Potential impacts of water hyacinth invasion and management on water quality and human health in Lake Tana watershed, Northwest Ethiopia

  • Ayenew Gezie
  • Workiyie Worie Assefa
  • Belachew Getnet
  • Wassie Anteneh
  • Eshete Dejen
  • Seid Tiku Mereta
Original Paper
  • 19 Downloads

Abstract

Incursion of water hyacinth, Eichhornia crassipes, has been a potential threat to Lake Tana and its ecosystem services. Its expansion is currently managed by abstraction (removing by hand); nonetheless, the disposal of mats and formation of pools are remaining problematic. This study aimed to assess the potential effects of water hyacinth and its management on water quality and human health. Biotic and abiotic data were collected on open water, water hyacinth covered and water hyacinth cleared out habitats. A total of 3673 invertebrates belonging to twenty-one families were collected from 45 sites. Culicidae was the most abundant family accounting (37.2%), followed by Unionoidae (19.4%) and Sphaeriidae (8.1%). Abundance of anopheline and culicine larvae were significantly higher in water hyacinth cleared out habitats (p < 0.05). Water conductivity and total dissolved solids were significantly higher in habitats covered with water hyacinth (p < 0.05). In conclusion, water hyacinth infestation had a negative impact on water quality and biotic communities. The physical abstraction of water hyacinth provided a very good habitat for the proliferation of mosquito larvae. Therefore, integrating water hyacinth management practices along with mosquito larvae control strategy could help to abate the potential risk of malaria outbreak in the region. In addition, developing watershed scale nutrient management systems could have a vital contribution for managing water hyacinth invasion in the study area.

Keywords

Blue Nile Lake Tana Macroinvertebrate Mosquito larvae Water hyacinth 

Introduction

Lake Tana, the largest natural freshwater lake in Ethiopia and the source of the Blue Nile, is well known for its biodiversity, cultural and socioeconomic values. It provides enormous ecosystem services such as fishing, water for drinking, tourism, transportation, electricity generation and irrigation (Wondie 2010; Anteneh et al. 2014, 2015). The lake is an important destination for numerous endangered migratory and endemic bird species (Aynalem and Bekele 2008). Both common and endemic fish species harbor in the lake and it is an important source of income for the local communities (Dejen et al. 2002, 2017). The lake and its watershed are the main focus area of the Ethiopian government to enhance economic growth and lessen poverty by the development of hydropower and irrigation schemes (Wondie 2010; Dessie et al. 2015). However, degradation of the lake and its associated services is escalating due to human-induced threats such as the release of untreated wastewater and solid wastes from Bahir Dar town, water abstraction for irrigation and agricultural activities and livestock grazing (Wondie 2010; Anteneh et al. 2014, 2015; Gezie et al. 2017).

Historically, water hyacinth has been considered as an ornamental plant and introduced to embellish water bodies. However, it is well recognized for its devastating repercussions on the stamina of water bodies (Villamagna and Murphy 2010). Water hyacinth is a perennial invasive macrophyte, which is indigenous to tropical South America (Güereña et al. 2015). This weed had been introduced to Ethiopia in 1950s, and recently found in Lake Ellen, Aba-Samuel dam, Koka dam, Rift valley water bodies as well as in Lake Tana (Yirefu et al. 2007; Anteneh et al. 2014; Firehun et al. 2014; Asmare 2017). Water hyacinth has been categorized among the 100 topmost aggressive invasive species in the world and recognized as one of the top 10 notorious invasive aquatic weeds (Lowe et al. 2000; Téllez et al. 2008). Its rapid reproduction and lavish growth, extensive dispersal capabilities and extensive environmental tolerance are important features of this weed (Kateregga and Sterne 2007; Zhang 2012). Barrett (1980) reported that water hyacinth had a very high reproduction rate and occupied a large area in a very short period of time. Water hyacinth can flower throughout the year and releases more than 3000 seeds per year. The seeds are long-lived, up to 20 years (UNEP 2013).

Water hyacinth can cause serious environmental and socioeconomic impacts. It exacerbate poverty via affecting agriculture, fisheries and biodiversity, which are a basis of livelihoods of people in developing countries (Lu et al. 2007; Ofulla et al. 2010; Villamagna and Murphy 2010; Güereña et al. 2015). It adversely affects aquatic biodiversity, economic development and human wellbeing (Masifwa et al. 2001; Vilà et al. 2011; Villamagna et al. 2012; Güereña et al. 2015). It also suppress relative abundance and survival of indigenous species by altering habitat complexity, thereby decline biodiversity (Rodriguez 2006; Theel et al. 2008; Villamagna et al. 2012). Water hyacinth impairs the structures and functions of aquatic ecosystems. For example, impenetrable thick mats of water hyacinth block light from reaching to deeper part of the waterbodies and negatively affect primary productivity (Malik 2007; Villamagna and Murphy 2010; Vilà et al. 2011; Patel 2012; Wang and Calderon 2012; Güereña et al. 2015). Furthermore, water hyacinth infestation increased the incidence of diseases such as malaria and schistosomiasis (Téllez et al. 2008; Güereña et al. 2015).

In Ethiopia manual abstraction is the most commonly used water hyacinth management system (Yirefu et al. 2007; Firehun et al. 2014). This water hyacinth management system has been implemented in Lake Tana since 2011 mainly during the dry season (Anteneh et al. 2015). In Lake Tana, physical abstraction was found to be unproductive due to the rapid expansion of the weed and disposal problems. Moreover, stagnant water bodies have been created during abstraction, which could provide a conducive breeding grounds for mosquitoes. Therefore, the present study investigates the impacts of water hyacinth invasion on water quality and biotic communities of Lake Tana. Furthermore, we assessed the impacts of water hyacinth management intervention on the occurrence and abundance of anopheline and culicine mosquito larvae. A fundamental understanding of the impacts of water hyacinth and its management is very important to plan and implement effective prevention and control intervention strategies.

Method and materials

Study area description

The data used in the present study were collected from shoreline of Lake Tana (Fig. 1). Lake Tana covers a surface area of about 3200 km2 and it is situated at an altitude of 1800 m in the Amhara National Regional State, Northwest of Ethiopia. It is a shallow lake with an average depth of 8 m and maximum depth of 14 m. Watershed of Lake Tana ranges from 10°58′ to 12°47′N and 36°45′ to 38°14′E with a surface area of approximately 16,500 km2. The lake and its watershed are the largest river basin in Ethiopia (Wondie et al. 2007). In the rainy season, Lake Tana forms a complex ecosystem network with Fogera flood plain on the East side, Dembia flood plain in the North, and Kunzila floodplain in the west (Wondie 2010). Seven perennial rivers (Arno-Garno, Dirma, Gelda, Gelgel Abay, Gumara, Rib, and Megech) and more than 60 seasonal rivers feed Lake Tana (Nagelkerke 1997). The only out flowing river from Lake Tana is the Blue Nile, which is the main source of water for the Grand Ethiopian Renaissance Dam, the largest hydroelectric power plant in Africa. The annual temperature of Lake Tana ranges from 20 to 27 °C and annual rainfall is around 1418 mm. The climate of Lake Tana is characterized by a major rainy season with heavy rains (June–October), sometimes a minor rainy season during February–March (Dejen et al. 2004). The Lake Tana watershed is highly populated and known for intensive farming and livestock production (Setegn et al. 2008; Wondie 2010). The major threats of the Lake Tana ecosystem are water use conflicts, unplanned land use, resource exploitation, pollution, high demographic pressure and water hyacinth invasion (Wondie 2010; Anteneh et al. 2014, 2015).
Fig. 1

Location of sampling sites (sites in open water represented by solid rectangle, sites in water hyacinth covered area represented by circles and sites in water hyacinth cleared out represented by solid polygon) in Lake Tana shoreline

Data collection

In this study, water quality, macroinvertebrate and mosquito data were collected in the shorelines of lake Tana during dry season (March 2016), when water hyacinth abstraction were carried out. A total of forty-five samples were collected from water hyacinth infested, open water and water hyacinth cleared out habitats (fifteen from each habitat type) (Fig. 2). The distance between each category of habitats and sampling sites was estimated to be 500 m (Baldwin et al. 2005). The percentage of water hyacinth cover was visually estimated according to Braun-Blanquet (1932) vegetation cover/abundance scale. Water depth and sludge layer thickness were measured at each observation site using a graduated stick. Conductivity, pH, daytime dissolved oxygen concentration, and water temperature were measured in the field using a multi-probe meter (HQ30d Single-Input Multi-Parameter Digital Meter, Hach). In vivo chlorophyll a concentration was used as a proxy of phytoplankton biomass and was measured in the field using a handheld fluorometer (Turner Design Aqua fluor). A water sample (200 ml) was taken from each site and subsequently filtered through a 0.45-µm filter paper in the field for the determination of nitrate, total dissolved solids (TDS), chloride, ammonia, and orthophosphate concentrations. Water samples were kept cool in the dark during transportation to the laboratory. Samples were analyzed in the laboratory following standard methods (APHA 1998).
Fig. 2

Photographs showing examples of the studied habitat types along the shorelines of Lake Tana. ac water hyacinth cleared out habitats, d cattle grazing on drying water hyacinth. e Open water habitat. f Water hyacinth infested habitat

The macroinvertebrate samples were collected by kick-net using standard hand net consisting of a metal frame holding a rectangular frame net (20 × 30 cm) with a mesh size of 300 µm (Gabriels et al. 2010). Macroinvertebrates were collected during 10 min sampling including all different microhabitat present in the sampling site (Gabriels et al. 2010). Sampling was performed through disturbing bottom sediment by foot vigorously and macroinvertebrate were collected by the kick-net. All collected invertebrates were sorted in the field and stored in labeled bottles within 80% ethanol. Afterwards, the invertebrates were transported to the laboratory and examined using a stereomicroscope (× 10 magnifications). Identification was conducted at family level using the identification key of Bouchard (2012). Total family richness, abundance, and family biotic index (FBI) (Hilsenhoff 1988) were calculated to characterize the community metrics of invertebrates for each sampling site. Additionally, mosquito larvae were sampled at sites in open water, water hyacinth cleared out and water hyacinth covered habitats. Twenty dips were taken at intervals along the edge of each larval habitat using standard mosquito dipper (350 mL Bio Quip Products, Inc. California, USA). For water hyacinth covered sites, the vegetation was carefully opened to allow for water pooling before dipping was done. The collected mosquito larvae from each habitat type emptied into a sorting tray and then sorted and stored in vials containing 80% ethanol and labeled accordingly and transported to the laboratory and identified morphologically at genus level as either anopheline or culicine (Farajollahi and Price 2013; Gunathilaka et al. 2014).

Data analysis

A Canonical Discriminant Analysis (CDA) was carried out using SPSS version 23 statistical software (SPSS Inc., Chicago, IL). CCA was used to derive a set of linear functions maximizing distinctions among the predefined groups. i.e. Open water, water hyacinth infested and water hyacinth cleared out habitats, and visualize their membership distributions on ordination diagram. CDA automatically determines optimal combination of variables so that the first canonical root represents the combination of variables that describes the greatest amount of discrimination among groups. The second root defines the next largest amount of discrimination and is independent of the first root, and so on (Cruz-Castillo et al. 1994). A plot of individual scores was used to visualize how the first 2 canonical discriminant functions (CDF1 and CDF2) accounted for separation between different habitats. Also, a biplot of canonical correlations (r) between the variables in the model and CDF1 and CDF2 was constructed. These values of “r” represent factor loadings of the variables on each CDF. A Chi square test based on Wilks’s lambda was used for ascertaining whether the variability that is systematically related to group differences is statistically significant. Person correlation coefficient was used to determine the extent of association of each predicator variable with each canonical discriminant function. In addition, Bonferroni multiple pairwise comparisons analyses was performed to test for differences between groups (Austin et al. 2006). Furthermore, the accuracy of the model was assessed from the classification (or confusion) matrix, which gives percent of correctly classified instances.

Box and whisker plots were made in STATISTICA 7.0 (Statsoft, Inc.) to visualize the relationships of biotic or abiotic environmental variables across the habitat categories using medians as a measure of central tendency. Kruskal–Wallis H test was performed to determine the existence of statistical significant differences of biotic or abiotic variables across habitat categories.

Results

Macroinvertebrate survey

A total of 3673 invertebrates belonging to twenty-one families were recorded (see the complete list in “Appendix”). The most abundant family was Culicidae with a relative abundance of 37.2%. The second most abundant family Unionoidae (9.4%) followed by Sphaeriidae (8.1%). The abundance of macroinvertebrates varied among different habitat types. Water hyacinth cleared habitat had a relatively higher relative abundance of Diptera as compared with other habitats (p < 0.05). Water hyacinth covered habitat had a relatively higher family richness, and gastropod abundance. On the other hand, the average Family biotic index score was relatively higher within open water habitat (p < 0.05) (Table 1, Fig. 3).
Table 1

Variations (Mean ± SD) in abundance of biotic parameters among open water, water hyacinth (WH) infested and water hyacinth cleared out shoreline habitats

Biotic parameters

Habitats

Open water (n = 15) Mean ± SD (abundance)

WH infested (n = 15) Mean ± SD (abundance)

WH cleared out (n = 15) Mean ± SD (abundance)

Anopheline

2.33 ± 2.85 (35)

8.67 ± 5.12 (130)

42.80 ± 26.10 (642)

Culicine

3.40 ± 3.25 (51)

7.40 ± 5.54 (111)

26.60 ± 15.85 (399)

Bivalvia

16.8 ± 9.70 (252)

18.67 ± 6.63 (280)

12 ± 7.43 (180)

Coleoptera

6.13 ± 4.42 (92)

11.27 ± 6.44 (169)

9.53 ± 9.19 (143)

Gastropoda

7.87 ± 5.21 (118)

19.93 ± 10.55 (299)

11.87 ± 6.14 (178)

Hemiptera

2.27 ± 3.40 (34)

9.93 ± 4.53 (149)

5.40 ± 3.91 (81)

Hirudinae

1.00 ± 1.31(15)

3.67 ± 4.22 (55)

1.00 ± 1.31 (15)

Odonata

1.53 ± 1.51 (23)

8.87 ± 8.72 (133)

5.93 ± 3.79 (89)

Relative abundance

620

1326

1727

Family richness

9.13 ± 1.19

12.73 ± 2.66

11.20 ± 2.21

FBI

8.47 ± 2.05

7.39 ± 0.71

7.54 ± 0.26

FBI Family biotic index

Fig. 3

Box and whisker plots showing the relationships of biotic metrics across categories of habitat. Small black squares represent median values, boxes represent interquartile ranges (25–75% percentiles), and range bars show maximum and minimum values. a, b and c indicate statistical significant differences shown by nonparametric Independent-Samples Median Tests (p < 0.05)

Relationships of macroinvertebrates with habitat types

The Box and whisker plots showed the abundance of macroinvertebrate taxa among different of habitat types (Fig. 3.). The Kruskal–Wallis test indicated that the abundance of both anopheline and culicine larvae were significantly higher in water hyacinth cleared out habitats (p < 0.05). On the other hand, total family richness, relative abundance, and the abundance of some macroinvertebrate taxa such as odonata, hemipteran, gastropoda and veroneida were significantly lower in open water habitats (p < 0.05). However, the abundance of coleopetera and hirudinae were not significantly different among habitat types (p > 0.05).

Relationships of environment variables with habitat types

The average values of water physicochemical variables of different habitat types are shown in Table 2. Sites covered with water hyacinth had a relatively higher electrical conductivity, chloride, orthophosphate, total dissolved solid and hardness. On the other hand, open water habitats had a relatively higher concentration of dissolved oxygen, Chlorophyll a, pH and water depth. Water hyacinth cleared out habitats had higher concentration of water alkalinity. The Kruskal–Wallis test indicated that water conductivity and total dissolved solids were significantly higher while pH and dissolved oxygen saturation were significantly lower at sites covered with water hyacinth (p < 0.05). The higher chloride ion concentration and water hardness were measured at the water hyacinth covered habitats, whereas higher chlorophyll a concentration was measured at open water habitats (Fig. 4).
Table 2

Variations (Mean ± SD) in abiotic variables among open water, water hyacinth (WH) infested and water cleared out habitats

Abiotic parameters

Habitats

Open water (n = 15) (Mean ± SD)

WH infested (n = 15) (Mean ± SD)

WH cleared out (n = 15) (Mean ± SD)

Orthophosphate (mg/l)

0.34 ± 0.20

0.50 ± 0.57

0.15 ± 0.17

Ammonium (mg/l)

0.22 ± 0.09

0.36 ± 0.31

0.54 ± 0.36

Nitrate (mg/l)

3.27 ± 1.52

4.43 ± 2.36

5.25 ± 2.76

Chlorophyll a (µg/l)

12.78 ± 3.80

9.14 ± 3.10

11.04 ± 2.53

Hardness (mg/l)

134.10 ± 26.70

296.10 ± 52.22

274.50 ± 128.77

Chloride (mg/l)

6.82 ± 6.96

21.80 ± 14.39

18.20 ± 14.00

Electrical conductivity (µS/cm)

271.10 ± 9.13

851.26 ± 318.32

291.70 ± 51.02

pH

9.00 ± 0.36

8.36 ± 0.25

8.92 ± 0.56

Alkalinity (mg/l)

128.70 ± 3.16

281.30 ± 104.84

384.25 ± 115.90

Bicarbonate (mg/l)

118.6 ± 5.8

260 ± 26.8

325.67 ± 36.90

Total dissolved solids (mg/l)

167.47 ± 7.60

546.80 ± 190.55

183.11 ± 36.44

Dissolved oxygen saturation (%)

84.96 ± 30.42

18.18 ± 11.65

69.02 ± 12.03

Water depth (cm)

20.67 ± 4.35

15.87 ± 4.75

19.27 ± 5.71

Sludge depth (cm)

9.53 ± 7.00

16.79 ± 3.31

20.27 ± 8.13

Fig. 4

Box and whisker plots of physicochemical parameters across categories of habitat. Small black squares represent median values, boxes represent interquartile ranges (25–75% percentiles), and range bars show maximum and minimum values. a, and b indicate statistically significant differences shown by nonparametric Independent-Samples Median Tests (p < 0.05)

Correlation of invertebrate taxa with abiotic variables

Table 3 portrayed the correlation between the invertebrate taxa and abiotic variables. The Spearman rank order correlation showed that most of the water physicochemical parameters showed no significant correlation with macroinvertebrates (p > 0.05). Family richness was negatively correlated with oxygen saturation while it was positively correlated with water hardness (p < 0.05). Culicine larvae had a significant negative correlation with Orthophosphate (p < 0.05). Gastropods had significant positive correlation with water electric conductivity and total dissolved solids while they had negative correlation with daytime dissolved oxygen saturation and pH (p < 0.05). In addition, Unionoida was negative correlated with anopheline larvae abundance.
Table 3

Correlation of macroinvertebrate metrics and environmental variables using Spearman rank order correlation

 

Bi

Ha

Al

OP

Cl

NH4

NO3

Os

EC

TDS

pH

Chl a

Bicarbonate

            

Hardness

0.92*

           

Alkalinity

0.97*

0.93*

          

Orthophosphate

− 0.11

− 0.06

− 0.22

         

Chloride

0.60*

0.59*

0.59*

− 0.29

        

Ammonium

0.79*

0.78*

0.80*

− 0.16

0.65*

       

Nitrate

0.18

0.31

0.27

− 0.08

− 0.33

0.15

      

Os

− 0.21

− 0.33

− 0.12

− 0.32

− 0.24

0.16

0.02

     

EC

0.20

0.25

0.12

0.31

0.36

− 0.05

− 0.24

− 0.76*

    

TDS

0.24

0.35

0.18

0.33

0.40

0.01

− 0.16

− 0.81*

0.97*

   

pH

− 0.06

− 0.15

− 0.04

0.11

− 0.17

0.28

0.10

0.71

− 0.63*

− 0.65*

  

Chl a

− 0.01

− 0.12

− 0.07

− 0.07

0.05

0.10

− 0.43

0.34

− 0.30

− 0.31

0.18

 

Anopheline

0.46

0.28

0.47

− 0.39

0.12

0.30

0.38

0.17

0.03

− 0.03

0.02

− 0.03

Culicine

0.26

0.21

0.35

− 0.7*

0.21

0.2

0.21

0.33

− 0.32

− 0.29

0.12

0.06

Gastropoda

0.23

0.31

0.20

0.10

0.19

− 0.1

0.07

− 0.63*

0.80*

0.76*

− 0.71*

− 0.33

Unionoida

− 0.48

− 0.41

− 0.47

0.09

0.12

− 0.1

− 0.56*

0.20

− 0.20

− 0.19

0.23

0.05

Veneroida

− 0.14

− 0.15

− 0.18

− 0.33

− 0.10

− 0.2

0.13

− 0.13

0.14

0.06

− 0.11

0.15

Odonata

0.44

0.49

0.45

− 0.10

0.51*

0.35

0.09

− 0.24

0.41

0.43

− 0.23

− 0.15

Coleoptera

0.40

0.48

0.41

0.29

0.44

0.35

0.15

− 0.28

0.16

0.24

0.20

− 0.37

Hirudinae

0.05

− 0.01

− 0.03

0.08

0.12

0.15

0.10

− 0.18

0.15

0.13

− 0.26

− 0.06

Hemiptera

0.11

0.26

0.19

− 0.16

0.34

0.04

0.15

− 0.22

0.26

0.28

− 0.41

− 0.30

Abundance

0.47

0.44

0.52*

− 0.27

0.30

0.38

0.43

0.06

0.09

0.09

0.02

− 0.23

Family richness

0.43

0.54*

0.36

− 0.13

0.42

0.30

0.15

− 0.55*

0.45

0.47

− 0.39

− 0.22

FBI

− 0.41

− 0.31

− 0.43

− 0.11

− 0.08

− 0.07

− 0.25

0.21

− 0.03

− 0.09

0.01

0.34

 

An

Cu

Ga

Un

Ve

Od

Co

Hi

He

Ab

Fr

Bicarbonate

           

Hardness

           

Alkalinity

           

Orthophosphate

           

Chloride

           

Ammonium

           

Nitrate

           

Os

           

EC

           

TDS

           

pH

           

Chl a

           

Anopheline

           

Culicine

0.58*

          

Gastropoda

0.27

− 0.09

         

Unionoida

− 0.69*

− 0.26

− 0.36

        

Veneroida

0.41

0.36

0.28

− 0.42

       

Odonata

0.40

0.26

0.59*

− 0.05

0.08

      

Coleoptera

− 0.04

− 0.05

− 0.01

0.13

− 0.32

0.49

     

Hirudinae

0.04

− 0.40

0.08

− 0.14

− 0.07

− 0.08

− 0.07

    

Hemiptera

0.09

0.24

0.51*

− 0.09

0.04

0.46

0.26

0.09

   

Abundance

0.78*

0.56*

0.42

− 0.38

0.24

0.80*

0.36

− 0.09

0.47

  

Family richness

0.17

0.13

0.65*

− 0.15

0.40

0.66*

0.24

0.08

0.34

0.47

 

FBI

− 0.37

− 0.11

0.05

0.32

0.32

− 0.15

− 0.54

0.11

0.06

− 0.30

0.18

Bi Bicarbonate, Ha hardness, Al alkalinity, OP orthophosphate, Cl chloride, NH4 ammonium, Os oxygen saturation, EC electric conductivity, TDS total dissolved solids, Chl a chlorophyll a, An anopheline, Cu culicine, Ga gastropoda, Un unionoida, Ve veneroida, Od odonata, Co coleoptera, Hi hirudinae, He hemiptera, Ab abundance, Fr family richness, FBI family biotic index

*Significant at p < 0.05

Multivariate analysis

Canonical Discriminant Analysis (CDA) showed that the three habitat groups were distinctly clustered (Fig. 5). The eigenvalues of the first canonical discriminant function (CDF1) was 11.480. The eigenvalue of the first canonical discriminant function (CDF2) was 1.014. The percentage of variance explained by the first and the second canonical discriminant functions were 91.8 and 8.1%, respectively. Based on Wilks’ lambda and the Chi square tests the proportion of group differences accounted by both CDF1 and CDF2 were statistically significant (p ≤ 0.001) (Table 4). The canonical correlation of the first canonical discriminant function (CDF1) was 0.959 whereas the second canonical discriminant function (CDF2) was 0.709 (Table 4). On CDF1, orthophosphate, total dissolved solids, and hardness contributed to group discrimination on the function in the positive direction while alkalinity, pH and chlorophyll a contributed for the grouping in the negative direction. The high discriminant scores on CDF1 are associated with total dissolved solids. Total dissolved solids (r = 0.478) are most highly correlated with CDF1 (Table 5). On CDF2, alkalinity and hardness contributed to group discrimination on the function in the positive direction while orthophosphate, total dissolved solids, pH and chlorophyll a contributed for the grouping in the negative direction. Alkalinity contributed most to group discrimination on the canonical discriminant function 2. The correlation of alkalinity with the CDF2 was r = 0.710 (Table 5).
Fig. 5

Scatter plot of canonical discriminant analysis indicating of the three distinguished habitat categories based on the environmental variables

Table 4

Evaluating canonical discriminant functions (CDF1, CDF2)

CDF

Eigenvalue

Relative percent

Canonical correlation

Wilks’s lambda

Chi square

df

p

1

11.480

91.9

0.959

0.040

127.348

12

0.000

2

1.014

8.1

0.709

0.497

27.646

5

0.000

Table 5

Standardized function coefficients (SFC), discriminator variables and Pearson correlations (r) with their respective canonical discriminant functions (CDF1, CDF2)

Variable

Canonical discriminant function 1

Canonical discriminant function 2

SFC

r

SFC

r

Hardness

0.443

0.174

0.533

0.689

Alkalinity

− 0.162

0.052

0.642

0.710

Orthophosphate

1.190

0.098

− 0.513

− 0.249

Total dissolved solids

1.258

0.478

− 0.264

− 0.032

pH

− 0.610

− 0.213

− 0.120

− 0.033

Chlorophyll a

− 0.646

− 0.130

− 0.186

− 0.196

Discussion

This study has investigated the potential effects of water hyacinth invasion and its management on water quality, biotic communities and human health. Water hyacinth covered habitat had higher macroinvertebrate family richness and relative abundance mainly due to the high affinity of most of the macroinvertebrate taxa to aquatic vegetation (Mereta et al. 2012). Vegetation can provide shelter against water current and predation, can provide more food resources, and is important as oviposition site (Couceiro et al. 2007). Vegetation has been shown to decrease the efficiency of fish predation and provides a refuge for benthic macroinvertebrates against visual predators (Hanson and Butler 1994; Diehl 1995). On the other hand, invertebrate predators such as odonates are strongly associated with the presence of vegetation cover as they look for food around the roots and leaves of macrophytes (Shelly et al. 2011). Several studies have shown that water hyacinth infestation positively altered the biodiversity of invertebrate assemblages mainly due to the complex structures of water hyacinth morphology that provide better habitat condition for epiphytic invertebrates (Schramm et al. 1987; Masifwa et al. 2001; Rocha-Ramirez et al. 2007; Schultz and Dibble 2012). On the contrary, Coetzee et al. (2014) reported a reduction of invertebrate diversity as a result of water hyacinth invasion as mats of water hyacinth reducing primary productivity and causing asphyxia; thereby, decline invertebrate diversity (Toft et al. 2003; Malik 2007; Schultz and Dibble 2012).

Habitats covered with water hyacinth had higher values of electrical conductivity, total dissolved solids and orthophosphate. The value of conductivity in habitats covered with water hyacinth was virtually three times higher as compared to the values in the open water and the cleared-out habitats. This high level of water electric conductivity may be due to the release of ions from water hyacinth detritus decomposition. Cunha-Santino et al. (2008) indicated that electric conductivity increased as a result of the liberation of ions in the process of decomposition. On the other hand, the values of dissolved oxygen and pH were notably lower in the habitat covered with water hyacinth. Impenetrable thick extensive mats of water hyacinth block reaching of light deep into the waterbodies and negatively affect primary productivity (Ochumba and Kibaara 1989; Meerhoff et al. 2003; Malik 2007; Villamagna and Murphy 2010; Vilà et al. 2011; Patel 2012; Wang and Calderon 2012; Güereña et al. 2015). In addition, the average value of orthophosphate was higher in habitat covered with water hyacinth. Studies have shown that lower dissolved oxygen levels and acidic condition triggers releases of soluble phosphorus from sediments, thereby increasing orthophosphate ion concentration in the water column (Reddy and DeBussk 1991; Moore and Reddy 1993).

In general, the scatter plot of canonical discriminant analysis in Fig. 5 indicated that water hyacinth cleared out sites and water hyacinth infested sites have been discriminated by environment variables. Furthermore, Water hyacinth cleared out habitats supported a high abundance of anopheline and culicine mosquito larvae. This implies that mosquito larvae management interventions should target water hyacinth cleared out sites. These habitats are isolated muddy water bodies which are conducive breeding habitats for mosquitoes (Mereta et al. 2013). Smaller and isolated water bodies are generally characterized by high water temperature, which eventually led to rapid larval development time (Culler and Lamp 2009). On the other hand, the abundance of anopheline and culicine larvae was lower in water hyacinth covered habitats. Ofulla et al. (2010) also found that lower abundance of anopheline mosquito in the Nyanza Gulf of Lake Victoria in habitat with covered with water hyacinth and other macrophytes. These habitats are home to a wide diversity of vertebrate and invertebrate predators and competitors and their presence likely suppress the density of mosquito larvae (Paaijmans 2008).

Several studies pointed out that aquatic insects belonging to the orders Coleoptera, Odonata and Hemiptera are responsible for significant reductions in mosquito populations and could be considered in integrated vector management programs (Shaalan and Canyon 2009). Predators reduce the abundance of mosquito larvae directly via predation, avoidance of oviposition or indirectly via competition for food resources (Knight et al. 2004). Some predators (especially those with chewing mouthparts) eat their prey (Odonata) but others suck the body fluid (hemolymph) of the prey (many beetle larvae and Hemiptera) (Shaalan and Canyon 2009). Some species of mosquito larvae reduce the chance of predator detection by reducing their activities (Bond et al. 2005; Ferrari et al. 2010). However, this has the disadvantage of reducing feeding efficiency, which in turn prolongs larval development and is also likely to result in smaller adults with probably a reduced longevity and fecundity (Bond et al. 2005).

In conclusion, water hyacinth infestation influenced water quality and biotic communities. The physical abstraction of water hyacinth provides a very good habitat for the proliferation of anopheline and culicine mosquito larvae. This could have an important influence on adult mosquito population and hence the dynamics of malaria transmission. Therefore, integrating water hyacinth management practices along with mosquito larvae control strategy could help to abate the potential risk of malaria outbreak in Lake Tana watershed since the area is one of the malaria vulnerable regions in Ethiopia (Graves et al. 2009). In addition, developing watershed scale nutrient management initiative and implementation could have a vital contribution for managing water hyacinth incursion. Data for this study were collected within a short period and hence time series study considering the overall effects of water hyacinth invasion on fish productivity, waterfowl species composition and diversity and other ecosystem services of Lake Tana is recommended.

Notes

Acknowledgements

The authors would like to acknowledge Bahir Dar University, Blue Nile Water research institute and Jimma University for providing material and financial assistance for this study. The study area map was digitized by Yihun Abdie from the Department of Environmental Sciences and Technology, Jimma University. Furthermore, the authors wish to thank all people who helped with laboratory and fieldwork.

Authors’ contribution

AG has conceived the main idea of the paper, collected the data, and has written the paper. WWA and BG have been collected the data and reading paper. WA and ES have been helping in writing and reading the paper. STM has been analyzing the data and writing the paper.

Compliance with ethical standards

Conflict interest

The authors declare that they have no conflict interest.

Supplementary material

10530_2018_1717_MOESM1_ESM.jpg (6.4 mb)
Supplementary material 1 (JPEG 6514 kb)

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ayenew Gezie
    • 1
    • 5
  • Workiyie Worie Assefa
    • 1
    • 3
  • Belachew Getnet
    • 2
    • 3
  • Wassie Anteneh
    • 1
  • Eshete Dejen
    • 4
  • Seid Tiku Mereta
    • 5
  1. 1.Department of BiologyBahir Dar UniversityBahir DarEthiopia
  2. 2.Department of Political ScienceBahir Dar UniversityBahir DarEthiopia
  3. 3.Blue Nile Water InstituteBahir Dar UniversityBahir DarEthiopia
  4. 4.Intergovernmental Authority on DevelopmentDjiboutiRepublic of Djibouti
  5. 5.Department of Environmental Health Sciences and TechnologyJimma UniversityJimmaEthiopia

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