Study Sites

The study site was Wayne Farms broiler production unit located at 32° 4’ 2.2” N and 85° 42’ 35.9” W, on a 4 Hectare (Ha) land in Bullock County, AL, USA. The soil series of the study area were Alaga (loamy sand, thermic, coated Typic Quartzipsamments) and Conecuh (sandy loam, fine, smectic, thermic Vertic Hapludults). For the past 10 years, this land has been used as an industrial broiler production site. During each of those ten years, 5-6 batches (∼80,000 broilers per batch) were produced with residual litter being removed annually and stored outside of the poultry houses at a designated site until a market could be established for the litter. In addition, there was approximately 1 Ha of pasture for a herd of 10 horses to graze. This area was only lightly grazed, as the horses were released onto this part of the land for only 2-3 days per week.

A preliminary geostatistical study of soil biochemical characteristics provided the initial evidence that soil biochemical and biological factors spatially vary with respect to land use type on this site (Table 1).³² A stratified random sampling design was used in an effort to obtain a statistically useful dataset while being cost-efficient. The three sampling areas were constructed of different sizes, which were reflective of the amount of area covered by each on the farm and overlaid by sampling grids. Samples were randomly collected from vertices of the grid, such that the amount of samples collected was proportional to the size of the sampling areas.

Table 1.

Selected soil properties amongst different land use strata.

Dna Extraction

Whole community DNA was extracted from approximately 0.25 g of soil (oven dried basis of field-moist soil) using the Power Soil Extraction Kit (MO BIO Laboratories, Soloana Beach, CA, USA) according to the protocol provided by manufacturer. Extracted DNA (2 µL) was checked for purity and concentration using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA) as well as being run on an 0.8% agarose gel. Once quality and concentration were determined, 3 samples from each of the three land-use types were pooled at equal bacterial DNA ratios to create three pools of DNA, each representing a major category of land use across the agroecosystem. The samples were then submitted to Research and Testing Laboratories (Lubbock, TX, USA) for PCR optimization and pyrosequencing analysis. PCR, massively parallel pyrosequencing, and tag design were carried out according to a procedure described previously by Dowd et al.^33,34

All DNA samples were diluted to 20 ng/µL from which a 20 ng (1 µL) aliquot of each sample DNA was used for a 25 µL PCR reaction: 5 min denaturing at 95 °C, anneal for 30 cycles of 94°C for 30 sec, 52°C for 40 sec, 70°C for 40 sec, and final extension at 70°C for 5 min. Primers used were the 16S universal Eubacterial primers 28 F (5'-GGC GVA CGG GTG AGT AA) and 530 R (5'-CCG CNG CNG CTG GCA CS). The resulting amplicons were equally mixed and purified using Agencourt Ampure beads (Agencourt Bioscience Corporation, MA, USA). In preparation for pyrosequencing, the size and concentration of DNA fragments were measured using DNA chips under a Bio-Rad Experion Automated Electrophoresis Station (Bio-Rad Laboratories, Hercules, CA, USA) and a TBS-380 Fluorometer (Promega Corporation, Madison, WI, USA). Samples of double-stranded DNA (9.6 x 10⁶ molecules/µL with an average size of 625 bp) were combined with 9.6 million DNA capture beads for emulsion PCR. The resulting bead-attached DNAs were denatured with NaOH and sequencing primers were annealed. The 454 Titanium sequencing run was performed on a 70 x 75 GS Pico-TiterPlate by using a Genome Sequencer FLX System (Roche, Nutley, NJ, USA).

Bioinformatics and Statistical Analysis

As a result of pyrosequencing services, quality trimmed sequences and hierarchal taxonomic data were provided following the bioinformatic pipeline described by Acosta-Martinez et al.³⁵ Each sequence was trimmed back to utilize only high-quality sequence information. Tags whose sequences designated individual samples were extracted from the FLX-generated multi-FASTA file, while parsing that file into individual sample-specific files. Tags that did not have 100% homology to the original sample tag designation as well as sequences that were less than 200 bp after quality trimming were not considered. Samples were then depleted of definite chimeras using B2C2 software that is described and freely available from Research and Testing Laboratory (Lubbock, TX, USA, USA). The resulting sequences were then evaluated using BLASTn³⁶ against a custom database derived from the RDP-II database³⁷ and GenBank ( http://ncbi.nlm.nih.gov). The sequences contained within the curated 16S database were those considered to be high-quality based upon RDP-II³⁸ standards and which had complete taxonomic information within their annotations.

Identification at the species level for the purpose of this study was considered tentative, and these taxonomic groups are referred to as operational taxonomic units (OTUs) and not species. Following best-hit processing, a secondary post-processing algorithm was utilized to combine genus and other taxonomic designations generating compiled data with relative abundance of each taxonomic entity within the given sample. Phylogenetic assignments were based upon NCBI taxonomic designations. Further processing and out-based analyses were then carried out using the MOTHUR³⁹ suite of programs for sequence processing and diversity analysis [v.1.19.3]. Processing commands included those for identifying/consolidating unique sequences, removing low-quality sequences, filtering, chimera removal, multiple sequence alignment, distance matrix generation, and sequence clustering into OTUs. OTU-based analysis differentiates itself from other methods of phylogenetic analysis in that it quantifies richness, diversity, and similarity amongst and between samples. The resulting clusters were assessed at 3% dissimilarity to provide the data needed for downstream analysis given a previous explanation of the relationship percent dissimilarity and species estimation based upon rarefaction.⁴⁰ Clusters at 3% were then utilized to generate rarefaction curves and the (diversity) indices ACE⁴¹ and CHAO⁴² as well as unweighted UniFrac for principle coordinate analysis (PCoA) plots.

Richness and Diversity Estimates

Figure 1 shows the observed and expected OTUs at 3% dissimilarity. The maximum OTUs detected across the soilscape at the site according to the observed clusters (sobs) at 3% dissimilarity was 1035 (Fig. 1), found in the pastured bacterial community. All Chao1 values reported in Figure 1 were comparable to the maximum OTUs predicted by rarefaction models (Fig. 2), while ACE estimators predicted significantly higher OTUs. No significant differences were observed between the bacterial communities under various areas for any of the estimators (P < 0.05). A distinct trend was detected in all estimators, suggesting that the highest richness was detected in the pastured area, while the lowest richness was found in the bacterial community under the poultry houses.

Figure 1.

Richness/diversity estimators as calculated by mothur at levels of 3% dissimilarity.

Figure 2.

Richness as estimated by rarefaction in mothur at a level of 3% dissimilarity.

The amount of species richness seemed to be similar among the different areas of the agricultural ecosystem, as significant differences in the soils were only observable using the ACE diversity index (Figs. 1 and 2). With the application of organic amendments (particularly poultry litter) to soils, researchers have reported changes in microbial communities found within these soils.^43,28 An important exception was that reported in a study by Acosta-Martinez and Harmel²⁸ who observed that that when poultry litter was applied to pasture surface soils and not incorporated into the soil, there was an obvious lack of response by microbial communities at the highest application rates, suggesting the need of some type of mechanical mixing of soil and litter to aid enhancement. This observation can be compared to that of the litter storage area, which tends to be higher in richness, but has no significant differences except for the ACE estimate. There is no frequent mechanical disruption to this area to allow for colonization of the soil by poultry litter microbes, thus resulting in a moderate level of diversity.

Bacterial Taxa

The soils under the poultry litter houses showed significant shifts in the relative abundance of five of the eight top bacterial phyla (Fig. 3). Significant shifts (P < 0.005) occurred in the phylum Proteobacteria between the broiler house community (37.1%) and the pastured community (53.4%). Because of the observed dominance of Proteobacteria in soils, the major bacterial classes were assessed for significant shifts amongst the soil systems as well (Fig. 4). Among the five classes of Proteobacteria, α-Proteobacteria was the only class to show significant shifts. These shifts occurred between BRHS soil (10.9%) and soils under grazed pasture (32.8%, P < 0.01) and soils under litter storage (22.3%, P < 0.05). Another shift that occurred between the BRHS and grazed pasture soil systems was in the phyla Bacteroidetes, which dropped from 21.8% to 7.2% (P < 0.05). The classes Flavobacteria and Bacteroidetes showed similar trends (Fig. 4). Other major shifts observed between the broiler house area and the litter storage areas were a decrease in Acido-bacteria (9.4% to 1.9% at P < 0.005) and an increase in Chloroflexi (3.0% to 10.8% at P < 0.05). Chloroflexi was the only phyla showing a significant decrease in relative abundance between the litter storage area and the grazed pasture soil system. Chloroflexi relative abundance actually dropped from 10.8% in the litter storage soil to 3.0% in the pasture soil (P < 0.05).

Figure 3.

Relative abundance of major phyla across land use systems.

Figure 4.

Relative abundance of classes from the most dominant phyla identified.

Proteobacteria remained the most dominant phyla under the different soil conditions, suggesting their central role in the soil ecosystem. Along with Acti-nobacteria and Bacteroidetes, Proteobacteria have been suggested to be a copiotrophic group of organisms;⁴⁴ as such, it would be expected that these organisms would be found in high abundance where there is access to plenty of organic carbon. It was observed that there was a significant decrease in the Proteobacterial phyla from the poultry house soil compared to the pastured soil. A further examination of Proteobacterial classes showed that the only significant shift occurred in one of the five classes, α-Proteobacteria (Fig. 4). Another important shift observed was in the phyla Bacteroidetes, which are found in soils, but are also largely associated with the internal and external flora of animals.⁴⁵ The classes that played major roles in this shift are Flavobacteria and Bacteroidetes. The class Flavobacteria has recently been described as predatory, since a growing number of its members are being characterized as such.^{46–4748495051} As there is no literature on the predator-prey relationship of Fla-vobacteria and other microbes, their increase could not be readily explained by shifts in other groups. More research in the area of Flavobacteria predation could shed more light on this ecological feature of the group. Bacteroidetes, one of the most widely studied classes, have been ecologically associated with animal intestines and feces, but they also contain genera that are known to be associated with soils.⁵² Because both of these groups contain organisms that are considered opportunistic pathogens, and are associated with animal feces and soil colonies, the soils under the poultry house seem to be an optimal environment to find these copiotrophs, where there is a convergence of these two ecosystems. This convergence may provide the conditions necessary for cross colonization of the litter layer and soil layer under the poultry houses.

Clustering of Soil Samples

Bacterial classes were used in cluster analysis to generate the double dendrogram shown in Figure 5. The double dendrogram allowed visualization of the diversity found among microbial classes over the study site. The patterns appeared to show the community in the poultry house soils exhibiting a distinct pattern compared to in other systems. Even within the poultry house samples, sample BRHS_2 showed less similarity to the other poultry house samples. The relative abundance was more focused towards the classes at the top of the y-axis than in any of the other sampled areas. The individuality expressed by this sample was reflected in the clustering analysis, as this sample showed a closer relationship to the other 6 samples at a distance of 2.25. All other samples (litter storage and pasture) clustered together at ∼1.75, while individual samples for each of these strata clustered at ∼1.50, showing sample similarity according to their soil system. Further analysis using Unifrac metrics and PCoA supported this data.

Figure 5.

Hierarchal clustering based upon the relative abundance of orders across the 9 samples across the three land use systems. Clustering in the Y-direction is indicative of abundance, not phylogenetic similarity.

Abbreviations: RA, relative abundance; BRHS, poultry house samples; STRG, litter storage samples; PSTD, pasture samples.

The 3-dimensional plot visualized from the principle coordinates analysis based upon unweighted Unifrac metrics (Fig. 6) showed that the samples of the bacterial community contained under the poultry houses distinguished itself in response to the variation detected in the samples across three axes. The x, y, and z axes in the PCoA plots (Fig. 6) represented 16.8%, 16.1%, and 12.6% of variation, respectively. Similar clustering can be observed in Figure 4.5 in that the samples from the same type of land-use system clustered together, with the exception of the BRHS_2 sample, which differed in response to both axis 1 and primarily axis 3.

Figure 6.

A 3-dimensional PCoA plot showing the clustering of samples around the first three axes of variation based on unweighted Unifrac scores.

Genera of Interest

Although the sequences represented in Table 2 did not have high relative abundances compared to other taxonomic classes, scale is important determining their potential environmental impact. In order to assess the significance of these specific genera for the environmental quality of the production system, average relative abundances were calculated using percentages. T-tests were conducted to determine if there were higher abundances in specified soil systems across the site. Major groups of interest were genera known for their contribution to pathogenesis in human and animal systems. With the exception of Mycobacterium, all pathogenic genera exhibited their highest abundance under poultry houses, with Brevibacterium and Staphylococcus being significant. Soils under the poultry houses showed the pattern of BRHS > PSTD > STRG representing 8.12%, 1.61%, and 0.85% of the total sequences in each system.

Table 2.

Salient genera of potentially pathogenic bacteria are expressed as mean relative abundance in each of the sampled areas. Significant differences are denoted by different letters in rows.

When considering the genera present in the soils, these data suggest that groups important to pathogenicity are present in all soils, though particularly high relative abundance was observed for specific genera in the soils under the poultry houses (Table 4.1). In the present study, relative abundances of genera did not surpass 8.97%, as only 23 of the genera identified had a relative abundance greater than 1%. When considering the pathogenic bacterial genus detected in the samples, the major contributor was Mycobacterium. Mycobacterium avium subsp. paratuberculosis (Map), an organism with excellent survivorship in the environment, as it has been detected for up to 600 days in water and soil. Although it can survive long periods without a host, the organism requires a host to grow and propagate.⁵³ There has been growing concern about the movement of pathogenic bacteria through the soil profile to contaminate groundwater.⁵⁴ Though this possibility exists for some microbes, the results of a recent study suggest that the potential for ground-water contamination by Map is low; however, the organism may remain bound to the soil near the surface where it can be ingested by grazing animals or be released during runoff events.⁵⁵

Although the ability of the litter layer to prevent leaching and migration of nutrients and microorganisms, respectively, has been documented, it was observed that biochemical processes still take place under the layer at comparable levels to that in other areas. Pyrosequencing revealed that some of the same pathogenic genera present in studies characterizing microbial communities in the litter layer were present in the soil layer, but in differing amounts. The genera of note were Brevibacterium, Clostridium, Corynebacterium, Mycobacterium, Staphylococcus, and Streptococcus. Brevibacterium accounts for about 6% of the sequences that were found in the soils under the poultry houses, and two-thirds of those were Brevibacterium avium, which is thought to be a secondary invader of diseased animals.^56,57 We also detected Clostridium in samples from under the poultry house, but there were no hits for C. perfrigens or C. botulinum, which are infamous pathogens. Staphylococcus was found in abundance as it has been found in litter,^9,27,7 which included S. cohnii and S. endermititis, known pathogens to humans. Other Staphylococcus sp. included animal pathogens and non-pathogens, but species belonging to this genus and Enterococcus may serve as sinks for the transmission of antibiotic resistance to normal human commensalist flora.^9,58 Another pathogenic genus present was Streptococcus, which has been found in the ileum of chickens, along with Enterococcus and Clostridium.²⁶ S. constellatus was prominent and is part of the Streptococcus auginosus group (SAG) that has the propensity to cause disease in humans.⁵⁹ Bordetella, Enterococcus, and Escherichia were found, but they showed relative abundances of less than 0.1%. Although these populations were found in relatively small abundance, persistence of bacterial populations and the development of resistance is a complex ecological process, and perhaps easier to acquire and maintain for some species of bacteria than others.