Headwater biodiversity among different levels of stream ... - Temnerud

Biodivers Conserv (2014) 23:63–80 DOI 10.1007/s10531-013-0584-3 ORIGINAL PAPER

Headwater biodiversity among different levels of stream habitat hierarchy Emma Go¨the • Nikolai Friberg • Maria Kahlert • Johan Temnerud Leonard Sandin

•

Received: 12 June 2013 / Accepted: 5 November 2013 / Published online: 26 November 2013 Ó Springer Science+Business Media Dordrecht 2013

Abstract With the current loss of biodiversity and threats to freshwater ecosystems, it is crucial to identify hot-spots of biodiversity and on which spatial scale they can be resolved. Conservation and management of these important ecosystems needs insight into whether most of the regional biodiversity (i.e. c-diversity) can be found locally (i.e. high a-diversity) or whether it is distributed across the region (i.e. high b-diversity). Biodiversity patterns of benthic macroinvertebrates and diatoms were studied in 30 headwater streams in five Swedish catchments by comparing the relative contribution of a- and b-diversity to cdiversity between two levels of stream habitat hierarchy (catchment and region level). The relationship between species community structure and local environmental factors was also assessed. Our results show that both a- and b-diversity made a significant contribution to cdiversity. b-diversity remained relatively constant between the two levels of habitat hierarchy even though local environmental control of the biota decreased from the catchment to the region level. To capture most of headwater c-diversity, management should therefore target sites that are locally diverse, but at the same time select sites so that b-diversity is maximized. As environmental control of the biota peaked at the catchment level, the conservation of headwater stream diversity is likely to be most effective when management targets environmental conditions across multiple local sites within relatively small catchments. Keywords a-diversity b-diversity Headwater streams Diatoms Macroinvertebrates Conservation management

E. Go¨the (&) N. Friberg L. Sandin Department of Bioscience, Aarhus University, Vejlsøvej 25, P.O. Box 314, 8600 Silkeborg, Denmark e-mail: [email protected] M. Kahlert J. Temnerud Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, P.O. Box 7050, 750 07 Uppsala, Sweden J. Temnerud Hydrological Unit, Research Department, Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsva¨gen 1, 601 76 Norrko¨ping, Sweden

123

64

Biodivers Conserv (2014) 23:63–80

Introduction Biodiversity is decreasing at a faster rate than ever before on a global scale (Heywood 1995; Jenkins 2003) and freshwaters are among the most threatened ecosystems (Ricciardi and Rasmussen 1999; Revenga et al. 2005; Strayer and Dudgeon 2010). Freshwater habitats cover less than 1 % of the Earth’s surface area, but contain about 10 % of all known species (Strayer and Dudgeon 2010). There has been substantial global loss in freshwater biodiversity and it is estimated that between 10,000 and 20,000 species are either extinct or seriously threatened, which is a much higher value than for any other ecosystem (Sala et al. 2000; IUCN 2007). Most rivers of the world are affected by human activities (Vo¨ro¨smarty et al. 2010), with severe consequences for stream organisms because of their vulnerability to, for example, habitat degradation, water pollution, and flow modifications (Abromovitz 1996; Malmqvist and Rundle 2002; Dudgeon et al. 2006). Climate change is also predicted to severely affect streams and rivers, especially in combination with other environmental stressors such as land use changes (Meyer et al. 1999; Sala et al. 2000; Moss et al. 2009). With such strong human induced stress on natural ecosystems, it is vital to find approaches for strategic conservation planning, including locating and designing reserves that ‘‘should sample or represent the biodiversity of each region’’ as has been suggested for the terrestrial realm (Margules and Pressey 2000). River conservation planning and assessment using a similar approach has developed rapidly within the last 20 years (Collier 2011). However, freshwater ecosystems are understudied in conservation research, which is worrying because empirical evidence is needed to convince policy makers (Abell 2002). Different components of biodiversity describe different aspects of diversity, and each of these components has an inherent conservation value. Gamma (c) diversity is the total diversity of a region (Whittaker 1972), alpha (a) diversity is the diversity within a locality and beta (b) diversity is the diversity between localities (species turnover along a gradient and/or variation in species composition) (Anderson et al. 2011). An assessment of the relative contribution of a- and b-diversity to c-diversity is useful for determining the location and size of reserves that ‘‘represents the biodiversity of a given region’’. That is, if most of the regional biodiversity (i.e. c-diversity) is found locally (i.e. high a-diversity) management could target a few diverse local sites and still conserve most of the regional diversity. On the other hand, if the regional diversity is more evenly distributed across the region (i.e. high b-diversity), management would instead have to target multiple local sites with significantly different species communities. In addition, information is needed on how biodiversity patterns are formed and maintained in these systems (i.e. an assessment of what factors control species composition at different spatial scales). Together, this knowledge determines how conservation strategies and resources should be distributed in the most efficient way (Jost et al. 2010; Bengtsson 2010) and can also help to develop and improve environmental assessment tools (Heino 2013). In river landscapes, the largest proportion of stream reaches are headwater streams. It is estimated that first and second order streams represent[70 % of the total channel length in stream networks (Leopold et al. 1964) and recent estimates from Sweden shows that up to 90 % of the channel length consists of streams with catchments \15 km2 (Bishop et al. 2008). However, despite their wide distribution and vulnerability to human perturbation, as well as being vitally important for the total biodiversity in the river network (Lowe and Likens 2005; Meyer et al. 2007; Finn et al. 2011), headwater streams are often neglected in management policies (Meyer and Wallace 2001; Bishop et al. 2008). We can make predictions about how diversity is distributed in and across headwater streams based on recent studies investigating local and regional drivers of species community

123


65

structure in these ecosystems. Earlier studies show that species distributions in headwater streams are largely the result of local environmental control, whereas the effect of spatial factors is less pronounced (Heino and Mykra¨ 2008; Brown and Swan 2010; Heino et al. 2012). The relatively low effect of spatial factors may be a consequence of increasing spatial isolation with decreasing stream-order as distances between upstream sites are deemed to be (on average) longer than between sites further downstream (Clarke et al. 2008). The strong environmental gradients, coupled with moderate levels of dispersal (allowing species to be sorted along the environmental gradients) in upstream areas, are likely to lead to high species turnover (high b-diversity) (Cottenie and De Meester 2004; Leibold and Norberg 2004; Finn et al. 2011). However, because dispersal is not expected to be high enough to, for example, sustain populations with negative growth rates (Pulliam 1988; Loreau and Mouquet 1999), this should lead to relatively low local (a) diversity because new colonizers may be quickly filtered out by prevailing environmental conditions. However, few studies have attempted to distinguish different components of diversity (a- and b-diversity) and their contribution to the total (c-diversity) in headwater ecosystems (but see Clarke et al. 2010). In this study, we partitioned invertebrate and diatom c-diversity into independent a- and b-components (using the multiplicative method of Jost 2007) and assessed the relationship between local environmental factors and community composition. We used data from 30 headwater streams in five Swedish river catchments and performed analyses over three levels of stream habitat hierarchy (site, catchment and region) (see Schmera and Podani 2013), primarily focusing on comparing diversity patterns between the catchment and region level. We tested three hypotheses too elucidate whether there is a general and consistent pattern in headwater diversity irrespective of the organism group under study: (1)

(2) (3)

Headwater stream ecosystems are characterized by high b-diversity, but low adiversity due to their isolated position in the stream network and due to strong environmental filters operating at both the catchment and region level (e.g. Clarke et al. 2008; see previous paragraph) b-diversity increases from catchment to region level due to an increase in the dispersal limitation of the biota (Clarke et al. 2010; Declerck et al. 2011) Local environmental control of species turnover (b-diversity) decreases from catchment to region level as regional factors (e.g. dispersal limitation, climate) are expected to increase in importance at larger scales of observation (Poff 1997; Mykra¨ et al. 2007).

Methods Study sites and sampling design ¨ sterdala¨lven [OD], Va¨sterdala¨lven Five catchments (Lugna˚n [LU], Danshyttea˚n [DA], O [VD], and the lower branch of River Dala¨lven [LB]), situated in the boreal region of southcentral Sweden, were selected for this study. In each catchment, six headwater (first– second order) stream reaches (sites) were sampled (i.e. a total of 30 sites) (Fig. 1). The size of the catchments range from 80 km2 (DA) to 12,300 km2 (OD) and the distance between the southernmost (LU) and northernmost (OD) catchments is approximately 500 km. The study area covers three ecoregions: the central plains (14), the fennoscandian shield (22), and the borealic uplands (20) (Illies 1966), which are the three main ecoregions in Sweden (Sandin and Johnson 2000). The bedrock geology of Sweden is dominated by three main

123

66


Fig. 1 Map showing the location of the five catchments in Sweden and the location of the six sampling sites within each catchment

components: precambrian crystalline rocks (e.g. the fennoscandian shield), younger sedimentary rocks (parts of the central plains and fennoscandian shield), and the Caledonides (borealic uplands). There is, however, a gradual rather than abrupt change in community composition and taxon richness of invertebrates across the country with gradients in e.g. climatic conditions (from the warmer, wetter south with open land and mixed forests to the colder drier north with boreal forests and alpine areas) (Sandin 2003). The size of the upstream catchment area for each sampled site (sub-catchment) varies between 0.18 and 6.17 km2, with an altitude ranging from 146 to 540 masl (Table 1). The sub-catchments have similar land use; consisting mainly of boreal forest ([70 %). Mires and small humic lakes comprise most of the remaining parts of the catchments, and the proportion of agricultural areas is small (\10 %) (Table 1). The sub-catchments are only influenced by low-intensity forestry with no other human impacts present such as point source pollution. Benthic macroinvertebrate and diatom sampling Benthic macroinvertebrates were sampled once in autumn of 2007 (LU and DA), or 2009 (VD, OD, and LB). A 10–50 m long stretch, homogeneous regarding flow and bottom substratum (gravel, cobble, or small boulders) was sampled in each stream with either five (25 9 25 cm sampled area; LU and DA), or 15 (14 9 14 cm sampled area; VD, OD, and LB) Surber samples (mesh size: 500 lm), covering an equally large total area sampled per site of *0.3 m2. The samples were preserved in 70 % ethanol and brought back to the laboratory for sorting and identification. The macroinvertebrates were identified to the lowest possible taxonomical level, in most cases to species or genus, but some groups were identified to a higher taxonomical level, e.g. Simuliidae (identified to family), Chironomidae (identified to subfamily) and Coleoptera (identified to family).

123


67

Table 1 Range (minimum–maximum values) of local physical, catchment (land-use) and water chemistry descriptors of the six sampling sites in each of the five catchments Va¨sterdala¨lven (VD)

¨ sterdala¨lven O (OD)

Lower Branch (LB)

Lugna˚n (LU)

Danshyttea˚n (DA)

Area (km2)

1.36–3.21

1.42–2.51

1.62–3.37

0.31–6.17

0.18–7.87

Forest (%)

86–99

92–99

91–99

70–88

89–99

Mire (%)

1.3–8.0

0–8.3

0–6.5

0–4.7

0–1.0

0–0.7

2.8–9.4

0–0.6

Agriculture (%)

0

0–10.9

Altitude (masl)

186–540

222–453

146–375

201–235

208–311

Width (m)

0.4–1.4

0.6–1.5

0.7–1.3

0.5–3.0

0.2–2.0

Depth (cm)

10–24

10–24

9–21

10–50

20–70

pH

4.31–6.85

5.11–7.26

4.44–6.35

5.12–6.87

4.22–6.50

ANC (meq l-1)

0.17–0.44

0.16–0.39

0.08–0.40

0.12–0.28

0–0.16

Total-N (lg l-1)

436–889

374–979

433–723

180–570

62–1,139

Total-P (lg l-1)

12–18

2–28

7–18

8–27

0–17

TOC (mg l-1)

20–48

3–58

19–36

10–34

8–51

0.44–0.85

0.03–1.14

0.25–0.75

0.03–0.17

0.02–0.20

Water colour (absorbance)

Benthic diatom samples were taken at the same time as benthic macroinvertebrates. Diatoms were collected according to the European/Swedish standard method (SS–EN 13946; CEN 2003) by scraping biofilm from the upper surface of at least five cobbles using a toothbrush and pooled into one composite sample. The samples were stored in plastic containers (250 ml) and preserved in ethanol (70 %). The samples were then treated with H2O2 and embedded in NaphraxÒ. Approximately four hundred diatom valves from each sample were identified to species level (with very few exceptions) according to the European/Swedish standard method (SS–EN 14407; CEN 2004). Counting *400 valves captures approximately 85 % of all taxa present in the sample if the sample contains a total of 50 taxa (Pappas and Stoermer 1996), which is close to the asymptote (i.e. the probability of finding new taxa when counting more valves is low). Abiotic parameters Stream width and stream depth was measured either along a single transect (LU and DA) or at ten evenly spaced transects along the same stretch as the biotic sampling was performed (VD, OD, and LB). Stream depth was measured at three points at each transect (at each channel edge and at the middle of the channel). Water-chemistry samples were collected once at each sampling site and were analyzed for pH, acid neutralizing capacity (ANC), water colour (absorbance), total organic carbon (TOC), total nitrogen (Total-N) and total phosphorous (Total-P). After collection, all water samples were kept dark and cool until they were analysed. pH was measured shortly after returning to the laboratory using a Ross 8102 low-conductivity combination electrode (ThermoOrion) and diluted buffers. TOC was measured by combustion and analysis as CO2 using a Shimadzu TOCVPCH analyser after acidification and sparging to remove inorganic carbon. UV/Vis absorbance spectra were recorded with a HP-DAD (diode array detector) 8453 E spectrophotometer on 0.45 lm filtered samples in the range 190–1,100 nm (1 nm interval) using a 1 cm quartz cuvette. Total-N and Total-P (unfiltered) were determinate by autoanalyzer after digestion in persulphate (SS 028131-1 and SS–EN 15681-2:2005). Samples

123

68


for major cation analyses (K, Mg, Na, Ca) were preserved with ultrapure HNO3 (1 % v/v) and stored cool until elemental analysis by ICP-OES (inductively-coupled plasma optical emission spectroscopy). Samples for strong acid anions (SO42- and Cl-) were stored frozen until analysis on a Dionex ion chromatograph system, whereas NO3- was analysed by flow injection analysis. The typical precision in anion and cation analyses based on measurements of certified standards was better than 2 %. ANC was calculated as the difference between strong base cations and strong inorganic acid anions. Data analyses Data analyses were performed in statistical software PAST (version 2.14) (Hammer et al. 2001) or in R (version 3.0.1) (R Development Core Team 2011), package vegan (Oksanen et al. 2011). Estimating the total species pool To assess whether our sampling effort was sufficient for the purpose of the study, we estimated how large proportion of the total species pool we were likely to have captured. For macroinvertebrates, and also for diatoms at the region level (where we had replicates), we used a bootstrap estimate (a nonparametric resampling method) (Smith and van Belle 1984). The bootstrapping method needs a collection of sampling replicates for its calculations and is based on the following model: BOOT Sp ¼ SO þ

SO X ð1 pi ÞN i¼1

where Sp is the extrapolated richness, SO is the observed richness, N is the number of sampling replicates and pi is the proportion of species. For diatoms we had pooled samples at each site and therefore we used a different method. Here we used the Chao estimator (Chao 1984) based on individual counts instead of sampling replicates. The Chao richness estimate is based on the following model: CHAO Sp ¼ SO þ

a1 ða1 1Þ 2 ða2 þ 1Þ

where Sp is the extrapolated richness, SO is the observed richness and a1 and a2 is the number of species seen only once or twice, respectively. The results from these analyses showed that we are likely to have captured a sufficient proportion of the estimated total species pool per site and catchment (in most cases [80 %) (Appendix). Diversity partitioning c-diversity was partitioned into its a- and b-diversity components, at two different levels of stream habitat hierarchy (catchment and region level), using a multiplicative partitioning method which let the a- and b-diversity component vary independently of each other. That is, if a is high, b is not automatically low and vice versa—which is the case for additive partitioning methods (Jost 2007, see also Chao et al. 2012). We transformed the a- and b-diversity components into their ‘numbers equivalents’ because it is the recommended choice in any diversity partitioning (Ellison 2010). We based the diversity partitioning on taxon richness and the Shannon diversity index (S) as they have different properties; taxon richness does not depend on

123


69

the frequencies of taxa (but favours rare taxa), whereas the Shannon diversity index is calculated as the exponential of the Shannon entropy, which weights all taxa by abundance and does not favour either rare or common taxa (Jost 2006). Also, if a- and b-diversity is calculated across the landscape, and community or sample weights are unequal, only taxon richness and Shannon diversity will satisfy Lande’s condition that a B c (Jost 2007). Due to the bewildering number of different measures of b-diversity (Tuomisto 2010a, b), it is important to explain how we define the term b-diversity in this paper. Here, b-diversity represents ‘the effective number of elements’ or ‘numbers equivalent’ (i.e. the number of distinct communities within and across the five catchments or the numbers of equally likely elements needed to produce a given value of a diversity index) (Jost 2007). We calculate b-diversity as: b ¼ c= a where c is the total diversity of any investigated area and a is the average diversity per sample unit (Whittaker 1960; Anderson et al. 2011) calculated as: a¼

N X

ai =N

i¼1

where i is the ith sampling unit and N is the total number of samples. To calculate the ‘true’ a for the Shannon diversity index (the Shannon entropies) we first averaged a across all sites within or across catchments and then we calculated exp (a ), while c is exp (c), and b is exp (c - a ) (Jost 2007). Note that it was not necessary to calculate the exponential of taxon richness because it is a direct measure of diversity in its basic form (Jost 2007). This way, a is the average value of the diversity index (number equivalents) of the samples, b is the effective number of distinct communities (reaches at the catchment level and catchments at the region level), and c is the total diversity (a 9 b) of the region analyzed. To facilitate the interpretation of the results, we also calculated (1) the proportion of the total diversity that can be found locally within a region (a/c), (2) the proportion of sites in a region that comprises distinct species communities (b/N) and (3) the turnover rate per sample (a measure of regional heterogeneity) based on b as: Turnover ¼ ðb 1Þ = ðN 1Þ where N is either six (sites within each catchment) or five (catchments). Turnover ranges from zero (no turnover between samples) to unity (each sample is completely different from all other samples). The turnover increases with increasing b (Jost 2007). Determining local environmental control of the biota The function bioenv in R package vegan (Oksanen et al. 2011) finds the best subset of environmental variables so that the Euclidean distance of scaled environmental variables has the maximum rank correlation with community dissimilarities (Clarke and Ainsworth 1993). The routine was run using Spearman rank correlations including a maximum of four environmental variables to avoid over-fitting the model. The two matrices used in bioenv were (1) Euclidian distances of environmental data that included altitude, stream width, stream depth, pH, Total-P, Total-N, TOC, and water colour, and (2) Bray-Curtis dissimilarities of organism abundance data. The same analyses using presence-absence data was also performed and showed the same general pattern. We therefore only report the results based on abundance data (Bray–Curtis index) in the result section. We used local environmental variables that are known to structure benthic stream communities (e.g. Sandin and Johnson 2004; Soininen

123

70


2007). Altitude, Total-N, Total-P, and TOC were log10 transformed to approximate normal distribution whereas the organism abundance data was log10 (x ? 1) transformed to downweight the importance of common taxa. No statistical tests were run because significance tests based on a priori chosen variables are biased (Oksanen et al. 2011). To visually assess the relationships between species community structure and environmental variables we ran Non Metric Multidimensional Scaling (NMDS) for each organism group using the Bray-Curtis similarity index on abundance data. Environmental variables are not part of the NMDS ordination but were added to the ordination graphs after the NMDS. Correlation coefficients between each environmental variable and the scores are presented as vectors from the origin. The vectors are arbitrarily scaled so that only the relative lengths and directions of the environmental vectors are considered when analyzing the ordination plots. Results Environmental gradients The sampled stream reaches varied in width from 0.2 to 3 m, and in depth from 9 to 70 cm. Water chemistry varied among and within the catchment with relatively large ranges in pH (4.2–7.3), ANC (0–0.44 meq l-1), Total-P (0–28 lg l-1), Total-N (62–1,139 lg l-1), TOC (2.6–58.3 mg l-1) and water colour (absorbance at 420 nm) (0.02–1.14) (Table 1). Contribution of a- and b-diversity to c-diversity Macroinvertebrates True c-diversity for benthic macroinvertebrates at the region level was 111 taxa (Table 2a), while it was 14.24 for the Shannon index (Table 2b). Nineteen (17.1 %) of the taxa were found in all five catchments, whereas 41 taxa (36.9 %) were found only in one of the catchments. The mean a/c ratio per catchment was 0.48 (taxon richness) and 0.73 (Shannon index) suggesting that, on average, 48 % of the total taxon richness and 73 % of the total Shannon diversity in each catchment can be found in single stream reaches. Similar values of alpha diversity were found at the region level (Fig. 2a). In terms of macroinvertebrate b-diversity at the catchment level, we estimated that, on average, 35 % and 23 % of the stream reaches comprised distinct invertebrate communities when partitioning was based on taxon richness and the Shannon index respectively. At the region level, our results suggested that 42 and 25 % of the catchments comprised distinct invertebrate communities when partitioning was based on taxon richness and the Shannon index respectively (Fig. 2b). However, macroinvertebrate b-diversity was not consistently higher at the region level. For example, at four instances the catchment level turnover values were higher than the ones observed at the region level (Table 2a–b). Diatoms True diatom c-diversity at the region level was 271 taxa (Table 3a) while it was 42.69 for the Shannon index (Table 3b). Twenty-two (8.1 %) of the taxa were found in all five catchments, whereas 144 taxa (53.1 %) were found only in one of the catchments. The mean a/c ratio per catchment was 0.34 (taxon richness) and 0.44 (Shannon index) suggesting that, on average, 34 % of the total taxon richness and 44 % of the total Shannon diversity in each catchment can be found in single stream reaches. Similar values of alpha

123


71

diversity were found at the region level (Fig. 2a). Regarding b-diversity, we found that, on average, 48 and 38 % of the stream reaches comprised distinct diatom communities when partitioning was based on taxon richness and the Shannon index respectively. At the region level, we estimated that 51 and 36 % of the catchments comprised distinct diatom communities when partitioning was based on taxon richness and the Shannon index respectively (Fig. 2b). Similarly to invertebrate b-diversity, diatom b-diversity was not consistently higher at the region level (Table 3a–b). Interestingly, invertebrates had consistently higher values of a/c, while diatoms had consistently higher values of b/N independent of whether calculations were based on species richness or the Shannon index (Fig. 2a–b). The mean difference in Table 2 Partitioning of a) taxon richness and b) Shannon diversity (eS) of macroinvertebrate data at two levels of headwater habitat hierarchy (catchment and region level). a is the average of the diversity index of the samples, b is the effective number of distinct communities (reaches at the catchment level and catchments at the region level), c is the total diversity, and turnover is the turnover rate per site at the catchment level and per catchment at the region level a Range (a) Catchment Va¨sterdala¨lven (VD) ¨ sterdala¨lven (OD) O

a Mean

b

c

Turnover

21–35

25.8

1.98

51

0.19

23–45

35.8

1.79

64

0.16

Lower Branch (LB) Lugna˚n (LU)

22–30

26.5

1.89

50

0.18

18–28

22.3

2.69

60

0.34

Danshyttea˚n (DA)

13–24

18.7

2.25

42

0.25

42–64

53.4

2.08

111

0.27

Region

3.28–15.36

8.52

1.19

10.15

0.038

3.57–14.03

9.51

1.61

15.33

0.122

Lower Branch (LB) Lugna˚n (LU)

3.38–16.36

6.63

1.02

6.78

0.004

5.76–10.96

8.64

1.59

13.80

0.118

Danshyttea˚n (DA)

4.10–14.15

7.47

1.61

12.05

0.122

6.78–15.33

11.18

1.27

14.24

0.055

Region

(b)

Invertebrates Diatoms

0.5 0.4

beta/N

0.6

0.6

0.7

Invertebrates Diatoms

Catch (R)

0.2

0.2

0.3

0.4

alpha/gamma

0.8

(a)

0.8

(b) Catchment Va¨sterdala¨lven (VD) ¨ sterdala¨lven (OD) O

Region (R)

Catch (S)

Region (S)

Catch (R)

Region (R)

Catch (S)

Region (S)

Fig. 2 a Mean a/c and b mean b/N based on taxon richness (R) and Shannon index (S) at the catchment (Catch) and region level (Region) for benthic macroinvertebrates (black circles) and diatoms (white circles). Standard error bars are shown where replicates were available (i.e. at the catchment level, N = 5)

123

72


macroinvertebrate and diatom b-diversity (based on species richness) at the catchment level was 0.79. That is, there was almost one more distinct community at the six sites for diatoms compared to benthic macroinvertebrates. At the region level, the difference in bdiversity was 0.47. A similar result, albeit more pronounced, was found for the Shannon index, where the mean difference in b-diversity between diatoms and benthic macroinvertebrates was 0.87 at the catchment level, and 0.50 at the region level. Local environmental control of the biota Strong relationships between community composition and the environmental variables were detected, where pH and absorbance were most consistently associated with macroinvertebrate and diatom community composition. Other important variables were stream width, altitude, ANC, and Total-P (Table 4). At the catchment level, the correlation between biota (abundances) and environment varied between 0.59 (VD) and 0.93 (OD) for macroinvertebrates and between 0.25 (VD) and 0.93 (LB) for diatoms with similar results for presence-absence data (results not shown). At the region level, the correlation was 0.41 and 0.45 for macroinvertebrates and diatoms, respectively (Table 4). Furthermore, community composition differed among the five catchments both for benthic macroinvertebrates (Fig. 3a) and for diatoms (Fig. 3b). The first, strongest NMDS ordination axis was mainly related to differences in altitude and ANC between the two southern (DA and LU) and the three northern (VD, OD, LB) catchments, while the second NMDS axis was more related to within catchment variation in pH, stream width, TOC, Total-N and Total-P. The same pattern was evident for both organism groups (Fig. 3a–b).

Discussion In this study we investigated how the biodiversity of headwater stream organisms is distributed at different levels of habitat hierarchy and at which hierarchical level environmental control of the biota and b-diversity peaks. We hypothesized that headwater stream ecosystems would be characterized by low adiversity. Strong local environmental filters (e.g. large variation in water-chemistry conditions between sites) (Buffam et al. 2007) coupled with isolation (e.g. Clarke et al. 2008) was thought to lead to relatively low local (a) diversity. That is, dispersal rates may be low, which does not allow for any greater colonization of species in the first place. Additionally, any maladapted populations are likely to be quickly filtered out by environmental conditions in the absence of high dispersal rates (Pulliam 1988; Loreau and Mouquet 1999). a-diversity contributed slightly less to c-diversity in our headwater sites compared to what was observed in a study by Clarke et al. (2010). However, local richness of both macroinvertebrates and diatoms was still relatively high and our results suggested that a significant portion of the total macroinvertebrate and diatom richness within each catchment can be preserved by conserving single stream-reaches. The same estimate, based on Shannon index, was much higher, probably reflecting a high influence of relatively many rare and unique taxa on the analyses based on taxon richness. We suggest two mechanisms which could partly explain the adiversity patterns observed in these headwater streams: (1) a number of relatively ‘weedy’ taxa that tolerate a broad range of environmental conditions form the basis of the communities, and (2) less tolerant (and rare) taxa for which these headwater systems acts as sinks are also present. This would also suggest that the dispersal for both organism groups over relatively small spatial extents (i.e. within drainage basins) is not limited to any great extent.

123


73

Table 3 Partitioning of a) taxon richness and b) Shannon diversity (eS) of diatom data at two levels of headwater habitat hierarchy (catchment and region level). a is the average of the diversity index of the samples, b is the effective number of distinct communities (reaches at the catchment level and catchments at the region level), c is the total diversity, and turnover is the turnover rate per site at the catchment level and per catchment at the region level a Range (a) Catchment Va¨sterdala¨lven (VD) ¨ sterdala¨lven (OD) O

a Mean

b

c

Turnover

0.42

15–44

31.2

3.11

97

19–60

39.2

2.99

117

0.40

Lower branch (LB) Lugna˚n (LU)

18–40

24.8

2.86

71

0.38

30–51

41.8

2.96

124

0.39

Danshyttea˚n (DA)

26–71

46.5

2.62

122

0.32

71–124

106.2

2.55

271

0.39 0.29

Region (b) Catchment Va¨sterdala¨lven (VD) ¨ sterdala¨lven (OD) O

4.08–13.99

8.57

2.43

20.82

2.74–22.60

10.66

2.21

23.59

0.24


4.72–12.50

7.30

1.96

14.32

0.19

9.17–17.53

12.84

2.70

34.64

0.34

Danshyttea˚n (DA)

7.55–38.86

15.67

2.10

32.88

0.22

14.32–34.64

24.03

1.78

42.69

0.19

Region

Table 4 Relationship (Spearman rank correlations) between benthic macroinvetebrates and diatom community dissimilarity (Bray–Curtis index) and environmental (Euclidian) distances at two levels of headwater habitat hierarchy (catchment and region level). The test finds the best subset of environmental variables so that the Euclidean distance of scaled environmental variables have the maximum rank correlation with community dissimilarities Invertebrates

Catchment Va¨sterdala¨lven (VD) ¨ sterdala¨lven (OD) O

Diatoms

Spearman rank correlation

Variables (in order of importance)

Spearman rank correlation

Variables (in order of importance)

0.59

Altitude, absorbance

0.25

pH, absorbance

0.93

pH, absorbance

0.87

Altitude, pH


0.73

Width, pH, absorbance

0.93

pH, ANC

0.66

Width

0.90

pH, Total-P

Danshyttea˚n (DA)

0.69

pH

0.71

pH, absorbance

0.41

Width, pH, Total-P, absorbance

0.45

Altitude, width, pH, ANC

Region

We also hypothesized that b-diversity would be high at all hierarchical levels due to strong environmental gradients (which organisms respond to) and due to moderate (sufficient) and limited dispersal at the catchment and region level, respectively. Our results did provide some support for this prediction. Our average values of b for taxon richness corresponded to 35 and 48 % distinct stream reaches at the catchment level and 42 and 51 % distinct catchments at the region level for macroinvertebrates and diatoms, respectively. Clarke et al. (2010), studying eight headwater streams in Victoria, Australia, had a similar range of values for b-diversity, which they assigned as being ‘‘low’’ relative to values of a-

123

74


diversity. However, we are reluctant to compare the individual contribution of a- versus bdiversity to c-diversity based solely on absolute values as the number of sites and/or catchments included in the study influences them. That is, because the maximum value for b equals the number of samples (N), the highest value of b in this study is six and five at the catchment and region level, respectively (Jost 2007). Therefore, and in contrary to Clarke et al. (2010), we suggest that not only a-diversity, but also b-diversity made a significant contribution to headwater c-diversity. b-diversity have been found to contribute substantially to the regional diversity also in larger streams (Stendera and Johnson 2005; Er} os 2007), but the general pattern seem to be that headwaters do exhibit higher b-diversity in comparison to downstream reaches (Finn et al. 2011; Fernandes et al. 2004, Carrara et al. 2012). In addition, headwaters are known to inhabit a unique species community compared to downstream reaches (e.g. Meyer et al. 2007). These studies provide evidence that headwaters contribute to the regional diversity in entire stream networks. Our study further suggests that this diversity is best maintained by targeting not only local diverse sites, but also by selecting sites that have distinct species communities (i.e. maximising b-diversity). Our analyses of the association between local environmental variables and biota can, to some extent, explain the observed b-diversity within and among catchments. In this study, the variation in environmental conditions at the catchment-level was high, especially in terms of water chemistry variables (e.g. pH ranged on average 2.13 ± SE 0.14 units between sites). A similar variation has also been observed in other headwater catchments (Temnerud and Bishop 2005; Buffam et al. 2007) and is likely to cause a high species turnover, both for macroinvertebrates and diatoms, because both organism groups are known to respond strongly to acidity (Braukmann and Biss 2004; Hirst et al. 2004; Lewis et al. 2007). Indeed, this large environmental variation was clearly reflected in the biotic communities in this study, leading to relatively high species turnover (b-diversity) at the catchment and region level. We also expected that b-diversity would increase at the region-level and that the environmental control of community composition would decrease. Contrary to our

(a)

(b)

0.12

0.25

TOC Total N Total P

Water colour

0.2

pH

0.08

Width

0.04

LB Altitude

0

DA

ANC

-0.04 -0.08

Coordinate 2

Coordinate 2

0.15

VD OD

LU

0.1 DA

0.05

OD

0

ANC LB

-0.05 pH

-0.12

LU

Width

Total P Total N

VD

-0.1

Altitude

-0.16 -0.15

TOC

-0.2 -0.18

-0.12 -0.06

0

0.06

0.12

Coordinate 1

0.18

0.24

0.3

-0.2 -0.4

Water colour -0.32

-0.24

-0.16 -0.08

0

0.08

0.16

0.24

Coordinate 1

Fig. 3 Non Metric Multidimensional Scaling (NMDS) of a benthic macroinvertebrate data and b diatom data using the Bray–Curtis similarity index. Environmental variables are not part of the ordination, but were added after the NMDS. The correlation coefficients between each environmental variable and the scores are presented as vectors from the origin where the vectors are arbitrarily scaled. Only the relative lengths and directions of the environmental vectors should be considered when analyzing the ordination plots. The sampling sites (dots) within each catchment are connected using convex hulls. VD Va¨sterdala¨lven, OD ¨ sterdala¨lven, LB lower branch, LU Lugna˚n, and DA Danshyttea˚n O

123


75

predictions, neither benthic macroinvertebrates nor diatoms showed any clear indication of increased b-diversity with an increasing spatial extent, independent on the diversity index used. This finding indicates that the dispersal of organisms is not limited, even at the region level. That is, contemporary dispersal limitation (e.g. Brown and Swan 2010; Go¨the et al. 2013) may not be strong enough to cause any major divergence of species assemblages, and other regional (e.g. historical, climatic or geological) factors may not have caused any significant differences in the regional species pool available for each individual catchment (e.g. Poff 1997; Mykra¨ et al. 2007). However, we cannot exclude the possibility that bdiversity was shaped by other factors besides local ones. In agreement with our predictions, it was evident that local environmental control decreased with an increasing spatial extent (in all cases except for diatoms in VD) (e.g. Poff 1997; Mykra¨ et al. 2007), suggesting that regional factors may have increased slightly in importance at the region-level (Poff 1997). Another finding was that diatom b-diversity consistently contributed more to c-diversity relative to macroinvertebrate b-diversity, while an opposite pattern was observed for adiversity (i.e. macroinvertebrates demonstrated a higher contribution of a-diversity to cdiversity compared to diatoms). This is perhaps not surprising as diatoms are known to respond quickly and strongly to changes in water chemistry (e.g. acidity and nutrients) (e.g. Lewis et al. 2007; Soininen 2007). Also, it is assumed that unicellular organisms (e.g. diatoms) are more easily dispersed over larger spatial scales compared to multicellular organisms (e.g. invertebrates) (Finlay et al. 2002; Finlay and Tom 2004). Because sufficient dispersal is a prerequisite for local filtering to occur (e.g. Cottenie and De Meester 2004), this may serve as a reasonable explanation for this finding. It is important to note, however, that this view (i.e. that diatoms are dispersed ubiquitously) has been strongly challenged by recent studies that have found that diatoms are more dispersal limited (or at least more structured by regional factors) than previously thought (e.g. Vyverman et al. 2007, Verleyen et al. 2009; Heino et al. 2010). In addition, very little is known about the dispersal capacities of freshwater invertebrates (Bohonak and Jenkins 2003), which makes it hard to speculate further about whether differences in dispersal related factors could have contributed to the disparate findings between organism groups. However, irrespective of the underlying cause behind the disparate diversity patterns, our results imply that different management and conservation strategies may be applied to the organism groups. That is, more of the invertebrate cdiversity can be preserved by targeting locally diverse sites, while the conservation of diatom c-diversity would be more efficient by maximizing b-diversity between multiple local sites. In summary, our study shows that a decrease in headwater a- and b-diversity is likely to lead to significant decreases also in headwater c-diversity. When selecting headwater sites to be included in freshwater management it may therefore be recommended to select those that are locally diverse and at the same time inhabit significantly different biotic communities (i.e. maximizing b-diversity). The finding that environmental control peaks at small spatial scales also suggest that the conservation of headwater stream diversity is likely to be most effective when management targets (restore or conserve) environmental conditions across multiple local sites within relatively small catchments. Otherwise biological recovery may be slow or absent; something which is commonly observed in restoration projects (Palmer et al. 2010). However, more extensive studies are needed in similar headwater ecosystems (both in time and space) to elucidate at which scale hotspots of biodiversity can be found, and to identify the relevant scale for successful management of (manageable) environmental parameters. In future studies, it would also be useful to include larger stream reaches and compare the relative contributions of a- and b-diversity to c-diversity between stream sizes. This would help clarify to what extent headwater stream reaches contribute to biodiversity in the whole riverscape.

123

76


Acknowledgments Financial support for this research was provided by the Swedish University of Agricultural Sciences, the Swedish Environmental Protection Agency, Knowledge Foundation and Swedish Meteorological and Hydrological Institute (Johan Temnerud), and the Marie Curie Actions of the European Commission (FP7-2010-PEOPLE-IEF) (Leonard Sandin). Sampling of the Lugna˚n and Danshyttea˚n catchments were funded by the Swedish Environmental Protection Agency (contract no. 261 0803). We thank our fellow collaborators for excellent field- and laboratory work: Dan Evander, Amelie Jarlman, Leif Go¨the, Jan-Olov Johansson, Kaloyan Kenov, Anna-Karin Persson, Fredrik Pilstro¨m, Frida Svanstro¨m, Putte Olsson and Per Westerfelt. We also thank Lars Eriksson who was responsible for the macroinvertebrate taxonomy and Elaine McGoff, Richard K. Johnson and three reviewers for providing useful comments on an earlier version of the manuscript.

Appendix See Figs. 4 and 5.

LU

OD

VD

0.8 0.4

Obs/Exp richness LB

Catchment VD

0.0

0.8 0.4 0.0

Obs/Exp richness

(b)

Region

(a)

DA

U12

U38

Catchment

U47

U68

0.8 0.4

Obs/Exp richness U37

0.0

0.8

Obs/Exp richness

0.4

U33

U77

U73

U82

Site Catchment LU

A25

Site

U99

A36

A39

0.8 0.4

Obs/Exp richness A24

U93

0.0

0.8

A23

U88

Catchment DA

(f)

0.4 A14

U85

Site

0.0

Obs/Exp richness

(e)

U65 U106

Catchment LB

(d)

0.0 U3

U60

Site

Catchment OD

(c)

U53

K1

K12

K13

K16

K18

K19

Site

Fig. 4 Plots showing the observed/expected richness of macroinvertebrates within each catchment (a) and within each site (b–f). The expected richness was obtained through a bootstrap resampling method (see ¨ sterdala¨lven, LB lower branch, LU Lugna˚n, ‘‘Materials and methods’’ section). VD Va¨sterdala¨lven, OD O and DA Danshyttea˚n

123


(b)

LU

OD

VD

0.8 0.4

Obs/Exp richness

0.8 0.4 LB

Catchment VD

0.0

Region

0.0

Obs/Exp richness

(a)

77

DA

U12

U38

Catchment

(d)

U37

U47

U68

0.8

Catchment LB

0.4

Obs/Exp richness

0.8 0.4

U33

U77

U73

U82

Site

(f)

A24

A25

Site

U93

U99

A36

A39

0.8

Catchment DA

0.4

Obs/Exp richness

0.8 0.4

A23

U88

0.0

Catchment LU

A14

U85

Site

0.0

Obs/Exp richness

(e)

U65 U106

0.0

Catchment OD

U3

U60

Site

0.0

Obs/Exp richness

(c)

U53

K1

K12

K13

K16

K18

K19

Site

Fig. 5 Plots showing the observed/expected richness of diatoms within each catchment (a) and within each site (b–f). The expected richness was obtained either through a bootstrap resampling method where we had sampling replicates (a) or through the Chao estimator where we had pooled samples (b–f) (see ‘‘Materials ¨ sterdala¨lven, LB lower branch, LU Lugna˚n, and DA and methods’’ section). VD Va¨sterdala¨lven, OD O Danshyttea˚n

References Abell R (2002) Conservation biology for the biodiversity crisis: a freshwater follow-up. Conserv Biol 16:1435–1437. doi:10.1046/j.1523-1739.2002.01532.x Abromovitz JN (1996) Imperiled waters, impoverished future: The decline of freshwater ecosystems Anderson MJ, Crist TO, Chase JM, Vellend M, Inouye BD, Freestone AL, Sanders NJ, Cornell HV, Comita LS, Davies KF, Harrison SP, Kraft NJB, Stegen JC, Swenson NG (2011) Navigating the multiple meanings of b diversity: a roadmap for the practicing ecologist. Ecol Lett 14:19–28. doi:10.1111/j. 1461-0248.2010.01552.x Bengtsson J (2010) Applied (meta) community ecology: diversity and ecosystem services at the intersection of local and regional processes. In: Verhoef HA, Morin PJ (eds) Community ecology: processes, models, and applications. Oxford University Press, New York, pp 115–131 Bishop K, Buffam I, Erlandsson M, Fo¨lster J, Laudon H, Seibert J, Temnerud J (2008) Aqua Incognita: the unknown headwaters. Hydrol Processes 22:1239–1242. doi:10.1002/Hyp.7049

123

78


Bohonak AJ, Jenkins DG (2003) Ecological and evolutionary significance of dispersal by freshwater invertebrates. Ecol Lett 6:783–796. doi:10.1046/j.1461-0248.2003.00486.x Braukmann U, Biss R (2004) Conceptual study—an improved method to assess acidification in German streams by using benthic macroinvertebrates. Limnologica 34:433–450. doi:10.1016/S00759511(04)80011-2 Brown BL, Swan CM (2010) Dendritic network structure constrains metacommunity properties in riverine ecosystems. J Anim Ecol 79:571–580. doi:10.1111/j.1365-2656.2010.01668.x Buffam I, Laudon H, Temnerud J, Morth CM, Bishop K (2007) Landscape-scale variability of acidity and dissolved organic carbon during spring flood in a boreal stream network. J Geophys Res 112:G01022. doi:10.1029/2006jg000218 Carrara F, Altermatt F, Rodriguez-Iturbe I, Rinaldo A (2012) Dendritic connectivity controls biodiversity patterns in experimental metacommunities. Proc Natl Acad Sci 109:5761–5766. doi:10.1073/pnas. 1119651109 Chao A (1984) Non-parametric estimation of the number of classes in a population. Scand J Stat 11:265–270. doi:10.2307/4615964 Chao A, Chiu C, Hsieh TC (2012) Proposing a resolution to debates on diversity partitioning. Ecology 93:2037–2051. doi:10.1890/11-1817.1 Clarke KR, Ainsworth M (1993) A method of linking multivariate community structure to environmental variables. Mar Ecol Prog Ser 92:205–291 Clarke A, Mac Nally R, Bond N, Lake PS (2008) Macroinvertebrate diversity in headwater streams: a review. Freshwat Biol 53:1707–1721. doi:10.1111/j.1365-2427.2008.02041.x Clarke A, Mac Nally R, Bond NR, Lake PS (2010) Conserving macroinvertebrate diversity in headwater streams: the importance of knowing the relative contributions of a and b diversity. Divers Distrib 16:725–736. doi:10.1111/j.1472-4642.2010.00692.x Collier KJ (2011) The rapid rise of streams and rivers in conservation assessment. Aquat Conserv: Mar Freshwat Ecosyst 21:397–400. doi:10.1002/aqc.1196 Cottenie K, De Meester L (2004) Metacommunity structure: synergy of biotic interactions as selective agents and dispersal as fuel. Ecology 85:114–119. doi:10.1890/03-3004 Declerck SAJ, Coronel JS, Legendre P, Brendonck L (2011) Scale dependency of processes structuring metacommunities of cladocerans in temporary pools of High-Andes wetlands. Ecography 34:296–305. doi:10.1111/j.1600-0587.2010.06462.x Dudgeon D, Arthington AH, Gessner MO, Kawabata ZI, Knowler DJ, Leveque C, Naiman RJ, PrieurRichard AH, Soto D, Stiassny MLJ, Sullivan CA (2006) Freshwater biodiversity: importance, threats, status and conservation challenges. Biol Rev 81:163–182. doi:10.1017/S1464793105006950 Ellison AM (2010) Partitioning diversity. Ecology 91:1962–1963. doi:10.1890/09-1692.1 Er} os T (2007) Partitioning the diversity of riverine fish: the roles of habitat types and non-native species. Freshwat Biol 52:1400–1415. doi:10.1111/j.1365-2427.2007.01777.x Fernandes CC, Podos J, Lundberg JG (2004) Amazonian ecology: tributaries enhance the diversity of electric fishes. Science 305:1960–1962. doi:10.1126/science.1101240 Finlay BJ, Tom Fenchel (2004) Cosmopolitan metapopulations of free-living microbial eukaryotes. Protist 155:237–244. doi:10.1078/143446104774199619 Finlay BJ, Monaghan EB, Maberly SC (2002) Hypothesis: the rate and scale of dispersal of freshwater diatom species is a function of their global abundance. Protist 153:261–273. doi:10.1078/1434-4610-00103 Finn DS, Bonada N, Mu´rria C, Hughes JM (2011) Small but mighty: headwaters are vital to stream network biodiversity at two levels of organization. J N Am Benthol Soc 30:963–980. doi:10.1899/11-012.1 Go¨the E, Angeler DG, Sandin L (2013) Metacommunity structure in a small boreal stream network. J Anim Ecol 82:449–458. doi:10.1111/1365-2656.12004 Hammer O, Harper DAT, Ryan PD (2001) PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron 4:1–9 Heino J (2013) The importance of metacommunity ecology for environmental assessment research in the freshwater realm. Biol Rev 88:166–178. doi:10.1111/j.1469-185X.2012.00244.x Heino J, Mykra¨ H (2008) Control of stream insect assemblages: roles of spatial configuration and local environmental factors. Ecol Entomol 33:614–622. doi:10.1111/j.1365-2311.2008.01012.x Heino J, Bini LM, Karjalainen SM, Mykra¨ H, Soininen J, Vieira LCG, Diniz-Filho JAF (2010) Geographical patterns of micro-organismal community structure: are diatoms ubiquitously distributed across boreal streams? Oikos 119:129–137. doi:10.1111/j.1600-0706.2009.17778.x Heino J, Gro¨nroos M, Soininen J, Virtanen R, Muotka T (2012) Context dependency and metacommunity structuring in boreal headwater streams. Oikos 121:537–544. doi:10.1111/j.1600-0706.2011.19715.x Heywood VH (1995) Global Biodiversity Assessment. Cambridge University Press, Cambridge, United Nations Environment Programme

123


79

Hirst H, Chaud F, Delabie C, Ju¨ttner I, Ormerod SJ (2004) Assessing the short-term response of stream diatoms to acidity using inter-basin transplantations and chemical diffusing substrates. Freshwat Biol 49:1072–1088. doi:10.1111/j.1365-2427.2004.01242.x Illies J (1966) Verbreitung der su¨ßwasserfauna Europas. Verh Internat Verein Theor Angew Limnol 16:287–296 IUCN (2007) IUCN Red List of threatened species. International Union for Conservation of Nature and Natural resources, Cambridge Jenkins M (2003) Prospects for biodiversity. Science 302:1175–1177. doi:10.1126/science.1088666 Jost L (2006) Entropy and diversity. Oikos 113:363–375. doi:10.1111/j.2006.0030-1299.14714.x Jost L (2007) Partitioning diversity into independent alpha and beta components. Ecology 88:2427–2439. doi:10.1890/06-1736.1 Jost L, DeVries P, Walla T, Greeney H, Chao A, Ricotta C (2010) Partitioning diversity for conservation analyses. Divers Distrib 16:65–76. doi:10.1111/j.1472-4642.2009.00626.x Leibold MA, Norberg J (2004) Biodiversity in metacommunities: plankton as complex adaptive systems? Limnol Oceanogr 49:1278–1289. doi:10.2307/3597954 Leopold LB, Wolman MG, Miller JP (1964) Fluvial processes in geomorphology. W.H Freeman and Co, San Fransisco Lewis BR, Ju¨ttner I, Reynolds B, Ormerod SJ (2007) Comparative assessment of stream acidity using diatoms and macroinvertebrates: implications for river management and conservation. Aquat Conserv: Mar Freshwat Ecosyst 17:502–519. doi:10.1002/aqc.787 Loreau M, Mouquet N (1999) Immigration and the maintenance of local species diversity. Am Nat 154:427–440. doi:10.1086/303252 Lowe WH, Likens GE (2005) Moving headwater streams to the head of the class. Bioscience 55:196–197. doi:10.1641/0006-3568(2005)055[0196:mhstth]2.0.co;2 Malmqvist B, Rundle S (2002) Threats to the running water ecosystems of the world. Environ Conserv 29:134–153. doi:10.1017/S0376892902000097 Margules CR, Pressey RL (2000) Systematic conservation planning. Nature 405:243–253. doi:10.1038/ 35012251 Meyer JL, Wallace JB (2001) Lost linkages and lotic ecology: rediscovering small streams. In: Press MC, Huntly NJ, Levin S (eds) Ecology: achievement and challenge. Blackwell Science, Oxford, pp 295–317 Meyer JL, Sale MJ, Mulholland PJ, Poff NL (1999) Impacts of climate change on aquatic ecosystem functioning and health. J Amer Water Resour Assoc 35:1373–1386. doi:10.1111/j.1752-1688.1999.tb04222.x Meyer JL, Strayer DL, Wallace JB, Eggert SL, Helfman GS, Leonard NE (2007) The contribution of headwater streams to biodiversity in river networks. J Amer Water Resour Assoc 43:86–103. doi:10. 1111/j.1752-1688.2007.00008.x Moss B, Hering D, Green AJ, Aidoud A, Becares E, Beklioglu M, Bennion H, Boix D, Brucet S, Carvalho L, Clement B, Davidson T, Declerck S, Dobson M, van Donk E, Dudley B, Feuchtmayr H, Friberg N, Grenouillet G, Hillebrand H, Hobaek A, Irvine K, Jeppesen E, Johnson R, Jones I, Kernan M, Lauridsen TL, Manca M, Meerhoff M, Olafsson J, Ormerod S, Papastergiadou E, Penning WE, Ptacnik R, Quintana X, Sandin L, Seferlis M, Simpson G, Triga C, Verdonschot P, Verschoor AM, Weyhenmeyer GA (2009) Climate change and the future of freshwater biodiversity in europe: a primer for policymakers. Freshwater Reviews 2:103–130. doi:10.1608/frj-2.2.1 Mykra¨ H, Heino J, Muotka T (2007) Scale-related patterns in the spatial and environmental components of stream macroinvertebrate assemblage variation. Global Ecol Biogeogr 16:149–159. doi:10.1111/j. 1466-8238.2006.00272.x Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Wagner H (2011) Vegan: Community ecology package. R package version 2.0-0. http://CRAN. R-project.org/package=vegan. Accessed 13 Nov 2011 Palmer MA, Menninger HL, Bernhardt E (2010) River restoration, habitat heterogeneity and biodiversity: a failure of theory or practice? Freshwat Biol 55:205–222 Pappas JL, Stoermer EF (1996) Quantitative method for determining a representative algal sample count. J Phycol 32:693–696. doi:10.1111/j.0022-3646.1996.00693.x Poff NL (1997) Landscape filters and species traits: towards mechanistic understanding and prediction in stream ecology. J N Am Benthol Soc 16:391–409. doi:10.2307/1468026 Pulliam HR (1988) Sources, sinks, and population regulation. Am Nat 132:652–661. doi:10.2307/2461927 R Development Core Team (2011) R: A language and environment for statistical computing. R foundation for statistical computing Vienna, Austria ISBN 3-900051-07-0 http://www.R-project.org. Accessed 13 Nov 2011 Revenga C, Campbell I, Abell R, de Villiers P, Bryer M (2005) Prospects for monitoring freshwater ecosystems towards the 2010 targets. Philos Trans R Soc Lond B Biol Sci 360:397–413. doi:10.1098/ rstb 2004.1595

123

80


Ricciardi A, Rasmussen JB (1999) Extinction rates of north American freshwater fauna. Conserv Biol 13:1220–1222. doi:10.1046/j.1523-1739.1999.98380.x Sala OE, Chapin FS, Armesto JJ, Berlow E, Bloomfield J, Dirzo R, Huber-Sanwald E, Huenneke LF, Jackson RB, Kinzig A, Leemans R, Lodge DM, Mooney HA, Oesterheld Mn, Poff NL, Sykes MT, Walker BH, Walker M, Wall DH (2000) Global biodiversity scenarios for the year 2100. Science 287:1770–1774. doi:10.1126/science.287 5459.1770 Sandin L (2003) Benthic invertebrates in Swedish streams: community structure, taxon richness, and environmental relations. Ecography 26:269–282. doi:10.1034/j.1600-0587.2003.03380.x Sandin L, Johnson RK (2000) Ecoregions and benthic macroinvertebrate assemblages of Swedish streams. J N Am Benthol Soc 19:462–474. doi:10.2307/1468107 Sandin L, Johnson RK (2004) Local, landscape and regional factors structuring benthic macroinvertebrate assemblages in Swedish streams. Landscape Ecol 19:501–514. doi:10.1023/B:LAND.0000036116. 44231.1c Schmera D, Podani J (2013) Components of beta diversity in hierarchical sampling designs: a new approach. Ecol Indic 26:126–136. doi:10.1016/j.ecolind.2012.10.029 Smith EP, van Belle G (1984) Nonparametric estimation of species richness. Biometrics 40:119–129. doi:10.2307/2530750 Soininen J (2007) Environmental and spatial control of freshwater diatoms - a review. Diatom Res 22:473–490. doi:10.1080/0269249x.2007.9705724 Stendera SES, Johnson RK (2005) Additive partitioning of aquatic invertebrate species diversity across multiple spatial scales. Freshwat Biol 50:1360–1375. doi:10.1111/j.1365-2427.2005.01403.x Strayer DL, Dudgeon D (2010) Freshwater biodiversity conservation: recent progress and future challenges. J N Am Benthol Soc 29:344–358. doi:10.1899/08-171.1 Temnerud J, Bishop K (2005) Spatial variation of streamwater chemistry in two Swedish boreal catchments: implications for environmental assessment. Environ Sci Technol 39:1463–1469. doi:10.1021/es040045q Tuomisto H (2010a) A diversity of beta diversities: straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography 33:2–22. doi:10.1111/j.16000587.2009.05880.x Tuomisto H (2010b) A diversity of beta diversities: straightening up a concept gone awry. Part 2. Quantifying beta diversity and related phenomena. Ecography 33:23–45. doi:10.1111/j.1600-0587.2009. 06148.x Verleyen E, Vyverman W, Sterken M, Hodgson DA, De Wever A, Juggins S, Van de Vijver B, Jones VJ, Vanormelingen P, Roberts D, Flower R, Kilroy C, Souffreau C, Sabbe K (2009) The importance of dispersal related and local factors in shaping the taxonomic structure of diatom metacommunities. Oikos 118:1239–1249. doi:10.1111/j.1600-0706.2009.17575.x Vo¨ro¨smarty CJ, McIntyre PB, Gessner MO, Dudgeon D, Prusevich A, Green P, Glidden S, Bunn SE, Sullivan CA, Liermann CR, Davies PM (2010) Global threats to human water security and river biodiversity. Nature 467:555–561. doi:10.1038/nature09440 Vyverman W, Verleyen E, Sabbe K, Vanhoutte K, Sterken M, Hodgson DA, Mann DG, Juggins S, Vijver B, Jones V, Flower R, Roberts D, Chepurnov VA, Kilroy C, Vanormelingen P, Wever AD (2007) Historical processes constrain patterns in global diatom diversity. Ecology 88:1924–1931. doi:10.1890/06-1564.1 Whittaker RH (1960) Vegetation of the Siskiyou Mountains, Oregon and California. Ecol Monogr 30:279–338. doi:10.2307/1943563 Whittaker RH (1972) Evolution and measurement of species diversity. Taxon 21:213–251. doi:10.2307/ 1218190

123