MUNICIPALIDAD DE PLAYA GRANDE INFORME Enero-Junio 2020

Total bacterial numbers ranged from (0.21 × 106 cells/mL) at site S20 in February 2013, to (5.34 × 106 cells/mL) at site S1 in December 2011, with an overall mean of 1.1 × 106 cells/mL. Table 3.2 and Figure 3.3 show the spatial variation of total bacterial numbers in the River Wensum from June 2011 to February 2013.

Two-way ANOVA analysis showed a significant variation between sites (F = 11.17, p< 0.001). The highest bacterial numbers were at the WCM and WSM regions in the downstream sites of the river, with highest values at sites S8 and S18 (4th order), respectively. The lowest numbers were in BSC1 and UW regions, with lowest values at the upstream sites S20 and SE (2nd _order) (Table 3.2). However the differences between sites are not large. Total bacterial numbers increase as water moves to downstream sites (3rd – 4th order) of the river (Figure 3.9). Bacteria can multiply and increase at these sites, with the majority located in urban areas in the vicinity of sewage treatment works.

70 Table 3. 2 Mean, standard error, minimum and maximum of total bacterial numbers (cells/mL) in the River Wensum by sites from June 2011 to February 2013.

Individual site Mean ± standard error Minimum and maximum

S4 0.85 ± .046 0.22 - 1.19 S5 0.83 ± .042 0.28 - 3.88 S6 1.06 ± .042 0.54 - 2.05 S13 0.92 ± .039 0.38 - 3.02 S20 0.71 ± .042 0.21 - 1.57 S1 1.25 ± .042 0.32 - 5.34 S2 1.00 ± .039 0.47 - 2.47 S3 0.88 ± .042 0.42 - 1.81 S15 1.48 ± .042 0.51 - 3.34 S7 0.98 ± .042 0.42 - 2.15 S14 1.49 ± .042 0.48 - 3.97 S8 1.47 ± .039 0.62 - 3.79 S10 0.85 ± .042 0.45 - 3.51 S11 1.36 ± .042 0.73 - 4.07 S12 1.20 ± .039 0.22 - 3.72 S21 0.88 ± .042 0.43 - 1.57 S9 0.83 ± .042 0.38 - 3.14 S16 1.55 ± .042 0.72 - 2.78 S17 1.22 ± .042 0.49 - 2.64 S18 1.89 ± .042 0.81 - 3.26 SA 0.92 ± .042 0.25 - 2.59 SB 0.78 ± .042 0.28 - 2.36 SE 0.74 ± .042 0.23 - 1.85 SC 1.14 ± .042 0.30 - 1.68 SD 1.10 ± .042 0.42 - 2.01 SF 1.02 ± .042 0.32 - 2.84

71

Key symbols: UW = Upper Wensum, WB = Wensum Beck, MCT = Mid Catchment Tributaries, WCM = Wensum Swanton Morley, BC = Blackwater Catchment, LCT = Lower Catchment Tributaries, WCM: Wensum Costessey Mill, BSC1 = Blackwater subcatchment 1, BSC2 = Blackwater subcatchment 2.

Figure 3. 3 Box plot of spatial variation of total bacterial numbers (cells/mL) in the River

Wensum (sites as individuals and groups) from June 2011 to February 2013. Note that bacterial abundance is plotted on log scale.

A box plot is a useful graphical visualization for the data containing statistical measures and explaining the distribution of the data. Box plots presented in this thesis include the lower 25% quartile, the median separating the box into two parts, and the upper 75% quartile. Therefore, between the bottom and top of the box represents 50% of the observations. The two whiskers on the back of the box plot extend from the minimum value to the lower quartile and from the upper quartile to the maximum value. The Whiskers therefore represent the spread of the data. Points above or below the two whisker lines are called outliers and are plotted separately on the figure. Outliers are extreme values that are distant from other values. Outliers do not always include the minimum and maximum values.

72 3.4.1.2 Temporal variation of total bacterial numbers

Table 3.3 and Figure 3.4 show temporal variation of total bacterial numbers in the River Wensum from June 2011 to February 2013.

Two-way ANOVA showed significant differences between months (F = 39.93, p< 0.001), with greater variation between months than between sites, accounting for 50.5% and 18.6% of the overall variance in bacterial numbers, respectively.

Bacteria were highest in summer 2011 (warm months and during low river flow), with the highest values in June and August. Numbers were lowest in winter (cold months and during high river flow), with lowest values in February 2013 and December 2012 (Table 3.3). These results demonstrate the independent effects of temperature and river flow on bacterial numbers. River flow is high in winter, and temperature is low, and the strong effect of temperature partially hides the effect of river flow (Table 3.4).

Table 3. 3 Mean, standard error, minimum and maximum of total bacterial numbers (cells/mL) in the River Wensum by months from June 2011 to February 2013.

Year Month Mean ± standard error minimum and maximum

2011 June 2.91 ± .089 2.26 - 3.79 July 2.01 ± .056 1.01 - 2.59 August 2.70 ± .040 1.20 - 4.41 September 1.64 ± .035 0.37 - 3.57 October 1.44 ± .035 0.22 - 3.52 November 0.99 ± .034 0.49 - 1.69 December 1.14 ± .034 0.57 - 5.34 2012 January 0.66 ± .034 0.35 - 1.78 February 0.68 ± .034 0.42 - 1.39 March 0.90 ± .034 0.54 - 2.02 April 0.83 ± .034 0.50 - 1.89 May 0.80 ± .034 0.35 - 1.80 June 1.01 ± .034 0.50 - 2.06 July 1.15 ± .034 0.58 - 2.48 August 0.80 ± .034 0.39 - 2.17 September 0.73 ± .034 0.40 - 1.57 October 1.05 ± .034 0.40 - 2.85 November 0.57 ± .034 0.34 - 0.99 December 0.51 ± .034 0.23 - 0.97 2013 February 0.51 ± .034 0.21 - 1.05

73 Figure 3. 4 Box plot of temporal variation of total bacterial numbers (cells/mL) in the River

Wensum (by month) from June 2011 to February 2013. Note that bacterial abundance is plotted on log scale.

For an explanation of the presentation of the box plot, see Figure 3.3.

74 3.4.1.3 Environmental parameters and total bacterial numbers

The mean, standard error, minimum and maximum of environmental parameter results are all presented in Appendix Tables A3-1 to A3-22.

Table 3.4 shows the correlations between environmental parameters and total bacterial numbers in the River Wensum from June 2011 to February 2013 using spearman’s rank correlation and the results of the stepwise multiple regression analyses.

Spearman’s rank correlation analysis revealed that total bacterial numbers were positively related to temperature, stream order, TOC, TP, STWs, urban area, improved grassland and other grassland, respectively, while they were negatively related to flow rate, TC, arable land, TN, and TSS, respectively.

However, among these significant parameters, stepwise multiple regression analysis revealed that the most significant parameters correlated positively with total bacterial numbers were temperature (Figure 3.5), TP (Figure 3.7), TOC (Figure 3.12), stream order (Figure 3.9), STWs (Figure 3.10), while the most significant parameters correlated negatively with total bacterial numbers were TC (Figure 3.6), flow rate (Figure 3.11) and TN (Figure 3.8). All of these environmental parameters explain approximately 52% of the variation of total bacterial numbers.

Overall, bacteria enter the river from different sources including land drainage (agricultural, particularly at upstream sites and urban at the downstream sites), sewage treatment works (particularly downstream sites). Bacteria increase as water moves downstream of the river (3rd – 4th order). Hierarchical partitioning showed an independent effect of temperature and flow. The highest numbers are in summer 2011 when water temperature is high and during periods of low river flow when residence time is long. However, bacterial numbers are lower during cold months when water temperature is low and also during periods of high flow when residence time is short. The abundance of bacteria showed a strong negative relationship with TC. TC increased during the wet year 2012 compared with the dry year 2011 (Appendix A 1.3).

75 Table 3. 4 Relationships between environmental parameters and total bacterial number (cell/mL) in the River Wensum from June 2011 to February 2013 using Spearman’s rank correlation and stepwise multiple regression analysis.

Environmental parameter Spearman’s rank correlation Stepwise multiple regression

Temperature (˚C) .437, p < 0.001 .309, p < 0.001

pH N.S. -

Total nitrogen TN (mg/L) - .284, p < 0.001 - .143, p < 0.001

Total phosphorus TP (µg/L) .263, p < 0.001 .208, p < 0.001

Total carbon TC (mg/L) - .344, p < 0.001 - .398, p < 0.001

Total organic carbon TOC (mg/L)

.284, p < 0.001 .204, p < 0.001 Total suspended solid TSS

(mg/L) -.103, p < 0.05 - Arable land (%) - .296, P < 0.001 - Improved grassland (%) .188, p < 0.001 - Other grassland (%) .140, p < 0.001 - Urban area (%) .226, p < 0.001 - Rainfall (mm) N.S -

Sewage treatment works (n) .253, p < 0.001c _{.101, p < 0.01}

Stream order (n) .336, p < 0.001c _{.181, p < 0.001}

76 Figure 3. 5 Relationship between total bacterial numbers (cells/mL) and temperature (˚C) from

June 2011 to February 2013 for all sites.

Figure 3. 6 Relationship between total bacterial numbers (cells/mL) and total carbon (mg/L)

from June 2011 to February 2013 for all sites.

rs= 437, p< .001

77 Figure 3. 7 Relationship between total bacterial numbers (cells/mL) and total phosphorus

(µg/L) from June 2011 to February 2013 for all sites.

Figure 3. 8 Relationship between total bacterial numbers (cells/mL) and total nitrogen (mg/L)

from June 2011 to February 2013 for all sites.

rs= 263, p< .001

78 Figure 3. 9 Box plot of the relationship between total bacterial numbers (cells/mL) and stream

order (number) from June 2011 to February 2013 for all sites. Note that bacterial abundance is plotted on log scale.

Figure 3. 10 Box plot of the relationship between total bacterial numbers (cells/mL) and

sewage treatment works (number) from June 2011 to February 2013 for all sites. Note that bacterial abundance is plotted on log scale.

rs= .366, p< .001

79 Figure 3. 11 Relationship between total bacterial numbers (cells/mL) and river flow (m3/s) from June 2011 to February 2013 for all sites.

Figure 3. 12 Relationship between total bacterial numbers (cells/mL) and total organic carbon

(mg/L) from June 2011 to February 2013 for all sites.

rs= -.499, p< .001

80 3.4.2 Total heterotrophic bacteria numbers

The mean number of heterotrophic bacteria in the River Wensum in February 2013 was 1.35 × 104 _{CFU/mL) with a range from 0.50 × 10}4 _{to 2.95 × 10}4 _{CFU/mL. There were significant} differences in numbers between sites (F = 3.12, p< .001; Figure 3.13).

Numbers show an increase as water moves downstream in the river (3rd – 4th stream order). The highest numbers were at sites S8 (4th order), S14 (3rd order) and S15 (4th order), while the lowest numbers were recorded at site SD. No significant effects of sites as groups were observed on the shifts in total heterotrophic bacteria (F = 1.39, p> .05).

Key symbols: UW = Upper Wensum, WB = Wensum Beck, MCT = Mid Catchment Tributaries, WCM = Wensum Swanton Morley, BC = Blackwater Catchment, LCT = Lower Catchment Tributaries, WCM: Wensum Costessey Mill, BSC1 = Blackwater subcatchment 1, BSC2 = Blackwater subcatchment 2.

Figure 3. 13 Total culturable bacteria (CFU/mL) in the River Wensum (as individual sites and

groups) from February 2013.

81 The percentages of total bacterial numbers that were culturable varied from 0.48% (site SD) to more than 7% at sites SA and S20 (Figure 3.14). Culturability was strongly negatively correlated with total bacterial numbers (Figure 3.15; rs= -.795, P< .001). In the River Wensum,

the abundance of some bacterial taxa increase as water moves downstream. This presumably reflects the fact that those bacteria are actively growing in the river, and diluting the abundance of other taxa including that are heterotrophic.

No significant correlations (P > .05) were found between total heterotrophic bacteria and any of the environmental parameters measured, but the number of data points is much smaller than for the total bacterial counts.

Key symbols: UW = Upper Wensum, WB = Wensum Beck, MCT = Mid Catchment Tributaries, WCM = Wensum Swanton Morley, BC = Blackwater Catchment, LCT = Lower Catchment Tributaries, WCM: Wensum Costessey Mill, BSC1 = Blackwater subcatchment 1, BSC2 = Blackwater subcatchment 2.

Figure 3. 14 The percentages of total culturable bacteria relative to total bacterial numbers in

82 Figure 3. 15 Relationship between percentages of total culturable bacteria and total bacterial

numbers in the River Wensum in February 2013.

83 3.5 Discussion

3.5.1 Total bacterial numbers

This research conducted in the Wensum catchment using epifluorescence microscopy (EFM) with DAPI staining as a standard technique (Hobbie et al. 1977; Porter and Feig 1980; Kepner and Pratt 1994). This technique was found to be suitable for the sizes of bacterioplankton populations investigated (Grivet et al. 2001).

Total bacterial numbers in the River Wensum range from 0.21 – 5.34 × 106 cells/mL and are similar to those found in freshwaters worldwide. Hobbie et al. (1983) reported bacterial numbers in the River Kuparuk, US of 0.3 × 106 cells/mL to 2.7 × 106 cells/mL with the highest abundance in the summer season. Also, Velimirov et al. (2011) reported 7.7 × 105 to 5.1 ×106 cells/mL in the River Danube, with numbers increasing as the river approached the sea. Castillo et al. (2004) recorded similar numbers of bacteria (0.6 × 106 and 0.8 × 106 cells/mL) in several lowland rivers of the Orinoco basin over a two year period, with increases during periods of low flow. The mean bacterial abundance in the River Traun in Austria was 1.2 × 106 cells/mL (Klammer et al. 2002). Freese et al. (2006) recorded the maximum numbers of total bacteria 24 × 106 _{cells/mL in the River Warnow, Germany. They attributed the high values to its} eutrophic status and the presence of large amounts of organic matter in the river. In the River Hull, UK and three smaller water courses, Yamakanamardi and Goulder (1995) reported bacterial numbers between 0.7 and 22.4 × 106 cells/mL, with a mean of 4.3 × 106 cells/mL, with highest values in spring and summer.

Schumann et al. (2003) found that bacterial abundance ranged from 4.4 × 106 _{cells/mL in} mesotrophic water habitats to 10.9 × 106 cells/mL in eutrophic water habitats. Raw water abstracted from Lake Zurich contained 1 × 106 cells ml-1 (Hammes et al. 2008) and in eighty natural lakes distributed through the Pyrenees mountains between France and Spain, bacterial numbers were between 3 × 104 to 3 × 106 cells/mL (Felip et al. 2007).

In this study, total bacterial abundance generally varies more temporally than spatially. This is due to the variation of water temperature and river flow as revealed by hierarchical partitioning analysis. The highest numbers were recorded in summer, while lowest numbers occurred in winter. This is in agreement with other studies. For example, in the Ogilvie and Swift rivers, Yukon Territory, Canada, heterotrophic counts and total bacterioplankton numbers changed seasonally, with higher numbers in the spring and summer and low numbers in the winter (Albright et al. 1980). In the Ogilvie River, the average numbers of heterotrophic bacteria and total bacterioplankton were 2.5 x 102 and 1.6 x 104 cells/mL,respectively in winter (average temperature, 0 ˚C) compared with 7 × 103 and 8.4 × 105 cells/mL, respectively in summer (average temperature, 12˚C). In the eutrophic River Warnow, total counts were 24 × 106 cells/mL in the summer season at an average water temperature of 22˚C, while the average

84 total numbers in spring were 6 × 106 cells/mL when the average water temperature was 8˚C (Freese et al. 2006).

The highest numbers of bacteria in the river water were recorded at sites S18 and S8 in the 4th order downstream section of the river, suggesting that bacteria increase in abundance as the water moves downstream. There may also be contributions from urban area runoff (urban area represents 4.9% of the sub-catchment area draining to S18 and 3.1% to S8 and discharges from sewage treatment works (3 STWs upstream of S8 and 2 STWs upstream of S18).