J. N. Am. Benthol. Soc., 2009, 28(2):331–351 ! 2009 by The North American Benthological Society DOI: 10.1899/08-010.1 Published online: 7 April 2009
Longitudinal macroinvertebrate organization over contrasting discontinuities: effects of a dam and a tributary
Izumi Katano1,4, Junjiro N. Negishi1,5, Tomoko Minagawa1,6, Hideyuki Doi2,7, Yoˆichi Kawaguchi3,8, AND Yuichi Kayaba1,9 1
Aqua Restoration Research Center, Public Works Research Institute, Kawashima Kasada-machi, Kakamigahara, Gifu 501-6021, Japan 2 Laboratory of Aquatic Food Web Dynamics, Faculty of Agriculture, Ehime University, 3-5-7 Tarumi, Matsuyama 790-8566, Ehime, Japan 3 Faculty of Engineering, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan
Abstract. Macroinvertebrate organization along a river was examined to relate biological responses to environmental changes observed across 2 discontinuities (a dam and a tributary). Benthic macroinvertebrates and a range of environmental variables were sampled from 4 study segments (above the dam, below the dam, below the tributary confluence, and in the tributary). Substrate was significantly coarser below than above the dam. In contrast, water-quality variables, such as water temperature and dissolved O2, changed little below the dam. The most striking discontinuity was substrate coarseness at the tributary confluence. Substrate below the confluence was finer than substrate below the dam and similar to the substrate above the dam. Macroinvertebrate organization differed across the 2 discontinuities. Assemblage composition above the dam was more similar to composition below the confluence than to composition below the dam. The longitudinal organization of the macroinvertebrates could be explained largely by changes in substrate characteristics and habitat preferences of the indicator species. The densities of drifting zooplankton and phytoplankton were higher below than above the dam and were higher below the dam than below the confluence. However, the density of drifting plankton did not differ between the reach immediately above the confluence and the reaches below the confluence. This result suggests that the decrease of zooplankton and phytoplankton occurred above the tributary, probably because of biological entrapment or passive deposition rather than the contribution of the tributary inflow. The dam and tributary caused contrasting discontinuities in macroinvertebrate organization. The tributary generally reversed the dam-related changes to the main stem habitat and the macroinvertebrate community. A key management implication of our study is that efforts to restore dam-related environmental impacts would be facilitated by understanding the role of tributaries downstream of the dam. Key words:
biodiversity, confluence, impoundments, riverbed environment, regulated rivers.
Stanford and Ward 2001). Adverse effects of dams on river ecosystems have been widely recognized (Poff and Hart 2002) and are mostly related to the alteration of geomorphic processes (Petts et al. 1993, Kondolf 1997, Osmundson et al. 2002), physicochemical regimes (Ward 1974, Storey et al. 1991, Stevens et al. 1997, Pardo et al. 1998, Vinson 2001), and flow regimes (Trotzky and Gregory 1974, Morgan et al. 1991, Ce´re´ghino et al. 1997). In contrast, tributary discontinuities generally exert positive effects on riverine biodiversity in unregulated rivers (Rice et al. 2001, Benda et al. 2004b, Kiffney et al. 2006). Empirical understanding of faunal organization in the context of the environmental gradients across these discontinu-
A river ecosystem might be a longitudinal continuum at a large spatial scale, but at smaller scales, the continuum appears as a series of discontinuities (i.e., serial discontinuity concept; Ward and Stanford 1983, Rice et al. 2001). Artificial structures, such as dams, and tributary confluences are 2 major types of discontinuities in a river system (Rice et al. 2001, 4 5 6 7 8 9
E-mail addresses:
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ities is crucial when generating research hypotheses and formulating predictive models (Stanford and Ward 2001). Dam-caused discontinuities are pervasive in major river ecosystems worldwide, and they pose a threat to aquatic biodiversity (Nilsson et al. 2005). Despite the environmental impacts caused by dams and impoundments, dams continue to be constructed worldwide, especially in Asia, where increased human demands on water resources and the need for flood control severely constrain the preservation of biodiversity (Dudgeon 2002). When construction is necessary and unavoidable, operational modifications and technological advancements in dam construction can reduce the environmental impacts of dams (Olmsted and Bolin 1996, Schmidt et al. 1998). For example, timing, duration, and magnitude of flow releases can be controlled to maintain quasi-natural flow regimes (Morgan et al. 1991, Bednarek and Hart 2005), and multilevel water release can moderate temperature anomalies below the dam (Olmsted and Bolin 1996, Vinson 2001). An understanding of the efficacy of such measures in reducing environmental impacts is critical when designing dam structures and planning operational schemes. Tributaries might be important attenuators of degradation of main channels caused by dams (Armitage 1978, Petts and Greenwood 1985, Munn and Brusven 1991, Takao et al. 2008). Relatively few studies have rigorously linked faunal organization to environmental gradients (Storey et al. 1991, Stevens et al. 1997). Two critical knowledge gaps exist in our understanding of the role of tributaries on regulated rivers. First, previous studies have focused primarily on water-quality variables, such as clarity, temperature, and dissolved O2, without quantifying one of the most prominent effects of dams and tributaries, changes in the composition of substrata. Second, the degree to which tributaries attenuate the effects caused by dams has been quantified only rarely in the context of pre-impoundment conditions (e.g., Storey et al. 1991, but see Vinson 2001). This lack of pre-impoundment data constrains such analyses in many cases, but data collected above the impoundment or from adjacent unregulated tributaries can be used as alternative data when the confounding effects of longitudinal transitions of biota along river continua are minimal (Stanford and Ward 2001). Our objective was to examine longitudinal patterns of environmental variables and macroinvertebrate assemblages in riffles across 2 discontinuities (dam and tributary confluence). The discontinuity caused by the dam was examined at a dam with a multilevel water intake. We hypothesized that changes in
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macroinvertebrate assemblage structure below the dam could be explained largely by the changes in substrate environments and that changes in water quality (e.g., temperature and dissolved O2) would be small, at least in part, because of the multilevel water intake. We also predicted that a tributary below the dam would attenuate the discontinuity caused by the dam by providing relatively fine substrate materials. Methods Study area The study was conducted around Agi-gawa Dam (110 km from the river mouth, 418 m above sea level [asl]) along the Agi-gawa River, a tributary in the Kisogawa River system in central Japan (lat 35823’42’’– 35826’49’’N, long 137825’12’’–137828’01’’E; Fig. 1). The Agi-gawa River is a 3rd- to 4th-order river with a naturally sand-rich bed derived from the weathered granite that characterizes the local geology (Moriyama 1987). The Agi-gawa Dam (lat 35825’32’’N, long 137825’55’’E), which began operating in 1990, is a 102-m-high rockfill dam with a catchment area of 82 km2, a storage capacity of 4.8 3 107 m3, a mean depth of ;45 to 50 m at the dam site, and a hydraulic residence time of 71 d. Three small subdams at the upstream end of the impoundment trap particulates. The dam is operated for multiple purposes, including flood control, industrial water supply, city water supply, and maintenance of baseflow. The flow regime was altered by the impoundment such that discharge is now relatively constant during November–April (Fig. 2A–C). The impounded water usually is stratified from April to December and is vertically mixed from January to March (Fig. 2D). A multilevel water intake is operated to moderate the effect of the dam on the thermal regime of the outlet water (Fig. 2D, E). Field sampling was conducted between 15 and 18 March 2005, a period of stable flow (1.3 m3/s; Fig. 2B, C). No rainfall occurred during the study period. Four study segments (length: 1–2 km each) were selected consisting of 2 study reaches each (a total of 8 reaches) along an 8.7-km-long stretch of the Agi-gawa River and a 1.0-km-long stretch of the Iinuma-gawa Stream (catchment area ¼ 24 km2), which flows into the Agi-gawa River 2.7 km downstream of the dam (Fig. 1, Table 1). The 4 study segments were: 1) upstream of the dam and impoundment (UD), 2) downstream of the dam (DD), 3) downstream of the tributary confluence (DC), and 4) in the lower tributary (TR). Each study reach was 160 m long with several pool–riffle sequences, and all reaches were .300 m apart. A preliminary survey indicated that TR and UD had comparable geology and biota, except
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FIG. 1. Study area showing 8 study reaches in 4 stream segments along the Agi-gawa River and Iinuma-gawa Stream, Gifu Prefecture, Japan. Gray circles denote the reaches, which are numbered from upstream to downstream within each segment: UD1 and UD2 are upstream of the dam and impoundment, DD1 and DD2 are downstream of the dam, DC1 and DC2 are downstream of the tributary confluence, and TR1 and TR2 are in the tributary. The 3 small rectangles at upstream ends of the impoundment are subdams, constructed to reduce inputs of particulates to the impoundment.
that UD was characterized by a wider channel and greater discharge, probably because of their differing catchment areas (Table 1). The 2 tributaries that joined the main channel between UD1 and UD2 were assumed to cause negligible changes in the biophysical characteristics of the main channel because of their extremely low discharge. The dominant land use along the study reaches was paddy fields, along with sparse riparian forests. Measurements at the 2 reaches within
the same segment were completed on same day, and reaches were surveyed in an upstream direction. Physical environment At each study reach, 6 riffles were selected, and 1 sampling location (50 cm 3 50 cm quadrat) was established in the midchannel area of each riffle. Physical environmental variables were measured before invertebrate collection at each sampling location. Substrate coarseness was measured by gently
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FIG. 2. Precipitation (A), inflow to the impoundment (B), outflow from the Agi-gawa Dam (C), depth of the water intake measured from the impoundment bottom and the vertical profile of temperature within the impoundment (D), and temperature of inflow and outflow (E). The vertical broken line indicates the study period. Note that the y-axes for (B) and (C) have a logarithmic scale. In panel (D), the dotted line denotes the water surface.
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TABLE 1. General characteristics of the 4 study segments. The distance of the tributary (TR) from the dam is the sum of the distances from the dam to the tributary confluence and from the confluence to TR; values in parentheses show the distance from the confluence. Data are shown as mean 61 SD (n ¼ 6) where applicable. UD ¼ upstream of the dam and impoundment, DD ¼ downstream of the dam, DC ¼ downstream of the tributary confluence. Study segments and reaches UD
DD
DC
TR
Variables
1
2
1
2
1
2
1
2
Distance from dam (km) Elevation (m asl) Catchment area (km2) Slope (%)a Discharge (m3/s)b Wetted width (m)b
5.3 469 21.5 3.5 1.6 6 0.3 10.3 6 2.1
4.1 432 35.4 2.6 1.4 6 0.8 10.2 6 1.8
1.1 304 83.4 1.1 1.1 6 0.4 8.4 6 1.9
2.4 286 84.7 1.4 1.5 6 0.4 8.9 6 1.6
2.9 284 106.6 1.7 2.3 6 0.5 18.5 6 1.2
3.4 279 108.8 0.9 1.4 6 0.1 14.4 6 1.7
4.3 (1.6) 324 20.7 2.6 0.4 6 0.1 4.5 6 0.6
3.3 289 21.2 2.5 0.5 6 0.1 5.6 6 1.2
a
Calculated from geographical information system elevation data (50-m resolution; Geographical Survey Institute of Japan) Based on 6 transect lines across the channel at each study reach; flow discharge estimated from velocity at 60% of the depth and water depth at 5 equidistant points along each transect b
floating a Plexiglas observation box (50 cm 3 50 cm 3 10 cm deep) divided into 4 grid squares (25 cm 3 25 cm) on the surface of the water so that the grid was projected onto the streambed. The size of the substrate material was coded based on the intermediate-axis length as follows: 1 ¼ sand (particles ,2 mm), 2 ¼ gravel (2–16 mm), 3 ¼ pebbles (17–64 mm), 4 ¼ cobbles (65–256 mm), and 5 ¼ boulders (257–1024 mm). The percentage of each grid square covered by each coded category was measured, and substrate coarseness in the grid square was calculated as: Substrate coarseness ¼ R (size category code 3 % covered by that category). The average substrate coarseness of the 4 grid squares was used to represent the substrate coarseness at each sampling location. Water depth and current velocity at 60% of the depth were measured at the center and at each corner of the quadrat with a ruler and an electromagnetic current meter (AEM-1D; Alec Electronics Co., Ltd., Kobe, Japan). The averages of the 5 velocity and depth measurements was used to represent velocity and depth at each sampling location. Water quality and transported materials Water quality (water temperature, conductivity, turbidity, and dissolved O2 [DO]) was measured 4 times (every 6 h starting from 0600 h) near the upstream end of each reach to obtain diel changes. Water temperature and DO were measured with a thermometer and a DO meter (YSI-58; Yellow Springs Instruments, Yellow Springs, Ohio), respectively, and conductivity and turbidity were measured with a water-quality probe (U-10; Horiba Ltd., Kyoto, Japan). Bedload typically refers to sediment particles trans-
ported during high-flow events. Measurements during high-flow events were not feasible or safe at the study site. Therefore, particles transportable near the streambed at baseflow were collected to measure the bedload flux. Three bedload sediment samples were collected using handmade bedload traps (mouth opening ¼ 20 cm 3 30 cm, mesh size ¼ 250 lm, catch bag length ¼ 1 m) positioned at equal intervals in a row perpendicular to flow at the upstream end of each reach. The 3 traps were placed with the opening flush with the riverbed and were opened for ;1 h, during which the current velocity at the center of the mouth opening was measured twice (at the beginning and end of the sampling) to estimate the water volume (m3) passing through the traps. Each bedload sample was later combusted in a muffle furnace (FO610; Yamato Scientific Co., Tokyo, Japan) at 5508C for 4 h, and the inorganic fraction was determined with an electronic balance (AW220; Shimadzu Co., Kyoto, Japan). The bedload flux (mg/m3) was obtained by dividing the mass of inorganic sediment by the water volume passing through the trap. One sample from reach DD1 was contaminated and was excluded from subsequent analyses. Suspended particulate organic matter (POM) and zooplankton were collected using 2 drift nets (mouth opening ¼ 20 cm 3 20 cm, mesh size ¼ 250 lm, catch bag length ¼ 0.8 m) anchored side-by-side perpendicular to the flow and near the bedload collection points. Samples were collected 4 times (every 6 h starting at 1800 h) for 20 min each time. Each sample was immediately preserved in 5% formalin. The water volume passing through the nets was calculated as described for the bedload traps. The samples were later divided into 2 size fractions: drifting coarse POM
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(DCPOM, .1 mm) and drifting fine POM (DFPOM, 0.25–1 mm). Zooplankton were separated from the DFPOM samples with forceps under a dissection microscope (MZ12; Leica Microsystems GmbH., Wetzlar, Germany), identified to the lowest taxonomic level possible following Mizuno and Takahashi (2000) and Tanaka (2002), and counted. Total drift density of zooplankton (number of individuals/m3) at each trap was determined by dividing the total number of zooplankton individuals by the water volume passing through the trap. Residual DFPOM and DCPOM samples were dried at 608C for 48 h and weighed. The samples were then combusted at 5508C for 2 h in the muffle furnace, reweighed, and the ash-free dry mass (AFDM) of the residual DFPOM and DCPOM (mg/m3) was calculated. Values from the 2 drift nets on each sampling occasion were averaged to give a total of 4 values for each reach. Surface water for phytoplankton analysis was sampled in a polyethylene bottle (250 mL) 4 times (every 6 h starting at 1800 h) near each of the bedload collection points. Each sample was immediately preserved in 5% formalin. Well-mixed samples were placed in a counting chamber (Burker–Turk hemocytometer; ERMA, Tokyo, Japan), identified to the lowest taxonomic level possible with the taxonomic keys in Krammer and Lange-Bertalot (1986–1991) and Hirose and Yamagishi (1977), and counted under a light microscope (BX50; Olympus Co., Tokyo, Japan). The total drift density of the phytoplankton was then calculated (cells/mL). Benthic organic matter, macroinvertebrates, and periphyton All bed materials to 20-cm depth were collected with a Surber sampler (frame size ¼ 50 cm 3 50 cm, mouth opening ¼ 50 cm 3 50 cm, mesh size ¼ 250 lm, catch bag length ¼ 1 m) at each sampling location. Immediately after collection, invertebrates and organic matter were brushed off substrates larger than pebbles (.16 mm) and sieved through a 0.25-mm-mesh sieve. The sieved samples and substrate material smaller than pebbles were mixed in a container and preserved in 5% formalin in the field. The material in each container was later divided into 2 size fractions using 1-mm- and 0.25-mm-mesh sieves. All material retained on the 0.25-mm sieve was mixed and divided into 2n subsamples (maximum n ¼ 32) using a splitter (Idea Co., Tokyo, Japan) following the method described by Vinson and Hawkins (1996). All macroinvertebrates in subsamples of both size fractions were counted and identified to the lowest taxonomic level possible, usually genus or species, using the taxonomic keys in Kawamura and Ueno
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(1973), Merritt and Cummins (1996), Kathman and Brinkhurst (1999), Kawai and Tanida (2005), and Torii (2006). Invertebrate taxa also were classified into 5 functional feeding groups (FFGs) according to the above references. FFGs were defined as: collector– filterer, collector–gatherer, predator, scraper, and shredder. If a species belonged to "2 FFGs, the number of individuals was apportioned across the FFGs. After all invertebrates were removed, AFDM (mg/m2) of benthic coarse POM (BCPOM, .1 mm) and benthic fine POM (BFPOM) in subsamples was determined by the same procedure as described for drifting POM. The total number of invertebrate individuals and the AFDM of BFPOM (mg/m2) in each sample were estimated by multiplying results by the corresponding 2n value. The number of taxa (/m2), density of invertebrates (/m2), number of FFGs, and relative abundance (%) of each FFG in each sample were calculated as the sum of the values in both size fractions. Shannon’s diversity index (H 0 ), Simpson’s evenness index, and % Ephemeroptera, Plecoptera, Trichoptera (EPT) individuals were calculated (Krebs 1999). One sample from UD2 was lost and was not included in subsequent analyses. Periphyton were sampled from a cobble adjacent to each sampling location. Periphyton were removed from a 5-cm 3 5-cm area on the upper surface of each cobble with a toothbrush. Each sample was placed in a separate container with 250 mL of water. Within 24 h after collection, a subsample of the well-mixed contents of each container was filtered through a glass-fiber filter (GF/C; Whatman Co., Maidstone, UK). Each filter was placed in a separate vial with 20 mL of 99.5% ethanol. The subsamples were stored in a dark refrigerator at 48C for 24 h. The extracted pigments were measured by using a spectrophotometer (U-1800; Shimadzu Co., Kyoto, Japan) following the method of Lorenzen (1967). Statistical analyses Variables measured at the same spatial or temporal scales were more or less associated. Therefore, nested multivariate analysis of variance (MANOVA) was used to test whether any of the variables measured at the riffle scale differed among segments (UD, DD, DC, and TR). Two replicate reaches were nested within each segment, and measurements within each reach were treated as subsamples. We assumed that temporal variability would be greater than spatial variability within each reach for variables measured over 24 h (e.g., water quality) and that the opposite would hold true for variables measured only once (e.g., macroin-
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vertebrates). Thus, subsamples within each reach were either spatially or temporally replicated depending on the variable type. Temporal replicates (4 samples collected every 6 h) were treated as a repeated factor (time factor). A nested MANOVA was used for the variables quantified once at each location (e.g., macroinvertebrates), and a nested repeated-measures MANOVA was used for variables quantified over a 24h period at each reach (e.g., water quality). When a significant difference was detected by MANOVA, each variable was tested separately with a nested analysis of variance (ANOVA) or nested repeated-measures ANOVA, as appropriate for the particular variable; the risk of inflating type-1 errors was reduced by Bonferroni adjustments. Temporal and spatial scales of bedload measurement were different from those of the other variables, so bedload was analyzed using a separate nested ANOVA. All analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) tests were done with R (version 2.7.1; R Development Core Team 2008). Tukey’s multiple comparison test was used if statistical significance was detected by ANOVA models. Residuals of each variable in each ANOVA model were checked with the Shapiro–Wilk normality test prior to the analyses, and normality was improved by using arcsine(x) or log(x þ 1) transformation when appropriate. Any significant changes in values for variables from UD to DD were interpreted as effects of the dam based on the assumption that conditions in UD, DD, and DC were similar to each other before the construction of the dam. Attenuating effects of the tributary were inferred when the changes from UD to DD were reversed from DD to DC. First, similarity of variables between TR and UD sites was confirmed statistically. If the values at DC were similar to the levels at UD or TR and different from values at DD, the discontinuity between DD and DC was assumed to have been caused by the tributary (abrupt recovery of the system toward the above-dam condition). Changes in variables from DD to DC also were examined at the reach scale to further elucidate tributary-caused discontinuity by comparisons among reaches with one-way ANOVA and Tukey’s test. Multivariate analyses were done with PRIMER software (version 5.0; Primer-E Ltd., Ivybridge, UK) to compare the structure of invertebrate assemblages among segments. Bray–Curtis coefficients were used to calculate a dissimilarity matrix between samples based on the number of different taxa in the macroinvertebrate assemblages. In addition, the similarity percentage species contributions (SIMPER) function of PRIMER was used to identify those taxa
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responsible for the observed differences in assemblages. SIMPER uses the Bray–Curtis similarity matrix to calculate how much each taxon contributes to the observed dissimilarities between groups. Macroinvertebrate assemblage organization in relation to environmental gradients across the dam and tributary confluence was analyzed by canonical correspondence analysis (CCA) in PC-ORD (version 4.0; MjM Software Design, Gleneden Beach, Oregon). Two matrices were included in the analyses: 1) density of each taxon 3 sampling site (response variable), and 2) environmental variables 3 sampling site (explanatory variables). Rare taxa (,3% relative abundance in any of the samples), which accounted for 83.7% of total taxa, were excluded from the analysis. Only the environmental variables that were found to be significantly different among segments by ANOVA were included in the CCA. For variables that were not collected at all sampling locations (e.g., bedload and suspended material), average values from replicate subsamples were used for each location within respective reaches. In all, 47 samples with 28 taxa were used for the analyses. Hill’s scaling was used to construct an ecologically interpretable ordination (a ¼ 0.5). Monte Carlo simulations (1000 iterations) were used to test whether eigenvalues from the CCA were significantly greater than those generated from a randomized matrix. Normality of the density of each taxon and the environmental variables were improved by arcsine(x) or log(x þ 1) transformation. Results Environmental variables Bedload samples consisted of sand-sized substrate materials (0.25–2 mm) and contained very little detritus of terrestrial or epilithic origin. Dominant zooplankton taxa were Bosmina longirostris (Branchiopoda) and Cyclopoida (Copepoda), and dominant phytoplankton were Asterionella formosa and Aulacoseira granulata (Bacillariophyceae) (Appendices 1, 2). Significant effects among segments were observed for environmental and biological variables that were measured multiple times and variables that were measured only once (MANOVA, Pillai-Bartlett tests, p , 0.0001). Thirteen variables differed significantly among segments (Table 2). DO was slightly, but significantly, lower in DC than in other segments, but this result did not indicate a dam effect. Conductivity, substrate coarseness, chlorophyll a, zooplankton, and phytoplankton were significantly higher at DD than at UD, whereas turbidity, % sand, % gravel, % pebble, bedload, DCPOM, and DFPOM were significantly lower at DD than at UD.
Environmental Water temperature (8C) DO (mg/L) Conductivity (mS/m) Turbidity (NTU) Depth (cm) Velocity (cm/s) Substrate composition % sand % gravel % pebble % cobble % boulder Substrate coarseness Bedload (mg/m3) Benthic Chlorophyll a (mg/m2) BCPOM (g/m2) BFPOM (g/m2) Drift DCPOM (mg/m3) DFPOM (mg/m3) Zooplankton (no./m3) Phytoplankton (cells/mL)
Variables
SD
12 12 12 12 12 12 5 5.2 4.1 2.0
3.3C 4.3 0.9
4.5 2.7A 21.1 7.3A 17.5 7.0B 40.8 7.7 16.0 14.3 3.4 0.3B 10.4 8.7B
8 6.4 6.4bC 8 3.2 2.1B 8 0.5 0.4B 8 195.1 36.2B
87.7A 12 9.3 12 1.9 12
1.5B 3.5B 4.6B 9.1 13.8 0.3A 3.6B
8 1.7 1.2C 8 1.6 0.7C 8 145.2 197.3A 8 420.6 244.5A
87.0 9.2 3.6
9.1B 12 5.1 12 1.6 12
18.5 9.3 3.5 10.5 3.1A 12.5 3.7A 0.0 0.1B 77.9 33.5C
1.5 6.0 15.7 45.9 30.9 4.0 3.6
12 12 12 12 12 12 6
4.6A 10.9A 7.8A 11.5 15.4 0.4B 56.3A
SD
DC
7.4 1.9 11.2 0.7B 3.6 0.1C 1.3 0.5A 24.4 5.3 46.6 16.0
n Mean
1.4 8 0.2A 8 0.1B 8 0.0B 8 6.7 12 17.1 12
SD
DD SD
n
F
Segment p
F
p
Reach (segment)
6.5 4.3A 17.4 6.3A 25.7 8.3A 37.2 9.3 13.1 13.4 3.3 0.3B 30.4 20.7A 12 12 12 12 12 12 6
8 6.0 8 4.1 8 0.2 8 23.1
4.5B 2.1B 0.5B 5.7D
,0.0001 ,0.0001 ,0.0001 0.0048 0.0046 ,0.0001 ,0.0001 38.83,40 ,0.0001 7.83,40 0.0003 3.03,40 0.0398
11.53,40 17.73,40 8.73,40 5.03,40 5.03,40 15.43,40 15.13,15
1.44,40 3.54,40 3.74,40
2.54,40 2.94,40 3.94,40 3.14,40 3.24,40 3.14,40 7.14,15
0.2481 0.0163 0.0122
0.0594 0.0305 0.0086 0.0274 0.0229 0.0248 0.0021
8 13.13,12 0.0004 3.24,12 0.0505 8 129.43,12 ,0.0001 18.34,12 ,0.0001 8 102.53,12 ,0.0001 80.24,12 ,0.0001 8 129.13,12 ,0.0001 2.84,12 0.0763
12 2.3 1.4C 12 12 17.2 13.8 12 12 4.3 3.6 12
12 12 12 12 12 12 6
8 6.7 1.5 8 1.93,12 0.1763 1.04,12 0.4413 8 12.3 0.2A 8 50.83,12 ,0.0001 1.04,12 0.4352 8 5.7 0.1A 8 1211.13,12 ,0.0001 22.94,12 ,0.0001 8 1.0 0.0A 8 36.23,12 ,0.0001 1.04,12 0.4504 12 25.2 6.6 12 3.83,40 0.0172 4.24,40 0.0059 12 57.4 11.1 12 4.13,40 0.0131 0.74,40 0.6188
n Mean
TR
ANOVA
3.212,12 2.012,12 0.412,12 1.312,12
– – –
– – – – – – –
11.212,12 23.812,12 1.412,12 0.412,12 – –
F
0.0265 0.1206 0.9366 0.3113
– – –
– – – – – – –
,0.0001 ,0.0001 0.2973 0.9406 – –
p
Time (reach [segment])
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7.2 20.7 25.7 32.7 13.6 3.2 47.8
7.0 12.2 5.3 0.0 31.7 37.5
n Mean
6.3 3.1 8 12.1 1.1A 8 3.1 0.4D 8 0.9 0.4A 8 26.8 7.8 12 58.2 20.9 12
Mean
UD
Study segments and reaches
TABLE 2. Mean (SD) values of environmental, benthic, and planktonic variables at each study segment and results of nested or nested repeated-measures analyses of variance (ANOVA) (see Statistical analyses section for explanation); n ¼ total number of subsamples in each segment. Significance level a for ANOVA was corrected by Bonferroni adjustments when necessary. Effects that were significant with corrected p-values are shown in bold font. Values of a variable labeled with the same letters (A–D) are not significantly different (Tukey’s multiple comparison test). UD ¼ upstream of the dam and impoundment, DD ¼ downstream of the dam, DC ¼ downstream of the tributary confluence, TR ¼ in the tributary. DO ¼ dissolved O2, POM ¼ particulate organic matter, BCPOM ¼ benthic coarse particulate organic matter, BFPOM ¼ benthic fine POM, DCPOM ¼ drifting coarse POM, and DFPOM ¼ drifting fine POM.
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FIG. 3. Mean (61 SD) values of % sand (A), % gravel (B), substrate coarseness (C), periphyton chlorophyll a (chl a) (D), turbidity (E), drifting fine particulate organic matter (DFPOM) (F), zooplankton drift density (G), and phytoplankton drift density (H) in reaches downstream of the dam (DD) and downstream of the confluence of the tributary (DC). The vertical broken line indicates the location of the tributary confluence, and the vertical dotted lines indicate the locations of the study reaches along the main channel. Means labeled with the same letters do not differ significantly. DFPOM did not differ significantly among reaches.
TABLE 3. Summary of nested analysis of variance (ANOVA) results testing for the effects of segment, within which reaches were nested, for variables related to macroinvertebrate assemblage structure. Bold font denotes statistically significant effects after Bonferroni adjustments. H 0 ¼ Shannon diversity; EPT ¼ Ephemeroptera, Plecoptera, Trichoptera, FFG ¼ functional feeding group. ANOVA Segment
Reach (segment)
Variable
F
p
F
p
Number of taxa Density H0 Evenness % EPT % FFG Collector–filterer Collector–gatherer Predator Scraper Shredder
14.13,40 58.73,40 17.13,40 13.83,40 1.53,40
,0.0001 ,0.0001 ,0.0001 ,0.0001 0.2272
3.34,24 3.64,24 1.84,24 2.84,24 5.24,25
0.0190 0.0142 0.1565 0.0379 0.0018
18.53,40 ,0.0001 2.13,40 0.1165 17.53,40 ,0.0001 1.53,40 0.2336 27.03,40 ,0.0001
1.54,40 1.04,40 1.04,40 1.04,40 10.84,40
0.2115 0.4384 0.4310 0.4370 ,0.0001
Substrate composition and coarseness, BCPOM, BFPOM, and zooplankton drift density did not differ significantly among the unregulated reaches, UD, and TR (Table 2). The mean values of 11 of the 12 environmental variables (all except conductivity) at DC were more similar to values at TR or UD than to values at DD. Percent sand (Fig. 3A), % gravel (Fig. 3B), substrate coarseness (Fig. 3C), benthic chlorophyll a (Fig. 3D), turbidity (Fig. 3E), DFPOM (Fig. 3F), zooplankton drift density (Fig. 3G), and phytoplankton cell density (Fig. 3H) were significantly higher or significantly lower at DD than at DC, in accordance with the attenuating effects of the tributary in our prediction. Values for these variables differed significantly between DD and DC but did not differ between DC and UD or TR (Table 2). Analyses at the reach scale indicated that changes from DD to DC could not be explained by the location of the confluence for phytoplankton and zooplankton densities. Densities in the reach immediately upstream of the confluence (DD2) did not differ from those in the reach immediately downstream of the confluence (DC1) (p . 0.05; Fig. 3G, H).
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FIG. 4. Mean (þ1 SD) taxonomic richness (A), total density and functional feeding groups (FFGs) (B), Shannon diversity (H 0 ) (C), Simpson’s evenness (D), % Ephemeroptera, Plecoptera, Trichoptera (EPT) individuals (E), % collector–filterers (CF) (F), % collector– grazers (CG) (G), % predators (PR) (H), % scrapers (SC) (I), and % shredders (SH) (J) in study segments. Bars labeled with the same letters are not significantly different among segments. UD ¼ upstream of the dam and impoundment, DD ¼ downstream of the dam, DC ¼ downstream of the tributary confluence, TR ¼ in the tributary.
Macroinvertebrate assemblage structure In total, 233,934 individuals in 172 invertebrate taxa were collected (see Appendix 3 for taxa with .0.1% occurrence). Mean density and numbers of taxa in each study segment ranged from 1586 to 9022 individuals (ind.)/0.25 m2 and from 45.7 to 60.6 taxa, respectively. Seven of 10 macroinvertebrate assemblage-structure variables differed significantly among segments (Table 3, Fig. 4A–J). Number of taxa (Fig. 4A), total density (Fig. 4B), H 0 (Fig. 4C), evenness (Fig. 4D), % collector–
filterers (Fig. 4F), % predators (Fig. 4H), and % shredders (Fig. 4J) differed significantly between UD and TR. Values for all of these variables except % shredders were higher at TR than at UD. In contrast, only total density (Fig. 4B), % collector–filterers (Fig. 4F), % predators (Fig. 4H), and % shredders (Fig. 4J) differed significantly between UD and DD, indicating that differences in community structure were more evident between unregulated segments (UD and TR) than between segments upstream and downstream of
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TABLE 4. Bray–Curtis dissimilarity matrix of macroinvertebrate assemblages among study segments. UD ¼ upstream of the dam and impoundment, DD ¼ downstream of the dam, DC ¼ downstream of the tributary confluence, TR ¼ in the tributary. Segment
DD
DC
TR
UD DD DC
61.2 —
54.3 40.8 —
46.7 56.3 42.0
the dam (UD and DD). Total density was significantly higher at DD than at UD or at DC; total density at TR was higher than at UD but lower than at DD or DC (Fig. 4B). The change in total density from UD to DD was accompanied by an increase in % collector–filterer and decreases in % predators and % shredders, whereas % collector–gatherers and % scrapers, the most abundant FFGs, did not differ significantly among segments (Fig. 4F, H, J). Number of taxa (Fig. 4A), total density (Fig. 4B), H 0 (Fig. 4C), evenness (Fig. 4D), % collector–filterers (Fig. 4F), and % predators (Fig. 4H) differed significantly between DD and DC. Each of these variables changed in the direction such that mean values became more similar to those at TR than to those at DD. Percent EPT individuals did not differ significantly among segments (Fig. 4E). Assemblage composition differed most between UD
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and DD (Bray–Curtis dissimilarity ¼ 61.2%; Table 4). The lowest dissimilarity values were between DD and DC and between TR and DC, whereas the dissimilarity between UD and TR was intermediate. DD and UD were dissimilar because Propappus volki and Drunella sachalinensis were more abundant and Uracanthella punctisetae was less abundant at UD than at DD; these taxa were the 3 largest contributors to the dissimilarity between 2 segments and accounted for 22.0% of the observed dissimilarity. Assemblage composition at DC and DD were dissimilar because Choroterpes altioculus and Elminae (Coleoptera) were more abundant and U. punctisetae was less abundant at DC than at DD (20.4% of the dissimilarity). Assemblage composition differed between TR and UD because P. volki and Diamesinae were less abundant and Drunella sp. were more abundant at TR than at UD (18.4% of the dissimilarity). Macroinvertebrate organization along environmental gradients Three CCA ordination axes with eigenvalues of 0.157, 0.047, and 0.016 were produced (Table 5). Environmental variables significantly influenced macroinvertebrate assemblages (Monte Carlo test; p ¼ 0.001). Ordination scores were plotted for only the first 2 axes because of the small eigenvalue and variance of species data explained by axis 3 (Table 5). Sampling locations within each segment clustered close together
TABLE 5. Summary statistics of the canonical correspondence analysis (CCA) done on macroinvertebrate assemblages and environmental variables in the 4 study segments. Values for each environmental variable in the axis columns denote the correlation coefficients of each variable with the corresponding axis. DO ¼ dissolved O2, POM ¼ particulate organic matter, DCPOM ¼ drifting coarse POM, DFPOM ¼ drifting fine POM. CCA axes Statistic Eigenvalue Variance in species data % of variance explained Cumulative % explained Pearson’s correlation species-environment Variables DFPOM Phytoplankton Bedload Turbidity DCPOM Chlorophyll a Zooplankton Substrate coarseness % sand % pebble % gravel DO Conductivity
1
2
3
Contribution to axes 1 and 2 (vector length)
0.157
0.047
0.016
—
38.6 38.6 0.919
11.7 50.3 0.876
4.1 54.4 0.648
— — —
0.42 $0.45 0.45 0.37 0.43 $0.27 $0.33 $0.34 0.35 0.31 0.25 0.08 $0.15
0.24 0.14 0.00 $0.23 $0.02 0.30 0.14 0.08 $0.07 $0.02 $0.08 0.21 $0.12
$0.01 $0.02 0.11 $0.01 $0.01 $0.06 0.22 0.04 0.04 0.02 $0.05 0.09 0.11
0.485 0.468 0.445 0.432 0.431 0.399 0.363 0.354 0.351 0.306 0.257 0.226 0.189
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in the ordination plot, and different segments were clearly distinguished by the first 2 axes (Fig. 5A). The taxa characteristic of each segment in the CCA plot were consistent with those identified as important by SIMPER. Thus, the CCA plot reflected assemblage structures along the environmental gradients. For example, P. volki and D. sachalinensis plotted near UD reaches, whereas U. punctisetae plotted near DD, and C. altioculus and Elminae plotted near DC reaches. Drunella sp. plotted closer to TR than to UD reaches (Fig. 5B). Axis 1 was positively correlated with bedload, DCPOM, DFPOM, turbidity, % sand, % pebble, % gravel, and negatively correlated with phytoplankton cell density, substrate coarseness, zooplankton drift density, and chlorophyll a (Table 5, Fig. 5A). Values of bedload, DCPOM, DFPOM, turbidity, % sand, % pebble, and % gravel were relatively high in UD and TR segments, whereas values of the phytoplankton cell density, substrate coarseness, zooplankton drift density, and chlorophyll a were generally low in UD and TR segments and high in DD segment (Fig. 5A, Table 2). Vector lengths for DO and conductivity were shorter than those of other environmental variables and probably reflected the ecologically negligible changes across the dam and tributary confluence. UD and DD, which were separated by the dam, were most clearly differentiated along axis 1. Therefore, axis 1 was interpreted as characterizing the influence of the dam (Fig. 5A). Unregulated TR reaches also were separated from the DD and DC locations along axis 1, and this result further supported our interpretation of the axis. Correlation coefficients of variables with axis 2 generally were low compared with those for axis 1 (Table 5). Axis 2 was positively correlated with chlorophyll a and negatively correlated with turbidity. Chlorophyll a values tended to be lowest at TR, whereas turbidity was relatively high at TR (Fig. 5A, Table 2). Main channel locations (UD and DD) were separated from the tributary (TR) and the tributaryaffected (DC) sites along axis 2. Therefore, Axis 2 was interpreted as distinguishing the tributary and main channel. The CCA plots and the dissimilarity matrix (Table 4) indicated that assemblage structure differed most between UD and DD and changed between DD to DC to become similar to TR, which was most similar to UD. Discussion Macroinvertebrate assemblage organization was distinct across both the dam and tributary confluence discontinuities. The greatest differences in assemblage structure occurred across the dam (between UD and
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DD). At segments below the dam, assemblage structure differed across the tributary confluence (between DD and DC), such that the assemblage at DC was similar to the assemblages at TR and UD. These results are in agreement with the predictions of the serial discontinuity concept (Ward and Stanford 1983). Because pre-impoundment data were unavailable, we were unable to ascertain whether the environmental and biological conditions of the segments below the dam were originally (before dam construction) similar to those in the UD segment. The following discussion assumes that this was the case. Assemblage structure at TR was comparable to assemblage structure at UD, even though the elevation of the tributary was as low as that of the main stem segments below the dam. Thus, the confounding effects of elevation on faunal zonation (Ce´re´ghino et al. 1997) appeared to be negligible. The effects of local geomorphology on assemblage structure (see Walters et al. 2003) also must be considered because the slope of the UD segment was ;23 the slope of the DD segment (Table 1). We expected substrate at DD, which had a lower slope than UD, to be similar to or finer than substrate at UD if the continuity of sediment transport was intact. Substrate was significantly coarser below than above the dam, indicating disconnected sediment transport. These points support our argument that changes caused by the dam accounted for a large portion of the observed changes in the variables below the dam. Dam-caused discontinuity As predicted, the effects of the dam on water quality were slight. Conductivity was higher at DD than at UD, but the magnitude of the change (;2 mS/m) was regarded as biologically negligible. Turbidity was slightly lower below than above the dam, which is consistent with findings of previous studies (e.g., Stevens et al. 1997). The source of the turbidity was probably DFPOM rather than drifting plankton from the impoundment because drifting POM was lower below than above the dam, whereas plankton was substantially more abundant below than above the dam. Water temperature did not change across the dam, apparently because the input water from the impoundment had not warmed during the study period (Fig. 2D). Types of available food resources changed below the dam. The substrate coarseness, and periphyton and drifting plankton were higher at DD than at UD. The riverbed environment is highly susceptible to change because dams interrupt sediment transport (Petts et al. 1993, Kondolf 1997). Lower bedload flux and flow
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FIG. 5. Biplots drawn from the canonical correspondence analysis (CCA) results showing the relationships among segment types and environmental variables (A) and dominant taxa (B). Only the environmental variables that were significantly different among study segments by nested or nested repeated measures analysis of variance (Table 2) were included in the CCA.
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pulses probably resulted in an algal bloom (i.e., increased chlorophyll a) below the dam (see Collier 2002). Palatable diatoms were overwhelmingly dominant in the periphyton, whereas filamentous green algae (e.g., Cladophora), which are not preferred food resources for macroinvertebrates (Dodds and Gudder 1992), did not occur (TM, unpublished data). Macroinvertebrate densities were significantly higher at DD than at UD, whereas taxon richness was approximately the same in the 2 segments. Increased downstream density of macroinvertebrates is one major consequence of dams (Spence and Hynes 1971, Munn and Brusven 1991, Petts et al. 1993, Vinson 2001), except for hydropower dams, below which a decrease in density is common (Trotzky and Gregory 1974, De Jalon et al. 1994). The decrease in bedload flux, which can disturb invertebrates (e.g., Shaw and Richardson 2001), might have directly favored crawling taxa such as U. punctisetae and Naididae; these taxa characterized the assemblage below the dam (Fig. 5B). The reduced bedload probably negatively affected some species that depend on fine-sediment deposition; for example, densities of burrowing (P. volki) and casebearing species (Glossosoma sp.) were lower at DD than at UD sites. In particular, P. volki, which prefers a substrate composed of abundant fine sediment (Torii 2006), was a key species that distinguished the assemblages between UD and DD. Besides the greater abundance of palatable food resources for grazers, the DD segment was characterized by drifting highquality plankton-rich organic matter (Spence and Hynes 1971, Richardson 1984, Mackay and Waters 1986, Valett and Stanford 1987, Doi et al. 2008). The higher densities of filter-feeding Hydropsyche species and % collector–filterers at DD than at UD are attributable to the higher quality of drifting food resources at DD. Tributary-caused discontinuity The tributary confluence appeared to attenuate the effects of several biologically important environmental changes associated with the dam. Turbidity at DC was comparable to turbidity at UD, probably because the tributary reconnected the main stem to upstream source areas of DFPOM (Stevens et al. 1997, Kiffney et al. 2006). The most obvious tributary-related discontinuity was the change in the substrate composition; the substrate was finer, and had higher % sand and % gravel, at DC than at DD. Responses of key taxa that distinguished assemblages above and below the confluence were explained by the environmental changes and their habitat preference. For example, Elminae and C. altioculus, which were more abundant
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at DC than at DD, prefer habitats with fine sediment (Kawai and Tanida 2005), whereas U. punctisetae was present at DD and was less abundant at DC than at DD. The importance of a tributary in mediating substrate characteristics of the main stem has been highlighted in unregulated rivers (Rice et al. 2001, Benda et al. 2003) and inferred for dammed rivers (Petts and Greenwood 1985, Stevens et al. 1997). Rice et al. (2001) and Kiffney et al. (2006) both emphasized the role of tributaries in increasing the coarseness of a riverbed, whereas our study and that of Stevens et al. (1997) indicate that tributaries are sources of fine materials that decrease the coarseness of the main stem substrate. This disparity with regard to how tributaries change the substrate of main stems appears to depend on differences in the prevailing geomorphic processes in regulated and unregulated rivers. The abundance of epilithic food resources was lower at DC than at DD and was similar to levels at UD. However, contrary to our prediction, the bedload flux was not higher at DC than at DD. An increase in the bedload flux would have partly explained the decrease in algal resources through abrasion processes. We think that a significant difference in fluxes of largersized bedload probably exists between DD and DC during floods. Close examination of drifting plankton along the channel across the confluence revealed that the discontinuity of these variables was not caused by inputs from the tributary (Fig. 3G, H). Plankton might have been depleted through predation by filter-feeding organisms (Doi et al. 2008). Removal of plankton by filter-feeding organisms, such as Hydropsyche (Richardson 1984, Walks and Cyr 2004), has been reported in lake outlets. In our study site, Hydropsyche was one of the dominant taxa at DD (Appendix 3). Also, the dominant phytoplankton (e.g., Asterionella formosa) and zooplankton were large enough to be trapped by Hydropsyche (net mesh size ¼ 53–432 lm; Wotton 1994), so these filter feeders could have consumed drifting plankton. Macroinvertebrate density increases below a confluence with a tributary when habitat conditions below a dam are degraded (e.g., Armitage 1978). In our study, total macroinvertebrate density was highest at DD, and the tributary caused a reduction in total density. Furthermore, TR had higher taxonomic richness and a higher H 0 value than did UD. Thus, the tributary apparently increased these diversity measures in the main channel at DC. This result supports the findings of previous studies that diversity measures increase across tributary confluences (Armitage 1978, Stevens et al. 1997, Rice et al. 2001, Kiffney et al. 2006, Takao et al. 2008). There are 3 possible explanations for this pattern. First, abrupt increases in environmental
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heterogeneity across the confluence might have affected macroinvertebrates by providing a diverse habitat (Rice et al. 2001, Benda et al. 2004b). Second, available food resources and invertebrates in the tributary flow might have allowed taxa from DD and taxa from TR to coexist in DC (Cellot 1996, Kiffney et al. 2006). Third, recruitment of colonizers might have been limited at UD compared with areas below the dam because the dam and the impoundment acted as a barrier to upstream migration (Watanabe and Omura 2007). Future research and management implications An increase in macroinvertebrate density below dams compared to above-dam sites commonly is accompanied by a significant reduction in taxonomic diversity, disappearance of EPT taxa, or dominance of dipterans (Munn and Brusven 1991, De Jalon et al. 1994, Vinson 2001). Such a pattern has been linked to the negative effects of cold-water release from the dam (Lehmkuhl 1972, Ward 1974, Armitage 1978, Brittain and Saltveit 1989). In our study, the change in assemblage structure did not involve a substantial loss of taxa or the disappearance of EPT taxa; instead, a shift occurred in the dominant taxa. Our short-term survey was conducted during a period without any dam-caused temperature anomaly, but the assemblage structure probably reflected environmental conditions over the generation time of the macroinvertebrates (0.5–1 y for many taxa). Thus, moderation of the damcaused discontinuity, at least in water temperature, caused by the operation of the multilevel intake might have moderated dam impacts on macroinvertebrates below the dam. At least one other system without adjustable intakes in a similar biogeophysical setting should be examined to test the efficacy of multilevel intakes. An alternative is to examine long-term data before and after installation of such a facility (Vinson 2001). Abrupt shifts in food resources, during which plankton became the dominant component, are relatively well known below small dams and shallow impoundments (Mackay and Waters 1986, Akopian et al. 1999) but not in large dammed rivers. The depth of intakes relative to the impoundment strata characterized by high primary production probably affects the quantity of plankton discharged from impoundments. In general, plankton productivity, especially phytoplankton productivity, is highest at relatively shallow depths (Wetzel 2001). Thus, if impounded water is released from relatively deep areas, a practice that is typical for conventional dams with hypolimnetic releases, a low concentration of plankton will enter the downstream system. If the intention of the water
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release is to reduce the temperature anomaly downstream of the dam, relatively shallow water should be released (see Fig. 2D). Therefore, efforts to restore thermal regimes might cause shifts in the downstream energy base (see Doi et al. 2008). This issue deserves further attention because thermal restoration has not been evaluated with regard to the ecological consequences of shifts in the energy base. The serial discontinuity concept predicts that recovery of regulated rivers in downstream areas depends on the relative size of tributaries to the main channel (Ward and Stanford 1983). In our study, Iinuma-gawa Stream appeared to be sufficiently large to attenuate dam-caused downstream impacts. Our results also underscore the importance of the distance of a tributary confluence downstream from the dam for discontinuity formation. For example, if the tributary had entered above the point of natural plankton depletion, it might have further reduced plankton abundance, possibly through dilution. The degree to which a tributary affects the morphology of the main channel has been quantified using drainage area ratios (Benda et al. 2004a), but the efficacy of such an index for measuring the biological importance of a tributary has yet to be tested. A key management implication is that, when planning dam construction and restoration efforts of dam-caused environmental impacts, better decision making could be facilitated by understanding the role of tributaries in downstream areas (see Benda et al. 2007, Bigelow et al. 2007). For example, tributaries that currently attenuate the dam-caused environmental impacts should be preserved. Also, dam planners should take into account ecological roles of tributaries when determining appropriate dam placement. Acknowledgements We thank T. Makino of the Agi-gawa Dam Control Center for providing water temperature and dam discharge data, H. Ozawa and members of the field crew for help with our field work, N. Satomi, A. Yamakawa, and Y. Dotetsuka of Aqua Restoration Research Center for help with the laboratory analyses, G. Yoshinari and T. Torii for advice on the identification of macroinvertebrates, and K. Fritz and 2 anonymous referees for their valuable comments on earlier versions of our manuscript. The present study was partly supported by a Grant-in-Aid for Young Scientists (B18710031) from the Japan Society for the Promotion of Science (JSPS), and by a Grant for Applied Ecological Research (2006-04) from the Water Resources Environment Technology Center, Japan.
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APPENDIX 1. Mean (SD) density of zooplankton taxa from drift net samples (10$5 individuals/m3). UD ¼ upstream of the dam, DD ¼ downstream of the dam, DC ¼ downstream of the tributary confluence, TR ¼ in the tributary. UD
DD
DC
TR
Order
Taxon
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Branchiopoda
Daphnia galeata Bosmina longirostris Alona sp. Monospilus dispar Ostracoda Cyclops vicinus Eucyclops serrulatus Macrocyclops fuscus Paracyclops fimbriatus Cyclopoida Harpacticoida
0.0 0.0 0.0 0.0 3.2 0.0 3.1 0.0 3.4 7.9 3.2
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 7.4 0.0
698.9 12,797.2 49.4 0.0 0.0 374.8 3.2 95.7 0.0 2182.3 0.0
714.6 15,415.9 65.4 0.0 0.0 484.0 0.0 0.0 0.0 3697.3 0.0
0.0 16.0 3.5 2.4 2.2 0.0 2.7 0.0 0.0 34.3 2.2
0.0 28.6 2.0 0.0 0.3 0.0 0.9 0.0 0.0 26.7 0.3
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 35.3 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 66.9 0.0
Ostracoda Copepoda
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APPENDIX 2. Mean (SD) density of phytoplankton taxa found in water samples (cells/mL; except for Oscillatoria sp., for which the number of filaments/mL is given). UD ¼ upstream of the dam and impoundment, DD ¼ downstream of the dam, DC ¼ downstream of the tributary confluence, TR ¼ in the tributary. UD
DD
DC
TR
Class
Taxon
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Cyanophyceae Chrysophyceae Bacillariophyceae
Oscillatoria sp. Mallomonas sp. Stephanodiscus sp. Thalassiosira pseudonana Aulacoseira distans Aulacoseira granulata Melosira varians Asterionella formosa Diatoma mesodon Fragilaria capucina var. vaucheriae Meridion circulare var. constricta Synedra acus Synedra ulna Synedra sp. Cymbella minuta Cymbella sinuata Cymbella tumida Gomphonema parvulum Gomphonema quadripunctatum Gomphonema sp. Navicula cryptocephala Navicula cryptotenella Navicula decussis Navicula gregaria Navicula pupula Navicula sp. Achnanthes japonica Achnanthes lanceolata Achnanthes minutissima Achnanthes sp. Nitzschia dissipata Nitzschia linearis Nitzschia palea Nitzschia spp. Surirella angusta Trachelomonas sp. Closterium sp.
1.0 0.0 0.0 0.0 0.0 5.0 5.3 0.0 5.0 2.0 1.0 0.0 1.7 0.0 45.3 2.8 0.0 2.7 7.7 1.0 1.7 1.0 0.0 1.0 0.0 0.0 4.0 2.0 3.3 0.0 1.3 1.0 1.0 1.6 0.0 0.0 1.0
0.0 0.0 0.0 0.0 0.0 0.0 4.5 0.0 2.3 0.9 0.0 0.0 0.6 0.0 24.1 1.7 0.0 1.2 3.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 1.9 1.4 3.2 0.0 0.6 0.0 0.0 0.8 0.0 0.0 0.0
0.0 1.8 3.0 103.5 21.4 106.8 7.0 171.3 1.0 1.5 1.0 1.5 1.3 0.0 2.8 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 3.1 0.0 0.0 0.0
0.0 0.8 2.4 156.3 11.7 35.0 6.9 86.4 0.0 0.6 0.0 0.7 0.6 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 2.3 0.0 0.0 0.0
0.0 1.0 2.6 48.1 15.3 52.8 4.5 61.6 1.0 1.3 1.0 0.0 1.5 2.0 2.8 0.0 0.0 1.0 1.5 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 4.4 0.0 1.0 0.0
0.0 0.0 1.3 28.3 15.0 11.1 2.6 23.1 0.0 0.6 0.0 0.0 0.7 0.0 1.6 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.4 0.0 0.0 0.0
0.0 1.0 2.0 0.0 6.0 0.0 3.4 6.2 1.0 2.0 0.0 0.0 0.0 1.0 5.8 1.0 0.0 1.3 1.3 0.0 1.0 0.0 1.0 1.0 1.0 0.0 1.5 2.0 0.0 0.0 0.0 1.0 0.0 2.1 1.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 2.0 4.8 0.0 1.3 0.0 0.0 0.0 0.0 3.3 0.0 0.0 0.6 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
Euglenophyceae Chlorophyceae
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APPENDIX 3. Mean (SD) density (individuals/0.25 m2) of benthic invertebrate taxa with .0.1% occurrence. Taxa with asterisks (* indicates .3% relative abundance in any of the samples) were included in the canonical correspondence analysis. UD ¼ upstream of the dam and impoundment, DD ¼ downstream of the dam, DC ¼ downstream of the tributary confluence, TR ¼ in the tributary. FFG ¼ functional feeding group, CF ¼ collector–filterer, PR ¼ predator, CG ¼ collector–gatherer, SC ¼ scraper, SH ¼ shredder. Study reaches UD Order
Family
Haplosclerida
Spongillidae
Tricladida Nemertina Nematoda Veneroida Tubificida
Dugesiidae Nemertina* Nematoda* Corbiculidae Tubificidae
Genus, Species Eunapius fragilis (gemmule) Dugesia japonica*
FFG CF
Mean 0.0
DD SD 0.0
Mean 29.3
DC SD 7.3
Mean
TR SD
0.1
0.2
PR 0.5 1.0 162.9 65.9 74.3 PR 0.0 0.0 5.0 4.0 44.3 PR 14.6 5.5 109.7 97.6 207.6 Corbicula sp. CG 0.0 0.0 82.1 134.2 10.3 Tubificidae CG 0.0 0.0 1.3 2.2 7.8 Naididae* CG 28.9 16.1 382.2 365.8 516.3 Propappidae Propappus volki* CG 306.4 142.2 0.0 0.0 2.7 Acarina Acarina PR 8.8 5.3 98.4 74.0 88.2 Ephemeroptera Leptophlebiidae Choroterpes altioculus* CG/SC 0.8 1.0 10.7 13.2 582.7 Paraleptophlebia sp. SC/SH 0.1 0.3 12.3 24.5 0.0 Potamanthidae Potamanthus formosus* CF 0.0 0.0 170.3 255.4 22.9 Caenidae Caenis sp.* CG 0.2 0.3 233.8 126.8 177.8 Ephemerellidae Cincticostella elongatula CG/SC 1.3 1.0 0.5 0.4 1.8 Cincticostella nigra CG/SC 1.7 1.3 0.5 0.6 4.3 Drunella basalis PR 0.6 0.3 0.1 0.2 5.1 Drunella ishiyamana PR 0.0 0.0 0.0 0.0 0.7 Drunella sachalinensis* PR 87.6 39.2 0.5 0.6 44.4 Drunella sp.* PR 8.5 3.9 0.7 1.3 74.9 Ephemerella sp.* CG/SC 0.1 0.2 17.7 35.3 0.0 Torleya japonica CG/SC 0.0 0.0 50.8 33.2 4.8 Uracanthella punctisetae* CG/SC 0.0 0.0 1579.8 1099.7 122.8 Ameletidae Ameletus sp. CG/SC 3.0 1.5 0.0 0.0 0.0 Baetidae Alainites yoshinensis CG/SC 2.8 0.9 17.2 21.7 14.6 Baetiella japonica* CG/SC 230.3 128.9 1047.3 978.0 680.7 Baetis thermicus* CG/SC 13.1 10.1 1.3 2.7 12.4 Baetis sp. F* CG/SC 6.4 8.7 7.9 5.3 27.3 Baetis sp. M1 CG/SC 5.3 10.4 0.0 0.0 0.0 Baetis sp. CG/SC 8.8 8.7 10.3 17.3 6.3 Nigrobaetis chocoratus CG/SC 0.0 0.0 15.3 19.4 2.6 Tenuibaetis sp. E* CG/SC 8.9 13.4 142.8 64.2 60.8 Tenuibaetis sp. H CG/SC 0.2 0.3 51.6 89.9 5.0 Heptageniidae Epeorus latifolium* CG/SC 0.1 0.3 108.5 93.3 32.7 Epeorus nipponicus CG/SC 1.8 2.7 0.0 0.0 2.1 Epeorus curvatulus CG/SC 3.7 5.6 0.1 0.2 4.5 Epeorus ikanonis CG/SC 0.7 0.8 10.9 19.2 8.7 Epeorus sp. CG/SC 8.3 0.9 54.3 39.3 34.8 Heptagenia sp. CG/SC 0.0 0.0 0.0 0.0 7.5 Rhithrogena tetrapunctigera CG/SC 9.6 9.8 0.0 0.0 10.9 Rhithrogena japonica CG/SC 4.3 4.2 0.0 0.0 0.2 Rhithrogena sp.* CG/SC 38.2 24.3 0.0 0.0 15.6 Plecoptera Perlodidae Isoperla nipponica PR/CG 1.3 0.9 0.0 0.0 1.8 Stavsolus sp. PR 1.1 0.6 2.8 1.5 2.3 Perlidae Gibosia sp. PR 1.7 1.8 0.0 0.0 0.0 Neoperla sp. PR 0.1 0.2 15.7 6.7 4.6 Perlidae PR 2.0 1.8 0.1 0.2 0.0 Chloroperlidae Chloroperlidae CG 5.3 4.4 0.0 0.0 0.0 Taeniopterygidae Strophopteryx nohirae SC/SH 0.9 0.6 0.0 0.0 0.3 Nemouridae Nemoura sp. SH 2.0 1.2 0.6 0.6 0.2 Trichoptera Rhyacophilidae Rhyacophila nigrocephala PR 0.7 0.9 14.6 7.1 13.4 Rhyacophila transquilla PR 0.1 0.2 0.0 0.0 2.3 Rhyacophila sp. Sibirica PR 1.7 2.5 13.3 12.1 11.3 Rhyacophila impar PR 0.1 0.2 19.0 11.4 10.3
18.4 48.7 96.5 12.0 6.0 295.8 2.9 47.8 184.5 0.0 24.0 109.7 1.4 5.4 5.1 1.3 20.3 48.9 0.0 2.8 62.9 0.0 11.5 307.1 11.1 42.7 0.0 9.5 2.7 34.7 6.5 23.8 1.6 4.3 7.3 30.8 9.2 10.2 0.3 10.2 1.9 2.3 0.0 3.5 0.0 0.0 0.5 0.2 5.1 1.5 6.2 4.0
Mean 0.0
SD 0.0
18.4 14.3 1.7 1.3 38.6 21.7 1.3 2.7 1.6 1.6 269.2 278.0 230.2 393.8 57.8 30.2 45.3 32.1 0.0 0.0 0.0 0.0 12.6 4.0 8.8 7.7 4.7 3.5 2.9 1.9 19.0 3.5 18.0 8.7 190.4 69.7 0.0 0.0 0.4 0.6 7.3 7.3 0.6 0.6 11.6 4.9 306.9 48.2 39.8 29.0 28.8 20.8 0.0 0.0 0.0 0.0 0.0 0.0 3.3 2.3 0.5 0.4 8.9 8.4 11.8 7.0 2.1 1.2 2.6 2.1 21.7 7.1 16.4 10.5 31.8 18.6 0.6 1.0 141.3 57.2 6.0 5.4 4.8 4.7 0.1 0.2 0.2 0.2 0.7 1.3 0.0 0.0 6.0 5.8 2.7 4.5 3.3 2.7 4.9 2.0 9.7 10.1 0.4 0.4
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APPENDIX 3. Continued. Study reaches UD Order
Family Glossosomatidae Stenopsychidae Psychomyiidae Hydropsychidae
Coleoptara Diptera
Brachycentridae Lepidostomatidae Uenoidae Elmidae Psephenidae Tipulidae Blephariceridae
Ceratopogonidae Chironomidae
Simuliidae
DD
DC
TR
Genus, Species
FFG
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Rhyacophila brevicephala Agapetus sp. Glossosoma sp.* Stenopsyche sauteri Stenopsyche marmorata Psychomyia sp. Cheumatopsyche sp. Hydropsyche orientalis* Hydropsyche sp.* Micrasema sp. Lepidostoma sp. Neophylax sp. Elminae gen. sp.* Psephenoides japonicus Psephenoides sp. Antocha sp.* Dicranota sp. Agathon japonicus Agathon longispinus Agathon sp. Bibiocephala infuscata var. minor
PR SC SC CF CF CG/SC CF CF CF CG/SC SH SC CG/SC SC SC CG PR SC SC SC SC
0.1 0.0 76.7 0.0 0.0 0.8 0.5 14.5 4.0 1.8 4.6 3.2 6.2 0.0 0.0 13.1 2.2 0.5 0.1 0.0 0.1
0.2 0.0 65.4 0.0 0.0 1.3 0.4 8.6 2.3 1.5 4.7 2.9 3.7 0.0 0.0 8.5 2.1 1.0 0.2 0.0 0.2
10.3 23.6 26.3 143.8 10.9 1.8 13.3 254.8 80.3 0.0 0.1 0.0 23.1 22.2 19.0 192.9 0.1 0.0 0.0 0.0 0.0
10.7 29.4 27.0 43.6 12.5 2.3 12.9 294.3 74.7 0.0 0.2 0.0 22.0 15.3 18.6 84.3 0.2 0.0 0.0 0.0 0.0
2.4 26.5 157.9 68.5 3.3 9.8 3.1 99.2 65.2 0.0 0.2 0.1 133.2 1.8 0.6 246.8 0.8 0.6 0.0 0.0 1.1
0.7 8.7 38.1 42.1 5.6 8.2 2.0 54.5 65.3 0.0 0.2 0.2 29.4 1.5 0.2 67.3 0.4 1.2 0.0 0.0 0.6
0.4 0.0 157.0 8.3 0.2 0.0 1.0 36.5 64.9 0.0 0.0 0.1 51.5 0.0 0.0 39.3 1.7 9.8 5.0 3.0 17.0
0.6 0.0 53.8 6.3 0.2 0.0 0.6 11.6 29.1 0.0 0.0 0.2 14.2 0.0 0.0 27.3 1.3 5.6 3.4 3.8 10.1
CG/CF PR CG/SC CG/SC CG/CF CF
1.1 1.8 113.5 488.2 13.9 0.8
0.6 0.5 21.0 117.4 8.4 0.2
1.8 18.1 35.2 3489.6 107.9 11.9
3.5 18.2 21.7 801.5 86.5 14.3
5.2 55.7 19.6 2159.3 80.0 7.9
2.8 46.0 11.8 856.8 55.1 12.6
3.2 10.4 9.8 776.3 35.1 1.4
3.8 6.1 5.9 233.0 9.9 1.1
Tanypodinae Diamesinae* Orthocladiinae* Chironominae* Simulium sp.
