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Bachelor’s degree, 180 ETC Degree thesis in Biology 15 ETC
Spring term 2019
Mouthpart deformities of
Chironomid larvae as an
indicator of heavy metal
polluted water
Tim Lindström Jonsson
Abstract
Freshwater ecosystems are under increasing pressure from a variety of contaminants, including
heavy metals from mining operations, which can have complex effects that are difficult to
evaluate. To detect early warnings from elevated concentrations of metals, organisms are sought
to be used as monitoring tools. For example, mouthpart deformities in Chironomid larvae have
been proposed as a bioindicator of stress in aquatic environments. However, the frequency and
cause of these deformations, and their sensitivity to different stressors remain uncertain. In this
study, I evaluated the usefulness of mouthpart deformities as a tool to monitor the effects of
heavy metals from mining in northern Sweden. To do this, the mouthparts of 3789 Chironomid
individuals analyzed from 17 sites closely located to mining operations and tested against
concentrations of metals and DOC in the water chemistry of lakes and rivers. The frequency of
deformities ranged from 0.00 – 4.79 % across all sites. Metal concentrations ranged from ‘very
low’ to ‘low’ based on biological effect risk assessments. Of these, copper (R2 = 0.73) and cobalt
(R2 = 0.66) were found to be significantly correlated with frequency of deformities.
Additionally, the occurrence of deformities declined with DOC concentration, this was a non-
linear relationship. Frequencies of deformities observed in this study were lower than what have
been reported to similar studies. The result from this study, together with other studies, suggest
that deformities in Chironomid larvae are sensitive to even low levels of certain metals and
could potentially be a good biomonitoring tool for early warnings of contamination in
freshwater environments.
Table of Contents
1 Introduction................................................................ 1
1.1 Background ......................................................................................... 1
1.2 Mouthpart deformations ..................................................................... 2
1.3 Chironomid deformities in freshwaters .............................................. 2
1.4 Aim of the study .................................................................................. 3
2 Materials and methods ............................................... 4
2.1 Study sites ........................................................................................... 4
2.2 Field methods ..................................................................................... 5
2.3 Lab methods ....................................................................................... 6
2.3.1 Assembly of the Chironomids mouthpart ................................................ 6
2.4 Data analysis ...................................................................................... 6
2.4.1 Bioavailability of metals .......................................................................... 6
2.4.2 Statistical analyses .................................................................................. 6
3 Results ........................................................................ 7
3.1 Metals ................................................................................................. 7
3.2 Frequency of mouthpart deformities .................................................. 9
3.3 Does DOC influence metal effects? ..................................................... 11
4 Discussion ................................................................. 11
5 Acknowledgement .................................................... 14
6 References ................................................................ 14
Appendix 1 ................................................................... 18
Normal and deformed mouthparts ......................................................... 18
Appendix 2 .................................................................. 19
Assessment of biological effects of metals .............................................. 19
Appendix 3 .................................................................. 20
Data ....................................................................................................... 20
1
1 Introduction
1.1 Background
As human population grows, freshwater ecosystems are increasingly affected by pollution
globally and locally from a variety of industrial and agricultural activities. As a result, rivers and
lakes are among the most threatened natural resources in the world (Machado 2015). Among
the substances that contaminate these ecosystems as a result of anthropogenic activities, heavy
metals are a major contributor (Ancion et al. 2012; Lesvenetal.2008; Lourino-Cabana et al.
2011). While some heavy metals (e.g. Copper (Cu), Cobalt (Co), Nickel (Ni), Zinc (Zn); Rengel
1999) originate from natural sources such as rock weathering (Maldonado and Wendling 2009),
and are essential for organisms when occurring at low levels, at high concentrations they often
have a wide range of toxic effects (Govind and Madhuri 2014).
Waste from industries, for example mining operations, deliver metals into the water where they
can form ions. These electrically charged particles either settle to the sediment, which then acts
as a sink, or they remain suspended in the water where they may contaminate the food chain
(Deliberalli et al. 2018). Additionally, metals in the sediment can be found in multiple chemical
forms, which behave differently in terms of mobility, chemical interactions, potential toxicity,
and bioavailability (Arnason and Fletcher 2003; Singh et al. 2005; Liu et al., 2009). The
bioavailability of metals refers to the relative amount of total metal that is available for biota to
absorb (Yuan et al. 2014). This could be of relevance to compare since total and bioavailable
metal concentrations are not necessarily equal (John and Leventhal 2004). For instance, sulfide
minerals may be integrated in quartz, which is a common component of the sediment (John and
Leventhal 2004). While there is a high concentration of total metals in quartz, these may not be
available for the biota to absorb (John and Leventhal 2004).
Excess metal ions can be absorbed by aquatic organisms through feeding or adsorbed through
contact with polluted water (Tüzen 2009). Once these contaminants are introduced into an
organism, they can basically be found in all tissues and organs (Božanić et al. 2018). In either
case, metals can cause harmful effects that range from mortality, to behavioral changes, to
morphological deformations across a broad range of amphibian (e.g., Burger and Snodgrass
2000), fish (e.g., Sun et al. 1998), and invertebrate taxa (e.g., Weeraprapan et al. 2018).
Many aquatic biota, including insect larvae, live in or close to the river and lake sediments,
where they are exposed to inorganic particles and organic detritus that bind chemical substances
(Di veroli et al. 2012). In particular, the larvae of numerous species in the insect family
Chironomidae are widely distributed, and often in high densities, which makes them important
for the benthic food web (Di veroli et al. 2012; Di veroli et al. 2014). In addition, many
Chironomids can also tolerate low oxygen levels and are considered to be good accumulators of
heavy metals (Roosa et al. 2016). In fact, certain Chironomid genera persist in even the most
severely polluted waters and thus can be diagnostic for certain water quality stressors
(Swansburg et al. 2002). For all of these reasons, Chironomid larvae have emerged as important
biomonitoring group. In this context, some studies have used taxonomic patterns of
2
Chironomids (e.g., richness and traits) among lakes to characterize water quality (Rosenberg
1992). However, already in the early 1970’s, Hamilton and Saether (1971) identified
deformations in Chironomid larvae that could potentially be used a ‘biomarker’ for pollutants.
Consequently, this biological assessment tool has developed into a quick and cost-effective
method to monitor the health of aquatic ecosystems (Arimoro et al. 2015).
1.2 Mouthpart deformations
Deformities is a term that refers to
morphological features that depart from
the normal structure (Nazarova et al.
2004). Mouthpart deformations in
Chironomids occur in different parts of the
cephalic capsule, such as mentum, ligula,
maxillary palps, and mandibles; however,
the main morphological effect caused by
metals are deformities in the mentum and
ligula. An example of a normal and a
malformed mentum can be seen in figure
1A and B. These type of deformations have
been observed in several previous studies
from different parts of the world (Arimoro
et al. 2018; Deliberalli et al. 2018; Di veroli
et al. 2012; MacDonald and Taylor 2006;
Nazarova et al. 2004; Ochieng et al. 2008;
Swansburg et al 2002; Dickman and Rygiel
1996) and are considered to be a good
stress-indicator that emerge from pollution
of metals (Nazarova et al. 2004). The
proportions of deformities in high metal-
concentrated water varies considerably,
but in extreme cases have reported
deformity proportions as high as 83%
(Swansburg et al. 2002). Natural
deformities do occur, but these are
considered in previous studies to be less
than 1% in field studies (i.e. Burt et al.
2003; Hudson and Ciborowski 1996;
Wiederholm 1982). Sites in rivers and
lakes that have a frequency of deformations above 6% are considered to be of bad quality and
expected to be affected by chemical contaminants (Moura e Silva et al., 2019).
1.3 Chironomid deformities in freshwaters
Studies of deformations in Chironomid larvae in Swedish lakes were performed already in the
early 80’s by Torgny Weiderholm (1982). He found that in waters with a high concentration of
A
B
Figure 1. A: Chironomus anthracinus. Normal mentum. Mats
Uppman (2017), B: Chironomus anthracinus. Deformed
mentum with a Köhns gap. Mats Uppman (2017).
3
heavy metals also had a high frequency of deformations in the mouthparts (Weiderholm 1982).
However, which metals that caused these deformities could not be determined at the time. In
more recent studies where Chironomid larvae were cultured in artificial sediment free from
heavy metals, it was shown that an increased occurrence of deformations in mouthparts could
be a result of elevated levels of copper, zinc, lead and arsenic in the water (Janssens de
Bisthoven, Vermeulen & Ollevier, 1998; Martinez, Moore, Schaumloffel & Dasgupta, 2003;
Martinez, Moore, Schaumloffel & Dasgupta, 2006; Beneberu & Mengistou, 2014).
Deformities in Chironomids related to heavy metals during the last decade have been further
studied, although these efforts have mostly targeted mixtures of metals including Cr, Ni, Zn, Pb,
and Cd (i.e. Adham et al. 2016; Arimoro et al. 2015; Di Veroli et al 2014; Di Veroli et al. 2012;
Deliberalli et al 2018; Ochieng et al. 2008; Roosa et al 2016). Some studies have even
investigated specific metals. For example, Weeraprapan et al. (2018) evaluated mouthpart
deformities in chironomids collected from a Cd polluted stream, and Božanić et al. (2018) tested
the effects of elevated Cu concentration in carp pond sediment. Correlations between heavy
metals and mouthpart deformations has been found in each case (Adham et al. 2016; Arimoro et
al. 2015; Božanić et al. 2018; Di Veroli et al 2014; Di Veroli et al. 2012; Deliberalli et al 2018;
Ochieng et al. 2008; Al-Shami et al., 2010; Roosa et al 2016; Weeraprapan et al. 2018), but the
specific cause of the deformations is complex and still not fully understood.
1.4 Aim of the study
This study aims to examine if there is a correlation between mouthpart deformities of
Chironomid larvae and heavy metals concentration in lakes and rivers influenced by mining
operations in northern Sweden. Sweden has a history of mining since about the 12th century,
but modern mining at industrial scales started about 100 years ago (Eilu et al. 2012). Some of
the products from today’s mining operations are Cu, Pb, Ni, Zi and Co (Eilu et al. 2012).
Considering that Sweden is one of the EU’s leading ore and metal producers, the mining
industry of Sweden is important for the country’s growth and economy (Sweden’s mineral
strategy 2013). The downsides of mining these metals are many, and among them are water
pollution from waste rock, tailings, or mine water that could contain high levels of heavy metals
(Sgu.se, 2019). Despite the widespread occurrence of mines in northern Sweden, little seems to
be known how these may influence the benthic fauna.
A secondary aim of the study is to determine if there are differences in the frequency of
deformations as a function of bioavailable versus total concentrations of heavy metals found in
the water chemistry analyses. Finally, the bioavailability of heavy metals has been shown to
decrease with an increase of dissolved organic carbon (DOC). Specifically, waters with high
concentrations of DOC contains more ligands to bind metals (Winch et al. 2002). Because high
DOC concentrations are common in northern Swedish lakes (Seekell et al. 2015), this may
influence the effect of metals on benthic fauna. Therefore, it is of interest to include an analysis
of DOC in relation to mouthpart deformations.
4
2 Materials and methods
2.1 Study sites
Figure 2. Map showing the examination sites (red dots).
The study areas described below and are sorted by examination year, starting with the oldest.
Kringelgruvan 2012
Kringelgruvan is an open pit mining that is located about 20 km northwest of Edsbyn in the
municipality of Ovanåker, Gävleborgs County (Figure 2). Mining here has been used to break
graphite periodically since 1996 and the last production of graphite was in 2015 (Leading Edge
Material 2019). The settling basin has its outflow to the river Älman in which chironomids have
been sampled and examined. Sampling and examination of chironomids have also been done in
the nearby river Voxnan, including the sites KG1 (116 individuals), KG2 (1 individuals), KG3
5
(12), KG4 (93 individuals), KG5 (568), KG6 (553) between the days 11th and 14th of September
2012. From this system, a total of 1343 individuals were collected.
Fäboliden 2016
Fäbodliden is a gold exploration project that is located about 35 km west of Lycksele,
Västerbotten County (Figure 2). The area had its first exploration in 1993 and has since then
been periodically explored with drill holes and the latest exploration was in 2018 (Dragon
mining 2019). Sampling and examination of chironomid larvae have been made in the smaller
stream Örträsket (70 individuals) and the lakes Sörträsket (49 individuals), Sörsjön (24
individuals) and Rusträsket (142 individuals) between the days 20th and 22nd of September 2016.
A total of 285 individuals were collected from this area.
Aitik 2017
Aitikgruvan is mining operation that has been active since 1968 located about 15 km southeast
of Gällivare, Norrbotten County (Figure 2). It is the largest open pit in Scandinavia and it mainly
produces copper (Nationalencyklopedin 2019). The receptors from this mine is mainly Leipojoki
(92 individuals), Vassara älv (187 individuals) and Lina älv (1271 individuals). There has been
sampling and examination of Chironomids from each of these streams between the days 5th to
8th of September 2017. A total of 1550 individuals were collected from this area.
Vitåfors 2017
Vitåfors mining site is located 3 km northeast of Malmberget, Gällivare, Norrbotten County
(Figure 2). Chironomids were sampled and examined in Lina älv (611 individuals) between the
dates 6th and 7th of September 2017. A total of 611 individuals were collected in this area.
2.2 Field methods
Sampling in streams
Sampling of chironomids in all streams was performed using the standardized kick-method “SS
EN 10870:2012” (SIS 2012). The kick-method is used mainly in streams and rivers and involves
a sweep or kick with the foot on a square area of 0.3 x 1.0 meter for 60 seconds. Downstream of
the area where the kicking is performed, a landing net is held to catch all the detached material.
The subsample was repeated five times at each locale within a reach of 10 meters. On the same
reach, the sampling was supplemented with a qualitative sweep sample using a M42-hand net in
areas where the kicking method is not possible to perform due to i.e. water depth or bed
substrate.
Sampling in lakes
The sampling of the Chironomids in all lakes (Fäbodliden: Sörsjön, Rusträsket and Sörträsket,
Viscaria: Södra tvillingtjärn and Leväjärvi) was performed in accordance with “Handledning för
miljöövervakning ”Bottenfauna i sjöars profundal och sublitoral”” (HaV 2016b) and “Svensk
Standard SS-EN 10870:2012” (SIS 2012). At each site, sampling has been performed with an
Ekman Grab (Ekmanhämtare) and supplemented with a qualitative sweep sample using a m42-
hand net.
6
Chemistry data
The sampling for water quality was performed according to ISO 5667-6:2014 standard along
with the Swedish guidance for environmental monitoring “Handledning för miljöövervakning
“vattenkemi i vattendrag” version 1:3 2010-02-17” (Naturvårdsverket 2010). In Fäbodliden, the
reported concentrations of all metals are based on mean values from seasonal samples during
spring, summer, autumn and winter between the years 2014 - 2016. In Kringelgruvan the levels
of metals are based on two rounds of sampling in 2012. In Aitik and Vitåfors, mean values of
metals are based on samples from winter, spring, summer and autumn during 2017. All
calculations of the water chemical parameters (metals, pH and DOC) has been performed by the
company Pelagia Nature & Environment AB. The analyses of the metals have been performed by
ALS Scandinavia AB. In each examination site, sampling of water chemistry and Chironomids
were both performed during the same year.
2.3 Lab methods
2.3.1 Assembly of the Chironomids mouthpart
In the laboratory, Chironomid larvae were picked and identified to genera. The cephalic
capsules were then removed and mounted. The head capsules were then photographed and
analyzed using an ocular camera and a stereomicroscope. To evaluate the deformities in the
mouthparts of the Chironomid larvae, a normal mentum or ligula with no signs of deformity (for
examples, see figure 7, 9 and 11) were used within in each genus to compare with mouthparts
with deformities. The mouthparts were considered to be deformed if it was lacking a tooth or
teeth (figure 10), Köhns gap (figure 12), had additional (figure 8) or fused teeth in the middle of
the mentum or ligula (figure 8). Köhns gap is a distinct and easily recognized deformity that
occurs when Chironomids get exposed to mixtures of toxicants, such as heavy metals, which in
turn leads to an inhibition of the epidermal tissue growth (Groenendijk et al. 1998).
2.4 Data analysis
2.4.1 Bioavailability of metals
The bioavailability of Cu, Ni, Zn and Pb was estimated using the tool “bio-met bioavailability
tool v4.0”. This tool is a biotic ligand model (BLM) which is used to determine the metal
speciation and to predict the toxicity to biota in aquatic ecosystems (Smith, Balistrieri and Todd,
2015). The calculations are based on the concentration of the metal, pH, calcium (Ca) and DOC.
The model used in this study can manage to analyze in a total of four metals for bioavailability
which is why only the before mentioned and not all metals were calculated.
2.4.2 Statistical analyses
I used two approaches to characterize the patterns of metal concentration across sampling sites.
First, I used Pearson product-moment correlation to explore the relationships across all metals.
To further evaluate how metals are distributed among lakes, I also used principal component
analysis (PCA). For this analysis, I included all metals (BLM metals included), pH, and DOC as
7
variables. Data for BLM metals, DOC, and pH were missing from two sample sites in Aitik. I
used the default imputing procedure in JMP software to estimate these missing data.
To assess Chironomid deformity, I first generated a simple frequency distribution of deformities
to characterize their occurrence across all sampling locations. Then I used simple linear
regression to 1) determine the relationship between % deformation and individual metal
concentrations, and 2) to assess whether the occurrence of deformation varied with DOC
concentration among sites. In all statistical analyses of this study, the level of significance was
set to 5% (P = 0.05).
3 Results
3.1 Metals
The metals analyzed were in general within the range of very low levels (class 1) or low levels
(class 2) with one exception in Fäboliden where one was assessed as moderately high level
(class) according to the classification from Naturvårdsverket of lakes and streams (Appendix 2).
I generated data table including mean values of each metal, pH and DOC to illustrate how the
levels differed between each site (Table 1). In general, the levels of each metal were equable with
a few exceptions; Fäbodliden had approximately 25 times higher level of As than the other sites,
Aitik had approx. 2,5 times higher copper than the other sites and Kringelgruvan had approx. 5
times higher Nickel and 3 times higher Zink than the other sites. Furthermore, the DOC was
about 2 times higher in Kringelgruvan and Fäbodliden than in the other two sites.
Table 1. Mean values of all metals (µg/L), pH and DOC (mg/L) in each site based on values from table 4. The standard
deviation is written in the parentheses. As = Arsenic, Cd = Cadmium, Cr = Chromium, Cu = Copper, BLM = Binding
ligand model, Pb = Lead, Zn = Zink and DOC = Dissolved organic carbon. Standard error in non-bold text.
The relationship among and between all metals, pH and DOC is presented in table 2 in a
Pearson product-moment correlation matrix. Out of 81 correlation tests, 27 were significant.
Element As Cd Cr Cu BLM Cu Ni BLM Ni Pb BLM Pb Zn BLM Zn pH DOC
Vitåfors 0.074 0.003 0.098 0.416 0.025 0.579 0.203 0.029 0.005 1.971 0.780 7.005 4.169
0.010 0.001 0.014 0.105 0.011 0.120 0.053 0.009 0.003 1.070 0.557 0.095 0.904
Aitik 0.082 0.004 0.119 1.148 0.057 0.446 0.116 0.031 0.005 3.243 0.942 7.009 4.928
0.007 0.002 0.014 0.348 0.028 0.086 0.040 0.006 0.000 0.415 0.005 0.084 0.303
Fäbodliden 2.596 0.010 0.212 0.452 0.013 0.585 0.120 0.069 0.003 4.685 1.149 6.975 9.595
1.326 0.002 0.040 0.031 0.001 0.139 0.017 0.010 0.000 0.522 0.048 0.095 1.824
Kringelgruvan 0.225 0.008 0.192 0.473 0.011 2.572 0.537 0.059 0.004 8.738 2.175 7.192 8.753
0.054 0.003 0.024 0.054 0.002 1.046 0.221 0.006 0.001 2.118 0.545 0.020 0.617
8
Table 2. Pearson product-moment correlation matrix. Bold texts are significant correlations (P = <0.05).
The PCA provided additional insight into the spatial correlation of these metals across sampling
locations. Figure 3 shows that with the first two axes together, the principal components
analysis explains 68,5% of the total variance. PC1 alone explains 44% of the total variance while
component and PC2 explains 24.5%. Three well defined groups of variables can be
distinguished; To the upper left Cu, BLM cu and BLM Pb, to the upper right pH, Ni, BLM Ni, Zn
and BLM Zn and to bottom right As, Pb, DOC and Cr. Cd are not closely related to any of the
groups. Additionally, the PCA indicated that Cu, As, Pb and Cr are regulated by DOC while pH
indicates to regulate BLM Ni, Ni, BLM Zn and Zn.
9
Figure 3. Principal component analysis of the chemical and hydrological variables (black dots) along with the sites
evaluated (grey dots).
3.2 Frequency of mouthpart deformities
In a total of 4 sites, 17 sampling sites with 3789 individuals sampled, the interval of deformities
in the sites ranged from 0.0 to 4.79%. The distribution of deformations with intervals with 0.8%
in range can be seen in figure 4. The largest frequency of % deformations was in the range of 0.0
- 0.8%, total of ten samples, where eight samples had 0 % and two had 0.5%. Five of the samples
with 0% deformations were from Kringelgruvan and the three residual samples were from
Fäboliden. Finally, the highest frequency of deformation was 4.79% and was found in Aitik.
10
Figure 4. Box plot showing the frequency of deformation sites in relation to the absolute percentage of deformations
with an interval of 0.8%.
Kringelgruvan 2012
In Kringelgruvan, sampling of Chironomids were performed in a total of six sites. Out of these
sites, only one individual was found with malformed mouthparts. This individual of the genera
Macropelopia sp. was found in a sample site of the stream Voxnan.
Fäboliden 2016
In Fäboliden, only one site (Rusträsket) out of four had Chironomids of the genera Chironomus
(Chironomus anthracinus) with mouthpart deformations of total 0.5 %, which is within the
considered range of natural occurrence of deformities.
Aitik 2017
In Aitik, samples included individuals with deformations from the genera Macropelopia,
Procladius, Paratendipes, Stictochironomus, and Sergentia. The highest reported % deformities
in this study was found in Leipojoki, 4.79%, which is a small stream and can be assessed as
distinctly affected. In Vassara älv, 2.10 % were found with deformities which could be a sign of
some contaminant effect. For the sampling sites in Lina älv, downstream of Leipojoki and
Vassaraälv, deformations showed a frequency of 3.73%, which is assessed as a clear sign of
contaminant effects.
Vitåfors 2017
In Vitåfors, three sampling sites were performed in the stream Lina älv where individuals from
the genera Sergentia and Procladius were found with deformations. In the sampling site
upstream of the mine the frequency was found in 0.5% of the individuals. The other two
sampling sites showed a frequency of 1.59% and 0.90%.
Only two metals were significantly correlated with % deformation among sample sites: Co and
Cu (Figure 5 A and B). For Co, deformation increased with concentration (R2 = 0.66, p =
0.002), but only 11 of the 17 sample sites had Co data. Across all lakes deformation increased
with the concentration of copper (Cu) (R2 = 0.73, p < 0.001).
11
Figure 5. A: % deformations in relation to levels of µg/l copper. B: % deformations in relation to levels of µg/l cobalt.
3.3 Does DOC influence metal effects?
DOC concentrations varied from 3.3 to 15.0 mg/l (mean value = 7.6 mg/l) across the sample
sites. The occurrence of deformations appeared to decline with DOC concentration (Fig. 6), but
this relationship was non-linear and could not be evaluated using a standard regression.
Regardless, this plot suggests a threshold concentration of 10 mg/l DOC beyond which no
deformities were observed in this dataset.
Figure 6. Deformations (%) and amount of DOC (mg/l).
4 Discussion
A major goal of this project was to examine if there is a correlation between mouthpart
deformities of Chironomid larvae and heavy metals in rivers and lakes in northern Sweden, as
one concern is that the mining operations may have a negative influence on the aquatic biota
(Deliberalli et al. 2018). Overall, I found that the rates of deformations varied among the sites
12
but were only as high as 4.79%, which was observed only in one of the sites in Aitik. Indeed, for
the majority of examined sample sites (12 out of 17), the frequency of deformations was <1%
(figure 4), which is within the range of natural deformity occurrence (i.e. Burt et al. 2003;
Hudson and Ciborowski 1996; Wiederholm 1982). That is to say, the mouthpart deformities
found in this study was relatively low compared to what has been reported from other regions.
For example, Deliberalli et al. (2018) reported an incidence of deformities was above 30% in
southern Brazil. Similarly, working in Nigeria, Arimoro et al. (2015) reported deformities up to
43% that was correlated with increased concentrations of Ni, Pb, Cu, Cr and Fe.
One likely reason for this low occurrence of deformities is that metal concentration was not
particularly high in these study systems. For example, in accordance to Naturvårdsverket
guidelines, the concentration of metals found in this study were in general ‘very low’ to ‘low’
(Table 3). The only exception was As at on site in Fäbodliden, which was observed at
concentrations deemed ‘moderately high’. Interestingly, at this same sampling site, I found zero
individuals with deformations. Consequently, As in this study did not correlate with deformities
and so for that reason the result suggests that increased levels of As alone may not cause
deformities in Chironomid larvae.
Despite the low occurrence of deformities and low concentrations of metal, a few relationships
still emerged from the data. For example, Figure 5 A and B shows that Cu and Co were positively
correlated (R2 = 0.73 respectively 0.66) with mouthpart deformities. Since these two metals
comes from the same minerals, Co is frequently found in waters that are located in areas related
to copper mines (Marr et al. 1998). Deformities in several previous studies have been correlated
to Cu (e.g. Deliberalli et al. 2018; Di veroli et al. 2014; Arimoro et al. 2015; Božanić et al. 2018;
Ocheing et al. 2008). However, Co was not even included in the analysis of either study. Given
that the chemical structure of Cu and Co are analogous, there is a chance of them reacting in
similar ways when the environment changes (Mason 2013). Therefore, the result in this study
raise the question if Co may have an impact on Chironomid larvae.
A second aim of this study was to find out whether the bioavailability of the metals would be
more strongly related to the frequency of deformations when compared to the total
concentrations. Overall, I found no evidence for strong relationships with estimate of
bioavailable concentrations. However, since Cu was the only metal significantly related to
deformations that could be calculated to bioavailable concentrations, the data are too weak to
support further discussion about this topic. Regardless, I still believe it is reasonable to separate
these two forms of metals and that this issue remains a knowledge gap. In addition, literature
with comparisons of this kind are scarce. One possible reason for this could be due to that tools
for calculations of metal bioavailability are complex and not available for all metals.
While this study tentatively links mouthpart deformations to concentrations of Cu and Co, it
also suggests that metal effects may be reduced by high DOC concentrations. Considering that
the bioavailability of heavy metals decrease with an increase of DOC (Winch et al. 2002) and
that waters in northern boreal regions of Sweden tend to have high concentrations of DOC
(Sobek et al. 2007; Seekell et al. 2015), it is possible that natural variation in lake chemistry may
mediate how Chironomids respond to metal loads. However, out of four BLM metals tested for
correlation with DOC, two (BLM Cu and BLM Pb) were found to be negatively correlated (r = -
13
0.50 and -0.64, respectively). In a previous study of Borg (1983), significant correlations
between metals in water and water color were found for several metals, including a few analyzed
in this study: As, Pb, Cr and Co. This is consistent with the PCA performed in this study (Figure
3) and the Pearson correlation matrix (table 2). Organic matter in water can be estimated by the
color of the water instead of using more advanced analysis of TOC (total organic carbon). This is
interesting because DOC is a fraction of TOC (Hoover 2008), and if water with high
concentrations of DOC also has high levels of metals but are bound to the dissolved organic
matter, then this may strengthen the hypothesis that high levels of DOC will have low %
deformations. Given that there is natural variation in surface water DOC across Sweden (e.g.,
Seekell et al. 2015), it would be interesting to further explore whether and how this mediates
aquatic ecosystem response to metal pollution.
In the present study, deformations and metal levels were both low in general. This could be
interpreted in two ways: it could either indicate that Chironomid larvae are sensitive even in low
levels of metals in water, or that mines may not have a large impact on Chironomids in lakes and
rivers located in northern Sweden. Therefore, in order to get a better understanding of what is
causing the deformations found in Chironomid larvae, sites with both higher concentrations of
metals and deformation would be useful. In addition, data from metals found in sediments
would be interesting to compare with, especially since many aquatic Chironomid larvae genera
live in or close to the bottom and can metabolize contaminants from both sediment and water
(Božanić et al. 2018). The genera found and examined in this study were Sergentia sp.,
Procladius sp., Macropelopia sp., Paratendipes sp., Stictochironomus sp., and some were only
identified to subfamily Tanypodinae sp. Since these have different feeding and living habits
(Nilsson 1997), one should do separate analysis for each genus to be sure that the sensitivity is
as similar as possible between the examined individuals.
On the basis of the result presented in this and previous studies, there are clearly still
uncertainties regarding what is causing the deformities. However, copper was, despite its
relative low levels, significantly correlated with mouthpart deformations in this investigation,
and this has also been reported to be the case in earlier studies (e.g. Deliberalli et al. 2018; Di
veroli et al. 2014; Arimoro et al. 2015; Božanić et al. 2018; Ochieng et al. 2008). Therefore, there
is some reason to believe that elevated levels of copper can be harmful to aquatic Chironomid
larvae. Furthermore, since deformations were found in this study even though the waters were
classified to have none to very low risk of biological effects (table 3), results also suggest that
Chironomids are sensitive to metals even at low concentrations. Having these results in mind, I
believe that Chironomids could potentially be a good monitoring tool for early warnings from
pollution. Nevertheless, there are still gaps in knowledge of what DOC, metals in sediment, and
the response to changes in the chemical variables in the different genera has to do with
deformations. To improve our understanding of the effect from these variables, future work
should strive to fill these gaps.
14
5 Acknowledgements
I would like to thank my supervisor Ryan Sponseller for great feedback with quick responses
and guidance along the way. I would also like to give many thanks to my supervisors at Pelagia
Peder Larsson and Ludvig Hagberg for giving me the opportunity to this thesis. Thanks goes to
Mats Uppman for sharing his photos of Chironomids mouthparts. I’m also grateful to all the
personal at Pelagia for providing me an office and a nice working environment. Huge thanks to
my beloved partner Maria Bjerknes Börestam who’s always there to support me and for giving
me constructive feedback to this work. Lastly, I would like to thank Johan Lidman for providing
me literature and for the help with my PCA.
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Appendix 1
Normal and deformed mouthparts
Figure 7. Macropelopia sp. Normal Figure 8. Macropelopia sp. Deformed ligula
ligula. Mats Uppman (2017). with fused teeth to the left, lack of tooth in the
middle and additional teeth to the right side.
Mats Uppman (2017).
Figure 9. Procladius sp. Normal Figure 10. Procladius sp. Deformed ligula
ligula. Mats Uppman (2017). with a missing tooth. Mats Uppman (2017)
Figure 11. Chironomus plumosus. Figure 12. Chironomus plumosus. Deformed
Normal mentum. mentum with a so-called Köhns gap.
Mats Uppman (2017). Mats Uppman (2017).
19
Appendix 2
Assessment of biological effects of metals
Short description of the classes in the assessment template from Naturvårdsverket (2000):
Class 1: None or very small risk for biological effects. Class 2: Small risks for biological effects.
Class 3: Effects may occur. The risk tends to be higher in waters with low pH and soft,
oligotrophic waters. Effects in this class refers to reproduction or survival in early life stages,
which normally shows as a reduction of individuals.
Class 4 & 5: Increased risks for biological effects. The survival of aquatic biota are affected even
of a short exposure.
Table 3. Classification table of metals in water (Naturvårdsverket 2000).
20
Appendix 3
Data Table 4. Table of all metals, pH and DOC. Blue indicates very low levels, green low levels, yellow moderately high
levels and grey are missing assessment. All metals and pH are average per sampling site. DOC is based on annual
mean values. The colors indicate the classification of the levels of metals seen in table 3.