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1 Spatial heterogeneity and ecology of algal communities in an ephemeral sandstone stream in the Bohemian Switzerland National Park, Czech Republic by Jana Veselá * Department of Botany, Faculty of Science, Charles University, Benátská 2, CZ-12801 Praha 2, Czech Republic With 5 figures and 3 tables Received 22 December 2007, accepted in revised form 24 November 2008 * e-mail: [email protected]

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Page 1: vesela spatial heterogeneity - Univerzita Karlova · total of 109 infrageneric algal taxa were identified with diatoms ... diatom slides were prepared following ... Within-substrate

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Spatial heterogeneity and ecology of algal communities in an ephemeral sandstone stream in the Bohemian Switzerland National Park, Czech

Republic

by

Jana Veselá *

Department of Botany, Faculty of Science, Charles University, Benátská 2, CZ-12801 Praha 2, Czech Republic

With 5 figures and 3 tables

Received 22 December 2007, accepted in revised form 24 November 2008

* e-mail: [email protected]

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Abstract: Spatial patterns and biodiversity of algae assemblages, and the effect of environmental factors on species composition were studied in an ephemeral pristine stream. Samples were taken along a longitudinal stream transect beginning at the source. At each sampling site, different types of substrates were collected and water chemistry investigated. A total of 109 infrageneric algal taxa were identified with diatoms being the most diverse group (85 taxa). Characteristic diatom species included: Chamaepinnularia soehrensis, Diadesmis laevissima, D. paracontenta, Eunotia exigua agg., E. incisa, E. ursamaioris, Fragilariforma virescens, Microcostatus krasskei, Pinnularia silvatica agg., and P. subcapitata agg. In addition, green algae of the Chlococcaceae agg., and Klebsormidium flaccidum were also abundant. Composition of algal assemblages were primarily influenced by the type of substratum (20.5% variance explained, p = 0.001) and by the spatial distance from the stream origin (3.0%, p < 0.05). Variation in species composition explained by conductivity, pH, temperature and nitrogen was relatively low (10.1%).

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Introduction The Bohemian Switzerland National Park was established to protect the sandstone landscape formed by the erosion of Cretaceous sediments. Most of the streams in the area are seasonally dry due to the high permeability of the sandstone bedrock. These ephemeral streams are unique biotopes on the boundary of aquatic and aerial habitats (Hoffmann 1989). In comparison with aquatic biotopes, there are relatively few studies on algae in aerial habitats (e.g. Ettl & Gärtner 1995; Johansen 1999; Casamatta et al. 2001; Van de Vijver et al. 2002) or in ephemeral streams (McCormick 1990; McKnight et al. 1999; Round 2001; Veselá 2006).

Patchiness of algal species distribution, richness, and biodiversity in streams have been observed along altitudinal and longitudinal gradients (Douglas 1958; Vavilova & Lewis 1999), over seasons (Dillard 1969; Müller-Haeckel & Håkansson 1978; Cantonati 1998), between habitats (Van de Vijver et al. 2001) and substratum types (Stevenson & Hashim 1989; Pringle 1990; Cantonati et al. 2006). Small scale spatial distribution of periphytic species and communities were studied along cross section transects and longitudinal stream transects (Rolland et al. 1997; Passy 2001; Poulíčková et al. 2004; Soininen 2004; Heino & Soininen 2006). The spatial patterns were influenced by biotic factors (life-cycle, morphology, species interactions), abiotic environment (physico-chemical parameters), microsite differences (heterogeneity), historical events (succession, colonization, disturbance) and geographical and stochastic factors (Allan 1995; Stevenson et al. 1996; Dale 1999; Willig et al. 2003; Cardinale et al. 2006).

In this study, diversity, spatial patterns and ecology of the algal assemblages, sampled longitudinally along an ephemeral sandstone stream transect, were investigated. The objectives of this study were to determine species richness and variation in algal species composition and to assess how these are influenced by environmental variables such as distance from the stream origin, substratum type and water chemistry.

Materials and Methods

The pristine forest stream Suchá Bělá, located on sandstone bedrock in the north-west part of the Bohemian Switzerland National Park (Fig. 1), is primarily rain-fed, and therefore, falls episodically dry throughout the year. The stream runs continuously after heavy rain or snowfall, mainly during early spring. The stream depth varies from 5 to 15 cm, and the stream width from 20 to 50 cm. In April 2006, samples were collected between 450 m (50°53.65N, 14°16.15E) and 270 m a.m.s.l. (50°53.34N, 14°16.04E) at 11 sampling sites located along a 500 m long longitudinal transect. Five to six samples were taken from all available substrates every 50 meters beginning at the origin. Algae were collected by scraping cobbles and tree sticks, by squeezing mosses or liverworts and by removing the soft sediment surface with a sample container that was moved along the sediment. A total of 66 samples has been collected. Conductivity, pH and temperature were measured using a Hanna Combo HI 98129 pH/EC/TDS tester/thermometer. Nitrate concentrations were determined using a 3205 JENWAY Ion meter. Fresh material was examined within 48 hours after sampling. Permanent diatom slides were prepared following methods by Houk (2003) using Naphrax® as a mountant. The algal taxa were identified at 1000x magnification using a light microscope Olympus BX 51 with DIC. A scanning electron microscope SEM JEOL 6380 was used to identify problematic diatom taxa and sample preparation for SEM followed Nebesářová (2002). Algal taxa were identified according to Alles et al. (1991), Ettl (1983), Ettl & Gärtner

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(1988, 1995), Hindák (1996), Houk & Klee (2007), Krammer (1992, 1997a,b, 2000, 2002, 2003), Krammer & Lange-Bertalot (1985, 1986, 1988, 1991a,b), Lange-Bertalot (1993, 2001), Lokhorst (1996, 1999), Růžička (1977, 1981), Starmach (1985), Werum & Lange-Bertalot (2004) and Wołowski (1998). Some taxa that were morphologically similar and difficult to separate were combined in aggregates (Tab. 1).

Frequencies of individual taxa were estimated using a semi-quantitative scale (Borcard et al. 1992; Kinross et al. 1993; Marhold & Suda 2002): rare (one or two cells per sample), uncommon (less than 5% of the cells), common (5-25%), frequent (25-50%) and dominant (more than 50%). In addition, the presence/absence of species from individual sites, with species from individual substrates pooled, was used in statistical analyses to evaluate the similarity between sites, as well as in permutation tests assessing the effects of environmental factors on species composition. Non-metric Multidimensional Scaling (NMDS, PRIMER software version 6, Clarke & Gorley 2006) was used to describe major patterns in community structure. Resemblance between localities and between substrates was assessed by Bray-Curtis similarity index (Bray & Curtis 1957). The similarity between sites using identical substrates was evaluated using the Bray-Curtis index and nonparametric analysis of variance was performed using the Kruskal-Wallis test to examine differences in mean diatom species richness and similarity of assemblages between different substrates. Algae preferences of certain substratum types and of downstream or upstream locations were calculated from their correlation with i) substrates and with ii) the first three upstream and the last three downstream sites (PAST software version 1.74, Hammer et al. 2001). Percentage of specific species for soft or hard substrates was assessed from the occurrence of common taxa. Mantel tests and partial Mantel tests (Mantel 1967; Smouse et al. 1986; Legendre & Legendre 1998) were used to examine whether community dissimilarity was increasing with distance while controlling for the effect of environmental factors. The species matrix was calculated from pairwise comparisons of species composition (Bray-Curtis and Sørensen index) and environmental distances from difference in parameter values between two samples. In addition, parameter distances were calculated as Euclidian distances between sites based on four environmental variables standardized to standard deviation (Soininen 2008). The significance of correlation between distance and similarity matrices was tested with simulation of 1000 randomizations. Mantel tests were performed using the software ZT version 1.0 (Bonnet & Van de Peer 2002).

The effects of conductivity, pH, temperature, nitrate, distance from the origin of the stream and substratum type on species composition were evaluated using linear ordination techniques including redundancy analysis (RDA) and partial RDA (Lepš & Šmilauer 2000). Percentage of variation in species data explained by environmental and/or spatial factors was partitioned according to Borcard et al. (1992). Nitrate concentrations was excluded from the RDA analysis because of the high correlation of nitrate content with the conductivity (r = 0.78) and with temperature (r = 0.64). Relevant environmental variables were chosen in a stepwise procedure, and the effects of the remaining parameters were removed by treating them as covariables. Split-plot permutation tests were used to assess the statistical significance of the impact of each environmental factor on species composition. Data were permuted between sites (parameters, distance - tests) or between samples (substrate - test) within each site (Lepš & Šmilauer 1999). Data were freely exchangeable within sites and permutations between sites

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were restricted to line transect model. Ordination methods and permutation tests were performed in CANOCO for Windows version 4.5 (ter Braak & Šmilauer 1998, 2002).

Results Conductivity varied between 46 μS·cm-1 and 61 μS·cm-1, pH values between 3.95 and 5.30, nitrate concentrations between 0.62 mg·l-1 and 2.42 mg·l-1 and temperature between 8.5 °C and 14.9 °C (Tab. 1). The conductivity and pH values of sites downstream of the third sampling site, where the peat bog stream flowed into the Suchá Bělá stream, change noticeably. Conductivity increased whereas pH decreased over one unit.

A total of 109 infrageneric algal taxa were identified in 66 samples including: 85 diatoms, 19 chlorophytes, 2 chrysophytes, 2 euglenophytes and 1 cryptophyte (Tab 2). The total number of taxa per sample ranged from 1 to 51 with a median value of 23. Species richness per sampling site varied from 42 (stream origin) to 68 (the last downstream site; Tab. 1). The most abundant taxa were the diatoms Chamaepinnularia soehrensis, Diadesmis laevissima, D. paracontenta, Eunotia exigua agg., E. incisa, E. ursamaioris, Fragilariforma virescens, Microcostatus krasskei, Pinnularia silvatica agg., P. subcapitata agg.; the green algae Chlococcaceae agg. and Klebsormidium flaccidum, and Chrysophycean cysts. The most species rich genera were Eunotia (15) and Pinnularia (15).

NMDS analysis (2D stress = 0.11) showed that assemblages at the first three sampling sites were similar to each other but different from assemblages at sampling sites further downstream (Fig. 2). The separation of site 4 is due to the nearby peat-bog inflow and sites 5 and 6 clustered together reflecting the dominance of the genus Chrysosphaera. Mantel tests revealed statistically significant spatial autocorrelation between the species data matrix and matrices of substratum types and geographical distances (Tab. 3). The strongest relationship was found between algal composition (Bray-Curtis index) and the substratum type, with spatial distance as a covariate (r = 0.24, p < 0.001). This indicates that after removing the effect of distance on the species data there was still a statistically significant correlation between species distribution and substratum type.

In the redundancy analyses, the spatial component and substratum type explained 23.5% and conductivity, pH, temperature and nitrate explained an additional 10.1% of the total variance. The proportion of variation explained by the combined effect of spatial and environmental component accounted for 6.4% of the explained variation in community composition. Effects of individual abiotic factors on species composition were not statistically significant, probably as a result of the strong correlation of the abiotic factors (Tab. 3). In the partial RDA, distance from the stream origin with other factors controlled, significantly affected the species composition (p < 0.05) and explained 3.0% of variance in species data. Diatoms significantly correlated with downstream sites were e.g., Chamaepinnularia soehrensis, Diadesmis contenta, D. gallica, D. laevissima, Microcostatus krasskei and Pinnularia subcapitata agg. Brachysira brebisonii, Eunotia incisa and Tabellaria flocculosa were associated with upstream sites (Tab. 2). Substratum type explained most of the variation in assemblage composition (20.5%, p = 0.001, Fig. 3), indicating that individual substrates differed in assemblage composition along the longitudinal transect. Distinct differences between assemblages on soft (bryophyte, sediment) and hard substrates (stone, wood) along the stream transect were shown by NMDS biplot (2D stress = 0.20, 3D stress = 0.12; Fig. 4). Assemblages on bryophytes and sediment were also more species rich than those on wood

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and stones (Fig. 5a). A further distinction between hard and soft substrates is demonstrated by the specificity of algae within substrates. Within-substrate similarity indices were higher for soft substrates (Fig. 5b) and 45.5% of abundant species were specific for soft substrates whereas on hard substrates 20.0% specific species were found. Characteristic species on bryophytes included diatom species such as Brachysira brebissonii, Chamaepinnularia tongatensis, Eunotia bigibba, E. muscicola var. tridentula, E. paludosa, E. rhomboidea, E. valida, Fragilariforma virescens, Frustulia crassinervia and Microcostatus krasskei (Tab. 2). Sediment samples contained more Cryptomonas sp., Diatoma mesodon, Eunotia trinacria, Gomphonema parvulum, Neidium alpinum, Pinnularia silvatica agg., P. rupestris, P. schoenfelderi and P. subcapitata agg. Green algae (Stichococcus bacillaris) were more abundant on stones.

Discussion

The algae assemblages of the ephemeral sandstone stream predominantly contained taxa that were found elsewhere in subaerial biotopes (e.g. Schorler 1915; Bock 1963, 1970; Neustupa 2001; Casamatta et al. 2002; Nováková & Poulíčková 2004) or in acidic freshwater ecosystems (DeNicola 2000; Gaiser & Johansen 2000; Nováková 2002; Werum & Lange-Bertalot 2004; Poulíčková et al. 2005). The autecological preferences of these species reflected episodic drying of the stream, low pH and low conductivity. Distribution of taxa changed along the longitudinal transect and this is most likely related to certain spatial environmental factors (Borcard et al. 1992; Passy 2001; Parris 2004; Soininen 2007), e.g. the inflow of a peatbog stream, subsequent change in water chemistry, variation of the desiccation and shading between sites. The explained variance in assemblage composition due to conductivity, pH or temperature was relatively low and probably reflected the relatively short gradients of environmental factors along the stream transect (Allan 1995). Desiccation of the stream probably represents the principal factor for the biota. Aerophytic genera (Chamaepinnularia, Diadesmis and Microcostatus) preferred downstream sites that were exposed to longer periods of desiccation. Other studies also reported a significant effect of moisture and frequency of drying on species composition (Camburn 1982; Gaiser & Johansen 2000; Casamatta et al. 2002; Poulíčková et al. 2004). The influence of shading and desiccation on assemblages is evident from the greater diversity and frequency of Diadesmis species that mainly dominates the species composition in caves where light is a limiting factor (Albertano et al. 1995; Garbacki et al. 1999). Although Diadesmis gallica and D. laevissima were found to be especially confined to limestone caves (Johansen 1999; Poulíčková & Hašler 2007), in the present study these species were abundant at strongly shaded downstream sites with pH lower than 4.5.

Substratum type significantly affected algal assemblages and explained the highest proportion of variation in assemblage composition. Although preferences of species for particular substrates are still poorly understood, several studies found differences among substrates in algal composition, species richness, biovolume and diversity (e.g., Maier 1994; Allan 1995; Antoniades & Douglas 2002), growth form (Allan 1995; Lowe & LaLiberte 1996), nutrient uptake (Pringle 1990), and metabolism (Sabater et al. 1998). However, in other studies the substratum differences with respect to assemblage structure were less pronounced (O’Quinn & Sullivan 1983; Jüttner et al. 1996; Winter & Duthie 2000; Kitner & Poulíčková 2003), possibly because the effect of other environmental factors were more important (Potapova &

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Charles 2005). In this study, a higher variability in composition of assemblages was found on stone and wood. Bryophytes and sediment samples were more species rich, which confirms the findings from previous studies on bryophyte assemblages (Cantonati 1998, 2001) and sediment assemblages (Vilbaste 2001). Differences in algal assemblages from hard versus soft substrates have also been reported in other publications (Stevenson & Hashim 1989; Allan 1995; Stevenson et al. 1996; Ledger & Hildrew 1998; Stevenson & Bahls 1999; Denys 2004; Potapova & Charles 2005; Townsend & Gell 2005). Similarity within hard and soft substrates might be influenced by the characteristic growth form of algae (Allan 1995; Méléder et al. 2007). Loosely attached Eunotia species and Cylindrocystis brebissonii were significantly more abundant on bryophytes whereas sediment samples contained more motile algae (Cryptomonas sp., Pinnularia spp.). Lower species number and negative correlation of diatoms with hard substrates may have resulted from the dominance of coccoid or filamentous green algae on stones (Ledger & Hildrew 1998; Soininen 2003; Potapova & Charles 2005; Greenwood & Lowe 2006) and greater abundance of bacteria and fungi on wood substrates (Sabater et al. 1998). It is probable that high species richness among sediment samples is influenced by the accumulation of diatoms from multiple habitats and by the larger portion of dead cells among epipelic diatoms than in other habitats (Vilbaste 2001; Soininen & Eloranta 2004). Greater complexity of bryophytes (Downes et al. 1998; Palmer et al. 2000; Taniguchi & Tokeshi 2004) and their ability to absorb and retain water (Poulíčková et al. 2005) possibly favour higher species numbers. The findings of Soininen & Heino (2007) suggested a relationship between niche breadth and species richness in diatoms, which corresponds well with their prediction that more diverse diatom assemblages tend to be dominated by species that are primarily specialists with few generalists and species-poor sites are primarily occupied by generalists. This prediction is congruent with more specific and species rich assemblages associated with soft substrates and variable, species poor assemblages on hard substrates. In summary, the nature of microhabitat is an essential factor that influences diversity and assemblage composition particularly in the absence of strong chemical gradients.

Acknowledgements I am grateful to Jeffrey R. Johansen and to Jiří Neustupa for valuable critique of the manuscript, and to Jan Šťastný for assisting in sampling of the stream transect. I would like to thank two anonymous reviewers for their helpful comments on this paper. Support for this study was provided by the Czech Science Foundation project no. 206/07/0115, by the Charles University Science Foundation project B-Bio/141/2006 and by the research project of the Czech Ministry of Education no. 0021620828.

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Received 22 December 2007, accepted in revised form 24 November 2008

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Tab. 1. Environmental characteristics and species richness of the sites on the Suchá Bělá (1 - upstream, 11 – downstream). Sampling

site Conductivity

(μS·cm-1) pH Temperature (°C)

NO3-

(mg·l-1) Species richness

1 46 5.24 12.5 0.93 42 2 48 5.30 12.0 1.03 54 3 48 5.18 12.5 0.62 58 4 61 4.12 10.3 1.63 62 5 54 4.11 14.6 1.01 48 6 58 3.95 9.9 1.61 46 7 54 4.16 14.9 0.97 60 8 58 4.19 10.3 1.84 62 9 53 4.36 14.6 1.11 55

10 60 4.16 8.5 2.42 61 11 57 4.43 12.9 2.23 68

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Tab. 2. Algae taxa in Suchá Bělá with frequencies of occurrence and with significant correlations (p < 0.05*, p < 0.01**, p < 0.001***) with specific substrate (s - stone, w - wood, b - bryophyte, t - sediment) and downstream sites (ds - downstream) Taxon name occurrence substrate and

downstream preferences

Euglenophyta Euglena mutabilis F. Schmitz rare Trachelomonas sp. uncommon Cryptophyta Cryptomonas sp. rare 0.44t** Chrysophyta Chrysosphaera sp. common Chrysophyceae cysts uncommon Bacillariophyta Achnanthes spp. rare Achnanthes lanceolata (Brébisson) Grunow rare A. minutissima Kützing rare Brachysira brebissonii R. Ross uncommon -0.38w* 0.52b*** -0.46ds* Brachysira sp. rare Caloneis aerophila W. Bock uncommon C. vasileyevae Lange-Bertalot, Genkal et Vekhov rare Cavinula cocconeiformis (W. Gregory) D.G. Mann et Stickle rare Chamaepinnularia cf. begeri (Krasske) Lange-Bertalot rare C. soehrensis (Krasske) Lange-Bertalot et Krammer common -0.47w** 0.33b* 0.69ds*** C. tongatensis (Hustedt) Lange-Bertalot uncommon -0.43w** 0.46b** 0.47ds* Chamaepinnularia sp. rare Cymbopleura cf. naviculiformis Auerswald rare Diadesmis brekkaensis (Krasske) D.G. Mann rare D. contenta (Grunow ex Van Heurck) D.G. Mann uncommon 0.65ds*** D. contenta var. biceps (Grunow ex Van Heurck) P.B. Hamilton uncommon D. contenta var. parallela (J.B. Petersen) P.B. Hamilton rare 0.46s** D. gallica W. Smith uncommon 0.65ds*** D. laevissima (Cleve) D.G. Mann uncommon -0.39w** 0.77ds*** D. paracontenta Lange-Bertalot et Werum frequent -0.50w*** 0.51ds* D. perpusilla (Grunow) D.G. Mann rare -0.33w* Diatoma mesodon Kützing rare 0.41t** Diploneis fontanella Lange-Bertalot rare Encyonema cf. minutum (Hilse in Rabenhorst) D.G. Mann rare Eunotia bigibba Kützing uncommon -0.43w** 0.43b** E. bilunaris (Ehrenberg) Mills uncommon -0.51w*** 0.34b* E. botuliformis F. Wild, Nörpel et Lange-Bertalot rare -0.34w* E. exigua (Brébisson) Rabenhorst and E. tenella (Grunow) Hustedt frequent E. fallax Cleve rare E. incisa W. Gregory common -0.37w* -0.77ds*** E. minor (Kützing) Grunow uncommon -0.41w** 0.34b* E. muscicola var. tridentula Nörpel et Lange-Bertalot rare -0.30s,w* 0.46b** E. paludosa Grunow uncommon -0.48w*** 0.42b** E. praerupta Ehrenberg rare 0.36b* E. rhomboidea Hustedt uncommon -0.36w* 0.44b** E. trinacria Krasske uncommon -0.50w*** 0.33t* 0.51ds* E. ursamaioris Lange-Bertalot et Nörpel-Schempp common -0.51w*** 0.37b* 0.54ds** E. valida Hustedt uncommon -0.30s,w* 0.44b** Fallacia vitrea (Østrup) D.G. Mann rare Fragilaria capucina Desmazières rare 0.36t* 0.45ds* Fragilariforma virescens (Ralfs) D.M. Williams et Round frequent -0.44w** 0.41b** Frustulia crassinervia (Brébisson) Lange-Bertalot et Krammer uncommon -0.37s,w* 0.46b**

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F. saxonica Rabenhorst uncommon F. vulgaris (Thwaites) De Toni rare Gomphonema clavatum Ehrenberg rare G. gracile Ehrenberg rare G. parvulum (Kützing) Kützing uncommon -0.37w* 0.37t* Luticola mutica (Kützing) D.G. Mann rare Meridion circulare var. constrictum (Ralfs) Van Heurck rare Microcostatus krasskei (Hustedt) J.R. Johansen et Sray common -0.43w** 0.43b** 0.78ds*** Mayamaea recondita (Hustedt) Lange-Bertalot uncommon -0.33w* 0.33t* Navicula gregaria Donkin rare N. radiosa Kützing rare Neidium affine (Ehrenberg) Pfitzer rare N. alpinum Hustedt uncommon -0.31w* 0.55t*** N. carteri Krammer rare N. hercynicum Ant. Mayer rare Nitzschia sp. rare Nupela lapidosa (Krasske) Lange-Bertalot rare Orthoseira roseana (Rabenhorst) O'Meara rare Pinnularia borealis Ehrenberg rare 0.41t** P. frequentis Krammer rare P. cf. julma Krammer et Metzeltin rare P. microstauron (Ehrenberg) Cleve rare P. obscura Krasske uncommon -0.34s,w* 0.34b,t* P. pseudogibba Krammer rare P. rupestris Hantzsch uncommon -0.40w** 0.48t*** P. schoenfelderi Krammer common -0.54w*** 0.51t*** P. silvatica J.B. Petersen and P. perirrorata Krammer frequent -0.47w** 0.34t* P. subcapitata W. Gregory and P. sinistra Krammer frequent -0.52w*** 0.31t* 0.65ds*** P. subinterrupta Krammer et S. Schroeter rare P. submicrostauron S. Schroeter rare P. viridiformis Krammer rare 0.38t* Placoneis hambergii (Hustedt) Bruder rare Planothidium marginulatum (Grunow) Bukhtiyarova et Round rare Psammothidium helveticum (Hustedt) Bukhtiyarova et Round uncommon -0.46w** 0.32b,t* Stauroneis thermicola (J.B. Petersen) J.W.G. Lund rare Surirella roba Leclercq rare Tabellaria flocculosa (Roth) Kützing uncommon -0.53ds** Chlorophyta / Charophyta Actinotaenium sp. rare Chlamydomonas sp. rare Chlorococcaceae and Radiococcaceae frequent -0.33t* Cylindrocystis brebissonii (Meneghini ex Ralfs) de Bary uncommon 0.47b** Mesotaenium sp. uncommon Microspora tumidula Hazen rare Microthamnion kützingianum Nägeli rare Mougeoutia sp. rare 0.37b* Keratococcus sp. and Raphidonemopsis sp. uncommon Klebsormidium crenulatum (Kützing) Lokhorst rare K. flaccidum (Kützing) P.C. Silva, Mattox et W.H. Blackwell common K. mucosum (J.B. Petersen) Lokhorst rare K. cf. scopulinum (Hazen) H. Ettl et G. Gärtner rare Monoraphidium terrestre (Bristol) Krienitz et G. Klein common Roya anglica G.S. West rare Scotiellopsis terrestris (Reisigl) Punčochářová et Kalina uncommon Stichococcus bacillaris Nägeli common 0.46s** -0.31b* -0.35t* Ulothrix sp. rare

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Tab. 3. Results of Mantel test evaluating the autocorrelation between different distance matrices (p < 0.05*, p < 0.01**, p < 0.001***). Covariate - separation of the effect of selected factor on species composition.

Autocorrelation matrices Covariate Correlation Autocorrelation matrices Covariate Correlation

Bray-Curtis vs. substrate - 0.23*** Sørensen vs. parameters - 0.04 Bray-Curtis vs. substrate distance 0.24*** Sørensen vs. parameters distance 0.14 Bray-Curtis vs. distance - - 0.15** Sørensen vs. pH - - 0.30 Bray-Curtis vs. distance substrate - 0.18** Sørensen vs. pH distance - 0.24 Sørensen vs. distance - - 0.22 Sørensen vs. conductivity - 0.22 Sørensen vs. distance parameters - 0.26 Sørensen vs. conductivity distance 0.14 Sørensen vs. distance pH - 0.12 distance vs. pH - 0.40* Sørensen vs. distance conductivity - 0.15 distance vs. conductivity - - 0.42** Sørensen vs. distance temperature - 0.23 pH vs. conductivity distance - 0.79***

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Fig. 1. Map of the Czech Republic and Bohemian Switzerland National Park showing the location of the stream Suchá Bělá.

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Fig. 2. Non-metric multidimensional scaling analysis (NMDS) of algae assemblages from 11 sites, Suchá Bělá stream.

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Fig. 3. Partial RDA analysis constrained by the habitat matrix and ordination of species. Brabre - Brachysira brebissonii, Chasoe - Chamaepinnularia soehrensis, Chaton - C. tongatensis, ChlRad - Chlorococcaceae agg., Diames - Diatoma mesodon, Diapar - Diadesmis paracontenta, Eumutr - Eunotia muscicola var. tridentula, Eunbig - E. bigibba, Eunbil - E. bilunaris, Eunmin - E. minor, Eunpal - E. paludosa, Eunrho - E. rhomboidea, Euntri - E. trinacria, Eunurs - E. ursamaioris, Eunval - E. valida, Fravir - Fragilariforma virescens, Frucra - Frustulia crassinervia, Gompar - Gomphonema parvulum, Mickra - Microcostatus krasskei, Neialp - Neidium alpinum, Pinbor - Pinnularia borealis, Pinobs - P. obscura, Pinrup - P. rupestris, Pinsch - P. schoenfelderi, Pisuca - P. subcapitata agg., Psahel – Psammothidium helveticum, Stibac - Stichococcus bacillaris

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Fig. 4. Non-metric multidimensional scaling (NMDS) of samples from different habitats (s - stone, w - wood, b - bryophyte, t - sediment).

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Fig. 5. Box plot diagrams. Variation in: a) diatom species richness, and b) Bray-Curtis similarity in four microhabitats along the stream transect. Both pair-wise between habitat comparisons (Kruskal-Wallis test) were statistically significant at p < 0.001.