ecological assessment of portoviejo river basin...
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Faculty of Bioscience Engineering
Academic year 2015 – 2016
Ecological assessment of Portoviejo river basin (Ecuador)
Juan Antonio Dueñas Utreras
Promotor: Prof. dr. ir. Peter L.M. Goethals
Tutor: MSc. Marie Anne Eurie Forio
Master’s dissertation submitted in partial fulfillment of the requirements
for the degree of
Master of Science in Environmental Sanitation
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COPYRIGHT PAGE
I, JUAN ANTONIO DUEÑAS UTRERAS, herewith declare that this dissertation is the result of my
own work and the submission of this dissertation is only made here in this university. Other
studies used here have been duly acknowledged through references of the authors and served
as information resources.
The author and the promoter give authorization to consult and copy parts of this dissertation for
personal use only. Any other use of this dissertation is subject to copyrights laws, and the source
should be specified after having received the written permission from the author and the
promoter.
Laboratory for Environmental Toxicology and Aquatic Ecology
Department of Applied Ecology and Environmental Biology
Faculty of Bio-engineering Sciences, Ghent University
Jozef Plateaustraat 22, B-9000 Gent (Belgium)
Tel. 0032 (0)9643765 Fax. 0032 (0) 9 2644199
…………………………………………
19/08/2016
Prof. Dr. ir. Peter L.M. Goethals
(Promoter)
Email: [email protected]
……………………………………………
19/08/2016
Juan Antonio Dueñas Utreras
(Master thesis author)
Email: [email protected]
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ACKNOWLEDGMENTS
First at all, I want to thanks God who has given to me the strength to remain firm in my convictions
and who holding me in my moments of sadness.
To my Promotor Prof. Peter Goethals and my tutor Marie Anne Eurie Forio, who have contributed
to my training, thanks for all your help in the realization of this thesis.
Special thanks to the Ministry of Higher Education, Science and Technology and Innovation
(SENESCYT) of Ecuador for the scholarship that was awarded in 2014 that made me able to realize
my dreams of studying abroad.
The Technical University of Manabí, institution where I work, thank you very much for the trust.
To my beloved Erika for giving me unconditional love and constant support at all times, my little
Lina for giving me their love and happiness when I needed it.
My mother, who provide their prayers and affection throughout my life, to each of the members
of my family, my grandfather, uncles, sisters, nephews, nieces and cousins.
To my friends in Ghent, who tolerated my jokes, for his words of encouragement and especially
for his sincere friendship in these two years where they became my family in foreign land.
To Anne-Marie and Guy for accepting me into your home and make me feel at home.
My eternal gratitude.
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TABLE OF CONTENT
COPYRIGHT PAGE .............................................................................................................................. i
ACKNOWLEDGMENTS ...................................................................................................................... ii
TABLE OF CONTENT ......................................................................................................................... iii
LIST OF ABBREVIATIONS ................................................................................................................... v
ABSTRACT ....................................................................................................................................... vii
1. INTRODUCTION ............................................................................................................................ 1
2. LITERATURE REVIEW ..................................................................................................................... 3 2.1 Water Quality in freshwater ecosystems .............................................................................. 3
2.2 Quality indices ....................................................................................................................... 3
2.2.1 Biological Indices ............................................................................................................ 3
2.2.2 Physicochemical water quality ....................................................................................... 6
2.3 River Continuum Concept (RCC) ............................................................................................ 8
2.4 Impacts of pollution............................................................................................................. 11
3. MATERIALS AND METHODS ............................................................................................... 13
3.1. Study area ........................................................................................................................... 13
3.2 Data collection ..................................................................................................................... 14
3.2.1 Macroinvertebrates ...................................................................................................... 14
3.2.2. Physicochemical characteristics .................................................................................. 14
3.2.3. Hydromorphological characteristics ............................................................................ 15
3.3. Chemical and ecological assessment .................................................................................. 15
3.3.1. Chemical indices .......................................................................................................... 15
3.3.2. Ecological indices ......................................................................................................... 16
3.4. Scatter plots and boxplots .................................................................................................. 17
3.5. Data analysis ....................................................................................................................... 18
4. RESULTS ...................................................................................................................................... 19 4.1 Physicochemical results ....................................................................................................... 19
4.2. Macroinvertebrates ............................................................................................................ 20
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4.3 Water Quality Indices .......................................................................................................... 23
4.4. Gradients of environmental variables from mouth to source. .......................................... 25
4.5. Impacts of dams ................................................................................................................. 29
4.6. Impact of Land use ............................................................................................................. 31
4.7 Effect of municipal waste water treatment plants ............................................................. 34
5. DISCUSSION ................................................................................................................................ 36 5.1 Water quality ....................................................................................................................... 36
5.2 River continuum/ Gradients from source to mouth ........................................................... 37
5.3 Impacts ................................................................................................................................ 39
5.3.1 Impact causes by dams in Portoviejo river ................................................................... 39
5.3.2 Impact of Portoviejo river cause by land use ............................................................... 40
5.3.3 Effects of municipal wastewater treatment plant in the Portoviejo river basin .......... 41
6. CONCLUSIONS AND RECOMMENDATIONS ................................................................................ 43 6.1 Conclusions .......................................................................................................................... 43
6.2 Recommendations. .............................................................................................................. 44
REFERENCES ................................................................................................................................... 45
APPENDICES .................................................................................................................................... 54
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LIST OF ABBREVIATIONS
BMWP: Biological Monitoring Working Party
BOD: Biological Oxygen Demand
BOD5: Five days Biological Oxygen Demand
COD: Chemical Oxygen Demand
CPOM: Coarse Particulate Organic Matter
DO: Dissolved Oxygen
DOM: Dissolved Organic matter
DOsat: Dissolved Oxygen Saturation
EC: Electrical Conductivity
EIFA-WP: European Inland Fishery Advisory
commission Working Party
EPT: Ephemeroptera, Plecoptera and Trichoptera
FFG: Functional Feeding Groups
FISRWG: Federal Interagency Stream Restoration
Working Group
INAMHI: Instituto Nacional de Meteorología e
Hidrología
INEC: Instituto Nacional de Estadística y Censos
MAGAP: Ministerio de Agricultura, Ganadería,
Acuacultura y Pesca.
MMIF: Multimetric Macroinvertebrate Index for
Flanders
PCBs: Poly-Chlorobiphenols
PHCs: Poly Aromatic Hydrocarbons
RCC: River Continuum Concept
SENAGUA: Secretaria Nacional del Agua
TOC: Total Organic Carbon
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ABSTRACT
Water is essential for life of organisms and necessary in civilization. However, the fast growth of
the human population, changes in land use and rapid urbanization damage natural ecosystems
and reduce their value for delivering goods and services for human societies. Around the world,
various researches determine how ecosystems respond to external stressors. However, some
regions are hardly investigated and characterized. For instance, the water and ecological quality
of the Portoviejo river basin is unknown.
Thus, the present study assesses the water quality of the Portoviejo River in Ecuador.
Furthermore, the evolution of various environmental variables was determined along the
disturbance gradient within the river. Additionally, the impacts of irrigation dams, agriculture,
urbanization, wastewater discharge on water and ecological quality was assessed. For this,
physical, chemical and biological (macroinvertebrates) characteristics of the rivers were sampled
at 31 sampling sites along the main river, some tributaries and within the reservoir. Ecological
quality, expressed as Biological Monitoring Working Party-Colombia (BMWP-Col), and chemical
indices (the Dutch and LISEC methods) were calculated. Majority of sampling sites (45%) had poor
quality. The good ecological quality was associated with high flow velocities, low temperatures,
low conductivity, low chlorophyll a content, low biological oxygen demand (BOD5) and low
nutrient concentrations. Additionally, good water quality was also associated with the presence
of sensitive taxa and high diversity. Bad quality, mainly at the downstream of the river, is related
to urbanization and inputs of untreated domestic wastewater. In general, there was an increase
in conductivity, chlorophyll a, available nutrients, and total organic carbon along the gradient
from source to the mouth. This observation was related to changes in land use. Predators and
collectors were dominant at upstream, more scrapers were found at the midstream and collectors
dominated near to the mouth. Deviation from the prediction of the River Continuum Concept
(RCC) can be explained by the presence of a series of dams along the river and differences in food
availability in tropical zones. Flow velocity, pH and temperature are low before dams. While,
turbidity is relatively high after dams. Chlorophyll a is higher in residential areas than in forest and
arable land. While conductivity and nutrients in forest areas are relatively low compared with
arable land. Conversely, BOD5 in forest areas is relatively higher than in arable and residential
zones. Physicochemical variables are not statistically affected by the presence of a municipal
WWTP in the Portoviejo River. Nonetheless, chlorophyll a, BOD5, TOC, total phosphorus and total
nitrogen after WWTP are relatively higher than before WWTP. Probably, the WWTP is insufficient
in organic and nutrients removal or an overload of waste is present.
In general, Portoviejo River follows the pollution gradient typical by the presence of
anthropogenic perturbations. Based on the findings, a sustainable management of the river
catchment is necessary, combining the reduction of inflow of pollutants via wastewater
treatment, and minimizing the habitat alteration of banks, and restoring flows affected by
hydropower dams.
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1. INTRODUCTION
Water is essential for life of organisms. It is necessary for development of natural ecosystems, for
human well-being and for progress of cities (Virha et al., 2011; Haldar et al., 2014). Around the
world, freshwater is mainly obtained from natural streams which are exposed to external pressure
that could influence its water quality. Water quality, in most cases, is caused by human activities
such as water pollution, modification of natural hydrology, river impoundments and land-use
changes (Geist, 2011). Assessment of water quality is crucial to determine the health of
ecosystems, control of environmental pollution and hence to maintain human safety (Bilotta et
al., 2008). Nowadays, water quality is assessed by measuring environmental variables and by
freshwater organisms in order to determine the environmental status of the ecosystem
(Sundermann et al., 2015). The use of macroinvertebrates together with physicochemical
variables has been used worldwide to assess water quality. Some adaptations were made to allow
its use in all regions including South America (Damanik-Ambarita et al., 2016). Macroinvertebrates
are broadly used because it provides an easy and less costly tool to monitor freshwater
ecosystems, which make it the best option in developing countries (Pander and Geist, 2013).
In Ecuador, a number of investigations on water quality and ecological status on freshwater
ecosystems based on macroinvertebrates were implemented (Alvarez-Mieles et al., 2013,
Damanik-Ambarita et al., 2016). However, this method is not yet spread along the whole country,
such as the case of the Portoviejo River. The Portoviejo River is an important source of water for
the inhabitants of the region for drinking water and irrigation. To know its water quality status is
crucial in order to take actions for reducing sources and impacts of pollution. However, the water
and ecological quality of the Portoviejo river basin is unknown. It is currently monitored based on
physicochemical variables by the water secretariat (Secretaría del agua SENAGUA), a government
organization which is also in charge to assure the access to good quality freshwater for human
consumption, irrigation and other uses. Furthermore, the conservation of natural environment is
in charge of the ministry of environment (Ministerio del ambiente MAE), which is in charge to
ensure sustainable management of strategic natural resources. Together, both agencies are
making good effort to assure water supply in the region but the rapid population grow,
urbanization, changes in land use, together with limited budget make it challenging to control and
continuously monitor the freshwater streams. Furthermore, the municipal government is putting
a great effort to recover the water quality of the river but positive results are not yet obtained.
Portoviejo river consists of a series of dams. The impacts of irrigation dams in water and ecological
quality are unknown. Furthermore, little is known on the impact of a series of dams along a
tropical river on the functional feeding groups (FFG). In the same way, limited knowledge exists
in how the nutrients, organic matter and others physicochemical variables evolve in this system.
The Portoviejo offers an interesting advantage for study as it is a small catchment (system) and
thus it is easy to explore and be investigated. As various land uses such as agriculture, and
urbanization and the presence of dams are found along the Portoviejo river impacts of these land
uses can be easily studied. Thus, changes in land use and other anthropogenic activities could be
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anticipated in order to reduce pollution to the river. Findings of this study can be used as a
baseline on the effects of these anthropogenic activities in a similar tropical river system
worldwide.
For reasons cited above, an ecological monitoring based on macroinvertebrates is proposed to
identify multiple stressors in freshwater stream to help decision makers to take actions for
management and control pollution.
For this research, it is aimed (1) to assess the ecological water quality in the Portoviejo River
(Ecuador) based on macroinvertebrates community, (2) to analyze the environmental gradients
along the river based on the river continuum concept and (3) to estimate different impacts caused
by land use, dams and waste water treatment plants within the Portoviejo River.
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2. LITERATURE REVIEW
2.1 Water Quality in freshwater ecosystems
Water is essential for the life in the planet (Virha et al., 2011). Freshwater streams support the
natural ecosystem (Haldar et al., 2014). People use freshwater mainly from rivers for their daily
activities. Utilization of water resources is indispensable for humans and its use has allowed the
development of cities and countries (Geist, 2011; Kaushal et al., 2015; Pander et al., 2013).
Water quality is important for sustaining development of both human and ecological communities
(Srebotnjak et al., 2012). Water quality could be defined as a group of chemical and physical
characteristics of any stream that could be used as indicator of ecosystem health and useful for
deducting environmental pollution (Bilotta et al, 2008). To assess water quality, environmental
measurements are needed. These values are usually contrasted with a reference point that has
previously been considered as good water quality site (De Rosemond et al., 2009).
The sources of pollutants are different and are present in many forms. Diffuse and point source
pollution affects streams in distinct ways. They may degrade various aquatic habitats through
accumulation. For instance, major intensity usually occurs in the lower section of a stream,
impoverishing the water quality and results to a decrease in diversity of aquatic fauna (Snook and
Whitehead, 2004). Furthermore, other types of river alteration, such as modification of natural
hydrology of a stream, also leads to the detriment of water quality (Castello and Macedo, 2016).
European Inland Fishery Advisory commission Working Party (EIFA-WP) defined parameters for
water quality in 1969. They demarcated safe pH range for fish, which is between 5 and 9.
Furthermore, they also established healthy temperature and ammonia ranges for aquatic animals
(EIFA-WP, 1969). In various studies, freshwater quality is derived from physicochemical, biological
and microbiological parameters (Antonietti et al., 1996; Da Silva and Sacomani, 2001; Reisenhofer
et al., 1998). These include pH, COD, orthophosphates, conductivity, dissolved oxygen, total
plating count, ammonia, nitrate, alkalinity, coliform fecal CF, adenosine triphosphate ATP,
carbohydrates and macrobenthos composition (Antonietti et al., 1996). In 2008, a Water Quality
Index (WATQI) was developed based on five parameters: dissolved oxygen, electrical conductivity,
total phosphorous, total nitrogen and pH (Srebotnjak et al., 2012).
2.2 Quality indices
2.2.1 Biological Indices
Several studies revealed that biota depends on water quality. Mostly, water quality is influenced
by anthropogenic pressures such as urbanization and agriculture (Kail et al., 2012). Organisms in
freshwater bodies seem to suffer multiple stressors from human activities and therefore these
organisms serve as indicators for pollution (Sundermann et al., 2015). Globally, freshwater
organisms are used to assess water quality and determine the environmental status of an
ecosystem.
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Fish, invertebrates, algae and macrophytes are commonly used to assess quality of aquatic
environments. They provide an easy and less costly tool to monitor the ecological status of
freshwater ecosystems (Southerland et al., 2007; Pander et al., 2013). But some of them have
limitations. For instance, fish monitoring cannot be applied to very small streams due to the
impairment of space to their developing (Southerland et al., 2007). Additionally, stream quality
classifications are usually based on the presence of expected fauna from a reference site (pristine
location). In this way, the absence of fauna means presence of stressors. Nevertheless, it needs
to take into account that these variations could depend on other physical characteristics, such as
landscape and flow velocity (Skoulikidis et al., 2009).
Macroinvertebrates are broadly used in environmental assessment as they are sensitive for a wide
range of pollutants. They have a broad variety of taxonomic groups whose responses towards
environmental variations are very valuable to evaluate freshwater ecosystems (Carlisle et al.,
2007; Smith et al., 2007). Cao et al., (1997) found as water quality was reduced along the stream,
some species loses their average quantity. On the other hand, the number of some tolerant
species increased in the contaminated sites. Furthermore, they measure the cumulative response
to habitat changes due to their long life cycle (Azevedo et al., 2015).
Macroinvertebrates has been used as water quality indicators since early 1950s (Gabriels, 2007)
and several assessment methods has been developed worldwide to evaluate water quality
(Skoulikidis et al., 2009). As an example a Multimetric Macroinvertebrates Index for Flanders
(MMIF) was developed by Gabriels et al. (2010) to assess quality in rivers and lakes within Belgium.
In Bulgaria and Vietnam, freshwater quality was determined with macroinvertebrates (Lock et al.,
2011; Nguyen et al., 2014). Furthermore, hydromorphological quality on surface water was also
examined in Estonia based on invertebrates (Timm et al., 2011).
2.2.1.1 Common indices based on Macroinvertebrates
There is an ample quantity of indices based on macroinvertebrates communities to assess
freshwater quality (Gabriels, 2007). Some of them used in the present study are discussed in the
following paragraphs.
Biological Monitoring Working Party (BMWP)
The Biological Monitoring Working Party (BMWP) (Armitage et al., 1983) developed in UK and
revised by the National Water Council, is based in a score system (Couto-Mendoza et al., 2015).
The BMWP score provides a suitable classification for monitoring and assessing quality in
freshwater ecosystem (Armitage et al., 1983). Zamora-Muñoz et al. (1995) demonstrated that
BMWP is negatively related to pollution. Their study also indicates that BMWP is not seasonally
dependent (Zamora-Muñoz et al., 1995) making it suitable for monitoring campaign during all
seasons.
Some adaptations to BMWP index were made in Europe. For example, the Iberian Biological
Monitoring Working Party (IBMWP) for Spain (Alba-Tercedor 2000; Alba-Tercedor et al., 2004)
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was developed. According to Couto-Mendoza et al. (2015), IBMWP was more used during the last
two and a half decades in Spain to determine ecological status in freshwater.
Several adaptations from BMWP have been developed in Latin America. For instance, in Costa
Rica, the Biological Monitoring Working Party Costa Rica (BMWP-CR) is employed (Gutiérrez and
Lorion, 2014). In Colombia, Roldán (2003) established Biological Monitoring Working Party
Colombia (BMWP/Col) to make an approximation on the ecological status of water bodies in
Colombia (Roldán, 2003). Some researches were conducted applying BMWP-Col in Colombia
(Montoya et al., 2011; Forero and Reinoso, 2013) and in Ecuador (Alvarez-Mieles et al., 2013;
Damanik-Ambarita et al., 2016) to assess freshwater quality and wetland ecosystems.
Diversity indices
Shannon-Wiener (Shannon and Weaver, 1949) and Margalef index (Margalef, 1958) are non-
taxonomic metrics (Gabriels, 2007), also referred as diversity indices. Both metrics make use of
richness, evenness and abundance on macroinvertebrate community. In an unpolluted
environment, high richness, even-spreading and abundant organisms is expected (Metcalfe,
1989). Metcalfe (1989) describes some advantages of diversity indices. They are exclusively
quantitative, independent of the proportions of the sample, no suppositions about tolerances are
needed and can measure biomass instead count individuals. Criticism against it includes values
rely on the equation used, there are variations depending on the standard used, some species are
neglected, it considers response to pollution as linear, and there is few testing in middle range
pollution (Metcalfe, 1989).
Taxonomic species richness
Freshwater invertebrate richness in pristine locations is influenced by environmental factors such
as geology, ecosystem productivity, competition and predation. Interactions of these factors
determine the gradients of species richness (Compin and Céréghino, 2003). The richness along
the stream is influenced by anthropogenic interference (Céréghino et al. 2003). The number of
taxa reduced (Brittain and Saltveit as cited by Compin and Céréghino, 2003) and expected gradient
is disrupted (Ward and Stanford as cited by Compin and Céréghino, 2003) as a result of human
activities.
Because Ephemeroptera, Plecoptera and Trichoptera (EPT) have an extensive distribution, they
are highly associated with tendencies in richness and vastly related with ecological variations
(Shah et al. 2015). Thus, EPT is a good indicator of stream disturbances (Céréghino et al. 2003).
EPT taxa were used to assess stream ecosystem health in Burkina Faso in Africa (Kaboré et al.
2016) and in Latin America and the Caribbean (Soldner et al. 2014).
The number of macroinvertebrate families are also used as an indicator of pollution in freshwater
streams (Carlisle et al. 2007). Carlisle et al. (2007) found that genera and families are strongly
correlated to road density as a result of urbanization. The total number of taxa is also utilized to
derive the multimetric index in Belgium (Gabriels, 2010) and in Vietnam (Nguyen et al. 2014).
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2.2.2 Physicochemical water quality
Physical and chemical properties depict water of a stream (Bilotta et al. 2008) and are essential
determining the stream’s quality (Virha et al. 2011). As the population grows, the needs of
freshwater increase as well. Furthermore, as a result of the increase of anthropogenic activities,
biological and physicochemical conditions of rivers deteriorate (Forero-Céspedes and Reinoso-
Flórez, 2013).
Pollution caused by chemicals is the main stressor in freshwater ecosystems (Berger el al. 2016).
Berger el al. (2016) found that some chemical affects ecosystems in lower concentration than
expected from laboratory analysis. They suggest that chemical pollution is an important factor in
the distribution of macroinvertebrates which are widely used as indicators of water quality (Smith
et al. 2007).
There are numerous physicochemical indicators used for determining water quality. Several
studies worldwide characterized water quality in freshwater streams based exclusively on
physicochemical parameters (Da Silva and Sacomani 2001; Reisenhofer et al. 1998) and others in
combination with biological and microbiological components (Charalampous et al. 2015; Haldar
et al. 2014; Antonietti et al. 1996). In 2008 a first approach named Water Quality Index (WATQI)
intended to be worldwide used was published. WATQI was based on measurements of dissolved
oxygen, electrical conductivity, total phosphorous, total nitrogen and pH (Srebotnjak et al., 2012).
2.2.2.1 Physicochemical water quality indicators
Physicochemical indicators are briefly deliberated below.
pH
pH can be easily measured in the field. Natural pH in rivers ranges between 6.7 and 8.6 which
could vary due to direct discharges, runoff, heavy rainfall events or mine drainage (Lloid et al
1969). With relation to the aquatic biota, Lloid et al. (1969) reported that pH range in between 5
to 9 is not directly harmful to fish.
Nitrogen and phosphorous
Nitrogen and phosphorous determine the trophic status and eutrophication in freshwater
ecosystems (Jarvie et al. 2002). The main sources of these nutrients are application of fertilizers
and combustion of fossil fuel (Smith et al. 2007). Nevertheless, eutrophication in the river also
depends on interacting elements along the stream (Honty 2015).
Suspended solids
It is clear that suspended solids are very important in the assessment of water quality in a river
ecosystem. Suspended solids not only affect the light availability within the water column and in
the visual effect of the river but also interfere negatively with the ecological life, e.g. reduction in
primary production and temperature change because of reduction of light penetration and,
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chemical alterations by release of contaminant into the water column from absorption places in
sediments (Bilotta et al., 2008). Moreover, Xia et al. (2004) revealed that the presence of
suspended solids could also enhance the process of nitrification.
Dissolved Organic matter (DOM)
The dissolved organic matter present in streams is connected with human interactions. Williams
et. al. (2016) established that DOM composition is strongly related with human activities. It is also
associated with land cover and human density. The DOM composition is different between
urbanized watershed and natural land cover and agricultural places. Ecosystem with low human
densities have DOM composition more similar to clear water ecosystem. So, highly populated
areas strongly alter the quality of DOM (Williams et al., 2016).
Stream Flow
Barreto et al. (2014) indicate that the flow rate is strongly related with other parameters. They
described that flow rate is positive correlated with total dissolved solid and salinity, while pH is
inversely correlated with flow rate. On the other hand, phosphorus that phosphorus increased
exponentially as flow rate increased (Barreto et al., 2014).
Temperature
Water temperature in freshwater ecosystems is a key element for subsistence of aquatic
organisms (Verones et al. 2010) and regulation of its compartment (Whitehead et al., 2009).
Thermal emissions (Verones et al. 2010), hydrological alterations (Olden and Naiman, 2010) and
climate change are increasing freshwater temperature (Dietrich et al. 2014). This increment has
negative ecological consequences as it accelerates kinetic reactions of some chemicals and
pollutants (Whiteheaed et al., 2009). For example, Laetz et al. (2014) found that some insecticide
mixtures increased its relative toxicity for Pacific Salmon with increasing temperature.
Electrical Conductivity (EC)
The electrical conductivity (EC) measures the total dissolved ions in freshwater ecosystems as an
indicator for pollution by human activity (Srebotnjak et al., 2012). It is frequently associated with
sewage discharge (Ribeiro de Sousa et al., 2014). However, Srebotnjak et al., (2012) indicates that
measurements of EC could be influenced by meteorological conditions, geology, water body size,
evaporation and metabolism of bacteria community. EC is inversely related with aquatic life
(Thompson et al., 2012). Furthermore, EC has been used together with other physicochemical
parameters to determine freshwater quality in rivers (Cicek and Ertan 2012; Akkoyunlu et al.,
2012), its effects in aquatic organisms (Patnode et al., 2015; Haddaway et al., 2015) and impact
of mining activities on water chemistry (Wright et al., 2015)
Indices based on chemical water variables
Below the LISEC index developed based on chemical water quality parameters.
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LISEC index
The LISEC Index is commonly used to evaluate quality in surface waters. It uses classification (5
classes) of 4 parameters (% O2 saturation, BOD, ammonium and orthophosphate). The LISEC
Index is then the sum of each individual variable class. Since it sums up pollution produced by
individual parameters, LISEC index classifies water quality with low scores as “very good”, and
high scores as “very bad”, (Lamia and Hocine 2012). This index was used to measure freshwater
streams quality in Congo (Bagalwa et al., 2013) and in Algeria (Lamia and Hocine 2012; Chaoui et
al., 2013).
2.3 River Continuum Concept (RCC)
The river continuum concept (RCC) (Figure 2.1) proposed by Vannote et. al. (1980) attempt to
explain a continuous gradient of physical conditions from source to mouth within a river system.
It also indicated that structures of biotic communities and functional characteristics are adapted
in function of energy inputs along the river (e.g. organic matter). So, the RCC offers a probable
composition in its functional feeding groups FFG, for example, at headwater, the organisms are
dominated by shredders as the organic matter debris are bigger, and collectors due to food source
as fine particulate organic matter (FPOM) from fragmented leafs (coarse particulate organic
matter (CPOM)) within a river ecosystem (Vannote et. al.,1980). The RCC was conceived based
on “existing data” from geomorphology, hydrology, biogeography, and natural history (Resh and
Kobzina 2003). The RCC defines a unidirectional transport of materials and organisms in
watercourses resulting in longitudinal variations along the stream gradient (Barquin and Death
2011; Minton et al., 2008). It predicts biotic diversity from little streams to big rivers (Dettmers et
al., 2001). Furthermore, RCC indicates that changes in physical conditions and food availability in
rivers leads to a longitudinal pattern of macroinvertebrates and fishes conditioning the trophic
group compositions of aquatic community (Ibanñez et al 2009; Wolff el al. 2013). However,
Dettmers et al., (2011) indicated that the RCC predicts a highest diversity in rivers of middle order
(4–6) but does not predict arrangements for fishes of large rivers (>6th order).
The longitudinal arrangements within the stream ecosystems in the RCC are dominant in early
ecological studies (Lamberti et al., 2010) producing a new paradigm and motivating a good deal
of discussion (Resh and Kobzina 2003). The RCC is widely applied in many studies. Wolff el al.
(2013) found that fish assemblages follow the pattern expected by the River continuum concept.
It was also used to describe the freshwater-saltwater interface in estuarine ecosystems (Dame et
al., 1992), the zooplankton in a reservoir, river and estuary pathway (Akopian et al. 2002) and the
bacterial diversity along the river (Savio et al., 2015). The RCC was also employed to investigate
the gradient in infection level produced by fish parasites (Blasco-Costa et al., 2013), to predict
linear gradients in two fish species (Schaefer et al., 2011) and quantified phenotypic gradients in
freshwater snails (Minton et al., 2008). From a water basin protection perspective, Saunders et
al. (2002) indicated that following the RCC, headwater streams are more vulnerable to changes in
land use as a result of changes in energy input. On the other hand, downstream riparian
9
vegetation is required for shading, smoothing hydrological fluctuation, regulating nutrient loads
and avoiding erosion.
Nevertheless, Newson and Newson (2000) found that macroinvertebrate biological patterns
respond to longitudinal zonation like the RCC, but there is a noticeable secondary indicator
controlled by local habitat patterns. In addition, Greathouse and Pringle (2006) indicated that
macroinvertebrates distribution in a tropical island normally follow RCC however, additional
studies are needed to polish the influence of functional feeding groups distribution caused by
trophic regulators. Furthermore, Covich et al. (2009) indicated that the RCC do not consider
impediment related with neither sloped basin nor dissimilarities between streams that controls
predation and macroinvertebrate spreading. The RCC also considers a strong influence of coarse
particulate organic matter (CPOM) from terrestrial sources as primary energy input on headwater,
whereas downstream internal production rises generating its own energy sources (Saunders et al.
2002).
Additionally, some deviations to the RCC are found in literature. In a comparison between tropical
and temperate fish assemblages, Ibañez et al. (2009) found some differences in the expected
predictions of the RCC that can be linked to differences in energy availability between temperate
and tropical systems. Covich et al. (2009) indicated that for a diadromous shrimp, complexity in
tropical insular drainages combined with temporal variability and land use induces dissemination
and abundance of this shrimp in tropical stream ecosystems, which do not meet the RCC. They
also stated that geomorphic obstacles can influence the plenty of shrimps and impede the
spreading of their predatory fishes (Covich el al., 2009).
Blanco et al. (2013) found that in short coastal streams (1 to 10 km) do not follow the principles
of the RCC because of the steep gradient and the presence of waterfalls and cascades. Thus is
consistent with the typology observed in volcanic oceanic islands in the Caribbean and the Pacific.
They also indicated that low order (<3) streams usually ended on the sea quickly. Thus, periphyton
production and transportation debris is avoided. Downstream, riparian vegetation have mainly
shredders (specially shrimps) spreading along the stream. This is contrary to the RCC, which
expects shredders (mostly insects) mainly in headwater (Blanco et al., 2013).
10
Figure 2.1: The River Continuum Concept from Vannote et al. (1980). Source: FISRWG (1998).
Stream Corridor Restoration: Principles, Processes, and Practices. Federal Interagency Stream
Restoration Working Group (FISRWG) 1998.
http://www.d.umn.edu/~seawww/depth/rivers/art/figure1_4.jpg
11
2.4 Impacts of pollution
Pollution could be defined as any change in the characteristics of a natural environment that may
affect the normal behavior of living organisms (Morse et al., 2017). Impacts of environmental
pollution are related with health risk through food safety (Lu et al., 2015) and negative distresses
on water usage such as drinking, bathing or fishing (Gosset et al., 2016). Furthermore, pollution
of freshwater is considered one of the major manmade origin of global change in biota (Gonzalo
and Camargo 2013).
Pollution in freshwater systems have been studied. For instance, pollution due to artificial salinity
caused by anthropogenic activities affects the ecology of freshwater macroinvertebrates
(Cañedo-Argüelles et al., 2012). Pollution produced by pesticides and sediment induced by rainfall
event were investigated in order to quantify pollution (Dabrowski et al., 2002; Schulz, 2001), and
to determine the effect on biota (Thiere and Schulz 2004) in South-African rivers. Chemical
pollution was also studied in Iberian river basins. Kuzmanović et al. (2016) found that it is
negatively linked to river macroinvertebrates.
Pollution of watercourses in developing countries is mainly produced by exploitation of natural
resources (Da Silva and Sacomani 2001) but also is caused by fecal contamination and inorganic
compounds from natural or anthropogenic sources (Shah et al., 2007). In Malaysia, the major
sources of pollution in aquatic environment are domestic sewage and animal wastes. There are
also an increase of toxic and hazardous waste through the application of pesticides as well as the
development of industries and urbanization (Abdullah 1995).
Land use
The land use affects watercourses in different ways, which produce various complications on
water ecosystems and water quality (Bu et al., 2014). Differences are found by comparing water
quality parameters from diverse water catchments (Shah et al., 2007). Urban and industrial land
uses are associated with heavy metals, nutrients and organic pollutions while agriculture is linked
with nutrients (Bu et al., 2014). Recently studies were made to understand the relation between
land use and streams. (Ding et al., 2016; Bu et al., 2014). Ding et al. (2016) determined that minor
water quality was linked with cropland, orchard and grassland in highland catchments while it was
connected with biggest urban areas in the lowland catchments. Furthermore, Ding et al., (2016)
showed that the river suffered organic and nutrients pollution and regions controlled by
cultivated and urbanized land uses in the river basin have a tendency to have poorer water quality
than other zones.
Some studies associate agricultural land use to bad freshwater quality mainly induced by the
presence of pesticides. Chemical pollution as a result of agricultural activities are brought from
soil surface to the stream via runoff (Dabrowski et al., 2002; Schulz 2001; Thiere and Schulz 2004),
increasing the quantities of nitrogen and phosphorous in surface waters (Bu et al., 2014).
Pesticides negatively affect aquatic organisms including macroinvertebrates (Dabrowski et al.,
12
2002; Thiere and Schulz, 2004). Thiere and Schulz (2004) determined that pesticides and turbidity
exerted a great pressure on macroinvertebrate community.
Urbanization/wastewater
The growth of urbanization produced great impact on aquatic environments especially by
transporting substantial concentration of micro and macro pollutants by rainwater runoff. Diverse
kind of pollutants like Poly Aromatic Hydrocarbons (PHCs), heavy metals, poly-chlorobiphenols
(PCBs), pesticides, bacteria and others has been detected in streams. Because of these mixed
composition of urban rainwater runoff, the diversity of organisms is disturbed (Gosset et al.,
2016).
Other problem found in urban areas are misconnections of sewage that produce extra discharges
and polluted surface water (Revitt and Ellis, 2016; Ellis and Butler, 2015). Gosset et al. (2016)
indicated that environmental effect of discharges cannot be anticipated only with
physicochemical studies, it is necessary to combine them with biological indices and
ecotoxicological studies.
Impacts of dams
Dams are assumed to affect negatively stream biota. (Singer and Gangloff, 2011; Gonzalo and
Camargo 2013; Mbaka and Schaefer 2016). It is considered as the main threat by altering habitat
and reducing chances for several river fish (Mazumder et al. 2016). Furthermore, hydrological
modifications degrade freshwater ecosystems and could cause biodiversity losses, increment of
watercourse temperatures, affectation in the structure and functioning of freshwater community
(Castello and Macedo, 2016). Bredenhand and Sanways (2009) described that disturbance caused
by dams have a result in the loss of local biodiversity. Glowacki, et al. (2011) found that
Chironomids declined their diversity in upstream of reservoir and amplified in downstream of it,
while the contrary happen to fish. However, Singer and Gangloff (2011) found that some small
dams improves conditions for freshwater mussel growth downstream. Mbaka and Schaefer
(2016) state that small impoundments has a slight effect on biota and could need minor attention
than other stressors.
13
3. MATERIALS AND METHODS
3.1. Study area
The Portoviejo River basin is located in the coastal central area of Manabí province (Thielen et al
2016). It covers an area of 2108.29 km2 (Macías and Díaz, 2010). Within the basin, Poza-Honda
reservoir is located which is 12 km in length and covers an area of 6 km2. It was built to provide
irrigation and drinking water. Water drains to the Pacific Ocean with an average discharge of 11
m3/s recorded on 2013 (INAMHI (b), 2015). The zone has two marked seasons, the rain season
starts on December and finish on April or May. The rest of the year is the dry season with almost
no precipitation occurs, excluding the years when El Niño phenomenon occurs (INAMHI, 2015).
Average precipitation within the basin is 1334 mm recorded on 2012 (INAMHI (a), 2015).
In total, thirty-one sites were sampled (Fig. 3.1), three within the reservoir (Poza Honda), five in
small tributary streams, twenty-two sites were along the Portoviejo river, the main river of the
basin, and one was sampled in the estuarine system. The sites were selected based on accessibility
and along a pollution gradient. Three sites were considered as pristine which served as reference
sites.
Figure 3.1: Map of study area in the Portoviejo River with indications of sampling sites.
Portoviejo river starts after the dam and runs along 152 km passing the cities of Santa Ana and
Portoviejo. The Santa Ana canton has a population of 47,385 inhabitants (INEC, 2010) in which
14
20% of the population are residing in the urban area of 1000 km2. On the other hand, the
Portoviejo canton has an extension of 968 km2 and a population of 280,029 inhabitants (INEC,
2010). The Portoviejo city has 72% of the total population of the Portoviejo canton, which means
there are 201,620 inhabitants in an area of 32 km2.
Fifty percent of the Portoviejo canton is a conservation area. Eighteen percent is an agricultural
area for cultivating corn, cocoa, coffee, plantain, rice, coconut, lemon, peanuts, cassava, pearl
onions, sugarcane, tomato kidney, sweet pepper, bean, melon, watermelon, papaya, mango,
passion fruit, cucumber, badea, higuerilla and achiote and some others fruits. Cattle pastures
constitute fourteen percent while anthropogenic use (urban area and others) is four percent
forest comprises nine percent while the rest of the territory is for other uses (MAGAP, 2012).
There are 645 inhabitants at the upstream of the dam. Most of the land is covered by forest, but
small portions of the land are cultivated with coffee, cocoa, lemon, orange, banana, tagua palm,
plantain, yucca, tree beans and papaya, peanuts, yucca and beans and cattle are also pastured.
3.2 Data collection
3.2.1 Macroinvertebrates
Sampling campaign was directed during July and August, 2015. Macroinvertebrates were
collected by kick sampling method using a standard conical hand net with a frame size of 20×30
cm and a mesh size of 500 µm, attached to a stick as specified by Gabriels et al. (2010). Each
sampling site was sampled for 5 min. along a 10-20 m segment. Sampling effort was uniformly
distributed over all aquatics habitats at the sampling site including stones, sands or mud,
macrophytes and others natural or artificial substrates. In addition, macroinvertebrates were also
picked by hands from stones, logs and leaves. Macroinvertebrates were sorted alive and identified
at family level.
3.2.2. Physicochemical characteristics
At each sample site, physicochemical data were collected. Temperature, specific conductivity,
chlorophyll, water pH and turbidity were measured by using multiprobes YSI-6600 while dissolved
oxygen (DO) and dissolved oxygen saturation (DOsat) were measured by multiprobes YSI-6920.
Both probes were submerged in a bucket with approximate 10 liters of water sample. Data was
recorded over several minutes per location. The final values were obtained by calculating the
average of the last 15 recorded measurements. Chemical oxygen demand (COD), total nitrogen
(Total N), total phosphorous (Total P), orthophosphate-P, nitrate-N (NO3--N), nitrite-N (NO2
--N),
ammonium-N (NH4+-N), biological oxygen demand (BOD) and total organic carbon (TOC) were
measured ex situ. For each sampling site, water samples were taken and stored in a cool and dark
container. The samples were analysed in the laboratory using Hach-Lange spectrophotometer
kits. Average water velocity were measured with the flow meter model höntzsch HFA, Höntzsch
GmbH manufacturer.
15
3.2.3. Hydromorphological characteristics
For each sampling site, mean and maximum depth of the water body, average and maximum
width of the river, floodplain and flood prone width, sludge layer were measured manually.
Furthermore, the type of watercourse, land use surrounding each bank (10 x 100 m), shading,
abundance of macrophytes, presence of water hyacinth, valley form, channel form, profile of the
bank, extent of bank erosion, type of bank material, bank shape, bank slope, variation in flow,
variation in width, presence of dead wood, pool-riffle class, types of mineral substrates, sediment
type, bed compaction, sediment matrix and sediment angularity were estimated through field
inspection. Additionally, sampling site elevation were recorded using GPS Garmin eTrex®30. An
overview of the classes of each of these variables can be found in Appendix A.
3.3. Chemical and ecological assessment
3.3.1. Chemical indices
In this study, the chemical water quality was determined with The Dutch method and LISEC
method.
The Dutch method
To calculate the Dutch method, 3 parameters were used. The score of each parameter was
assigned based on ranges. For oxygen saturation of 91% to 100%, a score of 1 was assigned, DO
saturation ranges of 71 to 90% and 111 to 120% was assigned to a score of 2, score 3 from 51%
to 70% and 121% to 130%, score 4 from 31% to 50% and finally for score 5 for values lower or
equal to 30 and bigger than 130%. For BOD5 a score 1 is given for values lower or equal to 3, a
score 2 for range from 3.1 to 6, score of 3 for range from 6.1 to 9, score of 4 from 9.1 to 15 and
score of 5 for values bigger than 15. For ammonium a score of 1 for values lower than 0.5, score
of 2 from 0.5 to 1, score of 3 from 1.1 to 2, score of 4 from 2.1 to 5, and score of 5 for a value
bigger than 5. The scores are presented in Table 3.1.
Table 3.1: Score based on 3 parameters – Dutch method
Score %O2 saturation BOD (mg/l) NH4+-N (mg N/l)
1 91 –100 <= 3 < 0.5
2 71 – 90
3.1 – 6.0 0.5 – 1.0 111 – 120
3 51 – 70
6.1 – 9.0 1.1 – 2.0 121 – 130
4 31 – 50
9.1 – 15.0 2.1 – 5.0 131 – 150
5 <=30 –>150 >15.0 >5.0
The index was derived by the addition of each individual score. A color code was assigned to the
total score: blue, green, yellow and red as indicated in Table 3.2.
16
Table 3.2: Water quality assessment according to Dutch Method
Class Color code Score Quality
1 blue 3 – 4.5 Excellent, very pure
2 green 4.6 – 7.5 Good, pure
3 yellow 7.6 – 10.5 Moderate, doubtful
4 orange 10.6 – 13.5 Bad, polluted
5 red 13.6 - 15 Very bad, heavily polluted
The LISEC method
The LISEC method was derived using 4 parameters, one variable more than the Dutch method.
For LISEC index orthophosphate was used with a score of 1 for values lower or equal to 0.05, a
score of 2 from 0.5 to 0.25, score of 3 from 0.25 to 0.9, score of 4 from 0.9 to 1.5 and score of 5
for values bigger or equal to 1.5. The parameters and scores are presented in Table 3.3.
Table 3.3: Score system based on 4 parameters – LISEC method
Score %O2 saturation BOD (mg/l) NH4+-N (mg N/l) t.an. PO4
3-P (mg P/l)
1 91 –100 <= 3 < 0.5 <= 0.05
2 71 – 90
3.1 – 6.0 0.5 – 1.0 <0.05 - <0.25 111 – 120
3 51 – 70
6.1 – 9.0 1.1 – 2.0 0.25 - <0.90 121 – 130
4 31 – 50
9.1 – 15.0 2.1 – 5.0 0.90 - <1.50 131 – 150
5 <=30 –>150 >15.0 >5.0 <= 1.5
Individual scores were summed. A color code was assigned to the total score: blue, green, yellow
and red as indicated in Table 3.4.
Table 3.4: Water quality assessment according to LISEC method
Class Color code SUM score Quality
1 Blue 4 – <6 Excellent, very pure
2 Green 6 – <10 Good, pure
3 Yellow 10 – <14 Moderate, doubtful
4 Orange 14 – <18 Bad, polluted
5 Red 18 – 20 Very bad, heavily polluted
3.3.2. Ecological indices
The ecological quality of each site was assessed with Biological Monitoring Working Party adapted
for Colombia (BMWP-Col, Alvarez, 2005). The BMWP-Col was used since it is considered an
17
appropriate index for Ecuador (Damanik-Ambarita et al. 2016). Representative macroinvertebrate
taxa were assigned tolerance/sensitivity scores which ranged from 1 to 10. The higher is the
tolerance/sensitivity score, the more sensitive is the taxa towards disturbance. The BMWP-Col
score for each site was obtained by the sum of the total tolerance/sensitivity scores of all families
present at a site. BMWP-Col index was assigned into five quality classes, which are good,
moderate, poor, bad, very bad for a total score of more than 100, between 61 to 100, between36-
60, between16-35 and between1-15, respectively. The scores and its respective quality classes for
BMWP-Col are indicated in table 3.5.
Table 3.5: Water quality assessment according to BMWP-Colombia.
Class Color code Score Quality
5 Blue >100 Good
4 Green 61-100 Moderate
3 Yellow 36-60 Poor
2 Orange 16-35 Bad
1 Red 0-15 Very bad
Diversity of macroinvertebrates was assessed by family richness, Margalef diversity index and
Shannon-Wiener diversity index. Family richness was calculated by the number of different
families at each site. Margalef diversity index (d) (Margalef, 1958) is often used to measure taxa
richness which can be calculated by
𝑑 = 𝑆 − 1
𝑙𝑛𝑁
where S is the number of taxa, and N is the total number of individuals in the sample. The Shannon
diversity index (H) (Shannon and Weaver, 1949) is a diversity index commonly used to characterize
species diversity in a community, which accounts for both abundance and evenness dimensions
of diversity. It is calculated by
𝐻 = − ∑ 𝑃𝑖𝑥𝑙𝑛𝑃𝑖
𝑠
𝑖=𝑠
where Pi represents the relative abundance of ith taxon in the sample, s is the total number of
taxa in the sample. The index identifies major changes in community structure of taxa (Pettersson,
1998). The higher the calculated value, the more diverse is the given site.
3.4. Scatter plots and boxplots
Environmental gradients (River continuum concept)
Scatter plots were made in order to understand chemical and physical gradients from upstream
to downstream. Scatter plots of each variable (flow velocity, temperature, pH, DO, DO saturation,
chlorophyll, turbidity, BOD5, nitrate-N, nitrite-N, ammonium-N, total N, orthophosphate-P, total
18
P, TOC, percent predators, percent scrapers, percent collectors, percent scrapers, percent
odonates) were plotted in function of the distance from the mouth to determine the change of
each variable from upstream to downstream.
Ecological quality and environmental variables
Boxplots of each BMWP-Col class as a function of each environmental variable (flow velocity,
temperature, pH, DO, DO saturation, chlorophyll, turbidity, BOD5, nitrate-N, nitrite-N,
ammonium-N, total N, orthophosphate-P, total P and TOC) were plotted to relate the impacts of
these variables on ecological water quality. Furthermore, boxplots of BMWP-Col scores were built
in function of each hydromorphological variable (type of watercourse, land use, shading,
macrophytes abundance, water hyacinth presence, valley form, channel form, profile of the bank,
bank erosion, bank material, bank shape, bank slope, variation in flow, variation in width , dead
wood, pool-riffle class, types of mineral substrates, sediment type, bed compaction, sediment
matrix and sediment angularity) to compare the relation between BMWP-Col and these variables.
Impacts of land use, dams, and waste water treatment plant (WWTP)
Boxplots of each land use class in function of each environmental variable (flow velocity,
temperature, pH, DO, DO saturation, chlorophyll a, turbidity, BOD5, nitrate-N, nitrite-N,
ammonium-N, total N, orthophosphate-P, total P and TOC) and BMWP-Col were made to compare
the impacts of land uses. The impact of dams was also determined. Boxplots of each dam category
(before dam, after dam, reference sites, and other impacted sites) in function of each
environmental variable and BMWP-Col were built. To assess the effect of waste water treatment
plants (WWTP), boxplots of each WWTP class (before the WWTP, after the WWTP, reference sites,
and other impacted sites) were made in function of each environmental variables and BMWP-Col.
3.5. Data analysis
All statistical analysis and plots were made in R Software (R Core Team, 2016). As the software’s
language is easy to apply in syntax with many built-in statistical functions and excellent graphical
capabilities (Verzani, 2002).
The non-parametric “Kruskal-Wallis rank-sum test" was implemented to determine if there is a
difference of means in each environmental variable among each land use, dam and WWTP
category. Kruskal-Wallis rank-sum test can be viewed as the generalization of the Wilcoxon rank-
sum test for more than two groups. Kruskal-Wallis was used to assess the hydrological and
anthropogenic influence in the reservoir in Ethiopia (Ambelu et al. 2013) and the influences of
environmental factor on macroinvertebrates in Zimbabwe (Dalu et al., 2012). Furthermore, to
assess. the differences of means among each group (category), a pairwise post-hoc comparison
of means by Wilcoxon rank-sum test was performed
19
4. RESULTS
4.1 Physicochemical results
Table 4.1 presents the physicochemical water measurements of the Portoviejo river. Temperature
ranged from 27.82 ◦C to 31.33 ◦C. The site Po9, which is after the Poza Honda dam, had the lowest
conductivity (164 µS/cm), while highest conductivity was found at site Po46 (near to the mouth)
which is mainly brackish water due to the presence of sea water (49384 µS/cm). More than half
of the sampling sites (16 out of 31) did not exceed 500 µS/cm, which is considered as having a
moderate impact on aquatic life (Behar, 1997). Six of the sites were below 200 µS/cm and were
considered as having a low impact on aquatic life (Behar, 1997; New Hampshire Volunteer River
Assessment Program VRAP, 2011). Water pH ranged from 6.50 to 8.81, wherein 22 sampling sites
were considered as normal quality standard (between 6.5 to 8) and the remaining 9 sampling sites
were regarded as having a low or moderate impact (8 to 9). Chlorophyll a concentration ranged
from 1.86 µg/L to 55.16 µg/L. The lowest value (1.86 µg/L) was observed at an upstream location
(Po7) which is a small tributary of the Poza-Honda dam while the highest value (55.16 µg/L) was
observed at the small reservoir sampling site Po47 located downstream. Four locations have less
than 3 µg/L which is according to the standards considered as excellent water quality. Ten of the
sampling sites presented good water quality (3 to 7 µg/L), eleven have a concentration less than
desirable (7 to 15 µg/L) and the remaining seven sites have concentrations that is nuisance to
surface waters (VRAP, 2011). The dissolved oxygen (DO) ranged from 2.22 mg/L to 18.29 mg/L,
wherein the lowest value (2.22 mg/L) was observed at location Po3 where the lowest pH was
observed. Except the lowest value in Po3, DO concentrations have values higher than the
minimum standard (5mg/L) which is considered good for many aquatic animals. The highest DO
(18.29 mg/L), chlorophyll a, total organic carbon (37.7 mg/L) and nitrite-nitrogen (0.143 mg/L)
were observed at the small reservoir in the sampling site Po47. The location Po11 had the highest
turbidity (34.54 NTU). Seven (fourteen) location were below the good standard for turbidity (10
NTU), while the remaining sampling sites were (above 10 NTU) is regarded as having a moderate
impact in freshwater streams. The biological oxygen demand (BOD5) ranged from 0.79 mg/L to
5.86 mg/L, with the lowest value (0.79 mg/L) after the Santa Ana dam (Po19), while the highest
value (5.86 mg/L) was observed at location Po3 within the Poza-Honda reservoir where the lowest
pH and dissolved oxygen were measured. BOD5 concentrations were in general acceptable as
(below6 mg/L). The highest measured total nitrogen concentration (5.70 mg/L) was observed in
Portoviejo city, which is the largest urban area within the watershed. Seven sampling sites were
below 1 mg/L total nitrogen concentrations which support moderate diversity (Behar, 1997). The
highest value of nitrate-nitrogen (2.81 mg/L) was observed before the municipal waste water
treatment plant of Portoviejo city. All concentration of nitrate-nitrogen within the watershed
exceeded the natural concentrations occurring in natural freshwater streams (0.1 to 1.6 mg/L in
water streams) (Zang et al., 2016). With nineteen with less than 1 mg/L that if often an indication
of human activities. Ammonium-nitrogen ranged from 0.035 mg/L to 0.185 mg/L, with the highest
value observed before the discharge of the municipal waste water from treatment plant of Santa
Ana city, while the lowest value (0.035 mg/L) was observed in the small downstream reservoir at
sampling site Po47. The highest value of total phosphorus (0.53 mg/L) and orthophosphate-
20
phosphorus (0.33 mg/L) were observed in a downstream location at the confluence of Portoviejo
river and Rio Chico river, which is the second large stream within the river basin. With exception
of sampling site Po3, total phosphorus exceeds the minimum allowed concentration (0.05 mg/L)
which is considered as potential nuisance concentration for freshwater ecosystems (VRAP, 2011).
Table 4.1. Mean, median, maximum and minimum of physicochemical variables measured in the Portoviejo river basin.
Variables Unit Mean Median Maximum Minimum
Temperature ◦C 27.69 27.82 31.33 25.56
Conductivity µS/cm 2445.30 425.00 49384.00 164.00
pH - 7.88 7.87 8.81 6.50
Chlorophyll a µg/L 13.28 7.17 55.16 1.86
Dissolved oxygen mg/L 7.97 7.71 18.29 2.22
Dissolved oxygen demand-sat. % 102.75 98.31 243.18 28.29
Turbidity NTU 15.06 14.22 34.54 0.00
Chemical oxygen demand mg/L 9.29 3.00 142.00 *3.00
Biological oxygen demand mg/L 3.00 2.82 5.86 0.79
Total nitrogen mg/L 1.77 1.20 5.70 *0.50
Total phosphorus mg/L 0.23 0.21 0.53 *0.05
Nitrate-nitrogen mg/L 1.02 0.53 2.81 *0.23
Nitrite-nitrogen mg/L 0.0372 0.0260 0.1430 *0.0015
Ammonium-nitrogen mg/L 0.085 0.079 0.185 0.035
Orthophosphate-phosphorus mg/L 0.20 0.20 0.33 *0.05
Total organic carbon mg/L 15.99 17.00 37.70 *3.00
Flow velocity m/s 0.38 0.44 0.88 0.00
Elevation m 59.06 59.00 121.00 3.00
Distance from the mouth Km 82.53 93.22 138.24 0.45
*Values expressed as the detection limit of the kit
4.2. Macroinvertebrates
In total, more than 8000 macroinvertebrates of 54 different families were sorted and identified.
The highest richness was observed in a small tributary of the main river with 22 families. The
insect constituted the highest number of families (39 out of 54 families), with Hemiptera, Diptera,
Coleoptera and Trichoptera (9, 8, 7 and 5 families respectively) as the main orders. Chironomidae
occurred most frequently, succeeded by Coenagrionidae, Libellulidae and Baetidae at 30, 21, 20
and 19 sites, respectively. Thiaridae was the most abundant family, followed by Chironomidae,
Corbiculidae and Libellulidae (5231, 808, 247 and 231 individuals respectively). Table 4.2 shows
the list of orders, families, abundance and percentage of occurrence encountered in the
Portoviejo river basin.
21
Table 4.2: List of orders, families, abundance and percentage of occurrence of macroinvertebrates
in the Portoviejo river basin.
Taxa Abundance % Occurrence
Coleoptera
Dryopidae 28 10%
Elmidae 8 19%
Haliplidae 13 29%
Hydrophilidae 29 16%
Lampyridae 2 3%
Ptilodactylidae 1 3%
Scirtidae 4 10%
Diptera
Ceratopogonidae 19 26%
Chironomidae 808 97%
Culicidae 1 3%
Ephydridae 1 3%
Limoniidae 15 23%
Simuliidae 2 6%
Stratiomyidae 12 16%
Tabanidae 6 10%
Ephemeroptera
Baetidae 181 61%
Leptohyphidae 175 35%
Leptophlebiidae 61 26%
Hemiptera
Belostomatidae 12 29%
Gelastocoridae 1 3%
Gerridae 8 16%
Naucoridae 42 39%
Nepidae 8 10%
Notonectidae 135 6%
Ochteridae 1 3%
Pleidae 12 19%
Veliidae 56 35%
Lepidoptera
Pyralidae 11 13%
Megaloptera
Corydalidae 19 10%
22
Table 4.2: List of orders, families, abundance and percentage of occurrence of macroinvertebrates
in the Portoviejo river basin. Continuation…
Taxa Abundance % Occurrence
Odonata
Calopterygidae 93 35%
Coenagrionidae 124 68%
Gomphidae 71 55%
Libellulidae 231 65%
Plecoptera
Perlidae 3 3%
Trichoptera
Hydropsychidae 74 29%
Hydroptilidae 7 13%
Leptoceridae 22 29%
Philopotamidae 7 3%
Polycentropodidae 5 3%
Decapoda
Atyidae 202 23%
Cambaridae 16 19%
Palaemonidae 30 13%
Portunidae 8 3%
Mysida
Mysidae 16 6%
Rhynchobdellida
Glossiphoniidae 8 3%
Canalipalpata
Spionidae 14 6%
Haplotaxida
Tubificidae 18 19%
Veneroida
Corbiculidae 247 29%
Others
Acari 184 19%
Hydrobiidae 60 6%
Littorinidae 21 6%
Lymnaeidae 1 3%
Physidae 2 6%
Thiaridae 5231 55%
23
4.3 Water Quality Indices
The water quality score for all 31 sampling sites based on BMWP-Col (Roldán, 2003) ranged from
16 to 140 (Fig 4.1; Appendix B: Table B1). High scores were observed at sites with dissolved oxygen
concentrations between 7 and 9 mg/L, conductivity between 239 and 890 µg/L, a chlorophyll
concentration lower than or equal to 6 mg/L, a turbidity lower than 29 NTU, a flow velocity higher
than or equal to 0.56 m/s, water temperature between 25.8 and 26.2 °C, a thin sludge layer (less
than 5cm), the pH between 7.7 and 8.3, a biological oxygen demand lower than 3.1 mg/L, total
nitrogen lower than or equal to 1.3 mg/L, total phosphorus lower than 2.4 and a total organic
carbon content lower than or equal to 13.4 mg/L. All physicochemical variables are presented in
Appendix B: Table B2. Sites with tightly packed bed, filled contact matrix and a well-rounded
sediment angularity were associated with high BMWP-Col score. The site with highest BMWP-Col
score had gravel bed bank substrate. Boxplots are presented in Appendix C: Fig. C1 to Fig. C5. High
BMWP-Col values were perceived at locations where the number of taxa was also the high
(between 20 and 22 taxa).
Figure 4.1: Map showing BMWP-Col quality classes of sampling sites in Portoviejo river basin.
The Margalef's diversity index (Margalef, 1958) ranged from 0.7708 to 3.9550 (Fig. 4.2a ; Appendix
B: Table B1) and Shannon Wiener diversity index (Shannon and Weaver, 1949) ranged from
24
0.2354 to 2.5875 (Fig. 4.2b; Appendix B: Table B1). There is a positive correlation between
Margalef’s diversity index and the BMWP-Col (R2=0.7). On the other hand, BMWP-Col was weakly
correlated with Shannon Wiener diversity index (R2=0.35).
Figure 4.2: Map indicating for each sampling site the classes according to (a) Margalef's diversity index, and (b) Shannon Wiener diversity index.
Regarding the chemical quality indices, the Dutch method indicated a moderate (3 sampling sites
out of 30) to excellent (21 sampling sites out of 30) water quality within the watershed., while the
LISEC index qualified them from poor (2 sample sites out of 30) to excellent (10 sampling sites out
of 30) (Fig. 4.3).
25
Figure 4.1: Maps indicating the chemical water quality (a) Dutch method, and (b) LISEC index. The
sampling site Po47 was excluded from calculation of both methods due to unmeasured BOD5.
4.4. Gradients of environmental variables from mouth to source.
Gradients of environmental variables were plotted in function of the distance from the mouth
(Appendix C: Fig. C6). Fig. 4.4 shows that conductivity increases as the distance from the source
increases. Near to the source, the pH had values that ranged from 8 to 8.9 except for one site
where the pH was 6.5. Then, the pH ranged from 7.1 to 8.4 (Fig. 4.4). Chlorophyll concentration
appeared very low at the source and increases with a small increment as the distance from the
source increases. Then, in the last segment of the river the chlorophyll a concentration abruptly
increased (>40 µg/L) passing sampling site Po31, after WWTP discharge, and dropped at sampling
sites near the mouth (Fig. 4.4). Near to the source, turbidity was relatively low but it suddenly
increased after Poza Honda dam (sampling site Po9) and then decreased as the distance from the
source increases. Nitrate-nitrogen, nitrite-nitrogen and total-nitrogen have a similar pattern
showing a slow increase as the distance from the source increases, then passing the middle, it
increased greatly and then decreased near to the mouth (fig.4.5). On the other hand, the
ammonium-nitrogen was relatively high near the source but at the midstream, the same pattern
was observed as the other nitrogen components (Fig. 4.5). The Biological oxygen demand showed
a none-uniform pattern with several peaks along the river (Fig 4.6). The orthophosphate-
phosphorus concentration increased as the distance from the source increases. Total-phosphorus
concentration showed similar patter as orthophosphate-phosphorus (Fig. 4.6). Total organic
carbon increases as the distance from the source increases (Fig. 4.6). Plots of other
26
physicochemical variables in relation with the distance from the mouth are presented in Appendix
C: Fig. C6.
Figure 4.4: Plots of conductivity, pH, chlorophyll and turbidity in relation with distance from the mouth.
Figure 4.5: Plots of nitrate-nitrogen, nitrite-nitrogen, ammonium-nitrogen and total-nitrogen in
relation with distance from the mouth.
27
Figure 4.2: Plots of biological oxygen demand, orthophosphate-phosphorus, total-phosphorus and total organic carbon in relation with the distance from the mouth.
Fig. 4.7 presents the plots of functional feeding groups (FFG) in function of the distance from the
mouth. Predators had an average abundance of 26%. They ranged from 0% (in two sampling sites)
to 73%. Predators were abundant near to the source and in the middle while near to the mouth
they were found in lower percentages in comparison with the other functional feeding group (Fig.
4.7). Shredders ranged from 0 to 38% with an average of 7%. They have low abundance near the
source and the midstream while they are more abundant near the mouth. Scraper ranged from
0% to 95% with an average of 31%. Scrappers have low occurrence near the source while they
appear mostly in the middle and near to the mouth. Collectors are the dominant with an average
of 36% and ranged from 2 to 76%. They occur at almost the same rate near to the source, in the
middle as well as near to the mouth (Fig. 4.7).
BMWP-Colombia in relation with distance from the mouth is shown in Fig. 4.8. BMWP-Col
increases as the distance from the mouth increases. The same pattern as BMWP/Col are observed
as the number of families, numbers of EPT taxa, percentage of odonates and Shannon Wiener
diversity index (Appendix C: Fig. C7)
28
Figure 4.7: Plots of functional feeding groups in function of distance from the mouth.
Figure 4.8: Plot of BMWP-Colombia in relation with the distance from the mouth.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 20 40 60 80 100 120 140
FFG
(%
)
Distance from the mouth (Km)
Functional Feeding Groups (FFG) vs Distance from the mouth
% Predator % Shredder % Scraper % collectors
Poly. (% Predator) Poly. (% Shredder) Poly. (% Scraper) Poly. (% collectors)
29
4.5. Impacts of dams
Boxplots were made to examine the impact of dams in the Portoviejo river (Fig. 4.9). The non-
parametric Kruskal-Wallis rank-sum test indicated that flow velocity, pH, dissolved oxygen and
chlorophyll in the Portoviejo river basin significantly differed between references, other impacts
(different from a dam), before a dam and after a dam sampling sites (p-value < 0.05) Appendix B:
Table B3. Boxplot shows that pH has differences from at least one categorical sampling site. A
pairwise post-hoc comparison of means by using Wilcoxon rank-sum tests indicated that pH
before a dam is significantly different than the other impacted sites. However, pH has no
significant difference among the remaining clusters. A significant difference in dissolved oxygen
content was observed among the clustered sampling sites (chi-squared = 13.968, df = 3, p-value
= 0.003; Kruskal-Wallis). The DO concentration at the impacted sampling sites (other than dams)
was significantly different from the reference, before a dam and after a dam sampling sites. There
were no significant differences in the DO concentration between remaining clusters. Flow velocity
(chi-squared = 7.900, df = 3, p-value = 0.048; Kruskal-Wallis) is significantly higher at impacted
sampling sites (other than dams) than in sites before a dam (Fig. 4.9). There are no significant
differences of flow velocities between remaining clusters. The chlorophyll content (chi-squared =
8.574, df = 3, p-value = 0.036; Kruskal-Wallis) is significantly low at reference sampling sites than
in sites with impacted sampling sites. There were no significant differences in the chlorophyll
content between remaining clusters (Fig. 4.9).
Figure 4.9: Boxplot of velocity, pH, dissolved oxygen and chlorophyll-a in relation with impacts
produced by the presence of a dam Reference, other, before, after refers to pristine site, sites
impacted by other activities, sites before dams(reservoir), sites after the dams, respectively.
30
On the other hand, water quality parameters such as temperature, conductivity, turbidity, COD,
BOD5, Nitrate-nitrogen, nitrite-nitrogen, ammonium-nitrogen, total-nitrogen, orthophosphate-
phosphorus, total-phosphorus and total organic carbon does not differ significantly between the
clustered sampling sites. The boxplot of these variables in relation with impact of a dam are
presented in Appendix C: Fig. C8 and Fig. C9.
Regarding to chemical indices, both the Dutch method and LISEC index are significantly different
among clustered sampling sites. (p-value <0.05). (chi-squared = 8.1886, df = 3, p-value = 0.04227
and chi-squared = 8.6666, df = 3, p-value = 0.03407 respectively). Dutch method and LISEC index
have significantly high score in before the dam sites than in other impacted sampling sites. There
were no significant differences in score of Dutch method and LISEC index among other clustered
sampling sites
Figure 4.10: Boxplot of Dutch method and LISEC index in relation with impacts produced by the
presence of a dam. Reference, other, before, after refers to pristine site, sites impacted by other
activities, sites before dams (reservoir), sites after the dams, respectively.
Additionally, BMWP-Col, number of families, percentage of odonates, Margalef’s diversity index
and Shannon Wiener diversity index does not differ significantly between clustered sampling sites
(Appendix Table C3). Moreover, numbers of EPT (Ephemeroptera, Plecoptera and Trichoptera)
taxa differ significantly between clustered sampling sites (chi-squared = 9.546, df = 3, p-value =
0.02285; Kruskal-Wallis). EPT taxa in reference sampling sites were significantly higher than other
impacted sampling sites. There were no significant differences in numbers of EPT taxa among
other clustered sampling locations. Appendix C Figure C10.
31
4.6. Impact of Land use
Boxplot in Fig. 4.11, 4.12 and 4.13 shows physicochemical variables in relation with land use. The
Kruskal-Wallis test presented in Table 4.3 indicates that the distance from the mouth, flow
velocity, chlorophyll, conductivity, turbidity, biological oxygen demand, nitrate-nitrogen, nitrite-
nitrogen, total-nitrogen, orthophosphate-phosphorus, total-phosphorus and total organic carbon
in the Portoviejo river basin significantly differed between forest, agriculture and residential land
uses (p-value < 0.05). The flow velocity in the forest land use is significantly lower from flow
velocity in arable land use. There were no significant differences in flow velocities among other
land uses. Chlorophyll concentration shows significant differences among land uses (Fig. 4.9). The
chlorophyll a concentration in forest was significantly lower from residential areas. None of the
other land uses show a difference in chlorophyll a concentration at 5% significance level. The
boxplot of conductivity shows differences in land uses (Fig. 4.9), which is confirm by Kruskal-Wallis
test (Table 4.3). It was observed that conductivity in forest was lower than arable land use and
residential areas. There were no significant differences in conductivity between arable and
residential areas. Boxplot in Fig. 4.10 shows differences in turbidity between land uses. The
average turbidity in forest was lower than on the arable land and residential areas. There were
no significant differences between the average turbidity in arable zones and residential areas. The
boxplots indicate differences in the average of 5 days’ biological oxygen demand between forest,
arable and residential land uses. The average BOD5 in forest was higher than in arable areas and
in residential areas while there was no difference between arable and residential areas (fig 4.10).
Total organic carbon shows differences in concentrations among land uses. Total organic carbon
concentrations were lower in forest than arable and residential areas (Fig. 4.11). Nitrate-nitrogen
concentration was significantly difference among different land uses. Figure 4.10 shows that
nitrate-nitrogen concentration is lower in forest zones than in residential and arable areas
(confirmed by a pairwise comparison), while no differences were observed between residential
and arable areas. Boxplots shows differences between nitrite-nitrogen concentrations between
land uses. Nitrite-nitrogen were lower in forest areas than in arable and residential areas (Fig.
4.10). There was no difference between arable and residential land use. Boxplots in Fig. 4.11
indicated differences in the total nitrogen concentrations between land uses. The average total
nitrogen concentration in forest areas is significantly lower than in residential area. There were
no differences in total nitrogen concentration between forest and residential nor between arable
and residential areas. There were some differences in orthophosphate-phosphorus concentration
regarding land uses (fig. 4.11). The orthophosphate-phosphorus and total phosphorus
concentration were lower in forest areas than in arable land and residential zones. (Fig. 4.11).
32
Figure 4.11: Boxplots of distance from the mouth, velocity, chlorophyll and conductivity in relation with land uses. Table 4.2. Kruskal-Wallis test comparing physicochemical variables with land uses. Degrees of freedom, chi-square and p-values are listed. P-values in bold indicates different among land uses at 5% level of significance.
Variable df chi-square p-value
Distance from the mouth 2 19.695 5.29E-05
Flow velocity 2 6.438 0.040
Chlorophyll 2 8.575 0.014
Conductivity 2 11.530 0.003
Turbidity 2 10.443 0.005
BOD5 2 12.223 0.002
Nitrate-nitrogen 2 18.890 7.91E-05
Nitrite-nitrogen 2 15.545 4.21E-04
Total-nitrogen 2 8.542 0.014
Orthophosphate-P 2 11.149 0.004
Total-phosphorus 2 11.973 0.003
TOC 2 19.508 5.81E-05
pH 2 3.211 0.201
Temperature 2 2.2327 0.328
Dissolved oxygen 2 5.4324 0.066
Ammonium-nitrogen 2 1.6296 0.443
COD 2 1.8448 0.398
33
Figure 4.12: Boxplots of turbidity, biological oxygen demand, nitrate-nitrogen and nitrite-nitrogen
in relation with land uses
Figure 4.13: Boxplots of total-nitrogen, orthophosphate-phosphorus, total phosphorus and total
organic carbon in relation with land uses.
On the other hand, pH, temperature, dissolved oxygen and ammonium-nitrogen does not differ
significantly among the land uses (at 5% significant level; Kruskal-Wallis; Table 4.3). Boxplot of
these variables are shown in appendix C: Fig. C11.
Regarding to chemical indices, both Dutch method and LISEC index does not differ significantly
among land uses (Table 4.3; Appendix C: Fig. C12).
34
In addition, BMWP-Col, number of families, numbers of EPT taxa, percentage of odonates,
Margalef’s diversity index and Shannon Wiener diversity index does not differ significantly among
the land uses (at 5% significant level; Kruskal-Wallis; Table 4.3; Appendix C: Fig. C13).
4.7 Effect of municipal waste water treatment plants
Boxplot in Figure 4.14 indicates differences in turbidity among reference, before and after waste
water treatment plants (WWTP) and other impacts. It is confirming by Kruskal-Wallis test that
there were significant differences (chi-squared = 8.806, df = 3, p-value = 0.03; Kruskal-Wallis). A
post-hoc analysis indicated significant difference in turbidity between references sampling sites
and other impacted sampling sites (5% of significant level). There were no significant differences
between references and before municipal waste water treatment plant, nor between reference
sampling sites and after municipal waste water sampling sites, in the same way the other
comparisons show no significant differences.
Figure 4.14: Boxplot of turbidity in relation with impacts caused by WWTP. Reference, other,
before, after refers to pristine site, sites impacted by other activities, sites before WWTP, sites
after the WWTP, respectively.
The other physicochemical variables such as velocity, temperature, pH, DO, chlorophyll, BOD5,
nitrogen, phosphorus compounds and TOC have no significant differences in relation with impacts
caused by waste water treatment plant (p value> 0.05; Kruskal-Wallis test) (Appendix B: Table
B4). Boxplots of these variables are shown in Appendix C: Fig. C14 and Fig. C15.
Concerning to chemical indices, both the Dutch method and LISEC index have no significant
differences in relation with the impacts caused by waste water treatment plant (p-value> 0.05;
Kruskal-Wallis test). Boxplots are shown in Appendix Figure C16.
35
On the other hand, BMWP-Col scores were significantly higher in references sampling sites than
sites impacted by other activities other than WWTP. There were no significant differences in
BMWP-Col scores between the remaining clusters (Figure 4.14). As similar as the former, numbers
of families and EPT taxa were significantly higher in references sampling sites than in sites
impacted by other activities. There were no significant differences in numbers of families and EPT
taxa between before and after WWTP and the remaining clusters. Moreover, Margalef’s diversity
index, Shannon Wiener diversity index and percentage of odonates do not differed significantly
among clustered impacts related to waste water treatment plant (Appendix C Table C4 and Figure
C18).
36
5. DISCUSSION
5.1 Water quality
The water of Portoviejo River is mainly used for irrigation, human consumption and recreation.
The Poza-Honda dam assures water availability during the dry season. Physicochemical water
quality in the river and within the reservoir is influenced by anthropogenic activity by settlements
nearby. Poza Honda reservoir and their tributaries, which is the upstream of the Portoviejo river
are considered as protected area by the national secretariat of water (Secretaria Nacional del
Agua SENAGUA). Due to limited human activities, a very low conductivity is expected. This is
similar to what was found in Abras de Mantequilla with conductivities lower than 35 µS/cm
(Alvarez et al. 2013). However, due to uncontrolled settlement and agricultural activities at the
upstream areas within the dam, the tributaries and the Portoviejo River had values closer to 500
µS/cm. In areas downstream of the Portoviejo River with human settlements and agricultural
activities, values higher than 900 µS/cm were recorded. This is in line with expected values within
areas with anthropogenic activities such as wastewater discharge (Damanik-Ambarita et al. 2016),
urbanization (Mereta et al. 2012), and run-off of pesticides (Prado et al. 2015) as a result of
agricultural activities in the Portoviejo river valley (INIAP, 2011). The highest conductivity value in
sampling site Po46 corresponds to estuarine system (Jun et al. 2016). High concentrations of
chlorophyll-a (translated in phytoplankton abundance) found downstream could be caused by
untreated wastewater. Those sampling sites are located after the inlet of the municipal waste
water treatment plant of Portoviejo city which were also characterized by nutrient enrichment
(NO3--N, NO2--N, Total N and Total P). Most of the sampling sites had Dissolved Oxygen (DO)
concentration higher than 5 mg/L except site Po3 which is located within the dam, with a DO
concentration of 2.22 mg/L. This value in site Po3 could be due to higher oxygen consumption as
confirmed with high BOD5 (5.86 mg/L) measurement. This is due to the dense presence of water
hyacinth (up to 75%) which avoid the contact of water with wind and the degradation of dead
water hyacinth, and shading produced by trees on the vicinity which avoid sunlight in the water
column that avoid oxygen production by aquatic plants. Total nitrogen concentration in upstream
areas is in average higher than the values reported by Alvarez et al. (2013) in Abras de
Mantequilla. However, values lower than 1 mg/L were recorded that are considered good enough
to support moderate diversity life (Behar, 1997). This total nitrogen concentration could be
produced by organic matter loads from natural sources as indicated by Bustamante et al. (2015)
that occurs in South America’s rivers. While in downstream areas the total nitrogen content
exceeded the proposed guideline for drinking water (Devic et al. 2014). This load could be due to
agricultural runoff from numerous small inputs over a wide area (Zevallos, 2002; INIAP, 2011), or
from fecal pollution in no sewer settlements (WHO, 1996) that are present in the zone. The
highest concentration of phosphorus content, reported in Po36 (0.53 mg/L), due to the presence
of septic systems, sewage, animal waste and fertilizer along Portoviejo and Rio Chico river, which
merge in site Po36 (Zevallos, 2002). However, the highest total phosphorus concentration is below
the standards for drinking water by WHO (1993). Nitrate-nitrogen were below the detection limit
(0.23 mg/L) of the chemical kits within the Poza Honda reservoir and their tributaries and nitrite-
nitrogen were lowest than 0.003 mg/L is in line with values found by Alvarez et al., (2013). Whilst
37
ammonium-nitrogen concentration in the same zone was higher than the maximum values found
in wetlands and rivers in Abras de Mantequilla (Alvarez et al, 2013). On the other hand,
concentrations of nitrate-nitrogen, nitrite-nitrogen and ammonium-nitrogen measured in the
other sampling sites in Portoviejo river were lower than concentrations recommended for
drinking water use (WHO, 2011). In general, the lower concentrations recorded indicate that there
was no eutrophication in the Portoviejo river despite of presence of settlements and cultivation
in their riparian zone. Turbidity in upstream areas within the Poza Honda dam and tributaries was
very low (>5 NTU) which means that in upstream zones the amount of suspended material in the
water, such as algae, silt and clay, suspended sediment and decaying plant material was very low.
While passing the Poza Honda dam the turbidity was higher initially caused by sediments that are
carried out from the bottom of the reservoir due to the configuration of the tunnel (bottom
tunnel) and the bank erosion that introduce suspended material in the water column.
Based on BMWP-Colombia, a good ecological quality status in Portoviejo river is associated with
flow velocities, low temperatures, low conductivity, low chlorophyll-a content, low BOD5 and low
nutrient concentrations. Good quality status is better in upstream areas that have lesser human
impact areas. This is in line with results found by Alvarez et al., (2013) in Abras de Mantequilla.
However, a bad water quality was found in sampling site Po3 within the reservoir where the
lowest DO content and pH and highest BOD5 were measured. On the other hand, bad quality
classes downstream are related to urbanization and possible input of untreated domestic
wastewater, similar to values found downstream by Damanik-Ambarita et al. (2016) in the Guayas
river basin. Regarding to the hydro-morphological variables, high BMWP-Colombia scores were
observed in sampling sites with gravel bank material and gravel mineral substrate. This in line with
Lemes da Silva et al. (2016) who explained that grain particle size composition is determinant for
macroinvertebrate taxonomic richness and density. Furthermore, Niba and Mafereka (2015)
indicated that substrate is important in defining species dissemination pattern.
Based on BMWP-Colombia the ecological quality ranged from good to bad, while based on the
Dutch method and LISEC index, water quality ranged from moderate to excellent, indicating
differences in classification between biotic and abiotic water quality indices. Higher diversity and
higher number of EPT taxa in Portoviejo river were associated with good quality of BMWP-Col.
Damanik-Ambarita et al. (2016) indicates that high diversity and sensitive taxa are good water
quality indicators. High scores of Margalef's diversity index and Shannon Wiener diversity index
were calculated on sampling sites with good quality classes based on BMWP-Col (Table C.1).
5.2 River continuum/ Gradients from source to mouth
Sampling sites located at upstream areas generally has low human impacts. As we expected, near
to the source is forested area, followed by arable land and residential areas at the middle and
lastly residential areas near to the mouth. In general, an increase of conductivity, chlorophyll a,
available nutrients (e.g. total nitrogen and total phosphorus) and total organic carbon was
detected from source to downstream areas. Studies have shown that levels of conductivity can
increase in freshwater due to urbanization (Mereta et al. 2012). Furthermore, nutrient levels in
38
freshwater can increase due to the use of chemical fertilizer in riverine (Damanik-Ambarita et al.
2016) which also allow phytoplankton increment and hence an increase of chlorophyll a.
On the other hand, turbidity near to the source is relatively low as a result of the presence of trees
at the riparian zone which avoid erosion of the banks. Then, turbidity increased just right after
the dam due to sediments brought from the bottom of the dam by the bottom tunnel of Poza
Honda dam and by erosion of the riverine. As the distance from the source increases, less trees
are present in the riverine zone due to the shift of land use into agricultural areas. However,
turbidity increase along the downstream zone of the river. This in concordance to the results of
Sherriff et al. (2015) who indicated that agriculture activities could accelerate erosion of the soil.
The BOD5 near the source is relatively higher than downstream. This could be as the result of
degradation of organic matter from decaying leaves and organic material from sewage of
settlements near the brooks that discharge water to the main river. At the middle, the BOD5
decreases showing relatively low values. This could be the result of sewage system which avoid
inputs of organic matter in this part of the river. At the downstream of the river, the BOD5
increases probably due to effluents of untreated sewage directly to the river. Most of the sampling
sites have a DO concentration between 5 to 12 mg/L. Relatively high values were measured
upstream near the source until the middle (between 7 to 12 mg/L), except sampling site Po3
within the reservoir which has a DO concentration of 2.22 mg/L, the lowest pH (6.5) and the
highest BOD (5.86 mg/L). This low DO could be explained by accumulation of decaying leafs.
Relatively low values are found near the mouth with exception of sampling site Po47 which has
the highest DO concentration (18.29 mg/L). This value could be the result of oxygen
oversaturation by photosynthesis of aquatic plants (e.g. phytoplankton) since the highest value of
chlorophyll a (55.16 µg/L) was measured in that sampling site. With the exception of sites Po3 and
Po47, a decreasing trend in DO concentration occurs from source to the mouth.
In general, BMWP-Col scores increases as the distance from the mouth increases. This pattern
indicates that near the source, water quality is better than near to the mouth due to less
anthropogenic activity which influence species richness (Céréghino et al. 2003). This results are in
line with those in the Guayas river basin by Damanik-Ambarita et al. (2016) who indicated that
rivers upstream have better quality status due to lesser anthropogenic activities, while
downstream it affects negatively the water quality. The same pattern is shown by richness and
diversity indices (e.g. numbers of families, numbers of EPT taxa, percentage of odonates,
Margalef’s index and Shannon Wiener). The high diversity and the sensitive taxa are indication of
good water quality in upstream areas.
Regarding to functional feeding groups (FFG), predators and collectors are the dominant
upstream. While at the middle, scrapers are relatively high in percentage. Near to the mouth,
collectors are dominant (Fig. 4.7). From the river continuum concept (Vannotte et al) shredders
and collectors are the dominants at upstream areas due to presence of available food such as
leaves from trees and fine particulate organic matter (FPOM) from fragmented leafs. While
grazers and predators are present in relatively lower percentages. However, in Portoviejo river,
shredders have the lowest percentage in upstream areas. This result could be explained due to
39
predation which is high numbers of predators at the upstream. This plenty of predators could be
due to absence of natural predators (e.g. fish) of macroinvertebrate predators as indicated by
Covich el al. (2009) who stated that geomorphic obstacles (e.g. dam) can influence the abundance
of these macroinvertebrates predators and impede the presence of predatory fishes. Collectors
and scrapers in upstream areas are in line with the expectation from the RCC. At the middle,
scrapers are the dominant in the Portoviejo river. While collectors and predators tend to decrease
and shredders have the lowest percentages. Thus, in line with predictions of the RCC at midstream
the expected high percentage of scrapers is accomplished mainly due to food availability (e.g.
periphyton and biofilm), and low percentage of shredders because at midstream is expected that
most of the leaves are already consumed by shredders near to the mouth (Vannote et al.,1980).
However, contrary to RCC which expected collectors to be the dominant at midstream, at the
midstream of the Portoviejo river, collectors have percentage lower than scrapers. As expected
from RCC, collectors are dominant near the mouth in the Portoviejo river. Collectors feed on
FPOM which is found in this part of the river. On the other hand, scraper and shredder are present
in lower percentage than collectors. However, the RCC indicates that near to the mouth nearly no
shredders are found as leaves were already consumed by shredders near to the source and in the
middle, and limited or no grazers are expected. The presence of shredder near to the mouth could
be explained by the presence of some trees in the banks, which are not dominant regarding to
land use but enough to bring leaves to the river, while scraper could eat periphyton and biofilm
on surface of stones, vascular hydrophytes or sediments. As expected, predators are present in
low percentage near to the mouth. Furthermore, the differences found in our research are in line
with Ibañez et al., (2009) who found some differences in the expected predictions of the RCC due
to differences in energy availability between temperate and tropical systems and could the
presence of dam be also a reason as explained by Covich el al. (2009).
5.3 Impacts
5.3.1 Impact causes by dams in Portoviejo river
Damming causes changes in the natural flow regimen and could affect negatively stream
ecosystems (Gonzalo and Camargo 2013). The first evident change was the flow velocity after and
before a dam. Water pH after dams is lower than before dams probably due to the presence of
sediment or accumulation of decaying material. Findings of Mwedzi et al. (2016) indicated that
dissolved oxygen concentration in sampling sites downstream of impoundments is lower than in
non-dammed sites. Contrarily, in the Portoviejo river, dissolved oxygen (DO) concentration in
other non-dammed sampling sites was lower than in the reference, before a dam and after a dam
sampling sites. There are no significant differences in DO concentrations between after a dam and
before a dam sampling sites. Other physicochemical variables measured in the Portoviejo river
have no significant downstream effects probably because the dam has small impoundments,
which is in concordance with Mbaka et al. (2015) who indicate that in small impoundments does
not significantly change physicochemical parameters. Furthermore, Mendoza-Lera et al. (2012)
found negligible alteration in physicochemical variables due to presence of reservoirs. However,
from boxplots (Fig. C8) a decrease in temperature trend and an increase trend in turbidity can be
40
reported. Temperature reduction is due to the heat exchange with the air caused by the water
flow. Turbidity increment could be explained by the presence of sediments dragged from the
bottom of the dam due to the configuration of the tunnel (bottom tunnel) in the Poza Honda
reservoir (SENAGUA, 2015).
The Dutch method and LISEC index have higher scores before dams indicating bad water chemical
status comparing to sampling sites after dams, reference and other impacted sampling sites.
Contrary to what was stated above that dams have minimal impact on chemical parameters other
than flow, temp, DO, turbidity, with the LISEC or DUCTH method, it shows differences. So, based
on those indices, dams affect negatively freshwater quality. This means that the effect is more
pronounced when chemical parameters are taken together such as done in LISEC or DUTCH
method than when looking at the chemical parameters separately.
Wang et al. (2013) observe reduction on richness and EPT taxa as a result of flow regulation
caused by dams. On the contrary, there were no significant differences found in richness and EPT
taxa after and before a dam in Portoviejo River (Fig. C10). In the same way, BMWP-Col, percentage
of odonates, Margalef’s diversity index and Shannon Wiener diversity index did not reflect
significant differences between after and before dams. Those results could be explained by the
finding of Brainwood and Burgin (2006) that indicated that species richness and abundance of
communities varied greatly between dams, influenced by water quality and habitat feature that
defined equilibrium conditions. As a result, local condition equilibrates macroinvertebrate
communities, affecting feeding groups rather than specific taxa (Brainwood and Burgin, 2006). On
the other hand, as we expected, references sampling sites possess better water quality than the
other clustered sampling sites, as these sites have low human impacts.
5.3.2 Impact of Portoviejo river cause by land use
Population growth and rapid urbanization of upstream riverine areas mean a threat to freshwater
ecosystems (De Troyer et al. 2016), which leads in changes in land use and generation of organic
pollution. A first observation is that forest area is located near the sources while arable land at
the middle and residential areas near to the mouth (Fig. 4.11) which is in line with the river
continuum (Vannote et al., 1980). Flow velocity are lower in forest areas, upstream of the river
due to the presence of the dam, and low in residential areas due to impairments to catch water,
while arable areas have relatively higher flow velocities due to the landscape which is more steep
in the midstream than in the upstream. The chlorophyll a is higher in residential areas than in
forest and in arable land, due to the input of nutrients from untreated domestic wastewater
(Kawasaky et al. 2009). Conductivity and nutrients (e.g. Phosphorus and nitrogen) in forest areas
are relatively low compared with arable land. This can be due to the addition of pesticides and
nutrients in agricultural area as explained by Collins et al. (2013). High conductivity and nutrients
in residential areas can be due to inputs of decaying organic matter of human origin like found by
Kaup and Burgess (2002) in surface water. Conversely, BOD5 in forest areas is relatively higher
than in arable and residential zones. This could be explained by the leaf litters from the trees that
can be source of organic matter. Furthermore, BOD5 input from untreated domestic wastewater
41
that comes from settlements in forest areas that not possess sewage systems which are
discharged in the brooks that reach the main river. Similar findings are observed by Scheren et al.
(2000) who found domestic BOD loads in the Lake Victoria in Kenya. In addition, physicochemical
indices (e.g. Dutch method and LISEC index) present no significant differences between land uses.
In addition, biotic indices (e.g. BMWP-Col, number of families, numbers of EPT taxa, percentage
of odonates, Margalef’s diversity index and Shannon Wiener diversity index) are not significantly
different among land uses, in opposition to Mwedzi et al. (2016) who found that
macroinvertebrates in urban sites indicate severe pollution while in forested and farming sites
indicates relatively clean water. However, the BMWP/Col at the Portoviejo river with arable land
use has relatively higher scores than with forested area. This in contrast to Damanik-Ambarita et
al. (2016) who found that forested areas possess relatively higher scores than in arable land use
within the Guayas river basin. Those results could indicate that in arable land areas there is not
yet too much contamination to affect species sensitive to pollution. On the other hand, it is
possible that some degree of contamination influenced forested areas affecting those sensitive
to pollution species. This unexpected contribution to pollution in forested area could be explain
by inputs that come from settlements upstream which are brought by small brooks to the main
stream.
5.3.3 Effects of municipal wastewater treatment plant in the Portoviejo river basin
Among the physicochemical variables, turbidity is higher in sampling sites after and before WWTP
discharges. However, there is no significant difference in turbidity after and before WWTP
discharge which means that WWTP did not contribute to the variation in turbidity. The other
physicochemical variables are not statistically affected by the presence of a municipal WWTP.
Although, Fig. C15 shows that the chlorophyll a concentration after WWTP is relatively higher than
before WWTP, reference and other impacted sampling sites. Furthermore, the BOD5 after WWTP
is also relatively higher than before WWTP which could indicate an enrichment of organic waste
perhaps due to an insufficient removal of organic waste. Both chlorophyll a and BOD5 could be
an indication of insufficient treatment of wastewater or could be outflow due to low quantity of
wastewater that come to the WWTP at that specific time and it perhaps is release untreated. In
the same way, TOC and total phosphorus after WWTP are higher than before WWTP, while total
nitrogen after WWTP is relatively lower than before WWTP. However, reference sites indicated
less pollution mainly due to lesser anthropogenic disturbances.
Regarding to physicochemical indices, the boxplot of Dutch method (Fig. C18) shows lower score
which means that water quality after WWTP is relatively lower than other clustered sampling sites
while the LISEC index indicates that water quality is the same after and before WWTP. The
differences between both indices could be explained because the Dutch method used % oxygen
concentration, BOD5 and ammonium while LISEC index includes the variables included in Dutch
method and also orthophosphate-P to calculate the total score. Nevertheless, both (Dutch
method and LISEC index) have no significant variation between and among clustered sampling
sites with a relative better quality in reference sampling sites.
42
As expected, references sampling sites have better BMWP scores than the other clustered
sampling sites, mainly due to less anthropogenic activities at reference zones. BMWP-Col is not
significantly different between sites after and before municipal WWTP discharges. The same is
indicated by EPT taxa, Margalef’s diversity index, Shannon Wiener diversity index and percentage
of odonates. However, BMWP-Col scores before WWTP are relatively higher than after WWTP.
Similar results are observed with number of families and EPT taxa. This result indicates that
WWTPs is adding some degree of pollution reducing richness and diversity and affecting
sensitivities species used to calculated BMWP-Col scores. Maybe because the WWTP is not
efficient enough to remove pollution. Perhaps it helps as perhaps without it, water quality would
be worse. But maybe additional treatment is needed to fully treat the waste. Or maybe the load
is too much for the current WWTP that already exists. Those findings indicate that municipal
WWTP discharges affect the distribution and richness of macroinvertebrates in the Portoviejo
river.
43
6. CONCLUSIONS AND RECOMMENDATIONS
6.1 Conclusions
Ecological quality, expressed as BMWP-Colombia, classified majority of sampling sites at
Portoviejo River with a poor quality. This classification is associated to nutrient inputs from
agricultural areas. However, some upstream sampling sites were classified with good and
moderate status. In general, the good ecological quality is associated with high flow velocities,
low temperatures, low conductivity, low chlorophyll a content, low BOD5 and low nutrient
concentrations. Additionally, good water quality is also associated with the presence of sensitive
taxa and high diversity. On the other hand, some sampling sites were classified with bad quality,
mainly downstream sites, where the cumulative effects of pollution are present. Bad quality at
the downstream of the river is related to urbanization and inputs of untreated domestic
wastewater.
Locations near the source have lower pollution impact. In general, an increase in conductivity,
chlorophyll a, available nutrients, and total organic carbon was measure along the gradient from
source to the mouth. This is related to land use changes that come from forested areas upstream
of the river, passing an agricultural zone at the midstream and residential areas near the mouth.
DO decreases along the river gradient. Turbidity is low near the source but high at the middle due
to agriculture, then it tends to be low near to the mouth where residential areas are found. BOD
is high near to the source because the presence of decomposed leaf, decreases at the middle and
increase near to the mouth due to untreated sewage discharge. From the mouth to source, there
is a clear pattern of increasing scores of BWWP-Colombia. The same for richness and diversity
indices (e.g. numbers of families, numbers of EPT taxa, percentage of odonates, Margalef’s index
and Shannon Wiener). This indicates that upstream areas of Portoviejo River have better quality
status than downstream zones. There is an evolution of functional feeding groups (FFG) along the
gradient from source to the mouth. Predators and collectors are dominant upstream of the river.
While at the middle, scrapers are relatively high in percentage. Near to the mouth, collectors are
dominant. There is some deviation in the gradient predicted by the River Continuum Concept
(RCC). This deviation is explained by the presence of a series of dams along the river and also
differences in food availability between tropical systems and temperate zones where the concept
was started.
Dams affect freshwater ecosystems in different ways. Flow velocity, pH and temperature are low
before dams. While, turbidity is relatively high after dams. Other physicochemical variables
measured in the Portoviejo river have no significant effects at the downstream of dams. This
because, except the Poza Honda Dam, other dams present in the Portoviejo River are rather small.
However, more pronounced effects are found when chemical parameters are taken together (e.g.
LISEC and DUTCH indices). There were no significant differences found in biotic indices after and
before a dam in the Portoviejo River.
Gradients of land use are observed along the river. Forest is located in upstream areas, arable
land at the middle and residential zones near to the mouth. The chlorophyll a is higher in
44
residential areas than in forest and arable land. Conductivity and nutrients in forest areas are
relatively low compared with arable land. Conversely, BOD5 in forest areas is relatively higher
than in arable and residential zones. In addition, physicochemical indices (e.g. DUTCH method and
LISEC index) and biotic indices (e.g. BMWP-Col, number of families, numbers of EPT taxa,
percentage of odonates, Margalef’s diversity index and Shannon Wiener diversity index) are not
significantly different among residential zones, forested and agricultural land. However, BMWP-
Colombia has relatively higher scores at arable land than at forested area. This could be explained
by pollution input from settlements in upstream areas.
Physicochemical variables are not statistically affected by the presence of a municipal WWTP in
the Portoviejo River. Nonetheless, chlorophyll a, BOD5, TOC, total phosphorus and total nitrogen
after WWTP are relatively higher than before WWTP. Similar results are observed with BMWP-
Colombia, number of families and EPT taxa. Maybe because the WWTP is insufficient in organic
and nutrient removal or the load is more than the treatment plant can handle.
6.2 Recommendations.
A continuing monitoring to identify sources of pollution in the Portoviejo river is suggested.
Besides physicochemical monitoring, the ecological quality monitoring offers a tool to assess the
effects of pollution in the ecosystems. Thus, the BMWP-Colombia or other adaptations of
biological monitoring for Ecuadorians rivers is highly recommended to assess water quality in the
Portoviejo river. In the same way, knowledge about pollution gradients could help decision
makers to take actions to reduce the impacts of pollution along the river. However, in comparing
the River Continuum Concept, differences in tropical ecological communities, structures and
functions should be taken into account.
Although dams do not present serious effects within the Portoviejo River, its’ impacts on long
term and seasonality need to be studied in order to get better knowledge of the effects on
ecological ecosystem within the Portoviejo river. Changes in land use and pollution inputs
upstream should be controlled to avoid deterioration of the natural habitat along the riverine
zones. Actions to reduce changes in land uses and recuperation of natural vegetation within the
buffer zone in the riverine of Portoviejo River are necessary to reduce pollution. Since a great part
of the zone is used for agriculture, measurements of pesticides are suggested for future studies.
A revision of the efficiency and sufficiency of municipal Wastewater Treatment Plants is
suggested. Adaptations to the current configuration could be needed in order to avoid negative
impacts on water quality in the Portoviejo river.
45
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APPENDICES
Appendix A: List of variables and the definition of each category (modified from (Parsons,
2002 #447) Parsons et al. (2002) and Raven et al. (1998)). Environmental variables Categories Definition
Main land use Forest Land with high density of trees. Includes primary, secondary or tertiary forests.
Arable Land with agricultural crops (e.g. maize, vegetables) Residential Land with residential houses Orchard Land with fruit or nut-bearing trees
Shading 1. No shading No shading in the sampling site 2. partly shaded, limited stretch <33% Less than 33% of the sampling site is partly shaded 3. partly shaded, longer stretch 33-90% About 33 – 90 % of the sampling site is partly shaded 4. partly shaded, whole stretch >90% Greater than 90% of the sampling site is partly shaded 5. completely shaded, limited stretch
>33% Less than 33% of the sampling site is completely shaded
6. completely shaded, longer stretch 33-90%
About 33 – 90% of the sampling site is completely shaded
7. completely shaded, whole stretch >90%
Greater than 90% of the sampling site is completely shaded
Type of macrophytes cover No macrophytes
Macrophytes not present
Interrupted Macrophytes that are not sharing a common border at more than a single point of intersection
Contiguous Macrophytes that are sharing a common border at more than a single point of intersection
Dominant macrophytes Absent No macrophytes present Submerged macrophytes Macrophytes rooted in the bottom sediment with the
vegetative parts predominantly submerged Emerged macrophytes Macrophytes rooted in the bottom sediment
with vegetative parts emerging above the water surface
Floating macrophytes Macrophytes with roots, if present, hang free in the water and are not anchored to the bottom
Presence of water hyacinth
Absent No water hyacinth found in the sampling site
Present Water hyacinth is found in the sampling site Valley form Canyon
V-shaped valley
Trough
Meander valley
U-shaped valley
Plain floodwidth n.a. Macroinvertebrates collected at macrophytes, far
from the bank Channel form Meandering Braided Anabranching Sinuate Constrained (natural) Constrained (artificial) n.a. Macroinvertebrates collected at macrophytes, far
from the bank Variation in width 0 Data collected at the reservoir 1 2 3 4 5 Extent of erosion Absent Erosion is not visible Limited Less than 30 % is eroded
55
Abundant More than 30% is eroded Bank profile Vertical Steep Gradually not trampled Composite not trampled
Variation of flow Absent No variation in flow At human constructions Variation of flow at human construction Low Variation of flow is less than 20% Moderate Variation of flow is between 20 – 50% High Variation of flow is greater than 50% Depth of sludge layer Absent There is no sludge layer present. <5 cm When the depth of sludge layer is less than 5 cm 5-20 cm When the depth of sludge layer is in between 5-20 cm >20 cm When depth of sludge layer is greater than 20 cm Abundance of dead wood Absent No dead wood present (twigs/branch/logs) Limited When dead wood present are less than 5 %. Abundant When dead wood present are more than 5%. Pool/Riffle class Class 1 Pool-riffle pattern is (nearly) pristine: extensive
sequences of pools and riffles. Class 2 Pool-riffle pattern is well developed: high variety in
pools and riffles. Class 3 Pool-riffle pattern is moderately developed: variety in
pools and riffles but locally. Class 4 Pool-riffle pattern is poorly developed: low variety in
pools and riffles. Class 5 Pool-riffle pattern is absent: uniform pool-riffle
pattern. Class 6 Pool-riffle pattern is absent due to structural changes:
uniform pool-riffle pattern due to reinforced bank and bed structures.
Bank shapea n.a. Macroinvertebrates collected at macrophytes, far from the bank
Concave
Convex
Stepped
Wide lower bench
Undercut
Bank slopea n.a. Macroinvertebrates collected at macrophytes, far from the bank
Vertical 80-900 bank sloping Steep 60-80o bank sloping Moderate 30-60o bank sloping Low 10-30o bank sloping Flat Less than 10o bank sloping Bed compactiona Invisible Bed not visible Tightly packed Array of sediment sizes overlapping, tightly packed
and very hard to dislodge Packed Array of sediment sizes overlapping, tightly packed
but can be dislodged moderately Moderate compaction Array of sediment sizes, little overlapping, some
packing but can be dislodged moderately Low compaction (1) Limited range of sediment sizes, little overlapping,
some packing and structure but can be dislodged very easily.
Low compaction (2) Loose array of fine sediments, no overlapping, no packing, and no structure and can be dislodge very easily.
Sediment matrixa Bedrock Composed of bedrocks Open framework 0-5% fine sediment, high availability of interstitial
space Matrix filled contact 5-32% fine sediments, moderate availability of
interstitial space
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Framework dilated 32-60% fine sediments, low availability of interstitial space
Matrix dominated Greater than 60% fine sediments, interstitial space virtually absent
Sediment angularitya Very angular
Angular
Sub-angular Rounded
Well rounded
Cobble, pebbles and gravel fractions not
present
Main sediment type Coarse Consists of boulder, cobble, pebbles, sand (except fine sand)
Fines Consists of fine sand, silt, clay
57
Appendix B
Table B1. Sampling sites with their respective water Quality indices. BMWP-Col water quality classes 5(good), 4(moderate) 3(poor) and 2(bad).
Sampling site ID
BMWP-Col Classes
BMWP-Col Score
Margalef's diversity index
Shannon Wiener diversity index
Po1 5 126.00 3.73 2.59
Po2 3 54.00 2.15 1.49
Po3 2 34.00 1.95 1.94
Po4 3 45.00 1.60 1.40
Po5 4 67.00 2.37 1.95
Po6 5 140.00 3.66 2.35
Po7 3 44.00 1.80 1.64
Po8 4 68.00 2.16 1.56
Po9 3 58.00 3.23 2.26
Po11 3 50.00 2.67 1.91
Po13 4 94.00 3.48 2.41
Po15 4 66.00 3.03 2.26
Po17 5 117.00 3.96 2.53
Po18 3 37.00 1.28 1.59
P019 3 45.00 1.53 0.99
Po21 4 71.00 2.54 1.52
Po22 3 49.00 2.91 2.20
Po24 4 86.00 3.94 2.40
Po26 3 59.00 2.30 1.83
Po27 3 57.00 2.13 1.81
Po28 3 60.00 1.78 0.26
Po29 3 37.00 2.47 1.66
Po30 4 80.00 2.21 0.91
Po31 2 16.00 0.77 0.55
Po34 2 34.00 0.98 0.24
Po36 3 50.00 2.00 1.10
Po38 3 46.00 2.04 1.73
Po40 2 30.00 1.46 1.49
Po43 2 24.00 1.20 1.31
Po46 2 17.00 1.46 1.37
Po47 2 34.00 0.90 0.90
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Table B2: Physicochemical variables
Sampling site ID
Velocity Temp. Cond. pH DO DO-Sat Chlor. a Turbidity
COD BOD5
Nitrate-N
Nitrite-N
NH4-N
Total-N PO3
−4-P Total-
P TOC
BMWP-Col
(scores) (m/s) (°C) (µS/cm) (-) (mg/L
) (%) (µg/L) (mg/L) (mg/L)
(mg/L) (mg/L) (mg/L)
(mg/L) (mg/L) (mg/L)
(mg/L) (mg/L)
Po1 126 0.56 26.16 294.00 8.26 8.72 108.34 2.97 0.00 *3 2.34 *0.230 0.0020 0.126 0.70 0.122 0.119 *3.00
Po2 54 0.07 26.66 270.00 8.34 8.34 104.46 1.88 0.26 *3 3.54 *0.230 0.0020 0.067 *0.5 0.126 0.111 *3.00
Po3 34 0.00 27.47 253.00 6.50 2.22 28.29 4.25 0.64 4 5.86 0.243 *0.0015 0.054 0.80 0.050 *0.05
0 4.36
Po4 45 0.00 29.93 196.87 8.10 9.25 122.76 9.39 2.97 9 4.41 *0.230 0.0020 0.057 0.60 *0.050 0.062 5.24
Po5 67 0.35 26.67 316.40 8.17 8.30 104.11 2.41 0.00 *3 5.45 *0.230 0.0020 0.063 1.10 0.119 0.120 *3.00
Po6 140 0.56 25.83 878.47 7.92 7.94 98.16 4.71 2.79 *3 3.04 0.508 0.0050 0.044 0.80 0.238 0.156 3.01
Po7 44 0.10 26.33 425.00 8.09 7.94 98.99 1.86 0.01 *3 5.58 *0.230 0.0020 0.08 0.80 0.157 0.211 *3.00
Po8 68 0.00 31.33 195.00 8.81 11.84 160.96 16.61 3.15 7 4.72 *0.230 0.0030 0.04 1.60 0.051 0.096 5.81
Po9 58 0.00 26.48 164.47 7.14 7.71 96.30 6.61 23.77 4 4.33 0.265 0.0230 0.136 0.90 0.195 0.237 3.68
Po11 50 0.54 26.70 169.00 7.38 7.19 90.19 7.17 34.54 *3 3.89 0.285 0.0320 0.079 *0.5 0.198 0.201 3.22
Po13 94 0.88 25.69 176.00 7.54 7.42 91.37 5.64 25.24 *3 4.18 0.346 0.0340 0.141 *0.5 0.187 0.201 10.30
Po15 66 0.81 25.56 198.00 7.64 7.36 90.37 5.77 26.71 *3 1.95 0.590 0.0300 0.044 *0.5 0.192 0.209 14.00
Po17 117 0.69 25.89 239.00 7.77 7.48 92.46 6.01 28.85 *3 0.90 0.525 0.0210 0.098 1.30 0.211 0.233 13.40
Po18 37 0.00 27.21 310.00 8.35 9.74 123.25 8.01 27.15 *3 2.10 0.481 0.0170 0.101 1.20 0.247 0.267 17.00
Po19 45 0.54 26.38 310.33 7.91 8.14 101.53 7.26 28.35 *3 0.79 0.549 0.0170 0.055 0.60 0.237 0.254 16.30
Po21 71 0.44 28.01 343.00 7.82 7.74 99.34 5.73 28.60 *3 1.10 0.531 0.0220 0.185 0.80 0.262 0.243 16.70
Po22 49 0.54 27.82 345.00 7.81 7.66 98.07 6.57 30.37 *3 3.91 0.528 0.0220 0.096 1.20 0.235 0.204 18.90
Po24 86 0.63 28.55 981.73 7.83 7.57 98.31 11.61 26.71 5 2.59 0.616 0.0260 0.069 2.90 0.242 0.274 29.20
Po26 59 0.87 28.31 1751.00 7.82 7.64 99.07 9.68 16.55 5 1.10 1.250 0.0470 0.087 1.40 0.243 0.204 22.30
Po27 57 0.72 28.55 1503.73 7.94 8.00 104.10 8.47 14.22 11 1.30 1.810 0.0440 0.134 2.20 0.186 0.132 27.90
Po28 60 0.45 28.38 1547.87 7.96 7.79 100.98 7.01 10.57 4 1.10 1.900 0.0480 0.123 5.70 0.109 0.117 20.60
Po29 37 0.39 28.52 1560.87 7.83 6.79 88.27 7.99 18.42 8 1.23 2.360 0.0490 0.152 2.40 0.190 0.209 19.10
Po30 80 0.20 28.42 1449.00 7.87 6.66 86.43 5.94 16.35 *3 2.47 2.810 0.0300 0.099 5.40 0.255 0.286 19.80
Po31 16 0.19 28.35 1418.27 7.99 6.73 87.12 40.89 18.54 *3 4.40 2.210 0.0340 0.061 4.20 0.217 0.435 31.10
Po34 34 0.54 28.73 1499.73 7.70 6.79 88.61 32.05 9.87 *3 2.76 2.540 0.1010 0.055 2.60 0.303 0.446 23.60
Po36 50 0.60 28.56 1575.00 7.66 5.89 76.63 19.03 8.49 *3 3.50 2.650 0.1140 0.084 4.20 0.333 0.534 32.00
Po38 46 0.11 28.20 1749.00 7.83 7.79 100.75 43.59 9.51 *3 2.88 2.390 0.0980 0.095 2.50 0.238 0.354 25.30
Po40 30 0.07 29.19 1931.00 8.21 10.39 136.81 53.09 7.30 *3 3.77 1.610 0.1100 0.063 1.00 0.308 0.335 23.50
Po43 24 0.50 27.56 2447.00 7.74 6.15 78.86 7.51 7.45 14 2.38 1.470 0.0590 0.058 1.70 0.315 0.333 17.30
Po46 17 0.30 27.32 49383.5
3 7.97 5.73 87.07 6.71 32.94 142 2.33 0.310 0.0130 0.041 *0.5 0.144 0.236 22.40
Po47 34 0.00 29.76 1922.07 8.36 18.29 243.18 55.16 6.44 18 n/a 1.470 0.1430 0.035 3.80 0.200 0.295 37.70
*Values expressed as the detection limit of the kit
n/a = not available
1
Table B3: Kruskal-Wallis test comparing physicochemical variables with the impacts of dams.
Degrees of freedom, chi-square and p-values are listed. P-values in bold indicates different
among clusters related to dams at 5% level of significance.
Variable df chi-square p-value
Distance from the mouth 3 4.025 2.59E-01
Flow velocity 3 7.900 0.048
Chlorophyll a 3 8.574 0.036
Conductivity 3 0.733 0.865
Turbidity 3 7.208 0.066
BOD5 3 1.109 0.775
Nitrate-nitrogen 3 4.403 0.221
Nitrite-nitrogen 3 4.796 0.187
Total-nitrogen 3 0.989 0.804
Orthophosphate-P 3 1.323 0.724
Total-phosphorus 3 3.972 0.265
TOC 3 6.479 0.090
pH 3 9.834 0.020
Temperature 3 5.894 0.117
Dissolved oxygen 3 13.968 0.003
Ammonium-nitrogen 3 1.371 0.713
Table B3: Kruskal-Wallis test comparing physicochemical variables with the WWTP. Degrees of
freedom, chi-square and p-values are listed. P-values in bold indicates different among clusters
related to WWTP at 5% level of significance.
Variable df chi-square p-value
Distance from the mouth 3 4.514 0.211
Flow velocity 3 0.824 0.844
Chlorophill a 3 7.359 0.061
Conductivity 3 0.315 0.957
Turbidity 3 8.806 0.032
BOD5 3 3.382 0.337
Nitrate-nitrogen 3 5.057 0.168
Nitrite-nitrogen 3 4.666 0.198
Total-nitrogen 3 2.347 0.504
Orthophosphate-P 3 3.844 0.279
Total-phosphorus 3 4.155 0.245
TOC 3 7.455 0.059
pH 3 2.231 0.526
Temperature 3 4.205 0.240
Dissolved oxygen 3 3.381 0.337
Ammonium-nitrogen 3 3.176 0.365
2
Appendix C
Figure C1: Boxplots of dominand land use, shading, main macrophyte, water hyacinth, valley
form and chanel form in relation with BMWP-Colombia scores.
Figure C2: Boxplots of variation in with, erosion, curvature of erosion, with of erosion, profile of
the banks and variation in flow in relation with BMWP-Colombia scores.
3
Figure C3: Boxplots of sludge layer, twigs, branches, logs, dominant mineral substrates and
dominant bank material in relation with BMWP-Colombia scores
Figure C4: Boxplots of bank shapes, bank slopes, bed compaction, sediemt matrix, sediment
angularity and riffle classes in relation with BMWP-Colombia scores.
4
Figure C5: Boxplots of Dutch method, LISEC index, land use, dams and municipal wastewater
treatment plant (MWWTP) and type of water course in relation with BMWP-Colombia scores.
Figure C6: Physicochemical variables in relation with distance from the mouth.
5
Figure C7: Plots of BMWP-Col, numbers of families, numbers of EPT taxa, percentage of odonates,
Margalef’s diversity index and Shannon Wiener diversity index in relation with distance from the
mouth
Figure C8: Boxplots of temperature, conductivity, turbidity, COD BOD5 and nitrate-nitrogen, in
relation with impacts caused by the presence of a dam. Reference, other, before, after refers to
pristine site, sites impacted by other activities, sites before dams(reservoir), sites after the dams,
respectively.
6
Figure C9: Boxplots of nitrite-nitrogen, ammonium-nitrogen, total-nitrogen, orthophosphate-
phosphorus, total-phosphorus and total organic carbon in relation with the impacts caused by
the presence of a dam. Reference, other, before, after refers to pristine site, sites impacted by
other activities, sites before dams(reservoir), sites after the dams, respectively.
Figure C10: Boxplots of BMWP-Col, number of families, numbers of EPT taxa, percentage of
odonates, Margalef’s diversity index and Shannon Wiener diversity index, in relation with the
impacts caused by the presence of a dam. Reference, other, before, after refers to pristine site,
sites impacted by other activities, sites before dams(reservoir), sites after the dams, respectively.
7
Figure C11: Boxplots of pH, dissolved oxygen, temperature and ammonium-nitrogen in relation
with land uses.
Figure C12: Boxplots of Dutch method and LISEC index in relation with land uses
8
Figure C13: Boxplots of BMWP-Col, number of families, numbers of EPT taxa, percentage of
odonates, Margalef’s diversity index and Shannon Wiener diversity index in relation with land
uses.
Figure C14: Boxplots of distance from the mouth, elevation, velocity and temperature in relation
with impacts of WWTP. Reference, other, before, after refers to pristine site, sites impacted by
other activities, sites before WWTP, sites after the WWTP, respectively.
9
Figure C15: Boxplots of conductivity, pH, dissolved oxygen and chlorophyll in relation with impacts
of WWTP. Reference, other, before, after refers to pristine site, sites impacted by other activities,
sites before WWTP, sites after the WWTP, respectively.
Figure C16: Boxplots of nitrate-nitrogen, nitrite-nitrogen, ammonium-nitrogen and total-nitrogen
in relation with impacts of WWTP. Reference, other, before, after refers to pristine site, sites
impacted by other activities, sites before WWTP, sites after the WWTP, respectively.
10
Figure C17: Boxplots of orthophosphate-phosphorus, total phosphorus, total organic carbon and
biological oxygen demand in relation with impacts of urbanization. Reference, other, before, after
refers to pristine site, sites impacted by other activities, sites before WWTP, sites after WWTP,
respectively.
Figure C18: Boxplot of Dutch method and LISEC index in relation with impacts of MWWTP.
Reference, other, before, after refers to pristine site, sites impacted by other activities, sites
before WWTP, sites after WWTP, respectively.
11
Figure C 19: Boxplots of BMWP-Col, number of families, numbers of EPT taxa, percentage of
odonates, Margalef’s diversity index and Shannon Wiener diversity index in relation with WWTP.
Reference, other, before, after refers to pristine site, sites impacted by other activities, sites
before WWTP, sites after WWTP, respectively.