Bacteria as emerging indicators of soil condition

35 Pages·2016·1.65 MB·English

AEM Accepted Manuscript Posted Online 28 October 2016
Appl. Environ. Microbiol. doi:10.1128/AEM.02826-16
Copyright © 2016, American Society for Microbiology. All Rights Reserved.
1 Title: Bacteria as emerging indicators of soil condition
2 Running Title: Bacterial indicators of soil health
3 1Syrie M. Hermans, 2Hannah L. Buckley, 3Bradley S. Case, 4Fiona Curran-Cournane, 5Matthew
4 Taylor, 1Gavin Lear#
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6 1School of Biological Sciences, University of Auckland, 3A Symonds Street, Auckland, New lo
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8 2Department of Ecology, Faculty of Agriculture and Life Sciences, PO Box 85084, Lincoln o
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9 University, Lincoln 7647, Canterbury, New Zealand. t
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10 3Department of Informatics and Enabling Technologies, Faculty of Environment, Society and /a
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11 Design, PO Box 85084, Lincoln University, Lincoln 7647, Canterbury, New Zealand. .
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12 4Auckland Council, 135 Albert Street, Auckland, New Zealand
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13 5Waikato Regional Council, Private Bag 3038, Waikato Mail Centre, Hamilton 3240, New g/

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17 #Correspondence: b
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18 Dr. Gavin Lear u
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19 School of Biological Sciences, University of Auckland t
20 3A Symonds Street, Auckland 1010, New Zealand.
21 E-mail: [email protected].
22 Tel: [phone]
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23 Abstract
24 Bacterial communities are important for the health and productivity of soil ecosystems, and have
25 great potential as novel indicators of environmental perturbations. To assess how they are
26 affected by anthropogenic activity, and determine their ability to provide alternative metrics of
27 environmental health, we sought to define which soil variables bacteria respond to across
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28 multiple soil types and land uses. We determined, through 16S rRNA amplicon sequencing, the
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29 composition of bacterial communities in soil samples from 110 natural or human-impacted sites, a
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30 located up to 300 km apart. Overall, soil bacterial communities varied more in response to
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31 changing soil environments than changes in climate or increasing geographic distance. We

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32 identified strong correlations between the relative abundance of members of Pirellulaceae and p
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33 soil pH, members of Gaiellaceae and carbon to nitrogen ratio, Bradyrhizobium and the levels of e
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34 Olsen P, and members of Chitinophagaceae and aluminium concentrations. These relationships a
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35 between specific soil attributes and individual soil taxa not only highlight ecological o
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36 characteristics of these organisms, but also demonstrates the ability of key bacterial taxonomic o
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37 groups to reflect the impact of specific anthropogenic activities, even when comparing samples p
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38 across large geographic areas and diverse soil types. Overall, we provide strong evidence that 1
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39 there is scope to use relative taxa abundances as biological indicators of soil condition. 1
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40 Importance g
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41 The impact of land use change and management on soil microbial community composition t
42 remains poorly understood. Therefore, we explored the relationship between a wide range of soil
43 factors and soil bacterial community composition. We included variables related to
44 anthropogenic activity, and collected samples across a large spatial scale to interrogate the
45 complex relationships between various bacterial community attributes and soil condition. We
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46 provide evidence of strong relationships between individual taxa and specific soil attributes even
47 across large spatial scales, soil, and land use types. Collectively, we were able to demonstrate the
48 largely untapped potential of microorganisms to indicate the condition of soil, and thereby
49 influence the way we monitor the effects of anthropogenic activity on soil ecosystems into the
50 future.
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51 Introduction
52 Soil bacterial communities provide a multitude of ecosystem services which directly, and
53 indirectly, affect the overall functioning of the soil environment (1–3). This has resulted in many
54 studies describing variation in bacterial community composition (4, 5) and functional roles (6–8);
55 however, less effort has been invested to explore how this variation correlates with soil health.
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56 There is great promise for using bacterial community composition, or the relative abundances of w
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57 individual taxa, as indicators of the state of soil environments at regional or even continental a
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58 scales. Recent advances in next-generation sequencing technologies now make this a plausible d

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59 and attractive avenue of research, leading bacterial community data to be proposed as capable of m

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60 providing alternative metrics of environmental health and production potential (9). If shown to tt
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61 be reliable, microbial community indicators could offer significant advantages over traditional a
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62 chemical and biological measures in terms of the relative speed and ease of data analysis, and the .a
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63 minimisation of site disturbance during sample collection (10). .
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64 For bacterial community attributes to be a viable indicator of soil condition, it is desirable /
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65 that natural spatial variation in bacterial community composition be less than variation caused by A
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66 anthropogenic factors. A consensus appears to have emerged that environmental factors, rather il 1
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67 than dispersal limitation, are dominant drivers of bacterial community composition (5, 11). The 0
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68 reduced role of dispersal limitation for determining the beta-diversity of microbial communities, b
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69 even across broad spatial scales, is presumed to be supported by a global dispersal of microbial u
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70 cells, including on major atmospheric (12) and oceanic currents (13). While there have been
71 numerous bacterial biogeography studies that have employed DNA-fingerprinting techniques
72 over large spatial scales (4, 5), DNA-sequencing studies at similar scales are comparatively
73 scarce. To date, most sequencing studies have analysed relatively small numbers of samples,
74 environments and land uses, or only across small spatial scales where the effect of dispersal
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75 limitation can already be presumed to be minimal. More studies that simultaneously analyse
76 large spatial scales, and a variety of soil and land use types, are required to confirm if
77 relationships observed between bacterial communities and soil environmental factors are
78 pervasive, or if instead, they are strongly mediated by geographic location. This would be the
79 first step in supporting the broad-scale use of bacterial data as a viable indicator of soil health.
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80 In support of their ability to indicate the condition of the soil environment, previous w
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81 biogeographic studies have identified several variables that correlate with changes in soil a
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82 bacterial community composition. Most notable is the evidence that pH influences bacterial d

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83 communities at regional (14), and continental scales (4, 5). Other variables such as the carbon to m

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84 nitrogen ratio, moisture content, and soil temperature also correlate with changes in soil bacterial tt
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85 communities (5). However, relationships between bacterial communities and critical variables a
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86 associated with the nature and intensity of human land use are frequently overlooked or are .a
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87 studied in isolation. These include concentrations of the many heavy metals that accumulate and .
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88 impact biological communities in urban (e.g., zinc, lead; (15)) and rural settings (e.g., copper, /
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89 chromium; (16)), as well as core soil physical attributes, such as porosity, which can correlate A
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90 negatively with stock density, and ultimately impact the production potential of agricultural land il 1
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91 (17, 18). The pairing of large scale surveys of soil bacterial communities to data gathered 0
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92 through long-term soil monitoring programs (5, 19, 20) provides opportunities to uncover and b
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93 quantify the strength of relationships between bacterial communities and a much wider range of u
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94 soil physicochemical variables than what has been previously achieved. This would then define
95 which chemical and physical stresses on the soil environment are reliably portrayed by bacterial
96 communities.
97 To date, many studies have only assessed changes in bacterial communities as a whole,
98 rather than the responses of individual taxa (4, 19). Others have restricted their analyses to
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99 investigate changes in the most dominant phyla (21, 22), although these are not necessarily the
100 most important, or only ones, driving the changes observed in the overall community (23).
101 Assessing community responses at lower taxonomic levels, such as genera, could highlight
102 important trends that might not always be observed in the higher taxonomic ranks (24). The
103 absence of studies assessing the responses of important taxa on an individual basis not only
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104 hinders our ability to expand our knowledge of the ecological attributes of these important w
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105 community members, but also our ability to truly assess the potential of bacterial taxa to serve as a
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106 biological indicators of ecosystem health. d

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107 By pairing bacterial community data and extensive metadata gathered from 110 sites, we m

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108 asked three main questions. Firstly, is variation in bacterial communities more strongly related to tt
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109 environmental changes or geographic distance separating the communities? We predict that soil a
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110 bacterial communities will be more strongly correlated with soil environmental factors, rather .a
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111 than purely spatial factors. This would suggest that bacterial community data may be suitable for .
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112 the assessment of soil status across the geographic area from which samples were taken. Our /
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113 second question is: Which environmental variables correlate with changes in bacterial A
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114 community composition? While we expect to find, consistent with other studies, that pH will il 1
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115 have a dominant effect on bacterial communities, we also anticipate that we will be able to 0
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116 uncover important relationships between bacterial community structure and other soil variables b
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117 that are indicators of soil condition. Our final question is: Can the abundances of different u
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118 individual taxa be used to monitor soil condition? Determining how individual taxa respond to a
119 range of environmental factors, and especially changes in soil variables brought about by
120 anthropogenic activity, may have important implications for how we monitor the health of our
121 soils in the future.
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122 Materials and Methods
123 Sample collection
124 We collected samples between 2013 and 2014 from 110 sites in northern New Zealand
125 (Fig 1). Our sampling area covered approximately 29,500 km2 of land consisting of diverse soil
126 types; over half of this area is used for pastoral farming and horticulture, the remainder is
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127 covered in forest, bare rock (volcanic cones), native tussock or urban areas ((25); Waikato w
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128 Regional Council, 2015 [http://www.waikatoregion.govt.nz/Environment/Environmental- a
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129 Information/Environmental-indicators/Land-and-soil/Land/land1-key-points/]). We classified d

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130 sites to Soil Order level according to the New Zealand Soil Classification (26) and the World m

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131 Reference Base for Soil Resources (27). Soil Order classification included (and equivalent in tt
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132 WRB) were Granular Soils (Ferralsols, n=23), Allophanic Soils (Andosols, n=25), Ultic Soils a
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133 (Acrisols, n=17), Pumice Soils (Andosols, n=14), Gleys (n=11), Organic Soils (Histosols, n=8), .a
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134 Brown Soils (Cambisols, n=8) and Recent Soils (Fluvisols & Arenosols, n=4). Sites are further .
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135 categorised as being dominated by indigenous forest, exotic forest, dairy pasture, dry stock /
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136 pasture or horticulture ((28); Table S1). A
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137 For microbial analyses, we collected five soil cores at each site (0-10 cm deep, 2.5 cm il 1
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138 diameter), after removing leaf litter and plant biomass, across a transect at 10 m intervals. These 0
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139 samples were kept on ice until they could be transferred to -20°C storage until further use. For b
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140 soil chemical analyses (Table 1), we composited an additional 25 soil cores collected from the u
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141 same transect at 2 m intervals, while for soil physical analyses (Table 1) we took intact soil cores
142 (0-9 cm deep, 10 cm in diameter) each separated by 15 m (28).
143 Molecular methods
144 Before DNA extraction, we homogenised each thawed soil sample by manual mixing.
145 We used PowerSoil® -htp 96 well DNA Isolation Kits (Mo Bio Laboratories Inc., CA, USA)
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146 following the manufacturer’s instructions, but with minor modification: (i) mechanical lysis was
147 performed by agitating the plates in a Qiagen TissueLyser II (Retch) for 4 minutes at a frequency
148 of 30 Hz, (ii) ethanol air drying time was extended to 15 min, and (iii) plates incubated at room
149 temperature for five minutes after elution buffer was added. In total, we extracted DNA from 550
150 samples, which we stored at -20 °C until further analysis.
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151 To characterise the diversity and composition of soil bacterial communities in each w
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152 sample, we amplified V3/V4 regions of bacterial 16S rRNA genes from each soil extract using a
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153 modifications of the primers 341F (5’- d

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154 TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3) and m

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155 785R (5’- tt
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156 GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC- a
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157 3’). This primer pair has been demonstrated to provide good coverage for bacteria and is .a
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158 purposefully designed for optimal use on Illumina MiSeq DNA sequencing platforms (29). The .
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159 primers include Illumina adapter sequences (underlined) that are required for downstream /
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160 sequencing. We amplified DNA from each sample, as well as mock community DNA (BEI A
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161 Resources; item HM-783D), with the following amplification conditions: (i) 95 °C for 3 minutes; il 1
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162 (ii) 25 cycles of 95 °C for 30 seconds, 55 °C for 30 seconds, 72 °C for 30 seconds, and then (iii) 0
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163 72 °C for 5 minutes. We individually purified PCR products using SequalPrep Normalisation b
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164 Plates (Invitrogen) or DNA Clean & Concentrator kits (Zymo Research), as per manufacturer’s u
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165 instructions. Finally, we measured and recorded the concentration of purified PCR products
166 using a Qubit® dsDNA HS Assay Kit (Life Technologies, USA), and normalised the
167 concentrations where required. The amplified material was then submitted to New Zealand
168 Genomics Ltd., for sequencing on an Illumina MiSeq instrument using 2 x 300 bp chemistry.
169 Prior to DNA sequencing, the sequencing provider attached a unique combination of Nextera XT
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170 dual indices (Illumina Inc., USA) to the DNA from each sample, to allow for multiplex
171 sequencing.
172 Bioinformatic methods
173 The DNA sequence data were quality-filtered, after which we picked de novo operational
174 taxonomic units (OTUs) using USEARCH v 7.0 (30). Forward and reverse reads were merged D
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175 using the fastq_mergepairs command. We truncated reads at the first position that had a quality n
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176 score (Q score) of less than 3, and set the minimum length of the merged read to 200 bp. We d
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177 then trimmed the first 20 bp from the start of all the merged sequences using the -fastq_filter f
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178 command since this region had a high probability of error. Reads with more than one expected

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179 error were discarded. Finally, we dereplicated sequence data (-derep_fulllength), removed p
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180 singletons (-sortbysize), and clustered sequences into OTUs at 97% sequence similarity, using e
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181 the UPARSE-OTU algorithm (31). s
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182 We performed taxonomic assignment within QIIME (Quantitative Insights into Microbial o
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183 Ecology, version 1.8) by comparison against the Greengenes reference database version 13.8 o
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184 (32) before randomly rarefying to a depth of 2,000 sequences per sample to achieve a standard p
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185 sequencing ‘depth’ across all samples. ,

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186 Analysis of soil physicochemical and climatic data 9

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187 We coupled our bacterial and soil physicochemical datasets with further environmental g
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188 data collated for each sampling location using ArcGIS 10.3 s
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189 (Environmental Systems Research Institute (ESRI), Redlands, CA). The extraction tools within
190 the spatial analyst toolset were used to obtain climate and site aspect data (Table S2), based on
191 the site location data (NZTM Eastings and Northings). When a large range of soil and climatic
192 variables are measured, several of these are usually correlated with each other, and this could
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193 have undesirable effects for downstream analyses. We therefore identified highly correlated
194 explanatory variables (with a Pearson’s correlation value greater than 0.6; either negative or
195 positive), and only included one of the representative variables in downstream analyses. This led
196 us to keep 22 variables (we discarded 22 variables; Table S3). For all reported results, any
197 significant correlation of bacterial community composition, or taxa abundance, with a
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198 representative explanatory variable could equally be caused by variability any of the removed w
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199 variables that correlated with the representative variable. a
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200 Statistical analyses fr
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201 We removed 16 samples from our analysis because they had fewer than 2,000 DNA
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202 sequence reads. To eliminate any biases associated with unequal coverage across sites, we :
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203 calculated centroid bacterial community data for each site by taking the mean abundance value m
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204 for each OTU from three randomly selected samples on each transect. We then assessed s
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205 differences in bacterial community composition by calculating the Bray-Curtis dissimilarities for r
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206 each pair of samples, using the averaged OTU abundances for each site. We also assessed n

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207 differences in community composition at the taxonomic levels of phylum, class and genus. Bray- r
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208 Curtis dissimilarity matrices were generated in R v3.2.1 using the package ‘vegan’. ,
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209 We used non-metric multidimensional scaling (nMDS) of the Bray-Curtis dissimilarity 9

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210 matrix from phylum abundances to obtain site scores in compositional space using the Primer v.7
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211 computer program (33). Using the ArcGIS kriging function in the extrapolation toolset, we then s
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212 mapped the first and second nMDS axis scores to generate a geographical representation of the
213 spatial patterns in community composition.
214 We used distance decay analysis and linear regression to further investigate patterns in
215 the bacterial communities, for each of the five land uses separately. For this, pairwise
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