Soil Biology & Biochemistry 112 (2017) 153e164
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Using community trait-distributions to assign microbial responses to pH changes and Cd in forest soils treated with wood ash Carla Cruz-Paredes a, Håkan Wallander b, Rasmus Kjøller a, Johannes Rousk b, * a b
Department of Biology, Terrestrial Ecology Section, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark Section of Microbial Ecology, Department of Biology, Lund University, 22362 Lund, Sweden
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 September 2016 Received in revised form 8 April 2017 Accepted 5 May 2017
The identification of causal links between microbial community structure and ecosystem functions are required for a mechanistic understanding of ecosystem responses to environmental change. One of the most influential factors affecting plants and microbial communities in soil in managed ecosystems is the current land-use. In forestry, wood ash has been proposed as a liming agent and a fertilizer, but has been questioned due to the risk associated with its Cd content. The aim of this study was to determine the effects of wood ash on the structure and function of decomposer microbial communities in forest soils and to assign them to causal mechanisms. To do this, we assessed the responses to wood ash application of (i) the microbial community size and structure, (ii) microbial community trait-distributions, including bacterial pH relationships and Cd-tolerance, to assign the microbial responses to pH and Cd, and (iii) consequences for proxies of the function soil organic matter (SOM) turnover including respiration and microbial growth rates. Two sets of field-experiments in temperate conifer forest plantations were combined with laboratory microcosm experiments where wood ash additions were compared to additions of lime and Cd. Wood ash induced structural changes in the microbial community in both field experiments, and striking similarities were observed between the application of ash and that of lime in the microcosm experiments. Wood ash increased pH, and led to a shift toward faster SOM decomposition and a reduced importance of fungi. This coincided with shifts in bacterial community trait distributions for pH, with pH optima closely tracking the new soil pH. A Cd solution could induce Cd-tolerance in the microcosm experiments, but the ash did not affect the microbial tolerance to Cd in field or microcosm experiments. We demonstrate that the microbial community responded strongly to the application of wood ash to forest soils with consequences for its functional capabilities in terms of respiration and growth rates. The bacterial community's trait distributions revealed that the increased pH directly caused the microbial responses, while the wood ash associated Cd has no detectable effects on the microbial community. The study demonstrates the power of community trait distributions to (i) causally link microbial structural responses to environmental change and (ii) potential to predict the ecosystem functional consequences. © 2017 Elsevier Ltd. All rights reserved.
Keywords: Plantesoil (below-ground) interactions Wood ash Decomposer ecology Soil organic matter pH Heavy metals
1. Introduction A central interest in ecology is the connection between the composition of organisms and the processes and elemental fluxes through an ecosystem (Odum and Odum, 1976). Global biogeochemical cycles are dominated by microorganisms. In most terrestrial ecosystems, the turnover of carbon (C) and the
* Corresponding author. Section of Microbial Ecology, Department of Biology, Lund University, Ecology Building, 22362 Lund, Sweden. E-mail address:
[email protected] (J. Rousk). http://dx.doi.org/10.1016/j.soilbio.2017.05.004 0038-0717/© 2017 Elsevier Ltd. All rights reserved.
regeneration of the nutrients that sustain primary productivity are rate-limited by the microbial turnover of soil organic matter (SOM). Thus, it has been argued that a mechanistic understanding of ecosystem functioning is only attainable with a clear understanding of the factors that structure microbial communities (Raes and Bork, 2008; Schmidt et al., 2011). Research of recent decades has highlighted environmental factors that are important for structuring microbial communities, including e.g. pH in terrestrial ecosystems (Lauber et al., 2009; Rousk et al., 2010a) and salinity in aquatic ecosystems (Lozupone and Knight, 2007; Mohamed and Martiny, 2011; Rath and Rousk, 2015). Although these observations have
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advanced our understanding of how microbial communities are shaped by their environment, important knowledge gaps remain to be filled, including the causal links between community structure and function (Jansson, 2013; Prosser, 2013). Within the field of microbial ecology, parallel responses of microbial structure and function to experimental treatments reported in most contemporary work is suggestive, but notoriously difficult to disentangle (Bier et al., 2016). For instance, it has frequently been observed that a community composition has responded to a change in its environment such as warming (Bradford, 2013). However, what actually caused the change e indirect effects such as nutrient availability, C quality or direct effects such as microbial tolerance to temperature e normally remains unresolved. Extending ideas from plant and animal ecology (Grime, 1977; Southwood, 1977) to the realm of microbial ecology, Green et al. (2008) and Webb et al. (2010) argue that a trait based approach can bridge from structure to function. The responses of communities to their environment, and subsequent feedback, is determined by the traits of included species or individuals (Allison, 2012; Norberg et al., 2001; Webb et al., 2010). A logical way to estimate a community's dependence on, and feedback to, its environment is thus to identify traits of all individual organisms, weigh up their relative abundance, and add them up to establish the contribution of a community to ecosystem function (Raes and Bork, 2008). Problematically, although some reports are beginning to appear characterizing single traits for selected microbial taxa (Martiny et al., 2015), the determination of traits for all the microbial taxa that make up natural communities is a daunting task. While this strategy may be acceptable for communities where most species can be identified, this is a nearly impossible enterprise for microbial communities given their staggering diversity (Delmont et al., 2011). As an alternative strategy, estimates of the distribution of traits that define whole communities could be more tractable (Norberg et al., 2001). The determination of an aggregate trait distribution for a whole community could then be used as a rcenas-Moreno et al., 2016; Norberg et al., 2001). way forward (Ba The dependence of the intrinsic growth rate of a microbial community on environmental factors, or its tolerance to e.g. toxins, could define such community trait distributions (Bååth and rcenas-Moreno et al., 2016). This approach has Kritzberg, 2015; Ba been used to determine bacterial community trait distributions for rcenas-Moreno et al., 2009; Birgander et al., 2013; temperature (Ba Li and Dickie, 1987), salt (Rath et al., 2016), and pH (Bååth and ndez-Calvin ~ o et al., 2011). For instance, the Kritzberg, 2015; Ferna pH trait distributions of bacterial communities are readily determined in an environmental sample by measuring the growth rate at a range of different pHs, forming a unimodal distribution with ~ o et al., highest values at the pH-optimum (e.g. Fern andez-Calvin 2011). Bacterial community trait distributions for inhibitory environmental factors like salt can be determined by establishing doseresponse relationships to estimate tolerance, often forming a sigmoidal relationship from highest values without toxins, and lower values at increasing toxin concentrations (e.g. Rath et al., 2016). Based on these approaches, the relative importance of environmental factors have been compared with the initial composition of the source community for formed community trait rcenas-Moreno et al., 2016; Pettersson and Bååth, distributions (Ba 2013, 2004). Recently, the alignment between community trait distributions and environmental conditions have also been linked to functional consequences (B arcenas-Moreno et al., 2016). In contrast with most alternative methods to assess microbial community change, monitoring responses in community trait distributions also have the power to assign community change to causal factors. For instance, if microbial communities become more warmtolerant in response to warming, this provides unambiguous evidence that the community was, at least partly, driven by the
rcenastemperature increase, rather than indirect effects (Ba Moreno et al., 2009; Li and Dickie, 1987). The current land-use characterizes the environment controlling plant and microbial communities in most managed ecosystems. In forestry, various treatments are made to enhance and maintain soil fertility. One such treatment is the application of wood ash, which has been proposed as a liming agent and a slow release fertilizer suitable for forest soils (Jacobson, 2003). In addition to increasing soil pH (Bååth et al., 1995; Fritze et al., 2000; Knapp and Insam, € ma €ki and Fritze, 2002), wood ash contains nutrients, 2011; Perkio e.g. calcium, potassium and magnesium, along with heavy metals, including e.g. cadmium (Cd) (Knapp and Insam, 2011). Of these, Cd is of major environmental concern, often setting the limits for the € ma €ki and Fritze, 2004). Given that wood ash use of wood ash (Perkio is a cocktail of potential drivers of microbial community change, the underlying causal factors e its alkaline properties, its nutrient content, or Cd content e often remain undifferentiated. The aim of this study was to determine the effects of wood ash on soil microbial communities and their ability to use and decompose soil organic matter (SOM). Specifically, our objectives were to: (1) assess the structural responses of the microbial community in terms of biomass and community structure, (2) determine how causal factors associated with the wood ash (i) pH and (ii) Cd had affected the community trait distribution and had thus structured the community composition, and (3) assess the consequences these microbial responses had for decomposer functioning of the soil, as indicated by the performance of the community measured as microbial growth rates and respiration. To accomplish this, we combined a range of field-experiments of ash application at field scale with parallel laboratory microcosm experiments comparing the effects of ash with factorial additions of lime and Cd. These experiments were thus designed to (a) disentangle the pH and Cd effects of ash amendments, and (b) to characterize the dynamics of how the anticipated induction of tolerance to pH and metals developed over time. Soil pH is known as a strong driver of microbial community structure (Lauber et al., 2009; Rousk et al., 2010a) along with fungal and bacterial growth rates (Rousk et al., 2009) both when soil pH varies due to pedology (Lauber et al., 2009) and when it is controlled by liming (Rousk et al., 2010a). Thus we hypothesized (H1) that the dominant factor affecting microbial communities and processes would be the soil pH change induced by the ash appli€ rk et al., 2010; Demeyer et al., 2001; Insam et al., 2009; cation (Bjo Reid and Watmough, 2014). We predicted that (a) lime additions should be able to reproduce the effect of ash in microcosms e the soil pH changes induced by both treatments likely dominating their influence on microbial communities e and that (b) microbial pH trait distributions would be induced to shift by ash in field experiments, and that microbial pH trait distributions would respond similarly in ash and lime additions in the microcosm experiments. It is known that Cd can induce tolerance in soil microbial com~ a and munities when applied in high concentrations (Díaz-Ravin Bååth, 1996). However, at the rates and forms of metal associated with ash, earlier studies have been unable to detect a microbial € ma €ki and Fritze, 2004). Using apresponse linked to Cd (Perkio proaches to measure microbial community tolerance to metals in soil (Berg et al., 2012; Lekfeldt et al., 2014), we expected that Cd additions could induce microbial Cd-tolerance in the microcosm experiments, but we also hypothesized (H2) that the Cd associated with ash applications in the field experiments (up to 90 t ash ha1) would not result in any responses in microbial Cd tolerance. In accordance with earlier findings, we hypothesized (H3) that the ash applications leading to higher pH in the field experiment would favor bacterial over fungal growth and result in a faster decomposition of soil organic matter (Rousk et al., 2009), with a similar
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outcome for ash and lime treatments also in the microcosms. Due to the previously reported higher tolerance of fungal growth than bacterial growth to metal exposure (Rajapaksha et al., 2004), we hypothesized (H4) that the Cd addition in the microcosms would inhibit bacterial more than fungal growth, and lead to a reduced rate of decomposition.
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(CaCO3), Cd (CdSO4), or lime þ Cd (Table 1) with three replicates for each treatment (n ¼ 3) maintaining the replicate blocks of the big plot field experiment. Six time-points were sampled; 1 d, 2 d, 5 d, 9 d, 19 d and 36 d; and fungal and bacterial growth rates were estimated as well as respiration rate (method descriptions follow below). At the final time-point, PLFA, along with bacterial pH relationships and Cd tolerance were evaluated as described below.
2. Materials and methods 2.4. Microbial measurements used for field- and microcosm experiment characterizations
2.1. Field experiments The study plots were established in April 2014 in Gedhus Plantation (56 16.3922N; 9 5.672E) located in mid Jutland, Denmark, 51 m above sea level. The climate is temperate with a mean annual temperature of 8.4 C and a mean annual precipitation of 850 mm. A Norway spruce (Picea abies (L.) Karst.) monoculture stand was established 57 years before ash treatments were initiated. The forest is a second-generation forest on land previously occupied by heathland, with a well-developed podzolation formed on a well-drained, sandy glacial till. The following treatments were performed in randomized split block design: control, and wood ash at three different application rate 0, 3, 4.5 and 6 t ha1, with 20 25 m plots including 24e39 trees per plot. Treatments were organized in a randomized block design with three blocks each including all treatments (n ¼ 3). These are henceforth referred to as “big plots”. In a proximal area, a reduced scale experiment including higher application rates was also established. A control treatment (0 t ha1) and wood ash at five different ash application rates 3, 9, 15, 30, 90 t ha1 were established, also in a randomized block design with 2 2 m plots five replicate blocks (n ¼ 5). These are henceforth referred to as “extreme plots”. In both sets of field experiments wood ash was spread manually with a spade evenly over the soil surface, in a single addition. The wood ash used in this experiment is a wood chip fuel mixed ash from a combustion plant in Brande, Denmark, with a particle size smaller than 1 mm, a pH of 13, and a Cd content of 4 mg kg1. 2.2. Soil sampling Soil samples were collected in January 2015 using a soil corer 5 cm in diameter to 3 cm depth, including only O-horizon. Composite samples were formed from at least 4 randomly taken cores. All 12 big plots and 30 extreme plots were sampled. The fresh soil was sieved through 6 mm mesh. Samples were stored at 4 C for one week before characterization of field experiments were conducted and microcosm experiments run. 2.3. Microcosm laboratory incubation experiments of ash, lime, and Cd Laboratory experiments were conducted to isolate the potential liming (pH) and Cd effects. Microcosms were established in plastic 200 mL containers with 30 g of soil from the control treatments of the big plots. Soil samples were exposed to three levels of ash, lime
Table 1 Amounts added of the different treatments with ash, lime, and Cd to the microcosms for the laboratory incubation experiment. Treatments
Low
Medium
High
Ash Lime Cd Lime þ Cd
0.4 t ha1 0.1 t ha1 0.006 M 2 t ha1þ0.006 M
1.5 t ha1 0.5 t ha1 0.03M 2 t ha1þ0.03 M
6 t ha1 2 t ha1 0.1M 2 t ha1þ0.1 M
The bacterial growth was estimated using leucine incorporation in bacteria extracted from soil using the homogenization/centrifugation technique (Bååth et al., 2001) with modifications (Rousk et al., 2009; Supplemental methods). Fungal growth and biomass concentrations were estimated using the technique of acetate incorporation into ergosterol (Newell and Fallon, 1991) with modifications (Rousk et al., 2009; Supplemental methods). The respiration rate and SIR-biomass were estimated in headspace vials measured on a gas chromatograph equipped with a methanizer and a flame ionization detector (Supplemental methods). The soil microbial community structure was analyzed by phospholipid fatty acid (PLFA) (Frostegård et al., 1993) with modifications including using pre-packed solid phase silica columns (Bond Elut 1 cc, 100 mg, Agilent, USA) for the lipid fractionation (Supplemental methods). 2.5. Bacterial community trait distributions: pH relationships and Cd tolerance The pH relationships of bacterial communities were assessed by measuring bacterial growth (as above) adjusting the pH in the bacterial suspension to a range of different pH levels (Bååth, 1996; ndez-Calvin ~ o et al., 2011). Aliquots (1.35 mL) of the bacterial Ferna suspension were transferred to 2 mL microcentrifugation tubes that contained 0.15 mL pH buffer. The pH of the bacterial suspension was adjusted to 11 different verified pHs using buffers, between pH 4 (final concentration 1.1 mM K2HPO4 and 0.5 mM citric acid) and 8 (final concentration 0.25 mM KH2PO4 and 6.4 mM K2HPO4). In addition one sample was treated with only water instead of buffer (in situ pH). The buffer concentrations used did not affect bacterial Leu incorporation rates within the short time-frame studied ndez-Calvin ~ o et al., 2011). The bacterial growth was then (Ferna estimated using Leu incorporation as described above. The Cd tolerance of the bacterial community was estimated similarly by exposing 1.35 ml of the bacterial suspension to 150 ml of different concentrations of CdSO4 and measuring bacterial growth as above. The final CdSO4 concentrations in the bacterial suspension were between 24 mmol and 10 mmol per l bacterial suspension. Distilled water was used as a control. 2.6. Statistical analyses Differences in studied microbial variables in both sets of field experiments were separately analyzed using a randomized block ANOVA with ash treatment as a fixed factor and block as a random factor, and Tukey's HSD pair-wise comparisons were used to correct for multiple comparisons. In the microcosm experiments estimates of microbial growth rates and respiration over the course of the experiment were summed up in cumulative values during the 36 day study which subsequently were subjected to statistical tests. Here, the effect of treatment concentration was evaluated in separate one-way ANOVAs for each of the different treatments (ash, lime, Cd and lime þ Cd, yielding 4 ANOVAs per variable). For factors with multiple levels, Tukey's HSD pair-wise comparisons were used
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Kaleidagraph 4.5.2 (Synergy Software, Reading, PA, USA). This is based on the assumption of two inhibition curves, where deviation from the optimal pH for growth decreases towards both low and high pHs.
to correct for multiple comparisons. All values were logtransformed prior to analysis to stabilize variance. Linear regressions were used to establish relationships between measured microbial variables (fungal and bacterial growth, respiration, biomass estimates, etc.) and soil pH levels. All the analyses were done with the R project 3.1 (R Core Team, 2014). The composition of the microbial PLFAs were analyzed with a principal component analysis (PCA) after standardizing to unit variance using Multi Variate Statistical Package (MVSP) 3.0 (Kovach computing services, Anglesey, Wales, UK). To test for the factors that affected the PLFA composition in the microcosm experiments, the PLFA data was split into 4 subsets defined by treatment factors, as for the other microbial variables (see above). The determined PCs were subjected to ANOVAs to test for treatment differences with Tukey HSD corrections for multiple comparisons, as described for the other microbial variables above. In order to estimate microbial tolerance to Cd, dose-response relationships were determined. To do this, values measured at different levels of Cd additions were first normalized by dividing them by the average of the values measured in the samples that received no Cd and where no inhibition was observed. Normalized values would then fall on a range between 1 (no inhibition of process) and 0 (complete inhibition of process). Values obtained in repeated runs of the experiment were combined to generate a single curve. Log (IC50) values (the logarithm of the metal concentration resulting in 50% inhibition) were estimated using a logistic model (Eq. (1)):
y¼c
i .h 1 þ ebðxaÞ
Bacterial growth .n h io 1 þ exp BlowpH pH AlowpH ¼ Copt .n h io 1 þ exp BhighpH pH AhighpH Copt þ Copt
(2)
where B is a fitted slope parameter indicating the rate of decrease towards lower or higher pHs. A is a fitted parameter defining the pH resulting in 50% inhibition of bacterial growth (IC50) towards lower or higher pH, and Copt is the bacterial growth at the optimal pH. The indices “low-pH” and “high-pH” denote which part of the curve is described (towards lower or higher pH compared with the sample's optimum, respectively). The slope (“B”) and IC50 parameters (“A”) parameters were numerically fitted using the LevenbergMarquardt algorithm in Kaleidagraph 4.5.2. Bacterial growth data were normalized prior to establishing the pH-tolerance curves, yielding Copt values of unity. As such, the highest values in all curves were set to unity, to only resolve the pH-trait distribution differences. Following normalization, the pH at optimal growth was solved from equation (2) with bacterial growth set to 1. Following the determination of the optimal pH for growth for all replicates, one-way ANOVAs were used to compare optimal pH between treatments using Tukey's HSD corrections for multiple comparisons, as described for the other studied microbial variables above. In addition, linear regression analyses were used to link the mean pH optima of each treatment to soil pH.
(1)
where y is the relative normalized process rate, x is the logarithm of the Cd concentration, a is the value of log (IC50), b is a fitted parameter (slope) indicating the inhibition rate and c is the process rate measured in the control without added Cd. Kaleidagraph 4.5.2 (Synergy Software, Reading, PA, USA) was used to fit inhibition curves using the equation above using least-square curve-fits and Pearson's R estimates. To statistically compare tolerance between treatments, 95% confidence intervals were estimated for the log (IC50) values based on the logistic model. We used the criterion of non-overlapping 95% confidence intervals as our significance threshold, which has been shown to be a conservative threshold for statistical comparison of single value estimates along with estimates of variance derived from the fitted curves (Payton et al., 2000; Rath et al., 2016). The pH relationships for bacterial communities were established using a double-logistic function, recently pioneered for this use in aquatic samples (Bååth and Kritzberg, 2015), using
3. Results 3.1. Soil chemical properties In the field experiments, soil pH increased significantly with ash (big plots F3,6 ¼ 7.6; p ¼ 0.02; extreme plots F5,20 ¼ 13; p < 0.001; Table 2, Tables S1, S2). In the “big plots” we only found an increase from pH 4 to 4.8 in the highest ash application used (6 t ha1). In the reduced scale experiments, “extreme plots”, we found that the pH increased by ca. one unit with 9 t ha1, by ca. two units with 15 t ha1, and by ca. three units with the two highest applications of ash. The SOM and water content were unaffected by ash treatments in the big plots (Table 2 and Table S1). In the extreme plots, SOM content was significantly different (F5,20 ¼ 4.3; p ¼ 0.008; Table 2 and Table S2), but water content was unaffected.
Table 2 Soil chemical properties in the different ash applications in the big and the extreme plots. Treatments Big plots 0 t ha1 3 t ha1 4.5 t ha1 6 t ha1 Extreme plots 0 t ha1 3 t ha1 9 t ha1 15 t ha1 30 t ha1 90 t ha1
pH
Soil organic matter (% dry matter)
Water content (% wet weight)
4.05 4.36 4.03 4.77
± ± ± ±
0.1b 0.2ab 0.1b 0.1a
79.8 74.3 74.1 77.0
± ± ± ±
8.2 15.5 9.1 14.3
74.4 72.3 71.5 73.7
± ± ± ±
1.9 5.9 3.4 4.1
4.22 4.68 5.59 6.12 7.39 7.09
± ± ± ± ± ±
0.1d 0.1cd 0.4bc 0.5ab 0.2a 0.5ab
63.6 85.2 79.7 84.6 54.6 62.0
± ± ± ± ± ±
8.9ab 6.2a 4.5ab 4.2a 7.1b 10.3ab
68.3 74.5 72.2 74.1 65.4 67.6
± ± ± ± ± ±
3.0 1.9 2.1 0.8 2.1 4.6
Different letters show significant differences (p < 0.05). Soil pH was measured in water extractions in 1:5 (w:w) ratios. Loss on ignition at 600 C during 24 h was used to estimate SOM. Water content was estimated gravimetrically (105 C during 24 h).
abc
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3.2. Microbial responses to experimental treatments 3.2.1. Microbial growth, respiration and biomass In the field experiments, bacterial growth measured by [3H]Leu incorporation increased significantly with higher application rates of ash in the extreme plots (F5,20 ¼ 4.7; p ¼ 0.005; Table S2), but not in the big plots (Fig. 1a; Table S1). Applications from 3 to 6 t ha1 had minor effects, while applications from 9 to 90 t ha1 increased bacterial growth by ca. 3e4-fold. This resulted in positive linear relationship between bacterial growth and soil pH (R2 ¼ 0.50; p < 0.001). Fungal growth measured by acetate incorporation into ergosterol decreased significantly with higher ash applications in the extreme plots (F5,20 ¼ 7.3; p < 0.001; Table S2), but not in the big plots (Fig. 1b; Table S1). While only marginal effects could be seen up to 15 t ash ha1, the highest application rates reduced fungal growth by half (Fig. 1b). Although the respiration rate tended to increase with higher ash application, the differences were not significant (Fig. 1c; Tables S1 and S2). The treatments administered in the microcosms resulted in pronounced changes in microbial growth and respiration over time (Figs. S1eS3, Fig. 2). With higher ash additions, cumulative bacterial growth increased (F3,6 ¼ 92; p < 0.001; Table S3) while cumulative fungal growth was unaffected (Fig. 2a; Table S3). When adding lime, we reproduced the effect of ash (F3,6 ¼ 64; p < 0.001; Table S3) and could observe nearly identical results (Fig. 2b). In the microcosms with the addition of only Cd the opposite effect was observed; the cumulative bacterial growth decreased (F3,6 ¼ 40; p < 0.001; Table S3) and the cumulative fungal growth increased (F3,6 ¼ 340; p < 0.001; Table S3) (Fig. 2c). The microcosm treated with lime and Cd resulted in no differences in cumulative bacterial growth and a significant decrease (F3,6 ¼ 20; p ¼ 0.002; Table S3) in fungal growth (Fig. 2d). The cumulative respiration rate tended to increase with the ash and lime treatments (Fig. 2a and b) and significantly decreased (F3,6 ¼ 12; p ¼ 0.006; Table S3) in the Cd treatments (Fig. 2c). The microbial biomass estimated with the SIR method along with the fungal biomass measured as ergosterol were unaffected by the ash application in both big plots and extreme plots (Table 3, Tables S1, S2). Total, bacterial and fungal PLFA concentrations were not significantly affected by ash addition in the big plots but increased in the extreme plots (F5,19 ¼ 3.8; p ¼ 0.01, F5,19 ¼ 4.2; p ¼ 0.009, F5,19 ¼ 4.1; p ¼ 0.01, respectively; Table 3, Tables S1, S2). In the microcosm experiment, total and bacterial PLFA concentrations were unaffected by all treatments (Table 4 and Table S3). Total PLFA ranged between 610 and 770 nmol g1 and bacterial PLFA were between 300 and 420 nmol g1. Fungal PLFA concentrations decreased in the treatment with Cd and with lime and high Cd additions (F3,6 ¼ 8.5; p ¼ 0.01, F3,6 ¼ 15; p ¼ 0.004, respectively; Table S3) with a value of 28 ± 1 nmol g1 and 19 ± 2 nmol g1 compare to the control with 41 ± 2 nmol g1. In contrast, the fungal biomarker ergosterol measured in the last sampling day, did not differ between treatments (Table 4 and Table S3). 3.2.2. Microbial PLFA composition In the field experiments, the microbial PLFA composition was affected by ash application in the extreme plots (Fig. 3a and Fig. S4c) with similar tendencies in the big plots (Fig. S4a). The PC1 of the PLFA pattern accounted for 28.4% of the variation in the data, while PC2 accounted for 20.5% (Fig. 3). Changes in the microbial community structure in the extreme plots were significantly different along the PC2 axis (F5,19 ¼ 2.9; p ¼ 0.004; Table S2), but not along PC1. PC1 was correlated to soil pH (R2 ¼ 0.40; p ¼ 0.003). The loading plot of the individual PLFAs (Fig. 3b, Fig. S4b, d) showed that the effect of ash on the PLFA pattern was driven by higher concentrations of the PLFAs 16:1u5, 18:1u7 16:1u7c and cy17:0 at
Fig. 1. Functional responses to wood ash application in the two sets of field experiments (Big plots, and Extreme plots, respectively). Values (mean ± SEM) for a) bacterial growth, b) fungal growth and c) respiration rate in the big plots (circles) and the extreme plots (squares). Different letters show significant differences (p < 0.05).
higher ash applications, while at lower applications PLFAs i16:0, cy19:0, 10Me17:0, 10Me16:0 and i15:0 were in higher relative abundances.
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Fig. 2. Functional responses to wood ash application, lime application, and Cd application in microcosm experiments. Cumulative values (mean ± SEM) for bacterial growth, respiration rate and fungal growth in the microcosms with different levels of a) ash, b) lime (CaCO3), c) Cd (CdSO4) and d) lime and Cd.
Table 3 Microbial biomass (total, bacterial and fungal) evaluated by SIR, ergosterol concentration, and total, bacterial and fungal PLFAs in the different ash applications in the big and extreme plots. Treatments Big plots 0 t ha1 3 t ha1 4.5 t ha1 6 t ha1 Extreme plots 0 t ha1 3 t ha1 9 t ha1 15 t ha1 30 t ha1 90 t ha1 ab
SIR (mg C g1)
Ergosterol (mg)
Total PLFA (nmol g1)
Bacterial PLFA (nmol g1)
Fungal PLFA (nmol g1)
2.0 1.7 1.5 2.2
± ± ± ±
0.2 0.3 0.3 0.3
14.7 11.2 11.9 15.2
± ± ± ±
0.8 1.0 0.9 2.3
808 689 672 765
± ± ± ±
79 201 60 163
390 352 352 342
± ± ± ±
33 90 14 55
56 46 38 60
± ± ± ±
1.4 2.2 2.9 3.1 2.5 2.7
± ± ± ± ± ±
0.2 0.2 0.6 0.3 0.2 0.5
11.9 16.4 17.2 16.4 11.0 15.1
± ± ± ± ± ±
1.7 1.4 0.9 1.1 1.7 3.0
645 896 883 730 530 649
± ± ± ± ± ±
101ab 63a 101a 34ab 80b 113ab
324 427 431 347 267 320
± ± ± ± ± ±
33ab 29a 43a 16ab 37b 54ab
37±9b 70±7a 66 ± 13ab 47±5ab 34±7b 44±9ab
4 13 8 23
Different letters show significant differences (p < 0.05).
Also in the microcosm experiments, the microbial PLFA composition responded to the treatments (Fig. 3c and Fig. S5). The PC1 of the PLFA pattern accounted for 35.4% of the variation in the data, while PC2 accounted for 17.1% (Fig. 3). As in the field experiment the PC1 was correlated to soil pH (R2 ¼ 0.87; p < 0.001). The loading plot of the individual PLFAs (Fig. 3d) showed that the effect of ash and lime on the PLFA pattern was higher concentrations of
the PLFAs 16:1u5, 16:1u7c, 18:1u7 and cy17:0 as in the field experiment, but also others including cy19:0 and i17:0 at the highest ash and lime applications. At lower ash and lime applications and Cd applications PLFAs i16:0, 10Me17:0, 10Me16:0, 10Me18:0, 16:1u7t and br18:0 occurred at higher relative abundances (Fig. 3d). The microcosms with lime and Cd showed very similar PCA scores to the high ash and lime applications (Fig. 3c).
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Table 4 Microbial biomass (total, bacterial and fungal) evaluated by ergosterol concentration, and total, bacterial and fungal PLFAs in the different microcosms at the end of the incubation experiment (day 36). Treatments Ash 0 t ha1 0.4 t ha1 1.5 t ha1 6 t ha1 Lime 0 t ha1 0.1 t ha1 0.5 t ha1 2 t ha1 Cd 0M 0.006 M 0.03M 0.1M Lime þ Cd 0 t ha1 þ 0 M 2 t ha1þ0.006 M 2 t ha1þ0.03 M 2 t ha1þ0.1 M
Ergosterol* (mg g1)
Total PLFA (nmol g1)
10.7 ± 0.8 10.2 ± 0.4 9.9 ± 0.2 9.4 ± 0.4
766 739 746 731
± ± ± ±
42 70 64 56
396 376 392 386
± ± ± ±
25 40 38 26
41 40 37 33
± ± ± ±
2 5 4 4
10.7 ± 0.8 11.1 ± 1.1 9.2 ± 1.0 9.9 ± 0.5
766 736 743 687
± ± ± ±
42 73 101 9
396 385 390 351
± ± ± ±
25 39 57 16
41 37 37 33
± ± ± ±
2 4 4 3
± ± ± ±
0.8 1.4 1.4 1.1
766 682 682 633
± ± ± ±
42 19 46 41
396 349 357 298
± ± ± ±
25 10 26 19
41±2a 37±4ab 28±1b 38±3a
10.7 ± 0.8 10.2 ± 0.9 9.5 ± 1.2 11.0 ± 1.7
766 755 774 609
± ± ± ±
42 13 38 81
396 390 421 329
± ± ± ±
25 4 17 44
41±2a 34±3a 27±1ab 19±2b
10.7 10.8 11.6 10.9
Bacterial PLFA (nmol g1)
Fungal PLFA (nmol g1)
ab Different letters show significant differences (p < 0.05). *Last sampling point value.
Fig. 3. Microbial community PLFA composition responses to different treatments. . The effect of wood ash applications on the PLFA pattern of the soil microbial community in the field experiments (Big and Extreme plots, respectively) showed in a) PCA plot, score values (mean ± SEM) for big plots (circles) and the extreme plots (squares); b) The loadings of the individual PLFAs from the PCA data. PLFAs to the right bottom in the plot indicate those that are more common in high ash applications, while those in the left indicate those that are more common in the lower applications. The effect of the different treatments on the PLFA composition of the soil microbial community in the microcosms showed in c) PCA plot, score values (mean ± SEM); d) The loadings of the individual PLFAs from the PCA data. PLFAs to the right bottom in the plot indicate those that are more common in the highest ash and lime applications (high pH), while those in the left indicate those that are more common in the Cd alone applications (low pH).
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However, as Cd increased the scores separated slightly along PC1 and more pronounced, along PC2. The microcosms with only Cd addition separated from the control in both PC1 and PC2 with higher rates of Cd application, with a stronger separation along PC1 (Fig. 3c). To statistically compare the effects by the different treatment factors, separate PCAs were also run on subsets of the microcosm samples defined by treatment factors (Fig. S5, Table S3). These analyses showed that the PLFA composition was substantially affected by ash, lime, Cd, and the combined ash and Cd treatment when compared with the untreated control samples (P < 0.001 for all four ANOVAs; Table S3). 3.2.3. Bacterial community trait distribution for Cd tolerance In the field experiments, the logistic model described the doseresponse curves well (with a mean R2 ¼ 0.89) and the 95% confidence intervals of estimates EC50 values overlapped, showing that the bacterial tolerance to Cd did not increase with the application rate of ash (Fig. S6). For the microcosm experiments, the logistic model described the dose-response curves well (with a mean R2 ¼ 0.99), enabling comparisons of tolerance. Ash and lime treatments did not increase the bacterial tolerance to Cd in the microcosms (Fig. 4a and b). Rather, we found that the IC50 decreased, as shown by non-overlapping 95% confidence intervals of IC50 values, suggesting a lowered tolerance, when either ash or lime were added (Figs. S7a and b). However, in the microcosms
with Cd additions we were able to induce tolerance to Cd (Fig. 4c and d), leading to 95% confidence intervals clearly distinguishable (Figs. S7c and d). The logistic model described the dose-response curves for these treatments well (with a mean R2 ¼ 0.97). The highest Cd addition had an IC50 value of 30 mM while for control soils the IC50 was 0.1 mM (Fig. S7). 3.2.4. Bacterial community trait distribution for pH relationships In the field experiments, the bacterial community trait distributions for pH varied with the ash application rate in the extreme plots (R2 ¼ 0.82; p ¼ 0.008). The increase in pH induced by the ash application was matched by a similar change in the pH-relationship (Fig. 5a). As such, with higher ash application rates, the curves shifted to be centered around higher pH optima. The double logistic model described the relationships well (with a mean R2 ¼ 0.82). The shape of the curves appeared to remain similar. This was shown by the difference in pH between 10% inhibition at low pH and 10% inhibition at high pH, which remained similar at ca 1.0e1.3 at all ash application rates, and had no correlation with soil pH. The bacterial community trait distributions in the big plots were shifted by a 1-pH unit increase in pH optimum in the 6 t ha1 addition treatments (Fig. 5a). The responses in bacterial community trait distributions were similar in the extreme plots. At higher addition rates, over 9 t ha1, a further increase of 1.5 units higher than the control was observed (Fig. 5a). Finally, the pH optimum was up to 2
Fig. 4. Cd-trait distribution responses to wood ash application, lime application, and Cd application in microcosm experiments. Microbial tolerance to Cd dose-response relationships in the different microcosms a) ash, b) lime (CaCO3), c) Cd (CdSO4) and d) lime and Cd at the end of the incubation experiment (day 36), estimated with logistic curves.
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Fig. 5. pH-trait distribution responses to wood ash application in the two sets of field experiments (Big plots, and Extreme plots, respectively). (a) pH relationships for bacterial communities estimated with double-logistic curves for the big and extreme plots with different ash applications and (b) the relationship between the pH-optima and the soil pH in each big plot (circles), extreme plot (squares) and microcosm (diamonds) estimated with linear regressions.
Fig. 6. pH-trait distribution responses to wood ash application, lime application, and Cd application in the microcosm experiments. pH relationships for bacterial communities estimated with double-logistic curves in the different microcosms a) ash, b) lime (CaCO3), c) Cd (CdSO4) and d) lime and Cd at the end of the incubation experiment (day 36).
units over the control in the 90 t ha1 treatment. The pH-optima estimated correlated with the soil pH in each treatment, indicating a strong positive relationship (Fig. 5b), but with a gradual increase in the offset of the pH optima over the soil pH towards acid pHs.
In the microcosm experiments, the double logistic model described the relationship curves for pH well (with a mean R2 ¼ 0.97). The responses of the community trait distributions for pH were similar to those found in the field (Fig. 6a and b). However, the curves were much wider in the low concentration treatments in
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the microcosms compared to the field treatments (10% inhibition level pH spans had a mean width of 1.1 pH units in the field experiments). In the microcosm experiments, 10% inhibition spans ranged from 2.2 to 1.1 pH units with a mean of 1.7. There was no correlation between the size of the 10% inhibition span and soil pH. The bacterial trait distribution for pH shifted to have higher pHoptimum following the ash and lime additions. With higher addition rates of ash or lime the soil pH increased and therefore the bacterial pH-trait distribution followed (Fig. 6a and b). Addition of 6 t ha1 ash or 2 t ha1 lime increased the pH-optimum compared to the control by almost two units, while the rest of the additions resulted in smaller changes (Fig. 6a and b). The Cd treatments resulted in the opposite shift in the bacterial community trait distributions for pH, shifting the optimal pH to more acid values (Fig. 6c and d). The community pH optima correlated positively with the soil pH in each treatment, similarly to results from the field experiment, and aligning with the same slope (Fig. 5b). 4. Discussion 4.1. Microbial responses assigned to pH changes We hypothesized (H1) that the dominant factor affecting microbial communities and processes would be the soil pH change induced by the ash application. In the field experiments the microbial PLFA composition changed drastically with the addition of 30 t ha1 and 90 t ha1. We observed an increase in the relative abundance of specific PLFAs such as 16:1u7c, 16:1u5 and 18:1u7 with a decrease in the relative abundance of i16:0 and cy19:0. These patterns have previously been identified as biomarkers for pH-induced microbial PLFA composition changes (Bååth and Anderson, 2003; Nilsson et al., 2007; Rousk et al., 2010b; Rousk and Bååth, 2011). In the microcosms with ash and lime we could verify these results. The additions of 2 t ha1 of lime and 6 t ha1 of ash changed the relative abundance of specific PLFAs in a similar way as in the field experiments. In addition, most of the variation in PLFA composition in both field and microcosm experiments were correlated with pH (i.e. PC1 correlated with soil pH; Fig. 3) strengthening the evidence for the causal link between the changes of the community structure and that of pH. The pronounced changes of community structures coincided with shifts in community trait distributions for pH. The pH optima of community trait distribution curves for pH correlated with the environmental soil pH (Fig. 5b), as predicted (H1). These responses were consistent in both sets of field experiments and in the microcosm experiments with ash and lime applications. This relationship was similar to those found before in natural and experimental ranges of pH (Bååth, 1996; B arcenas-Moreno et al., 2016; ndez-Calvin ~ o et al., 2011). Ferna Similar responses in bacterial community pH optima were found in the microcosm with ash and lime compared to the field experiment. Still, we observed interesting qualitative differences between the shorter-term microcosm experiments and the field data. The widths of pH trait distribution curves in the microcosms were wider compared with the narrower curves of the field experiments (c.f. Figs. 5a and 6). The curves in the field experiment showed that a 10% inhibition will occur with a change of ±1.1 pH units, while the wider curves of the microcosm experiments yielded more variable indices with an average of ±1.7 pH units. Bååth and Kritzberg (2015) found that pH optima for bacterial community growth in lakes and streams were closely correlated with in situ pHs, and that the variation in the width of the pH tolerance (at 10% inhibition) varied between 1 and 3 pH units. They proposed that these differences in width could be attributed to the stability of pH in the environment. This would suggest that environments
exposed to larger recent fluctuations in pH, or other disturbance, would have pH tolerance curves with broader optima compared to more stable environments, without recent perturbation. In the field experiments wood ash was added ca. 1 year before analysis. The microcosms were constructed from soil samples following homogenization and treatment with various applications during 36 days. It would be reasonable, therefore, that the field experimental community trait alignment had reached a situation closer to stablestate, while the shorter-term microcosm communities were still in a state of ongoing change undergoing further alignment of its trait variation to the new environmental conditions. 4.2. Microbial responses assigned to Cd The microcosm experiments were designed to disentangle the toxicological effects of ash amendments, and characterize the dynamics of how the induction of tolerance to Cd developed over time. In the field experiment Cd additions corresponded to 12 kg ha1 in the 3 t ha1 ash application, up to 360 kg ha1 in the 90 t ha1 ash application. In the microcosms, up to 24 kg ha1 (0.12 mg g1 soil) Cd was added with 6 t ha1 ash application. When Cd was added in a solution the concentrations ranged from 0.11 up to 1.69 mg g1 soil. With an assumed bulk density of ca 1 g cm3 for the fresh soil in the top 3 cm of the O-horizon this would correspond to ca. 4 and 60 kg Cd ha1. With soluble Cd additions, we found that the community structure evaluated with PLFAs changed drastically, contrasting with the results found from ash-associated Cd in the field experiment. In the microcosms treated with Cd solution we observed an increase in the relative abundance of i16:0, 10Me16:0, a17:0 and 10Me17:0 previously associated with metal toxicity (Åkerblom et al., 2007; Frey et al., 2006; Pennanen et al., 1996). In addition, in the microcosms with Cd solution and lime the relative abundance of cy19:0 increased, which is a marker previously associated with Cd stress (Åkerblom et al., 2007). One of the major concerns of the use of ash is the content of heavy metals such as Cd. However, we did not find any response in microbial Cd tolerance (up to 90 t ha1) in accordance with our hypothesis (H2). In the field experiments and the microcosms with ash, the bacterial community trait distribution showed that Cd contained in wood ash did not induce tolerance to Cd (Fig. S6 and Fig. 4a). On the other hand, soluble Cd additions with or without lime clearly induced tolerance to Cd (Fig. 4c and d). Many heavy metals like Cd are less bioavailable at high pH values (Brady and Weil, 1996). Previous studies reported that Cd in ash is not € ma €ki et al., 2003), even when bioavailable (Fritze et al., 2001; Perkio added at rates of 400 and 1000 mg Cd g1 ash, because it is probably in forms with low water solubility, such as CdO or CdSiO3 (Hansen et al., 2001). Studies in contaminated soils have shown that is possible to induced tolerance to Cd (Almås et al., 2004; Witter et al., 2000), as was also observed in microcosms treated with Cd solution (Fig. 4c and d), emphasizing that the bioavailable metal concentration in soil is decisive. As previously reported, we observed that wood ash could even counteract the toxic effect of Cd due to its capacity to increase soil pH and decrease Cd bioavailability. These findings imply that even though wood ash application certainly affects the microbial community, the changes are not related to the Cd content. 4.3. Ash-induced changes in microbial functions We hypothesized (H3) that ash applications leading to higher pH in the field experiment would favor bacterial over fungal growth. Ash applications increased soil pH substantially, matching € rk et al., 2010; Demeyer et al., results of many earlier studies (Bjo
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€ m€ 2001; Insam et al., 2009; Perkio aki and Fritze, 2002; Reid and Watmough, 2014). In response to these changes, we observed a broad shift toward higher bacterial and reduced fungal dominance in the field experiment assessed by microbial growth rates. As expected, we found a similar outcome in the microcosms treated with ash and lime as in the field experiment. These results echo a series of recent reports where acid environments favor fungal growth while neutral or slightly alkaline conditions favor bacterial growth €gberg et al., 2007; Rousk et al., 2010a, 2009). The mechanisms (Ho by which the pH of the soil environment regulates the relative importance of the decomposer groups remain unresolved (but see discussion about candidate mechanisms in Rousk et al., 2010b). However, the extreme control by which the pH-trait distributions of bacterial communities were matched to the soil pH of their environment (Fig. 5b) strongly suggest that soil pH exerted a direct effect on the bacterial community. The shifts in fungal-to-bacterial growth induced by the application of ash coincided with an € m€ increased respiration rate, matching earlier reports (e.g. Perkio aki and Fritze, 2002). However, the relatively limited responses in respiration compared to shifts in fungal-to-bacterial growth seen in the more controlled microcosms experiments (Fig. 2), suggest a high degree of functional redundancy within the microbial community. As such, the effect of ash induced increases in C availability driven by a higher solubility of SOC with higher pH (Curtin et al., 2016) are more likely to have characterized the higher respiration observed in the field experiment than the induced changes in the microbial decomposer groups. When assessing functional consequences of wood ash additions, our approach to use microbial trait distributions to link microbial responses to Cd could link none of the observed variation in the field treatments to Cd. This was consistent with results from microcosms treated with ash where we did not find functional effects due to Cd added with ash. However, as we hypothesized (H4) soluble Cd addition at relatively high concentrations in the microcosms inhibited bacterial more than fungal growth. With soluble Cd additions, we observed a shift toward higher fungal and reduced bacterial dominance. It is known that bacteria and fungi tend to compete for resources (Rousk et al., 2008). Therefore, these results suggest that Cd reduced bacterial growth which allowed fungi to proliferate; however, overall microbial activity, measured as respiration, remained unaffected. Presumably this was due to the fungal growth increases compensating for bacterial growth reductions terms of overall decomposer function. 5. Conclusions Using the comparative analysis of two sets of field experiments with wood ash application to parallel microcosm experiments including wood ash along with its two candidate component factors pH (lime) and Cd we could show that the microbial structural and ecosystem functional responses induced by wood ash were associated with its pH increasing effect. Moreover, by also investigating how ash affected the trait distributions of the microbial communities we could unambiguously link the detected microbial responses to being directly induced by soil pH changes rather than caused by indirect effects of the ash. Finally, using the same approaches, we could determine that the levels of Cd associated with the wood ash applied (360 kg Cd ha1 carried with the 90 t ha1 wood ash application) has no effect on the soil microbial community or their ability to provide decomposer functioning as proxied by growth rates and respiration. Acknowledgments This work was supported by a STSM grant from the EU Cost
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Further reading Evans, S.E., Wallenstein, M.D., 2012. Soil microbial community response to drying and rewetting stress: does historical precipitation regime matter? Biogeochemistry 109, 101e116. http://dx.doi.org/10.1007/s10533-011-9638-3. Schimel, J., Balser, T.C., Wallenstein, M., 2007. Microbial stress-response physiology and its implications for ecosystem function. Ecology 88, 1386e1394. http:// dx.doi.org/10.1890/06-0219.
