min:1.0 - max:3.1 min: 2.6 - max: 9.1 min: 1.8 - max: 7.2

post-hoc test SK vs KE NK vs KE NK vs SK Z=-1.08, P=0.54 Z=4.57, P<0.001 Z=-7.64, P<0.001

CHAO1 INDEX South Kinangop North Kinangop Kedong

27.3 (±5.72) 188.1 (± 33.40) 131.3 (±27.05)

min: 11.8 - max: 87.3 min: 48.1 - max: 330.1 min: 46.9 - max: 289.3 post-hoc test SK vs KE NK vs KE NK vs SK

Z=-1.03, P=0.55 Z=3.36, P=0.002 Z=-5.94, P<0.001

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Table S5.3 - Linear mixed-effect models examining variation in two α-diversity metrics associated with eggshells among habitats. ‘Sites’ corresponds to the three Kenyan habitats (South Kinangop, North Kinangop, and Kedong). Models are based on backward elimination procedure. F tests and P-values are reported for each model; P-values are marked up in bold when significant (P<0.05).

Explanatory variables df F P

FAITH'S PHYLOGENETIC DIVERSITY site * clutch age 2,14 1.01 0.39

site * Julian day 2,16 2.49 0.11

laying order 1,8 0.48 0.51

Julian day 1,18 13.22 0.002

site 2,18 29.67 <0.001

clutch age 1,18 2.45 0.13

CHAO1 INDEX site * Julian day 2,14 0.69 0.52

site * clutch age 2,16 2.37 0.13

laying order 1,8 0.42 0.54

Julian day 1,18 8.42 0.01

site 2,18 17.80 <0.001

clutch age 1,18 2.26 0.15

Table S5.4 - Linear mixed-effect models examining variations in antimicrobial compounds among habitats. ‘Sites’ corresponds to the three Kenyan habitats (South Kinangop, North Kinangop, and Kedong). Models are based on backward elimination procedure. F tests and P-values are reported for each model; P-values are marked up in bold if significant (P<0.05).

Explanatory variables df F P

pH site * Julian day 2,29 0.69 0.51

site * clutch age 2,31 1.72 0.20

Julian day 1,33 4.51 0.04

site 2,33 0.23 0.80

clutch age 1,33 3.23 0.08

LYSOZYME CONCENTRATION site * Julian day 2,29 0.47 0.63

(mg/ml) site * clutch age 2,31 1.67 0.20

site * pH 2,27 3.35 0.05

Julian day 1,33 0.49 0.49

pH 1,29 2.43 0.13

site 2,34 0.60 0.56

clutch age 1,34 3.56 0.07

OVOTRANSFERRIN CONCENTRATION site * clutch age 2,28 0.92 0.41

(mg/ml) (Log) site * pH 2,26 0.67 0.53

site * Julian day 2,30 1.28 0.29

Julian day 1,32 0.006 0.94

pH 1,28 2.55 0.12

site 2,33 1.26 0.30

clutch age 1,33 1.02 0.32

131

Tabl

e S5

.5 -

Line

ar m

ixed

-eff

ect

mod

els

exam

inin

g co

rrel

atio

ns b

etw

een

eggs

hell

bact

eria

l com

mun

ities

and

in a

ntim

icro

bial

com

poun

ds a

mon

g ha

bita

ts. ‘

Site

s’

corr

espo

nd to

the

thre

e Ke

nyan

hab

itats

(Sou

th K

inan

gop,

Nor

th K

inan

gop,

and

Ked

ong)

. Mod

els

are

base

d on

bac

kwar

d el

imin

atio

n pr

oced

ure.

F te

sts

and

P-va

lues

ar

e re

port

ed p

er m

odel

; P-v

alue

s ar

e m

arke

d up

in b

old

if si

gnifi

cant

(P<0

.05)

. ‘PD

’ sta

nds

for p

hylo

gene

tic d

iver

sity

.

Ly

sozy

me

conc

entr

atio

n

Ovo

tran

sfer

rin

conc

entr

atio

n

pH

Ex

plan

ator

y va

riabl

es

df

F

P

Ex

plan

ator

y va

riabl

es

df

F

P

Ex

plan

ator

y va

riabl

es

df

F

P

si

tes

* Ju

lian

day

2, 2

3 0.

07

0.93

site

s *

pH

2, 9

0.

15

0.86

site

s *

log

16S

2, 13

0.

26

0.77

si

tes

* pH

2,

8

2.45

0.

15

si

tes

* lo

g 16

S 2,

11

1.09

0.37

site

s *

Julia

n da

y 2,

23

0.60

0.

56

si

tes

* cl

utch

age

2,

25

1.74

0.20

site

s *

clut

ch a

ge

2, 2

3 3.

80

0.04

site

s *

clut

ch a

ge

2, 2

5 0.

29

0.75

Ba

cter

ial a

bund

ance

si

tes

* lo

g 16

S *

2, 10

2.

27

0.15

site

s *

Julia

n da

y 2,

23

4.42

0.

02

Ju

lian

day

1, 27

2.

21

0.15

da

tase

t: pH

1,

12

0.04

0.

85

pH

1,

13

2.20

0.

16

si

tes

2, 2

8 2.

87

0.07

SK

, NK

and

KE

Julia

n da

y 1,

27

0.12

0.

73

Ju

lian

day

1, 23

4.

65

0.04

clut

ch a

ge

1, 28

3.

47

0.07

si

tes

2, 2

8 0.

12

0.88

site

s 2,

23

2.46

0.

11

lo

g 16

S 1,

15

14.3

3 0.

002

cl

utch

age

1,

28

2.19

0.

15

cl

utch

age

1,

23

9.34

0.

006

log

16S

1, 13

1.0

7 0.

32

lo

g 16

S 1,

14

13.5

5 0.

003

si

tes

* Ju

lian

day

1, 8

0.18

0.

68

si

tes

* sh

anno

n 1,

2 0.

42

0.58

site

s *

Julia

n da

y 1,

8 0.

24

0.63

si

tes

* pH

1,

2 0.

11

0.77

site

s *

Julia

n da

y 1,

8 0.

96

0.36

site

s *

clut

ch a

ge

1, 9

1.46

0.26

si

tes

* sh

anno

n 1,

2 0.

47

0.54

site

s *

pH

1, 3

10.7

7 0.

04

si

tes

* sh

anno

n 1,

4 6.

73

0.06

si

tes

* cl

utch

age

1,

9 0.

87

0.38

site

s *

clut

ch a

ge

1, 9

5.70

0.

04

Ju

lian

day

1, 10

2.

43

0.15

Ju

lian

day

1, 10

0.

07

0.79

Julia

n da

y 1,

9 1.2

4 0.

30

si

tes

1, 11

0.

55

0.47

pH

1,

4 2.

30

0.20

pH

1, 3

8.31

0.

06

cl

utch

age

1,

11

4.58

0.

06

si

tes

1, 11

2.

13

0.17

site

s 1,

10

18.7

4 0.

002

sh

anno

n 1,

5 1.2

8 0.

31

cl

utch

age

1,

11

5.42

0.

04

cl

utch

age

1,

10

4.46

0.

06

A

lpha

-div

ersi

ty

shan

non

1, 5

0.02

0.

89

sh

anno

n 1,

3 14

.46

0.03

indi

ces

site

s *

pH

1, 2

0.08

0.

81

si

tes

* ot

u 1,

2 1.2

6 0.

38

si

tes

* cl

utch

age

1,

8 0.

62

0.45

da

tase

t: si

tes

* Ju

lian

day

1, 8

0.30

0.

60

si

tes

* Ju

lian

day

1, 8

0.86

0.

38

si

tes

* Ju

lian

day

1, 9

1.36

0.27

N

K an

d KE

si

tes

* cl

utch

age

1,

9 0.

35

0.57

site

s *

pH

1, 3

10.7

0 0.

04

si

tes

* ot

u 1,

4 35

.04

0.00

4

si

tes

* ot

u 1,

2 1.1

3 0.

37

si

tes

* cl

utch

age

1,

9 8.

24

0.02

Julia

n da

y 1,

10

0.02

0.

89

Ju

lian

day

1, 10

0.

06

0.81

Julia

n da

y 1,

9 0.

32

0.58

site

s 1,

11

23.4

6 0.

0005

pH

1,

4 1.5

4 0.

28

pH

1,

3 4.

92

0.11

clut

ch a

ge

1, 11

4.

05

0.07

si

tes

1, 11

3.

52

0.09

site

s 1,

10

17.4

3 0.

002

ot

u 1,

4 5.

27

0.08

cl

utch

age

1,

11

6.56

0.

03

cl

utch

age

1,

10

4.64

0.

06

otu

1, 5

0.30

0.

61

ot

u 1,

3 13

.15

0.04

Cha

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132

Tabl

e S5

.5 -

cont

inue

d.

Ly

sozy

me

conc

entr

atio

n

Ovo

tran

sfer

rin

conc

entr

atio

n

pH

Ex

plan

ator

y va

riabl

es

df

F

P

Ex

plan

ator

y va

riabl

es

df

F

P

Ex

plan

ator

y va

riabl

es

df

F

P

si

tes

* Ju

lian

day

1, 8

0.02

0.

89

si

tes

* Ju

lian

day

1, 8

0.46

0.

52

si

tes

* cl

utch

age

1,

8 0.

38

0.55

si

tes

* PD

1,

2 0.

009

0.93

site

s *

PD

1, 2

0.28

0.

65

si

tes

* Ju

lian

day

1, 9

1.34

0.28

si

tes

* pH

1,

2 0.

24

0.66

site

s *

clut

ch a

ge

1, 9

5.59

0.

04

si

tes

* PD

1,

4 10

.77

0.04

si

tes

* cl

utch

age

1,

9 0.

91

0.37

site

s *

pH

1, 3

13.7

2 0.

03

Ju

lian

day

1, 10

<0

.001

0.

99

Ju

lian

day

1, 10

<0

.001

0.

98

Ju

lian

day

1, 9

0.02

0.

91

si

tes

1, 11

9.

63

0.01

pH

1,

4 2.

26

0.21

pH

1, 3

5.52

0.

10

cl

utch

age

1,

11

4.65

0.

054

si

tes

1, 11

2.

58

0.14

site

s 1,

10

16.4

9 0.

002

PD

1,

4 1.4

4 0.

29

Alp

ha-d

iver

sity

cl

utch

age

1,

11

5.37

0.

04

cl

utch

age

1,

10

5.73

0.

03

in

dice

s

PD

1, 5

0.00

1 0.

97

PD

1,

3 13

.18

0.04

data

set:

site

s *

pH

1, 2

0.06

0.

84

si

tes

* Ju

lian

day

1, 8

1.78

0.22

site

s *

clut

ch a

ge

1, 8

0.90

0.

37

NK

and

KE

site

s *

Julia

n da

y 1,

8 0.

24

0.63

site

s *

Chao

1 1,

2 1.5

6 0.

34

si

tes

* Ju

lian

day

1, 9

1.80

0.21

si

tes

* Ch

ao1

1, 2

0.28

0.

63

si

tes

* cl

utch

age

1,

9 9.

65

0.01

site

s *

Chao

1 1,

4 30

.85

0.00

5

si

tes

* cl

utch

age

1,

9 0.

74

0.41

site

s *

pH

1, 3

17.0

6 0.

03

Ju

lian

day

1, 10

0.

14

0.71

Ju

lian

day

1, 10

0.

04

0.84

Julia

n da

y 1,

9 0.

47

0.51

site

s 1,

11

19.9

2 0.

001

pH

1,

4 1.4

9 0.

29

pH

1,

3 2.

82

0.19

clut

ch a

ge

1, 11

5.

09

0.04

5

si

tes

1, 11

3.

73

0.08

site

s 1,

10

17.5

0 0.

002

Ch

ao1

1, 4

2.62

0.

18

cl

utch

age

1,

11

6.74

0.

03

cl

utch

age

1,

10

5.19

0.

046

Chao

1 1,

5 0.

38

0.56

Chao

1 1,

3 12

.56

0.04

133

Fi

gure

S5.

1 - D

otpl

ots

of t

hree

α-d

iver

sity

met

rics

amon

g ha

bita

ts. (

A) S

peci

es r

ichn

ess

(num

ber

of o

bser

ved

OTU

s), (

B) F

aith

’s p

hylo

gene

tic d

iver

sity

inde

x, a

nd (

C)

Chao

1 ind

ex a

re p

lott

ed p

er h

abita

t: So

uth

Kina

ngop

(SK)

, Nor

th K

inan

gop

(NK)

, and

Ked

ong

(KE)

. Dat

a ar

e re

pres

ente

d by

gre

y op

en c

ircle

s an

d ea

ch m

ean

by a

bla

ck

diam

ond

with

its

stan

dard

err

or.

SK

NK

KE

050100

150

Hab

itat

Species richness (number of OTUs)

SK

NK

KE

2468

Hab

itat

Phylogenetic diversityS

KN

KK

E

050100

150

200

250

300

Hab

itat

Chao 1

(A)

(B)

(C)

134

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Figure S5.2 - Heatmaps of the taxonomical distribution of eggshell bacterial communities among three habitats. Heatmaps are based on Operational Taxonomic Units (OTUs) retrieved from pyrosequencing; each OTU corresponds to one single representative sequence at 97% nucleotide identity. Only OTUs including at least fifteen sequences and present in at least eight samples are plotted. The heatmap on the top represents OTUs at phylum/class level; the lower one is a zoom on Betaproteobacteria (family/order levels). Lanes are ordered by habitats (South Kinangop (SK), North Kinangop (NK), and Kedong (KE)), per nest (in ascending order), and per clutch age (day 1 (d1), day 2 (d2) and day 3 (d3) after clutch completion). When the two eggs of a same nest are present, they are also noted with a or b.

SK1a-d1

SK1b-d1

SK2-d1

SK3a-d1

SK3b-d1

SK4-d1

SK5-d1

SK6-d1

SK7a-d2

SK7b-d2

SK8a-d3

SK8b-d3

NK1a-d1

NK1b-d1

NK2-d1

NK3a-d1

NK3b-d1

NK4a-d2

NK4b-d2

NK5-d2

NK6-d2

NK7a-d3

NK7b-d3

KE1a-d1

KE1b-d1

KE2-d1

KE3a-d1

KE3b-d1

KE4a-d2

KE4b-d2

KE5-d2

KE6a-d3

KE6b-d3

Other_PhylaVerrucomicrobiaThermi(Proteobacteria)Alpha-(Proteobacteria)Beta-(Proteobacteria)Delta-(Proteobacteria)Gamma-PlanctomycetesGemmatimonadetes(Firmicutes)Bacilli(Firmicutes)ClostridiaCyanobacteriaCrenarchaeotaChloroflexiBacteroidetesActinobacteriaAcidobacteria

0

50

100

150

200

Oxalobacteraceae(f3)Oxalobacteraceae(f2)Oxalobacteraceae(f1)Comamonadaceae(f)Burkholderiaceae(f4)Burkholderiaceae(f3)Burkholderiaceae(f2)Burkholderiaceae(f1)Burkholderiales(o3)Burkholderiales(o2)Burkholderiales(o1)

135

Fi

gure

S5.

3 -

Prin

cipa

l Co

ordi

nate

s An

alys

is (

PCoA

) pl

ots

of e

ggsh

ell

bact

eria

l co

mm

uniti

es a

mon

g th

ree

habi

tats

. Pl

ots

are

base

d on

wei

ghte

d (A

, B,

C)

and

unw

eigh

ted

(D, E

, F) U

niFr

ac d

ista

nce

mat

rices

. Egg

shel

l com

mun

ity v

aria

bilit

y is

bas

ed o

n th

e th

ree

first

axe

s of

the

PCoA

, whi

ch a

ccou

nt fo

r 86.

50%

of th

e va

riabi

lity

base

d on

wei

ghte

d U

niFr

ac a

nd 4

6.14

% on

unw

eigh

ted

Uni

Frac

. The

per

cent

age

of v

aria

tion

expl

aine

d pe

r ax

is (

PC)

is m

entio

ned

on t

he g

raph

. Eac

h do

t re

pres

ents

th

e ba

cter

ial c

omm

unity

ass

ocia

ted

with

one

egg

shel

l. So

uth

Kina

ngop

com

mun

ities

are

sym

boliz

ed b

y bl

ue c

ircle

s, N

orth

Kin

ango

p by

gre

en s

quar

es, a

nd K

edon

g by

re

d tr

iang

les.

(A)

(D)

(E)

(F)

(B)

(C)

136

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Appendix 5.1 - Egg sampling and associated data from albumen antimicrobial compounds and eggshell bacterial communities in the three Kenyan habitats. SOUTH KINANGOP SAMPLING ANTIMICROBIALS

Nest code (1) Date (2012) Clutch age

(day(s) after clutch completion) (2)

Lysozyme concentrations

(mg/ml)

Ovotransferrin concentrations

(mg/ml)

SK12010 14-Mar 1 - - SK12010 14-Mar 1 1.11 8.68 SK12011 22-Mar 1 0.74 8.96 SK12011 22-Mar 1 - - SK12012 15-Mar 1 - - SK12012 15-Mar 1 - - SK12019 15-Mar 1 2.51 - SK12019 15-Mar 1 - - SK12020 21-Mar 1 1.25 12.33 SK12020 21-Mar 1 1.65 6.94 SK12028 24-Mar 1 2.43 7.06 SK12028 24-Mar 1 1.66 10.38 SK12032 28-Mar 1 (unk) 2.36 6.80 SK12032 28-Mar 1 (unk) 2.74 9.07 SK12034 25-Mar 1 2.20 11.44 SK12034 25-Mar 1 2.23 8.80 SK12040 28-Mar 1 1.65 11.47 SK12040 28-Mar 1 1.63 11.69 SK120B 30-Jan 1 (unk) 1.38 9.74 SK120B 30-Jan 1 (unk) 1.63 5.72 SK12009 14-Mar 2 2.50 10.48 SK12009 14-Mar 2 1.77 9.06 SK12024 16-Mar 2 2.72 16.19 SK12024 16-Mar 2 2.29 6.56 SK12025 16-Mar 2 (unk) 1.76 14.09 SK12025 16-Mar 2 (unk) 1.37 11.02 SK12053 23-Apr 2 0.73 16.91 SK12053 23-Apr 2 0.81 7.45 SK120A 30-Jan 3 1.97 7.62 SK120A 30-Jan 3 2.01 7.33

137

BACTERIAL COMMUNITIES

pH Bacterial

abundance (log 16S rRNA gene)

Beta- proteobacteria

(%)

Actinobacteria (%)

Bacilli (%)

Shannon's diversity index

8.50 3.84 82.92 2.08 0.00 1.29 8.28 3.31 - - - - 8.43 3.79 93.75 2.50 0.00 0.80

- - - - - - - 4.04 93.75 0.42 0.00 0.64 - 4.12 87.08 1.25 0.83 1.38

7.33 - - - - - - - - - - -

7.92 5.19 89.17 1.67 0.00 0.82 8.28 5.01 86.25 3.75 0.00 1.05 8.10 - - - - - 7.74 3.25 91.25 0.00 0.00 0.55 8.42 - - - - - 7.96 - - - - - 8.33 3.41 - - - - 7.39 - - - - - 8.18 3.75 94.58 0.83 0.00 0.71 7.96 - - - - - 8.35 - - - - - 7.96 3.64 - - - - 8.01 - - - - - 8.13 4.40 - - - - 7.86 2.77 91.67 0.00 0.00 0.78 8.22 3.61 - - - - 7.48 2.03 - - - - 7.78 3.53 77.50 7.50 1.25 1.69 8.04 - - - - - 7.44 - - - - - 7.79 3.11 72.92 12.92 0.42 2.79 8.19 2.89 81.67 2.50 0.00 1.63

(1) Eggshells cracked or smashed during fieldwork, thus unavailable for further analyses, are marked up in italics. Eggshells retrieved after quality trimming and rarefied at 240 sequences are marked up in bold. (2) When clutch age was unknown upon egg collection, we assigned it as 'unk' (standing for 'unknown'). Age was determined after egg dissection.

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Appendix 5.1 – Continued. NORTH KINANGOP SAMPLING ANTIMICROBIALS

Nest code (1) Date (2012) Clutch age

(day(s) after clutch completion) (2)

Lysozyme concentrations

(mg/ml)

Ovotransferrin concentrations

(mg/ml)

NK12034 10-Apr 1 1.75 12.79 NK12034 10-Apr 1 1.80 9.86 NK12035 6-Apr 1 2.70 - NK12035 6-Apr 1 2.54 9.51 NK12037 3-Apr 1 1.61 17.71 NK12037 3-Apr 1 2.18 8.33 NK12040 11-Apr 1 1.45 11.97 NK12040 11-Apr 1 1.25 9.87 NK12041 11-Apr 1 - - NK12041 11-Apr 1 4.54 14.29 NK12042 12-Apr 1 2.53 9.29 NK12042 12-Apr 1 1.54 5.61 NK12049 21-Apr 1 2.37 9.98 NK12049 21-Apr 1 1.63 9.64 NK12050 21-Apr 1 2.29 6.62 NK12050 21-Apr 1 3.51 7.09 NK12022 27-Jan 2 2.02 12.29 NK12022 27-Jan 2 1.24 10.60 NK12031 22-Mar 2 - - NK12031 22-Mar 2 0.59 6.49 NK12039 4-Apr 2 1.40 10.13 NK12039 4-Apr 2 2.72 14.99 NK12028 13-Mar 3 1.86 11.10 NK12028 13-Mar 3 1.53 11.30

139

BACTERIAL COMMUNITIES

pH Bacterial

abundance (log 16S rRNA gene)

Beta-proteobacteria

(%)

Actinobacteria (%)

Bacilli (%)

Shannon's diversity index

8.21 1.16 - - - - 8.43 - - - - - 8.17 - - - - - 8.06 - - - - - 7.86 1.54 22.50 54.17 3.75 5.76 8.33 3.60 5.42 50.83 17.08 6.47 8.05 1.52 - - - - 7.70 1.54 - - - -

- - - - - - 7.22 - 32.50 7.92 7.08 4.63 8.39 3.14 1.67 85.42 0.83 5.46 8.19 - -

-

7.96 2.35 40.83 3.33 4.17 3.83 8.04 - - - - - 7.85 - - - - - 7.75 2.55 - - - - 8.39 3.80 13.33 26.67 14.17 6.66 8.18 2.39 40.00 23.75 3.75 6.09

- - - - - - 8.31 2.67 12.50 31.67 16.67 6.59 8.14 - - - - - 7.56 1.52 25.83 16.67 25.83 5.41 7.66 1.07 64.17 7.08 5.42 4.29 7.44 1.24 84.17 6.25 0.42 1.78

(1) Eggshells cracked or smashed during fieldwork, thus unavailable for further analyses, are marked up in italics. Eggshells retrieved after quality trimming and rarefied at 240 sequences are marked up in bold. (2) When clutch age was unknown upon egg collection, we assigned it as 'unk' (standing for 'unknown'). Age was determined after egg dissection

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Appendix 5.1 – Continued. KEDONG SAMPLING ANTIMICROBIALS

Nest code (1) Date (2012) Clutch age

(day(s) after clutch completion) (2)

Lysozyme concentrations

(mg/ml)

Ovotransferrin concentrations

(mg/ml)

KE12041 8-Jan 1 2.09 7.77 KE12041 8-Jan 1 1.16 - KE12044 12-Jan 1 0.95 12.54 KE12044 12-Jan 1 2.74 7.98 KE12047 13-Jan 1 1.59 7.42 KE12047 13-Jan 1 1.32 11.19 KE12054 17-Jan 1 1.94 5.59 KE12054 17-Jan 1 3.99 6.17 KE12057 18-Jan 1 2.80 7.78 KE12057 18-Jan 1 1.73 7.37 KE12038 8-Jan 2 0.73 4.44 KE12038 8-Jan 2 - 7.75 KE12040 8-Jan 2 - - KE12040 8-Jan 2 1.00 11.20 KE12046 15-Jan 2 2.89 4.08 KE12046 15-Jan 2 2.44 - KE12048 15-Jan 2 2.14 7.16 KE12048 15-Jan 2 2.12 6.23 KE12049 17-Jan 2 1.47 19.01 KE12049 17-Jan 2 1.64 20.35 KE12055 17-Jan 3 - 20.72 KE12055 17-Jan 3 1.16 8.69 KE12066 11-Mar 3 0.74 9.95 KE12066 11-Mar 3 0.71 6.33

141

BACTERIAL COMMUNITIES

pH Bacterial

abundance (log 16S rRNA gene)

Beta-proteobacteria

(%)

Actinobacteria (%)

Bacilli (%)

Shannon's diversity index

7.99 2.04 - - - - 7.72 - - - - - 8.46 2.33 - - - - 8.22 1.41 - - - - 8.27 2.86 95.83 2.08 0.00 0.85 8.01 1.98 30.00 27.08 11.67 5.79 8.19 2.70 81.25 4.17 2.92 2.33 8.20 2.60 - - - - 7.93 1.69 33.33 30.83 5.00 4.99 8.03 3.18 75.83 15.83 2.08 3.06 8.14 2.98 78.33 11.25 0.83 2.26 8.31 - - - - -

- - - - - - 8.04 3.09 85.83 3.75 4.58 1.97 8.51 2.32 - - - - 8.33 - - - - - 8.28 - - - - - 8.35 - - - - - 8.36 1.72 - - - - 8.34 1.83 25.42 16.25 19.58 5.92 7.76 2.72 - - - - 8.40 4.20 - - - - 7.77 1.89 56.67 2.50 1.25 3.03 7.65 2.85 55.00 28.33 0.00 3.55

(1) Eggshells cracked or smashed during fieldwork, thus unavailable for further analyses, are marked up in italics. Eggshells retrieved after quality trimming and rarefied at 240 sequences are marked up in bold. (2) When clutch age was unknown upon egg collection, we assigned it as 'unk' (standing for 'unknown'). Age was determined after egg dissection.