Vegetation and flow regime in lowland streams

Vegetation and flow regime in lowland streams

TENNA RIIS* , ALASTAIR M. SUREN†, BENTE CLAUSEN‡, 1 AND KAJ SAND-JENSEN§

*Department of Biological Sciences, University of Aarhus, Ole Worms Alle, Arhus, Denmark†National Institute of Water and Atmospheric Research Ltd., Riccarton, Christchurch, New Zealand‡National Environmental Research Institute, Vejlsøvej, Silkeborg, Denmark§Biological Institute, Freshwater Biological Laboratory, University of Copenhagen, Helsingørsgade, Hillerød, Denmark

SUMMARY

1. The hydrological regime is important to the distribution of benthic organisms in streams.

The objective of this study was to identify relationships between hydrological variables,

describing the flow regime, and macrophyte cover, species richness, diversity and

community composition in Danish lowland streams.

2. We quantified macrophyte vegetation in 44 Danish streams during summer by cover,

species richness and diversity. Flow regime was characterized by 18 non-intercorrelated

variables describing magnitude, frequency and duration of low and high flow events,

timing or predictability of flow and general flow variability.

3. We found support in the stepwise multiple regressions analysis for our expectation that

macrophyte cover is lowest in streams with high flow variability and highest in streams

with long duration of low flow and low flow variability. We found support for the

intermediate disturbance hypothesis as there were significant quadratic relationships

between species richness and diversity as functions of disturbance frequency. There was

poor discrimination in a detrended correspondence analysis (DCA) analysis of macrophyte

community composition between four TWINSPANTWINSPAN groups separating streams with

different hydrological properties. Moreover, we did not find any relationship between the

presence of disturbance-tolerant species and hydrological disturbance, suggesting that

plant community composition developed independently of stream hydrology.

Keywords: disturbance, flow regime, hydrology, macrophyte, stream

Introduction

The hydrological regime has been widely recognized

as an important factor affecting the distribution of

benthic biota in streams (e.g. Minshall, 1988; Poff &

Ward, 1989; Biggs, 1996). From studies relating

different aspects of the flow regime with stream

biology we know that flood frequency, flow variabil-

ity and flow magnitude influence biomass and

species composition of periphyton, invertebrates

(e.g. Scarsbrook & Townsend, 1993; Biggs, 1995; Suren

et al., 2003a,b) and macrophytes (e.g. Suren & Duncan,

1999; Riis, Sand-Jensen & Vestergaard, 2000; Riis &

Biggs, 2003). Macrophytes, in turn, play an important

ecological role by increasing physical heterogeneity,

trapping sediments, and providing extensive habitats

for periphyton, invertebrates and fish (e.g. Sand-

Jensen, 1997; Collier, 2002).

Recently, Riis & Biggs (2003) demonstrated that

hydrological disturbance is a major controlling factor

for macrophyte abundance and diversity in New

Zealand streams. The hydrological regime in New

Zealand varies from stable spring-fed streams to hill

and mountain streams characterized by highly vari-

able and unpredictable hydrological regimes (Biggs,

Nikora & Snelder, 2005). In contrast to New Zealand

streams, Danish streams have more stable and regular

flow regimes with high winter and spring flows, and

Correspondence: T. Riis, Department of Biological Sciences,

Aarhus University, Ole Worms Alle, 8000 Arhus, Denmark.

E-mail: [email protected] address: Bente Clausen, Rosenvængets Alle 30, 9700

Brønderslev, Denmark

Freshwater Biology (2008) 53, 1531–1543 doi:10.1111/j.1365-2427.2008.01987.x

� 2008 The Authors, Journal compilation � 2008 Blackwell Publishing Ltd 1531

low summer and autumn flows. Given the fact that

macrophytes in New Zealand streams are strongly

controlled by hydrology we posed the questions

whether this would also be the case in a region with

less hydrological variability such as Denmark, and if

so, are the controlling mechanisms similar.

The mechanisms behind hydrological control of

macrophyte biomass can be divided into biomass loss

and gain processes. Loss processes are caused by

increased shear stress that break and dislodge the

plants and take place at high flow events of variable

frequency, duration and intensity (e.g. Biggs, Duncan

& Smith, 1999; Riis & Biggs, 2003; Biggs et al., 2005).

High flow events are thus regarded as disturbances

according to Grime (1977). In contrast, biomass gain

processes take place at median to low flow conditions

and in the absence of frequent disturbance. Thus, light

and nutrient conditions being equal, we would expect

a higher plant cover in streams where flood frequency

is low or where low flow events are common and

extended.

Biomass loss and gain processes can also affect

macrophyte species richness, diversity and commu-

nity composition along the lines espoused by Grime

(1973), in his intermediate disturbance hypothesis

(IDH). At one extreme, habitats with high disturbance

frequency or intensity are expected to have low

species richness, because few species are able to

colonize during the short period between distur-

bances or to resist the physical impact of the distur-

bance. At the other extreme, habitats with low

disturbance frequency or intensity are also expected

to have low species richness, because the vegetation

becomes dominated by competitively superior spe-

cies. Hence, species richness should be highest at an

intermediate disturbance frequency or intensity

because both fast colonizers and competitive species

can grow together (Grime, 1973). However, this

pattern has not been demonstrated unambiguously

in stream studies. For example, Willby, Pygott &

Eaton (2001) found that most macrophyte species

were present at the intermediate disturbed reaches in

the British Canal system where disturbance was

caused by boat traffic. On the other hand Riis & Biggs

(2003) found a linear decrease in macrophyte species

diversity as a function of flood frequency in New

Zealand streams. In riparian vegetation, Nilsson et al.

(1989) and Pollock, Naiman & Hanley (1998) found

support for the IDH, whereas Bornette, Amoros &

Lamouroux (1998) recorded the highest species rich-

ness in the most frequent flooded cut-off arm channels

in a French river. An obvious explanation for the

discrepancy is that the amplitude of disturbances

differs among the studies and the entire range of

disturbances is rarely present in the studies.

Community composition is expected to change

directionally with increased hydrological disturbance

as described in a conceptual model by Riis & Biggs

(2001) and found by Riis & Biggs (2003) and Mackay

et al. (2003). In accordance with that conceptual model

we expect a higher dominance of species that are

resistant or resilient to disturbance in frequently

disturbed streams in contrast to streams with a low

frequency of disturbance, where more competitive

species will constitute a greater proportion of the

vegetation.

Besides biomass loss and gain processes, other

aspects of the hydrological regime are probably to

affect the macrophyte community. Flow magnitude has

often been found to affect the distribution of macro-

phytes in streams (e.g. Riis et al., 2000; Demars &

Harper, 2005). On the other hand, timing of high or low

flow during the year has not received much attention,

even though it is probably that the phenology of the

plants may be strongly affected. For example, it is

probably that a high flow event in spring will cause

great harm to some species that might not be affected if

the high flow event was in autumn. Moreover, variation

in the timing of the annual maximum flow between

years might have an overall negative effect on species

diversity because both the presence of species occur-

ring early and species occurring late might be affected

negatively over a longer period, reducing overall

diversity in the river.

The objective of this study was to identify relation-

ships between hydrological variables describing the

flow regime, and macrophyte cover, species richness,

diversity and community composition in Danish

lowland streams. More specifically we wanted to

determine (i) how hydrological variables controlling

biomass loss and gain processes, and variables

describing magnitude and timing of the flow regime,

relate to vegetation cover, species richness and diver-

sity; (ii) whether macrophyte cover and diversity

decrease monotonically with increasing hydrological

disturbance, as found in New Zealand streams (Riis &

Biggs, 2003), or whether the IDH works in the

streams and (iii) whether species composition reflects

1532 T. Riis et al.

� 2008 The Authors, Journal compilation � 2008 Blackwell Publishing Ltd, Freshwater Biology, 53, 1531–1543

differences in hydrological disturbance, as proposed

by Riis & Biggs (2001).

Methods

Selection and calculation of flow variables

A total of 45 flow variables (Table 1) was selected

from a long list of flow variables used in other studies

(Clausen & Biggs, 1997, 2000; Clausen, Iversen &

Ovesen, 2000; Olden & Poff, 2003) and organized into

five categories (magnitude of flow, frequency and

duration of high flows, frequency and duration of low

flows, timing or predictability and flow variability), as

suggested by Richter et al. (1996) and Poff et al. (1997).

The selected variables were included either because

they directly express events that were expected to

have high impact on macrophyte communities, such

as magnitude of flow (Riis et al., 2000; Mackay et al.,

2003), frequency and duration of high flows (Riis &

Biggs, 2003) and frequency and duration of low flows

(Holmes, 1999; Westwood et al., 2006) or because

other studies have suggested that they could be

ecologically important (Poff & Ward, 1989; Jowett &

Duncan, 1990).

We calculated all flow variables from daily mean

flows for the 10-year period 1988–97, which is the

period previous to and including the year (1997) of

macrophyte collection. A longer record would have

enabled calculation of more stable flow variables

(closer to the long-term values) but would also have

reduced the number of sites with adequate flow data.

A shorter period would not have increased the

number of sites considerably. The same time period

was used for all sites, and all flow data were

normalized to the median flow (Q50) to enable

comparison of streams with very different flow

magnitudes (Table 1, variables marked by ‘*’).

Description of variables

To illustrate the flow pattern and the meaning of some

of the flow variables, the hydrographs from three sites

with contrasting flow regimes are shown in Fig. 1. All

three streams are small with Q50 between 0.5 and

2.5 m3 s)1. Vindinge was the smallest stream (lowest

Q50) with the highest flow fluctuation. Uggerby was

the largest stream (highest Q50) with medium flow

fluctuation, and Salten had the most stable flow.

Magnitude. The mean (Qmean) and median flow (Q50)

representing the average flow conditions were deter-

mined (Table 1).

Frequency and duration of high flows and low flows. To

determine frequency and duration of high-flow and

low-flow, a threshold flow was used to select partial

duration series of high flows and low flows (i.e. peaks

over or under a selected threshold). For example, for

the 1994 hydrograph for Vindinge (Fig. 1), 10 high

flows exceeded a threshold of 7 · Q50 (3.36 m3 s)1),

giving a high-flow frequency (FRE7) of 10 for 1994. For

the 10-year period the annual frequency was calcu-

lated as the total number of high or low flows divided

by 10. The mean duration of high flows, using 7 · Q50

as the threshold (DUR7), was 5.3 days for this site. To

investigate the influence of the level of threshold we

used three threshold values for high flows, 3 and

7 · Q50, and the 25th percentile from the flow dura-

tion curve (Q25). For low flows we used two threshold

values, the 75th percentile from the flow duration

curve (Q75) and the median of the 1-day annual

minima (MIN50).

Predictability and timing of flow. Predictability (PRE) is

defined by Colwell (1974) and varies between 0 (no

predictability on a daily basis from year to year) and 1

(identical flows on the same dates). The matrix used to

define PRE (and CON, see below) is the same as the

one described by Clausen & Biggs (1997, 2000). The

variable is sensitive to the number of years of record

and requires more than 20 years of record to obtain its

long-term value (Gan, McMahon & Finlayson, 1991).

Thus, given that we only have 10 years of data this

variable should be interpreted with care. Timing was

represented by the dates (in terms of the number of

day in the year starting 1 May) for the mean annual

maximum (JULMAX) and the mean annual minimum

(JULMIN). The coefficient of variation for the date

of maximum or minimum flows (JULMAXCV and

JULMINCV) in terms of Julian day was used to express

variability in timing of flows.

Overall flow variability. Baseflow Index (BFI) is

defined as the area below the baseflow line divided

by the total area below the hydrograph (Gustard,

Bullock & Dixon, 1992). Values of BFI range from 0

(indicating an intermittent or ephemeral stream) to

Vegetation and flow regime in lowland streams 1533


Table 1 Flow variables used to characterize the flow regime in the initial analysis of 44 lowland stream sites

Symbol (unit) Description

Magnitude

Qmean (m3 s)1) Mean flow

Q50 (m3 s)1) Median flow, i.e. flow exceeded 50% of the time

Frequency and duration

High-flow events Average number and duration of high-flow events per

year using a threshold of

FRE3 (year)1) 3 times the median flow (Q50)

FRE7 (year)1) 7 times the median flow (Q50)

FRE25 (year)1) 25th percentile from the 1-day flow duration curve (Q25)

DUR3 (days) 3 times the median flow (Q50)

DUR7 (days) 7 times the median flow (Q50)

DUR25 (days) 25th percentile from the 1-day flow duration curve (Q25)

Low-flow events Average number and duration of low-flow events per

year using a threshold of

FRE75 (year)1) 75th percentile from the 1-day flow duration curve (Q75)

FREMIN50 (year)1) Median of the 1-day annual minima (MEDMIN)

DUR75 (days) 75th percentile from the 1-day flow duration curve (Q75)

DURMIN50 (year)1) Median of the 1-day annual minima (MEDMIN)

Timing or predictability of flow

PRE ()) Predictability of daily flows (Colwell, 1974)

JULMAX ()) Mean Julian day of annual maximum flow (day 1 is 1 May)

JULMAXCV ()) Variability of Julian date of annual maximum flow

JULMIN ()) Mean Julian day of annual minimum flow (day 1 is 1 May)

JULMINCV ()) Variability of Julian date of annual minimum flow

Flow variability

Overall

BFI ()) Baseflow index, baseflow volume divided by total flow volume

SK ()) Skewness defined as MF ⁄ Q50

CV ()) Coefficient of variation of daily mean flows

CON ()) Constance (Colwell, 1974)

NORISES ()) Average ratio of days with increasing flow

NOFALLS ()) Average ratio of days with decreasing flow

KPOS (m3 s)1) Median of differences between natural log of flows

between two consecutive days with increasing flow

KNEG (m3 s)1) Median of differences between natural log of flows

between two consecutive days with decreasing flow

High flow

Q10* ()) 10th percentile from the flow duration curve


MAMAX* ()) Mean annual 1-day maximum

MAMAX7* ()) Mean annual 7-day maximum

MAMAX30* ()) Mean annual 30-day maximum

MAX50* ()) Median of the 1-day annual maxima

PEA3* ()) Mean peak flow for events higher than 3 times Q50

PEA7* ()) Mean peak flow for events higher than 7 times Q50

PEA25* ()) Mean peak flow for events higher than the 25 percentile

VOL3* (days) Mean volume of flooded water using the threshold of 3 times Q50

VOL7* (days) Mean volume of flooded water using the threshold of 7 times Q50

VOL25* (days) Mean volume of flooded water using the

threshold of the 25 percentile

TIM3 (days year)1) Mean number of days in a year in flood, FRE times

DUR, using a threshold of 3 times Q50

TIM7 (days year)1) Mean number of days in a year in flood, FRE times

DUR, using a threshold of 7 times Q50

Low flow


1534 T. Riis et al.


close to 1 (indicating that the flow remains fairly

constant over the year). Skewness, calculated as Qmean

divided by Q50, expresses the degree by which the

mean is affected by extreme events compared to the

median. The CV is the standard deviation of the daily

mean flows divided by Qmean and is a measure of the

spread of data. Constancy (CON) is based on the same

principles as PRE and also uses values from 0 to 1. It is

a measure of how constant the daily flow values

are over the year (Colwell, 1974). NORISES and

NOFALLS describe the average ratio of days during

which the flow increases or decreases respectively.

Flashiness is expressed by the median values of

the increase (KPOS) and decrease (KNEG) in flow

(in terms of natural logarithmic values) from one

day to the next. In Danish streams, a high KPOS

and low KNEG are present in eastern areas of

the country with slow draining clay soils and corre-

spond to a high flow variability, whereas low KPOS

and high KNEG are present in western areas with

Table 1 (Continued)

Symbol (unit) Description


MAMIN* ()) Mean annual 1-day minimum

MAMIN* ()) Mean annual 7-day minimum

MAMIN30* ()) Mean annual 30-day minimum

MIN50* ()) Median of the 1-day annual minima

*Flow has been standardized relative to the median flow (Q50).

0

2

4

6

8

10

12

14

16

18

20

1994

Flo

w (

rati

o o

f Q

50)

UggerbySaltenVindinge

Uggerby Salten VindingeQ50 (m

3 s–1) 2.5 1.8 0.5 BFI (–) 0.6 0.9 0.6 CV (–) 0.8 0.2 1.4 Q10 (ratio of Q50) 2.8 1.4 5.0 Q90 (ratio of Q50) 0.5 0.8 0.3 FRE7 (year–1) 0.3 0 4.3 DUR7 (days) 2.7 - 5.3

1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241 253 265 277 289 301 313 325 337 349 361

Fig. 1 Illustrative hydrographs for 1994 for three stream sites with contrasting flow regimes, showing mean flow conditions (averaged

over a 10 years from 1988 to 1997) against Julian day (day 1 = 1 January). See explanation of variables in Table 1.



sandy, fast draining soils and correspond to a low

flow variability.

High-flow variability. The 10% and 25% exceedance

flows (Q10 and Q25) express the flows that are exceeded

10% and 25% of the time, respectively, relative to Q50.

Q10 and Q25 are usually regarded as high-flow vari-

ables, but because they have been standardized in

terms of Q50, here they express the general variation of

the high flow regime. This is also the case for the

mean annual 1-, 7- and 30-day maximum (MAMAX,

MAMAX7, MAMAX30) and the peak flows (PEA) of the

selected high flow periods [peaks over defined thresh-

olds of 3 and 7 · Q50, and mean peak flow for events

>25th percentile (PEA25)]. The time the flow was high is

expressed by TIM and is calculated as: TIM = FRE ·DUR for the different thresholds (3 and 7 · Q50 or the

25th percentile). The high-flow volume (VOL) is the

area between the hydrograph and three defined thresh-

olds (either 3 or 7 · Q50 or the 25th percentile) during

the high-flow periods. The dimension of VOL is time

because the volume is divided by Q50.

Low-flow variability. Six variables were used: the 75%

and 90% exceedance flow (Q75 and Q90), the

mean annual 1-, 7- and 30-day minimum (MAMIN,

MAMIN7, MAMIN30) and the median of the 1-day

annual minima (MIN50), all standardized by Q50.

These variables express how extreme the lowest flows

are compared to the average conditions.

Macrophyte data

Data were compiled from 44 unshaded lowland stream

sites throughout Denmark. Vegetation data were col-

lected during the summer of 1997. Ten cross-sections

(or transects) were evenly placed along a 100 m long

reach of each stream. Quadrats (25 · 25 cm) were then

placed side by side from one bank to the other along

each transect. In each quadrat, the presence of vascular

plant species and bryophytes was recorded using an

underwater viewing tube. Where possible, plants were

identified to species level (Hansen, 1981; Moeslund

et al., 1990). The species or species group (taxon) were

subsequently categorized as mainly aquatic (corre-

sponding to obligate and amphibious plants in Riis,

Sand-Jensen & Larsen, 2001) or mainly terrestrial

plants. Aquatic plants live most of the time in water

but some can survive and even grow on land, usually

by developing land forms that are morphologically

distinct from the water forms. Terrestrial plants can

occasionally be observed within streams, but they

never form permanently submerged populations and

do not develop morphologically distinct water forms.

Total vegetation cover (CALL) was calculated as the

proportion of the total number of surveyed quadrats

containing plants. A minimum of 100 quadrats with

plants per site were analysed to ensure accurate cover

estimations. If 10 transects did not provide 100 quadrats

with plants, additional transects were included.

Species richness (S) and diversity (H’) were calcu-

lated for all sites only using aquatic species, because the

number of terrestrial species will, to a great extent,

depend on landuse in the riparian areas (Riis et al.,

2000).

Analysis

To detect relationships between hydrological variables

and vegetation community descriptors (CALL, S and H’

we first classified sites into similar hydrological

regimes, and then explored relationships between the

three derived vegetation variables and hydrological

variables. Pearson correlation coefficients were first

calculated for all 45 hydrological variables. All vari-

ables that were highly inter-correlated with other

variables (r2 > 0.7, >40% of the time) were removed

from the data. Variables with missing data were also

removed from the analysis. The remaining hydrologi-

cal data were classified using TWINSPANTWINSPAN (PC-ORDPC-ORD, MJMMJM

Software Design 1999, Gleneden Beach, OR, U.S.A.) to

produce site groups with similar hydrological regimes.

TWINSPANTWINSPAN analysis was performed using ranked data,

with pseudo-species cut levels set to the 10th, 25th, 50th,

75th and 90th percentile. TWINSPANTWINSPAN produces dichot-

omous groups at each level of the classification hierar-

chy. All hydrological data was subsequently tested by

ANOVAANOVA to determine the differences in hydrology

among the four TWINSPANTWINSPAN groups.

Relationships between vegetation descriptors (i.e.

CALL, S and H’) and hydrological variables were

analysed in two ways. First, we compared the vege-

tation descriptors between the four TWINSPANTWINSPAN site

groupings by ANOVAANOVA. Secondly, we used stepwise

multiple regressions (SMR) to examine relationships

between hydrological variables and vegetation com-

munity descriptors. Both forwards and backwards

SMRs were performed for each model, and the model

1536 T. Riis et al.


with the highest r2-value was chosen. In all cases, the

same models were derived using both methods. All

models were examined for remaining intercorrelated

variables, which were removed. The importance of the

selected variables chosen in each of the SMR model

was assessed on the basis of their final significance

level in the complete model.

To determine whether macrophyte species richness

and diversity were related monotonic or quadratic to

hydrological disturbance, we tested for any significant

linear or quadratic relationship between the hydro-

logical variables describing disturbance (FRE25 and

FRE3) and species richness or Shannon diversity.

To explore relationships between species composi-

tion and hydrological variables, macrophyte species

composition data were ordinated using detrended

correspondence analysis (DCA) to see whether differ-

ent communities existed in the 44 surveyed stream

sites. Resultant ordination scores were also coded

with their appropriate TWINSPANTWINSPAN classification codes,

and ANOSIMANOSIM was used to determine whether ordina-

tion scores differed between the a priori TWINSPANTWINSPAN

groups.

Moreover, we constructed a disturbance index

describing the resistance and resilience of the com-

munity to hydrological disturbance, to test whether

species composition described by functional groups

varied in relation to disturbance. The index was based

on the ability of each species (i) to have multiple

apical growth points; (ii) to root at nodes; (iii) to

produce fragments for dispersal (resilience trait); (iv)

to produce a high number of reproductive organs

(resilience trait); (v) to have annual perennation; (vi)

to grow amphibiously and (vii) to have a highly

flexible stem (resistance trait) and to have soft leaves.

Species information was derived from Willby,

Abernethy & Demars (2000) (traits number 9, 22, 25,

33, 34, 35, 39, 46, 47 and 48 in the Table 1). For each

trait the species were given a number from 0 to 2,

where 2 denotes a high affinity to the trait (Willby

et al., 2000). For each species we summed up the

affinity scores for each trait. The highest disturbance

tolerance score for a species was therefore 16, and

the lowest 0 (Appendix S1). The tolerance score

was weighted according to species abundance by

multiplying by the relative abundance of each species.

Then for each stream site the tolerance scores of the

species were summed, and normalized to the mean

cover at the stream sites, to give the disturbance index

for a site. We then used ANOVAANOVA to test whether there

were significant differences in disturbance index

between the four TWINSPANTWINSPAN site groupings, and

included the disturbance index as a dependent

variable in the SMR.

Results

Flow variables between site groups

A total of 27 inter-correlated variables were removed

from the original matrix of 45 variables, leaving 18

variables as predictors. The 18 variables were allo-

cated to one of four groups that included hydrological

variables associated with: (i) biomass-loss processes

(e.g. variables related to flood and high-flow variabil-

ity); (ii) mass-gain processes (e.g. variables related to

low flow and low-flow variability); (iii) magnitude of

flow and (iv) timing of flow. Summary statistics of

these variables showed a large variability in the range

of hydrological conditions in the study streams

(Table 2).

The TWINSPANTWINSPAN classification of the 18 hydrological

variables produced four groups after the second

classification division. Classification was terminated

at the second TWINSPANTWINSPAN division, because this was

where most of the differences in hydrological vari-

ables occurred. There were significant differences in

15 of the 18 hydrological variables between the four

TWINSPANTWINSPAN groups (Table 2). The largest differences

between the site groups were found in the frequency

and duration of high and low-flow events. The most

harsh plant habitats were found in stream sites in

group 3, where flow variability was highest and there

were frequent high and low flows of short duration.

On the other hand, stream sites in groups 1 and 2

were more benign plant habitats with low flow

variability from the median water level. Group 4 sites

were characterized by less variability but long dura-

tion of low flow events. From KPOS and KNEG it was

clear that sites in groups 1 and 2 were located in fast

draining sandy soils (low KPOS and high KNEG) and

sites in groups 3 and 4 were located in slow draining

clay soil (high KPOS and low KNEG).

Regarding the timing of the flow regime, all sites

were relatively similar. All sites showed a relatively

high predictability (PRE), and a low variability in the

timing of maximum and minimum flows, which gen-

erally occurred in winter and summer respectively.



Macrophyte cover, diversity and flow regime

A total of 55 macrophyte taxa were found in the 44

streams (Appendix S1). Species richness and Shannon

diversity were lowest in the stream sites in group 3,

which was identified as the group with the most

variable flow regime and thus the harshest habitat for

macrophytes (Table 2). There were no differences

among the other three groups of sites. Moreover,

there was no difference in vegetation cover among the

four site groups.

Stepwise regression analysis of the three vegetation

variables (CALL, S, H’) against the 18 hydrological

variables produced significant models that could

Table 2 Summary statistics of the mean (±SD) and median (min.–max.) of 18 hydrological variables describing aspects associated with

biomass loss and gain processes for aquatic macrophytes, and aspects describing magnitude and timing of flow

Variable type Variable Group 1 Group 2 Group 3 Group 4

Hydrological variables

associated with biomass

loss processes

DUR25 16.6 (10.4)B 10.8 (5.1)AB 8.4 (3.1)A 14.7 (5.9)B

10.7 (10.7–41.5) 9.5 (6.6–26.96) 7.7 (5.1–15.7) 15.2 (7.7–30.4)

FRE25 5.9 (2.9)A 8.2 (2.3)B 11.6 (3.0)C 7.1 (2.6)A

6.6 (0.4–8.5) 9.6 (3.4–12.4) 11.9 (5.8–15.3) 6.0 (3.0–11.9)

FRE3 1.5 (1.6)A 1.6 (1.7)A 6.4 (1.8)B 5.6 (1.3)B

0.7 (0–4.3) 0.8 (0–4.9) 6.6 (2.1–8.6) 5.6 (3.2–7.8)

KPOS 0.03 (0.02)A 0.03 (0.01)A 0.09 (0.02)C 0.07 (0.01)B

0.39 (0.23–0.49) 0.03 (0.01–0.05) 0.09 (0.05–0.11) 0.07 (0.05–0.09)

NORISES 0.40 (0.05)C 0.39 (0.03)AB 0.37 (0.02)BC 0.35 (0.01)A

0.39 (0.23–0.49) 0.38 (0.35–0.45) 0.38 (0.34–0.40) 0.35 (0.33–0.36)

Hydrological variables

associated with biomass

gain processes

DUR75 13.0 (4.7)B 11.8 (2.1)AB 9.0 (1.3)A 17.9 (3.9)C

12.1 (4.3–19.8) 11.4 (8.1–15.2) 9.0 (7.0–10.7) 16.9 (12.1–24.1)

DURMIN50 5.8 (3.9)AB 6.0 (2.3)AB 3.8 (1.9)A 8.2 (4.1)B

5.5 (1.8–175.7) 6.2 (2.3–9.0) 4.4 (1.5–7.6) 7.1 (2.8–17.5)

FRE75 7.7 (5.6)AB 7.8 (1.5)B 10.0 (1.6)B 5.3 (1.1)A

6.8 (0.9–21.2) 7.8 (6.0–11.3) 9.4 (8.4–13.0) 5.4 (3.8–7.5)

FREMIN50 1.2 (0.6)A 2.0 (0.8)B 1.7 (0.6)AB 1.4 (0.5)A

1.2 (0.5–2.2) 1.9 (0.8–3.4) 1.5 (1.0–3.0) 1.4 (0.6–2.3)

KNEG )0.03 (0.02)C )0.02 (0.01)C )0.06 (0.01)A )0.05 (0.01)B

)0.02 ()0.06–0) )0.02 ()0.04–0.01) )0.07 ()0.08–0.04) )0.05 ()0.07–0.03)

NOFALLS 0.60 (0.04)A 0.61 (0.03)AB 0.63 (0.02)BC 0.65 (0.01)C

0.61 (0.51–0.77) 0.62 (0.55–0.65) 0.62 (0.60–0.66) 0.65 (0.64–0.67)

Q90 0.61 (0.15)B 0.77 (0.07)C 0.53 (0.12)B 0.36 (0.14)A

0.65 (0.27–0.76) 0.79 (0.63–0.85) 0.51 (0.33–0.71) 0.35 (0.11–0.54)

Magnitude Q50 3.87 (5.63) 2.27 (3.72) 0.79 (0.77) 1.25 (0.89)

2.79 (0.14–18.27) 1.78 (0.09–14.22) 0.42 (0.02–2.50) 1.04 (0.07–2.60)

Timing JULMAX 280.1 (15.4)B 276.4 (17.9)B 242.5 (42.2)A 268.0 (12.2)B

288 (251–294) 276.7 (239.5–299.1) 242.4 (179.4–307.0) 265.8 (244.2–289.0)

JULMAXCV 12.6 (3.7)A 17.7 (10.8)A 32.3 (19.2)B 16.2 (3.7)A

13.7(5.3–16.7) 13.8 (7.0–38.6) 31.7 (8.1–61.5) 15.3 (10.3–22.9)

JULMIN 105 (50) 101.7 (13.5) 107.7 (16.4) 100.8 (10.6)

87 (75–230) 102.3 (76.2–123.1) 105.0 (88.9–136.5) 100.2 (84.1–127.7)

JULMINCV 33.4 (11.0) 34.0 (16.3) 35.3 (22.2) 28.5 (8.9)

31.1 (19.9–51.2) 28.4 (17.3–73.4) 30.1 (11.0–89.6) 25.1 (18.4–51.4)

PRE 0.76 (0.07)B 0.78 (0.02)B 0.69 (0.06)A 0.64 (0.08)A

0.78 (0.60–0.83) 0.78 (0.74–0.82) 0.70 (0.59–0.76) 0.64 (0.50–0.76)

Vegetation community

descriptors

CALL 70.2 (22.1) 69.0 (20.6) 50.3 (18.0) 69.6 (19.0)

76.5 (27.3–97.5) 70.3 (31.6–92.3) 52.9 (14.4–73.8) 71.3 (36.4–94.1)

S 9.7 (2.6)B 10.1 (3.2)B 5.4 (2.5)A 11.1 (3.6)B

9 (6–14) 10 (7–17) 5 (2–10) 10 (5–17)

H’ 1.6 (0.2)B 1.6 (0.3)B 1.1 (0.5)A 1.6 (0.3)B

1.5 (1.2–1.9) 1.7 (1.1–2.0) 1.1 (0.3–1.7) 1.6 (0.8–2.4)

Disturbance

index

13.1 (3.1) 13.0 (1.8) 13.2 (1.4) 12.8 (1.3)

12.1 (9.7–18.1) 12.8 (10.1–17.1) 13.3 (11.3–15.4) 12.5 (10.9–15.2)

Letters indicate significant differences among groups.

1538 T. Riis et al.


account for 31–48% of the variation (Table 3). Vegeta-

tion cover showed a negative relationship with high-

flow variation (KPOS and NORISES) and positive

relationships with low-flow frequency and duration.

Species richness was primarily correlated to Qmean

(magnitude) and KNEG (gain process). Shannon diver-

sity at the sites was strongly related to variability in the

timing of the annual maximum flow.

We tested whether there was a monotonic or

quadratic relationship between species richness and

Shannon diversity as a function of FRE25 or FRE3

(Fig. 2). For both S and H’, FRE25 was the best

predictor and explained up to 24% of the variation.

A quadratic relationship explained about twice as

much of the variation in both datasets compared to a

linear relationship.

Community composition and hydrological variables

The most common macrophytes were Sparganium

emersum Rehman, found in 69% of all sites, followed

Table 3 Models derived from stepwise multiple regression analysis of relationships between selected variables describing macro-

phyte communities in 44 Danish lowland streams and hydrological variables associated with biomass loss and gain processes, timing

of flow and magnitude of flow

Dependent

variable

Loss processes Gain processes Timing ⁄ magnitude Model

Variable + ⁄) P-value Variable + ⁄ ) P-value Variable + ⁄) P-value R2 F P-value

CALL KPOS

NORISES

))

0.001

0.003

DURMIN50

FRE75

Q90

+

+

+

0.018

0.007

0.023

0.484 4.83 0.001

S DUR25 ) 0.014 KNEG

NOFALLS

+

+

0.006

0.016

Qmean + 0.003 0.458 5.21 0.001

H¢ JULMAXCV ) 0.001 0.310 16.89 0.001

Table shows which variables were selected for the regression models (P < 0.05), the nature of the relationship (positive or negative;

+ ⁄)), the P-value for each independent variable, the % variance explained by the model (R2 value) and the overall significance of the

model (F-ratio and P-value).

CALL, total cover of vegetation; S, number of aquatic species; H¢, Shannon diversity of aquatic species.

Fig. 2 Species richness (S) and Shannon diversity (H¢) of aquatic

plants in 44 Danish streams as a function of hydrological

disturbance frequency (FRE25). Species richness and Shannon

diversity both vary quadratically with disturbance frequency

(S = 5.42 + 1.54x ) 0.11x2; R2 = 0.26; P < 0.01 and

H¢ = 1.33 + 0.11x ) 0.01x2; R2 = 0.24; P < 0.01).

Fig. 3 Results of the DCA of macrophyte species composition in

the 44 streams showing the sites coded for the T W I N S P A NT W I N S P A N

groups 1–4.



by Callitriche sp., Glyceria maxima (Hartman) Holmberg

(65%) and Elodea canadensis L.C. Rich (63%; Appendix

S1). Ordination of the macrophyte data revealed only

weak sample clusters, suggesting that few environ-

mental gradients were controlling macrophyte species

assemblages (Fig. 3). The gradient lengths of both axes

1 and 2 were moderate (3.33 and 3.52, respectively),

implying that complete species turnover does not

occur. When coded for their respective TWINSPANTWINSPAN

groups, no pattern was apparent (Fig. 3), suggesting

that macrophyte communities were not strongly cor-

related to hydrological variables. This finding was

reinforced by the absence of strong correlations

(r < 0.3) between the ordination scores and individual

hydrological variables.

To further investigate the relationship between flow

regime and community composition, we analysed the

effect of hydrological disturbance on the presence of a

proposed more disturbance resistant ⁄ resilient macro-

phyte community, described by a high disturbance

index. However, there was no apparent relationship

between hydrological disturbance and the presence of a

disturbance tolerant macrophyte community, as there

was no significant difference in disturbance index

between the four TWINSPANTWINSPAN groups, and no significant

relationships in the stepwise regressions.

Discussion

Macrophyte cover in relation to flow regime

We found support in the stepwise multiple regression

analysis for our expectation that macrophyte cover is

lowest in streams with high-flow variability and

highest in streams with long durations of low flow

and low-flow variability, supporting the contention of

Biggs (1996). Thus, the study also supported the

findings from New Zealand streams where hydrolog-

ical disturbance was identified as a main controller of

vegetation cover (Riis & Biggs, 2003). However, there

is a major difference in the flow regime most closely

related to vegetation cover in the two regions, as we

found that a more extreme high flow parameter

(FRE7) was most strongly related to vegetation cover

in New Zealand streams whereas a variable describ-

ing less harsh conditions (FRE25) was best related to

vegetation cover in the Danish streams. There are two

possible explanations for this difference. First, the

results may reflect the presence of more sandy and

mobile stream bottom substrates in Danish streams,

causing the vegetation to become disturbed at a much

lower level of high flow. Previous studies have shown

that the mechanism behind biomass reduction and the

subsequent removal of whole plant beds at high flow

is erosion rather than breakage of single stems and

leaves (Riis & Biggs, 2003). Thus lower high flow

events causing sand transport can control the vegeta-

tion cover in Danish streams whereas higher flow

events are needed to move substrate and thereby

control vegetation in New Zealand streams with

coarser substrate. Secondly, the differences might

arise because stronger disturbances strengthen the

relationship between disturbance and vegetation

cover. Thus if high flow events of the magnitude of

seven times the median flow was as common in

Denmark as in New Zealand, FRE7 would also be a

stronger controller than FRE25 in Danish streams.

Vegetation cover appeared to be most strongly

related to loss processes. Our results indicate that the

degree of flashiness and the pattern of flow rise

during high flow were the most important loss

processes. This intriguing result implies that even in

the low gradient Danish streams, rapid increases in

flows were more detrimental to macrophyte cover

than slow increases. This result may reflect more

extensive sediment erosion and macrophyte dislodge-

ment with a rapidly rising hydrograph, whose turbu-

lent eddies may disturb the stream bed and remove

plants. This aspect is open to further investigation.

Thus, in conclusion, even though the hydrological

regime in the Danish streams was less variable than in

the New Zealand streams, vegetation cover was

nevertheless controlled by hydrological variables,

mainly through biomass-loss processes.

Macrophyte species diversity in relation to flow regime

We obtained support for the IDH because there were

significant quadratic relationships between species

richness and diversity as functions of disturbance

frequency, as previously reported by Willby et al.

(2001). This verification contrasts with the findings for

New Zealand streams where the IDH could not be

demonstrated for in-stream vegetation, and where a

linear relationship best described the relationship

between species richness and diversity as a function

of hydrological disturbance. One reason for the IDH

working in Danish but not New Zealand streams

1540 T. Riis et al.


could be the use of different disturbance variables

(FRE7 and FRE25) in the two regions. In New Zealand

streams where FRE7 is 0 there could still be some

disturbance occurring at a less intense level of

disturbance. Therefore, stable conditions might not

be present in streams with FRE7 = 0 and thus species

richness is not reduced by competition. On the other

hand, in Danish streams the disturbance frequency

must be really low when FRE25 = 0 because the high

flow is less severe, and therefore the decline in species

richness and diversity in these streams might be

reduced by competition.

A decline in species richness at the high disturbance

frequency was also supported by the TWINSPANTWINSPAN

analysis. Streams in TWINSPANTWINSPAN group 3 were charac-

terized by flashy hydrographs, with frequent, short-

lived floods, and species richness and diversity were

significantly lower in this group compared to the other

three groups, and represented high disturbance sites in

the quadratic relationship. In the stepwise multiple

regression, species richness was most strongly related

to flow magnitude. This result suggests that large rivers

have a higher species richness of aquatic plants than

small rivers. Similar results were reported by Riis &

Sand-Jensen (2001) in Danish and Riis, Biggs & Flan-

agan (2003) in New Zealand streams. Large rivers have

a greater source of potential plant propagules from

upstream areas, and are probably to have a greater

variety of growth habitats.

Timing of the annual maximum flow influenced the

Shannon diversity on the stream sites. However, there

was a generally high predictability at all sites, with

annual minimum flow occurring in summer and

annual maximum flow in winter. Thus, no important

variation in vegetation because of timing could be

expected among the streams sites.

Between 31% and 48% of the variation in vegeta-

tion cover, species richness and diversity could be

explained in the stepwise multiple regression models.

The residual variation in the data set is probably to

relate to other factors such as water alkalinity, bottom

substrata, location in the stream system, and human

impacts (e.g. Riis & Sand-Jensen, 2001; Demars &

Harper, 2005).

Species composition in relation to flow regime

There was poor discrimination of macrophyte com-

munity composition among the four TWINSPANTWINSPAN

groups of streams in the DCA analysis. Moreover,

we did not find any relationship between the presence

of disturbance-tolerant species and hydrological

disturbance. Lack of strong discrimination and rela-

tionships suggests that the composition of the aquatic

macrophyte community in Denmark is not strongly

regulated by stream hydrology. Previous studies of

aquatic macrophyte distribution have also only found

weak associations between species composition and

flow (Henry, Bornette & Amoros, 1994; Riis & Biggs,

2003). Thus, the hypotheses posed by Riis & Biggs

(2001) could not be verified in this study. Other factors

that might be more important in Danish streams are

water alkalinity and stream size (Riis et al., 2000), but

still other factors such as nutrient concentration and

substrate size might also be influential.

The higher species richness associated with an

intermediate frequency of high flow events is

expected to be a result of coexistence of more

competitive species, that generally do well in stable

streams, with more ruderal species that colonize open

stream bed from upstream or bank areas. Successful

colonization from upstream populations is dependent

on retention of dispersal propagules, establishment by

roots, and colonization by lateral growth (Riis, 2008).

Colonization from riparian areas into the stream

channel most probably occurs in two ways. First,

many terrestrial plants will spread efficiently into the

stream channel by lateral growth by rhizomes (Henry

et al., 1994; Barrat-Segretain & Amoros, 1996). Sec-

ondly, during low flows, seeds from terrestrial species

are probably to sprout in depositional areas such as

meanders near the stream banks.

Acknowledgments

We thank The Danish Natural Science Research

Council and Carlsberg Foundation for financial

support. Also we are grateful to two anonymous

reviewers that helped improving our manuscript.

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Supplementary material

The following supplementary material is available for

this article:

Appendix S1. Aquatic plant species found at 44

stream sites in Denmark. Relative frequency is the

percentage of the 44 sites at which the species was

present. Disturbance tolerance scores describe low (0)

to high (6) disturbance tolerance of a species based on

information on five traits derived from Willby et al.

(2000). Numbers in italics are based on own observa-

tion and not included in Willby et al. (2000), and

indicate that these reed plants use the bank as a

refugium from which they can spread back into the

stream bed after a disturbance

This material is available as part of the online article

from:

http://www.blackwell-synergy.com/doi/abs/

10.1111/j.1365-2427.2007.01881.x

(This link will take you to the article abstract).

Please note: Blackwell Publishing are not responsi-

ble for the content or functionality of any supplemen-

tary materials supplied by the authors. Any queries

(other than missing material) should be directed to the

corresponding author for the article.



Vegetation and flow regime in lowland streams

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