INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

Land-Use Practices Affect Water Quality Parameters and Mayfly

(Order Ephemeroptera) Assemblage Along River Nzoia (Kenya)

Juliana Mwachi Anyango, Charles Odhiambo and Eunice Kairu

Kenyatta University

DOI : https://doi.org/10.51583/IJLTEMAS.2025.1411000061

Received: 27 November 2025; Accepted: 02 December 2025; Published: 09 December 2025

ABSTRACT

Several river ecosystems are undergoing varied land-use practices, whose monitoring should be continuous. This

study evaluated the influence of land-use practices on water quality and macro-invertebrate taxa, specifically the

mayfly (order Ephemeroptera) assemblage, along the River Nzoia in Kenya. Four dominant land-use activities

were identified as undisturbed, sugarcane growing, settlement, and industrial activities. All the physicochemical

water quality parameters displayed significant (P < 0.05) spatial variations. Areas with industrial activities had

low DO, as well as high BOD, TA, pH and conductivity, settlement and sugarcane growing areas had high levels

of phosphates and nitrates. Land use patterns dictated the macro-invertebrate community structure, where sites

with low disturbances had high composition, abundance and diversity and were dominated by order

Ephemeroptera, Plecoptera, and Trichoptera (EPT). The distribution of mayfly was significant relative to land-

use practice (P < 0.05), where undisturbed sites followed by industrial sites had the highest occurrence and

abundance of mayfly taxa, suggesting the occurrence of more tolerant species of mayfly in sites near industrial

areas. Dominance of Baetis, and Caenis in undisturbed sites and settlement areas, coupled with Heptagenia and

Ephemerella dominance in the sugarcane growing region, but none of the mayfly taxa dominated industrial sites,

suggests that they are influenced by anthropogenic activities. PCA plots showed a clear distinction between land-

use practices, with ephemeroptera taxa composition being clearly distinguished in the tri-plot. The present study

indicates that different types of land-use practices within the study area caused changes in the abundances of the

macro-invertebrates and, particularly, mayfly taxa. Thus, all stakeholders should formulate immediate policies

that will reduce human impacts on the water quality in River Nzoia. There is also a need to sensitize the local

community members to avoid harmful activities along the River Nzoia.

Key words: Macroinvertebrates; Metrics; Human activities; River Nzoia (Kenya), phemeroptera taxa

INTRODUCTION

The pivotal role of rivers in provisioning of vital ecosystem services, including drinking water, habitat for biotic

community, conservation of biodiversity, and attenuation of downstream fluxes of sediments, water, organic

carbon, and nutrients, is well known (Erős and Lowe, 2019 ). Rivers in high altitude areas conventionally occur

in catchment areas where land use activities should be minimal. In the contemporary world as well as in the past,

the fingerprints of numerous land use activities within proximity of the riverine environments (Thai-Hoang et

al., 2022; Ajwang, 2023; Zhang et al., 2023) have created long-lasting legacies of the negative impacts of human

activities on land near rivers (Li et al., 2020; Li et al., 2022). These land use activities include agriculture

(Edegbene et al., 2020; Comte et al., 2022), urbanization and urban development (Qi et al., 2020; Bi et al.,

2023), industrial development (Zhang et al., 2022; Zhu et al., 2022), large-scale water abstraction (Jiang et al.,

2022; Zogaris et al., 2023), or a combination of various land use practices (Ren et al., 2022; Islam et al., 2023).

By virtue of the strong interconnections between the riverine environment, their ecotones and fluvial ecosystems,

land use activities may alter the hydrological regime, damage habitats, as well modify ecological structures and

processes within the lotic aquatic ecosystems (Brontowiyono et al., 2022; Yao et al., 2023). Therefore,

monitoring of water quality changes at the riverine ecosystems remains a prerequisite for managing human-

induced land use changes.

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Traditional methods of monitoring the ecological integrity of the riverine ecosystems is usually performed using

physical and chemical methods (Farrell-Poe, 2005; Omer, 2019). Monitoring of surface waters using

physicochemical parameters provides conditions of the land use practices over a shorter time span (Tanjung and

Hamuna, 2019; Ewaid et al., 2020). Hitherto, the unreliability for long-term monitoring of water quality changes

remains the drawback of using physical and chemical water quality parameters for monitoring purposes.

Moreover, the results of water quality do not account for the impacts of land use activities from non-point sources

(Bhatia et al., 2018; Islam et al., 2018). Incorporating suitable biological methods and/or metrics ensures long-

term approaches in monitoring of water quality due to land use activities (Simon, 2020; Ustaoğlu et al., 2020).

In electing appropriate methods, selecting biological organisms that are easy and affordable to collect is more

convenient to achieve the best biological monitoring programme (Parmar et al., 2016; Motlagh and Yang, 2019).

Macro-invertebrates assemblage may provide an integrative measure of scientifically defensible evidence of

environmental condition (Mzungu et al., 2022) including reflection of land use activities on the water quality

(Sripanya et al., 2023). Several quantifiable population attributes that assess macro-invertebrate assemblage,

structure, composition, and response have been evaluated, and some have been found to massively correlate with

anthropogenic activities (Tampo et al., 2021). Measuring changes in macroinvertebrates' characteristics only at

the population level may allow only indirect inference to be made with regard to the cause of the population

decline. Assemblages of macroinvertebrates together with metrics of the population attributes are useful

surrogates for ecosystem attributes, reflecting on anthropogenic impact (Retnaningdyah et al., 2023; Sripanya et

al., 2023). The use of macro-invertebrates in the assessment of water quality in Kenya is historical (Mathooko,

1988; Mathooko and Mavuti, 1992; Ndaruga et al., 2004; Kibichii et al., 2007) and even in recent years (Lubanga

et al., 2021; Chamia and Kutuny, 2022; Fekadu et al., 2022). Several quantifiable attributes that assess macro-

invertebrate assemblage, structure, composition, and functional response have been evaluated, and some have

been found to massively correlate with water quality changes (Tampo et al., 2021).

Aquatic insects have gained prominence in studies aimed at monitoring water quality through biological ways

(Prommi and Payakka, 2015; Sitati et al., 2021). Research into this realm has consistently established that aquatic

insects belonging to Ephemeroptera (mayfly), Plecoptera (stonefly) and Trichoptera (caddisfly), generally

referred to as (EPT), show immense sensitivity to water quality changes (Abong'o et al., 2015; Oruta et al., 2017;

Fekadu et al., 2022). Insects belonging to these three groups cannot tolerate degraded water conditions, and thus

their high abundance in a particular localized region of a water body depicts high water quality integrity (Mzungu

et al., 2022). Among the EPT community of aquatic insect taxa, mayflies (order Ephemeroptera) have shown

much higher sensitivity to water quality changes compared to their counterparts in this taxa and thus are currently

being given leeway as biological water quality monitors (Maina et al., 2021). Mayflies are good bio-indicators

of the freshness in water quality due to their ability to swell in water of good biological quality (Mir et al.,

2021). However, land-use practices that introduce changes in water quality may also negatively impact the

mayfly abundance and community attributes.

In Kenya, Massive extensions of human settlements, human population growth and increasing industrial

activities along the River Nzoia Catchment have been reported (Nyilitya et al., 2020; Kadeka, 2021; Tarus et

al., 2022). The intensification of human activities within the catchment of River Nzoia threatens water quality

and functional integrity of the river, which occurs in the form of adjustments of biotic abundance and assemblage

(Achieng et al., 2021). Fortunately, the changes in biological assemblage can be used to decipher the extent to

which humans are affecting the ecological integrity of the river through diverse land-use practices. Although

several studies have employed aquatic macroinvertebrates to study changes in water quality along River Nzoia,

surprisingly, very few of these studies have directly linked them to land-use practices. Indeed, most of the studies

just describe spatial variations in macro-invertebrate assemblage without reference to land-use activities (Aura

et al., 2010; Aura et al., 2011; Sitati et al., 2021). There is also a consistent lack of studies on the mayfly

assemblage relative to land-use activities. Prolonged deficiency of such data will hamper the protocol for the

management and conservation of rivers undergoing differential land-use activities in Kenya.

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MATERIALS AND METHODS

Study area land use classification

The study was conducted along the River Nzoia in the larger Nzoia Catchment in Kenya. River Nzoia cachment

is located within the Lake Victoria Basin (Figure 3.1), lying at latitudes 1º30'N and 0º05'S, and longitude 34º

and 35º45'E. The river is 320 km long with a riparian catchment area of 275 km²(Githui, 2008). It lies at 1134-

2700 m above sea level. The rivers flow from the Kakamega Forest, and some of its tributaries pass through the

Mt. Elgon region, the Cherangany Hills and the lowland areas of Mumias and Webuye, and finally drain into

Lake Victoria. There are several sources of pollutants along the river located in urban areas in Bungoma,

Webuye, Mumias, and Webuye. Bungoma is an urban area that has industrial effluents from the municipal

region, Mumias and Nzoia discharge effluents from sugar mills, and at Webuye, there is the Pulp and Paper

Mills. Herein, espoused is the role played by agricultural chemicals, especially Mumias sugar and pan paper

mills, respectively.

The weather pattern within the catchment is typical of the equatorial climate type, where there is sunlight for 12

hours and darkness for 12 hours throughout the year. Temperature varies from a low of 812ºC to a high of

2529ºC (Othieno Odwori, 2021). The lowest temperatures are recorded in September, and the highest

temperatures are in March.

The rainfall in the region of study is bimodal and is fairly high, with a mean of 1200 to 1400 mm per year. Long

rains fall between the end of the month of March to the end of April-May, while June to the end of August is a

dry period that ushers in short rains in September, and afterwards is a long dry spell from October to the end of

March.

Fig. 1. Map of River Nzoia Basin, the study area and the sampling stations chosen for the study.

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Selection of study sites

Based on the categorization of the major land use activities and the delineation of catchments, the study sites

were selected. Undisturbed land use was found in the forest at latitude E035.07.272 and longitudinal

N00.52.403; sugarcane regions were located at latitude N00.21.005 and E034.29.462, with large-scale sugarcane

farming being the dominant activity; and areas near industrial activities were sampled at latitude E34.46.33 and

longitudinal N036.036, hereby referred to as industrial regions. On the other hand, settlement (at latitude

E034.20.049 and longitude N00.34.29.400) was the dominant land-use activity, with scattered subsistence farms

serving as a backdrop. Bathing, subsistence farming, fishing, and sand gathering were notable activities in

population areas.

Measuring physico-chemical and hydrological parameters

Five samples for physico-chemical parameters were taken for 8 months in January, March, May, June and

August. All samples were measured in triplicate. During measurements of in-situ samples, samples for laboratory

analysis were also collected in triplicate.

Physico-chemical parameters such as pH, dissolved oxygen (DO), electrical conductivity and total alkalinity

were measured in situ using the JENWAY® 3405 electrochemical analyzer that had probes for each independent

variable. Calibration of the probes was done before any sampling activity. Three measurements were done within

the vicinity of the sampling points.

Water was collected in plastic bottles for measurement of BOD₅, nitrate and phosphates. Water samples were

drawn from the selected sites in triplicate using labelled plastic bottles. The bottles were stored in cool boxes

during transportation from the field to the water quality laboratory. In the laboratory, samples were refrigerated

at 4 °C until analyzed using standard methods (Adams, 2017). In all analyses, 100 ml of the sample was used

after filtration through Whatman No. 42 filters. BOD₅was analyzed using the light and dark bottle method

(Young et al., 1981). NO₃-N was analyzed through flow injection spectrophotometric, diphenylamine sulphonic

2-

acid chromogene method (Asan et al., 2008). PO₄was analyzed using the standard ascorbic acid method,

reduction of phosphomolybdic acid (Towns, 1986). Due to financial limitations, other parameters such as total

nitrogen (TN), total phosphorus (TN), ammonia (NH₄), and total organic carbon (TOC) were not analyzed.

Macro-invertebrate sampling

Over the course of eight months, five sampling occasions were conducted in January, March, May, June, and

August to collect macro-invertebrate samples in tandem with physico-chemical parameters. In order to account

for the stydy area's seasonality, the samples were taken during both the dry and wet seasons. Macro invertabrates

were collected using Distrubance Removal Sampling Technique (DRST) and a Hess Sampler. The method

involve defining a particular sampling area and holding the Hess sampler in water as one disturbs the substrate

for approximately three minutes to ensure adequate dislodgement within the designated area and the

macrobenthos were then washed down into the Hess sampler. At each site, three samples of Macro invertabrates

were collected that is, from the middle and about 1m from the left and right banks. The Hess sampler was used

in riffles, shallow sites with rocky substrates while an Eckman grab sampler was utilised in soft sediment. Large

debris was then removed from the samples after careful washing the samples through a 500um mesh sieve of the

attached organisms, and placed in lidded sample jars. The sample jars were then appropriately labelled, and fixed

with 4% formalin (Kage, 2003), right in the field.

Laboratory processing

In the laboratory, the macro-neveterbrate samples were filtered and washed free of sediments through a 250um

sieve. The samples were spread evenly on a white tray for sorting (Kage, 2003). All the Ephemeroptera were

sorted out and identified to the lowest genera level and preserved with 70% Ethanol in well labelled and lidded

sample jars. Later the samples were taken to a specialist in Kenyan Museum for further identification of the

specific names and confirmation of genera. The unidentified samples were taken to South Africa for further

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attention where it was possible. The other samples were preserved in 70% Alcohol and kept in the University of

Eldoret Museum.

Assessment of species diversity indices

In calculating the indices, abundance data were obtained by counting macroinvertebrates. Several key attributes

were determined:

1. Absence/presence data of taxa of benthic macroinvertebrates at the various sampling sites relative to the

human activities by counting the visible macroinvertebrates

2. The abundance of each macroinvertebrate at various sampling sites relative to human activities by

quantification of the numbers determined

3. Shannon index (H’) of benthic macroinvertebrates at the various sampling sites relative to the human

n

activities using the formula: H' P(LnP) (Murphy, 1978)



i

i1

Where H’ = Shannon’s diversity index; Pi = the abundance of the i^thspecies

expressed as a proportion of total cover; n = number of species

1. Percentage of oligochaetes and chironomids compared to the total abundance of the macroinvertebrates

2. Percentage of Ephemeroptera, Plecoptera and Trichoptera (%EPT) compared to the total abundance of the

macroinvertebrates

3. Percentage of mayfly (Ephemeroptera) compared to the total abundance of the macroinvertebrates

Data analysis

The data obtained after sample analysis were analyzed using STATISTICA 6.0 (StaSoft, 2001). All assumptions

of the parametric test were achieved through a normality test using the Shapiro-Wilk W statistic (Shapiro and

Francia, 1972). Differences in physico-chemical parameters among land-use practices were analyzed using One-

Way Analysis of Variance (ANOVA). Significantly different means were discriminated using Duncan's Multiple

Range test after ANOVA (Tallarida et al., 1987).

Differences in macroinvertebrate community attributes (abundance, diversity, %OC, %EPT, %mayfly) were

analysed using the nonparametric Kruskal–Wallis test, as the data did not meet normality assumptions. Analysis

of the variation in the occurrence of macroinvertebrates among land-use practices was done using chi-square

(²). All results were declared significant at P < 0.05.

Principal Component Analysis (PCA) was used to analyze the spatial relationships between physico-chemical

parameters relative to catchment land-use as well as the relationships between mayfly taxa relative to catchment

land-use cover and the Interrelationship between land-use practices, water quality parameters and ephemeroptera

(mayfly) taxa. This was done through the PCA procedure uses multiple regressions to fit attributes to an

ordination space as vectors. The significance of Principal Axis Correlation coefficients was tested using a Monte-

Carlo procedure. Discriminant functions of each attribute on land-use were determined through measures of

constancy and fidelity. Constancy is the quantity of land-use areas where macroinvertebrate taxa occur, while

fidelity is the ability of the macroinvertebrate attribute to predict variations.

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RESULTS

Effects of land-use practices on physico-chemical water quality parameters in River Nzoia

A summary of the analyzed physico-chemical properties of riverine water relative to the four human activities

along River Nzoia is shown in Table 1. All the analyzed physicochemical water quality parameters demonstrated

significant (P < 0.05) spatial changes relative to land-use practices. The pH ranged from 3.5 to 8.1 and displayed

significant (F = 19.3223, df = 3, P = 0.0021) differences relative to land use. The pH value was highest in

sugarcane farms, while the pH of industrial areas was somewhat lower than 4.0.

Table 1. Physico-chemical attributes (mean  SEM) of river water with respect to land-use practices along

River Nzoia from January to August 2020

UNDS

6.59 ±

INDS

SUGS

SETL

P-value

pH

3.99 ± 0.27^a7.85 ± 0.74^d5.94 ± 0.73^b0.0021

3.49 ± 0.35^a5.40 ± 0.45^b4.87 ± 0.44^b0.0005

8.13 ± 0.51^d2.39 ± 0.18^b3.86 ± 0.68^c0.0015

0.72

^c

DO (mg/L)

12.71 ±

0.90

^c

BOD₅(mg/L)

Electrical conductivity

1.24 ±

0.17

^a

32.4 ± 4.9^a341.5 ±

148.8 ± 25.9^c99.4 ± 14.9^b0.0076

7.94 ± 1.06^c6.41 ± 0.78^b0.0124

(µS/cm)

62.2

^d

3.22

^d

Total Alkalinity (mg/L)

3.91± 0.48^a19.72 ±

-

NO₃(mg/L)

PO₄^2-(mg/L)

0.28 ±

0.37 ± 0.07^b0.71 ± 0.11^c0.64 ± 0.12^c0.0035

0.24 ± 0.03^a0.36 ± 0.06^b0.65 ± 0.23^c0.0128

0.36

^a

0.22 ±

0.04

^a

¹Means with the same letters as superscripts are not significantly different (P > 0.05).

²SE: Standard Error of the mean

³UNDS = Undisturbed sites, INDS = Industrial sites, SUGS = Sugarcane growing sites and SETL = Settlement

locations

Macro-invertebrate species composition and abundance in Nzoia River

A total of 43 species belonging to 31 genera and 12 orders of macroinvertebrates were observed relative to the

land-use activities. The macroinvertebrate genera occurrence in terms of presence/absence along a gradient of

human activities along River Nzoia is provided in Table 2. There were significant differences in the occurrence

of macroinvertebrates among sites (Chi-square; ²= 9.234, df = 3, P = 0.0263). The undisturbed sites had the

highest occurrence of macroinvertebrate genera (36 genera), which was followed by occurrence in settlement

areas (21 genera), sugarcane growing areas (20 genera), while sites adjacent to the industrial areas had the lowest

macroinvertebrate occurrence (17 species).

Table 2. Information concerning presence (+)/absence (-) of various genera of benthic macroinvertebrates

from January to August 2020

Order

Family

Genus

UNDS SUGS

SETL

INDS

Pulmonata

Limnaeidae

Physidae

Lymnaea

Physa

+

-

+

-

+

Planorbidae

Dytiscidae

Planorbis

Coptotomus

-

Coleoptera

-

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Ilybius

+

-

Platambus

Gyrinus

-

Gyrinidae

+

-

Haliplidae

Haliplus

Limnophora

Chironomus

Tipula

-

+

-

Diptera

Anthomyiidae

Chironomidae

Tipulidae

+

-

+

-

+

-

+

-

Simulidae

Simulium

Baetis

+

-

Ephemeroptera

Baetidae

+

-

+

-

Caenidae

Caenis

Ecdyonuridae

Ephemerallidae

Leptophtebiidae

Heptageniidae

Heptagenia

Ephemerella

Habrophlebia +

-

Epeorus

+

-

+

-

Rhithrogena

Collicorixa

Corixa

-

Hemiptera

Corixidae

-

+

-

+

-

Gerridae

Gerris

-

Hydrometridae

Mesoralidae

Notonectidae

Physidae

Hydrometra

Mesorelia

Notonecta

Phymata

-

+

-

+

Lamellibrandiat Sphaeriidae

a

Sphaerium

-

Odonata

Agridae

Agrion

+

-

+

-

Cordulegasterida Cordulegaste

e

r

Gomphidae

Gomphus

+

-

+

-

Plactyenemididae Pyrrhosoma

+

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Enallagma

Platycnemis

Valvata

+

-

+

-

Prosobranchiata Valvatidae

-

Trichoptera

Hydropsychidae

Hydropsyche

Tinodes

+

-

+

21

+

-

Plecoptera

Nemouridae

Neoperla

-

Oligochaeta

Crustacea

Frequency

Lumbricidae

Decapoda

Lumbricus

Eriocheir

-

+

17

36

20

¹UNDS = Undisturbed sites, INDS = Industrial sites, SUGS = Sugarcane growing sites and SETL = Settlement

locations

The abundance of macroinvertebrate genera relative to land-use activities is provided in Table 3. Generally,

there was no genera of macro-vertebrate exceeding 10% in relative abundance, while those whose abundance

was more than 4% were Baetis (9.5%), Caenis (5.4%), Hydropsyche (4.7%), Gerris (4.5%), and Spaherium

(4.2%). Differences in the abundance of macro-invertebrate genera were significant relative to land-use practices

(Kruskal-Wallis ANOVA; H = 19.234, df = 3, P = 0.0004). Undisturbed sites had the highest abundance of

macro-invertebrate genera (n = 5692), which was dominated by Baetis (8.6%), Caenis (5.9%), Tinoides (5.3%),

Hydropsyche (5.0%), Heptagenia (4.7%), and Notonecta (4.6).

Meanwhile, the settlement areas had the second most abundant macroinvertebrate (n = 3182), which was

dominated by macroinvertebrates of the genera Baetis (16.8%), Sphaerium (11.6%), Caenis (11.5%), and

Chironomus (9.5%). The areas within industrial regions had the least abundance of macro-invertebrates (n =

1692), which was dominated by genera: Planorbis (12.5%), Baetis (12.5%), Rhithrogena (10.8%) and Tipula

(9.9%). In the sugarcane-dominated growing, the total abundance of macroinvertebrates (n = 2510) was lower

than that of the settlement region but higher than the industrial region. The macroinvertebrate was dominated by

Platambus (11.3%), Cordulegaster (10.9%), Gerris (9.2%), Agrion (9.0%), and Ephemerella (4.2%).

Table 3. Information concerning the abundance of macroinvertebrates at the four land-use practices from

January to August 2020

Landuse type

Order

Family

Genera

UNDS SUGS

SETL

110

33

INDS

0

Pulmonata

Limnaeidae

Physidae

Lymnaea

Physa

44

54

30

26

21

0

123

98

0

Planorbidae

Dytisadae

Planorbis

Coptotomus

Ilybius

72

15

213

159

132

142

Coleoptera

89

27

132

0

Platambus

285

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Gyrinidae

Gyrinus

31

0

Haliplidae

Haliplus

0

231

0

Diptera

Anthomyiidae

Chironomidae

Tipulidae

Limnophora

Chironomus

Tipula

123

75

0

132

0

303

0

27

168

0

Simulidae

Simulium

Baetis

49

34

0

Ephemeroptera

Baetidae

489

336

270

201

54

534

366

0

213

0

Caenidae

Caenis

0

Ecdyonuridae

Ephemerallidae

Leptophtebiidae

Heptageniidae

Heptagenia

Ephemerella

Habrophlebia

Epeorus

211

213

0

69

33

0

44

183

0

Rhithrogena

Collicorixa

Corixa

30

0

Hemiptera

Corixidae

127

261

159

138

228

264

137

192

212

207

195

69

0

78

231

0

33

201

15

0

Garridae

Gerris

0

Hydrometridae

Mesoralidae

Notonectidae

Physidae

Hydrometra

Mesorelia

Notonecta

Phymata

0

15

54

0

135

369

84

0

Lamellibrandiata

Odonata

Sphaeriidae

Agridae

Sphaerium

Agrion

0

225

273

117

0

33

0

Cordulegasteridae

Gomphidae

Plactyenemididae

Cordulegaster

Gomphus

Pyrrhosoma

Enallagma

Platycnemis

Valvata

39

0

21

0

78

0

228

234

285

301

153

225

70

156

0

Prosobranchiata

Trichoptera

Valvatidae

0

Hydropsychidae

Hydropsyche

Tinodes

53

45

15

0

159

48

0

115

101

27

51

0

Plecoptera

Hydropsychidae

Nemouridae

Neoperla

Lumbricus

Eriocheir

228

39

81

Oligochaeta

Crustacea

Lumbricidae

Decapoda

0

21

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Total abundance

5692

2510

3182

1692

¹UNDS = Undisturbed sites, INDS = Industrial sites, SUGS = Sugarcane growing sites and SETL = Settlement

locations

The total abundance of each order in River Nzoia is shown in Figure 2. Ephemeroptra was the most abundant at

24% relative abundance, followed by Hemiptera (15.9%), Odonata (14.8%) and Coleoptera (10.0%). There were

significant differences in the abundance of macro-invertebrate orders relative to the land-use activities (Kruskal-

Wallis ANOVA; H = 76.312, df = 33, P < 0.0001). Higher abundance of Ephemeroptera, Hemiptera, Odonata,

Trichoptera and Plecoptera was recorded in areas that are less disturbed. In the settlement areas, Lamebranchiata,

Coleoptera, Diptera and Oligochaetes dominated. In sugarcane growing areas, Pulmonata, Coleptera, and

Odonata were more dominant. Meanwhile, no macro-invertebrate-dominated areas within the industrial

activities.

1600

UNDS

SETL

SUGS

INDS

1400

1200

1000

800

600

400

200

0

a

t

a

t

a

t

a

t

a

r

e

a

r

a

e

r

a

t

r

e

t

e

i

e

a

e

t

a

i

e

t

a

t

c

t

n

d

n

p

i

a

t

p

i

p

h

p

o

h

c

p

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p

o

c

n

a

o

s

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r

o

d

r

m

l

m

D

o

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i

h

u

r

e

l

c

e

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i

O

c

i

r

e

l

u

r

b

o

l

C

m

e

H

P

l

P

C

e

T

O

h

s

m

p

o

a

r

P

E

L

Macro-inverterbrate order

Figure 2. Total abundance of benthic macro-invertebrates’ orders in Nzoia River from January to August 2020

¹UNDS = Undisturbed sites, INDS = Industrial sites, SUGS = Sugarcane growing sites and SETL = Settlement

locations

The benthic macroinvertebrate community species diversity under different land-use attributes is shown in

Figure 3. The highest species diversity occurred in the undisturbed areas, followed by sugarcane growing areas,

while industrial areas had low species diversity. The high diversity of species in the least disturbed regions

indicates that macroinvertebrates prefer less polluted sites in the river ecosystem, as previously stated.

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

UNDS

SUGR

SETL

INDS

Land use activities

Figure 3. Species diversity relative to various land-use practices in Nzoia River from January to August 2020

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¹UNDS = Undisturbed sites, INDS = Industrial sites, SUGS = Sugarcane growing sites and SETL = Settlement

locations

The overall %EPT was highest in the industrial areas and lowest at the undisturbed sites, with the other two sites

being intermediate (Figure 4).

25.0

Ephemeroptera Plecoptera Trichoptera

20.0

15.0

10.0

5.0

0.0

INDS

SUGS

SETL

UNDS

Land use activities

Figure 4. The %EPT relative to various land-use practices in River Nzoia from January to August 2020

¹UNDS = Undisturbed sites, INDS = Industrial sites, SUGS = Sugarcane growing sites and SETL = Settlement

locations

Influence of land-use on the composition and diversity of mayfly

The results showing the presence/absence of mafly taxa are shown in Table 4. There were significant differences

in the abundance of the species among the sampling sites (Chi-square,  = 7.987, df = 3, P = 0.0193). Undisturbed

sites had the highest occurrence of mayfly, followed by sites near industrial activities, and the least in settlement

regions.

Table 4. Presence (+)/absence (-) of various genera of Ephemeroptera (Mayfly) taxa with respect to land-

use practices along Nzoia River from January to August 2020

Family

Genus

INDS SUGS SETL UNDS

Baetidae

Caenidae

Coenidaie

Baetis

Caenis

Coenus

+

-

+

-

+

-

+

-

Ecdyonuridae

Ephemerallidae

Leptophtebiidae

Heptageniidae

Heptogenia

Ephemeralla

Habrophlebia

Epeorus

Rhithrogenia

Heptagena

+

-

+

7

+

-

+

-

+

-

+

-

+

-

5

-

3

Total occurrence

6

¹UNDS = Undisturbed sites, INDS = Industrial sites, SUGS = Sugarcane growing sites and SETL = Settlement

locations

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Absolute abundance (No. ind.) and relative abundance (%) of mayfly (Ephemeroptera) with respect to land-use

practices along Nzoia River are shown in Figure 5. There was a significant spatial variation in the abundance of

mayflies in relation to land-use practices Kruskal-Wallis ANOVA; H = 1223.234, df = 18, P < 0.0001).

Dominance of Baetis (15.7%), Caenis (10.4%) and Heptagenia (8.3%) was observed in undisturbed sites. In the

sugarcane growing region, the dominant genera of mayfly were Heptagenia (6.5%) and Ephemerella (6.6%),

while in the settlement region, two species occurring in high abundance were Baetis (16.5%) and Caenis

(11.3%). Meanwhile, at the industry sites, there was no dominant species except Baetis, which occurred at 6.6.%

and Rhithrogena (5.6%).

600

UNDS

SETL

SUGS

INDS

500

400

300

200

100

0

Baetis

Caenis

Heptagenia

Ephemerella

Habrophlebia

Epeorus

Rhithrogena

18.0

16.0

14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

Baetis

Caenis

Heptagenia

Ephemerella

Habrophlebia

Epeorus

Rhithrogena

Macro-inverterbrate genera

Figure 5. Absolute abundance (No. ind.) and relative abundance (%) of mayfly (Ephemeroptera) with respect to

land-use practices along Nzoia River from January to August 2020

¹UNDS = Undisturbed sites, INDS = Industrial sites, SUGS = Sugarcane growing sites and SETL = Settlement

locations

Interrelationship between land-use practices, water quality parameters and Ephemeroptera (mayfly) taxa

The relationships among water quality parameters and Ephemeroptera taxa composition at the sampling sites

during the study are shown in Figure 6. There was a clear distinction between the land-use sites, with the

ephemeroptera taxa composition being clearly distinguished in the PCA tri-plot. Areas of industrial activities

were significantly correlated with DO, BOD, TA, pH and electrical conductivity, showing positive correlation

with Baetis, Coenus, Habrophlebia, Epeorus, and Heptagenia, as well as with industrial sites. Settlement and

sugarcane growing areas were correlated positively with nitrates and phosphates, as well as mayfly of genera

Ephemaralla, Heptogenia, and Caenis. Undisturbed areas did not control the population of any mayfly, as well

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any water quality parameters. Phosphates and nitrates were positively associated with settlement and sugarcane

growing. Meanwhile, undisturbed sites had no effects on any physico-chemical parameters.

Figure 6: Principal Component Analysis (PCA) of mayfly taxa relative to catchment land-use cover along

River Nzoia from January to August 2020

¹UNDS = Undisturbed sites, INDS = Industrial sites, SUGS = Sugarcane growing sites and SETL = Settlement

locations

The interrelationships among water quality parameters, land-use and ephemeroptera taxa composition at the

sampling sites during the study are shown in Figure 7. There was a clear distinction between the land-use sites,

with the ephemeroptera taxa composition being clearly distinguished in the PCA tri-plot. There was a clear

distinction between the land-use sites, with the ephemeroptera taxa composition being clearly distinguished in

the PCA tri-plot. Areas of industrial activities were significantly correlated with DO, BOD, TA, pH and electrical

conductivity, showing positive correlation with Baetis, Coenus, Habrophlebia, Epeorus, and Heptagnia, as well

as with industrial sites. Settlement and sugarcane growing areas were correlated positively with nitrates and

phosphates, as well as mayfly of genera Ephemaralla, Heptogenia, and Caenis.

1.0

Coenus

Baetis

INDS

Habrophlebia

pH

TA

Heptagena

BOD

DO

Conductivity

Rhithrogenia

Epeorus

0.0

Phosphates

Nitrates

SETL

UNDS

SUGC

Ephemeralla

Heptogenia

Caenius

-1.0

0.0

Factor 1 : 32.18%

1.0

Figure 7. Principal Component Analysis (PCA) of mayfly taxa and water quality relative to catchment land-use

cover along River Nzoia from January to August 2020

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¹UNDS = Undisturbed sites, INDS = Industrial sites, SUGS = Sugarcane growing sites and SETL = Settlement

locations

DISCUSSION

There were differences in physico-chemical parameters relative to land use activities along River Nzoia. The

study, high pH values in the sugarcane growing zone, is most likely associated with alkaline conditions owing

to the burning of sugarcane during harvesting, which produces ash which mixes with rainwater to form a weak

alkaline solution (Trujillo-Narcía et al., 2019). It is also probable that the fertilizers used in the sugarcane farms

are mostly alkaline in nature, an assumption that needs further validation. The settlement areas had pH values

between 4.66.7, indicating large variations from acidic to near neutral pH, which may be associated with the

discharge of domestic wastes.

A total of 43 species belonging to 31 genera and 12 orders of macroinvertebrates were observed relative to the

land-use activities. The rich biodiversity of macroinvertebrates in the undisturbed site could be attributed to the

good water quality due to low human disturbances, as found in previous studies (Mathooko and Mavuti, 1992;

Abong'o et al., 2015; Gholizadeh et al., 2021; Maina et al., 2021). It must be highlighted that studies conducted

in the upstream River Naromoru (Mathooko and Mavuti, 1992), River Omubira in Kakamega County (Mzungu

et al., 2022) and Thika (Maina et al., 2021) found much higher species composition than in the current selected

undisturbed sampling sites in the River Nzoia, suggesting that the river may be undergoing a more serious

problem of water quality degradation.

The present study suggests that land-use practices are affecting species abundance since less disturbed sites had

high genera abundance, mainly of the order Ephemeroptera, Plecoptera, and Trichoptera (EPT), as found in other

studies (Menetrey et al., 2010; Ab Hamid and Rawi, 2017; Firmiano et al., 2017). Macro-invertebrates of the

EPT taxa prefer less polluted sites and will be in higher abundance in areas with less human disturbance and

pollution. The species count in areas undergoing human activities in the selected sampling sites in the River

Nzoia rarely exceeds 50, perhaps due to the higher intensity of human activities, which may also serve as

pollutants. No specific dominance of macroinvertebrates was observed in the region, and therefore occurrence

of Hydropsyche, Gerris, Heptagenia, and Spaherium may have been possible because they are multivoltine,

which allows them to be rapid colonizers (Merrit et al., 2008). Chronomus sp., Caenis sp. and Hydropsyche sp.

have been shown to increase in floodplain rivers (Chessman et al., 2006), suggesting that the river has a tendency

to flood during the rainy season. Meanwhile while Baetis sp. and Caenis sp. have been found in areas of intense

food crop production (Johnson et al., 2013; Gutzlera et al., 2015). These patterns clearly indicate that different

human activities contribute differentially to species abundance and somewhat validate the use of

macroinvertebrate abundance in the study area to detect the influence of land-use practices.

The highest species diversity occurred in the undisturbed areas, followed by sugarcane growing areas, while

industrial areas had low species diversity. The high diversity of species in the least disturbed regions indicates

that macroinvertebrates prefer less polluted sites in the river ecosystem, as previously stated. The current results

support the idea that agricultural activities and industrial land-use resulted in negative changes in water quality

as reflected in the macroinvertebrate genera patterns.

The presence of low abundance of EPT in areas with varying land-use practices lends credence that human

activities negatively impacted water quality and thus lower the quality of water, indicating some level of water

pollution along the river, as established in other comparable studies (Camargo, 1992; Camargo, 1994; Živić et

al., 2009; Guilpart et al., 2012). This could be attributed to the human disturbances that reduce canopy cover

and introduce many pollutants into the water body. The low species diversity in settlement areas and sugarcane

farms can be attributed to the use of various chemicals for agriculture, human wastes, together with a host of

other activities that consequently lower the water quality.

Undisturbed sites had the highest occurrence of mayfly, followed by sites near industrial activities, and the least

in settlement regions. Apart from the sites near undisturbed sites, a higher occurrence of mayfly in the sites near

industries compared to settlement and sugarcane growing is rather peculiar. It could, however, be possible that

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these mayfly genera that inhabit those areas undergoing constant pollution develop to become tolerant and exist,

but with low abundance, which is now widely documented (Edegbene et al., 2020; Edegbene et al., 2021; Hu et

al., 2022). This may be confirmed by reports that have indicated that there are some species of mayfly that may

tolerate oxygen depletion (Firmiano et al., 2017), high ammonia (Echols et al., 2010), acidification (Šupina et

al., 2022), and extreme pH (Sivaruban et al., 2020). Nevertheless, the current study cannot adequately verify

this hypothesis since identification of the macroinvertebrates was done to genus levels, but determining which

species tolerate pollution requires identification to species level.

It is still not clear why Baetis dominated undisturbed, settlement and industry sites but was absent in the

sugarcane farm. Perhaps it is species-wide in distribution since among all the mayfly taxa, the genus Baetis is

categorized as the most sensitive (Anuradha et al., 2020; Elias, 2020; Strungaru et al., 2021). Baetis sp. has been

established as being very tolerant, with the ability to survive with constraints to a range of water parameters

(Thakur et al., 2023). Previously, it was established that abundance and distribution of Baetis sp. in several

polluted rivers were mainly influenced by anthropogenic activities, which cause alterations in conductivity,

dissolved oxygen, and nutrients (Strungaru et al., 2021). Similarly, population dynamics of Baetis sp. are rarely

decided by changes in the kind and accessibility of diet, but by the competition from other species inhabiting the

sites (Thakur et al., 2023). It has been established that the alteration in the water quality parameters is more

likely to alter their abundance of Baetis sp., but may not eliminate them (Hamid et al., 2021), which is similar

to the present study. Heptagenia and Habrophlebia dominated the undisturbed region and settlement region,

which shows that they respond to moderate pollution. Rhithrogena was more dominant in areas of wide industrial

practice because this species has been established to tolerate a wide range of pollutants, including acid mines

and heavy metals (Hamid et al., 2021). Meanwhile, it also established that Species such as Habrophlebia

occurred only in undisturbed sites and may be an indicator that they are not tolerant mayfly genera even low

levels of pollution.

The present study indicates that different types of land-use practices within the study area caused changes in the

abundances of the mayfly taxa. It is possible that undisturbed sites within the forested region allow for high

abundance due to the presence of appropriate conditions necessary for the survival of larvae. However, the

presence of diverse organic and inorganic pollutants in the areas undergoing human activities, there is a

possibility that water quality may not be suitable for some species of mayfly or may allow for them to exist in

low abundance. The highest species diversity occurred in the undisturbed areas, followed by sugarcane growing

areas, while industrial areas had low species diversity. The high diversity of species in the least disturbed regions

indicates that macroinvertebrates prefer less polluted sites in the river ecosystem, as previously stated. The

current results support the idea that agricultural activities and industrial land-use resulted in negative changes in

water quality as reflected in the macroinvertebrate genera patterns.

It is possible that undisturbed sites within the forested region allow for high abundance due to the presence of

appropriate conditions for the survival of larvae. However, the presence of diverse organic and inorganic

pollutants in the areas undergoing human activities, there is a possibility that the water quality may not be

suitable for some species of mayfly. Baetis sp. It has been established that the changing land-use and alteration

in the water quality parameters are more likely to alter their abundance of several species of mayfly (Hamid et

al., 2021). Rhithrogena was associated with any land-use activity since this species has been established to

tolerate a wide range of waterbodies, including acid mines and heavy metals (Hamid et al., 2021).

CONCLUSIONS

The present findings demonstrate that land use activities influence the physicochemical parameters and

macroinvertebrate assemblage. All the physico-chemical water quality parameters displayed significant (P <

0.05) spatial variations. The concentration of DO was lowest and BOD highest in areas with industrial activities,

-

2-

followed by sugarcane farms, but was lowest BOD₅in the undisturbed areas. The highest of NO₃and PO₄

occurred in sugarcane farms and settlement sites. Macroinvertebrate species composition was highest at the

undisturbed sites, followed by sugarcane, while sites adjacent to the settlement and industrial areas had the lowest

species numbers. There was a clear pattern showing that different land use patterns affected macro-invertebrate

community structure, where sites with low disturbances had high composition, abundance and diversity and

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were dominated by order EPT, while sites closer to industrial activities had low species composition, abundance

and diversity. Meanwhile, mayfly occurrence was significant relative to land-use practice (P < 0.05).

Undisturbed sites had the highest occurrence of mayfly, followed by sites near industrial activities, and the least

in settlement regions, suggesting possible occurrence of more tolerant species of mayfly in sites near industrial

areas. There were significant interrelationships between the land-use practices, water quality and Ephemeroptera

taxa composition. The concentrations of DO, BOD, TA, pH and conductivity were positively associated with

industrial activities, while the phosphates and nitrates were positively associated with settlement and sugarcane

growing, leading to the conclusion that the present study indicates that different types of land-use practices

within the study area caused changes in abundances of the mayfly taxa.

The macroinvertebrate richness in terms of family: genus: species ratio reflects that a loss of species in any site

would mean loss of the entire genus (or family). This indicates the necessity of preserving all the sampled rivers

if species or genus richness conservation should be a priority. It is also time to declare these selected sampling

sites in the Upstream River Nzoia as endangered, and that the extinction will result in the extinction of unique

macroinvertebrate species. As such, human activities along the Upstream River Nzoia should be regulated. The

presence of a larger number of species tolerant to anthropogenic impacts could signal human-induced

perturbations; thus, all stakeholders should formulate immediate policies that will reduce human impacts on the

macroinvertebrate composition of the selected sampling sites in the River Nzoia.

RECOMMENDATIONS

Several recommendations were formulated based on the research findings as follows: First, there is a need to

protect existing riparian forest strips by maintaining a natural canopy to control temperature and sediment input,

as well as engaging local communities in monitoring EPT indicators every six to twelve months. This includes

Capacity building of farmers on how to use controlled fertilizer application on sugarcane farms by introducing

soil-testing-guided fertilization to reduce excess nutrient runoff. Secondly, there is a need to establish a

catchment management committee to involve farmers, industries, NEMA, WRA, and KFS through public

awareness programmes. This would include building the capacity to educate households on safe disposal and

the ecological role of Ephemeroptera as bioindicators.

Third, farmers should be encouraged to plant napier grass and indigenous trees to trap sediments and

agrochemicals, which serve as riparian buffers. And for this to happen, it is necessary for riparian demarcation

and rehabilitation to remove illegal structures along the Nzoia River. Fourth, the government should introduce

pollution effluent compliant monitoring monthly programmes for BOD, total suspended solids, and Ammonia

at the factory discharge point. Fifth, there is also, a need to upgrade the effluent Pre-treatment system by

introducing chlorine-free bleaching practices at Webuye Pan Paper industry. And, finally, there is a need to

establish a pollution-controlled Buffer Zone by re-vegetating the river banks along the Nzoia River with water

purifying plants such as reeds (Phragmites Spp.)

ACKNOWLEDGEMENTS

We thank the University of Eldoret Research Laboratory and staff for the use of their facilities for

macroinvertebrates identification and analysis. Our sincere gratitude also goes to technical assistants, Mr.

Lubanga and Mr. Kibet, for their support in macroinvertebrate analysis.

Conflicts of interest/Competing interests: The authors declare no conflict of interest in the publication of this

manuscript

Availability of data and material: Data will be available on request from the author(s)

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