BA CHELOR THESIS
Bachelor's Programme in Nature Conservation and Species Diversity, 240 cred
Malformation in different species of benthic diatoms in three herbicide polluted streams in southern Sweden
Helena Spångfors
Biology, 15 credits
Halmstad 2017-09-11
SAMMANFATTNING
Målet med den här studien var att undersöka kiselalger och dess eventuella missbildningar i tre vattendrag i södra Sverige - Höje å, Skivarpsån och M42. Kiselalger används som bioindikator för vattenkvalitet i hela Europa, men de befintliga kiselalgsindexen visar inte eventuell förekomst av miljöfarliga ämnen. Det har dock visats att en förekomst > 1 % av missbildade kiselalger indikerar påverkan av miljöfarliga ämnen, såsom pesticider och tungmetaller. Denna studie är en av få som har undersökt kiselalgers missbildningar i vattendrag som är mer eller mindre påverkade av herbicider. Sex prov från varje vattendrag analyserades - kiselalger räknades och identifierades till artnivå och missbildningar dokumenterades och kategoriserades. Vattendragen delades in efter PTI (Pesticide Toxicity Index), där Höje å hade lägst PTI och ansågs vara minst påverkad av herbicider.
Skivarpsån och M42 hade högre PTI, och ansågs därför ha en högre herbicidpåverkan.
Det fanns en signifikant skillnad i missbildningsfrekvens vattendragen emellan, den kunde dock inte kopplas till PTI. Både Höje å och M42 hade > 1 % missbildningar. Skivarpsåns
missbildningsfrekvens var < 1 % trots vattendragets relativt höga PTI. Det är möjligt att
missbildningsfrekvensen bättre hade reflekterat PTI om herbicidprovtagningen skett någon månad tidigare, då herbicidhalter kan variera och kiselalger har visat sig kunna spegla ett vattendrags mående upp till tre månader bakåt i tiden. En annan förklaring till en varierande
missbildningsfrekvens kan vara en lika varierande artsammansättning. Vissa arter är mindre
“benägna” att missbildas än andra - ett prov som domineras av sådana skulle därför kunna innehålla
få missbildningar trots eventuell miljögiftspåverkan. Det krävs dock ytterligare studier för att bättre
förstå kiselalgers missbildningar i förhållande till herbicider.
Introduction
Diatoms are unicellular algae with a silica cell wall (“frustule”), and important primary producers in most aquatic systems (Morin et al. 2012). In freshwater, diatoms are usually the dominating taxa in biofilm communities of rocks and macrophytes - a few square centimeters of biofilm can contain more than one hundred different species (Kahlert et al. 2016). Since all diatom species have different ecological preferences they are useful in detecting environmental changes (Morin et al.
2008). Diverse, easily sampled and with different ecological preferences, diatoms are perfectly suited to serve as biological indicators (Kahlert et al. 2016).
Two diatom indexes are commonly used to determine water quality in Swedish lakes and streams:
IPS reflects levels of nutrients and organic pollution, and ACID is an acidity index (Kahlert et al.
2016). The IPS and ACID index enables detection of eutrophication and acidification in streams and lakes. However, there is yet no diatom index in Sweden that takes environmental pollution into account (Kahlert 2012). This means that the existing indexes might wrongfully report a good ecological status class even in a heavy metal- or pesticide polluted stream (Morin et al. 2009). In order to meet the demands of the Swedish Environmental Objectives such as 'A Non-Toxic
Environment’ it is thus imperative to further develop indicators that detect effects of environmental pollutants, such as heavy metals and pesticides (Kahlert 2016).
Malformed diatoms may be a valid indicator of environmental pollution (Falasco et al. 2009).
Falasco et al. (2009) have summarised cause-effect relationships of malformations from 222 studies from different countries (e.g. Australia, Canada, France, Great Britain, Wales, Hungary). Their review shows that environmental pollution such as metals and pesticides can cause frustule
malformation. Several studies have shown that the proportion of malformation is low in unpolluted
streams in Sweden, but increased tenfold in waterbodies with heavy metal or pesticide pollution
(Eriksson & Jarlman 2016, Goedkoop & Kahlert 2015, Kahlert 2012). Eriksson & Jarlman (2011)
have sampled and analysed diatoms from pesticide polluted streams in southern Sweden. They
found that the high proportion of malformed frustules was strongly positively correlated to total
stream water pesticide concentration. Following Kahlert’s (2012) recommendations, a preliminary
screening indicator has recently been added to the Swedish water quality assessment guidelines,
where frustule malformation percentage > 1 %, or number of diatom taxa < 20 indicates heavy
metal- or pesticide pollution. Kahlert (2012) also has contributed to a new parameter in the Swedish
standard method (SIS 2014b) of diatom analysis. The parameter divides malformed diatoms into
four categories - ”slight”- or ”strong” abnormal form, and ”slight”- or ”strong” abnormal pattern. It is clear that diatom malformation is an important variable in the development of a new bio indicator that reflects environmental pollution (Lavoie et al. 2017).
Herbicides are a type of pesticide used abundantly in agriculture, targeting unwanted plants. In agricultural dominated landscapes, such as southern Skåne, streams might contain traces of many different herbicides (Pirzadeh 2011). Many of these substances affect primary producers (such as algae) photosynthesis and growth in the same way as in the land based plants they were designed to eliminate (Goedkoop & Kahlert 2015, Morin et al. 2009). At present there exist few studies of diatom malformation as a response to herbicides. Debenest et al. (2008) isolated a diatom community from a natural biofilm of a French river, cultured and exposed them to maleic hydrazide, a commonly used herbicide in France. They found that the highest herbicide
concentration significantly increased the abundance of malformed frustules. Jan-Ers (2009) found a significant increased proportion of malformation in relation to both heavy metal- and pesticide (two of which were herbicides) water concentrations. She also compared malformations induced either by pesticides (two of which were herbicides) or by heavy metals in a laboratory experiment but found no difference in type of malformation between the two. Lavoie et al. (2017) suggests that the method of documenting pure presence of diatom malformation is excellent in identifying
contaminated environments. However, they highlight the need for studies that take the type and the severity of these malformations into account. They also highlight the need to identify species more prone to develop malformations than others, in order to fully understand causes of diatom
malformations and develop a bio indicator based on these. I have therefore conducted a study of diatom species and their types of malformations, in three streams that were herbicide polluted to varying degrees.
Aim
This study is a contribution to the development of a new bio indicator in Sweden that responds to environmental pollution. The aim was to investigate diatom malformation in three herbicide
polluted streams in southern Sweden. This was done by answering the following questions: 1) Is the proportion of diatom malformation higher in streams with higher herbicide pollution than in a less polluted stream? 2) Does the proportion of diatom malformation differ between sampling
occasions? 3) Are “strong” malformations a better indicator of herbicide pollution than “slight”
malformations?
Material & Methods
Study design and study locations
The study included 3 streams (Höje å, Skivarpsån and M42. See Fig. 1), each was sampled 6 times, but at 2 different sampling occasions (September and October) and at 3 slightly different loctions (N=18). The stream water differed in stream water herbicide concentrations, where Höje å had the lowest and Skivarpsån and M42 the highest.
Höje å is located in Lomma municipality in the county of Skåne, between Lund and Malmö (Fig.
1). Skivarpsån and M42 are both located in Skurup municipality in southern Skåne (Fig. 1) and are included in Swedens national environmental program for pesticides, which is administrated by SLU.
Fig. 1: Approximate locations of the three streams Höje å, Skivarpsån and M42, in the county of Skåne, southern Sweden. Map is created with pictures from VISS (Water information in Sweden).
The catchment area of all three streams is dominated by agricultural farmlands according to VISS
(Water information Sweden), and their physiochemical properties are presented in Table 1. Höje å
runs through a nature reserve upstream sampling points and had a low stream water herbicide
concentration. Skivarpsån and M42 had a relatively high stream water herbicide concentration.
Table 1: Physicochemical properties of the three streams studied (Höje Å, Skivarpsån and M42) during the two sampling occasions in September and October of 2016. “Number of herbicides” is the total amount of herbicides in the sample that is over detection limit. PTI represents the Pesticide toxicity index (only for herbicides). Metal classifications are taken from the Swedish Environmental Protection Agency (Naturvårdsverket 1999).
Water sampling and analyses
The three streams were sampled for stream water quality on the 13th of September and the 11th of October 2016. pH was measured in situ with the help of a portable multiparameter sensor (Hanna Instruments ® HI98194). Metal ions in the stream water were analysed by ICP-MS at SRT- University of Girona. The analysis of stream water PO4 was performed by Eurofins using SS-EN ISO 6878:2005. Stream water pesticide concentrations were measured by the laboratory for organic environmental chemistry (OMK) at SLU, using by Swedac accredited methods OMK 51:8, OMK 57:3, OMK 58:1 and OMK 59:0 (SLU 2017). Stream water herbicide concentrations were used to calculate a PTI (Pesticide Toxicity Index) for herbicides (Table 1). It is an index calculated of the sum of all herbicide substances found in each sample, and their concentration, in relation to each substance toxicity to aquatic organisms (Naturvårdsverket 2008). Water samples and water
chemistry data were provided by Natàlia Corcoll (University of Gothenburg). PTI were calculated by Andreas Håkansson (University of Gothenburg).
Diatom sampling
Diatom sampling was carried out at the same time as water sampling, taking three samples from each stream both September and in October. Sampling was performed according to European
Month No of
herb. PTI pH PO43-
(mg/L) Cu
(µg/L) Cu
(class) Zn
(µg/L) Zn
(class) Cd
(µg/L) Cd
(class) Pb
(µg/L) Pb (class)
Höj. 9 9 0.2 7.9 <0.02 1.1 Low 31.0 Mod.
high 0.02 Low 0.6 Low
10 8 0.2 7.8 <0.02 0.9 Low 130.2 High 0.02 Low 0.7 Low
Skiv. 9 15 2.3 8.3 0.3 1.4 Low 49.1 Mod.
high 0.03 Low 1.1 Mod.
high
10 19 4.0 8.3 0.2 0.3 Very
low 213.1 High 0.02 Low 0.4 Low
M42 9 23 7.2 7.8 0.6 1.7 Low 36.7 Mod.
high 0.05 Low 1.5 Mod.
high
10 27 17.7 7.7 0.2 0.2 Very
low 85.0 High 0.04 Low 0.5 Low
standard method SS-EN 13946 (SIS 2014a), scraping off the biofilm of small stones with a toothbrush. Diatom samples were provided by Natàlia Corcoll (University of Gothenburg).
Diatom preparation and analysis
Diatom samples was prepared in April and May 2017 at the Department of Soil and Environment at SLU. The samples were boiled in hydrogen peroxide (30% H
2O
2) and hydrochloric acid (35% HCl) to eliminate organic material and to clean the diatom silica frustules, following Jarlman (2007).
Permanent replicates were then prepared by mounting the frustules on glass microscope slides with Naphrax© (Brunel Microscopes Ltd, UK), a medium with refractive index 1.74. See Jarlman (2007) for a detailed method description. Diatom preparation was performed according to European
standard method SS-EN 13946 (SIS 2014a)
The diatom analysis was performed according to European standard method SS-EN 14407 (SIS 2014b) in the laboratory of Halmstad University. 400 valves were identified to species level on each replicate with a Nikon eclipse E600 microscope with 1000× magnification. In some cases only genera were identified - for example some “girdle views” (valves lying on their side) are difficult to identify to species level. Every taxon found was updated to the latest nomenclature according to the Swedish freshwater diatom taxa list (available from Department of Aquatic Sciences at SLU).
The taxonomic literature used in this study are from central Europe (Hofmann, Werum & Lange- Bertalot 2011, Krammer & Lange-Bertalot 1991a, Krammer & Lange-Bertalot 1991b), and was supplemented with Spaulding, Lubinski & Potapova (2010) and Guiry & Guiry (2017).
Diatom metrics and malformation categories
Indexes used in this study are IPS (Indice de Polluo-sensibilité Spécifique, Cemagref 1982), ACID (Andrén & Jarlman 2009) and Shannon’s diversity index (Shannon 1948) IPS is based on all identified taxa in the sample and shows the nutrient impact and organic pollution in the water. It is used with supportive parameters %PT (Pollution Tolerant valves, Kelly 1998) and TDI (Trophic Diatom Index, Kelly 1998), which classifies species based on their tolerance to eutrophication and (easily dissolved) organic pollution. ACID is an acidity index. Shannon’s diversity index,
showing species diversity, was also calculated. All index calculations in this study were made by
Anders Stehn (SLU), using Omnidia software (Omnidia, Roubaix, France). The ecological status of
each stream was determined by IPS value as recommended by the Swedish Environmental
Protection Agency (Naturvårdsverket 2007). Index values for Swedish conditions are shown in Table 2 & 3.
Table 2: Classes for IPS (Indice de Polluo-sensibilité Spécifique), %PT (Pollutant Tolerant valves), TDI (Trophic Diatom Index). IPS margin of error +/- 0,5 if IPS > 13, margin of error +/- 1 if IPS < 13 (Naturvårdsverket 2007).
Table 3: Classes for ACID (ACidity InDex) for Swedish conditions. Margin of error +/- 10 % (Naturvårdsverket 2007).
A total of 400 diatom valves were counted and identified on each slide. Every malformation was photographed with microscopic camera DFK 33UX174 Color Camera using IC Measure software (The Imaging Source Europe GmbH, Bremen, Germany). Malformations have been divided into four categories according to Kahlert (2012) - ”slight”- or ”strong” abnormal form, and ”slight”- or
”strong” abnormal pattern. The total number of malformations (i.e the sum of all malformations regardless of category) in each sample were transformed into %, where a total proportion of malformation > 1 % indicates toxic impact such as heavy metal- or possibly pesticide pollution (Kahlert 2012). Pictures of all malformations were sent to Maria Kahlert at SLU for validation.
Acidity class ACID index Equivalent to mean pH (mean of 12 months before sampling)
Equivalent to minimum pH (up to 12 months before sampling)
Alcaline ≥7,5 ≥7,3 -
Near neutral 5,8-7,5 6,5-7,3 -
Moderate acidic 4,2-5,8 5,9-6,5 <6,4
Acidic 2,2-4,2 5,5-5,9 <5,6
Highly acidic <2,2 <5,5 <4,8
Status IPS-value %PT TDI
Reference 19,6 - -
High ≥17,5 <10 <40
Good ≥14,5 and <17,5 <10 40-80
Moderate ≥11 and <14,5 <20 40-80
Unsatisfactory ≥8 and <11 20-40 >80
Poor <8 >40 >80
A mean value for each sample was calculated for number of taxa, proportion of malformations, IPS, TDI, %PT, ACID and Shannon. Ecological status for each sample was determined by the mean IPS- value according to the Swedish Environmental Protection Agency (Naturvårdsverket 2007)
Data processing and statistics
The first question to be answered in this study is if the total proportion of diatom malformation is higher in streams with higher herbicide pollution than in a less polluted stream. I sorted streams after stream water herbicide concentration using PTI (Table 1). Höje å has lowest PTI and is assumed to be less herbicide polluted than Skivarpsån and M42. M42 has the highest PTI and is assumed to be the most polluted. An one way ANOVA-test and a Tukey HSD test was used to find any differences between all samples total proportion of malformation and the three streams.
The second question is if the proportion of diatom malformation differs between sampling occassions. An independent samples t-test was used to find any difference between the samples proportion of malformation and sample occasions September and October.
The third question is if “strong” malformations are a better indicator of herbicide pollution than
“slight” malformations. A Kruskal-Wallis test was used to find any differences between “strong malformations” and the three streams. An one way ANOVA-test and a Tukey HSD test was used to find any differences between “slight malformations” and the three streams.
Statistical analysis
All statistical analyses were performed in SPSS (IBM Corp. Released 2016. IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp.). In this study there is a total of 18 samples.
These consists of 3 samples from 3 streams sampled at 2 occasions (N=18) (Table 3). All data except the variable for “strong malformations” were normally distributed according to a Kolmogorov-Smirnov test.
Results
Taxonomic composition of diatom communities
Amphora pediculus Kütz. Grunow is the most common species in all samples except the stream
Höje in October, where Cocconeis placentula Ehrenb. dominated. Table 4 describes the five most
common diatom taxa at each sampling occasion. Their photos are presented in Fig. 2. A complete taxa list for each stream is presented in Appendix A.
Table 4: The five most common diatom taxa from each sample at each sampling occasion, ranked 1-5 where 1 is the most common. Authors: Achnanthidium minutissimum-complex, Amphora pediculus Kütz. Grunow, Caloneis lancettula (Schulz) Lange-Bert. & Witkowski, Cocconeis placentulaEhrenb., Cyclotella meneghiniana Kütz., Eolimna minima (Grunow) Lange-Bert., Navicula gregaria Donkin, Planothidium frequentissimum (Lange-Bert.) Lange-Bert., Planothidium lanceolatum (Bréb. ex Kütz.) Lange-Bert.
Höje å
September Höje å
October Skivarpsån
September Skivarpsån
October M42
September M42 October 1 Amphora
pediculus Cocconeis
placentula Amphora
pediculus Amphora
pediculus Amphora
pediculus Amphora pediculus 2 Achnanthidium
minutissimum Aulacoseira sp. Achnanthidium
minutissimum Achnanthidium
minutissimum Cocconeis
placentula Achnanthidium minutissimum 3 Eolimna minima Amphora
pediculus Amphora sp. Amphora sp. Achnanthidium
minutissimum Planothidium frequentissimum 4 Planothidium
lanceolatum Staurosira sp. Planothidium
lanceolatum Caloneis
lancettula Aulacoseira
sp. Navicula gregaria
5 Planothidium
frequentissimum Cyclotella
meneghiniana Cocconeis
placentula Cocconeis placentula / Eolimna minima
Eolimna
minima Planothidium lanceolatum
Fig. 2: Pictures of the most common taxa in all samples. a) Amphora pediculus Kütz. Grunow, b) Achnanthidium minutissimum-complex c) Cocconeis placentulaEhrenb., d) Eolimna minima (Grunow) Lange-Bert., e) Amphora sp., f) Planothidium lanceolatum (Bréb. ex Kütz.) Lange-Bert., g) Navicula gregaria Donkin, h) Staurosira sp., i) Caloneis lancettula (Schulz) Lange-Bert. & Witkowski, j) Aulacoseira sp., k) Planothidium frequentissimum (Lange-Bert.) Lange-Bert., l) Cyclotella meneghiniana Kütz. Ⓒ Helena Spångfors
Diatom metrics
There was a significant difference between the total proportion of malformation and the three streams (one way ANOVA test; df = 17, F = 4.7, p = 0.0026). There was no significant difference between the total proportion of malformation and sampling months September and October (t-test;
p > 0.05).
Results of the diatom analysis is showed in Table 5. All three streams have a higher number of taxa in October than in September, but standard deviations are high in Höje å and in September sample of Skivarpsån. IPS values are higher in September than in October in all three streams. Samples from Skivarpsån have a malformation frequency < 1 %, whereas the other two streams have values
> 1 %. Both Höje å and M42 has a higher proportion of malformation in September than in October.
The ecological status is moderate in Höje å, good in Skivarpsån and moderate to good in M42.
Table 5: Mean with standard deviations of total number of taxa, proportion of malformations and index values from three samples per site. The ecological status is classified from index values, according to Swedish Environmental Protection Agency (Naturvårdsverket 2007). Abbreviations: IPS (Indice de Polluo-sensibilité Spécifique), TDI (Trophic Diatom Index), %PT (% Pollution Tolerant valves), ACID (ACidity InDex), Shannon (Shannon´s diversity index).
* > 1 % indicates environmental pollution (Kahlert 2012). ** On the boundary to “Moderate” (Naturvårdsverket 2007).
Month Taxa %
Malform. IPS TDI %PT ACID Shannon Ecological
status class
Höj. 9 27 ± 7.2 2.17 ± 1.2 14.4 ± 0.4 92.7 ± 2.3 12.2 ± 3.0 7.7 ± 0.2 2.5 ± 0.7 Moderate
10 30 ± 7.0 1.75 ± 0.9 14.1 ± 0.5 72.7 ± 1.8 3.0 ± 1.6 7.5 ± 0.2 3.4 ± 0.6 Moderate
Skiv. 9 26 ± 10.4 0.75 ± 0.3 15.2 ± 0.1 92.4 ± 3.2 5.3 ± 2.2 7.8 ± 0.1 2.1 ± 0.6 Good
10 35 ± 3.0 0.75 ± 0.9 14.6 ± 0.3 90.9 ± 3.3 7.2 ± 3.2 7.8 ± 0.2 2.5 ± 0.2 Good*
M42 9 27 ± 1.2 2.08 ± 0.4 14.9 ± 0.4 84.8 ± 11.7 6.0 ± 3.9 8.2 ± 0.6 2.6 ± 0.7 Good*
10 35 ± 1.0 1.33 ± 0.4 14.0 ± 0.7 95.9 ± 0.9 14.7 ± 2.9 8.5 ± 0.8 3.0 ± 0.3 Moderate
Malformation categories
Only two of four malformation categories were found in this study. These were ”slight”-
respectively ”strong” abnormal form. There was a significant difference between the proportion of
“slight” abnormal form and the three streams (one way ANOVA test; df = 17, F = 6.09, p = 0.012).
There was no significant difference between the proportion of ”strong” abnormal form and the three streams (Kruskal-Wallis test; p > 0.05). The graph in Fig. 3 presents the total proportion of
malformation of each category found in each sample. The red line in the graph shows the limit for which environmental pollution can be assumed (malformations > 1 %). There is one sample in Skivarpsån in October in which no malformations were found. “Strong” abnormal forms are found in all samples except M42 in October. Pictures in Fig. 4 are taken during analysis and show
examples of the two malformation categories, of the most commonly malformed species
(Achnathidium minutissimum, Eolimna minima (Grunow) Lange-Bert and Cocconeis placentula Ehrenb.
Fig. 3: Graph on total proportion of malformation of all 18 samples, including 2 categories: “Strong” abnormal form and “slight” abnormal form. The red line shows the 1% malformation limit ( > 1 % indicates environmental pollution).
Fig. 4: Pictures taken during analysis, showing examples of “strong”- and ”slight” abnormal forms found in this study.
a) “Slight” abnormal form of Achnanthidium minutissimum, b) “Strong” abnormal form of Achnanthidium
minutissimum, c) “Slight” abnormal form of Eolimna minima (Grunow) Lange-Bert, d) “Strong” abnormal form of Eolimna minima “Slight” abormal form of Cocconeis placentulaEhrenb., f) “Strong” abnormal form of Cocconeis placentulaEhrenb. Ⓒ Helena Spångfors
Discussion
The aim of this study was to investigate diatom malformations in Höje å, Skivarpsån and M42. The total proportion of diatom malformation were significantly different between the streams, but according to Tukey’s HSD test only between Skivarpsån and the other two streams. This means that in this study the proportion of malformation is high in the stream with the highest herbicide
pollution, but also in the stream with less herbicide pollution. This answers the first question in this study - the proportion of diatom malformation is not higher in the streams with higher herbicide pollution than in the less polluted stream. The proportion of “slight” abnormal form were also significantly different between the streams but with the same Tukey’s HSD test result as for the total proportion of malformation. The proportion of “strong” abnormal form were not significantly different between the streams, and the total proportion of diatom malformation was not significantly different between samling month September and October. This answers the second and third
question in the study - the proportion of malformation does not differ between the sampling occasions, and the ”strong” malformations are not a better indicator of herbicide pollution than the
”slight” malformations.
However, M42 showed expected results with regard to its relatively high PTI. It had a total
proportion of malformation > 1 % and was significantly different to Skivarpsån, which had lower
PTI. Its IPS value in October was the lowest of all samples, while its TDI and %PT for the same month were the highest. This means that M42 had the highest nutrient load and the highest proportion of pollution tolerant valves out of all streams. The mean number of malformations are higher in September than in October in both Höje å and M42, however, neither of the samples differed in malformation frequency between September and October according to the t-test. It is possible that a significant difference would appear had there been more streams in the study.
In Höje å, PTI was relatively low. The sample points were downstream a nature reserve and therefore expected to have low impact of environmental pollution. Despite this, the highest
proportion of both ”slight”- and ”strong” abnormal form were found here, and the total proportion of malformation was significantly different to Skivarpsån. One possible explanation to the high proportion of malformations could be the low level of phosphorus in Höje å. A nutrient deprivation could have further affected the (already vulnerable) diatom community and its negative response to pesticides (Morin et al. 2009). However, the IPS and TDI rather reflect a nutrient surplus in all three streams. Another explanation could be the high Zn levels in Höje å. In Hedenborg’s (2016) study it was observed that similar high Zn levels induced diatom malformation, and Ström (2016) found that in some cases in her study, low phosphorus levels raised the toxicity of Zn to diatoms.
Whatever the reason might be, from this study one can assume that there is some sort of environmental pollution in the samples from Höje å, because of the relatively high level of malformations and the only moderate ecological status class.
Regarding Skivarpsån, I had expected a higher frequency of diatom malformations due to a relatively high herbicide pollution (i.e. high PTI). Additionally, also the Zn and Pb content was found to be relatively high in the stream. However, the total proportion of diatom malformation was
< 1 % and IPS was higher in Skivarpsån than in the other streams. The low amount of
malformations could be temporary, because variations in malformations have been observed earlier (Kahlert 2012, Morin et al. 2009). It could also be that the proportion of malformation probably is not coupled to the PTI directly at sampling date, but to earlier impacts. According to Eriksson &
Jarlman (2011) and Kahlert (2012) malformations best reflects the environmental pollution impact of the previous 1-3 months. Another possibility could be that the diatom taxa composition in Skivarpsån was dominated by taxa which are less prone to develop malformations. Larras et al.
(2014) states that in a natural diatom community in Lake Geneva, sensitivity to a herbicide mixture
varied between seasons, with winter season species being more tolerant than the summer season
community. It is also known that some species are more tolerant to pollution than others, for example Achnanthidium minutissimum (Kahlert 2012, Wood et al. 2016) that is the 2nd most common species in Skivarpsån. Overall, the five most common species in Skivarpsån is not very different from the other streams. However, Amphora sp. (on 3rd place both September and October) and Caloneis lancettula (Schulz) Lange-Bert. & Witkowski (4th in October) did not have a single malformed valve in any of the 18 samples in this study. Amphora pediculus Kütz. Grunow which is the most dominant species in both sampling months in Skivarpsån only had two malformed valves out of all 18 samples. Also, two of this study’s most commonly malformed species (Cocconeis
placentula Ehrenb. and Eolimna minima (Grunow) Lange-Bert.) are on 5th place in Skivarpsånwhile Cocconeis placentula Ehrenb. is on 1st and 2nd place in the other two streams, and Eolimna
minima (Grunow) Lange-Bert. on the 3rd and 5th. It would be of interest to take more of the speciesfound into consideration, but this will have to be done in another study.
In general, most samples in the study were dominated by Amphora pediculus Kütz. Grunow,
Achnanthidium minutissimum and Eolimna minimia (Grunow) Lange-Bert. which are common taxa
in nutrient rich waters according to Goedkoop & Kahlert (2015). Regarding the number of taxa and diversity, they fall into the range which is assumed to be normal for Sweden according to Kahlert (2011), who states that 90% of Swedish streams have between 20 and 80 diatom taxa and a Shannon-diversity between 1.5 and 5. The mean number of taxa was higher in October than in September for all three streams, reflecting the seasonal variations of diatoms found earlier by Goedkoop & Kahlert (2015). Judging by the normal amount of taxa and diversity, and by the IPS values, all three streams in this study would probably come across as having reasonably high water quality. However, by counting malformed diatom valves, one will find that there is an
environmental pollution impact in two of the streams. Hopefully in a near future, with a new bio indicator based on diatom malformation and species composition, it will be possible to point out exactly what kind of environmental pollution is affecting Swedish streams.
Conclusions
The present study investigated diatom malformations in more or less herbicide polluted streams.
The results show that the stream Höje probably has some exposure to environmental pollution,
shown by its high proportion (> 1 %) of malformations - this is also confirmed by its ecological
status class (“moderate”). Considering the stream runs through a nature reserve upstream from the
sample points, and had a relatively low PTI, these findings are somewhat surprising and should be
investigated further. Skivarpsån does not show any impact, but more samples should be taken as this study indicates that monthly variations occur. Other months might present a higher proportion of malformation in Skivarpsån. In general, taxa composition could have impact on the lack of malformations. Also, to obtain a mean value of malformations that matches PTI in a stream, one probably needs to take several pesticide measurements, starting a couple of months ahead of diatom sampling. Overall, this study takes another step towards developing a new bio indicator for Swedish waters. When analysing its results with regards to earlier studies, it is clear that malformations are complex. This study indicates that diatom malformations might vary in relation to stream water herbicide concentration and probably due to taxa composition.
Acknowledgements
Thank you Antonia Liess for being an invaluable supervisor, and Maria Kahlert for taking me under your wings and teaching me (almost) everything about diatoms. Thank you Natalia Corcoll, Bonnie Bailet, Anders Stehn, Eva Herlitz, Isabel Quintana and Andreas Håkansson. Also, thank you Mikael Andersson and Kerstin Spångfors for your support and lunch boxes.
Diatom samples as well as pesticide and water chemistry data in this study were provided by Natàlia Corcoll as a part of the HERBEVOL Project No. 2015-1464, founded by the Swedish research council Formas. All index calculations were made by Anders Stehn at SLU. PTI were calculated by Andreas Håkansson at the University of Gothenburg.
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Appendix A
Complete taxa lists of all species found in each stream each sample occasion.
Location Date Taxa Author
Höje å 13/9-16 Achnanthidium lauenburgianum (Hust.) Monnier, Lange-Bert. & Ector, 2007 Höje å 13/9-16 Achnanthidium minutissimum group II
(mean width 2,2-2,8µm)
Höje å 13/9-16 Adlafia minuscula (Grunow) Lange-Bert., 1999 Höje å 13/9-16 Amphora pediculus (Kütz.) Grunow, 1880
Höje å 13/9-16 Amphora sp. Ehrenb. ex Kütz.
Höje å 13/9-16 Aulacoseira sp. Thwaites
Höje å 13/9-16 Caloneis lancettula (Schulz) Lange-Bert. & Witkowski, 1996 Höje å 13/9-16 Cocconeis pediculus Ehrenb., 1838
Höje å 13/9-16 Cocconeis placentula incl. varietes Ehrenb., 1838 Höje å 13/9-16 Cyclostephanos dubius (Hust.) Round., 1988 Höje å 13/9-16 Cyclotella meneghiniana Kütz., 1844
Höje å 13/9-16 Discostella pseudostelligera (Hust.) Houk & Klee, 2004 Höje å 13/9-16 Eolimna minima (Grunow) Lange-Bert., 1998 Höje å 13/9-16 Fragilaria capucina var. capucina Desmazières, 1825
Höje å 13/9-16 Fragilaria capucina var. vaucheriae (Kütz.) Lange-Bert.,1980 Höje å 13/9-16 Gomphonema micropus Kütz.,1844
Höje å 13/9-16 Gomphonema parvulum (Kütz.) Kütz., 1849
Höje å 13/9-16 Gomphonema pumilum s.lat. (Grunow) E.Reichardt & Lange-Bert.
Höje å 13/9-16 Gomphonema sp. Ehrenb.
Höje å 13/9-16 Hantzschia amphioxys (Ehrenb.) Grunow, 1880 Höje å 13/9-16 Luticola ventriconfusa Lange-Bert.
Höje å 13/9-16 Mayamaea atomus var. permitis (Hust.) Lange-Bert.
Höje å 13/9-16 Melosira varians C.Agardh, 1849
Höje å 13/9-16 Meridion circulare var. circulare (Grev.) C.Agardh, 1831
Höje å 13/9-16 Navicula gregaria Donkin, 1861
Höje å 13/9-16 Navicula lanceolata (C.Agardh) Ehrenb., 1838
Höje å 13/9-16 Navicula sp. Bory
Höje å 13/9-16 Navicula tripunctata (O.F.Müll.) Bory, 1822 Höje å 13/9-16 Nitzschia dissipata (Kütz.) Grunow, 1862 Höje å 13/9-16 Nitzschia fonticola Grunow, 1881
Höje å 13/9-16 Nitzschia sp. Hassall
Höje å 13/9-16 Planothidium delicatulum (Kütz.) Round & Bukht., 1996 Höje å 13/9-16 Planothidium frequentissimum (Lange-Bert.) Lange-Bert., 1999 Höje å 13/9-16 Planothidium lanceolatum (Bréb. ex Kütz.) Lange-Bert., 1999 Höje å 13/9-16 Platessa conspicua (A.Mayer) Lange-Bert.,2005 Höje å 13/9-16 Pseudostaurosira parasitica var.
subconstricta (Grunow) E.Morales, 2003
Höje å 13/9-16 Rhoicosphenia abbreviata (C.Agardh) Lange-Bert. 1980 Höje å 13/9-16 Sellaphora joubaudii (H.Germ.) Aboal, 2003 Höje å 13/9-16 Sellaphora seminulum (Grunow) D.G.Mann, 1990 Höje å 13/9-16 Sellaphora sp.
Höje å 13/9-16 Staurosira pinnata s.lat. Ehrenb.
Höje å 13/9-16 Stephanodiscus parvus Stoermer & Håk., 1984
Location Date Taxa Author
Location Date Taxa Author
Höje å 11/10-1
6 Achnanthidium catenatum (J.Bily & Marvan) Lange-Bert. 1999 Höje å 11/10-1
6 Achnanthidium lauenburgianum (Hust.) Monnier, Lange-Bert. & Ector, 2007 Höje å 11/10-1
6 Achnanthidium minutissimum group II (mean width 2,2-2,8µm)
Höje å 11/10-1
6 Adlafia minuscula (Grunow) Lange-Bert., 1999
Höje å 11/10-1
6 Adlafia suchlandtii Moser, Lange-Bert. & Metzeltin, 1998 Höje å 11/10-1
6 Amphora pediculus (Kütz.) Grunow, 1880
Höje å 11/10-1
6 Amphora sp. Ehrenb. ex Kütz.
Höje å 11/10-1
6 Asterionella formosa Hassall, 1850
Höje å 11/10-1
6 Aulacoseira sp. Thwaites
Höje å 11/10-1
6 Cocconeis pediculus Ehrenb., 1838
Höje å 11/10-1
6 Cocconeis placentula incl. varietes Ehrenb., 1838 Höje å 11/10-1
6 Cyclostephanos dubius (Hust.) Round., 1988
Höje å 11/10-1
6 Cyclotella meneghiniana Kütz., 1844
Höje å 11/10-1
6 Cyclotella sp. (Kütz.) Bréb.
Höje å 11/10-1
6 Discostella pseudostelligera (Hust.) Houk & Klee, 2004 Höje å 11/10-1
6 Encyonema silesiacum var. silesiacum (Bleisch) D.G.Mann, 1990 Höje å 11/10-1
6 Eolimna minima (Grunow) Lange-Bert., 1998
Höje å 11/10-1
6 Fragilaria berolinensis (Lemmerm.) Lange-Bert.
Höje å 11/10-1
6 Fragilaria capucina var. capucina Desmazières, 1825 Höje å 11/10-1
6 Fragilaria capucina var. vaucheriae (Kütz.) Lange-Bert., 1980 Höje å 11/10-1
6 Fragilaria gracilis Østrup, 1910
Höje å 11/10-1
6 Fragilaria rumpens (Kütz.) G.W.F. Carlson
Höje å 11/10-1
6 Fragilaria sp. Lyngb.
Höje å 11/10-1
6 Fragilaria tenera (W. Sm.) Lange-Bert., 1981
Höje å 11/10-1
6 Gomphonema parvulum (Kütz.) Kütz., 1849
Höje å 11/10-1
6 Gomphonema pumilum s.lat. (Grunow) E.Reichardt & Lange-Bert.
Höje å 11/10-1
6 Gomphonema sp. Ehrenb.
Höje å 11/10-1
6 Hippodonta capitata (Ehrenb.) Lange-Bert., Metzeltin & Witkowski, Höje å 11/10-1 1996
6 Hippodonta sp. Lange-Bert. et al.
Höje å 11/10-1
6 Melosira varians C.Agardh, 1849
Höje å 11/10-1
6 Navicula cincta (Ehrenb.) Ralfs, 1861
Höje å 11/10-1
6 Navicula cryptotenella Lange-Bert., 1985
Höje å 11/10-1
6 Navicula gregaria Donkin, 1861
Höje å 11/10-1
6 Navicula lanceolata (C.Agardh) Ehrenb., 1838
Höje å 11/10-1
6 Navicula reichardtiana Lange-Bert., 1989
Höje å 11/10-1
6 Navicula tripunctata (O.F.Müll.) Bory, 1822
Höje å 11/10-1
6 Navicula veneta Kütz., 1844
Höje å 11/10-1
6 Nitzschia dissipata (Kütz.) Grunow, 1862
Höje å 11/10-1
6 Nitzschia fonticola Grunow, 1881
Höje å 11/10-1
6 Nitzschia palea var. debilis (Kütz.) Grunow, 1880 Höje å 11/10-1
6 Nitzschia sp. Hassall
Höje å 11/10-1
6 Planothidium frequentissimum (Lange-Bert.) Lange-Bert., 1999 Höje å 11/10-1
6 Planothidium lanceolatum (Bréb. ex Kütz.) Lange-Bert., 1999 Höje å 11/10-1
6 Planothidium rostratum (Østrup) Lange-Bert., 1999
Höje å 11/10-1
6 Platessa conspicua (A.Mayer) Lange-Bert.,2005
Höje å 11/10-1
6 Reimeria sinuata (W.Greg.) Kociolek & Stoermer, 1987
Höje å 11/10-1
6 Staurosira construens var. construens Ehrenb., 1843 Höje å 11/10-1
6 Staurosira pinnata s.lat. Ehrenb.
Höje å 11/10-1
6 Staurosira sp. Ehrenb.
Höje å 11/10-1
6 Staurosira venter (Ehrenb.) Cleve & J.D.Möller, 1881 Höje å 11/10-1
6 Stephanodiscus hantzschii Grunow, 1880
Höje å 11/10-1
6 Stephanodiscus parvus Stoermer & Håk., 1984
Höje å 11/10-1
6 Stephanodiscus sp. Ehrenb.
Location Date Taxa Author
Location Date Taxa Author
Skivarpsån 13/9-16 Achnanthidium lauenburgianum (Hust.) Monnier, Lange-Bert. & Ector, 2007 Skivarpsån 13/9-16 Achnanthidium minutissimum group II (mean
width 2,2-2,8µm) ADM2
Skivarpsån 13/9-16 Adlafia minuscula (Grunow) Lange-Bert., 1999 Skivarpsån 13/9-16 Amphora pediculus (Kütz.) Grunow, 1880
Skivarpsån 13/9-16 Amphora sp. Ehrenb. ex Kütz.
Skivarpsån 13/9-16 Aulacoseira sp. Thwaites
Skivarpsån 13/9-16 Caloneis lancettula (Schulz) Lange-Bert. & Witkowski, 1996
Skivarpsån 13/9-16 Cocconeis placentula incl. varietes Ehrenb., 1838 Skivarpsån 13/9-16 Cyclotella meneghiniana Kütz., 1844
Skivarpsån 13/9-16 Cyclotella sp. (Kütz.) Bréb.
Skivarpsån 13/9-16 Cymatopleura elliptica var. elliptica (Bréb. ex Kütz.) W.Sm., 1851 Skivarpsån 13/9-16 Encyonema silesiacum var. silesiacum (Bleisch) D.G.Mann, 1990 Skivarpsån 13/9-16 Eolimna minima (Grunow) Lange-Bert., 1998 Skivarpsån 13/9-16 Fallacia subhamulata (Grunow) D.G.Mann, 1990 Skivarpsån 13/9-16 Fragilaria capucina var. vaucheriae (Kütz.) Lange-Bert., 1980 Skivarpsån 13/9-16 Gomphonema micropus Kütz.,1844
Skivarpsån 13/9-16 Gomphonema olivaceum (Hornem.) Kütz. 1844 Skivarpsån 13/9-16 Gomphonema parvulum (Kütz.) Kütz., 1849
Skivarpsån 13/9-16 Gomphonema pumilum s.lat. (Grunow) E.Reichardt & Lange-Bert.
Skivarpsån 13/9-16 Gomphonema sp. Ehrenb.
Skivarpsån 13/9-16 Hippodonta capitata (Ehrenb.) Lange-Bert., Metzeltin & Wit- kowski, 1996
Skivarpsån 13/9-16 Karayevia oblongella (Østrup) M.Aboal, 2003 Skivarpsån 13/9-16 Luticola mutica (Kütz.) D.G.Mann, 1990 Skivarpsån 13/9-16 Mayamaea atomus var. permitis (Hust.) Lange-Bert.
Skivarpsån 13/9-16 Melosira varians C.Agardh, 1849 Skivarpsån 13/9-16 Meridion circulare var. circulare (Grev.) C.Agardh, 1831 Skivarpsån 13/9-16 Navicula gregaria Donkin, 1861
Skivarpsån 13/9-16 Navicula lanceolata (C.Agardh) Ehrenb., 1838 Skivarpsån 13/9-16 Navicula reichardtiana Lange-Bert., 1989 Skivarpsån 13/9-16 Navicula tripunctata (O.F.Müll.) Bory, 1822
Skivarpsån 13/9-16 Navicula veneta Kütz., 1844
Skivarpsån 13/9-16 Nitzschia dissipata (Kütz.) Grunow, 1862 Skivarpsån 13/9-16 Nitzschia fonticola Grunow, 1881 Skivarpsån 13/9-16 Nitzschia linearis var. linearis (C.Agardh) W.Sm.
Skivarpsån 13/9-16 Nitzschia media Hantzsch, 1860
Skivarpsån 13/9-16 Planothidium delicatulum (Kütz.) Round & Bukht., 1996 Skivarpsån 13/9-16 Planothidium frequentissimum (Lange-Bert.) Lange-Bert., 1999 Skivarpsån 13/9-16 Planothidium lanceolatum (Bréb. ex Kütz.) Lange-Bert., 1999 Skivarpsån 13/9-16 Pseudostaurosira parasitica var. subconstricta (Grunow) E.Morales, 2003
Skivarpsån 13/9-16 Reimeria sinuata (W.Greg.) Kociolek & Stoermer, 1987 Skivarpsån 13/9-16 Rhoicosphenia abbreviata (C.Agardh) Lange-Bert. 1980 Skivarpsån 13/9-16 Sellaphora joubaudii (H.Germ.) Aboal, 2003 Skivarpsån 13/9-16 Sellaphora seminulum (Grunow) D.G.Mann, 1990
Skivarpsån 13/9-16 Sellaphora sp. Mereschk.
Skivarpsån 13/9-16 Staurosira dubia Grunow
Skivarpsån 13/9-16 Staurosira venter (Ehrenb.) Cleve & J.D.Möller, 1881 Skivarpsån 13/9-16 Stephanodiscus parvus Stoermer & Håk., 1984
Skivarpsån 13/9-16 Surirella brebissonii var. kuetzingii Krammer & Lange-Bert., 1987
Location Date Taxa Author
