BIOTECHNOLOGICAL PRODUCTS AND PROCESS ENGINEERING

Correlations between molecular and operational parametersin continuous lab-scale anaerobic reactors

Marta Carballa & Marianne Smits &

Claudia Etchebehere & Nico Boon & Willy Verstraete

Received: 19 May 2010 /Revised: 2 August 2010 /Accepted: 19 August 2010# Springer-Verlag 2010

Abstract In this study, the microbial community character-istics in continuous lab-scale anaerobic reactors werecorrelated to reactor functionality using the microbialresource management (MRM) approach. Two moleculartechniques, denaturing gradient gel electrophoresis (DGGE)and terminal-restriction fragment length polymorphism (T-RFLP), were applied to analyze the bacterial and archaealcommunities, and the results obtained have been compared.Clustering analyses showed a similar discrimination ofsamples with DGGE and T-RFLP data, with a clearseparation between the meso- and thermophilic communi-ties. Both techniques indicate that bacterial and mesophiliccommunities were richer and more even than archaeal andthermophilic communities, respectively. Remarkably, thecommunity composition was highly dynamic for bothBacteria and Archaea, with a rate of change between 30%

and 75% per 18 days, also in stable performing periods. Ahypothesis to explain the latter in the context of theconverging metabolism in anaerobic processes is proposed.Finally, a more even and diverse bacterial community wasfound to be statistically representative for a well-functioning reactor as evidenced by a low Ripley indexand high biogas production.

Keywords Anaerobic digestion . Communityorganization . Diversity . Dynamics .Microbial communitystructure . Richness

Introduction

The waste treatment concept will change drastically in thecoming years from a pollution control point of view to arecovery of valuable resources perspective. In this context,anaerobic digestion (AD) can make an important contribu-tion, since it offers the opportunity to stabilize organicwastes and to recover energy simultaneously. However,despite the well-known advantages of this process, AD isrestrained by some obstacles like the presence of poorlybiodegradable substrates (Liu et al. 2007; Hecht and Griehl2009) and the occurrence of process instabilities (Verstraeteet al. 2005). Therefore, a considerable monitoring andoptimization is still required.

The common process parameters used to characterize theperformance of anaerobic reactors are the volatile fatty acid(VFA) concentrations, the biogas production, and theRipley index, defined as the ratio between the free fattyacids and the buffer capacity of the reactor (Ripley et al.1986; Boe et al. 2008). Propionic acid (HPr) tends toaccumulate when a reactor is overloaded, and the maximumacceptable HPr concentrations as total acid vary from 0.8 to

Marta Carballa and Marianne Smits have equally contributed to thispaper.

M. Carballa (*)Department of Chemical Engineering, School of Engineering,University of Santiago de Compostela,Rúa Lope Gómez de Marzoa s/n, E-15782,Santiago de Compostela, Spaine-mail: marta.carballa@usc.es

M. Carballa :M. Smits :N. Boon :W. VerstraeteLaboratory of Microbial Ecology and Technology (LabMET),Faculty of Bioscience Engineering, University Ghent,Coupure Links 653,9000 Ghent, Belgium

C. EtchebehereMicrobiology Department,School of Science and School of Chemistry,University of the Republic,General Flores 2124,Montevideo, Uruguay

Appl Microbiol BiotechnolDOI 10.1007/s00253-010-2858-y

2.2 g/L, depending on the type of waste and reactor(Barredo and Evison 1991; Mosche and Jordening 1998).Biogas production and biogas composition allow a rapidevaluation of anaerobic reactors performance (Dearman etal. 2006; Boe et al. 2008; Rincón et al. 2008), and a Ripleyindex lower than 0.3 is an indication of a well-functioningreactor (Ripley et al. 1986). These parameters suffice toevaluate the ongoing performance of anaerobic reactors, buttheir power to predict the stability and future performanceof the reactors is limited.

Anaerobic digesters are characterized by complexmicrobial consortia (Riviere et al. 2009), and culture-independent molecular techniques, such as denaturinggradient gel electrophoresis (DGGE) and terminal restric-tion fragment length polymorphism (T-RFLP), have dem-onstrated that the microbial community characteristics(“who is there doing what with whom”) can play animportant role for a good reactor performance (McHugh etal. 2004). Many studies have postulated that biomonitoringof the microbial community characteristics could lead to anearly detection of operational problems, making preventiveaction possible (McHugh et al. 2004; Lee et al. 2008; Malinand Illmer 2008; Rincón et al. 2008; Talbot et al. 2008). Ingeneral, a higher diversity of the bacterial community,mainly at mesophilic temperature (Leven et al. 2007), isobserved compared to the archaeal community (Rincón etal. 2008). Microbial diversity was shown to be notimportant in the development of a functionally successfulanaerobic microbial community (Dearman et al. 2006). Incontrast herewith, a high rate of change of the microbialcommunity composition (Miura et al. 2007) and an initialcommunity evenness (Wittebolle et al. 2009) are importantfactors for stable operation of mixed microbial cultures.Thus far, no direct relationships between such microbialcommunity characteristics and process parameters havebeen established (Bouallagui et al. 2005).

The main objective of this work was the application ofthe microbial resource management (MRM) approach tocorrelate microbial community characteristics with theprocess performance in continuous lab-scale anaerobicdigesters. The MRM concept describes the microbialcommunity by three parameters (Verstraete et al. 2007;Marzorati et al. 2008): (a) the richness (Rr), which reflectsthe number of dominant species; (b) the dynamics ofchange (Dy), which reflects the specific rate of speciescoming to significance; and (c) the community organization(Co), which relates the task distribution of the microbialcommunity. To estimate these parameters, two differentmolecular techniques, DGGE and T-RFLP, have been used,and the results obtained have been compared. To ourknowledge, this is the first study applying not only MRMto AD processes but also the MRM concept with T-RFLPdata.

Materials and methods

Lab-scale anaerobic reactors

In the first experiment, four continuous tank reactors with avolume of 18 L were used: two reactors (M) were operatedin mesophilic range (34±2°C) and the other two (T) inthermophilic conditions (53±2°C). At each temperature,both reactors were operated similarly till the organicloading rate (OLR) reached 2 g chemical oxygen demand(COD) per liter per day. Afterwards, this OLR was keptconstant in one reactor (M1 and T1), while in the otherreactor (M2 and T2), the OLR was gradually increased todetermine the maximum applicable load.

At the onstart, 16 L of anaerobic sludge was used asinoculum with an initial in-reactor biomass concentration of35 g volatile suspended solids (VSS) per liter. Thethermophilic inoculum was taken from a thermophilicdigester treating pig manure and organic co-substrates(Bio Electric, Beernem, Belgium) and the mesophilic onefrom a mesophilic digester treating sewage sludge(Ossemeersen, Ghent, Belgium). Mixed kitchen waste(Trans Vanheede, Wervik, Belgium) diluted with sewageto obtain the desired OLR was used as substrate. The maincharacteristics of the mixed kitchen waste were: 215 gCODtotal/kilogram, 75 g CODsoluble/kilogram, 166 g totalsolids (TS)/kilogram, 155 g volatile solids (VS)/kilogramand pH 4. The reactors were stirred before effluentdischarge and after feeding addition and operated at ahydraulic retention time (HRT) of 18 days. During the lastdays of each experiment, when the reactors suffered fromacidification, the pH was maintained at around 7.5 by theaddition of NaOH (10 N). The theoretical gas productionswere based on the COD measurements performed on theconcentrated feed on the one hand and the volumetric gasproduction on the other hand.

In the second experiment, only the mesophilic reactors(M1 and M2) were operated, and their performance wasoptimized by installing mechanical mixers (IKA Labor-technik, Staufen, Germany), which worked 5 min/h, and byenhancing the biomass retention in the reactors (the mixedliquor was allowed to settle for at least 30 min beforeeffluent discharge).

The pH and the biogas production were monitored daily,and influent and effluent samples were taken thrice a weekfor solids, COD, and VFA analyses. Further on, the percentbiogas production refers to the amount of biogas producedrelative to the theoretical amount, which would beproduced based on the COD supplied, assuming a constantmethane content of 70% (0.5 L biogas/gram CODremoved). From days 54 and 80 for the thermophilic andmesophilic reactors, respectively, a sample of the anaerobicbiomass was taken every HRT to examine the microbial

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community. These samples were stored at −20°C untilDNA extraction was performed.

Analytical techniques

TS, VS, and COD were measured according to standardmethods (Greenberg et al. 1992). VFAs (acetic, propionic,i-butyric, and i-valeric) were extracted with diethyl etheras described by Holdeman et al. (1977) and measured in acapillary gas chromatograph (GC 8000 Carlo ErbaFractovap 4160, CE Instruments, Wigan, UK) equippedwith a flame-ionization detector and a Delsi-Nermagintegrator (Thermo Separation Products, Wilrijk, Bel-gium). The biogas production was followed by liquiddisplacement. The pH was measured with a C532 pHmeter (Consort, Turnhout, Belgium), and the partial andtotal alkalinities were determined according to Ripley etal. (1986). The Ripley index was calculated as the ratiobetween the intermediate alkalinity (total alkalinity−partial alkalinity) and the total alkalinity.

Molecular techniques

Conditions and references for deoxyribonucleic acid (DNA)extraction, polymerase chain reaction (PCR), and DGGEwere previously described in detail by Boon et al. (2003)and Rooney-Varga et al. (2007) and were based on theprimers 338F-GC and 518R for Bacteria and the primers915F and 1352aR-GC for Archaea. DGGE analyses wereperformed in a Bio-Rad DGenesystem (BioRad, Hercules,CA, USA) according to Muyzer et al. (1993).

For T-RFLP, the 16S rRNA genes were amplified byPCR using universal primers (27F and 907R for Bacteriaand 21F and 915R for Archaea). The forward primerswere fluorescently labeled with the dye 5-(6-carboxy-fluorescein). The amplification reactions were carried outas described by Lueders et al. (2004). The PCR productswere purified using PCR purification kit (Qiagen, Venlo,The Netherlands) and quantified using the ND-1000spectrophotometer (Nanodrop, Isogen Life Sciences,Sint-Pieters-Leeuw, Belgium). One hundred nanogramsof the purified PCR product was digested with the MspIand the TaqI restriction enzymes (Fermentas, St. Leon-Rot, Germany) for Bacteria and Archaea analyses,respectively (Lueders et al. 2004). DNA fragments wereprecipitated with 95% ethanol and washed with 70%ethanol. After drying in a Savant SpeedVac system(ThermoFischer Scientific, Tournai, Belgium), the DNAfragments were re-suspended with 8 μL formamide and0.3 μL of internal standard (GeneScan-500 Liz SizeStandard, Applied Biosystems, Halle, Belgium) andseparated on an 3130 Genetic Analyzer (Applied Bio-systems, Halle, Belgium).

Statistical analysis

Normalization and analysis of the obtained DGGE patternswere done with BioNumerics software v.2.0 (AppliedMaths, Sint-Martens-Latem, Belgium). The chromatogramsobtained with T-RFLP were analyzed using Peak ScannerSoftware v.1.0 (Applied Biosystems, Halle, Belgium). Inboth cases, bands or peaks with more than 1% intensity orabundance, respectively, were considered.

For the interpretation of the results, the three levels ofanalysis analogous to those described by Marzorati et al.(2008) were calculated. Rr was determined as the numberof bands or peaks in each DGGE pattern and electrophe-rogram, respectively. To calculate the Dy, profile similari-ties were obtained by determination of the Jaccardcoefficient, and cluster analyses were constructed usingUPGMA algorithm (Hammer et al. 2001) with PASTsoftware. The Co was calculated as the percentage of theGini coefficient, a value that describes a specific degree ofevenness of a microbial community by measuring thenormalized area between a given Pareto–Lorenz curve andthe perfect evenness line (Wittebolle et al. 2009).

The open-source statistical environment R (http://www.r-project.org/) was used to determine the correlation coef-ficients among the molecular parameters and between themolecular and the operational coefficients as well as toconduct significance tests in order to check if therelationship/correlation was significant. Statistical signifi-cance was established at the p<0.05 level.

Results

Anaerobic reactors operation

The results of the operation of the thermophilic andmesophilic reactors are shown in Figs. 1 and 2, respective-ly. Significant differences in performance (biogas produc-tion and COD removal) were observed neither between thetwo mesophilic reactors (M1 and M2) nor between thethermophilic ones (T1 and T2). A good performance of thethermophilic reactors was obtained until days 55–60(Fig. 1) when the OLR was kept below or around 2 gCOD per liter per day. However, it can be noted that HPrconcentrations already showed an increasing trend fromday 50 on. During this period, the biogas production was onaverage 75% of the maximum theoretical production basedon the OLR applied, and the CODtotal and HPr concen-trations remained below 30 g COD/liter and 0.5 g HPr-COD/liter, respectively. However, when the applied OLRsurpassed 2 g COD per liter per day, the CODtotal and HPrlevels in the reactors increased up to 45–70 g COD/liter and3.0–3.5 g HPr-COD/liter, respectively, with the concomi-

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tant decrease of the biogas production to negligible values.The reactors failed, and the experiment was terminated onday 84.

The mesophilic reactors showed a similar behavior as thethermophilic ones (Fig. 2). When the OLR was kept below2 g COD per liter per day, the biogas production was 85%of the maximum theoretical value, approximately, and theCODtotal and HPr concentrations remained below 25 gCOD/liter and 2.0 g HPr-COD/liter, respectively. Theincrease in the OLR to 2.2 g COD per liter per day(day 37) caused the failure of the reactors, since theCODtotal and HPr accumulated in the reactors (up to 50 gCOD/liter and 6–7 g HPr-COD/liter, respectively) and thebiogas production decreased to 0.2 L/Lday. The reactorsfailed and were stopped on day 72.

The transition of the mesophilic reactors between experi-ments 1 and 2 (day 80) was done by diluting 9 L of mixedliquor from experiment 1 with 5 L of mesophilic inoculumand 4 L of tap water. Since the HPr concentrations werestill high after dilution (around 3.5 g HPr-COD/liter, datanot shown), the digesters were operated without feedinguntil the residual HPr levels decreased, and biogas was

produced again. On day 108 (Fig. 2), the mixing and thefeeding started.

The results of experiment 2 confirmed those of exper-iment 1. Until day 151, in which the OLR was stepwiseincreased to 2 g COD per liter per day in M1 and to 2.4 gCOD per liter per day in M2, the performance of thereactors was quite good, with constant biogas productionrates (0.8 L/Lday) and CODtotal (<30 g COD/liter) and HPrconcentrations (around 3 g HPr-COD/liter). However, theincrease in the OLR to 2.5–2.7 resulted in the irreversibledecrease of the biogas production to negligible values.Concomitantly, the CODtotal concentrations rose up to 40–50 g COD/L. The reactors were stopped on day 162.

Microbial community results

Clustering analyses

Figure 3 shows the cluster analysis for Bacteria in the lab-scale anaerobic digesters based on the DGGE and the T-RFLP profiles. A similar discrimination of the samples wasobtained with DGGE and T-RFLP data, with a clear

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segregation between the mesophilic and the thermophiliccommunities. Among the mesophilic samples, those fromthe first experiment stirred manually twice a day (days 55and 72) could be clearly differentiated from those from thesecond experiment (days 128, 144, and 158) with mechan-ical stirring and enhanced biomass retention. The samplesfrom day 108 were situated in between since theycorresponded with the end and beginning of experiments1 and 2, respectively. Similar results were obtained with thearchaeal profiles (data not shown).

Richness

For Bacteria (Fig. 4a), a similar or even higher number ofbands were achieved with DGGE analyses (22±8) thanwith T-RFLP (17±5), while the opposite occurred forArchaea (Fig. 4b), 4±3 and 11±4, respectively. However,both techniques indicate that the bacterial community wasricher than the archaeal one independently of temperature,and that mesophilic communities were richer than thethermophilic ones (except for Archaea with T-RFLP). Noclear conclusions can be made about the evolution of therichness of bacterial and archaeal communities over time.

Microbial community organization

The microbial community organization was assessed bycalculating the community organization coefficient (Co,Fig. 4c, d). The higher is the Co, the more uneven is thecommunity. In general, higher Co values were obtainedwith DGGE (between 42 and 76 for Bacteria and between25 and 89 for Archaea) than with T-RFLP data (between 28and 51 for Bacteria and between 42 and 62 for Archaea).However, both techniques indicated that the archaealorganization was more uneven (Co values of 69±22 withDGGE and 54±7 with T-RFLP) than the bacterial organi-zation (Co values of 60±9 with DGGE and 41±6 with T-RFLP) and that both bacterial and archaeal communitieswere more even in mesophilic reactors (average Co valuesof 60 and 45, respectively) than in the thermophilic ones(average Co values of 70 and 50, respectively).

Dynamics of change

The values of the rate of change of the bacterial andarchaeal community were calculated for each reactor perHRT (18 days). The average rates of change were in the

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same range when comparing the DGGE (47–63% forBacteria and 43–53% for Archaea) and T-RFLP (48–61%for Bacteria and 35–74% for Archaea) data (Table 1).Overall, there were no significant differences between thetwo reactors operated at the same temperature, but themesophilic communities were somewhat less dynamic.

At each temperature, the rates of change of Bacteria andArchaea were in the same range (with a trend to be lower

for the archaeal community), but the evolution of thedynamics over time was more variable (higher standarddeviations) in mesophilic reactors and for the archaealcommunity.

Correlation among process parameters and microbialcommunity

Figure 5 shows the correlations among the three molecularparameters (Co, Rr, and Dy) for Bacteria and Archaea. Noclear correlations among the parameters could be obtainedfrom T-RFLP profiles (data not shown). However, theDGGE profiles show some general trends for both Bacteriaand Archaea. The more even the community was (lowerCo), the higher the richness (Fig. 5a, b) was. The higher thedynamics, the lower the richness (Fig. 5c, d) and the moreuneven the community (Fig. 5e, f). These correlations werestatistically significant at 95% confidence interval forBacteria (p<0.05), while for Archaea, only the correlationbetween Co and R was significant.

Figure 6 shows the correlations between the molecular(Co, Rr, and Dy) and the operational (Ripley index andbiogas production) parameters for Bacteria, while nocorrelations could be obtained for Archaea (data notshown). Some statistically significant trends could beobserved for Bacteria (p ranging from 0.0013 and0.0052). The more uneven the microbial community was,the poorer the reactor performance was, i.e., the higher theRipley index (Fig. 6a), and the lower the biogas production(Fig. 6b). On the contrary, the higher the richness, the betterthe performance, i.e., the lower the Ripley index (Fig. 6c)and the higher the biogas production (Fig. 6d). Althoughsome trends were observed between Dy and reactorperformance (Fig. 6e, f), the correlations were weak (R2<0.30) and not statistically significant (p>0.05).

Discussion

An ecological interpretation of the behavior of microbialcommunities under particular conditions in function of timecan provide useful information about process performanceand efficiency and can facilitate the comparison betweendifferent reactors situations. This ecological interpretationshould be independent of the fingerprinting method used,and in this study, the later statement was evaluated usingtwo molecular techniques (DGGE and T-RFLP) to charac-terize the bacterial and archaeal communities in continuouslab-scale anaerobic reactors.

Cluster analyses were independent of the moleculartechnique used. Recently, Smalla et al. (2007) showed that,although the amplified PCR fragments comprised differentvariable regions and lengths, DGGE and T-RFLP analyses

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led to similar findings in terms of clustering of thefingerprints and replicate variability. A clear segregationwas obtained between mesophilic and thermophilic commu-nities, but the microbial communities were not distinct enoughto distinguish between the two reactors operated at the sametemperature, probably due to the similar operational con-ditions and the same type of substrate. This finding is also inaccordance with previous studies, which stressed the predom-

inant influence of temperature (Karakashev et al. 2005; Levenet al. 2007) and type of substrate or operational conditions(Leclerc et al. 2004; Lee et al. 2009) on the composition ofthe microbial community.

R values are an estimation of “who is there” andinform on the carrying capacity of an environment, i.e.,whether an environment is very habitable or adverse/exclusive (Verstraete et al. 2007). Some differences were

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observed between DGGE and T-RFLP when evaluatingthe richness of the bacterial and archaeal communities. Itis well known that each technique has its own sensitivityand accuracy. In the case of Bacteria, the region of the gentargeted by DGGE (positions 338–518, Escherichia coli16S rRNA gene numbering) is included in the regiontargeted by T-RFLP (positions 27–907) and known as themost variable region of the gen for Bacteria (Li et al.2009). In contrast, the targeted regions for Archaea arequite different. Since a higher diversity of Archaea wasobserved with T-RFLP, it can be concluded that the regiontargeted by T-RFLP (positions 21–915) is more diverse/variable than the one targeted by DGGE (positions 915–1352), as previously reported (Yu et al. 2008). However,both molecular techniques showed that bacterial commu-nities are richer than archaeal communities, as previouslyjudged (Fernández et al. 1999; Tang et al. 2004; Dearmanet al. 2006; Hori et al. 2006; Malin and Illmer 2008). Thisappears logical since the first group relates to a multitudeof substrates, while the second group is confined to thenarrow niches of methane production from acetate andhydrogen. It was also observed from both techniques thatmesophilic communities are richer than thermophiliccommunities, a finding in agreement with previousreported data (Karakashev et al. 2005; Leven et al. 2007).

Co values are an estimation of “who is doing what withwhom” and inform on the functional organization of themicrobial community (Marzorati et al. 2008). Low (20–25)and high (>80) Co values indicate a highly even orspecialized community, respectively, and consequently, along lag phase resp. longer recovery times could be neededto counteract a sudden stress. Average Co values (45–60)represent balanced communities, which can potentially dealwith changing environmental conditions. Both techniquesindicate that the archaeal community is more uneven than thebacterial community, regardless the temperature of operation.In addition, mesophilic communities are more even thanthermophilic communities. The more even bacterial commu-nities (lower Co values) in mesophilic reactors are inaccordance with previous studies (LaPara et al. 2002).

However, the higher evenness of archaeal communities inmesophilic temperature has not been well-documented yet.

Dy values are an estimation of the rate of change in amicrobial community and inform on the number of speciesthat on average come to significant dominance at a givenhabitat, during a defined time interval (Marzorati et al.2008). Stable communities (low Dy values) represent smallreservoirs that limit the influx of new propagules, and theymight be useful in terms of technical performance butdangerous in terms of overall adaptability (Verstraete et al.2007). In this study, the community composition of bothBacteria and Archaea was highly dynamic (30–75% per18 days) during the whole experiment. The high dynamicsduring well-functioning periods can be explained by thefunctional redundancy among diverse phylogenetic groupsallowing oscillations of their populations with no effects onthe reactor function (Zumstein et al. 2000; Briones andRaskin 2003). Transitions between deteriorative and stablereactor conditions and considerable process events (e.g.,increase in OLR) are commonly related to significant shiftsin microbial populations (Hori et al. 2006; Lee et al. 2008).Overall, it was observed that archaeal and mesophiliccommunities were less dynamic than bacterial and thermo-philic communities, respectively. This finding is in agree-ment with previous studies showing that bacterialpopulations display a highly variable pattern of temporalvariation, even with stable reactor performance, whilearchaeal populations displayed less temporal variability(Fernández et al. 1999, Zumstein et al. 2000).

Regardless of the nature of community dynamics in aconstant environment, a stable system must possess theability to maintain process stability in response to dis-turbances. This does not imply that the microbial commu-nity be stably maintained. In this study, higher dynamics(>30%) were observed during well- and poor-functioningperiods, a finding in agreement with previous reported data(Fernández et al. 1999, 2000). However, in reactors withrelatively long sludge residence times (>50 days) and veryslow growing anaerobic microbial communities (celldoubling times of a week or more), it is surprising to finddifferent subpopulations emerging to dominance andbecoming subsequently less prominent again in a matterof weeks. However, it is well known that AD does notentail a single set of food chains but represents a multitudeof parallel converging bioconversions. Indeed, a wide arrayof metabolites (higher and lower fatty acids, alcohols,amines, etc.) has, each by a specific pathway, to beconverted to the two ultimate principal end products, i.e.,CH4 and CO2. In such a situation of a “funneling” ofbioconversions, it is conceivable that constantly all routesare experiencing hinder, e.g., because critical metabolites(such as hydrogen or HPr) of the one conversion influencethe kinetics of the other conversion. In this context, it stems

Table 1 Average dynamics between bacterial and archaeal profilesobtained with DGGE and T-RFLP using an 18-day window

Reactor Bacteria Archaea

DGGE T-RFLP DGGE T-RFLP

T1 61.6±1.6 54.1±7.4 nd 74.2±5.8

T2 62.5±2.5 61.2±5.6 nd 46.7±13.4

M1 47.4±18.1 48.4±10.1 42.7±16.8 34.9±13.1

M2 47.9±13.4 48.2±16.6 53.4±13.5 40.4±13.5

nd no data due to problems with the analyses

Appl Microbiol Biotechnol

to reason that the multi-routing toward the common funnelresults in a non-occurrence of a dominant and stable teamof organisms that governs the overall process. Hence,according to the latter hypothesis, the high Dy values maybe a most important characteristic of a well-functioning ADsystem in which all of the processes and their responsiblecross-feeding microbial associations properly come toaction. Obviously, further work is required to corroborateto what extent high dynamics in microbial patterns arecongruent with “equal opportunity” microbial populationdynamics in converging metabolic systems.

One of the novel aspects of this study was the attempt tocorrelate the overall behavior of the anaerobic microbialcommunity to process efficiency by means of the generictools/parameters proposed by MRM. Two sets of interestingand statistically significant trends could be observed withDGGE-based data of Bacteria and the Ripley index(Fig. 6a, c). Indeed, for this important index of processstability, reactors with low Ripley values (and hence goodperformance) typically had also low Co resp. high Rrvalues (R2 values above 0.75, p<0.006) indicating that aneven and rich association of Bacteria corresponded with

y = -0,8342x + 77,867R² = 0,5143

0

10

20

30

40

50

60

70

80

0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40

Co

Richness

y = -8,3595x + 100,16R² = 0,9832

0

20

40

60

80

100

0 2 4 6 8 10

0 2 4 6 8 10

Co

Richness

y = -1,1187x + 77,981R² = 0,4059

0

10

20

30

40

50

60

70

Dyn

amic

s (

%)

Richness

y = -2,8544x + 59,941R² = 0,0832

0

10

20

30

40

50

60

70

80

Dyn

amic

s (

%)

Richness

y = 1,0491x -9,3789R² = 0,3938

0

10

20

30

40

50

60

70

30 40 50 60 70 80

Dyn

amic

s (

%)

Co

y = 0,3367x + 25,154R² = 0,0833

0

10

20

30

40

50

60

70

80

30 40 50 60 70 80 90

Dyn

amic

s (

%)

Co

a

c

e

b

d

f

p = 0.0008 p = 6.3·10-13

p = 0.0350 p = 0.5305

p = 0.0393 p = 0.5294

Fig. 5 Correlations between DGGE-based molecular parameters for Bacteria (a, c, e) and Archaea (b, d, f)

Appl Microbiol Biotechnol

good conversion of fatty acids to methane. Wittebolle et al.(2009) also found that the initial community evenness is akey factor in preserving the functional stability of anecosystem against environmental stress. Although weakerlinear trends (R2 between 0.3 and 0.6) were observablebetween Co and Rr and the biogas production (Fig. 6b, d),these correlations were statistically significant (p<0.003),and therefore, these positive trends detected betweenrichness and community evenness and biogas yield certain-ly warrants further exploration.

To conclude, despite slight differences in the “absolute”values, the same ecological interpretation was derived fromDGGE and T-RFLP: (a) temperature has a strong effect on themicrobial composition of both Archaea and Bacteria; (b)bacterial and mesophilic communities are richer than thearchaeal and thermophilic ones, respectively; (c) archaeal andthermophilic populations are more uneven than bacterial andmesophilic ones, respectively; (d) The community composi-tion of both Bacteria and Archaea was highly dynamic (rateof change between 30% and 75% per 18 days) in well- and

y = 78,479x + 23,273R² = 0,8273

30

40

50

60

70

0,30 0,35 0,40 0,45 0,50 0,55

0,30 0,35 0,40 0,45 0,50 0,55

Co

Ripley index

y = -0,2229x + 70,741R² = 0,5619

20

30

40

50

60

70

80

0 20 40 60 80 100

0 20 40 60 80 100

0 20 40 60 80 100

Co

Biogas production

y = -58,373x + 50,848R² = 0,753

0

10

20

30

40

Ric

hn

ess

Ripley index

y = 0,1825x + 14,001R² = 0,4773

0

5

10

15

20

25

30

35

40

Ric

hn

ess

Biogas production

y = 202,61x -29,992R² = 0,2869

20

30

40

50

60

70

0,25 0,30 0,35 0,40 0,45

Dyn

amic

s (

%)

Ripley index

y = -0,2473x + 63,774R² = 0,297

20

30

40

50

60

70

Dyn

amic

s (

%)

Biogas production

p = 0.0017p = 0.0013

p = 0.0052 p = 0.0030

p = 0.0669p = 0.2734

a

c

e

b

d

f

Fig. 6 Correlations between DGGE-based molecular and operational parameters for Bacteria. Biogas production is shown as the percentage ofthe theoretical value based on the COD supplied

Appl Microbiol Biotechnol

also poor-functioning periods; and (5) a more even (low Covalues) and diverse (high richness) bacterial community wasindicative of a well-functioning reactor.

Acknowledgments This research was funded by the project SewagePlus 180B12A7 (MIP project, Milieu- & Energietechnologie–Innova-tieplatform, Berchem, Belgium), the Geconcerteerde Onderzoeksactie(GOA) of Ghent University contract grant (BOF09/GOA/005), thefellowship of CSIC-UdelaR (Uruguay) for Dr. Claudia Etchebehere,and by a postdoctoral contract for Dr. Marta Carballa from the Xuntade Galicia (Isidro Parga Pondal program, IPP-08-37). The authorsacknowledge Dr. Jingxing Ma and Varvara Tsilia for their support andcooperation with the lab work.

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