Role of protozoa in the formation of activated sludge (Experiment II)
Hypothesis 5: Protozoa and rotifers grazing cause changes in taxonomic structure of bacterial community
Detailed hypothesis: Protozoa and rotifers could eliminate some competitive bacterial species and simultaneously help to grow up other species (weaken competition between bacteria species).
The obtained data were analyzed in two different ways:
Firstly, using analysis of variance (ANOVA) and their non-parametric alternatives to check differences between three experimental groups, and secondly using analysis of regression and their non-parametric alternatives – Spearman rank correlation test to check influence of total microorganism (protozoans and metazoans) density and dry mass on bacteria dry mass, flocs area and N and P mineralization rate.
Principal coordinate analysis was used to show similarity of bacterial community from investigated bioreactors. ANOVA and regression analysis was performed in Statistica 13 (TIBCO Software Inc.
2017) and R 3.4.4 (R Core Team 2018). Principal coordinate analysis was performed in Canoco5 (Microcomputer Power, Ithaca NY, USA).
53 Bacteria dry mass
No significant difference was found between experimental groups in total bacteria dry mass in last day of experiment and no significant relationship between total bacteria dry mass and mean protozoa and metazoa dry mass (Table 4.2, 4.3 and 4.4).
Differences between experimental groups were not found in biofilm dry mass and we did not find any relationship between biofilm dry mass and mean total protozoa and metazoa dry mass (Table 4.2, 4.3 and 4.4).
Sludge yield did not differ between experimental groups and did not show any significant relationship with mean protozoa and metazoa dry mass community (Table 4.3, 4.4).
Table 4.2. Dry mass and sludge yield in different group of bioreactors.
Mean ± SD, values in mg/l and g for sludge yield.
Group MLSS Biofilm dry mass Total bacteria
dry mass Sludge yield Rotifers 0.44 ± 0.14 0.67 ± 0.46 1.11 ± 0.60 0.08 ± 0.04 Control 1.12 ± 0.47 0.54 ± 0.35 1.65 ± 0.82 0.11 ± 0.05 Ciliates 0.79 ± 0.58 0.75 ± 0.71 1.53 ± 0.13 0.11 ± 0.01
Table 4.3. Results of ANOVA for differences between experimental groups in bacteria dry mass in last day of experiment.
Parameter F(2,3) p value
Total bacteria dry mass 0.46 0.67
Biofilm dry mass 0.08 0.93
Sludge yield 0.41 0.70
Table 4.4. Spearman's rank correlation coefficients (rs) values between mean protozoa and metazoa dry mass and bacteria mass parameters.
54 Table 4.5. Mean total dry mass of protozoa and metazoa in different group of bioreactors.
Mean ± SD.
Group Mean protozoa and metazoa dry mass [g]
Rotifers 0.15 ± 0.03 Control 0.09 ± 0.01 Ciliates 0.11 ± 0.01
Flocs area
Median flocs area presented very high variation during whole experimental period in all bioreactors (Figure 4.1). Despite of high variance within groups, data from experiment did not met assumption (normal distribution of residuals and sphericity) to perform the most appropriate analysis for this type of data – repeated measure ANOVA.
Figure 4.1. Median flocs area during experimental period in treatment groups. The values on y axis is presented in 104 scale for transparent view. Wrinkles represent SD.
To compare growth (and increase) of flocs area one-way ANOVA test was used. For this purpose, median flocs area measured in day where mixture of protozoa was introduced were subtracted from the median flocs area in last experimental day in order to correction for differences between floc area in bioreactors before experimental treatment (Table 4.6).
55 Table 4.6. Floc area growth from introduction of protozoa to last experimental day. Mean ± SD.
Group Growth of floc area [µm2] Rotifers 9 134.1 ± 9 817.6
Control 27 903.2 ± 18 583.0 Ciliates 25 765.7 ± 7 801.5
Figure 4.2. The growth of flocs area in experimental groups. Raw data.
The growth of flocs area did not differ between experimental groups (F(2,3)= 1.26, p = 0.41), although the Figure 4.2 and Table 4.6 shows a tendency that the growth of flocs area in the presence of rotifers was lower. The lack of clear differences can be the consequence of the high variability within group as each group was represented only by two bioreactors and/or groups did not differ in mean dry mass of protozoa and metazoa.
Analysis performed only for the last day of experiment did not show significant differences between experimental groups in median flocs area (F(2,3)= 1.24, p = 0.40).
In control bioreactors flocs probably exceed area value which keep flocs stable therefore median flocs area in this bioreactors after 52nd day of experiment rapidly decreased (Figure 4.1 – red line and points) destroyed e.g. by shear forces.
Experimental groups clearly differ in median flocs surface area on average for whole experimental period (F(2,3)= 50.8, p = 0.005) and Table 4.7.
Table 4.7. Median floc area on average for whole experimental period in experimental groups.
Mean ± SD.
Group Median floc area [µm2] Rotifers 5 681.8 ± 676.7
Control 43 109.5 ± 2 970.8 Ciliates 22 337.1 ± 5 678.4
56 The flocs with the biggest median surface area were present in control bioreactors, whereas the flocs with the smallest mean surface area occurred in bioreactors with rotifers. Flocs from bioreactors with ciliates had intermediate values of surface area in comparison to mentioned above experimental groups.
Process effectiveness
High fluctuations in the N and P compounds reduction rate were observed over time in all bioreactors regardless from which experimental group they were (Figure 4.3 B, C).
Figure 4.3. Reduction rate of COD (A), Ntotal (B) and Ptotal (C), during experimental period in treatment groups. Mean ± SD.
57 Generally, presence of ciliates and rotifers seemed to did not have visible effect on N and P compounds reduction in experimental set. Only in 32nd day of experiment in bioreactors with rotifers, reduction of N and P compounds was visible lower than in groups of bioreactors without rotifers. But in next week situation stabilized and all bioreactors had similar reduction rate of mentioned above compounds.
COD, Ntotal and Ptotal percent of reduction did not differ between experimental groups on the last day of experiment (Table 4.8 and 4.9). Mean percent of reduction mentioned above chemical compounds calculated as the mean value of all sampling days also did not differ between experimental groups (Table 4.10 and 4.11).
Table 4.8. Reduction rate in last day of experiment. Presented values are in %. Mean ± SD.
Group COD N total P total
Rotifers 91.5 ± 4.2 53.9 ± 25.4 57.7 ± 25.5 Control 93.3 ± 7.1 56.5 ± 24.4 43.8 ± 31.9 Ciliates 92.4 ± 0.3 45.4 ± 21.9 57.3 ± 13.8
Table 4.9. Results of ANOVA for differences between experimental groups in reduction rates in last day of experiment.
Parameter F(2,3) p value
COD 0.07 0.93
Ntotal 0.12 0.89
Ptotal 0.20 0.83
Table 4.10. Mean reduction rates of chemical compounds in artificial sewage during whole experimental period. Presented values are in %. Mean ± SD.
Group COD N total P total
Rotifers 86.0 ± 1.3 51.4 ± 5.5 36.7 ± 3.9 Control 88.9 ± 3.0 53.8 ± 13.7 37.6 ± 12.9 Ciliates 89.5 ± 4.7 56.6 ± 3.3 38.0 ± 5.8
Table 4.11. Results of ANOVA for differences between experimental groups in mean values of effluent parameters.
Parameter F(2,3) p value
COD 0.63 0.59
Ntotal 0.18 0.85
Ptotal 0.01 0.99
58 The coefficients of variation (measure of process stability) of COD reduction rate were lower than the variation coefficients of N and P reduction rate, this indicated that reduction of COD was more stable over time than reduction of N and P (Table 4.12). The coefficient of variation of COD, N and P compounds reduction rate did not differ between experimental groups (Table 4.13).
Table 4.12. Coefficient of variation (CV) of chemical compounds reduction rate in experimental groups.
Mean ± SD.
Group COD Ntotal Ptotal
Rotifers 0.10 ± 0.06 0.41 ± 0.01 0.63 ± 0.05 Control 0.06 ± 0.01 0.52 ± 0.46 0.58 ± 0.25 Ciliates 0.06 ± 0.04 0.27 ± 0.03 0.59 ± 0.004
Table 4.13. Results of ANOVA on coefficient of variation (CV) values of effluent parameters between experimental groups.
CV F(2,3) p value
COD 0.48 0.66
Ntotal 0.42 0.69
Ptotal 0.09 0.92
Biodiversity and process performance
The experimental groups did not differ in mean bacteria Shannon-Wiener index (H’) and mean OTUs values (Table 4.14 and 4.15). The experimental groups also did not differ in bacteria Shannon-Wiener index and OTUs values in last day of experiment (Table 4.16 and 4.17).
Table 4.14. Mean values of biodiversity indexes in experimental groups. Mean ± SD.
Group H’ OTUs
Rotifers 3.93 ± 0.03 65 ± 5 Control 3.79 ± 0.52 61 ± 18 Ciliates 4.18 ± 0.17 69 ± 1
Table 4.15. Results of ANOVA in differences between experimental groups on mean values of S-W index and OTUs.
Parameter F(2,3) p value
H’ 0.80 0.53
OTU 0.04 0.96
59 Table 4.16. Values of biodiversity indexes in experimental groups in last experimental day.
Mean ± SD.
Group H’ OTUs
Rotifers 5.33 ± 0.19 105 ± 12 Control 4.51 ± 0.19 75 ± 17 Ciliates 4.92 ± 0.17 81 ± 1
Table 4.17. Results of ANOVA in differences between experimental groups in last experimental day.
Parameter F(2,3) p value
H’ 6.30 0.08
OTU 3.36 0.17
All 6 bioreactors were analyzed together and for that purpose non-parametric Spearman rank correlation test was used because assumption for regression analysis was not met. Analysis showed that Shannon-Wiener index of bacteria and OTUs values were not correlated with COD Ntotal and Ptotal reduction rate.
Additionally, Shannon-Wiener index of bacteria and OTUs values were not correlated with process stability expressed as CV of COD, Ntotal and Ptotal. Only one trend was observed, higher level of Ntotal
reduction tend to be connected with higher OTUs values (Table 4.18).
Table 4.18. Spearman's rank correlation coefficients (rs) values between coefficients of variation, reduction rates and biodiversity indexes.
Parameter OTUs H’
COD reduction 0.14 0.71
Ntotal reduction 0.94 0.77
Ptotal reduction 0.71 0.37
CV COD 0.37 -0.37
CV Ntotal -0.77 -0.77
CV Ptotal -0.60 -0.83
Protozoa and metazoa Shannon-Wiener index, dry mass density and number of species did not show any significant relationship with Shannon-Wiener index of bacteria and OTUs values (Table 4.19).
60 Table 4.19. Spearman's rank correlation coefficients (rs) values between protozoa and metazoa community parameters and bacteria biodiversity indexes.
Protozoa and metazoa
Bacteria OTUs
Bacteria H’
H’ -0.02 -0.20
Dry mass -0.20 -0.14
Density -0.66 -0.37
No. species 0.26 0.03
Bacterial community structure
The results of Principle Coordinate Analysis (PCO, PCoA) showed that bacterial community in bioreactors with ciliates and rotifers at the end of experiment were more similar to each other than to control bioreactors. Moreover, bacterial community in bioreactors with ciliates and other protozoa were more similar in last day of experiment to bioreactors with rotifers than to bioreactors only with flagellates and amoebas (Figure 4.4). First axis explained 51.17% of variation and it could be interpreted as time, second axis explained 12.82% of variation and could be interpreted as effect of protozoa and rotifers on bacterial community. Based on this analysis we supposed that ciliates and rotifers were able to stabilize bacterial community.
We could not quantify and describe directly changes in density of bacterial community in experimental bioreactors because data received from NGS analysis are qualitative and not quantitative and presented only relative abundance of bacteria. The resulting BIOM table contained 2 095 833 reads for 42 activated sludge samples with a mean of 49 900 ± 43 236 (SD) for each sample.
We checked whether experimental groups differ in total reads value and we did not find significant differences (F(2,3)= 0.33, p = 0.74). We can assume that in all experimental groups bacteria abundance was similar. But high differences in total reads between individual bioreactors in last day of experiment indicated that abundance of bacteria was highly diverse between bioreactors (Table 4.20).
61 Figure 4.4. Principle coordinate analysis diagram. Dots represent bacterial community in bioreactors, S – first day of experiment, F – last day of experiment. Color represent experimental groups: red – control bioreactors, black – bioreactors with rotifers, blue – bioreactors with ciliates. First axis explained 51.17% of variation, second axis explained 12.82%.
Axis 1
A x is 2
62 Table 4.20. Total number of sequences reads in each experimental bioreactor in last day of experiment.
Bioreactor ID Experimental
group No. of reads
bio1 Rotifers 15 663
bio2 Control 53 805
bio3 Ciliates 85 618
bio4 Control 26 586
bio5 Rotifers 32 213
bio6 Ciliates 13 711
Rotifers 23 938 ± 11 703
Mean ± SD Control 40 195 ± 19 247
Ciliates 49 664 ± 50 846
Generally, in all bioreactors similar bacteria groups were presented but in various proportions. At the beginning of experiment in all bioreactors dominated Gammaproteobacteria with small abundance of Betaproteobacteria, Bacteroidetes and Alphaproteobacteria. Bacterial community in all bioreactors were changed significantly (but not in statistical meaning) over time. Abundance of Gammaproteobacteria was constantly decreasing and simultaneously abundance of Bacteroidetes and Alphaproteobacteria was constantly increasing in all bioreactors. The changes in Betaproteobacteria abundance depended on bioreactor and did not show clear general pattern, the same observation applies to other groups of bacteria such as: Verrucomicrobia, Epsilonproteobacteria and Deltaproteobacteria (Figure 4.5 a, b, c).
In our experiment we observed some differences in bacterial community between experimental groups however we could not say anything about their significance because PCO similarly to PCA is descriptive analysis. Additionally, NGS get only information about relative abundance of bacteria species.
63 Figure 4.5a. Microbial community structure in bioreactors with flagellates, amoebas, ciliates and rotifers.
64 Figure 4.5b. Microbial community structure in bioreactors with flagellates and amoebas.
65 Figure 4.5c. Microbial community structure in bioreactors with flagellates, amoebas and ciliates.
66 Discussion
Performing “super clean” long time experiment especially with activated sludge microorganisms is very difficult task. Even Curds et al. (1968) in his most cited research did not avoid contamination of control bioreactors. In our study contamination of bioreactors on early stage of the experiment forced us to analyze data in another way that was originally planned.
High variability within experimental groups in all investigated aspects: bacteria and protozoa dry mass, flocs area, chemical compounds concentration in effluent, bacterial community structure clearly shows that the activated sludge process is very dynamic and testing any hypothesis and/or engineering procedures requires to use an appropriate experimental setup with replications. Unfortunately, the vast majority of experiments on lab-scale bioreactors (Park & Noguera 2004, Puigagut et al. 2007, Wittebolle et al. 2008, Dubber & Gray 2011b, Muszyński et al. 2013, Yadav et al. 2014) were carried out without replications.
Bacteria dry mass
We did not observe significant differences in bacteria biomass and sludge yield between experimental groups probably because experimental groups did not differ in protozoa and metazoa biomass. However, basing on this results we cannot conclude that in all experimental groups bacteria were under the same protozoa and metazoa grazing pressure as this group differ in community structure.
Our results suggest only that the same amount of protozoa and metazoan expressed as their dry mass had a similar effect on bacterial dry mass in spite of differences in species composition.
Moreover, the role of protozoa predation in reduction of sludge production is not clear and evident.
Seviuor and Nielsen (2010) only suggested that protozoa could consume flocculated bacteria and mineralize sludge. Seviuor and Nielsen (2010) quoted research conducted by Lee and Welander (1996a) and Lee and Oleszkiewicz (2003) as examples of experiments where protozoa predation reduced sludge production. But both publications showed that rather metazoan with protozoa then protozoa alone were main consumers of bacterial biomass. In earlier study Lee and Welander (1996b) suggest this fact in the discussion: (…) The rotifers probably have a stronger influence on the biomass decreasing capacity than the stalked ciliates do (…). In publication Lee and Oleszkiewicz (2003) some inconsistency was found, namely authors in abstract wrote about protozoa grazing inhibition but in main text they described rotifers grazing inhibition. The results and conclusions concerned the effect of rotifers (Metazoa) activity on nitrification rate and microbial dry mass (MLVSS) concentration. Taking into account above mentioned correction only two researches conducted by Ratsak et al. (1994) and Ghyoot and Verstraete (2000) present possible sludge reduction by protozoa. But Ghyoot and Verstraete (2000) did not make replications in their experiment. Our results showed high variation in sludge yield within all experimental groups (Table 4.2). However, Ratsak et al. (1994) experiment showed clear results but in
67 my opinion it is hard to extrapolate their results on full scale WWTP and suggested 19% reduction of sludge accumulation as did Pauli et al. (2001). Ratsak and co-workers (1994) used in their experiment only one bacteria and one ciliate species during short period of time. Because of many confusing results of different studies this topic still need a further research.
Flocs area
Differences in the median flocs area between experimental groups, taking into account the lack of differences in mean bacteria dry mass, suggest that protozoa and metazoa community with different structure shaped differently the spatial structure of sludge flocs.
The smallest median flocs surface area was noted in bioreactors with presence of Lecane inermis rotifers.
Our results are consisted with results of Puigagut et al. (2007) which showed that rotifers from Lecanidae family reduce mean floc surface area due to its grazing activity. However, contrary to Puigagut et al. (2007) suggestion and similarly to Kocerba-Soroka et al. (2013b) results, our outcome showed that higher rotifers grazing pressure did not cause negative effect on overall nitrogen removal process.
In bioreactors with ciliates and without rotifers median flocs surface area were significantly larger than in bioreactors with rotifers but significantly smaller than in bioreactors only with flagellates and amoebas. Some researchers claim that one of the most significant role of protozoa (mainly ciliates) in activated sludge is enhancing the flocculation of bacteria (Pillai & Subrahmanyan 1944, Watson 1945, Sudo & Aiba 1984). Additionally, Curds (1982) compile the substantial set of literature which show ability of protozoa in pure culture to flocculate suspended particulate matter and bacteria. Experiments performed by Arregui and co-workers (2008) demonstrated that crawling and attached ciliates can contribute to aggregation/flocculation by the active secretion of polymeric substances. But Curds (1982) made a note of the fact that many bacteria are able to flocculate or grow in flocs forms without the presence of protozoa. The same observation made McKinney and Gram (1956) which observed bacterial aggregates in control units without protozoa. For the other hand flocs produced in the presence of ciliates were more compacted than those produced by bacteria alone (Pauli et al. 2001). But the role of protozoa in floc formation in activated sludge in full scale systems could be overestimated because wastewater itself contains a high concentration of different particles and bacteria which can produce extracellular polymeric substances and aeration and mixing process could glue them together (Pauli et al. 2001).
Additionally, activated sludge process leads to the selection of faster falling particles - larger aggregates (flocs).
Macek (1989) also presented that protozoa influence on bacterial flocculation is not so obvious. He obtained different results depend on used protozoa species and system configuration. In laboratory system where MCRT = HRT and without biomass recycle, ciliates Vorticella microstoma and Colpidium campylum induced flocculation of bacteria but Glaucoma scintillans and Chilodonella uncinata favor growth of dispersed bacteria. In system with recycled biomass, MCRT = 5 days and presence of
68 flagellates, V. microstoma and C. campylum did not flocculated bacteria but in presence of Aspidisca cicada and Stylonychia putrina did.
Our results showed that grazing pressure of community consisted of ciliates, flagellates and amoebas had stronger effect on floc surface area than flagellates and amoebas alone.
In my opinion, nowadays the main open question connected with protozoa influence on bacteria flocculation is: How really strong protozoa pressure affects the bacteria flocculation in full-scale WWTPs where activated sludge in bioreactors is affected by significant hydraulic forces which themselves could favorable flocculation process?
Based on results of bacterial dry mass and flocs surface area we suggest that in our experiment different protozoa and metazoa community affected mainly the spatial floc structure.
Process effectiveness
Pilot study results strongly indicated positive effect of ciliates on COD, N and P compounds reduction. Results obtained from bench-scale experiment conducted in laboratory bioreactors were not so clear and easy to interpretation. It must be underlined that scale of experiment with microorganisms has huge importance on results. Problem of scale-up some biological process is still common problem in biotechnology (Takors 2012).
The main physical factors which could generate differences in results between small scale and bench scale experiment were: type of mixing (shaker vs stirrer), pH control (none vs automatic), type of oxygen supply (mixing vs compressed air), feeding regime (every two days vs daily), lack of anaerobic phase in small scale experiment.
In bench-scale experiment we did not find significant differences between experimental groups in COD, N and P compounds reduction rate and in coefficient of variance (measure of process stability).
We did not find any significant relationship between protozoa and metazoa dry mass and COD, Ntotal
and Ptotal reduction rate. The lack of differences between the experimental groups in mentioned above factors may resulted from the lack of differences in the protozoa and metazoa dry mass between the treatments. It could be assumed that bacteria in three experimental groups were under the similar protozoa and metazoan predatory pressure.
From the one side in ecological experiments two replications per treatment are definitely too few but for the other hand bioreactors are very expensive equipment this is a serious methodological problem in research from the borderline of ecology and technology.
From the obtained results, we can make an important conclusion: namely despite of differences in the species community of protozoa and rotifers, each group of experimental bioreactors in similar ways reduced COD, N and P compounds. Differences between bioreactors within experimental groups were so high that diminished differences between treatments. We suggest that during the start-up of the
69 activated sludge process protozoa and metazoa community composition was not the most meaningful
69 activated sludge process protozoa and metazoa community composition was not the most meaningful