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Diversity and structure of soil bacterial communities associated with vultures in an African savanna HOLLY H. GANZ,1,5, ULAS KARAOZ,2 WAYNE M. GETZ,1,3 WILFERD VERSFELD,4

AND

EOIN L. BRODIE2

1

Department of Environmental Science, Policy & Management, University of California, 137 Mulford Hall, Berkeley, California 94720-3114 USA 2 Ecology Department, Earth Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720 USA 3 School of Mathematical Sciences, University of KwaZulu-Natal, Private Bag X54001, Durban 4000 South Africa 4 Etosha Ecological Institute, P.O. Box 6, Okaukuejo via Outjo, Namibia Citation: Ganz, H. H., U. Karaoz, W. M. Getz, W. Versfeld, and E. L. Brodie. 2012. Diversity and structure of soil bacterial communities associated with vultures in an African savanna. Ecosphere 3(6):47. http://dx.doi.org/10.1890/ES11-00333.1

Abstract. Bird guano has been shown to alter the structure and function of ecological communities. Here we characterize the effects of vulture guano on the phylogenetic structure, taxa richness, and abundance in soil bacterial communities within an African savanna. By altering soil chemistry and nutrient status, vulture guano appears to play a role in influencing the structure of soil bacterial communities. DNA was extracted from soil collected under twenty trees: five African white-backed vulture (Gyps africanus, WBV) nesting sites, five lappet-faced vulture (Torgos tracheliotos, LFV) nesting sites and ten control sites where no sign of vulture activity was detected. Using a high-density phylogenetic microarray (PhyloChip G2), we identified 1,803 bacterial Operational Taxonomic Units (OTUs) in the twenty samples. Analysis of beta-diversity using the Unifrac distance metric demonstrated that WBV nesting sites were phylogenetically distinct from both control trees and LFV nesting sites. We detected a higher degree of phylogenetic clustering in soil bacterial communities associated with both WBV and LFV nesting sites compared to control sites, suggesting that the deposition of guano increases the strength of habitat filtering in these communities. Canonical correspondence analysis revealed that variation in OTU intensity (a measure of relative abundance) could be related to variations in pH, electrical conductivity and total nitrogen content. WBV sites explained 10% to 22% of the variation in OTU intensity. The elevated total nitrogen and lower pH characteristic of soils associated with vultures may favor Proteobacteria and suppress Firmicutes, particularly Clostridia and Bacilli. Acidic aggregations of vulture guano may be unlikely to support longterm survival of spore-forming Firmicute pathogens and thus may limit the role that vultures play as potential disease vectors. Key words: Bacillus anthracis; bacteria; competitive exclusion; Gyps africanus; habitat filtering; lappet-faced vulture; microbial ecology; pH; savanna; Torgos tracheliotos; white-backed vulture. Received 28 November 2011; revised 24 February 2012; accepted 28 February 2012; final version received 2 May 2012; published 1 June 2012. Corresponding Editor: K. Elgersma. Copyright: Ó 2012 Ganz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits restricted use, distribution, and reproduction in any medium, provided the original author and sources are credited. 5

Present address: University of California, Davis, School of Veterinary Medicine, Department of Population Health and

Reproduction, One Shields Avenue, Davis, California 95616 USA. E-mail: [email protected]

INTRODUCTION

for nesting birds. When birds deposit guano at these nesting and roosting sites, they create nutrient rich resource patches. Nutrient inputs from bird guano can have cascading effects in

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aboveground food webs (Wootton 1991, Polis and Hurd 1996, Anderson and Polis 1999, Sanchez-Pinero and Polis 2000, Croll et al. 2005). While little is known about how guano deposition alters the composition of belowground food webs, like other soils with organic enrichment, ornithogenic soils tend to have higher soil microbial biomass, respiration, and nitrogen mineralization (Tscherko et al. 2003, Barrett et al. 2006, Wright et al. 2010). A significant proportion of the metabolic waste in bird guano is crystalline uric acid, which is derived from excess dietary nitrogen and protein catabolism (Hutchinson 1950, Bird et al. 2008). Consequently, ornithogenic soils typically have increased organic matter, higher nitrogen (Hutchinson 1950, Wainright et al. 1998, Anderson and Polis 1999), and lower soil pH (Mccoll and Burger 1976, Sobey and Kenworthy 1979, Hogg and Morton 1983, Wait et al. 2005). Chemical changes arising from guano deposition in soil likely affect microbial community structure, which is known to be influenced by both nutrient availability (e.g., Meyer 1994, Tate 2000, Fierer et al. 2007) and soil pH (e.g., Baath and Anderson 2003, Nilsson et al. 2007, Fierer and Jackson 2006, Lauber et al. 2008, Rousk et al. 2010a, Rousk et al. 2010b, Osborne et al. 2011). The phylogenetic structure of ecological assemblages provides insight into the processes mediating the coexistence of species. Many assemblages exhibit phylogenetic clustering, such that they are composed of taxa that are more closely related to each other than would be expected by chance (Webb et al. 2002, CavenderBares et al. 2006, Kembel and Hubbell 2006, Horner-Devine and Bohannan 2006, Lovette and Hochachka 2006, Weiblen et al. 2006). Such phylogenetic clustering is indicative of habitat filtering, which selects for those species that share ancestral traits needed for survival (Webb et al. 2002, Horner-Devine and Bohannan 2006). Alternatively, competitive exclusion is expected to produce communities comprised of taxa that are less related to each other than expected by chance (phylogenetic evenness or overdispersion, Cavender-Bares et al. 2004, Kembel and Hubbell 2006). Here we used a high-density phylogenetic microarray (PhyloChip G2, Brodie et al. 2006) to collect data on bacterial composition to test v www.esajournals.org

whether soil bacterial communities are altered by aggregations of guano that collect under the nests and roosts of African white-backed vultures (Gyps africanus, WBV) and lappet-faced vultures (Torgos tracheliotos, LFV). Nutrient enrichment as a result of guano deposition may promote competitive exclusion of slower growing bacteria in nutrient rich soil or it may induce habitat filtering. Using contemporary genetic and phylogenetic methods, we characterize the composition and diversity of soil bacteria beneath roosting sites in order to explore the factors governing the structure of these soil bacterial communities. We hypothesize that nutrient enrichment from guano would reduce competitive exclusion while enhancing the role of habitat filtering. If competition is more important than habitat filtering in structuring these bacterial communities, we expect to find that these communities exhibit phylogenetic evenness and are composed of distantly related taxa. Alternatively, if habitat filtering is more important, we expect to find that the communities exhibit phylogenetic clustering and are composed of closely related taxa.

METHODS Study area and species This research was conducted in a thorn bush savanna in Etosha National Park (Etosha), a large wildlife reserve in northern Namibia. In Etosha, anthrax outbreaks occur annually in populations of herbivorous mammals, including zebra (Equus quagga), springbok (Antidorcas marsupialis), wildebeest (Connochaetes taurinus), and elephant (Loxodonta africana, Lindeque and Turnbull 1994). These outbreaks help to support a thriving scavenger guild that includes vultures, blackbacked jackal (Canis mesomelas), lion (Panthera leo), and spotted hyena (Crocuta crocuta). Vultures are among the most abundant scavengers in Etosha and form stable nests and roosts under which their waste materials collect. Populations of both WBV and LFV are declining in southern Africa from habitat loss, poisoning, and electrocution (Anderson 1994, 1995, Simmons 1995, van Rooyen 2000). The WBV is listed by the International Union of Conservation of Nature (IUCN) as near-threatened (BirdLife International 2008a). The LFV is globally threatened and is IUCN 2


GANZ ET AL.

listed as vulnerable (BirdLife International 2008b).

electrical conductivity were both determined in a 1:2.5 soil:deionized water extract. Organic matter was determined using the ignition-extraction method of Rowell (1994). Total nitrogen was determined using a modified Kjeldahl method (McGill and Figueiredo 1993). Phosphorus was determined using the ignition—extraction method of Olsen and Sommers (1982). Equilibrium extraction of soil for potassium, calcium, magnesium, and sodium was performed using 1M ammonium acetate (pH 7.0) with subsequent determination by inductively coupled plasma optical emission spectrometry (Soil and Plant Analysis Council 1999).

Sample collection In early April 2008 (near the end of the rainy season), we sampled under WBV nests occurring in umbrella thorn acacia (Acacia tortilis) and under LFV nests occurring in purple-pod terminalia (Terminalia prunioides) and worm-cure albizia (Albizia anthelmintica). At each site, we collected a single large soil core sample (10 cm diameter 3 10 cm depth) from the center of guano stained areas beneath the canopy under each vulture nesting and roosting site and beneath the canopy at control sites (neighboring trees of the same species with no clear evidence of usage by vultures). Tall grasses (predominantly hooked bristle grass, Setaria verticillata) were abundant beneath nesting sites and absent beneath trees selected as control sites. Control sites were located nearby in order to minimize changes in the soil bacterial communities that arise from different soil types. When we collected soil samples, pairs of vultures were observed at each nesting or roosting site and the soil below had fresh vulture guano. Soil core samples were homogenized by hand in sterile Whirl-Pak bags (Nasco, Fort Atkinson, WI, USA), and frozen (208C) within 24 hours of collection. DNA was extracted from 10 g of soil using the PowerMax Soil DNA extraction kit (MoBio, Carlsbad, CA, USA) according to the manufacturer’s instructions. DNA was concentrated by isopropanol-salt precipitation and quantified using a Picogreen Assay (Invitrogen, Carlsbad, CA, USA) on a NanoDrop 3300 fluorometer (Thermo Scientific, Wilmington, DE, USA).

Amplification of 16S rRNA genes We characterized the community of bacteria in soil samples using the PhyloChip G2 (Brodie et al. 2006; manufactured by Affymetrix Inc., Santa Clara, CA, USA), a high-density DNA microarray to detect and monitor 8,741 bacterial and archaeal OTUs. We used 20 PhyloChip microarrays to test for an effect of vultures on the soil microbial community: five WBV nests, five LFV nests, five matched white-back control trees (WBC), and five matched lappet-faced control trees (LFC). For the bacterial community characterization, we performed PCR with the following components per reaction: 0.02 U/lL ExTaq (Takara Bio Inc., Japan), 13 ExTaq buffer, 0.2 mM dNTP mixture, 1 lg/lL bovine serum albumin (BSA), and 300 pM each of universal bacterial primers: 27F (5 0 -AGAGTTTGATCCTGG CTCAG-3 0 ) and 1492R (5 0 -GGTTACCTTGTTAC GACTT-3 0 ) for each genomic DNA sample. We used 10–30 ng of DNA template in a total volume of 50 ll. For each sample, eight replicate PCR amplifications were performed, with a range of annealing temperatures from 48 to 588C, with an initial denaturation at 958C for 3 min, followed by 25 cycles of denaturation at 958C for 30 s, annealing for 30 s, extension at 728C for 2 min, followed by a final extension at 728C for 10 min. Subsequently, the PCR products from the 8 reactions were combined per sample and isopropanol precipitated using 1 ll linear acrylamide as a carrier molecule, washed twice with ice-cold 70% ethanol, and resuspended in 50 ll nuclease-free water. The pooled products were visualized using 2% agarose gels (E-gel, Invitrogen Corporation, Carlsbad, CA, USA). After gel

Analysis of soil chemistry The soil at the sample locations was classified as dominated by shallow to medium, weakly developed, carbonate-rich, silty loamy to sandyloamy Regosols and Leptosols from mainly aeolian origin that cover a limestone surface (Beugler-Bell and Buch 1997). Soil chemical analysis was performed on homogenized individual core samples. The gravimetric moisture content was determined by oven-drying freshly sieved soil at 1058C overnight. For other analyses, moist soil samples were air dried, followed by sieving through a 2mm sieve. Soil pH and soil v www.esajournals.org

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quantification, 500 ng of the pooled PCR products were hybridized onto G2 PhyloChips.

relationships between ten environmental variables (Table 1). Nitrogen and pH, and calcium and magnesium have an absolute cross-correlation of 0.87 and 0.92 respectively, indicating a strong linear relationship (true cross-correlations are provided in Appendix: Table A1). For each of these pairs of variables, one was omitted to avoid collinearity as they represent the same underlying ecological signal. For sample type, we created four new dummy variables GWB, GWBC, GLF, and GLFC with variable equal to 1 if the sample was from the corresponding vulture type, and 0 otherwise. To avoid perfect multicollinearity, one of these levels, arbitrarily chosen to be GLFC, was omitted. The remaining three variables were added to the eight environmental variables and were used as explanatory variables in CCA. Since CCA is known to be sensitive to abundances equal to zero, we used OTU level intensity data for all the detected OTUs, including those where the ratio of positive fraction probes fell below the threshold of 0.9. To explore the relative contribution of environmental variables in explaining differences in bacterial community composition, we used a forward selection procedure as follows. Separately for each variable, the variables were sorted according to the eigenvalue of the first and only axis (marginal effects). Next, starting with the variable with the highest corresponding eigenvalue, the increase in the total sum of eigenvalues was computed (conditional effects). The null hypothesis that the explained variation is larger than a random distribution was tested with a Monte Carlo permutation test procedure. Differences among samples in significant environmental parameters and species richness were tested using the nonparametric Wilcoxon two-sample test because the data were not normally distributed (based on the Shapiro-Wilk normality test).

PhyloChip microarray analysis of 16S rRNA gene diversity We added known concentrations of control amplicons derived from yeast and bacterial metabolic genes to the pooled PCR amplicons from each soil sample. This mix was subject to fragmentation, biotin labeling, and hybridization to the G2 PhyloChip microarrays as described previously (Brodie et al. 2006). Each PhyloChip was scanned and recorded as a pixel image, and initial data acquisition and intensity determination were performed using standard Affymetrix software (GeneChip microarray analysis suite, version 5.1). Background subtraction and probepair scoring were performed as reported previously by Brodie et al. (2006). PhyloChip intensities were normalized according to Ivanov et al. (2009). The positive fraction (pf ) was calculated for each probe set as the number of positive probe-pairs divided by the total number of probe-pairs in a probe set. An OTU was considered present if it had a positive fraction of greater than or equal to 0.9 of probes in the probe set and detected in at least three out of five replicates per sample type. For each taxon/probe set, hybridization intensity (intensity) was calculated in arbitrary units using a trimmed mean (highest and lowest values were removed before averaging) of the intensities of the perfect match (PM) probes minus the intensities of their corresponding mismatch probes (MM) for all of the probe pairs in a given probe set (Brodie et al. 2007).

Canonical Correspondence Analysis (CCA) In order to identify the environmental gradients that structure bacterial communities, we used the vegan R package (Oksanen et al. 2010) to perform CCA (Dixon 2003) and we performed partial CCA to investigate the effect of particular environmental variables (ter Braak 1987, 1988). Ten environmental variables (pH, soil moisture, percent organic matter, electrical conductivity, total Kjedahl nitrogen, phosphorus, potassium, sodium, calcium, and magnesium) and sample type (WBV, WBC, LFV, LFC) were used as explanatory variables for CCA as follows. First, we used pairwise correlation plots to explore the v www.esajournals.org

Phylogenetic analysis of the bacterial communities Sequences for all present taxa were exported from Greengenes (DeSantis et al. 2006). Sequences were aligned using MAFFT (Katoh et al. 2002) and imported into FastTree 2 (Price et al. 2010) for tree construction using algorithms that approximate maximum likelihood methods. FastUnifrac was used to calculate bacterial betadiversity metrics and generate a UPGMA tree 4


GANZ ET AL. Table 1. Chemical properties of ornithogenic soils and control site soils in Etosha National Park. Site

pH

LFV

8.0 6 0.13 LFC 8.1 6 0.13 WBV 6.4 6 0.35 WBC 7.9 6 0.11

Soil Organic Electrical conductivity Total Kjedahl moisture (%) matter (%) (lScm1) nitrogen (%) 0.08 6 0.006 0.17 6 0.10 0.26 6 0.02 0.24 6 0.02

2.09 6 0.19 1.97 6 0.11 3.28 6 0.20 3.17 6 0.41

296.8 6 77.22 133.6 6 15.4 584.2 6 128.8 471.4 6 74.91

0.17 6 0.02 0.14 6 0.01 0.37 6 0.06 0.22 6 0.02

P (ppm)

K (ppm)

Na (ppm)

Ca (ppm)

Mg (ppm)

45.48 879 66.4 3824 397 6 20.07 6 61 6 11.5 6 1255 6 77 10.24 667 61.6 4494 340 6 2.91 6 146 6 14.64 6 972 6 52 85.88 845 15.5 3766 462 6 7.57 6 78 6 2.90 6 1436 6 150 18.81 866 33.0 14146 1178 6 2.90 6 102 6 2.17 6 4392 6 335

Note: Means 6 SE. Boldface entries indicate significant differences between vulture sites (WBV and LFV) and respective control sites (WBC and LFC) at P , 0.05.

indicating the degree of clustering between samples (Hamady et al. 2010). We tested for differences between vulture sites and control sites in OTU intensity (a measure of relative abundance) and OTU richness using Student’s ttests with Benjamini-Hochberg correction to identify classes responsible for sample clusters. We used the picante package in R to calculate the net relatedness index (NRI) and nearest taxon index (NTI) to compare the phylogenetic structure of the microbial communities (Kembel et al. 2010). NRI and NTI are indices for the degree of phylogenetic clustering of taxa across a phylogenetic tree in a given sample relative to the regional pool of taxa (Webb et al. 2002). For both indices, a positive value indicates that a taxon cooccurs with related taxa more often than would be expected by chance (phylogenetic clustering), while a negative value indicates that a taxon cooccurs with unrelated taxa more often than would be expected by chance (phylogenetic evenness). NRI is a standardized measure of the mean pairwise distance of taxa in a sample that quantifies overall clustering of taxa in a phylogenic tree (Webb et al. 2002). NTI is a standardized measure that quantifies the extent of terminal clustering by measuring the phylogenetic distance of the nearest taxon for each taxon in the sample (Webb et al. 2002).

community structure to sample type (WBV nest site, WBV control site, LFV nest site, LFV control site) and soil chemical and physical measurements. We found that variation in OTU intensity (a measure of relative abundance) could be related to variations in pH, electrical conductivity and total nitrogen content. Overall, explanatory variables account for 68% of the total variation in OTU intensities. The CCA plot indicates that the WBV sites exhibit higher total nitrogen and lower pH, two main factors affecting variation in OTU intensities (Fig. 1). The first two axes explain 73% of this 68%, corresponding to 50% of the total variation (Fig. 1). We tested the importance of the explanatory variables using a forward selection procedure combined with a permutation test and found that electrical conductivity and soil pH, or total Kjedahl nitrogen, were significant at the 5% level (Tables 2 and 3). We used partial CCA and variance partitioning to explore the effect of different sets of environmental variables. Five different CCAs, each with a different set of explanatory variables, were applied (Appendix: Table A2). Taken together, the explanatory variables explain 68% of the total variation in OTU intensities. Decomposing this 68% shows that the pure sample classification effect is 10%, and the variation explained solely by the environmental variables is 47% (Appendix: Table A3). The shared explained variation (due to collinearity) is 12% (59%-47%). Hence, sample type explains between 10% and 22% of the variation in the OTU intensity data (Appendix: Table A3). Community composition was influenced by soil pH. When we ranked the association of OTUs with the environmental variables in the CCA, we found that OTUs within the phylum

RESULTS Effects of vultures on soil chemistry and bacterial community abundances Differences in soil chemistry associated with the presence of vulture guano appear to affect the abundance of bacteria in the soil. We used a constrained ordination method, CCA, to relate v www.esajournals.org

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Fig. 1. Canonical correspondence analysis of the relationship between physical and chemical parameters and bacterial relative abundances (OTU intensities). The projected length of the vectors centered in the panel on the two axis represents the strength of the labeled factors (as abbreviated below) with respect to the contributions to CCA transformed factors 1 and 2. Statistically significant vectors are indicated in bold. Abbreviations: SM ¼ soil moisture, OM ¼ percent organic matter, EC ¼ electrical conductivity, Na ¼ sodium ion concentration, pH ¼ soil pH, K ¼ potassium concentration, Mg ¼ magnesium concentration, P ¼ phosphorus concentration and N ¼ total Kjedahl nitrogen. Filled red circles represent WBV, open red circles: WBC, filled blue triangles: LFV and open blue triangles: LFC.

Table 2. Marginal effects of environmental variables in explaining bacterial community composition based on CCA when only one explanatory variable was used.

Table 3. Conditional effects of explanatory environmental variables of environmental variables in explaining bacterial community composition based on CCA.

Variable

Eigenvalue

Eigenvalue %

Variable

v2

F-statistic

Pr(.F)

Electrical conductivity WBV site pH/Total Kjedahl nitrogen Phosphorus Potassium LFC site Organic matter Sodium LFV site Soil moisture Calcium/Magnesium

0.00263 0.00224 0.00214 0.00177 0.00138 0.00128 0.00121 0.00118 0.00101 0.00063 0.00047

16.50 14.05 13.43 11.10 8.66 8.03 7.59 7.40 6.34 3.95 2.95

Electrical conductivity pH/total Kjedahl nitrogen Calcium/Magnesium Potassium Sodium Organic matter Phosphorus Soil moisture

0.0026 0.0023 0.0017 0.0013 0.0008 0.0007 0.0007 0.0003

3.90 3.38 2.50 1.93 1.23 1.09 1.04 0.51

0.016 0.024 0.068 0.12 0.28 0.35 0.37 0.75

Notes: Conditional effects were calculated using a Monte Carlo permutation test procedure, starting with the highest corresponding eigenvalue. Number of permutations was 9,999 for each variable. Statistically significant P-values are indicated in bold.

Note: Results are ranked based on eigenvalues and percentages of explained variation.

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Firmicutes were positively associated with pH compared to the other environmental variables (Appendix: Fig. A1). OTUs within the phylum Proteobacteria tended to be positively associated with the other environmental variables and negatively associated with pH (Appendix: Fig. A1). Of the 100 OTUs most positively associated with pH, 33% were within the phylum Firmicutes (which was composed of the following: 52% Clostridia, 36% Bacilli, 6% Catabacter, 3% Mollicutes, and 3% unclassified) and 17% were within Proteobacteria. Of the 100 OTUs most negatively associated with pH, only 2% were Firmicutes and 85% were Proteobacteria (which was composed of the following: 59% Gammaproteobacteria, 29% Alphaproteobacteria, 9% Betaproteobacteria, and 2% Deltaproteobacteria). Ornithogenic soils differed in soil conditions. Based on the results of the CCA, we restricted comparisons of soil conditions to pH, phosphorus, total nitrogen, and electrical conductivity. Soils beneath WBV nests were more acidic (v2 ¼ 6.82, DF ¼ 1, P , 0.01), had 4.6 times more phosphorus (v2 ¼ 6.82, DF ¼ 1, P , 0.01), 1.7 times more total nitrogen (v2 ¼ 4.87, DF ¼ 1, P ¼ 0.027) but did not differ in electrical conductivity compared to control sites. Soils associated with lappet-faced vulture nests had 4.4 times more phosphorus (v2 ¼ 6.82, DF ¼ 1, P , 0.01), 2.2 times higher electrical conductivity (v2 ¼ 4.81, DF ¼ 1, P ¼ 0.027), but did not differ significantly in pH or total nitrogen compared to control sites.

Analysis of beta-diversity using Unifrac revealed that the soil bacterial communities under WBV nest sites were phylogenetically distinct from all other sites with a well-supported node (Fig. 2). Soil bacterial communities associated with LFV did not differ from control sites (Fig. 2). Analysis of the phylogenetic structure within the soil bacterial communities using the net relatedness index (NRI) and nearest taxon index (NTI) values indicated that many of the bacterial communities exhibited phylogenetic clustering (Table 5). It is notable that soil bacterial communities associated with vulture sites were more likely to exhibit significant phylogenetic clustering (based on NRI and NTI) than control sites (logistic regression, v2 ¼ 5.94, DF ¼ 1, P ¼ 0.015). All white-backed vulture nest sites had significant NRI and NTI values, while only 40% of white-backed vulture control sites had significant NRI values and 80% had significant NTI values (Table 4). Eighty percent of lappet-faced vulture nest sites had significant NRI and NTI values, while we detected significant NRI values at 40% and significant NTI values at 20% of lappet-faced control sites. Control sites sometimes had negative NRI or NTI values, an indication of phylogenetic evenness, but NRI and NTI values were not both negative at any site. We tested for relationships between phylogenetic structure (NTI, NRI) across all taxa and pH; NTI decreased significantly with pH (Fig. 3) but NRI did not.

DISCUSSION Effects of vultures on bacterial community structure

The accumulation of vulture guano in the soil beneath nesting and roosting sites affects the structure of soil bacterial communities. The strength of the effect of ornithogenic inputs differed between the two vultures studied here. Although both vulture species affected local soil chemistry, bacterial communities associated with WBV sites were distinct from the other study sites and bacterial communities associated with LFV sites were not. This difference may be partially attributable to differences in the breeding behavior between the two species that would affect the amount of guano deposition on the soil. LFV are solitary breeders and typically change nesting sites each year (Mundy et al. 1992; Versfeld, personal observation). In contrast, WBV breed in colonies with up to four nests per tree

After filtering data to include only those OTUs that were present in at least 3 out of 5 replicates per treatment, we detected a total of 1,803 OTUs across all samples. Using the PhyloChip, we identified 185 (14%) more OTUs in WBV nesting sites compared with controls sites (v2 ¼ 4.81, DF ¼ 1, P ¼ 0.028, Table 4). OTUs within class Gammaproteobacteria were significantly more abundant at vulture sites compared to control sites (LFV vs. LFC: P , 0.0001 and WBV vs WBC: P , 0.0001, Appendix: Table A4). Epsilonproteobacteria were also significantly more abundant at LFV sites compared to LFC (P , 0.001, Appendix: Table A4). Bacilli were significantly more abundant at WBC sites compared to WBV sites (P ¼ 0.001, Appendix: Table A4). v www.esajournals.org

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GANZ ET AL. Table 4. Number of bacterial OTUs within each phylum or other taxonomic grouping detected by the PhyloChip G2 by sample type. Domain or Phylum

Class

LFV

LFC

WBV

WBC

Bacteria

Total

Proteobacteria

Total

Proteobacteria

Alphaproteobacteria

Proteobacteria

Betaproteobacteria

Proteobacteria

Deltaproteobacteria

Proteobacteria

Epsilonproteobacteria

Proteobacteria

Gammaproteobacteria

1394 6 57 609.8 6 26.64 189.8 6 6.49 106.4 6 1.63 70.2 6 4.88 31.4 6 1.47 212 6 14.93 245.2 6 9.38 109.6 6 2.21 114.4 6 6.01 183 6 4.56 85.2 6 5.76 53 6 2.49 25 6 1.76 6.4 6 0.4 15.4 6 0.4 52.4 6 3.39 30.2 6 1.53 26.4 6 4.01 19.4 6 1.47 12.2 6 1.72 7.4 6 0.4 7 6 0.55 6.8 6 0.97 5 60 5.2 6 0.8 4 60 3.6 6 0.4 3.4 6 0.4 3 60 2.8 6 0.2 2.8 6 0.2 2.6 6 0.25

1352 6 21 583 6 13.37 180.2 6 3.25 109.4 6 1.21 66.8 6 2.60 34.2 6 0.583 192.4 6 11.66 251.6 6 4.97 108.6 6 2.80 120.4 6 3.19 170 6 3.03 79.4 6 2.80 48 6 3.91 23 6 1.84 6.2 6 0.49 12.8 6 1.77 57.6 6 2.56 31 6 1.14 25.2 6 0.92 17.8 6 1.2 11.8 6 1.07 6.8 6 0.2 6.6 6 0.25 7.4 6 0.25 5.6 6 0.25 4.4 6 0.25 4 60 4.4 6 0.4 3.2 6 0.49 3 60 2.8 6 0.2 3 60 2.8 6 0.2

1489 6 45 703.4 6 18.44 216.6 6 7.20 123.4 6 5.97 74.6 6 2.46 34.4 6 0.678 254.4 6 5.31 248.4 6 11.39 101.6 6 6.82 124.4 6 3.53 187.8 6 6.34 84.4 6 5.82 56.6 6 1.69 24.8 6 1.39 7.6 6 0.51 16.8 6 0.49 40.8 6 1.93 29.2 6 0.735 28.2 6 1.59 18.2 6 1.32 11.4 6 0.68 8.4 6 0.25 6.2 6 0.37 7.6 6 0.25 5.8 6 0.2 6.2 6 0.49 4.6 6 0.25 4 6 0.45 3.2 6 0.49 3 60 2.2 6 0.49 2.8 6 0.2 2 60

1304 6 61 578.4 6 27.37 184.6 6 6.18 96.6 6 5.44 61.4 6 6.91 28.2 6 3.007 207.6 6 8.4 221 6 8.99 97.4 6 2.80 105 6 5.44 166.4 6 9.03 89.4 6 3.67 50.4 6 6.27 23.8 6 3.2 7.8 6 0.37 13.4 6 1.965 43.2 6 3.46 29.6 6 1.86 26.4 6 2.34 17.6 6 1.4 11 6 0.95 7.8 6 0.74 5.6 6 0.25 5.8 6 0.92 5 60 3.8 6 0.8 3.8 6 0.2 2.6 6 0.25 2.6 6 0.25 3 60 2.6 6 0.25 3 60 2.6 6 0.25

Firmicutes

Total

Firmicutes

Bacilli

Firmicutes

Clostridia

Actinobacteria

Total

Bacteroidetes

Total

Acidobacteria

Total

Acidobacteria

Acidobacteria

Acidobacteria

Acidobacteria-4

Acidobacteria

Acidobacteria-6

Cyanobacteria

Total

Chloroflexi

Total

Spirochaetes

Total

Verrucomicrobia

Total

Planctomycetes

Total

Planctomycetes

Gemmatimonadetes

Chlorobi

Total

Unclassified

Total

Synergistes

Total

Nitrospira

Total

OP10

Total

OP9/JS1

Total

OP9/JS1

Natronoanaerobium

Lentisphaerae

Total

Aquificae

Total

OP3

Total

Chlamydiae

Total


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GANZ ET AL. Table 4. Continued. Domain or Phylum Chlamydiae

Class

LFV

LFC

WBV

WBC

Deinococcus-Thermus

2 60 1.6 6 0.4 2.8 6 0.2

2 60 0.8 6 0.2 2.4 6 0.25

2.8 6 0.2 2.6 6 0.4 3.8 6 0.2

2 60 1 60 3.2 6 0.37

Termite group 1

Total

TM7

Total

Note: Taxonomic groups with two OTUs or fewer are not listed here.

Fig. 2. UPGMA tree with the twenty samples from FastUniFrac community analyses of PhyloChip detected sequences from the nesting sites and control trees. In the UPGMA tree, jackknife node supports are represented by the colored circles with black: .99.9%, grey: 70–90% and white: ,50%. Samples are color-coded as follows: red: WBV, black: WBC, blue: LFV and green: LFC.


9


GANZ ET AL. Table 5. Net relatedness index (NRI) and nearest taxon index (NTI) results for Operational Taxonomic Units (OTUs) detected by the PhyloChip G2. Site

Replicate

N

NRI

P

NRI_gt

NTI

P

NTI_gt

LFV LFV LFV LFV LFV LFC LFC LFC LFC LFC WBV WBV WBV WBV WBV WBC WBC WBC WBC WBC

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

1183 1416 1495 1434 1321 1381 1267 1332 1291 1358 1513 1522 1288 1519 1475 1357 1113 1462 1262 1215

3.24 1.68 1.93 1.78 3.06 3.45 1.27 2.21 1.36 0.64 4.31 3.91 5.32 2.93 2.03 2.43 1.39 1.20 2.11 0.31

0.001 0.052 0.036 0.038 0.002 0.001 0.11 0.016 0.087 0.27 0.001 0.001 0.001 0.002 0.021 0.009 0.91 0.12 0.022 0.62

999 948 964 962 998 999 892 984 913 734 999 999 999 998 979 991 93 884 978 383

3.02 1.70 1.71 1.76 1.23 1.36 2.11 0.63 1.14 0.77 3.99 3.51 2.18 2.64 3.12 1.96 2.78 1.50 2.20 2.35

0.002 0.044 0.043 0.030 0.109 0.097 0.017 0.72 0.13 0.22 0.001 0.001 0.014 0.004 0.001 0.020 0.006 0.061 0.014 0.015

998 956 957 970 891 903 983 276 867 781 999 999 986 996 999 980 994 939 986 985

Notes: N ¼ number of OTUs in a community. NRI_gt and NTI_gt represent the number of times the observed NRI and NTI values for a community were greater than the value of randomly permuted communities. Boldface entries indicate communities that were significantly structured at P , 0.05.

(Versfeld, personal observation). Moreover, a breeding pair of WBV will typically use the same nest for several years (Brown et al. 1982) and the same tree has been occupied for more than nine consecutive years (North 1944; Versfeld, personal observation). Greater rates of guano deposition would be expected to occur under WBV nesting sites that contain more nests and are occupied for a longer period of time than LFV nesting sites. Moreover, the A. tortilis trees preferred by WBV colonies for nesting can live for as long as 650 years (Andersen and Krzywinski 2007) and the mature trees sampled here may have been associated with the vultures for decades or centuries. This is the first study to apply contemporary genetic and phylogenetic methods to characterize how guano affects the composition and diversity of soil bacteria and explore the factors governing the structure of these soil microbial communities. Changes in the structure of soil bacterial communities associated with WBV nesting sites likely reflect changes in soil chemistry resulting from guano addition rather than the addition of exogenous organisms. Several studies have found that organic inputs in soil do not leave a trace of microbial biomass, suggesting that the exogenous organisms may not be able to compete with indigenous populations (Innerebv www.esajournals.org

ner et al. 2006, Saison et al. 2006). By lowering pH and increasing phosphorus and nitrogen in the soil, the deposition of vulture guano may alter the phylogenetic structure of soil microbial communities through habitat filtering. Analysis of the phylogenetic structure within the bacterial communities based on two relatedness measures (NRI and NTI) indicates that the soil associated with both vulture species contained bacterial taxa that were more closely related than expected by chance, suggesting that habitat filtering for certain traits may play a role in structuring these communities. In their study of the phylogenetic structure of bacterial communities, Horner-Devine and Bohannan (2006) suggested that habitat filtering often plays an important role in structuring many bacterial communities and observed that the strength of habitat filtering may vary along environmental gradients. One relatedness measure, NTI decreased significantly as soil pH increased (Fig. 3), suggesting that lower pH (and higher nitrogen, which was negatively correlated with pH) may select for taxa with a higher degree of terminal clustering in the phylogeny. Despite the phylogenetic analyses afforded by contemporary microbial diversity data, the roles of many bacterial taxa in the environment remain largely unknown. The increased phylogenetic 10


GANZ ET AL.

Fig. 3. Variation in nearest taxon index (NTI) along a pH gradient: N ¼ 20, R ¼0.67, P , 0.01. Filled red circles represent WBV, open red circles: WBC, filled blue triangles: LFV and open blue triangles: LFC.

structuring detected in bacterial communities associated with vulture guano deposition may arise from habitat selection for those individuals that have the ability to utilize uric acid and its byproducts as a nitrogen source. Uric acid is a major component of guano-enriched, ornithogenic soil (Speir and Cowling 1984), which can remain in arid soils (like the soils studied here) for extended periods (Ramsay and Stannard 1986). Relatively high numbers of culturable uric acid degrading bacteria are known to occur in ornithogenic soils (Pietr 1986), including members of genus Psychrobacter, which has been isolated from ornithogenic soil (Bowman et al. 1996) as well as penguin guano (Zdanowski et al. 2005). In addition to high uric acid concentrations, ornithogenic soils typically have lower soil pH (Mccoll and Burger 1976, Sobey and Kenworthy 1979, Hogg and Morton 1983, Wait et al. 2005), v www.esajournals.org

which may also promote habitat selection for more acidophilic taxa. Soil pH was an important driver of bacterial relative abundances (OTU intensities) and the degree of phylogenetic clustering in the current study. Soil pH has a strong influence on the composition of soil bacterial communities both within individual soil types and across biomes (e.g., Baath and Anderson 2003, Nilsson et al. 2007, Fierer and Jackson 2006, Lauber et al. 2008, Rousk et al. 2010a, Rousk et al. 2010b, Osborne et al. 2011). The strong effect of soil pH on bacterial communities may reflect narrow pH ranges for optimal bacterial growth (Rousk et al. 2010a, Rousk et al. 2010b). Across all samples in the present study, the abundance of OTUs within the Bacilli and Clostridia tended to be positively associated with soil pH (and negatively associated with total nitrogen). Spore-forming Bacilli were not more 11


GANZ ET AL.

frequent (Table 4) or abundant at vulture sites (Appendix: Table A4), including Bacillus anthracis, the causative agent of anthrax. This finding is surprising because both vulture species are regularly exposed to B. anthracis while consuming carcasses in Etosha (Lindeque and Turnbull 1994). Acidic aggregations of vulture droppings may be unsuitable as spore reservoirs because B. anthracis spores tend to occur in alkaline soils (Van Ness 1971, Smith et al. 2000, Hugh-Jones and Blackburn 2009, Hampson et al. 2011). Gammaproteobacteria and some Epsilonproteobacteria were more abundant in soils associated with vultures compared to control sites (Table A4). Gammaproteobacteria and Alphaproteobacteria tended to be negatively associated with soil pH (and positively associated with total nitrogen, Appendix: Table A2). The association of proteobacterial groups with ornithogenic soil is consistent with the findings of Aislabie et al. (2009) in soil associated with penguin colonies. The negative association with pH differs from that of Rousk et al. (2010a) who found that the relative abundances of proteobacterial groups tended to be positively related to pH. However, Gammaproteobacteria tend to occur in higher abundances in association with greater availability of carbon (McCaig et al. 1999, Axelrood et al. 2002, Kirchman 2002, Fazi et al. 2005, Fierer et al. 2007, Rousk et al. 2010a). Moreover, Rousk et al. (2010a) suggest that increases in proteobacterial abundance with pH may reflect a decrease in carbon availability at low pH in the Hoosfield acid strip. In conclusion, we characterized the effects of ornithogenic soil influenced by two vulture species and found that they differed in their effects on soil chemistry and soil bacterial communities. This difference may be partially attributable to the single nest of a solitary breeder (LFV) producing a lesser amount of guano than multiple nests in a breeding colony (WBV). Bacteria associated with WBV sites had greater taxa richness and were phylogenetically distinct from control sites. Soil microbial communities associated with both vulture species exhibited a greater degree of phylogenetic clustering in the bacterial communities, which may reflect habitat filtering for traits associated with survival in acidic, nutrient-enriched, ornithogenic soils. Proteobacteria were more abundant and Firmicutes v www.esajournals.org

were less abundant in soil bacterial communities associated with WBV nesting sites. Even though vultures may vector spores of pathogenic Firmicutes, including spore-forming Bacilli and Clostridia, the acidity associated with vulture guano aggregations may negatively affect the long-term survival of spores. Consequently, guano acidity may reduce the role that vultures play as potential disease vectors and enhance their role as environmental sanitizers.

ACKNOWLEDGMENTS We thank the Namibian Ministry of the Environment and Tourism and the Etosha Ecological Institute for logistical support. Werner Kilian provided thoughtful discussions that helped with the design of the study. Catherine A. Osborne, Anna C. Treydte, Wendy C. Turner, Kenneth J. Elgersma, and two anonymous reviewers provided helpful suggestions to improve the manuscript. Mateusz Plucinski and Pauline L. Kamath helped with phylogenetic tree construction. Carlton X. Osborne filtered and extracted the data making the Fast Unifrac analyses possible. Thanks to Tamara Banda, Katherine C. Goldfarb, and Clark A. Santee for assistance in the laboratory. Part of this work was performed at Lawrence Berkeley National Laboratory under Contract No. DE-AC02-05CH11231 from the U.S. Department of Energy, Office of Science. This research was supported by NIH Grant GM083863 to WMG.

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SUPPLEMENTAL MATERIAL APPENDIX Table A1. Pearson correlations between environmental variables across the 20 soil samples. Variable pH SM OM EC N P K Ca Mg Na

pH

SM

OM

EC

N

P

K

Ca

Mg

Na

1 0.3 0.55 0.6 0.87 0.67 0.16 0.14 0.065 0.47

1 0.37 0.35 0.38 0.1 0.3 0.14 0.22 0.55

1 0.46 0.57 0.17 0.27 0.0097 0.087 0.72

1 0.76 0.68 0.14 0.33 0.43 0.35

1 0.67 0.19 0.12 0.025 0.42

1 0.063 0.21 0.12 0.15

1 0.075 0.28 0.086

1 0.92 0.11

1 0.098

1

Notes: Boldface entries indicate absolute cross-correlations of 0.8 or greater between two different variables. Physical and chemical parameters are abbreviated as follows: soil moisture (SM), organic matter (OM), electrical conductivity (EC), total Kjedahl nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg) and sodium (Na).


15


GANZ ET AL.

Fig. A1. Proportion of different bacterial phyla in the 100 highest ranked OTUs associated with the environmental variables: (a) positive associations and (b) negative associations.


16


GANZ ET AL. Table A2. Results of partial CCA and variance partitioning. Step 1 2 3 4 5

Explanatory variables

Explained variation

%

Sample type and environmental Sample type Environmental Sample type with environmental as covariable Environmental with sample type as covariable

0.0123 0.0038 0.0105 0.0017 0.0085

68 21 59 10 47

Table A3. Variance partitioning in CCA with environmental, sample type and shared used as explanatory variables. Component A B C D Total

Source

Variation

%

Pure environmental Pure sample type Shared (3–5) Residual

0.0085 0.0017 0.0021 0.0057

47 10 12 32 100

Table A4. Intensity of OTUs within different bacterial classes by sample type (mean 6 SE). Class

LFV

LFC

WBV

WBC

Bacilli Gammaproteobacteria Epsilonproteobacteria Alphaproteobacteria Betaproteobacteria Anaerolineae Nitrospira Unclassified OP9 Deltaproteobacteria Acidobacteria-4 Acidobacteria Acidobacteria-6 Bacteroidetes Flavobacteria Sphingobacteria Chlorobia Planctomycetacia Cyanobacteria Spirochaetes Actinobacteria Desulfotomaculum Clostridia Mollicutes Verrucomicrobiae Symbiobacteria Thermodesulfobacteria Dictyoglomi Acidobacteria-5 TM7–3 Catabacter Acidobacteria-9 Solibacteres Anaerobranca Dehalococcoidetes JS1 Chloroflexi-4 CH21 cluster Chlamydiae Thermotogae

1219231 6 43940 2215044 6 144143 519566 6 63895 2369146 6 151487 1183876 6 37528 180233 6 13828 59714 6 9391 1043996 6 104967 29918 6 2295 923424 6 112062 72003 6 6847 377473 6 48497 217693 6 15311 311527 6 36010 187186 6 26961 454833 6 39103 39705 6 1641 143103 6 23663 507674 6 47039 341174 6 64928 2018487 6 78624 57057 6 6343 1402294 6 109371 80055 6 7774 226331 6 23131 24733 6 3409 13176 6 1482 12159 6 919 14007 6 3645 39974 6 3344 75200 6 8031 060 40619 6 4915 060 75623 6 4883 18393 6 4712 20269 6 3810 34183 6 2507 32753 6 2856 9155 6 108

1149086 6 68832 1867935 6 75421 492509 6 35656 2192870 6 89518 1198185 6 8880 179596 6 11691 56372 6 5210 1005818 6 78687 27389 6 1916 844505 6 68151 74430 6 5087 326845 6 41139 173621 6 33541 285448 6 14983 153528 6 13753 406401 6 24241 38371 6 1688 132304 6 14369 553047 6 43016 307080 6 22500 1856609 6 75612 58239 6 4023 1413277 6 76252 76267 6 8087 189636 6 20622 26940 6 1180 11550 6 504 11283 6 444 13352 6 3581 34582 6 3052 72706 6 5315 060 36325 6 7406 060 70385 6 3590 22942 6 4575 19706 6 3632 32313 6 1927 33882 6 3020 9447 6 177

1027255 6 59932 2721736 6 104141 482285 6 35645 2578915 6 102428 1338916 6 46943 165843 6 8030 67131 6 6937 958048 6 68388 27410 6 2887 934598 6 70506 85812 6 8742 354239 6 41182 231016 6 34883 266895 6 17452 216425 6 13814 418190 6 19621 40378 6 1424 125871 6 7246 337911 6 8069 331938 6 24256 2065287 6 52947 42535 6 2938 1403621 6 16748 81249 6 6646 179271 6 11100 28125 6 2384 11530 6 1020 6195 6 2548 15672 6 1721 54047 6 5059 66353 6 2889 6862 6 1745 37515 6 3418 5416 6 2225 67581 6 3789 19840 6 4841 21392 6 3340 29983 6 1518 26990 6 4089 9257 6 623

1072818 6 53916 2080575 6 140109 438989 6 84043 2310817 6 199009 1103439 6 102016 182832 6 15541 49784 6 12709 896135 6 108880 26106 6 4310 786391 6 133511 93957 6 9421 345427 6 73541 188863 6 37045 276938 6 19905 228553 6 14667 445794 6 23112 35595 6 3124 120930 6 15811 357754 6 22928 326546 6 46951 1967500 6 196054 48293 6 10876 1228448 6 110127 69970 6 9352 196529 6 24139 27603 6 2610 11211 6 1043 10058 6 233 14224 6 4074 44237 6 3781 58048 6 11340 060 31242 6 10584 060 66769 6 5775 8518 6 2282 19040 6 4562 31340 6 2615 32543 6 4363 8430 6 650


17


GANZ ET AL.

Table A4. Continued. Class

LFV

LFC

WBV

WBC

Acidobacteria-10 NC10–1 Thermomicrobia mgA-2 KSA1 BD2–10 group gut clone group mgA-1

15606 6 1400 060 22012 6 2854 15702 6 2088 12698 6 1736 12638 6 3249 17329 6 1252 8985 6 1121

14495 6 960 060 23871 6 895 13254 6 1125 12407 6 1081 9188 6 3810 23840 6 4090 9061 6 701

14375 6 1209 060 19672 6 2203 13531 6 1233 13847 6 2342 11329 6 2944 27683 6 1721 9240 6 552

14545 6 1616 9474 6 3913 17530 6 2881 13645 6 1644 13383 6 2437 10006 6 4145 10974 6 4567 6465 6 1705

Notes: Classes with significant differences between vulture sites and respective control sites are listed first. Boldface entries indicate significant differences between vulture sites (WBV and LFV) and respective control sites (WBC and LFC) at P , 0.05.


18
