1 Assessing patterns of diazotroph and ammonium oxidizing bacterial diversity in
2 managed second-growth and old-growth coastal Douglas fir (Pseudotsuga menziesii
3 ssp. menziesii (Mirb.) Franco) forest using DGGE analysis of 16S rDNA, amoA and
4 nitrogenase reductase (nifH) genes
5
6 Richard S. Winder*, Phyllis L. Dale, David J. Levy-Booth
7
8 Canadian Forest Service, Natural Resources Canada, Pacific Forestry Centre, 506
9 Burnside Rd. W., Victoria, B.C., Canada, V8Z 1M5
10
11 *Corresponding author: Richard S. Winder, Canadian Forest Service, Natural Resources
12 Canada, Pacific Forestry Centre, 506 Burnside Rd. W., Victoria, B.C., Canada, V8Z
13 1M5. (email: [email protected])
14
1 15 Abstract
16 Forest management practices that alter soil microbial community structure may
17 result in an alteration of soil nutrient cycling dynamics and nutrient availability to future
18 timber rotations. Nitrogen fixation and nitrification are essential components of the soil N
19 cycle that are potentially altered by the conversion of old-growth Douglas fir forests to
20 managed stands, variable tree retention and clear-cut harvest. In this study PCA analysis
21 of PCR-DGGE patterns was used to analyze the effects of forest management on nitrogen
22 fixing and nitrifying microorganisms in soil under three coastal Douglas fir forest
23 research plots in summer and fall 2004, and winter 2005, using taxon-specific and
24 universal 16S, nifH and amoA primer sets. The results of this study confirmed that
25 management-related effects were observed in Paenibacillus sp. 16S rDNA PC3 between
26 unthinned, second-growth plots and clear-cut plots (P = 0.001) at Shawnigan Lake and in
27 amoA diversity in unthinned and clear-cut plots (P = 0.032). Phylogenetic analysis
28 revealed nifH clustering by date and by management practice. These data allow for
29 comparison of the diversity of select N cycling microorganisms in soil Douglas fir stands
30 under various forms of forest management relative to old-growth forests, and reveal N
31 cycling microbial community shifts following clear-cut.
32
33 Keywords: 16S rDNA; amoA, Denaturing gradient gel electrophoresis; Diversity; Forest;
34 Indicators; nifH; Old-growth; Soil; Variable tree retention.
35
2 36 Introduction
37 Forest management practises can affect nitrogen availability by altering
38 ecological processes such as litter decomposition and nitrogen cycling. Reduction in
39 nitrogen availability relative to old-growth forests may suppress the growth potential of
40 second-growth stands. Management practices such as stand-thinning, burn-off and clear-
41 cutting can lead to increases in mineralizable and leachable N that contributes to a long-
42 term reduction in N availability (DeLuca and Zouhar 2000; Prescott 2002; Smith et al.
43 2008). These practices have the potential to significantly alter the soil microbial
44 community (Smith et al. 2008). Forest management that seek to retain plant-available N
45 must consider the sources of N availability in soil, and preserve the microbial
46 communities responsible. Plant-litter decomposition by soil micro- and macro-organisms
47 is often considered to be the primary source of cycled nitrogen (Berg and Tamm 1994;
48 Laiho and Prescott 1999; Prescott 2002). In a subalpine fir (Abies lasiocarpa) forest, for
49 example, Laiho and Prescott (1999) measured cumulative N inputs of 1.3 g m2 year-1
50 from decomposing fir canopy, ground vegetation and coarse woody debris. However,
51 dinitrogen fixation by soil diazotrophs also provides significant N inputs to the soil N
52 cycle. In terrestrial ecosystems without anthropogenic nitrogen additions biological
53 nitrogen fixation may be an important source of plant-available nitrogen (Cleavland et al.
54 1999; Philippot and Germon 2005). Biological nitrogen fixation accounts for 65% of total
55 nitrogen fixed globally (Newton 2000); in soil nitrogen fixation may account for between
56 90–130 Tg N year−1 world wide (Galloway 1998). Free-living diazotrophs in Canadian
57 pine forests contribute 0.03 - 0.28 g N m2 annually (Cleavland et al. 1999).
3 58 Diazotrophic microorganisms biologically fix nitrogen gas (N2O) into a plant-
+ 59 accessible form (NH3 ). The amount of biological nitrogen fixation that occurs in a
60 terrestrial ecosystem is in part dependent on the amount and type of nitrogen fixing
61 microorganisms present in the environment, as not all of the known genera of diazotrophs
62 have identical potential for nitrogen fixation. For example, endophytic diazotrophs fix
63 about 100 times more nitrogen than free-living strains (Philippot and Germon 2005).
64 However, free-living diazotrophs such as Azotobacter vinelandii and Azospirillum
65 brasilense may be the dominant nitrogen source in nutrient-poor soils that lack symbiotic
66 relationships (Cleavland et al. 1999). To maintain ecological functioning in second-
67 growth forests, to ensure the long-term viability of these forests, and to reduce timber
68 rotation times while retaining ecological sustainability, management practices that
69 maintain the populations of these diazotrophic microorganisms relative to those found in
70 old-growth forests is essential.
71 Diazotrophs may be studied using the nifH marker gene, which encodes the Fe
72 dinitrogenase reductase enzyme, and is contained within the nif gene cluster (e.g.,
73 Azotobacter vinelandii EC 1.18.6.1.1g1m; Bairoch 1993). General and taxon-specific
74 microbial diversity may also be studied using 16S rDNA. Molecular tools such as
75 restriction fragment length polymorphism (RFLP), fluorescent-labelled terminal RFLP
76 (T-RFLP), community level physiological profiling (CLPP), ester-linked phospholipid
77 fatty acid profiling (PLFA) and denaturing gradient gel electrophoresis (DGGE) can be
78 used to compare the diversity of soil microbial communities (Bossio et al., 1998; Rosado
79 et al. 1998; Widmer et al. 1999; Shaffer et al. 2000; Grayston et al. 2001; Poly et al.
80 2001; Bürgmann et al. 2003, 2004; Grayston et al. 2004; Yeager et al. 2005), and to
4 81 identify ecological indicators associated with productive forest. Diazotroph distribution
82 can be predicted on the basis of environmental characteristics (Zehr et al., 2003).
83 Conversely, the distribution and structure of the diazotroph community may act as a
84 potential indicator of a habitat’s ecological functioning, including nitrogen status
85 (Bürgmann et al., 2004). Molecular tools have helped elucidate the distribution of nifH
86 sequences in forest habitats and the effects of forest management on diazotroph
87 communities. For example, Widmer et al. (1999) demonstrated that distinct nifH groups
88 could be characterized in Douglas-fir (Pseudotsuga menziesii) forest floor litter, mineral
89 topsoil and deeper soil. Eight distinct nifH RFLP patterns were observed in the litter
90 layer, corresponding primarily to Rhizobium, Sinorhizobium and Azosprillium sequences
91 (Widmer et al., 2000). In the mineral layer, the cloned nifH sequences were homologous
92 to genera including Bradyrhizobium, Azorhizobium, Herbasprillum and Thiobacillus
93 (Widmer et al., 2000). DGGE and RFLP banding patterns indicate that litter and soil nifH
94 communities are taxonomically diverse (Widmer et al. 1999; Yeager et al. 2005), and that
95 the genetic diversity and plasticity of the nifH gene requires degenerative primers for
96 universal amplification (Widmer et al. 1999). Taxon-specific amplification is also
97 possible (Bürgmann et al. 2004). Using both universal and taxon-specific nifH
98 amplification, DGGE and may be used to assess diazotroph community structure.
99 Methods for assessing sequence diversity are well established (Rosado et al. 1998;
100 Shaffer et al. 2000; Widmer et al. 1999; Bürgmann et al. 2004; Yeager et al. 2005). Using
101 T-RFLP patterns, Yeager et al. (2005) showed that natural disturbance in the form of
102 wildfire significantly altered the nifH profile of mixed conifer soils. Spore-forming
103 diazotrophs such as Clostridium and Paenibacillus increased in prevalence following
5 104 burning. The nifH diversity following severe and moderate burns was shown to return to
105 a lesser baseline level (Yeager et al., 2005). It is unclear what effects manual disturbance
106 such as clear-cut, tree thinning and variable harvesting have on nifH sequence diversity or
107 diazotroph biomass in forest soils. However, the disruption of forest floor layers, the
108 alteration of soil nutrient status (e.g., through canopy thinning and removal of coarse
109 woody debris) may alter diazotroph habitats and result in a shift in nifH sequence
110 diversity and abundance. Clear-cutting, a common harvesting technique in Canadian
111 forests, represents an intensive ecological disturbance. It is less well known what effects
112 other disturbances may have on soil microbial populations; variable tree retention or
113 selective harvesting, for example.
114 Although the primary focus of this study was the characterization of diazotrophic
115 populations in old-growth and managed coastal Douglas fir (P. menziesii ssp. menziesii
116 (Mirb.) Franco) forests, it also included analysis of ammonium oxidizing bacteria (AOB),
117 an important driver of N cycling dynamics in forest soil. In this study, universal and
118 taxon-specific nifH 16S taxon-specific rDNA and amoA amplification methods were used
119 to characterize diazotroph and AOB community structure in old-growth and clear-cut
120 Douglas fir forests for base-line information regarding the effect of disturbance on
121 microbial diversity. Assessing the diversity of dinitrogen fixing and ammonium oxidizing
122 microorganisms can provide an indication of the possible effects of forest management
123 practices on the N cycling community in old-growth, logged and managed second growth
124 Douglas-fir on Vancouver Island, B.C., Canada, to determine management practices best
125 suited to preserve soil microbial community structure and functioning in forest
126 ecosystems.
6 127
128 Materials and Methods
129 Field site and soil sampling
130 This study was conducted at three field sites located on Vancouver Island, British
131 Columbia, Canada (Table 1). The Shawnigan Lake and Sayward Forest Level of Growth
132 Stock (LOGS) installations are 63- and 61-year-old second-growth Douglas fir
133 plantations, respectively. At Shawnigan Lake and Sayward, sampling of the leaf-litter
134 organic soil layer (LFH; 2-0 cm) and the mineral layer (Ae and Bh; 0-8 cm) took place in
135 0.5 ha second-growth un-thinned control plots, highly thinned (10% tree retention) and in
136 adjacent clear-cut plots. The 0.41 ha old-growth Shawnigan Lake coastal Douglas fir
137 chronosequence plots are >200 years old, while the chronosequence mature stand was 65-
138 85 years old at the time of sampling. Shawnigan Lake sampling took place on July 15,
139 2004; November 19, 2004; and [DATE] 2005 - while Sayward sampling took place on
140 August 10, 2004; October 10, 2004; and [DATE], 2005 - henceforth referred to as the
141 summer 2004, fall 2004 and spring 2005 sampling times, respectively. The Shawnigan
142 Lake chronosequence old-growth and mature plots were sampled on [I still don’t have
143 reliable sampling dates]. Soil samples were collected from the leaf-litter organic soil
144 layer and the mineral layer separately, with nine (chronosequence) or ten (LOGS) ~200
145 ml samples collected randomly throughout each plot. Samples within each treatment were
146 pooled and stored at -20oC prior to analysis.
147
148 Soil DNA extraction
7 149 DNA was extracted from pooled soil samples using the MoBio UltraClean
150 Microbial DNA Isolation Kit (MoBio Laboratories Inc. Carlsbad, CA, U.S.A.) with
151 modification to the manufactures protocol to remove forest soil humic substances that co-
152 extract with DNA. 260 µl of 1x Tris-EDTA (TE) (pH 8) buffer was added to the post-
153 extracted DNA with 40 μl ammonium acetate buffer and 6 μl of linear polyacrylamide
154 co-precipitant to aid alcohol precipitation of DNA (Bioline Ltd., London, UK). The
155 mixture was incubated at room temperature for 15 min and centrifuged at 12500 rpm.
156 The DNA pellet was isolated and washed with 70% EtOH, followed by a second
157 centrifuge step. Pellet was air-dried and suspended in 50 μl TE. DNA was stored at -20oC
158 prior to PCR.
159
160 PCR-DGGE of the 16S, amoA and nifH genes
161 PCR amplification and DGGE analysis of the nifH marker gene were performed
162 using conditions described in Widmer et al. (1999) and Bürgmann et al. (2004). The nifH
163 gene was amplified using the universal nifH semi-nested primer set (Burgmann et al.,
164 2004) under the following: 1x NH4 reaction buffer (Bioline Ltd., London, UK), 2.5 mM
165 MgCl2, each deoxynucleoside triphosphate at a concentration of 200 µM, each
166 oligonucleotide primer at a concentration of 1 µM and 0.2 µl (1 U) Diamond Taq
167 polymerase (Bioline Ltd., London, UK). For the first PCR (25 µl), 5 µl of DNA sample
168 was used and for the nested PCR (50 µl), 5 µl of the first PCR product was used. After
169 initial denaturation (6 min at 94°C) 30 amplification cycles were performed at 94oC for
170 11 s, 92oC for 15 s (denaturation), 8 s at 54°C and 30 s at 56°C for the first reaction and 8
171 s at 51°C and 30 s at 53°C for the nested reaction (annealing), and 25 s at 72oC
8 172 (extension). The nifH taxon-specific reactions took place under qPCR conditions
173 described above, although only a single 50 cycle reaction was used. Annealing
174 temperatures for the nifH-g1 (Azotobacter spp.), nifH-b1 (Herbaspirillum seropedica)
175 and nifH-a1 (Rhizobiales) primer set amplifications were 60oC for 35 s (Bürgmann et al.
176 2004). The amoA gene was amplified using the amoA-1F’/amoA-2R primer set and
177 protocol from Stephen et al. (1999). Analysis of amoA gene pools was conducted for the
178 2004 sampling points only. Paenibacillus sp.-specific 16S was amplified using a nested
179 protocol with primary Paen515F/R1401 and secondary F968-GC/R1401 primer sets and
180 amplification protocol from Araújo da Silva et al. (2003). Actinomyces-specific
181 amplification took place using the F234/R1378 and F234/R513-GC nested primer sets as
182 described in Heuer et al. (1997).
183 Following amplification, PCR products were subjected to DGGE analysis. DGGE
184 was performed using the BioRad (Hercules, CA, U.S.A.) universal mutation detection
185 system with at 6% (w/v) acrylamide/bisacrylaminde stacking gel with no denaturant and a
186 separating gel of 8% acrylamide with a denaturing gradient of 35-65% (100% denaturant
187 contained 7 mol l-1 urea and 40% formamide). The gel was run at 60oC for 180 minutes at
188 180 V in 1 x TAE buffer (40 mmol l-1 Tris-acetate, 1 mmol l-1 EDTA, pH 8.3). Gels were
189 stained for 20 min in 1x TAE with 1:10000 (v/v) SYBR Gold (Invitrogen Corp.,
190 Carlsbad, CA, U.S.A.) and documented using the Syngene Chemigenius Bioimaging
191 System (Cambridge, UK). Gel images presented in the manuscript have been modified
192 using the Invert function in Adobe Photoshop CS2 (Adobe Systems Inc., San Jose,
193 California, U.S.A.). DGGE bands were excised from the acrylamide gel with a sterile
o 194 scalpel, placed in 160 µl sterile dH2O, frozen at -20 C, masticated with a sealed pipette
9 195 tip, thawed for 1 h, frozen again at -70oC for 1 h, thawed at 8oC overnight and finally re-
196 amplified using the above listed PCR protocols. PCR products were purified using the
197 UltraClean DNA Purification Kit (MoBio Laboratories, Inc., Solona Beach, CA. U.S.A.)
198 and sequenced by Macrogen, Inc. (Korea).
199
200 Statistical and phylogenetic analysis
201 DGGE banding profiles were converted to a binary matrix and subjected to
202 multivariate analysis using Statistica 6.1 (StatSoft Inc. Tulsa, OK, U.S.A.). DGGE
203 profiles were compared by principal component analysis (PCA) and the resulting
204 principal component co-ordinates were compared with analysis of variance (ANOVA).
205 Significance was determined at = 0.05 for all analysis. Banding patterns were also
206 subjected to Shannon-Wiener diversity index analysis. The sequenced nifH gene
207 fragments were aligned with published nifH sequences in the GenBank database
208 (http://www.ncbi.nlm.nih.gov/) using the CLUSTAL W 2.0.6 program (Thompson et al.
209 1994). Phylogenetic analysis was performed using the neighbour-joining method on a
210 323 bp nifH fragment that excluded both primer sites to allow phylogenetic analysis with
211 a maximum number of published nifH reference sequences using BioEdit Sequence
212 Alignment Editor 7.0 (Hall 1999) and the TreeView 1.6.6 plug-in using 100 bootstrap
213 replicates and using Gram + (high GC) Frankia sp. nifH as the outgroup.
214 Nucleotide sequence accession numbers
215 The 30 nifH sequences isolated from soils were submitted to GenBank and are
216 available under accession numbers [submissions processing].
217
10 218 Results
219 DGGE of taxon-specific 16S genes
220 The Shawnigan Lake site Paenibacillus sp. PC3 coordinates were significantly (P
221 = 0.001) different between the unthinned control (2162 Douglas fir tree stems ha-1) and
222 clear-cut plots (Fig. 1a). PC3 accounted for 13.55% of the banding variability. There
223 were no significant effects of sampling data or soil layer on Paenibacillus sp. 16S rDNA
224 DGGE banding patterns (Fig. 2); likewise, at the Sayward Forest site management
225 practices did not result in a significant alteration in PCA coordinates. The first principal
226 component (PC1) of the Paenibacillus sp. 16S rDNA DGGE banding patterns accounted
227 for 41.89% of the variability of the data-set and correlated significantly (R2 = 0.74; P <
228 0.001) with Shannon’s diversity (H’) (Fig. 3). The remaining principal components did
229 not significantly correlate with H’. Significant differences in soil layer (mineral vs.
230 organic) were found in PC2 (P = 0.007) of the Actinomyces 16S community (Fig. 1b).
231 PC2 accounted for 16.11% of DGGE banding diversity. No Actinomyces 16S principal
232 component correlated significantly with H’ following regression analysis.
233
234 DGGE of amoA and nifH functional genes
235 Universal amoA and universal and taxon-specific nifH DGGE analyses were
236 performed to determine the effects of forest management practices on microbial
237 functional genes important to the N cycle. PC1 of amoA gene DGGE banding patterns
238 was significantly different (P = 0.032) between clear-cut and control plots at both
239 Shawnigan Lake and Sayward Forest, regardless of soil layer (mineral or organic) or
240 sampling date (summer 2004 or fall 2004) (Fig. 1c). The amoA PC1 significantly
11 241 correlated with H’ (P < 0.001) and explained 36.57% of the data-set variability. Fig. 1
242 demonstrates the potential for significant differences in the microbial community due to
243 management practices (Fig. 1a,c) or between soil layers (Fig. 1b). Sampling date did not
244 significantly affect the community structure between the analyzed groups, although
245 additional field seasons would help increase the confidence of this observation.
246 Interpretation of DGGE banding patterns can be used as a proxy of community structure
247 analysis but conclusions about alternations to specific gene sequences cannot be made
248 without DNA sequencing and phylogenetic analysis.
249 A composite, representative DGGE gel of universal nifH is shown in Fig. 4. There
250 were no significant differences between sampling time, soil layer, site and management
251 practices on soil diazotroph diversity. However, a number of treatment-specific bands
252 were observed in the soil mineral layer. These bands were excised, purified, re-amplified
253 and sequenced. Phylogenetic analysis revealed clustering of nifH sequences based on
254 management practices (clear-cut and unthinned control) (Fig. 5). Further analysis is
255 needed to determine if the clear-cut nifH clusters can be used as indicators of disturbance.
256 All nifH sequence fragments obtained in this analysis demonstrated varying degrees of
257 homology to Azospirillum brasilense (NBCI accession no. X51500) with NCBI Blast
258 alignment (http://www.ncbi.nlm.nih.gov/blast/bl2seq/wblast2.cgi) identities ranging from
259 85-91%.
260 PCA of the Rhizobiales-specific nifH DGGE banding matrix did not result in
261 significant differences between treatments (Fig. 6). However, coordinate locations
262 revealed that N-fertilization and high-thinning increased diversity in the order
263 Rhizobiales, while lowest DGGE band diversity was found in the old-growth and mature
12 264 forest plots from the Shawnigan Lake chronosequence plots. Aditionally, Azotobacter sp.
265 nifH and Herbaspirillum sp. nifH anaylsis did not result in significant differences when
266 PC coordinates were subjected to ANOVA, although bands that are found exclusively in
267 control plots and absent in clear-cut plots are highlighted in the composite, representative
268 DGGE gel image shown in Fig. 7. Together, these data provide insights into effects of
269 forest management on N-fixing and ammonium oxidizing bacteria (AOB), and provide
270 baseline diversity data for the forest plots sampled in this study.
271
272
273 Discussion
274 DGGE analysis of nitrogen fixing and ammonium oxidizing microorganisms was
275 used to assess forest management impacts on N cycling in soil under Douglas fir stands.
276 Taxon-specific 16S and nifH, as well as universal amoA and nifH PCR-DGGE assays
277 were used to target communities that have important ecological roles in forest soil.
278 Multivariate analysis was applied to DGGE banding patterns and compared with
279 univeriate measures of diversity. Multivariate analysis revealed shifts in the microbial
280 communities in soil under Douglas-fir forests that had been clear-cut relative to managed
281 second-growth stands. PCA analysis of DGGE profiles showed that the primary principal
282 components (PC1) for all communities examined, with the exception of Actinomyces
283 16S, correlated significantly with SWDI (H’) (e.g., Fig. 3). However significant effects of
284 management and soil layer were shown in principal components (e.g., PC2 and 3) that
285 did not significantly correlate with H’. For example, Paenibacillus sp. 16S diversity had
286 significant difference in management practices at one site only in PC3, and Actinomyces
13 287 16S diversity was significantly affected by soil layer only in PC2. It has been shown that
288 univariate measures of diversity such as H’ may not fully account for species shifts if
289 total diversity remains intact (Hartman and Widmer 2006).
290 PCA analysis of Paenibacillus sp. 16S diversity showed site-specific alternation
291 due to clear-cut logging, although total nifH diversity did not differ significantly. The
292 diversity of Paenibacillus sp., a diazotrophic genus of spore-forming bacteria, has been
293 shown to shift in response to disturbance such as burning (Yeager et al. 2005).
294 Additionally, population shifts may indicate the loss of substrate-specific microbial
295 strains. For example, clear-cut logging of Douglas fir forests was shown to remove
296 restriction fragments that were detected in unharvested tree stands; Shaffer et al., (2000)
297 showed that a nifH 237-303 bp RFLP band that was indigenous to the plant litter layer of
298 a Douglas fir forest was not present in clear-cut soils. Clear-cuts have a lower nitrogen
299 fixation rate than Douglas-fir forests (Shaffer 2000), and support less microbial biomass.
300 Clear-cuts also increased amoA diversity in this study, leading to the potential for
301 increased nitrification under this management practice, which may alter biogeochemical
302 cycling resulting in the loss of nitrogen in clear-cut plots. It is well known that N loss due
- 303 to nitrification and NO3 leaching leads to a loss of N from clear-cut soils (Sollins and
304 McCorison 1981; Sollins et al. 1981; Paavolainen and Smolander 1998; Bottomley et al.
305 2004); it is less well understood how alterations in AOB community structure lead to N
306 loss from forest soil. It has been shown that management-related effects on the soil
307 community were correlated with soil nutrient status and pH (Grayston et al. 2004). Other
308 management-related effects on forest soil organisms include the loss of ectomycorrhizal
309 fungal community structure and abundance (e.g., Jones et al. 2003) in clear-cut plots. The
14 310 loss of microbial functional groups and a reduction of overall microbial abundance is a
311 consequence of clear-cut and other methods of intense soil disturbance (Lundgren 1982;
312 Shaffer et al. 2000). Clear-cut may remove unique nifH gene pools in Douglas fir forests
313 due to the removal of their primary habitat: Douglas fir litter. The alternation of the nifH
314 community resulted in a halving of biological nitrogen fixation (Shaffer et al. 2000). It
315 remains to be seen if variable green-tree retention preserves community structure in soil
316 under Douglas fir stands. This study demonstrates that although diversity of the nifH
317 functional group is preserved in plots under variable retention, shifts in the nifH
318 community may have non-target effects on nutrient cycling processes in forest soil. The
319 disruption of these processes may have long-term effects on timber rotations and tree-
320 harvesting cycles. The methods used in this study may be used to facilitate high-
321 throughput screening of community structure alterations in forest soil as a method to
322 further assess the effect of forest management on microbial groups that play an important
323 role in N cycling.
324 There were no significant seasonal effects of 16S, amoA or nifH diversity
325 following PCA analysis of DGGE patterns. However, seasonal and management
326 clustering was observed in nifH phylogenetic analysis (Fig. 5). Seasonal effects on
327 microbial community structure have been observed in both managed and unmanaged
328 agricultural and forest soil ecosystems such as temperate grassland soil (Grayston et al.
329 2001), canola rhizospheres (Dunfield and Germida 2003) and tomato field soil (Bossio et
330 al. 1998), among others. Mergel et al. (2001) reported seasonal fluctuations of
331 diazotrophic bacteria in Norway spruce forest soil, as well as seasonal differences in
332 other microbial functional groups that are active in N cycling. Holmes and Zak (1999)
15 333 also reported seasonal alterations in N cycling microbial dynamics in their study of
334 hardwood soil ecosystems. However, while Piceno et al. (1999) reported minor seasonal
335 differences in nifH diversity in salt march rhizosphere sediments, the authors also
336 reported seasonal constancy in DGGE banding profiles. Improved long-term monitoring
337 of nifH diversity would allow for a better understanding of seasonal cycles of diazotroph
338 community structure in forest soil.
339 The results of this study indicate that implementing high-throughput methods to
340 assess effects of forest management on diazotroph communities is an appropriate method
341 of rapidly determining indicators of ecological function across treatments, soil layers and
342 seasons within the soil ecosystems assessed. The universal nifH banding patterns and
343 phylogenetic analysis shown in Figs. 4 and 5, respectively, have aided in the assessment
344 of diazotroph diversity in soils under various forest management practices, have provided
345 base-line community data for the plots used in this study that may be used in future
346 studies to assess further long-term management effects, and has provided indicators of
347 ecological disturbance in these research plots.
348 This study demonstrated that clear-cutting reduced Paenibacillus sp. and
349 ammonium oxidizing bacterial diversity (Fig. 1) and changed diazotroph community
350 structure (Figs. 4,5). Mature second growth stands retained Rhizobiales nifH diversity
351 relative to old-growth plots (Fig. 6), as did the leaf litter organic later in highly-thinned
352 (10% tree retention relative to unthinned control plots) treatments. However, the mineral
353 layer of the highly-thinned treatment showed a decrease in Rhizobiales nifH diversity.
354 These data allow for base-line comparison of the diversity of select N cycling
355 microorganisms in soil Douglas fir stands under various forms of forest management
16 356 relative to old-growth forests. As old-growth forests are thought to contain important
357 levels of biodiversity, including microbial community diversity, it is notable that second-
358 growth forests have comparable levels of genetic diversity to old growth stands and may
359 increase functional diversity due to management related shifts in microbial community
360 structure.
361
362 Acknowledgements
363
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24 521 Table 1. Field site characteristics at the Shawnigan Lake Chronosequence and the 522 Shawnigan Lake and Sayward Forest LOGS research forests. 523 Site Characteristics Primary Tree Field Site Treatments Soil Type Elevation Species Humo-Ferric Old Growth 465 m Coastal Podzol (40% Shawnigan Lake Douglas fir Mature Silty Loam slope) Chronosequencea
Unthinned control Highly thinned Humo-Ferric Shawnigan Lake Coastal (10% retention) Podzol 335 m LOGSb Douglas fir Clear-cut Loam N-Fertilized
Humo-Ferric Unthinned control Podzol Sayward Forest Coastal Highly thinned 274 m LOGSb Douglas fir Sandy Loam (10% retention) Clear-cut 524 525 aTrofymow 1997; bBeddows 2002. 526
25 A B 4 C 2 4 Mineral Control Shawnigan Lake - Clearcut 3 Organic Clearcut 3 Shawnigan Lake - Control 2 1 2 Sayward - Clearcut Sayward - Control 1 1 0 0 0 -1
-1
-2 -1 Principal Component(14.66%) 2 Principal
Principal Component(13.55%) 3 Principal -2 -3 Paenibacillus 16S 0.007) = (P Component(16.11% 2 Principal Actinomyces 16S AOB (amoA) -3 -4 -2 -3 -2 -1 0 1 2 3 -3 -1 1 3 5 -3 -2 -1 0 1 2 3 Principal Component 2 (14.89%) Principal Component 1: 17.41% (P = 0.442) Principal Component 1 (36.57%) (P = 0.032) 527 528 Fig. 1. Principal component analysis of PCR-DGGE banding patterns in Douglas fir
529 research plots for: a) Paenibacillus 16S rDNA demonstrating significant management
530 related effects at the Shawnigan Lake site in PC3 (P = 0.001) between the unthinned
531 control and clear-cut plots; b) Actinomyces 16S rDNA demonstrating significant effects
532 of soil layer on community structure; and c) ammonium oxidizing bacteria amoA gene
533 differences between clear-cut and control at both Sayward and Shawnigan lake sites
534 throughout all sampling dates (summer 2004, fall 2004 and spring 2005).
535
26 Summer 04 Fall 04 Spring 05 -CT- -CC- -CT- -CC- -CT- -CC- SL SY SL SY SL SY SL SY SL SY SL SY
536
537 Fig. 2. Composite, representative PCR-DGGE gel image for Paenibacillus 16S rDNA
538 analysis from two Douglas fir forest sites over three sampling times. The gene fragments
539 were obtained using primary Paen515F/R1401 and secondary F968-GC/R1401 primer
540 sets for a nested PCR reaction.
541
27 A 6 B 50 4 PC1 (41.89%) PC2 (14.89%) 45 PC3 (13.55%) 2 40 PC1 Fitting 0 PC2 Fitting 35 PC3 Fitting 30 -2 PC1 = -4.7161(SDI) + 9.6769 25 2 -4 R = 0.7369 (P = <0.001) Total (%) Total 20 PC2 = 0.7009(SDI) - 1.4381 -6 R2 = 0.0458 15
Principal ComponentCoordinates Principal -8 PC3 = 0.8466(SDI) - 1.7372 10 R2 = 0.0734 5 -10 -1 0 1 2 3 4 0 1 2 3 4 5 6 7 8 9 10 11 Shannon's Diversity Index Principal Component 542 543 Fig. 3. Treatment effects on microbial diversity measured using PCA and Shannon-
544 Weiner diversity (H’) following Paenibacillus 16S rDNA PCR-DGGE analysis. a)
545 Regression analysis of PCA coordinates against Shannon-Weiner diversity (H’)
546 demonstrating the significant correlation with H’ and PC1 coordinates. b) The percentage
547 of total variance explained by each component. While PC1 accounted most significantly
548 for overall variation and correlated with H’, the use of principal components increased
549 the dimensionality and sensitivity of DGGE banding analysis.
550
28 Summer 04 Fall 04 Spring 05
CTO CTM CCO CCM CTM CCO CCM CTO CTM CCO CCM Az SL SY SL SY SL SY SL SY SL SY SL SY SL SY AZ SL SY SL SY SL SY SL SY
551
552 Fig. 4. Composite, representative PCR-DGGE gel image for universal nifH analysis from
553 two Douglas fir forest research sites over three sampling dates. The gene fragments were
554 obtained using primary nifH-forA/nifH-Rev and secondary nifH-forB/GC-nifH-Rev
555 primer sets for a nested PCR reaction. Azotobacter vinelandii ATCC 12518
556 Was used as a positive control (Az).
557
29 Frankia sp. X73983 Frankia sp. X76399 Clostridium pasteurianum X07472 Clostridium pasteurianum X07475 Trichodesmium sp. L00688 Trichodesmium thiebautii M29709 Klebsiella pneumoniae V00631 Alcaligenes faecalis X96609 Azotobacter chroococcum MCD 1 X03916 Azotobacter vinelandii M20568 Burkholderia cepacia S7 2 AM110713 Herbaspirillum seropedicae Z54207 Bradyrhizobium japonicum USDA 110 K01620 Thiobacillus ferrooxidans M15238 Azorhizobium caulinodans str. ORS571 M16710 Rhodobacter capsulatus str. SB1003 X07866 Rhodospirillum rubrum M33774 Sinorhizobium fredii str. QB1130 L16503 Rhizobium sp. str. ANU240 M26961 Rhizobium etli bv. phaseoli M10587 Rhizobium leguminosarum bv. trifolii K00490 Azospirillum brasilense ATCC 29145 M64344 Azospirillum brasilense str. Sp7 X51500 Su04-Sy-CCm [Band 7] Su04-Sy-CTm [Band 3] Su04-Sy-CTm [Band 7] Su04-Sy-CTm [Band 4] Su04-SL-CTm [Band 5] Sp05-SL-CTm [Band 20] Su04-Sy-CTm [Band 1] Su04-Sy-CCm [Band 6] Fall04-SL-CTm [Band 9] Fall04-SL-CTm [Band 8] Sp05-Sy-CTm [Band 12] Sp05-SL-CTm [Band 17] Sp05-Sy-CTm [Band 13] Sp05-Sy-CTm [Band 11] Sp05-SL-CTm [Band 18] Sp05-Sy-CCm [Band 1] Sp05-SL-CTm [Band 19] Sp05-Sy-CTm [Band 15] Sp05-Sy-CTm [Band 14] Distance Sp05-SL-CTm [Band 16] Sp05-Sy-CTm [Band 10] 0.1 Fall04-Sy-CTm [Band 10] Sp05-Sy-CCm [Band 4] Sp05-Sy-CCm [Band 2] Sp05-SL-CCm [Band 5] Sp05-SL-CCm [Band 6] Sp05-SL-CCm [Band 9] Sp05-Sy-CCm [Band 3] Sp05-SL-CCm [Band 7] Sp05-SL-CCm [Band 8]
558
559 Fig. 5. Phylogenetic analysis of sequenced nitrogenase reductase (nifH) diazotroph
560 marker gene fragments (position: 131-334 bp from start of Azotobacter vinelandii
30 561 (M20568) nifH sequence) from Douglas fir forest plots compared to known nifH
562 sequences obtained from GenBank. Sequences were obtained following direct
563 amplification from isolated DGGE bands from mineral layer soil DNA extractions from
564 Summer 2004(Su04), Fall 2004 (Fall04) and Spring 2005 (Sp05) sampling dates at
565 Shawningan Lake (SL) or Sayward Forest (Sy) sites in clearcut (CC; underlined) or
566 unthinned control (CT; grey italics) plots. Frankia sp. (X73983, X76399) were used for
567 the out-group. Clustering based on sampling date and management type was observed.
568
31 569
2
Old Growth - m Mature - m
1 High Thinning - m N Fertilized - m Old Growth - o Mature - o 0 High Thinning - o N Fertilized - o
-1 Principal Component 2 (11.61%) 2 Component Principal
-2
-3 -3 -2 -1 0 1 2 3 4 Principal Component 1 (11.78%)
570
571 Fig. 6. Principal component analysis of Rhizobiales nifH PCR-DGGE banding patterns in
572 Douglas fir stands showing mineral and organic layer nifH diversity for old-growth and
573 mature forest stands at the Shawnigan Lake chronosequence and the high-thinning and N
574 fertilized plots at the Shawnigan Lake LOGS site. Diversity increased from the bottom
575 left (N-fertilized-organic) to the top right (high-thinning-mineral). In managed plots
576 higher numbers of DGGE bands were observed, suggesting nifH community shifts
577 relative to old-growth and mature Douglas fir forests.
578
32 nifH – universal Azotobacter sp. nifH Herbaspirillum sp. nifH CT CC CT CC CT CC Az SL Sy SL Sy Az SL Sy SL Sy Az SL Sy SL Sy
579
580 Fig. 7. Universal and taxon-specific nifH community structure in two Douglas fir forest
581 sites at the Spring 2005 sampling point. DGGE bands unique to clear-cut plots for
582 Azotobacter sp. nifH (diamond) and unthinned control plots for Herbasprillum sp. nifH
583 (triangle) may provide indicators of management impacts at the sites sampled in this
584 study. Azotobacter vinelandii ATCC 12518 was used as a positive control (Az).
585
33