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

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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.

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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

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(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.

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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.