Changes in lake diatom assemblages in Northwestern Finnish Lapland over recent decades and their relation to climate warming Master’s programme in Environmental Change and Global Sustainability Master’s thesis May 2021 Maxime Courroux Supervisors: Kaarina Weckström Jan Weckström Maija Heikkilä 2 Tiedekunta - Fakultet - Faculty Faculty of Biological and Environmental Sciences Tekijä - Författare - Author Maxime Courroux Työn nimi - Arbetets titel - Title Changes in lake diatom assemblages in Northwestern Finnish Lapland over recent decades and their relation to climate warming Oppiaine - Läroämne - Subject Environmental Change and Global Sustainability Työn laji/ Ohjaaja - Arbetets art/Handledare - Level/Instructor: Master’s Thesis Master’s Thesis / Supervisor’s Name Kaarina Weckström, Jan Weckström, Maija Heikkilä Aika - Datum - Month and year May 2021 Sivumäärä - Sidoantal - Number of pages 35 pp. + 9 pp. appendices Tiivistelmä - Referat - Abstract The changes in lake diatom assemblages as a response to climate warming over the past three decades were examined in 26 lakes across Northwestern Finnish Lapland using multivariate statistical techniques. The lakes are distributed along a steep climatic and vegetational gradient, covering three distinct vegetation zones spanning boreal coniferous forest, mountain birch woodland, and treeless tundra. Lakes were selected following a study realised by Weckström and Korhola in 2001, who had sampled the same lakes for surface-sediment diatom assemblages, physical, and chemical limnological variables. Climate data from the past 30 years was retrieved, showing a slow and steady yearly increase in temperature, with strong seasonal fluctuation and fall months experiencing the strongest warming. Surface sediment samples were taken from the lakes and their diatom communities analysed. A total of 185 diatom taxa representing 27 genera were recorded. Ordination techniques (DCA, CCA) at the genus and species level were performed to identify the main patterns of variation between diatom data from the original data set and the current study, and their relationship to environmental variables. Strong changes were recorded in four of the lakes with major shifts in dominant diatom species. Moderate changes were recorded in eight lakes, where dominance changes were recorded for a few species while the majority remained unchanged. The remaining 14 lakes did not show noticeable changes over the 30-year period. Changes observed in the studied lakes did not follow a widely observed pattern in northern Hemisphere lakes. The results indicate that while climate change is a driving factor behind changing lake dynamics with increasing temperatures and decreasing lake ice cover duration, it cannot be the only force responsible. Avainsanat - Nyckelord Keywords: Palaeolimnology, Finnish Lapland, Climate Change, Diatoms Säilytyspaikka - Förvaringsställe - Where deposited: E-Thesis Viikki Campus Library Muita tietoja - Övriga uppgifter - Additional information 3 Table of Contents 1. INTRODUCTION....................................................................................................... 4 1.1 Palaeolimnological approach .............................................................................................................. 5 1.2 Diatoms ............................................................................................................................................... 6 1.3 Original Study ..................................................................................................................................... 7 1.5 Objectives of the study ........................................................................................................................ 7 2. STUDY AREA ............................................................................................................. 8 2.1. Environmental characteristics ............................................................................................................ 8 2.2 Site Descriptions ................................................................................................................................. 9 3 METHODS ................................................................................................................. 10 3.1 Climate data ...................................................................................................................................... 10 3.2 Sampling ........................................................................................................................................... 10 3.3 Microfossil analysis .......................................................................................................................... 11 3.4 Data analysis ..................................................................................................................................... 11 4. RESULTS .................................................................................................................. 13 4.1. Regional climate Warming .............................................................................................................. 13 4.2. Diatom species ................................................................................................................................. 14 4.3. Comparison between old and new species data ............................................................................... 18 4.4. Diatom genera .................................................................................................................................. 19 4.5. Comparison between old and new genus data ................................................................................. 21 5. DISCUSSION: ........................................................................................................... 23 5.1. Climate ............................................................................................................................................. 23 5.2. Diatoms ............................................................................................................................................ 24 5.2.1 General diatom distribution according to vegetation zones ........................................................... 24 5.2.2 Observed changes over the past decades ....................................................................................... 25 5.3. The impact of climate warming on diatom communities ................................................................. 28 6. CONCLUSIONS ....................................................................................................... 30 7. ACKNOWLEDGEMENTS ..................................................................................... 31 8. CITATIONS .............................................................................................................. 32 9. APPENDIX ................................................................................................................ 36 4 1. Introduction Climate warming has increased during the last century, but especially over the last three decades, which, according to IPCC (2018) might have been the warmest of the last 800 years. Almost every year during the past decade new “records” of the warmest year have been broken. For example, 2014, 2015, and 2016 have all claimed the title of warmest year on record (Kennedy et al. 2016; Mann et al. 2016). Due to the strong feedback mechanisms, such as decrease in albedo and permafrost thaw, the Arctic is warming at twice the rate compared to the rest of the planet. Winter temperatures in northern boreal forest environments are expected to increase by 3 to 5oC by 2050 (SWIPA 2017). In Finland, the normal period between 1981-2010 was 0.4 degrees higher than during the previous normal period (1971-2000), and 0.7 degrees warmer than during the normal period between 1961 and 1990 (Aalto et al. 2012). The warming appears to be stronger in the south of Finland than in the north, with highly variable seasonal patterns, and will have important consequences for land use, biodiversity, and overall physical environmental characteristics. Since the 1970s, climate models have been able to accurately predict the development of climate change up to the present time period (Hausfather et al. 2020). Today, modelling tools are getting increasingly complex and accurate at predicting future climate conditions. To test and challenge these models, other approaches - such as palaeoecological tools - should be used to reconstruct past environmental conditions, to understand natural long-term variability and to provide data against which climate models can be hindcasted (Smol 2009). In this aspect, lakes in northern high latitudes are especially important to study as they are sentinels of climate change. Their remoteness and lack of anthropogenic influences make them a prime target to observe direct climate and environmental changes. In their 2001 study, Weckström and Korhola (2001) were already expressing concerns about the increasing impacts that climate warming would have on ice cover duration and growing season in Arctic and subarctic lakes. Over the past decades, many studies focusing on the environmental history of northern Fennoscandia have been conducted on lake sediments. Firstly, due to high lake density, with highest density values over 1000 lakes/100 km2 in Finnish Lapland (Tikkanen 2002), and secondly, because these lake archives consisting of biota remains are important tools to reconstruct 5 environmental conditions qualitatively and quantitatively. In the late 1990s, many studies describing either the chemical characteristics of lakes, or interaction between lake biota and various environmental factors were published (Weckström et al. 1997a, b; Blom et al. 1998; Korhola 1999). Due to the rapidly increasing rate of changes, these studies could be followed up to better understand the magnitude of these changes at a regional scale. 1.1 Palaeolimnological approach Palaeolimnology is the study of past aquatic ecosystems including freshwater, brackish waters, and saline lakes. It is used to reconstruct past ecological conditions of lakes, from aquatic communities to physico-chemical conditions and their interactions (Smol 2009). Sediments are continuously accumulating into lakes and if the sedimentation conditions are optimal, lake sediments are a book of environmental history in a chronological order (Cohen 2003). The sediment material originates from a large variety of sources within the drainage basin (catchment area), making it a very rich source of information on the surrounding environmental conditions (Smol 2009). The general stability of the sediment input makes it ideal to reconstruct how conditions might have evolved over time. The biological remains of different organisms can be used as indirect (proxy) tools in environmental reconstruction (Cohen 2003). Knowledge of the ecology of the modern biota in the study area enables the use of lake sediment series incorporating some of these biotas, where changes in species composition within the sediment record will reflect changes in environmental conditions. The choice of the study area is important as sufficient understanding of the local processes is needed to better understand any potential variations in environmental conditions (Smol 2009). Once the study sites have been selected, each lake needs to be sampled at the optimal location regarding the sedimentation processes (i.e. the sedimentation basin) so that the sediment archive is the most representative of its surroundings (Reeves 2014). With the continuous development of computer technology and statistical tools, marked progress has been made on how data can be analysed. During the past decades, multivariate numerical techniques have become a standard procedure in palaeolimnological studies. Direct gradient analysis techniques such as canonical correspondence analysis (CCA) can be used to detect patterns in species data and relate them to measured environmental variables controlling 6 their distribution (ter Braak 1986). To do so, the topmost 0.5-1 cm of the sediment representing the most recent years is collected from each lake and the environmental variables of interest are measured at the same site. After identifying and counting the biota of interest, multivariate statistical analysis methods are used to determine which of the measured environmental variables are statistically the most significant in explaining the species distribution and how the species are positioned along environmental gradients. 1.2 Diatoms Diatoms are unicellular siliceous microscopic algae, which appear everywhere where sufficient light and moisture are available for photosynthesis. They are responsible for 45% of Earth’s global primary production and therefore play a critical role in the functioning of aquatic ecosystems (Werner 1977; Benoiston et al. 2017). Due to their silica shell, or frustule, diatoms are generally well preserved in sediments and identifiable to the species or even subspecies and variety level. Their distribution is controlled by many ecological factors such as temperature, pH, nutrients or salinity, to which they are very sensitive (Stoermer and Smol 1999). Their short life cycle coupled with their narrow optima and tolerance to many environmental variables make them an excellent tool to observe and reconstruct past environmental conditions, and they are one of the most commonly employed palaeobioindicators (Moser et al. 1996). Diatom frustules are composed of two separate valves (epitheca and hypotheca), and a number of girdle bands holding the valves together. Identification of diatoms is based on the shape and various species-specific characteristics of the siliceous valves. They can be divided into two major groups according to their overall shape: centric and pennate diatoms (Stoermer and Smol 1999). Centric diatoms are mostly cylindrical and are often radially symmetric relative to the pervalvar axis, while pennates are elongated, with lateral symmetry (Patrick and Reimer 1966). Diatoms reproduce by cell division where each daughter cell will receive one original valve from the parent cell. The successive division of cells leads to a continuous reduction in size of the bottom valve, while the top valve will keep its size. This shrinkage will continue until the cell is too small to divide any further, in which case it is able to reproduce sexually to restore the cell to the original size (Round et al. 1990). Diatoms can be classified under two major groups according to their habitat: planktic and benthic diatoms. Planktic species are floating freely in the water column, 7 while benthic species live near the bottom of lakes and can be attached to the substrate. Within this benthic habitat group, species can be further subdivided according to their preferred growth habitat, such as epipsammic (attached to sand), epilithic (attached to stones/rocks), and epiphytic (attached to plants), showing their variability towards specific environmental conditions. 1.3 Original Study Weckström and Korhola (2001) aimed to explore relationships between diatom assemblage composition and species richness, and environmental variables in lakes in subarctic Lapland, with particular attention to air temperature. Their data was collected in July 1995 and 1998 from 64 lakes in Northwestern Finland. Surface sediment samples as well as 22 geographical and physico- chemical environmental variables were measured from each lake. They concluded that temperature, lake water pH, calcium, sediment organic content, and total organic carbon were the most influential variables determining the distribution of diatoms in the area (Weckström and Korhola 2001). Furthermore, air temperature was identified as one of the strongest external variables explaining variations in the diatom data. Temperature can impact diatoms either directly by stimulating their metabolic processes but also indirectly by altering water quality, lake stratification and various catchment factors. 1.5 Objectives of the study The main objective of this thesis is to study the impact of recent climate warming on diatom communities in NW Finnish Lapland by: 1. comparing recent diatom communities of 26 subarctic lakes with diatom communities sampled from the same lakes by Weckström and Korhola (2001) ca. 25 years ago, and by 2. evaluating the role of temperature and other environmental variables as drivers of the possibly observed changes 8 2. Study area The 26 lakes of the study are located in Northwestern Finnish Lapland (67o51’- 69o10’N and 20o39’-24o10’E; using the code WFL), except for one lake (WFL 26), which is located in Norway close to the Finnish border (Figure 1). The study area covers a long climatic and vegetational gradient. Northwestern Finland is ideal for studying the impact of climate warming on aquatic ecosystems as it has remained one of the most pristine European regions in terms of anthropogenic atmospheric pollution over the past 30 years (Hettelingh et al. 1992; WHO 2018; González 2020). The chosen lakes are located in an area where local anthropogenic catchment disturbance is very low, with little to no human activity. 2.1. Environmental characteristics Most of the lakes in the area are unnamed and were therefore numbered following the same coding system as in the original dataset by Weckström and Korhola (2001). The mean annual temperature ranges from about -1.3oC in the north (Kilpisjärvi) to -0.6oC in the south (Muonio) (Finnish Meteorological Institute 2020). While the yearly average has remained quite steady during the past 30 years, the winter average Figure 1: Map of the study area showing the location of the 26 lakes sampled. The isotherm of the mean july air temperatures (1961-1990) according to the original lake samples from 1995 (Weckstrom & Korhola 2001), and the main vegetation zones are displayed 9 temperatures have been slowly rising, the month of June being the only period of the year experiencing cooling (Aalto et al. 2012). Current mean annual precipitation of 530 mm is consistent across the study area. This is a considerable change compared to the measured values in 1986 (Hämet-Ahti et al. 1988), which recorded 600 mm in both north and south, with a decline in the central region. The snow cover duration ranges from 225 days in the north to 200 days in the south (Merkouriadi et al. 2017). Snow acts as a long-term insulator for lake ice, increasing the duration of ice cover. The ice-free period ranges from May-June to October-November (Hämet- Ahti et al. 1988). However, recently the lakes have been freezing later and the ice has melted earlier (Korhonen 2019). The bedrock in the area consists mainly of Precambrian orogenic plutonic rocks, but also karelidic schists occur (Simonen 1980). The bedrock is overall alkaline, which can lead to higher pH in some lakes. The vegetation varies along the study transect from coniferous boreal forest in the south referred to as the pine, spruce and birch (SPB) vegetation zone, to mountain birch woodland (MBW), and finally to barren (Ba) ground in the north and high-altitude sites. The coniferous boreal forest is dominated by Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) (Hämet-Ahti et al. 1988). Many lakes in the southern part are surrounded by peatlands. The central study area is dominated by pine and birch forests due to its sandy terrain, while only mountain birch (Betula pubescens subsp. czerepanovii) grows in the north, with only a few pine trees in the sunny valleys. When reaching the northernmost region of the study area, the vegetation shifts drastically to treeless tundra consisting mainly of ground vegetation like lichens, willows and shrubs (e.g. Salix spp. and Betula nana). 2.2 Site Descriptions The 26 lakes were selected from the original 64-lake data set described in Weckström & Korhola (2001). The northernmost area of the original data set was not sampled as thoroughly due to the remoteness and difficult accessibility of the lakes. The study lakes were selected on the basis that they were as pristine as possible, with minimal human influence. The physico-chemical 10 environmental variables (Appendix 2) have been collected as parts of multiple field campaigns throughout the years. The 26 lakes sampled are small (mean area of 18 ha, range 0.9-70 ha), shallow (average depth 7 meters, range 1-25 m), mostly slightly acidic (mean pH of 6.4, range 5-7.5), and are characterised by low alkalinity and conductivity. Lakes in the barren area are very clear with secchi depth often exceeding the depth of the lake. Lakes in the MBW and SPB areas are characterised by lower secchi depth as their water is often brown due to the surrounding peatlands and larger input of humic substances. 3 Methods 3.1 Climate data Looking specifically at how this warming trend has affected the study area of Northwestern Finnish Lapland, the past 30 years’ climate was reconstructed, taking into account seasonality adjusted to the regional conditions (Figure 2). Weather stations from the south (Muonio) and north (Kilpisjärvi) of the study area were chosen to observe the magnitude of meteorological changes (Finnish Meteorological Institute 2020). 3.2 Sampling The sediment samples were collected over two separate field sessions. Three lakes (WFL25, 26, and 27) were sampled during three days in August 2020 as part of the University of Helsinki Field course on Arctic ecosystems and climate. The remaining 24 lakes were sampled over the first week of September in 2020. One lake (WFL 10), however, was discarded due to the poor quality of the sample. The sampling was carried out from an inflatable boat from and the deepest point of each lake, which was located using a UWITEC water depth gauge. For each lake, one surface-sediment sample (0-2.5 mm) was collected with an HTH gravity corer (Renberg and Hansson 2008). The 11 sediments were stored in Minigrip plastic bags and kept dark and cool (4oC) conditions until freeze-dried (Savant ModulyoD Freeze Dryer). 3.3 Microfossil analysis Subsamples of dry sediment (ca. 0.1 g) were placed in a solution of H2O2 and heated at 90oC for two to three hours until all of the organic matter was oxidised. A few drops of HCl (37%) were added to remove H2O2 and possible carbonates from the sediment. The diatom solutions were then centrifuged (4 min at 1500 rpm) and washed at least five times. Excess water was removed using a pipette and topped up with distilled water. The resulting diatom suspension was evaporated onto coverslips and mounted on glass slides with Naphrax. From each slide, at least 500 diatom valves were enumerated using Zeiss Axiocam 506 colour microscope at 1000X magnification. The nomenclature mainly follows Hartley et al. (1986) to remain consistent with the nomenclature used in the original study by Weckström & Korhola (2001). The identification of diatoms was mainly based on Krammer and Lange-Bertalot (1986, 1988, 1991a, 1991b). 3.4 Data analysis Prior to statistical analysis the diatom data was organised to both the genus, and the species level as relative abundances (%). Since the present dataset is compared with the dataset from the original study (Weckström and Korhola 2001), some diatom species were merged or classified under an alternative name, to achieve a better level of consistency in the taxonomy. Environmental data was retrieved from the original study. Thus, the updated diatom data was compared to environmental gradients based on the original environmental data. Detrended correspondence analysis (DCA) was performed to analyse the main patterns of variation in both diatom species and genus data. As the gradient length of the first axis for the species data was 4.4 standard deviation units (SD), a unimodal approach, namely DCA, was used in order to compare the similarity between the species of the original study and the current study. Diatom species with relative abundance of at least 5% in one of the 52 sediment samples (26 12 original samples and 26 current samples) were displayed (Figure 3). Species data were square-root transformed and rare species were down-weighted prior to statistical analysis. The same approach was repeated using diatom data at the genus level. Diatom genera with relative abundance of at least 2% in at least two of the 52 sediment samples were displayed (Figure 2). While observations of unidentified species (spp.) were excluded from the species analyses, they were included in the genus analysis. Canonical correspondence analysis (CCA) was employed to reveal the relationship between the diatom data and the environmental variables. CCA was used as the length of the first axis in DCA was 4.5 SD, suggesting the use of a unimodal approach. Statistical significance was tested using Monte Carlo permutation test (999 permutations). Prior to the CCA, environmental variables were log(x+1) transformed, and three nominal variables were added for the vegetation zones as binary codes (0/1). Out of the original measured 20 environmental variables, 12 were chosen for the final statistical analyses after screening with Variance Inflation Factor (VIF). Environmental variables with VIF-values < 20 were included in addition to environmental variables with ecological importance. Species data from newly collected samples was plotted passively, as the environmental data could not be updated. Passive samples will not affect the analysis of species-environment relationship but will be plotted according to their species compositions along the defined species-environment biplot space. Species data were square-root transformed and rare species down-weighted prior to statistical analysis. The same method was repeated using diatom data at the genus level. Relative frequency diagrams were performed at both species and genus levels, showing the relative abundance of specific species within each lake. For the species level analysis, diatom taxa with relative occurrence of at least 5% in at least one sample were displayed. At the genus level, diatom genera with relative occurrence of at least 2% in at least two samples were displayed. All multivariate analyses were performed using Canoco for Windows 5.01 (ter Braak and Šmilauer 2002) and the stratigraphical diagrams using C2 v.1.7.7 (Juggins 2007). 13 Figure 2: Weather data retrieved from the Finnish Meterological Institute’s open database for both the Kilpisjarvi and Muonio weather stations. Yearly mean seasonal temperatures were calculated for the time period between 1990 and 2020. 4. Results 4.1. Regional climate Warming The magnitude of yearly temperature changes seems to be consistent between Kilpisjärvi and Muonio, with a slow, steady increase over the past three decades. However, some strong seasonal fluctuations can be observed in both ends of the gradient, with the fall months (September and October) experiencing the strongest warming. 14 4.2. Diatom species A total of 185 diatom taxa representing 27 genera were recorded from the modern surface sediment samples of the 26 lakes. Out of the 185 diatom taxa, 28% occurred in only one lake, 78% occurred in less than 10 lakes, and only 22% occurred in 10 lakes or more. In addition, 42% of the species had a maximum abundance below 1%, and only 11% had a maximum abundance over 10% (Appendix 3). Table 1: Species summary statistics The first two axes of the species DCA accounted for 18.9% of the cumulative variation in the data (Table 1). The DCA analysis was performed to compare changes in diatom communities between the modern and older samples. It provides a side by side view of each lake allowing to evaluate potential changes in diatom assemblages between the current and past diatom communities depending on their proximity with one another along the DCA axes (Figure 3). The species DCA shows a clear delimitation by vegetation zones, with SPB lakes located on the center- left side (Figure 3). These lakes are defined by taxa such as Eunotia rhomboidea, Pinnularia rupestris and Frustulia rhomboides var. saxonica. Lakes of the MBW zone are clustered around the central area of the DCA, along with their typical species Brachysira brebissonii, B vitrea, and Achnanthes pusilla. The Ba lakes are located on the upper right side of Figure 3, hosting species such as Aulacoseira distans, Achnanthes levanderi, and Cyclotella rossii. These trends can also be seen in the relative abundance diagram (Figure 4). Eigenvalue Cumulative Variance (%) Axis DCA CCA DCA CCA 1 0.439 0.465 12.5 14.5 2 0.221 0.241 18.9 22.1 3 0.173 0.214 23.8 28.8 4 0.093 0.159 26.0 33.7 15 Figure 3: Detrended Correespondence Analysis (DCA) of diatom observations at the species level for the 26 study lakes. Points including the prefix WFL represent new samples; points without the prefix represent samples collected between 1995 and 1998. The colour of the dots representing the study sites refer to the vegetation zones: Green = SPB, Yellow = MBW, Blue = Ba. Explanations for the abbreviations can be found in Appendix 1. 16 Figure 4: Relative frequency diagram of the dominant diatom taxa (>5% relative occurrence in at least one sample). For each lake, the 2020 (black bar) observation is on top of the samples collected between 1995 and 1998 (open bar) observation. Lakes are sorted from lowest (top) to highest altitude (bottom), which also coincides with their respective vegetation zones. 17 For the CCA, highly collinear variables were deleted according to their Variance Inflation Factors (VIF): variables with values above 20 were removed, leaving 12 environmental variables to be included in the analysis. Eigenvalues of the first two CCA axes were λ1=0.465 and λ2=0.241, representing 22.1% of the cumulative variance in the diatom species data. In the CCA biplots (Figures 5 and 8), taxa and genera data had to be plotted separately from the lake and environmental data due to the large difference in scale of the axes. In the species CCA biplot (Figure 5), many of the lakes in the SPB vegetation zone are located in the upper right quadrant characterised by higher amounts of mire area (MireArea) in their catchment, higher temperature (AirT), comparatively high amounts of total organic carbon (TOC), and lowest water pH. Diatom taxa indicative of these conditions lie on the right-hand side of the biplot. Species like Navicula hoeflerii and Eunotia rhomboidea are found in a few lakes where pH values were the lowest. The MBW zone can be considered as a transitional environment or ecotone between the Ba and the SPB regions. Lakes in this zone are mostly scattered on the lower half of the CCA biplot. These lakes have a large size and depth gradient and have elevated conductivity like the Ba lakes, but are cooler, more alkaline, and have higher pH values than the SPB lakes. Lakes in the Ba region are mostly located on the left-hand side of the biplot. These lakes are typically colder, deeper, and larger with low TOC concentrations. Diatom taxa on the top left quadrant are typically found in these lakes. Species such as Frustulia rhomboides var. rhomboides, Frustulia rhomboides var. saxonica, Brachysira brebissonii, or Pinnularia biceps were found consistently across the dataset, disregarding of the vegetation zone. On the other hand, species like Cyclotella glomerata and Cyclotella comensis were only found in one lake (WFL 24), which was the deepest lake of the dataset. Looking at the CCA biplot, it is also possible to observe along which environmental gradients changes between samples have occurred (see next chapter). As an example, looking at lake WFL 30, the changes are happening very clearly along the LOI, TOC, and pH axis. 18 Figure 5: Canonical Correespondence Analysis (CCA) ordination diagram showing the relationship between diatom observations and selected environmental variables. Points including the prefix WFL represent new samples; points without the prefix represent samples collected between 1995 and 1998. The qualitative variable of vegetation zones (SPB, MBW, Ba) was included passively and is labeled with squares. The colour of the dots representing the study sites refer to the vegetation zones: Green = SPB, Yellow = MBW, Blue = Ba. Explanations for the abbreviations can be found in Appendix 1. 4.3. Comparison between old and new species data In general, the 26 lakes can be categorised into three groups based on changes in conditions since 1995: large, moderate, and negligible (according to the distance between site symbols in Figure 3). WFL 49 seems to have undergone the largest changes with a marked shift in dominant species. In the original study the lake diatom assemblages were dominated by Brachysira vitrea, which was completely absent in the current sample, where instead Achnanthes levanderi has become dominant. WFL 30 experienced a large decline in the percentage abundance of Eunotia rhomboidea, which was rare in the current sample, in favour of species such as Navicula hoeflerii and Navicula subtilissima. WFL12 had a large increase in Frustulia rhomboides and Navicula hoeflerii from low relative abundances to the current ca. 20% abundance. On the other hand, the 19 relative abundance of Pinnularia rupestris decreased by half, from 30% to 15%. Finally, WFL37, which was earlier dominated by species such as Fragilaria brevistriata, has shifted towards the dominance of Brachysira vitrea, which was absent in the original samples. Eight of the lakes have undergone moderate changes; WFL3, 4, 6, 11, 18, 27, 41, 56. In most of these lakes the change occurred only between a few species, often marking a turnover of specific taxa, whereas the majority of other species remained similar between the old and current samples. For example, WFL6 had a noticeable switch from the original Eunotia rhomboidea dominance to Navicula hoeflerii. Another example is WFL11, with the absence of Pinnularia biceps in the newer samples. The remaining 14 lakes (WFL 5, 7, 13, 17, 24, 25, 26, 29, 32, 35, 36, 52, 54, and 55) show negligible to no differences between the original and the recent samples. 4.4. Diatom genera A DCA was also performed at the genus level (Figure 6) to observe more general changes in lake diatom communities. The first two axes of the genus DCA accounted for 29.3% of the cumulative variation in the data (Table 2). Table 2: Genus summary statistics Similarly to the species DCA, the genus DCA shows a clear delimitation by vegetation zones. SPB lakes are on the the left side of the figure (Figure 6), defined by genera such as Frustulia, Eunotia and Pinnularia. Lakes of the MBW zone are clustered on the central upper side of the figure, associated with genera like Brachysira and Fragilaria. The Ba lakes are spread across the right side of the plot, with frequent occurrence in samples of genera such as Achnantes, Aulacoseira, and Cyclotella. Genera such as Cymbella and Nitzschia are evenly found throughout the three vegetation zones.These trends can also be clearly observed in the genus relative abundance diagram (Figure 7). Eigenvalue Cumulative Variance (%) Axis DCA CCA DCA CCA 1 0.236 0.221 28.1 25.8 2 0.093 0.085 39.3 35.8 3 0.048 0.063 44.9 43.2 4 0.027 0.042 48.1 48.1 20 Figure 6: Detrended Correespondence Analysis (DCA) of diatom observations at the genus level for the 26 study lakes. Points including the prefix WFL represent new samples; points without the prefix represent samples collected in 1995 and 1998. The colour of the dots representing the study sites refer to the vegetation zones: Green = SPB, Yellow = MBW, Blue = Ba. Explanations for the abbreviations can be found in Appendix 1. Figure 7: Relative frequency diagram of the dominant diatom genera (>2% relative occurrence in at least two samples). For each lake, the 2020 (black bar) observation is on top of the 2001 (open bar) observation. Lakes are sorted from lowest (top) to highest (bottom) altitude, which also coincides with their respective vegetation zones. 21 For the genus CCA, eigenvalues of the of the first two CCA axes were λ1=0.221 and λ2=0.085, representing 35.8% of the cumulative variance in the diatom genus data. From the CCA biplot (Figure 8), the separation between vegetation zones is clearer than at the species level, making it possible to differentiate between Ba (top left quadrant) and MBW (bottom left quadrant). The ecological and environmental conditions attributed to each vegetation zone do not differ from the species level analysis. In the CCA biplot, lake WFL 24, located in top left quadrant, clearly differs from the other lakes. Diatom genera associated with this lake are Stenopterobia and Thalassiosira with very low abundances. Genera such as Cyclotella and Aulacoseira are indicators of the deep lakes located in the Ba zone. The acidic, humic conditions attributed to lakes in the SPB zone can be linked to genera rare in this data set such as Actinella and Peronia. While genera such as Navicula, Pinnularia, and Eunotia are present in all three vegetation zones, they are overall more abundant in the SPB area. The genus Tabellaria appears to be a good indicator for lakes in mire areas. As for MBW lakes, the genera Denticula, Epithemia and Rhopalodia seem to be a good indicator of their high alkalinity. 4.5. Comparison between old and new genus data While some of the lakes seemed to have experienced large changes at the species level, the magnitude of some of these changes tends to be more discrete at the genus level. For example, clear differences in the species relative abundance diagram of WFL 6 are not reflected in either of the DCA analyses or in the genus relative frequency diagrams. Inversely, some changes are more obvious at the genus level. This can be seen in Figure 7 for both WFL 49 and WFL 37, which already experienced substantial changes at the species level. In WFL 49, Brachysira, which was the dominant genus in the original sample, is almost completely absent from the recent sample. Similarly, Aulacoseira occurred in the recent sample but was not found in the earlier observations (Figure 7). In WFL 37, the genus Fragilaria largely dominated the original sample with a relative abundance of over 60%, whereas its current abundance is as low as 10%, mostly replaced by Brachysira. Some changes that were not observed at the species level were evident also in lake WFL 18, where a high relative abundance of about 25% of Eunotia decreased to below 5%, whereas Fragilaria appeared in the new sample having been absent in the original. 22 Figure 8: Canonical Correespondence Analysis (CCA) ordination diagram showing the relationship between the diatom observations and the selected environmental variables. Points including the prefix WFL represent new samples; points without the prefix represent samples collected in 1995. The qualitative variable of vegetation zones (SPB, MBW, Ba) was included passively and is labeled with squares. The colour of the dots representing the study sites refer to the vegetation zones: Green = SPB, Yellow = MBW, Blue = Ba. Explanations for the abbreviations can be found in Appendix 1. 23 5. Discussion: 5.1. Climate Over the past 30 years, climate has been steadily warming in the study area as shown in Figure 2 (Finnish Meteorological Institute, 2020). However, it is critical to note that fluctuations in temperatures observed cannot be attributed to climate change alone. Changes and improvements in the accuracy of the climate data, combined with an increased frequency of data collection and continuous monitoring of weather stations might play a role in explaining small variations in the regional temperature record (Aalto et al. 2012). Instead of observing the annual temperature only, it is crucial to separate the seasonal trends of climate warming, as these may be of higher importance in shaping the structure of subarctic ecosystems. During the past 30 years, each season has undergone continuous increases in temperature, with the fall months (September and October) experiencing the strongest warming. This continuous warming, combined with the increase in the frequency of extreme events - such as storms, heavy rainfall and heat waves - has had a strong impact on factors such as the ice cover dynamics of lakes (Gebre et al. 2014; Filazzola et al. 2020). Over the past century, ice break-up and ice freeze-up trends in Finnish lakes have varied by about 4-10 days, reducing the ice cover duration by 11-17 days (Korhonen 2019). A climate model by Gebre (2013) looking at lake ice phenology in the Nordic and Baltic regions of northern Europe has predicted that by 2041, ice freeze-ups are expected to be delayed by 1-3 weeks, while ice break-ups are expected to occur 1-10 weeks sooner, cumulatively reducing the overall ice duration by 1-11 weeks. All of these changes in ice dynamics will have important impacts on the seasonal variability of lake processes. This was nicely demonstrated by Sharma et al. (2019), who studied two Swedish lakes that exceptionally did not freeze over during the winter. Internal processes in these lakes were disturbed due to the increase of surface temperatures, which in turn led to increased primary production and algal biomass (Sharma et al. 2019). While shifts in the duration of ice cover are an important driver of many lake processes, increasing temperatures could impact aquatic ecosystems in many other ways. Higher temperatures could also be reflected by increased stratification of lakes, changing their mixing regime and circulation of nutrients (Havens and Jeppesen 2018; Woolway and Merchant 2019). These changes, associated with longer growing seasons, could have cascading effects on the food web of the lakes (Havens and Jeppesen 2018). 24 Changes in precipitation patterns tend to affect erosion around lakes, with more precipitation generally translating into higher inputs from the catchment, increasing organic carbon and nutrients (Vincent et al. 2013). Longer growing seasons and thawing of permafrost may lead to a northward migration of vegetation, possibly reaching previously treeless tundra regions (Garamvoelgyi and Hufnagel 2013). Migration of vegetation to these previously treeless areas could affect erosion in the catchment, as well as input of humic substances to lakes, leading to potential browning of these clear-water lakes. 5.2. Diatoms Large environmental gradients commonly result in low occurrence and abundance of many diatoms species. This is consistent with the results of the current study, where 78% of species occurred in less than 10 lakes. These large gradients enable the potential for turnover in species compositions and increase heterogeneity of the data (Pienitz et al. 1995; Birks 1998). A total of 185 species representing 27 genera were identified from the 26 lakes studied (Appendix 3). In comparison, Weckström and Korhola (2001) observed 370 species from 40 genera within the 64 lakes of their study. While this could initially be interpreted as a large decrease in species diversity, it is most likely a result of a smaller sampling size and, to a much lesser extent, due to some remaining identification differences after taxonomic harmonization of the data sets. The presence of two different analysers for diatom identification and counting might have resulted in small-scale inaccuracies in the data, which were an additional motivation behind the statistical analyses at the genus level. While species-level observation tends to provide more accurate results of changes in lake ecology, the main purpose of the genus level observations is to assess larger scale changes in overall diatom communities (Bennett et al. 2010). 5.2.1 General diatom distribution according to vegetation zones The 26 lakes were divided between three distinct vegetation zones, all reflecting different ecological conditions and water chemistry. The CCA analysis allowed for a direct interpretation of which characteristics where the most representative of each vegetation zone, highlighting their 25 most impactful ecological conditions. The SPB region was defined by its higher temperature (AirT), comparatively high rates of total organic carbon (TOC), and relatively low water pH. The most dominant diatom taxa in the area verified this observation with taxa such as Eunotia rhomboidea and Pinnularia rupestris generally indicating warmer lowland lakes, which are slightly acidic. At the genus level, these conditions were associated with Actinella and Peronia. As only one valve of these genera were found in one lake, their indicator value in this study is limited. The MBW vegetation zone is a transition zone between the SPB and Ba regions. This is also suggested by the species distribution, as MBW seemed to have the highest diversity (Weckström and Korhola 2001), including many diatom taxa that can be commonly found in both of the other vegetation zones. In all statistical analyses, MBW lakes showed a wider distribution, partly overlapping with the other vegetation zones. In the Ba region, colder, deeper, larger lakes were dominant, with low TOC concentrations. The clear spread of Ba lakes along the depth gradient on both species and genus CCA analyses (Figures 5 and 8) suggests that higher water depth is a common characteristic for these lakes. The dominant genera observed in these lakes seem to support this, as planktic species such as Cyclotella and Aulacoseira favour deeper water with elevated turbulence (especially in the case of Aulacoseira). Going back to the example of WFL 24, it contained many unique species (e.g. Cyclotella glomerata and Cyclotella comensis) and genera (e.g. Stenopterobia and Thalassiosira), which are good indicators for showing that it is the deepest lake of the dataset. 5.2.2 Observed changes over the past decades In general, the trends in diatom communities analysed in this study remained similar to the observations made by Weckström and Korhola (2001). A majority (14/26) of the 26 lakes did not show clear changes in diatom composition, indicating that lakes in the study area have not experienced any major turnover in water chemistry or other environmental variables despite changes in air temperatures and lake ice cover duration. Still, 12 of the 26 lakes were shown to have sustained some level of changes, with four lakes experiencing large changes, and eighth lakes experiencing moderate changes. 26 Of the lakes that experienced large changes in diatom communities, one was in the Ba vegetation zone (WFL 49), one in the MBW vegetation zone (WFL 37), and two in the SPB vegetation zone (WFL 12 & WFL 30). These lakes are located along the climate and vegetational gradient and reflect varying lake characteristics and ecologies, signifying that observed changes were not regionally focused. In lake WFL 30, a switch from Eunotia rhomboidea to Navicula hoeflerii and Navicula subtilissima was observed. While these three species seem to reflect similar ecologies (i.e. slightly acidic waters), the species CCA analysis shows a very clear gradient along the LOI, TOC and pH axes between the old and the new sample (Figure 5). This would indicate a lowering in LOI and TOC values, combined with a higher pH. Along this same LOI, TOC and pH gradient in the species CCA, lake WFL 12 showed marked changes with a large increase in Frustulia rhomboides and Navicula hoeflerii, associated with a clear decrease in Pinnularia rupestris. However, when looking at both CCA analyses at the genus level, these changes were not reflected by any important ecological turnover. Lakes WFL 37 and WFL 49 are in different vegetation zones (MBW and Ba, respectively) and vary highly in their size, depth, and altitude. Yet they display similar ecological conditions with neutral pH (7-7.3) and relatively high alkalinity (8- 9.5 mmol/l). These are the two lakes with the largest changes in their diatom compositions (genus Brachysira), occurring along the same environmental gradient (in opposite directions) according to the species CCA. Lake WFL 37 showed a large increase in Brachysira vitrea and Brachysira brebissonii, with a cumulative relative abundance of 20% in the modern sample compared to no occurrences in 1995. On the other hand, lake WFL 49 experienced the opposite trend, with a reduction in cumulative relative abundance of these species from 47% in 1995 to approximately 1% in the modern sample. Brachysira vitrea is known to thrive in oligotrophic, alkaline waters (Wolfel and Kling 2001), conditions that occur in both of these lakes. The environmental gradient along which the change occurs in these two lakes is not well captured by the variables available in this study. This could mean that changes in diatom communities could be attributed to other physical or chemical variables or even changes in the benthic habitat of the lakes. Most of the observed changes in the study lakes appear to be occurring in benthic diatom communities. Planktic diatoms appear to be rather insensitive to direct temperature changes, but instead seem dependent on processes like turbulence, light, or nutrient supplies, which ultimately are controlled by climate (Anderson 2000). In temperate lakes, this causes planktic diatoms to generally bloom in the spring when the conditions favour plankton growth (Reynolds 1984). However, with the 27 changing ice and temperature dynamics, especially with the fall months being warmer, the nature and timing of diatom responses may vary (Rühland et al. 2015). It might also become more frequent for lakes to experience a peak in biomass in the autumn. This trend of peak autumn biomass was previously observed in lake Saanajärvi (WFL 24) by Rautio et al. (2000). Regarding the lakes with moderate changes, four of them were located in the SPB vegetation zone, three in the MBW vegetation zone, and one in the Ba region, overall showing a more pronounced geographical pattern than the larger changes. One example from the SPB region is WFL 6. When looking at both CCA biplots (Figures 5 and 8), it is clear that changes are happening along the AirT and MireArea/pH gradients, indicating an increase in the air temperature and/or higher amounts of mire area in the lake catchment. However, this lake has crystal clear water with large water moss areas in the bottom, and very little mire in its catchment. As the mire area in the lake catchment is small, this change could have more to do with increased air temperature and changes in seasonality. In the MBW zone, lake WFL 18 showed moderate changes in species and genus composition in both relative frequency diagrams and DCAs, but this was not reflected in the CCAs. Overall, with two lakes having experienced large changes and four lakes with moderate changes, the SPB vegetation zone showed the largest difference to the original study. Due to its lower altitude compared to the other two vegetation zones, it is very likely that these lakes have been impacted the most by increasing temperatures and resulting changes in algal communities and primary production, despite the somewhat complex response of the diatom communities in these lakes For both studies, sediment samples were collected from the deepest point of each lake. The purpose of sampling the deepest point is to obtain a sample from the accumulation zone of the lake, providing the most complete archive of the basin’s sedimentation (Smol 2009). If one of the samples was taken on a flat central basin, and the other taken on a slight slope, it could have an impact on quality and quantity of the fossil remains in the sediment record. In addition, lakes in the SPB and MBW areas tend to have overall faster and steady sedimentation rates due to more vegetated catchment areas combined with higher productivity with warmer temperatures, providing higher OM flux and faster erosion rates compared to Ba lakes (Johansson 1985; Rekolainen et al. 1986). Thus, observations made for the SPB and MBW lakes could be more consistent with the aims of this study to compare two distinct samples from different time periods. 28 Lakes in the Ba region tend to have much lower sedimentation rates (Korhola and Weckström 2004; Frolova et al. 2018). This increases the risk that not enough new sediment has accumulated in 25 years on top of the reference sample. Thus, it could be possible that the recent surface sample (2.5 mm) also contains sediment from the reference sample. On the other hand, although their sedimentation rates are lower, the Ba lakes are deeper and their sediment contains less water, which decreases the possibility of the surface sample mixing with deeper layers. Overall, out of the 14 lakes that were considered to have experienced negligible changes, five were from the Ba vegetation zone. Considering that only 7 lakes were sampled in the Ba zone, a large majority of these lakes have not experienced noticeable changes over the past 25 years. While it is plausible that the more southern and lower-lying lakes in the SPB zone show earlier signs of warming compared to the lakes in the Ba zone, this lack of change in the Ba lakes could be due to overlap between the old and new samples. 5.3. The impact of climate warming on diatom communities Over the past decades, a large number of studies have been published, aiming to observe shifts in diatom communities as a result of recent climate warming (e.g. Battarbee et al. 2002; Rühland et al. 2003, 2008, 2015; Rühland and Smol 2005; Winder et al. 2009). In many of these studies, sediment records dating back to pre-industrial time were analysed to observe long-term changes. A common trend observed during the last decades is the significant increase in the relative abundances of the genus Cyclotella, associated with a decline of the genus Aulacoseira and Fragilaria (Battarbee et al. 2002; Rühland et al. 2003, 2008; Rühland and Smol 2005). Climate warming, a longer growing season, and longer ice-free periods will generally favour planktic diatom species such as the genus Cyclotella (Battarbee et al. 2002; Winder et al. 2009). Most of the large-scale increases in Cyclotella species have been found in relatively pristine, nutrient poor and non-acidified lakes (Rühland et al. 2008). This increasing trend in planktic diatoms was directly associated with a decrease in benthic Fragilaria and (tycho)planktic Aulacoseira species. The longer ice-free period should overall be favourable to many diatom species, increasing overall competition within the lake, which would be unfavourable to Fragilaria species that typically thrive under longer periods of ice cover where competition within primary producer communities is limited (Lotter and Bigler 2000). The decrease in Aulacoseira species could be more directly 29 explained by the increased competition with the Cyclotella species, which are favoured by the changing lake conditions (Rühland and Smol 2005). Heavily silicified Aulacoseira species generally thrive under longer ice cover duration where there is less time for warming up the epilimnion, hence stronger mixing of the water column and no stratification. Inversely, the light Cyclotella species prefer shorter ice cover duration, allowing for higher epilimnion temperatures and stronger stratification of the water column (Rühland et al. 2015). These trends have been observed in many lakes in the Northern Hemisphere, both in North America and Europe. However, possibly due to the shorter 25-year time span of this study, these longer time scale changes could not be observed. The absence of the trend could also be explained by the water chemistry of the lakes in this study. Cyclotella species have been shown to favour non-acidified lakes (Saros and Anderson 2015). In a study conducted by Rühland et al. (2003), observing 50 lakes in the Canadian Arctic, the pH of the 50 lakes were neutral to alkaline, which are ideal pH conditions for Cyclotella species. However, the 26 lakes in this study are dominantly slightly acidic, with pH values ranging from 5 to 7.5 (mean 6.4), which could partly explain their lack of presence in the recent samples. Additionally, these planktic species are favoured by greater water depth, which explains their presence in the Ba lakes. However, when these deeper Ba lakes are excluded, the average depth of the lakes in the dataset is 4.3m (ranging from 1.4m to 9.6m) which does not favour, although it does not completely preclude, planktic species. Finally, while most of the observed changes in this study were attributed to changing climate and increasing temperatures, it is key to remember that lakes are complex ecosystems. Changes in diatom assemblages cannot be attributed to one single external factor. Nutrient loading, water stratification, erosion rate, water pH, biological population of the lake, habitat availability, prey-predator relationship, as well as numerous other factors all work together and behave differently in each and every lake. 30 6. Conclusions The purpose of this thesis was to compare and analyse the impacts of evolving climate trends on diatom communities over a period of 25 years in Northwestern Finnish Lapland. While many palaeolimnological studies reconstruct ecosystems over long time frames, ranging from centuries to thousands of years, the present study aimed to observe changes in diatom communities over a short time scale. During the 25-year period between the original study by Weckström and Korhola (2001) and 2020, seasonal trends of climate warming were recorded in the region. These were defined by increases in yearly temperatures, but most strongly over the fall period between the months of September and October. Overall, a small majority of the 26 lakes studied have remained relatively stable with few to no changes to the diatom assemblages in the lake sediments. Only four of these lakes, scattered across the three vegetation zones, have sustained large changes at either the taxa or genus level. Most of the changes associated with these four lakes were related to major shifts in dominant diatom species. While some of these changes could be attributed to changing lake dynamics due to increasing temperatures and decreasing lake ice cover duration, they do not follow the global pattern observed in other literature. Many recent articles have observed an increase in dominance of Cyclotella species, directly associated to the decline of Fragilaria and Aulacoseira species. The shallow acidic nature of the lakes studied for this thesis could be the reason why this trend was not observed in the study lakes over the past 25 years. Another eight lakes were observed to have sustained moderate changes, where dominance changes were recorded for a few species while the majority remained unchanged. Of these eight lakes, half were from the SPB vegetation zone, which has experienced the most changes across the study area. This is likely due to more direct impacts of changing temperatures due to the lower altitude of the lakes. The remaining 14 lakes did not show noticeable changes in diatom communities and hence lake chemistry. In some cases, such as lakes in the Ba area, the lack of change could also be attributed to slow sedimentation rates, preventing the differentiation between old and newer samples. Repeating a similar study in another 25 years period (50 years after the original study) could enable validation of the current observations, shed new light regarding changes in the Ba lakes, and support determination of whether or not these lakes in Northwestern Finnish Lapland will experience the same changes as other northern hemisphere lakes under the continuing climate warming. 31 7. Acknowledgements This adventure has been a crazy ride, from the change of topic due to the pandemic, to the writer’s block after months of work on this project. It has been an extremely exciting experience, having the opportunity to meet and work with amazing people, and also visiting some of the most beautiful places I’ve had the chance to see. None of this would have been possible without my amazing supervisors. I would like to thank Jan Weckström for joining me during the field mission and for tolerating me and the countless diatom pictures I sent him asking for his help. I would like to thank Kaarina Weckström for rescuing me over a year ago when I was struggling to find a thesis topic, and also for the continuous support with the writing process. I am very grateful to Maija Heikkilä and the other members of the research group for the emotional and academic support with the very helpful “Monday morning coffees”. Financial support from the Arctic Avenue is also gratefully acknowledged. I would like to thank all of my friends and family for helping me get through this process. It is amazing to look back and see the incredible amount of support I have received over the past year. Thank you to my parents for putting up with me and my mood swings over this difficult process. I am very grateful to my friends in Finland that have not only helped me study but also helped me grow as a person. Thank you to all the people that took part in the Arctic field course for helping me collect some of the samples that were analysed in this thesis. Special thanks to the Järviryhmä group for the amazing time walking across the tundra while trying to teach me Finnish words. Thank you to Sanna and Kimmo for joining us last summer in Lapland for the field mission. There are so many other people that I could thank for their involvement in this adventure and I am sorry if I have not directly mentioned you here, but I hope that my actions have been able to show you my gratitude. 32 8. Citations Aalto J, Karlsson P, Kaukoranta J-P, et al (2012) Tilastoja Suomen ilmastosta 1981-2010. Ilmatieteen laitos, Helsinki Anderson NJ (2000) Miniview: diatoms, temperature and climatic change. European Journal of Phycology 35:307–314 Battarbee RW, Grytnes J-A, Thompson R, et al (2002) Comparing palaeolimnological and instrumental evidence of climate change for remote mountain lakes over the last 200 years. 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Studies on Diatoms (R Jahn, JP Kociolek, A Witkowski, P Compàre & H Lange-Bertalot) Koeltz, Koenigstein 243–264 Woolway RI, Merchant CJ (2019) Worldwide alteration of lake mixing regimes in response to climate change. Nature Geoscience 12:271–276 9. Appendix Appendix 1: Abbreviations of diatom species used in the DCA and CCA analyses. Taxa Abbreviation Achnanthes levanderi Ac. levanderi Achnanthes marginulata Ac. margi. Achnanthes pusilla Ac. pusilla Aulacoseira distans var. alpigena Au. distans alpi. Aulacoseira distans var. distans Au. distans Aulacoseira distans var. nivalis Au. distans. niv. Aulacoseira lirata var. lirata Au. lirata Aulacoseira perglabra Au. pergla. Brachysira brebissonii Bra. brebi. Brachysira vitrea Bra. vitrea Cyclotella comensis Cy. comensis Cyclotella glomerata Cy. glomerata Cyclotella rossii Cy. rossii Cymbella gaeumannii Cym. gaeum. Eunotia rhomboidea Eu. rhombo. Fragilaria brevistriata var. brevistriata Fr. brevi. Fragilaria construens var. exigua Fr. cons. exi. Fragilaria construens var. venter Fr. cons. ven. Fragilaria pinnata var. pinnata Fr. pinnata Fragilaria pseudoconstruens Fr. pseudo. Frustulia rhomboides var. rhomboides Fru. rhom. Frustulia rhomboides var. saxonica Fru. rh. sa. Navicula hoeflerii Nav. hoefl. Navicula pupula Nav. pup. Navicula radiosa var. radiosa Nav. radiosa Navicula subtilissima Nav. subti. Pinnularia biceps var. biceps Pin. biceps Pinnularia rupestris Pin. rupes. 37 A p p en d ix 2 L o ca ti o n s a n d e n v ir o n m en ta l ch a ra ct er is ti c o f th e st u d y l a k es . L a k e L a ti tu d e L o n g it u d e A lt it u d e A re a M ir e a re a S P B M B W B a M a x .d e p t h A ir T p H C o n d . A lk a lin it y L O I* * * T O C N a K M g W F L 3 6 7 .8 5 2 4 .1 8 2 6 8 .0 0 7 .1 4 4 0 .0 3 1 .0 0 0 .0 0 0 .0 0 4 .0 0 1 3 .3 0 6 .1 0 1 0 .6 9 2 .4 4 6 4 .1 0 9 .9 0 0 .6 5 0 .1 7 0 .2 7 W F L 4 6 7 .9 8 2 3 .6 8 2 6 2 .0 0 1 0 .6 7 2 8 .8 1 1 .0 0 0 .0 0 0 .0 0 3 .3 0 1 3 .1 0 4 .8 0 7 .2 0 0 .0 0 7 1 .8 0 8 .3 0 0 .4 3 0 .1 1 0 .1 4 W F L 5 6 8 .0 1 2 3 .4 0 2 4 9 .0 0 4 .3 4 4 5 .9 5 1 .0 0 0 .0 0 0 .0 0 3 .3 5 1 3 .2 0 5 .9 0 1 8 .7 0 9 .7 6 5 0 .1 0 1 1 .0 0 1 .2 0 0 .1 7 0 .7 1 W F L 6 6 8 .1 2 2 3 .3 7 2 5 2 .0 0 1 .5 2 4 .4 5 1 .0 0 0 .0 0 0 .0 0 4 .3 0 1 3 .1 0 6 .2 0 6 .4 0 1 .2 2 8 8 .0 0 5 .1 0 0 .1 5 0 .1 1 0 .0 3 W F L 7 6 8 .2 0 2 3 .1 8 2 6 3 .0 0 0 .9 0 0 .0 0 1 .0 0 0 .0 0 0 .0 0 4 .4 0 1 2 .6 0 6 .5 0 1 6 .0 0 4 .5 0 6 1 .2 0 7 .2 0 0 .8 0 0 .2 2 0 .3 6 W F L 1 1 6 8 .4 0 2 2 .8 5 3 1 3 .0 0 2 .1 8 2 .3 8 1 .0 0 0 .0 0 0 .0 0 4 .1 0 1 2 .6 0 6 .6 0 9 .4 0 4 .2 7 5 8 .6 0 8 .0 0 0 .7 3 0 .3 2 0 .2 7 W F L 1 2 6 8 .4 2 2 2 .5 8 3 3 2 .0 0 1 .9 7 5 8 .8 2 1 .0 0 0 .0 0 0 .0 0 1 .8 5 1 2 .4 0 4 .4 0 1 3 .5 0 0 .6 1 8 0 .8 0 1 1 .6 0 1 .3 2 0 .2 0 0 .2 0 W F L 1 3 6 8 .4 7 2 2 .4 3 3 2 2 .0 0 2 .5 5 2 5 .7 5 1 .0 0 0 .0 0 0 .0 0 4 .0 0 1 2 .5 0 6 .3 0 1 1 .9 0 4 .2 7 6 0 .4 0 8 .5 0 0 .6 0 0 .3 4 0 .4 1 W F L 1 7 6 8 .9 0 2 1 .0 7 4 6 3 .0 0 6 .0 5 0 .9 3 0 .0 0 1 .0 0 0 .0 0 1 .7 0 1 1 .3 0 6 .9 0 2 3 .9 0 6 .1 0 4 8 .3 0 1 0 .4 0 1 .5 6 0 .5 9 0 .5 6 W F L 1 8 6 8 .9 2 2 0 .9 7 5 2 6 .0 0 3 .8 5 0 .0 0 0 .0 0 1 .0 0 0 .0 0 1 .4 5 1 0 .8 0 7 .2 0 3 7 .7 0 8 .5 0 5 5 .0 0 8 .9 0 1 .3 9 0 .8 2 0 .5 4 W F L 2 4 6 9 .0 5 2 0 .8 7 6 7 9 .4 0 6 9 .8 6 0 .0 0 0 .0 0 0 .0 0 1 .0 0 2 4 .0 0 9 .9 0 6 .8 0 2 7 .7 0 0 .1 6 2 0 .5 0 3 .1 0 1 .1 5 0 .2 7 0 .6 7 W F L 2 5 6 9 .0 7 2 0 .8 8 9 4 1 .0 0 1 .6 9 0 .0 0 0 .0 0 0 .0 0 1 .0 0 3 .3 5 8 .5 0 6 .2 0 7 .2 0 2 .0 0 4 .6 9 3 .5 0 0 .3 3 0 .3 8 0 .0 8 W F L 2 6 6 9 .1 8 2 0 .7 2 3 6 0 .0 0 7 .2 1 0 .0 0 0 .0 0 1 .0 0 0 .0 0 3 .4 0 1 1 .1 0 6 .9 0 3 4 .2 0 6 .5 0 2 2 .6 7 9 .8 0 1 .6 9 2 .2 9 0 .4 3 W F L 2 7 6 9 .0 8 2 0 .8 0 5 9 3 .0 0 1 5 .6 8 0 .0 0 0 .0 0 0 .0 0 1 .0 0 1 3 .7 5 1 0 .4 0 7 .1 0 2 4 .0 0 5 .5 0 1 8 .4 2 4 .1 0 0 .8 6 1 .6 9 0 .2 2 W F L 2 9 6 8 .1 0 2 3 .4 2 2 4 9 .0 0 4 .0 1 1 2 .0 3 1 .0 0 0 .0 0 0 .0 0 9 .6 0 1 3 .2 0 6 .0 0 1 7 .2 0 6 .1 0 4 2 .3 0 1 2 .4 0 0 .9 0 0 .1 8 0 .4 9 W F L 3 0 6 8 .1 3 2 3 .3 7 2 5 3 .0 0 1 .3 3 0 .0 0 1 .0 0 0 .0 0 0 .0 0 4 .3 0 1 3 .2 0 4 .0 0 5 .2 0 0 .6 1 8 3 .6 0 1 1 .3 0 0 .2 2 0 .0 8 0 .0 8 W F L 3 2 6 8 .4 2 2 2 .9 0 3 1 9 .0 0 2 8 .1 8 2 2 .6 8 1 .0 0 0 .0 0 0 .0 0 6 .0 0 1 2 .6 0 7 .0 0 2 9 .3 0 1 0 .9 8 4 2 .3 0 8 .3 0 1 .3 4 0 .5 7 0 .9 0 W F L 3 5 6 8 .6 8 2 2 .0 5 5 2 6 .0 0 1 3 .9 1 0 .0 0 0 .0 0 1 .0 0 0 .0 0 5 .3 5 1 1 .0 0 7 .4 0 2 8 .0 0 9 .0 0 3 8 .2 0 4 .9 0 0 .8 0 0 .3 5 0 .4 0 W F L 3 6 6 8 .6 7 2 2 .0 5 4 9 8 .0 0 1 0 .2 7 0 .3 3 0 .0 0 1 .0 0 0 .0 0 1 7 .0 0 1 1 .3 0 7 .5 0 3 3 .5 0 9 .5 0 3 5 .7 0 6 .5 0 0 .9 8 0 .4 0 0 .4 8 W F L 3 7 6 8 .6 7 2 2 .0 3 5 0 8 .0 0 1 .3 9 0 .0 0 0 .0 0 1 .0 0 0 .0 0 2 .8 5 1 1 .2 0 7 .3 0 3 2 .8 0 9 .5 0 3 8 .2 0 4 .8 0 1 .0 1 0 .4 7 0 .3 8 W F L 4 1 6 8 .9 2 2 1 .0 5 5 9 6 .0 0 3 .4 7 0 .0 0 0 .0 0 1 .0 0 0 .0 0 8 .1 5 1 0 .5 0 6 .8 0 1 3 .3 0 3 .6 6 5 4 .5 0 4 .0 0 0 .9 5 0 .4 3 0 .2 7 W F L 4 9 6 9 .0 8 2 0 .6 7 7 7 8 .0 0 1 6 .1 0 0 .0 0 0 .0 0 0 .0 0 1 .0 0 1 2 .1 0 9 .4 0 7 .0 0 9 .9 0 8 .0 0 1 6 .9 0 2 .5 0 0 .5 8 0 .1 6 0 .1 1 W F L 5 2 6 9 .0 5 2 0 .9 8 6 8 7 .0 0 1 6 .8 9 0 .0 0 0 .0 0 0 .0 0 1 .0 0 9 .1 0 9 .9 0 6 .6 0 1 8 .2 0 0 .1 1 1 9 .6 0 4 .4 0 1 .1 4 0 .3 5 0 .4 1 W F L 5 4 6 9 .0 3 2 1 .1 3 7 9 6 .4 0 9 .3 3 0 .0 0 0 .0 0 0 .0 0 1 .0 0 8 .0 0 9 .2 0 7 .0 0 1 2 .8 0 3 .6 6 3 3 .6 1 3 .0 0 0 .8 4 0 .2 8 0 .1 9 W F L 5 5 6 9 .0 6 2 1 .0 5 7 7 4 .0 0 2 0 .4 4 0 .0 0 0 .0 0 0 .0 0 1 .0 0 2 .0 0 9 .3 0 7 .0 0 1 1 .6 0 4 .2 7 3 4 .0 4 2 .5 0 0 .5 3 0 .2 4 0 .2 0 W F L 5 6 6 9 .1 7 2 1 .0 5 1 0 0 9 .0 0 9 .6 1 0 .0 0 0 .0 0 0 .0 0 1 .0 0 1 0 .0 0 7 .9 0 5 .6 0 5 .9 0 0 .0 0 1 3 .4 9 1 .5 0 0 .4 4 0 .0 9 0 .0 6 38 Appendix 3: Species name and associated code of every diatom specie observed and examined in this thesis. The row # Occurrence represents the amount of lakes in which diatom species were observed. The row Max % represents the maximum occurrence of the selected specie within the lakes in which it appeared. Hill’s N2 represents the effective number of occurrences of the selected specie within the data. Taxon code Taxa Author # Occurrence Hill's N2 (Gadagkar 1989) Max % AC018A Achnanthes laterostrata Hust (1933) 4 3.58 0.38 AC022A Achnanthes marginulata Grun. in Cleve & Grun (1880) 4 1.51 15.5 9 AC046A Achnanthes altaica A. Cleve-Euler (1953) 6 4.54 1.30 AC152A Achnanthes carissima Lange-Bertalot (1990) 1 1.00 6.30 AC023A Achnanthes conspicua var. conspicua A. Mayer (1919) 1 1.00 0.18 FSN004 Achnanthes daonensis NORD-CHILL (1997) 9 5.17 1.79 AC024A Achnanthes depressa Hust (1933) 6 3.59 0.83 AC039A Achnanthes didyma Hust (1933) 1 1.00 0.17 AC025A Achnanthes flexella Brun (1880) 4 3.21 0.58 AC154A Achnanthes imperfecta Schimanski (1978) 5 4.51 0.35 AC083A Achnanthes laevis Ostr (1910) 1 1.00 0.17 AC044A Achnanthes levanderi Hust (1933) 23 8.54 20.6 7 FSN001 Achnanthes minutissima NORD-CHILL (1997) 18 9.32 9.91 AC019A Achnanthes nodosa A. Cleve-Euler (1900) 17 11.01 3.01 AC035B Achnanthes petersenii Hust (1936) 3 1.80 1.47 AC035A Achnanthes pusilla Grun. in Cleve & Grun (1880) 16 5.63 14.2 3 AC034A Achnanthes suchlandtii Hust (1933) 5 3.24 1.23 AC042A Achnanthes subatomoides Lange-Bertalot & Archibald (1985) 13 8.32 3.02 AC160A Achnanthes thermalis Rabenhorst (1907) 3 2.15 3.48 AC161A Achnanthes sublaevis Hust (1936) 3 2.81 0.38 AT001A Actinella punctata Lewis 2 1.49 0.76 AM011 A Amphora libyca Her 2 1.38 0.90 39 AM001 A Amphora ovalis Kutz (1844) 1 1.00 0.19 AU004D Aulacoseira distans var. alpigena Simonsen (1979) 1 1.00 11.3 0 AU001D Aulacoseira italica f. crenulata R. Ross in Hartley (1986) 2 1.26 3.78 AU005A Aulacoseira distans var. distans Simonsen (1979) 18 9.20 19.7 0 AU005E Aulacoseira distans var. nivalis 6 4.19 2.90 AU004B Aulacoseira lirata var. lacustris R. Ross in Hartley (1986) 3 2.81 2.25 AU004A Aulacoseira lirata var. lirata R. Ross in Hartley (1986) 6 4.48 4.73 AU014A Aulacoseira nygaardii Camburn 1 1.00 0.66 AU010A Aulacoseira perglabra 5 3.22 10.5 4 FSN013 Aulacoseira subarctica NORD-CHILL (1997) 3 1.97 14.6 3 AU001C Aulacoseira italica var. valida Simonsen (1979) 10 6.91 3.82 BR006A Brachysira brebissonii R. Ross in Hartley (1986) 20 11.19 13.9 1 BR002A Brachysira follis R. Ross in Hartley (1986) 1 1.00 0.17 BR003A Brachysira serians Round & Mann (1981) 4 2.10 3.15 BR004A Brachysira styriaca R. Ross in Hartley (1986) 1 1.00 0.16 BR001A Brachysira vitrea R. Ross in Hartley (1986) 20 8.20 16.1 8 BR005A Brachysira zellensis Round & Mann (1981) 1 1.00 1.47 CA002A Caloneis bacillum Cleve (1894) 1 1.00 0.56 CA031A Caloneis obtusa Cleve (1894) 1 1.00 0.18 CA018A Caloneis tenuis Gregory (1985) 2 1.32 1.10 CP002B Campylodiscus noricus var. hibernicus Grun (1862) 1 1.00 0.18 CO001A Cocconeis placentula var. placentula Ehrenb (1838) 2 1.83 0.35 CY013A Cyclotella antigua W. Sm (1853) 2 1.41 0.92 FSN020 Cyclotella bodanica var. lemanica NORD-CHILL (1997) 4 1.80 4.06 CY010A Cyclotella comensis Grun. in Van Heurck (1882) 2 1.03 13.4 0 40 CY052A Cyclotella rossii Hakansson (1990) 6 2.13 26.1 0 CY055A Cyclotella schumannii Hakansson (1990) 2 1.19 3.53 CY004A Cyclotella stelligera Grun. in Van Heurck (1882) 1 1.00 4.49 CL001A Cymatopleura solea W. Smith (1851) 1 1.00 0.19 ABC001 Cymbella alpina 1 1.00 0.39 CM016A Cymbella amphicephala Naegeli ex Kutz (1849) 1 1.00 0.39 CM015A Cymbella cesatii Grun. in A. Schmidt (1881) 8 5.36 5.60 CM026A Cymbella cuspidata Kutz (1844) 1 1.00 0.20 CM038A Cymbella delicatula Kutz (1849) 1 1.00 0.18 CM052A Cymbella descripta Krammer & Lange-Bertalot (1985) 9 5.15 3.90 EY006A Cymbella elginensis Krammer (1981) 1 1.00 0.36 CM020A Cymbella gaeumannii Meister (1934) 2 1.53 1.66 CM048A Cymbella lunata W. Sm. in Grev. (1855) 10 7.16 1.58 EY003A Cymbella hebridica Cleve (1894) 4 1.96 3.83 CM013A Cymbella helvetica var. helvetica Kutz (1844) 1 1.00 0.19 CM101B Cymbella incerta Grun. in Cleve & Moller (1878) 7 4.29 2.34 CM004A Cymbella microcephala Grun. in Van Heurck (1880) 5 3.58 1.10 EY011A Cymbella minuta Hilse ex Rabenh (1862) 3 2.08 1.42 CM009A Cymbella naviculiformis Auersw. ex Heib (1863) 13 5.72 9.61 EY013A Cymbella obscura Krasske (1938) 8 5.21 2.72 EY014A Cymbella perpusilla A. Cleve (1895) 13 6.75 3.16 ABC002 Cymbella proxima 4 3.62 0.35 ABC003 Cymbella pusilla 1 1.00 0.39 EY016A Cymbella silesiaca Bleisch ex Rabenh (1864) 24 13.72 4.71 RE001A Cymbella sinuata Greg (1856) 2 1.81 0.37 CM107A Cymbella subcuspidata Krammer (1982) 1 1.00 0.37 DE003A Denticula kuetzingii Grun 1 1.00 2.73 FSN028 Denticula tenuis NORD-CHILL (1997) 4 3.38 3.01 DP012A Diploneis marginestriata Hust (1922) 2 1.59 0.58 EU013A Eunotia arcus Ehrenb (1837) 1 1.00 0.18 41 EU014A Eunotia bactriana Ehrenb (1854) 2 1.22 3.58 EU049A Eunotia curvata Lagerst (1884) 12 5.37 6.60 EU049B Eunotia curvata var. subarcuata Woodhead & Tweed (1954) 2 1.62 0.57 EU015A Eunotia denticulata Rabenh (1864) 2 2.00 0.58 EU016A Eunotia diodon Ehrenb (1837) 4 2.12 1.39 EU043A Eunotia elegans Ostr (1910) 1 1.00 0.17 EU009A Eunotia exigua Rabenh (1864) 13 3.58 6.23 EU051A Eunotia vanheurckii Patr (1958) 9 2.81 6.79 EU017A Eunotia flexuosa Kutz (1849) 3 1.90 1.26 EU024A Eunotia glacialis Meister (1912) 9 6.63 1.18 EU054A Eunotia hexaglyphis Ehrenb (1854) 1 1.00 0.17 EU002E Eunotia pectinalis var. minor f. impressa Hust 2 2.00 0.18 EU047A Eunotia incisa W. Sm. ex Greg (1854) 10 7.96 0.70 EU048A Eunotia naegelii Migula (1907) 1 1.00 0.33 EU045A Eunotia nymanniana Grun. in Van Heurck (1881) 3 2.20 1.28 EU034A Eunotia parallela var. parallela Ehrenb (1843) 1 1.00 0.36 EU002D Eunotia pectinalis var. undulata Rabenh (1864) 2 2.00 0.18 EU003A Eunotia praerupta Ehrenb. (1843) 15 9.31 2.60 FSN034 Eunotia praerupta var. bigibba NORD-CHILL (1997) 1 1.00 0.19 EU011A Eunotia rhomboidea Hust (1950) 11 4.99 4.34 EU106A Eunotia rhynchocephala Hustedt (1936) 1 1.00 0.33 EU032A Eunotia serra Ehrenb (1837) 7 5.54 1.16 ABC005 Eunotia soleirolii 2 1.62 0.53 ABC006 Eunotia subarcuatoides 1 1.00 0.54 EU039A Eunotia triodon Ehrenb (1837) 2 1.98 0.93 PS001A Fragilaria brevistriata var. brevistriata Grun. in Van Heurck (1885) 12 5.82 3.77 FR009A Fragilaria capucina var. capucina Desm (1825) 2 1.84 0.97 FF003A Fragilaria constricta f. constricta Ehrenb (1843) 4 3.22 0.52 FR010B Fragilaria constricta f. stricta Hust (1931) 1 1.00 3.30 SR001A Fragilaria construens var. construens Grun (1862) 3 1.76 4.17 FR002B Fragilaria construens var. binodis Grun (1862) 2 2.00 0.19 42 FR002D Fragilaria construens var. exigua 13 6.87 13.6 9 FR002C Fragilaria construens var. venter Grun. in Van Heurck (1881) 5 2.75 36.0 7 SS001A Fragilaria lapponica Grun. in Van Heurck (1881) 3 1.91 1.51 SS002A Fragilaria pinnata var. pinnata Ehrenb (1843) 11 7.04 4.54 PS002A Fragilaria pseudoconstruens Marciniak (1982) 9 5.97 9.06 SF001A Fragilaria virescens var. exigua Grun. in Van Heurck (1881) 7 5.13 6.09 FU002A Frustulia rhomboides var. rhomboides De Toni (1891) 23 11.38 23.7 3 FU002B Frustulia rhomboides var. saxonica De Toni (1891) 18 10.54 24.7 9 FU002F Frustulia rhomboides var. viridula Cleve (1894) 7 4.04 4.95 GO006A Gomphonema acuminatum var. acuminatum Ehrenb (1832) 9 7.37 0.83 GO029A Gomphonema cf. clavatum Her 1 1.00 0.55 GO004A Gomphonema gracile Ehrenb (1838) 2 1.48 0.72 GO013A Gomphonema parvulum var. parvulum Kutz (1849) 15 7.85 3.60 ABC007 Gomphonema pseudoaugur 1 1.00 0.35 ABC008 Gyrosigma attenuatum 1 1.00 0.19 MR001A Meridion circulare var. circulare Ag (1831) 1 1.00 0.20 MR001B Meridion circulare var. constrictum Van Heurck (1885) 1 1.00 0.17 NA161A Navicula absoluta Hust (1950) 6 3.83 2.30 NA069A Navicula americana Ehrenb (1843) 2 2.00 0.39 NA038A Navicula arvensis Hust 1 1.00 0.59 CV001A Navicula cocconeiformis var. cocconeiformis Greg. ex Greville (1855) 5 2.43 1.74 NA007A Navicula cryptocephala var. cryptocephala Kutz (1844) 6 3.47 1.89 NA149A Navicula digitulus Hust (1943) 3 2.32 1.54 NA100A Navicula explanata Hust (1948) 2 1.82 0.36 NA015A Navicula hassiaca Krasske (1925) 1 1.00 1.10 NA433C Navicula ignota var. palustris J.W.G. Lund (1946) 1 1.00 0.19 43 NA101A Navicula jaagii Meister (1934) 7 5.26 1.65 CV002A Navicula jaernefeltii Hust (1942) 2 1.99 0.19 FSN047 Navicula pupula NORD-CHILL (1997) 15 3.37 16.6 4 NA156A Navicula leptostriata Jorgensen (1948) 8 4.74 5.60 NA006A Navicula mediocris Krasske (1932) 13 7.99 4.47 SL003A Navicula minima var. minima Grun. in Van Heurck (1880) 5 1.52 6.48 ABC009 Navicula muraliformis 1 1.00 0.93 CV004A Navicula pseudoscutiformis Hust (1930) 4 2.91 0.76 NA123A Navicula pseudoventralis Hust (1953) 1 1.00 0.19 NA003A Navicula radiosa var. radiosa Kutz (1844) 19 6.91 10.0 0 NA133A Navicula schassmannii Hust (1937) 10 4.02 3.68 SL002A Navicula seminulum Grun (1860) 17 9.10 7.03 NA048A Navicula soehrensis var. soehrensis Krasske (1923) 2 1.83 0.33 NA033A Navicula subtilissima Cleve (1891) 22 8.78 10.9 8 NA040A Navicula hoeflerii Choln. in Choln. & Schindler (1953) 12 4.10 30.0 0 NA738A Navicula vitiosa Schimanski (1978) 6 3.30 2.27 NE003A Neidium affine var. affine Pfitz (1871) 1 1.00 0.19 NE001C Neidium iridis var. ampliatum Cleve (1894) 4 2.30 2.88 NE004A Neidium bisulcatum var. bisulcatum Cleve (1894) 2 1.81 0.38 NE007A Neidium dubium var. dubium Cleve (1894) 6 3.19 3.18 NE001A Neidium iridis Cleve (1894) 10 5.53 2.75 NE9999 Neidium spp. 6 5.28 0.80 NI020A Nitzschia angustata var. angustata Grun. in Cleve & Grun (1880) 8 4.85 2.30 NI002A Nitzschia fonticola Grun. in Van Heurck (1881) 19 8.09 7.41 PI055A Pinnularia balfouriana Grun. ex Cleve (1896) 2 1.74 0.40 PI018A Pinnularia biceps var. biceps Greg (1856) 20 7.21 17.3 8 PI170A Pinnularia braunii Cleve 1 1.00 2.36 44 PI015A Pinnularia abaujensis R. Ross in Hartley (1986) 7 4.45 1.23 Pi015C Pinnularia abaujensis var. linearis Patr. in Patr. & Reimer (1966) 5 3.79 1.75 FSN056 Pinnularia interrupta var. gibberula NORD-CHILL (1997) 6 4.45 1.05 FSN057 Pinnularia microstauron NORD-CHILL (1997) 15 9.38 3.16 ABC010 Pinnularia microstauron var. brebissoni 3 2.34 0.71 FSN058 Pinnularia nodosa NORD-CHILL (1997) 3 2.72 0.75 ABC011 Pinnularia obscura 1 1.00 1.22 FSN084 Pinnularia pluviana Sovereign 1 1.00 0.20 PI056A Pinnularia rupestris Hantzsch in Rabenh (1861) 12 5.77 13.3 5 PI022A Pinnularia subcapitata var. subcapitata Greg (1856) 1 1.00 0.17 PI007A Pinnularia viridis var. viridis Ehrenb (1843) 14 8.44 2.46 RH001A Rhopalodia gibba var. gibba O. Mull (1895) 2 1.45 0.73 SA001A Stauroneis anceps var. anceps Ehrenb (1843) 15 9.14 5.19 SA006A Stauroneis phoenicenteron var. phoenicenteron Ehrenb (1843) 10 6.79 2.04 SP002A Stenopterobia sigmatella R. Ross in Hartley (1986) 3 2.77 1.11 ABC012 Stephanodiscus hantzschii 1 1.00 0.18 OX001A Tabellaria binalis Grun. in Van Heurck (1881) 2 1.86 2.16 OX001B Tabellaria binalis var. elliptica Flower (unpub) (1986) 1 1.00 0.20 FSN061 Tabellaria flocculosa NORD-CHILL (1997) 23 13.89 5.69 TA004A Tabellaria quadriseptata Knudson (1952) 2 1.55 0.58 ABC013 Tetracyclus emarginatus 1 1.00 0.88 TE001A Tetracyclus lacustris Ralfs (1853) 8 5.27 1.58 CY007A Cyclotella glomerata Bachm (1911) 1 1.00 7.94 SE001A Semiorbis hemicyclus Patr. in Patr. & Reimer (1966) 2 1.33 2.04