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HydrobiologiaThe International Journal of AquaticSciences ISSN 0018-8158Volume 703Number 1 Hydrobiologia (2013) 703:225-237DOI 10.1007/s10750-012-1362-z

Responses of epibenthic algal assemblagesto water abstraction in Hong Kong streams

Tao Tang, Sophia Qian Niu & DavidDudgeon

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PRIMARY RESEARCH PAPER

Responses of epibenthic algal assemblages to waterabstraction in Hong Kong streams

Tao Tang • Sophia Qian Niu • David Dudgeon

Received: 23 August 2012 / Accepted: 22 October 2012 / Published online: 1 November 2012

� Springer Science+Business Media Dordrecht 2012

Abstract We studied the effects of flow reduction on

epibenthic algal assemblages by comparing up- and

down-stream reaches of ten Hong Kong streams

subject to different degrees of water abstraction during

2007–2008. Downstream discharge declined by 71

and 54% during the wet and dry seasons, respectively.

Algal responses varied seasonally and according to

morphological guild and reflecting reductions in

discharge and current velocity, or changes in total

nitrogen and phosphate, or their combined effects.

Significant inter-reach assemblage differences were

observed during the both seasons, but wet-season

assemblage structure was not directly influenced by

any flow-related variable. During the dry season,

scouring-tolerant prostrate/adnate diatoms (especially

Achnanthes, Cocconeis) were relatively abundant

upstream, whereas intolerant stalked (Eunotia, Gom-

phonema) and mobile diatoms were more numerous

downstream. Both low- and high-profile (erect dia-

toms, Chamaesiphon, Calothrix) guilds were sensitive

to changes in nutrients. Low-flow conditions down-

stream, surprisingly, enhanced algal diversity, and

richness. Based on the linear response of Cocconeis

(mainly C. placentula) to discharge reduction, we

recommend that[65% of dry-season discharge should

be maintained in downstream reaches in order to

sustain near-natural algal assemblages; 32% of dis-

charge is required to avoid substantial alterations in

assemblage structure.

Keywords Flow reduction � Hydro–ecological

relationship � Diatoms � Morphological guilds �Hierarchical partitioning � Environmental flow

allocations

Introduction

Instream flows are fundamental drivers of spatiotem-

poral patterns and functions in lotic epibenthic algal

communities (Martınez de Fabricius et al., 2003;

Uehlinger et al., 2003; Luce et al., 2010). However,

globally many rivers and streams have been subjected

Handling editor: Judit Padisak

Electronic supplementary material The online version ofthis article (doi:10.1007/s10750-012-1362-z) containssupplementary material, which is available to authorized users.

T. Tang (&)

Institute of Hydrobiology, Chinese Academy of Sciences,

Wuhan 430072, People’s Republic of China

e-mail: tangtao@ihb.ac.cn

S. Q. Niu

Department of Biology, Saint Louis University,

St. Louis, MO, USA

e-mail: qniu1@slu.edu

D. Dudgeon

Chair of Ecology and Biodiversity, School of Biological

Sciences, The University of Hong Kong, Hong Kong

SAR, People’s Republic of China

e-mail: ddudgeon@hku.hk

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DOI 10.1007/s10750-012-1362-z

Author's personal copy

to hydrologic regulation and dam construction for

various purposes (e.g., March et al., 2003; Nilsson

et al., 2005), causing diversity declines and composi-

tional changes in aquatic taxa. Such responses have

been widely documented to inform management

decisions about the implementation of environmental

flows; i.e., the flows left in streams to sustain

biodiversity, ecological integrity and ecosystems

service (Acreman & Dunbar, 2004; Brisbane Decla-

ration, 2007; Arthington et al., 2010). Nonetheless

studies on the relationships between flow reduction

and responses of stream algal assemblages are scarce

compared with those dealing with macroinvertebrates

and fishes, and most such studies are focused mainly

on single streams in temperate or subtropical latitudes

(Clausen & Biggs, 1997; Downes et al., 2003;

Soininen, 2003; Suren et al., 2003; Wang et al.,

2009; Wu et al., 2009). Comparable studies on tropical

or monsoonal streams—with markedly seasonal flow

regimes—are scarce (Niu & Dudgeon, 2011a).

Responses of epibenthic algal assemblages to

altered flow regimes can be mediated by biotic as

well as abiotic processes. Physical shear stress

imposed by current strongly influences the relative

abundance of algae with different growth forms or

morphologies (e.g., erect, prostrate, or filamentous)

and also affects colonization dynamics, thereby influ-

encing species composition (Stevenson, 1996; Hart &

Finelli, 1999). With reduced current speed and flow

volume, algal community development is less con-

strained by shear stress, and compositional shifts can

take place from, for instance, low-stature diatoms to

nuisance growths of filamentous green algae, although

the precise outcome depends on nutrients and temper-

ature regimes (Biggs & Price, 1987; Suren et al., 2003;

Berthon et al., 2011). The strength of grazing pressure

upon epibenthic algae can vary according to flow

conditions (Poff & Ward, 1995; Chester & Norris,

2006), potentially influencing the intensity of top–

down control of assemblage composition and biomass

(Opsahl et al., 2003). Because of the complex

interactions among abiotic factors, such as discharge

and nutrients, and biotic factors (Peterson & Steven-

son, 1992; Ledger et al., 2008), the effects of flow on

epibenthic algal assemblages vary substantially

according to stream conditions (Jowett & Duncan,

1990; Downes et al., 2003; Wu et al., 2010).

Hong Kong streams suffer extensive and aggressive

water extraction to meet the demands of municipal

users, with no apparent concern for downstream impacts

or consideration of the need to sustain in-stream

environmental flows (Dudgeon, 1996). Reduced flow

or even dewatering may occur in downstream reaches

(typically at elevations below 100 m) especially during

the dry season, leading to increased algal biomass,

declines in macroinvertebrate species richness and

compositional changes, as well as decreased litter

breakdown rates (Dudgeon, 1992; Niu & Dudgeon,

2011a, b). This occurs against a background of seasonal

variation in epibenthic algal assemblages in stream

reaches that do not experience water extractions. These

may be induced by flow (Yang et al., 2009) as well as

top–down control of biomass and composition by

grazing fish (Yang & Dudgeon, 2010b). In this study,

we compared epibenthic algal assemblages between up-

and down-stream reaches of a number of streams

experiencing varying degrees of water abstraction. We

attempted to test the hypotheses that: (1) there will be

substantial differences in assemblage composition in

terms of both taxonomic diversity and growth forms—

between upstream (unaffected) and downstream

reaches; (2) this difference would vary seasonally due

to the monsoonal climate; and (3) the magnitude of algal

assemblage changes would be related to the magnitude

or degree of flow reduction.

Methods

Study sites and sampling design

We investigated 11 second- to fourth-order stony

hillstreams (see Supplementary material—Appendix 1

for detail). Sampling was conducted at approximately

monthly intervals on six occasions: three during the

wet season in 2007 (June to September 2007) and three

during the following dry season (December 2007 to

March 2008). Most streams were situated within or

adjacent to country parks (conservation areas), and

vegetation cover was mainly secondary forests and

shrubland. Although all streams were unpolluted (Niu

& Dudgeon, 2011a, b give details), they experienced

various degrees of water extraction. Ten streams, all

ext Shek Mun Kap Stream (SMK), were sampled in

the 2007 wet season. Wong Lung Hang stream (WLH)

was abandoned and substituted by adjacent SMK in

following dry season due to a fish-poisoning episode

on January 2008. Therefore, only wet season data from

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WLH and only dry-season data from SMK were used,

resulting in datasets for ten streams for both seasons.

At each stream, we selected two reaches 100–200 m

apart with comparable physical conditions and stream-

bed characteristics: one above (upstream) and one

below (downstream) a water abstraction point (a small

dam or catchment channel). The upstream reach served

as a reference site with natural flow conditions, and

downstream reach received reduced flows. To avoid

direct impacts arising from water impoundment or

scouring, both reaches were least 50 m away from

dams or water-abstraction infrastructure.

Measurements of flow and physicochemical

variables

At each sampling occasion and each study reach, flow

parameters were measured from three representative

cross-sections (at least 15 m apart), and a water sample

was collected. We measured wetted width, and water

depths at 50-cm intervals and current velocities at

10-cm intervals (at 40% vertical water depth above the

bottom) using a Swoffer Model 2100 current meter

(Swoffer Instruments Inc., Seattle, Washington, USA).

Discharge (Q in l s-1), the volume of flow moving past

a given cross-section per second, was calculated using

the velocity-wetted-area method (Gore, 2006), and the

mean value based on three cross-sections was used to

characterise each reach. Water samples were collected

with an opaque Nalgene bottle and transported to the

laboratory in a cool-box, where dissolved oxygen

(DO), conductivity, and pH were measured as soon as

possible using YSI 85 and YSI pH 100 m meters (YSI

Inc., Yellow Springs, Ohio, USA). Water samples for

nutrient measurements (nitrate, nitrite, ammonia com-

bines as total inorganic nitrogen [TN]; phosphate

[PO4-P]) were collected from both reaches of each

study stream once during each season. Nutrients were

analysed following APHA (1995) using the automated

phenate method for ammoniacal-N, and colorimetric

methods for nitrate-N, nitrite-N, and PO4-P on a flow

injection analyser (QuickChem 8000; Lachat Instru-

ments, Inc., Milwaukee, Wisconsin, USA).

Sampling and laboratory procedures for epibenthic

algae

In each reach, epibenthic algae were collected from

three randomly selected stones of 10–15 cm diameter

(Stewart et al., 2005; Yang & Dudgeon, 2010a; Niu &

Dudgeon, 2011a). Each stone was brushed vigorously

with a brass-bristled brush to remove all attached

periphyton, and the three perpendicular parameters

measured to the nearest in order to estimate surface

area. The sample was stored in an opaque Nalgene

bottle with 500 ml of with stream and transported to

the laboratory in a dark cool-box. A well-mixed 10 ml

sample of periphyton preserved with 1% formalin was

drawn from each bottle for further processing.

Algae in each sample were first identified and

counted to genus-level using a 0.1 ml counting chamber

at 4009 magnification under a compound microscope

(Olympus� BX50F-3: Olympus Optical Co., Japan). At

least 300 units were tallied, where unit represented

individual cells for unicellular taxa, a 10 lm length for

filaments, and 10 9 10 lm2 area for colonies (Steven-

son & Bahls, 1999). All diatoms were grouped into one

category during this step. Subsequently, species-level

identification and enumeration of diatoms was con-

ducted, after they had been acid-cleaned (Hu & Wei,

2006) and slide-mounted, at 10009 magnification with

an oil immersion objective. At least 500 valves were

counted for each sample. Density of all taxa was

quantified in terms of numbers counted per unit area of

stone surface (cm2), with stone area calculated from

measures of perpendicular perimeters of each stones

using a regression model applicable to Hong Kong

streams (see Salas & Dudgeon, 2003). Relative abun-

dance was calculated also. Algal identification was

based on Patrick & Reimer (1966, 1975), Jao (1988), Qi

(1995), Li & Bi (1998), Shi (2004), and Zhu (2007),

while species names for diatoms were updated follow-

ing the Catalogue of Diatom Names of California

Academy of Sciences (On-line Version, updated 19

Sept. 2011, http://research.calacademy.org/research/

diatoms/names/index.asp).

Data analysis

Wet- and dry-season data were analyzed separately

because of substantial variations in water flow char-

acteristics between the two seasons. All streams were

treated as replicates in those analyses.

Differences in assemblage composition between

up- and down-stream reaches across all streams were

examined using non-metric multi-dimensional scaling

(NMDS, with Bray–Curtis similarities) and two-way

crossed analysis of similarities (ANOSIM). Where

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significant variation was detected, Similarity Percent-

ages (SIMPER), a procedure examining the contribu-

tion of species to average resemblances/dissimilarity

between sample groups based on Bray–Curtis simi-

larities (Clarke & Warwick, 2001), was used to

estimate the percentage contribution of each taxon to

the community dissimilarity. The analyses were

performed using PRIMER version 6 (PRIMER-E

Ltd, Plymouth, UK), with arcsine square root trans-

formations applied to relative abundance data to

reduce the influences of abundant taxa.

A variety of algal metrics were used to assess the

response to flow changes. Algal taxa were grouped into

three ecological guilds: high-profile (tall-statured

unicellular, erect, stalked, tube-forming, or filamen-

tous algae), low-profile (short-statured prostrate,

adnate, small erect, solitary centrics species) and

mobile guild (fast moving taxa) following Passy

(2007) and Passy & Larson (2011). Diatoms were

further classified into four growth groups, namely:

prostrate/adnate, erect, stalked, and filamentous

(Wang et al., 2005; Steuer et al., 2009; Berthon et al.,

2011) (see supplementary material—Appendix 3). In

addition, four diatom genera, each with distinctive

morphology, were enumerated and investigated: Ach-

nanthes sensu lato spp. (i.e., as defined before sepa-

rating some species into other genera, henceforth

Achnanthes spp.: usually small in size and prostrate

orientation to substrate), Cocconeis (prostrate taxon

with special adherence to the substrate by large

quantity of mucilage), Navicula sensu lato spp.

(henceforth Navicula spp.: generally prostrate, fre-

quently motile taxon), and Eunotia (adhere to the

substrate by one or more jelly pores) (Molloy, 1992;

Kutka & Richards, 1996).

The abundance for each guild, diatom growth form,

or genus was the sum of relative abundance of all taxa

within that group. Morphospecies richness (henceforth

species richness) and the exponent of Shannon–

Wiener diversity (exp. H0), which is more sensitive

to environmental change than H0 (Gray, 2000), were

calculated also.

Four dry-season samples (one from Tung Chung

and Tai Po Kau stream, two from SMK stream) were

spilled during transportation, resulting in an unbal-

anced repeated-measures dataset. Accordingly, linear

mixed models (LMM), adjusted by the restricted

maximum likelihood (REML), were used to investi-

gate the between-reach differences in flow upon algal

metrics. LMM applies to unequal repeated measures,

and can model various types of repeated covariance

structures (correlations in the repeated measures) that

are assumed to unstructured in conventional repeated

measures analyses of variance (Jiang, 2007; Goldstein,

2010). We used stream reaches as fixed factor, with

streams and sampling times as repeated measures, and

all algal metrics including abundance of ecological

guilds, diatom growth forms, the four diatom genera,

algal density, richness, and exp. H0, as well as pH, DO,

and conductivity, as dependent variables. We ran

LMM repeatedly by alternately selecting various

covariance structures (ante-dependence, first-order

autoregressive, compound symmetry, diagonal, and

so on) with the model yielding a value of Akaike’s

information criterion (AIC) closest to zero chosen as

the best-fit (SPSS Inc., 2008). Arcsine square root and

log(x ? 1) transformation was applied for percentage

and non-percentage data to improve the normality of

data sets. Nutrient concentrations, which were only

measured once per season, were analyzed by repeated-

measures ANOVA without replication. The analyses

were conducted using SPSS 17 (SPSS Inc., Chicago,

IL, USA).

Algal taxa contributing *40% of accumulated

community dissimilarity revealed by SIMPER, and

algal metrics that identified by LMM as exhibiting

significant between-reach differences in each season

were regarded as indicative of the effects of flow

reduction. We then calculated the between-reach dif-

ferences of those indicator taxa and metrics, and related

them to between-reach differences in flow (i.e., the

focus was on the magnitude of relative difference, not

the absolute values). Particular attention was paid to

differences in current velocity and discharge given that

they are both known to influence algal assemblages

(Biggs et al., 1998; Stanish et al., 2011; Villeneuve et al.,

2011). Differences in percentage data between reaches

were calculated by simple subtraction (upstream minus

downstream), while ratio data were calculated from the

percentage difference (difference between reaches

divided by upstream value).

To confirm whether responses of indicative algal

metrics and taxa (4Y) were caused by changes of flow

parameters, we use hierarchical partitioning (HP) to

identify significant explanatory factors (based on

upper 0.95 confidence limit, Z C 1.65) (Mac Nally,

2002). HP computes all possible hierarchical multiple

regression models that can be developed with a set of

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predictive variables and estimates independent effects

of each predictor by averaging contribution of a given

variable to the goodness of fit for all involved models.

Averaging is likely to alleviate multicolinearity prob-

lems so that the actual casual variables can be revealed

(Chevan & Sutherland, 1991; Mac Nally, 2000).

Besides percentage reductions of discharge (4Q)

and current velocity (4V), percentage changes in TN

(4TN), and PO4-P (4PO4-P) were also used as

predictive variables, irrespective of whether or not

there were statistically significant inter-reach differ-

ences in nutrient concentrations, because nutrients can

strongly influence algal assemblages (reviewed by

Larned, 2010). Quadratic terms for the four variables

were also included as predictors to detect possible

nonlinear relationships (Quinn & Keough, 2002). HP

was conducted in R (version 2.15) with the hier.part

package (version 1.03, Walsh & Mac Nally, 2008).

Whenever 4Q, 4V, or their quadratic terms was

detected as the unique significant causal variable for

the dependent variable 4Y, regression was then used

to uncover a possible relationship between % flow

reduction and the response of the algal metric.

Kolmogorov–Smirnov and Durbin–Watson tests were

used to check dataset normality, variance, and the

independence of residuals. Regressions and associated

tests were conducted with SigmaPlot 11.0 (Systat

Software Inc., Chicago, IL, USA).

Results

Effects of water abstraction on flow

and physicochemical variables

Water abstraction substantially altered stream flow in

both seasons. During the wet season, mean discharge

was reduced from 107 l s-1 (upstream, range:

21–245 l s-1) to 30 l s-1 (downstream, range: 1–220

l s-1) or a 71% reduction (range: 15–98%). Current

velocity decreased by 46% (39–83%), wetted width by

29% (3–53%) and depth by 23% (5–56%). Dry-season

discharge declined from 10 l s-1 (2–25 l s-1) to

4 l s-1 (0.1–13 l s-1), or by 54% (9–98%), with

current velocity reduced by 18% (ranged from a 79%

reduction to an increase of 50%) wetted width reduced

by 11%, and water depth slightly increased by 12%

(Table 1).

Concentrations of TN or PO4-P did not differ

significantly between reaches during either season,

while differences among streams varied inconsistently

according to season. Mean TN loading increased from

9.5 (upstream) to 11 lg l-1 (downstream), with

Table 1 Mean (±1 SE) and the percentage differences (%

change) of hydrological characteristics and stream water

between up- (U) and downstream (D) reaches of ten Hong

Kong streams during the wet and dry seasons

Wet season Dry season

Discharge (l s-1)

U 106.9 ± 13.5 9.6 ± 1.3

D 29.9 ± 10.9 3.7 ± 0.77

% Change -71.1 ± 6.9 -53.8 ± 6.3

Velocity (cm s-1)

U 20.2 ± 1.7 10.2 ± 0.7

D 9.4 ± 1.4 7.7 ± 0.8

% Change -46.4 ± 9.1 -17.8 ± 10.7

Wetted width (m)

U 5.5 ± 0.3 3.9 ± 0.3

D 3.7 ± 0.2 3.13 ± 0.2

% Change -29.2 ± 4.7 -11.1 ± 5.7

Depth (cm)

U 23.4 ± 1.1 14.3 ± 1.3

D 17.6 ± 1.4 15.2 ± 1.5

% Change -23.4 ± 5.6 11.8 ± 9.5

DO (mg l-1)

U 7.2 ± 0.1 9.0 ± 0.2

D 6.9 ± 0.1 8.7 ± 0.2

% Change -3.6 ± 1.6 -3.2 ± 1.8

Conductivity (ls cm-1)

U 22.8 ± 2.9 43.2 ± 3.1

D 24.5 ± 3.1 50.0 ± 3.8

% Change 10.6 ± 12.3 16.8 ± 7.0

pH

U 6.8 ± 0.1 7.0 ± 0.1

D 6.51 ± 0.2 6.8 ± 0.1

% Change -4.6 ± 1.9 -1.7 ± 1.0

Total nitrogen (lg l-1)

U 9.5 ± 3.3 65.2 ± 21.0

D 11.0 ± 3.4 41.3 ± 13.5

% Change 62.3 ± 55.7 -21.7 ± 31.8

Phosphate (lg l-1)

U 2.2 ± 0.5 3.4 ± 0.7

D 2.7 ± 0.4 3.1 ± 0.5

% Change 33.4 ± 17.7 2.8 ± 16.1

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absolute differences among streams ranging from

-20.3 to 19.9 lg l-1 during the wet season, and from

65.2 (upstream) to 41.3 lg l-1 (downstream, differ-

ences range: -96.4 to 69.5 lg l-1) during the dry

season. The equivalent values for PO4-P were 2.2 to

2.7 lg l-1 (wet, -1.3 to 1.7 lg l-1) and 3.4 to

3.1 lg l-1 (dry, -2.5 to 2.4 lg l-1). In addition to a

lack of significance in between-reach differences in

nutrients, DO, conductivity, and pH did not vary

between reaches in either season (Table 1).

Responses of algal variables to water abstraction

Out of a total of 196 taxa, comprising 160 Bacilla-

riophyta, 17 Cyanophyta, 17 Chlorophyta, and 2

Rhodophyta were collected during the study; 162

during the dry season and 142 in the wet season

(supplementary material—Appendix 2). The vast

majority were diatoms: 96% of total counts during

the wet season, 89% in the dry season. Achnanthidium

minutissimum (Kutzing) Czarnecki (with a relative

abundance of 22.4% in the wet season and 25.8% in

the dry season), Cocconeis placentula Ehrenberg (wet:

15.4%; dry: 16.5%), Karayevia oblongella (Østrup)

Aboal (wet: 13.3%; dry: 7.5%), Gomphonema parv-

ulum (Kutzing) Kutzing (wet: 5.7%; dry: 7.0%) and

Gomphonema clevei Fricke (wet: 5.4%; dry: 3.2%)

were the dominant taxa, collectively contributing to

[60% of total algal abundance during both seasons.

Despite substantial among-stream differences in

algal assemblage structure (ANOSIM: stream effects,

wet season: global R = 0.759, P = 0.001; dry season:

global R = 0.946, P = 0.001) (Fig. 1), there were

nonetheless strong between-reach differences in

assemblage composition (reach effects, wet season:

global R = 0.205, P = 0.031; dry season: global

R = 0.532, P = 0.001). SIMPER showed that, in

combination, 11 taxa contributed*40% to inter-reach

assemblage dissimilarity during each season (Table 2).

Among these indicator taxa, eight were important in

both seasons, including the five most abundant taxa

which collectively contributed[20% dissimilarity.

LMM revealed that none of the algal metrics

differed between reaches during the wet season. In

contrast, ten metrics showed significant between-

reach differences during the dry season (Table 3). At

downstream reaches, % low profile guild, % prostrate

diatoms, % Achnanthes spp., and % Cocconeis spp.

were reduced by 6–11% and % high profile guild,

% erect diatoms, % Eunotia spp., and % Navicula spp.

increased by 1.4–5%. Diversity increased from 6.7 to

8.9 (difference: 46.7%) and this was reflected in

species richness (23–28 species; difference: 23.2%).

Regression relationships between water flow

change and algal responses

4Q and 4V were identified as unique or combined

causal variables for the responses of 11 algal metrics

or taxa by HP, and these causal relations may have

been simple or nonlinear. In addition, 4TN and

quadratic terms of4PO4-P were significant predictors

for 7 and 2 algal responses, respectively (Table 4).

The independent contributions of all of these predic-

tors to the explained variance of algal responses

ranged from 23 to 44%.

No response of algae was caused solely by discharge

or current velocity reduction during the wet season.

However, % Achnanthes spp., % Cocconeis spp.,

% Eunotia spp., % Navicula spp., exp. H0, Cocconeis

placentula, and Gomphonema clevei were affected by

flow conditions during the dry season and were used to

analyze algal responses to flow reduction. % Cocconeis

placentula and % Cocconeis spp. decreased linearly

Fig. 1 Non-metric multi-dimensional scaling (NMDS) ordina-

tion of epibenthic algal assemblages in up- and down-stream

reaches of ten Hong Kong streams during the a wet and b dry

seasons

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with DQ (Fig. 2), while % Achnanthes spp., and

diversity showed quadratic responses to DQ. % Ach-

nanthes spp. initially declined as discharge was

reduced, but increased when DQ exceeded 89%

(Fig. 2). Diversity increased quadratically with greater

% reduction in DQ (DY [ 0 when DQ exceeded 15%).

Only % Navicula spp. apparently increased linearly

when current velocity declined (Fig. 3).

Discussion

Partitioning the physical and chemical effects

of flow reduction

Despite reductions in flow beneath water abstraction

points during both seasons, the effects of decreased

flow on algal assemblages were only apparent during

the dry season. Presumably this result reflects the fact

that water abstraction during the wet season was not

sufficient to cause a response from the majority of

algae, because the ‘‘background’’ stream discharge

during this season was relatively high due to the high

precipitation. Indeed, downstream discharge in the

five streams with the most depleted flows during the

wet season were approximately the same as their

upstream (natural) flows during the dry season. In

other words, flow reduction did not reach a critical

threshold that triggered responses of any algae.

Algal responses were mainly caused by between-

reach changes of the two flow parameters: discharge

and current velocity. However, 7 metrics and taxa

responded to PO4-P and TN changes, despite the fact

that there were only minor and non-significant

Table 2 The top-ranked SIMPER contributors to % dissimilarity in epibenthic algal composition between up- (U) and down-

(D)stream reaches of ten Hong Kong streams during the wet and dry seasons

Taxon Abund. (%) % Contrib. % Cum.

U D

Wet season

Achnanthidium minutissimum (Kutzing) Czarnecki 24.1 21.2 7.3 7.3

Cocconeis placentula Ehrenberg 15.2 14.5 6.0 13.3

Karayevia oblongella (Østrup) Aboal 9.5 16.5 4.5 17.8

Gomphonema clevei (Fricke) Gil 5.9 5.1 4.0 21.8

Gomphonema parvulum (Kutzing) Kutzing 6.8 4.7 3.5 25.4

Brachysira brebissonii Ross 2.3 4.2 3.1 28.4

Encyonema minutum (Hilse) Mann 4.1 4.9 3.1 31.5

Achnanthidium exile (Kutzing) Round & Bukhtiyarova 5.2 0.2 3.0 34.5

Planothidium rostratum (Østrup) Lange-Bertalot 2.7 2.5 2.3 36.8

Schizonema radiosum (Kutzing) Kuntze 0.7 1.5 2.1 38.9

Psammothidium montanum (Krasske) Mayama 2.7 1.5 2.0 40.9

Dry season

Cocconeis placentula 20.2 13.2 5.2 5.2

Achnanthidium minutissimum 27.4 21.9 5.1 10.3

Gomphonema parvulum 6.3 6.0 4.2 14.4

Karayevia oblongella 4.9 10.3 4.0 18.4

Chamaesiphon Braun et Grunow 4.8 3.0 3.9 22.3

Brachysira brebissonii 0.9 6.6 3.8 26.0

Homoeothrix Kirchner 2.2 1.4 3.4 29.4

Gomphonema clevei 2.6 4.0 3.3 32.8

Encyonema minutum 3.5 3.7 2.6 35.3

Psammothidium montanum 3.7 1.1 2.5 37.9

Calothrix Agardh 1.0 0.7 2.3 40.2

Abund mean abundance; % Contrib. percentage contribution; % Cum. percentage cumulative contribution

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between-reach differences in concentrations of both

nutrients. Stream flow has opposing subsidy-stress

effects on algae assemblages; i.e., flow continually

brings nutrients but simultaneously exerts shear stress

(Allan & Castillo, 2007). Their joint influence can vary

with circumstances, with nutrient supply dominating at

low current speeds and the physical effects of shear

stress becoming more important when flow is high

(Biggs et al., 1998). The interactions between flow and

nutrients are complex and compound specific. In this

Table 3 Comparison of

algal metrics between

upstream (U) and

downstream (D) reaches

during the dry season in

ten Hong Kong streams

based on LMM (see text)

Only metrics with

significant between-reach

differences (P \ 0.05)

are presented

Algal metrics Mean (range) LMM tests of reach effects

% High profile guild

U 17.8 (1.4 to 65.3)

D 22.7 (6.2 to 51.4)

Change 5.0 (-28.5 to 48.0) F1,15.142 = 10.276, P = 0.006

% Low profile guild

U 79.9 (34.2 to 98.4)

D 73.0 (46.4 to 91.0)

Change -6.9 (-49.1 to 27.9) F1,21.059 = 11.291, P = 0.003

% Prostrate/adnate diatoms

U 70.0 (26.9 to 98.4)

D 59.0 (16.2 to 92.1)

Change -11.1 (-50.1 to 28.5) F1,28.238 = 19.470, P \ 0.001

% Erect diatoms

U 0.8 (0 to 2.4)

D 3.3 (0 to 18.8)

Change 2.5 (-.07 to 18.8) F1,7.454 = 69.366, P \ 0.001

% Achnanthes

U 47.7 (23.7 to 81.4)

D 41.4 (13.8 to 75.0)

Change -6.2 (-31.6 to 17.9) F1,40.918 = 5.465, P = 0.024

% Cocconeis

U 20.7 (0 to 65.8)

D 14.6 (0 to 61.2)

Change -6.1 (-44.9 to 30.6) F1,43.004 = 5.032, P = 0.030

% Eunotia

U 0.3 (0 to 2.2)

D 2.0 (0 to 15.5)

Change 1.7 (-0.7 to 15.5) F1,6.213 = 11.003, P = 0.015

% Navicula

U 1.7 (0 to 10.1)

D 2.7 (0 to 12.3)

Change 1.4 (0.5 to 2.4) F1,7.558 = 76.915, P \ 0.001

Shannon–Wiener diversity (exp. H’)

U 6.7 (3.2 to 11.6)

D 8.9 (4.5 to 16.5)

% Change 46.7 (-52.7 to 192.5) F1,48.604 = 8.302, P = 0.006

Richness

U 23 (17 to 33)

D 28 (17 to 42)

% Change 23.2 (-29.2 to 82.6) F1,14.446 = 12.359, P = 0.003

232 Hydrobiologia (2013) 703:225–237

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study, TN and PO4-P loads both increased during the

dry season when discharge was relatively low, and

there was a slight, but non-significant, increase in

PO4-P in downstream reaches. These changes are

highly likely to be due to a decrease in dilution capacity

and increase in residence times during under condi-

tions of reduced flow (Caruso, 2002; Lake, 2003).

Algal responses to flow reduction

Algal morphology strongly influenced their responses

to flow. Algae that are tolerant to scouring (Peterson,

1996; Biggs & Smith, 2002), such as members of

prostrate/adnate diatoms (especially Achnanthes spp.,

Cocconeis spp.), were more abundant in relative high

velocities or discharge upstream during the dry season.

Conversely, members of relative flow-intolerant

stalked species (Eunotia spp., Gomphonema clevei)

and mobile diatoms (Navicula spp., Brachysira breb-

issonii Ross) that able to crawl, avoid siltation and

select suitable habitats (Stevenson & Bahls, 1999)

were more abundant downstream. Other algal taxa and

metrics were more sensitive to nutrient changes than to

flow. Taxa in the high profile guild taxa (especially

erect diatoms, arborescent Chamaesiphon spp., Calo-

thrix spp.) were enhanced by small increases in PO4-P

or TN concentrations, while % low profile guild

diatoms in algal assemblages (including adnate Ach-

nanthidium exile (Kutzing) Round & Bukhtiyarova)

declined when PO4-P concentrations increased. This

result confirms the observation that low profile taxa

are more tolerant to resource-limited environments

(Passy, 2007; Berthon et al., 2011) where a complex

3D algal community cannot form due to low nutrient

availability (Passy, 2007; Passy & Larson, 2011).

Gomphonema parvulum and Psammothidium monta-

num (Krasske) Mayama were sensitive to both flow-

induced stresses, leading to combined responses.

Flow reduction appears to generate relatively stable

habitats that allow species with various attributes and

competitive abilities to coexist, resulting in increased

species richness (Biggs, 1996: Ghosh & Gaur, 1998)

as seen in downstream reaches of study streams. Flow-

related responses of diversity can be positive, negative

or shift from one to the other, depending on the studied

flow ranges (Wendker, 1992; Acs & Kiss, 1993;

Mackay et al., 2011) and it is notable that response of

diversity to discharge in this study was quadratic

(‘‘humped’’) rather than linear. Given the increased

species diversity downstream of water abstraction

points during the dry season, and higher periphyton

biomass reported by Niu & Dudgeon (2011a), it

appears that low-flow conditions have stimulated algal

assemblages in Hong Kong streams. In such cases

elsewhere, especially where nutrient supply is high.

Such conditions can give rise to excessive or nuisance

growths (Flinders & Hart, 2009).

Environmental flow allocation based on

eco–hydrological relationships

There have been few attempts to derive quantitative

eco–hydrological relationships between the extent of

Table 4 The significant predictors identified by hierarchical

partitioning and their independent contributions (I) to explain

between-reach difference of algal metrics and indicator taxa

during the wet and dry seasons (only present the metrics and

taxa whose causal variable(s) are detected)

Significant predictors I (%)

Algal metrics

Dry season

% High profile guild DPO4-P2 39.8

% Low profile guild DPO4-P2 44.0

% Erect diatoms DPO4-P2 38.1

% Achnanthes DQ2 43.5

% Cocconeis DQ 32.8

% Eunotia DV2 35.1

% Navicula DV 32.6

Exp. H0 DQ2 30.4

Richness DV 29.1

DQ 26.8

Indicator taxa

Wet season

Achnanthidium exile DPO4-P2 25.9

Dry season

Cocconeis placentula DQ 32.4

Gomphonema parvulum DQ 24.4

DPO4-P2 23.9

Chamaesiphon DTN 37.2

Brachysira brebissonii DV 35.6

DQ 29.4

Homoeothrix DPO4-P2 33.1

Gomphonema clevei DV2 37.4

Psammothidium montanum DPO4-P2 35.1

DV2 22.9

Calothrix DTN 26.5

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flow alternation and algal responses, and the manage-

ment implications of derived relationships can be

unclear partially because of the inconsistent responses

of ecological metrics (Larned, 2010; Poff & Zimmer-

man, 2010). The discharge reduction versus algal

response relationships reported herein, based on ten

streams with various degrees of flow reduction, could

be applied to determine the environmental flow

(e-flow) allocation needed to maintain epibenthic

algal assemblages in a close-to-natural condition in

Hong Kong, following a similar approach to that used

elsewhere (Arthington et al., 2006; Richter, 2010).

Using data from the same set of ten Hong Kong

streams, Niu and Dudgeon (2011a) proposed an e-flow

framework based on macroinvertebrates: a ‘‘thresh-

old’’ intended to maintain near-natural stream condi-

tions (4Y = 0), and a ‘‘degradation limit’’ that

allowed no more than 25% of the maximum indicator

Fig. 2 Linear or quadratic relationships between % discharge reduction (4Q) and between-reach differences in algal metrics during

the dry season. Only significant relationships (P \ 0.05) are presented

Fig. 3 Linear regressions between reductions in current veloc-

ity (4V, negative values representing increases) and between-

reach difference in algal metric during the dry season. Only

significant relationship (P \ 0.05) is presented

234 Hydrobiologia (2013) 703:225–237

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response to flow reduction (4Y = 25%). We applied

this method to calculate acceptable % discharge

reduction based on the linear responses of relative

abundance of Cocconeis placentula and % Cocconeis

spp. during the dry season (estimating from the fitted

4Q versus 4Y regression equations in Fig. 2).

Calculated threshold e-flows required that C65%

natural discharge be maintained, and the degradation

limit was C32% natural discharge for the dry season.

A threshold or limit for the wet season could not be

calculated because discharge reduction was not the

causal factors to algal responses in the context of high

background levels of wet-season flow. Flow require-

ments derived from studies of benthic macroinverte-

brates in Hong Kong streams indicated a C74%

threshold discharge for both seasons, and degradation

limit discharges of C27% in the dry season (Niu &

Dudgeon, 2011a). Thus, a rough rule-of-thumb is that

at least three-quarters of the downstream flow should

be maintained to protect benthic macroinvertebrates

and algal assemblages in Hong Kong streams during

the dry season. Furthermore, retaining one-third of

natural discharge should be sufficient to limit the

serious impacts of water abstraction on macroinver-

tebrates and algal assemblages.

We note that the direction of the responses of most

algae and macroinvertebrates to discharge reduction

was inconsistent: macroinvertebrate diversity and

abundance decreased as discharge declined, while

algal metrics tended to exhibit the opposite trend.

However, e-flow management decisions need to be

based around sustaining natural assemblage structure,

and not maximizing diversity of a single group.

Among the algae, the linear response of Cocconeis

placentula to discharge reduction may have value as

indicator for monitoring success of an e-flow imple-

mentation program in monsoonal streams based

around the rules-of-thumb set out above, although

we recommend that any such monitoring include

macroinvertebrates as well as diatoms, and might also

be combined with process-based measurements that

reflect ecosystem functioning (Niu & Dudgeon,

2011b). The information thereby generated could be

used to inform adaptive management and thereby

refine e-flow allocations for regulated monsoonal

streams such as those in Hong Kong.

Acknowledgments We thank Yixin Zhang for facilitating this

collaborative research, Lily C. Y. Ng for technical support in the

laboratory, and the Field Work Support Team of the Division of

Ecology and Biodiversity at the University of Hong Kong for

logistical assistance.

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