Arch. Hydrobiol.
158
4
431–460
Stuttgart, December 2003
Spatio-temporal patterns of benthic invertebrates
along the continuum of a braided Alpine river
Dave B. Arscott1 *, Klement Tockner1 and J. V. Ward 1
Department of Limnology, Swiss Federal Institute for Environmental Science
and Technology (EAWAG/ETH), Dübendorf, Switzerland
With 7 figures, 3 tables and 1 appendix
Abstract: Aquatic invertebrate community structure was quantified from six sites,
each located in distinctly different geomorphic reaches along the Tagliamento River
(N.E. Italy). Quantitative samples of benthic invertebrates were collected quarterly
over one year from six sites (from 5 –1100 m above sea level), while physico-chemical
data were collected monthly. Total abundance of benthic invertebrates was highly variable in space and time with a minimum density of 433 ± 158 individuals m – 2 ( ± 1
standard deviation; n = 3) occurring in the main channel of an island-braided flood
plain midway along the continuum after the autumnal floods (November 1998). At
most sites, maximum densities (111,226 ± 45,395 ind. m – 2) were observed during late
summer (August 1998). In August, insects comprised more than 87 % of all taxa in
each reach, with Chironomidae (Diptera) and Baetidae (Ephemeroptera) being the
most abundant insect families. Abundance of Insecta, Crustacea (primarily copepods
and amphipods), Oligochaeta, Nematoda, and Hydrachnidia all increased with distance
downstream. In general, dominant insect taxa were represented by highly mobile forms
with potential for multivoltinism (Chironomidae, Baetidae, Simuliidae). Non-insect
taxa were also well represented by high mobility (e.g., Echinogammarus spp.) and
ability to rapidly reproduce (e.g., Oligochaeta, Nematoda, Copepoda), life-cycle strategies critical for persistence in a highly dynamic and physically harsh environment.
Temperature, substrate size, nutrient concentrations, benthic organic matter, and biomass of epilithic algae all exhibited distinct changes along the continuum. Changes in
benthic invertebrate community structure (abundance and diversity) also occurred
along the longitudinal gradient and these changes were correlated with spatial and temporal shifts in various environmental factors. Longitudinal patterns revealed by CoInertia analysis, corroborated diversity and abundance patterns by illustrating the dis-
1
Authors’ addresses: Department of Limnology, EAWAG/ETH, Überlandstrasse
133, CH-8600 Dübendorf, Switzerland.
* Current address of author for correspondence: Stroud Water Research Center,
970 Spencer Road, Avondale, PA 19311, USA; E-mail: [email protected].
DOI: 10.1127/0003-9136/2003/0158-0431
0003-9136/ 03/0158-0431 $ 7.50
ã 2003 E. Schweizerbart’sche Verlagsbuchhandlung, D-70176 Stuttgart
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Dave B. Arscott, Klement Tockner and J. V. Ward
tinct nature of assemblages occurring in headwater reaches and the meandering lowland reach. Faunistic-to-environment concordance among reaches and dates was remarkably high and exhibited spatio-temporal dynamics. Concordance of communities
with their local environment was more variable from date-to-date in lower reaches
compared to headwater reaches, particularly following spring and autumn floods.
Floods caused massive removal and relocation of individuals and appeared to disrupt
the organizing pressures (selection pressures) experienced by communities after prolonged periods of low flow (i.e., competition, predation, and spatial organization of
food and habitat resources). Patterns observed using Co-Inertia analysis were driven
primarily by abundance and secondarily by community composition. In this way, it is
important to recognize the complementary nature of comparing and contrasting measures of diversity (driven primarily by composition and secondarily by abundance) with
multivariate analyses.
Key words: aquatic macroinvertebrates, abundance, richness, alpha diversity, beta
diversity, gamma diversity, community assemblages, Co-Inertia analysis, longitudinal
patterns, Tagliamento River, Italy.
Introduction
Spatial and temporal variation in taxa richness, abundance, and dominance are
important attributes of biotic communities (e.g., Huston 1994, Rosenzweig
1995). Spatial variation has been attributed to habitat character (e.g., Brown
1984, Brown et al. 1995) but may also manifest due to stochastic temporal
variation of the environment (Ives & Kopfer 1997). Temporal variation in
community assemblages can result from habitat seasonality, organism phenology, and/or disturbance (e.g., flooding and fire). Disturbances are important
determinants of biotic community structure and function (Grime 1977, Ward
& Stanford 1983, Junk et al. 1989, Pickett et al. 1989) and may manifest
hierarchically, affecting several levels of organization, through individual,
ecosystem, and landscape scales. According to White & Pickett (1985) disturbances are “… any relatively discrete event in time that disrupts ecosystem,
community, or population structure and changes resources and the physical
environment”.
Spatial variation in communities often reflects differences in recovery trajectories following disturbance (sensu O’Neil et al. 1986). For example, if two
identical communities are disturbed to differing degrees (e.g., magnitude, frequency, or timing of disturbance) then it is likely that community attributes
(composition and abundance) will differ because each is at a different point
along that communities recovery trajectory. O’Neil et al. (1986) described the
process of a system returning to its pre-perturbation trajectory (or rate of
change) rather than returning to an artificial “undisturbed” state as homeorhesis.
Spatio-temporal patterns of benthic invertebrates
433
Flooding represents the dominant type of natural disturbance along most
river corridors (Welcomme 1979, Junk et al. 1989, Puckridge et al. 1998)
and it is likely that all stream ecosystems are disturbed by fluvial forces to
some degree (Reice 1985, Statzner et al. 1987, Ward 1989). Flood dynamics have been well studied in riverine ecosystems; however, relatively little information is available on the effects of disturbance at a scale large enough
(e.g., an entire river corridor) to match that operating in nature (Fisher 1987,
Pickett et al. 1989, Dudgeon 1992), although recent important advances
have been made (Michener & Haeuber 1998, Swanson et al. 1998, Nakamura et al. 2000, Arscott et al. 2002). Moreover, current understanding of
stream invertebrate biodiversity patterns is heavily biased toward local studies
of small temperate forested streams, whereas data from large rivers or across
broad spatial scales are limited (Vinson & Hawkins 1998, Tockner & Ward
1999). Thermal patterns in rivers are also thought to be a major factor structuring zoobenthos diversity patterns (Vannote & Sweeney 1980, Ward 1985).
In particular, diel and seasonal pulses are presumed to be maximized mid-way
along the river corridor (Vannote & Sweeney 1980, Vannote et al. 1980)
and this peak in thermal heterogeneity, among other factors, is predicted to be
a major factor influencing stream invertebrate biodiversity. Changes in hydraulic stress due to tributary confluences or changes in channel geometry
(i.e., width, depth, slope) have also been suggested to influence longitudinal
patterns of invertebrate diversity (Statzner & Higler 1986).
The Tagliamento River in N. E. Italy, considered to represent a reference
condition for many rivers draining the European Alps because of its morphologically intact nature (Ward et al. 1999 a), provided an excellent opportunity
to investigate biotic structure and the dynamic-equilibrium between biota and
environment along an extensive longitudinal gradient. The goal of this study
was to determine the extent to which aquatic invertebrate abundance, richness,
distribution, and their concordance with the environment were influenced by
hydrological factors (in particular flooding), seasonality, and other prevailing
environmental conditions (e.g., substrate type and nutrients). Hypotheses were
(1) that taxa richness would be highest in reaches mid-order reaches where the
distribution of taxa restricted to colder headwater sites would overlap with
taxa more specific to lowland environments, (2) that richness and abundance
patterns are highly variable over time due to combined influences of seasonality and hydrological disturbance (i.e., flooding), and (3) that concordance
between fauna and the environment will vary temporally in response to the
flood regime.
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Dave B. Arscott, Klement Tockner and J. V. Ward
M aterial and m ethods
Study site
The Tagliamento River, N. E. Italy (Friuli-Venezia Giulia; 46ƒ N, 12ƒ 30¢ E), extends
from the southeastern corner of the European Alps to the Adriatic Sea mid-way between Venice and Trieste (Fig. 1). The catchment (2580 km2) is dominated by Triassic
dolomitic limestone in upper reaches and valley bottom deposits of glacial-fluvial sed-
Fig. 1. Tagliamento catchment, location of reaches I –VI (see text for site description),
and the hydrograph showing discharge (left axis) at river-km 59 and stage height at
river-km 84 (right axis). Dashed lines indicate invertebrate sampling dates.
Spatio-temporal patterns of benthic invertebrates
435
Table 1. Description of study reaches.
Reach
I – Constrained headwater streams
II – Headwater island-braided reach
III – Bar-braided reach
IV – Lower island-braided reach
V – Braided-to-meandering reach
VI – Meandering reach
Distance from Altitude
source (km)
(m a. s. l.)
0–2
13.3
73.7
80.3
120
127.5
1005 – 1095
705
165
140
20
5
Average Active floodplain
slope (%) width (m-range)
5.5 – 19.5
2.5
1
1
0.5
< 0.5
8 – 30
106 – 263
611 – 832
670 – 999
449 – 834
138 – 254
iments in the lower reaches (Tockner et al. 2003). Average catchment elevation is
1159 m a.s.l. (above sea level) with the highest peak at 2781 m a.s.l. The active mainstem corridor (150 km2, 172 km, 7th order) traverses a series of distinct geomorphological reaches characterized by open gravel flood plains up to 2 km wide and, in some locations, vegetated island density approaching 100 ha per river-km (Ward et al. 1999 a,
Gurnell et al. 2000). Study sites were located in six geomorphologically distinct
reaches aligned along the longitudinal continuum (Fig. 1 and Table 1), including: headwater constrained streams (reach I), a headwater island-braided flood plain (II), a barbraided flood plain (III), a lower island-braided flood plain (IV), a braided-to-meandering transition flood plain (V), and a meandering flood plain (VI).
Substrate size in the Tagliamento declines with decreasing elevation (Petts et al.
2000) and substrate heterogeneity within a habitat is highest in headwater reaches
(Arscott et al. 2000). In general, nutrient concentrations increase along the longitudinal continuum, primarily driven by anthropogenic influences. Specifically, nitrate
(NO3), ammonium (NH4), and total dissolved reactive phosphorus (TDP) increased
from 341 ± 14.5 mg l –1 ( ± 1 S.E.), 1.7 ± 0.3 mg l –1, and 0.5 ± 0.19 mg l –1, respectively, in
reach I to 1266 ± 92 mg l –1, 6.0 ± 1.6 mg l –1, and 3.2 ± 1.07 mg l –1 in reach VI (Arscott et
al. 2000). Water temperature increased along the continuum from summer averages of
11.3 ± 1.6 ƒC in the headwaters to 17.1 ± 1.3 ƒC in reach VI. During winter, water temperature fell to 2.0 ± 0.7 ƒC in the headwaters (I) and to 8.8 ± 0.5 ƒC in reach VI (Arscott et al. 2001).
The hydrological regime is flashy pluvio-nival, with highest discharges during the
spring and autumn (Ward et al. 1999 a). Hydrology is highly variable and discharge, at
a point mid-way along the continuum (~59 river-km; 7th order), averages between 60
and 80 m3 s –1 with 2, 5, and 10 year return period floods estimated to be 1100, 1500, and
2150 m3 s –1 (Gurnell et al. 2000). During the study period there were two highly relevant flow events, one just prior to the May 1998 sampling date and the other prior to
the November 1998 date (Fig. 1). In early May 1998, the flow event only influenced
main channel habitats (i.e., below bank-full), while the three large flood peaks in October 1998 almost completely inundated the 1.5 – 2.0 km flood plain in lower reaches.
Invertebrates
Benthic invertebrates were collected from main channel sites in each of the six reaches
in May, August, and November 1998, and March 1999 and preserved in 4 % formalde-
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Dave B. Arscott, Klement Tockner and J. V. Ward
hyde. Quantitative samples were collected in triplicate by randomly placing a modified
Hess sampler (415 cm2; 100 mm mesh net) on the substrate. Sampling methodology was
identical to Arscott et al. (2003). Substrate particle size (apportioned into 6 size classes, see Arscott et al. 2000), water depth, and velocity were recorded for each sample. In this study, we report on samples collected from the hard substrate in main channel areas of each of the six geomorphic reaches. Substrate size was largest in headwaters where bedrock, boulder, and cobble were dominant. In lower reaches, small and
large gravels were dominant (see Arscott et al. 2000). Flow velocity, integrated over
6 s, was measured at 60 % of the total depth with a Pocket MiniAir 2 Meter (Schiltknecht AG, Switzerland). Invertebrates were sorted from each sample with the aid of a
dissecting microscope at 10 ´ magnification. Most invertebrates were identified to genus (Appendix 1).
Physico-chemical charac teristics, benthic organic matter, and
benthic algae
Prior to invertebrate sampling on each date, water samples were collected for analysis
of physico-chemical variables. Specific conductance and temperature were measured
with a Universal Pocket Meter Multiline P4 (WTW GmbH, Weilheim Germany).
Three liters of water were collected from each site for analysis of sixteen particulate
and dissolved components. Analytical techniques are reported in Tockner et al.
(1997), Malard et al. (1999), and Arscott et al. (2000) and main channel concentrations are reported in Arscott et al. (2000).
Benthic organic matter (BOM) was quantified from Hess samples after removal of
invertebrates. Coarse and fine BOM (CBOM or FBOM) fractions were separated using
a 1mm sieve and needle, leaf, and macro algal fragments larger than ~1cm2 were separated from the CBOM fraction. All material was dried at 60 ƒC, weighed, ashed at
500 ƒC for 4 h, and re-weighed. Ash-free dry mass was calculated for CBOM, FBOM,
needles, leaf, and macro algae.
Standing stock of benthic chlorophyll-a (CHLben) was measured at each site from
the upper surface of five stones of average size (b-axis typically 5 –15 cm) collected
along a transect across the channel, in order to integrate the depth and flow gradients.
From each stone, a 2 by 3 cm area was scrubbed using a wire brush and a plastic
frame/template. The resulting slurry was filtered onto a Whatman GF/F filter and the
filter was placed immediately into 6 ml of 90 % ETOH. The vial containing the filter
was kept cool and in the dark until analysis. Analytical techniques for the measurement
of chlorophyll-a followed the rapid HPLC method of Meyns et al. (1994).
Diversity indices
Main channel samples were processed for calculation of diversity parameters in two
ways. First, in order to investigate temporal patterns of diversity among dates, replicate samples (n = 3) for each site-date combination (n = 24) were averaged to obtain
mean abundance (m – 2). Taxa richness (S), Fisher’s a (the parameter for the log series
Spatio-temporal patterns of benthic invertebrates
437
distribution), and the Simpson index (SI) were calculated for each site-date combination using Species Diversity version 2.3 software (Pisces Conservation Ltd., Lymington, UK, 1998). Confidence intervals (C.I.) at the 95 % level were also calculated for
each diversity index using bootstrapping techniques available in the software package.
The Simpson index was transformed to a non-bias form that increases with increasing
diversity using a negative natural log function suggested by Rosenzweig (1995).
Taxa richness (S) calculated for each main channel site (I–VI) on a specific date represented a date specific measure of alpha diversity and was designated date alpha diversity or ad.
Second, in order to determine overall diversity patterns among reaches, all samples
for a reach were averaged to produce abundance (m – 2) estimates for each taxon (i.e.,
compressing temporal data to one measure per reach). Again, S, Fisher’s a, SI, d, and
95 % C.I.’s were calculated for each reach. Taxa richness (S) calculated from a reach’s
full complement of samples (i.e., all dates) represented a temporal aspect of diversity
and was designated temporal gamma diversity or gt. In addition, the fit of the rank
abundance sequence of each reach to the geometric series, log series, truncated log
normal, and broken stick abundance models was tested using the c2 goodness of fit
test. This procedure was carried out to evaluate if Fisher’s a and SI were appropriate
indices of richness and dominance, respectively. Pearson’s product-moment correlation analysis was used to examine relationships between diversity indices and environmental variables.
To determine the importance of temporal changes in benthic invertebrate assemblage two measures of beta diversity were calculated. Beta diversity measures the turnover or change in taxa composition between two samples or among a collection of
samples. Harrison et al. (1992) modified Whittaker’s beta, the most robust measure of
beta diversity (Shmida & Wilson 1985), to allow for direct comparisons between
transects of unequal size (b1) and to examine how trends in alpha diversity (i.e., taxa
richness at a point) may influence beta diversity (b2). Beta-1 can be used to examine
the pairwise differentiation between sites and ranges from 0 (complete similarity) to
100 (complete dissimilarity). Beta-2 measures the amount by which regional diversity
exceeds the maximum diversity attained locally and also ranges from 0 to 100. Beta-2
converges on b1 when variability in alpha is small. We used b2 to measure the extent to
which alpha diversity (taxa richness in a reach on a single date; ad) converges with
gamma diversity for a reach (defining gamma diversity as the taxa richness of all dates
from a reach; gt) based on measures of taxa richness through time. Therefore, a low
value of b2 indicates that ad approaches gt and the closer that b2 is to b1 the lower is
variability in alpha over time. By using these measures of diversity, the relative importance of regional versus local factors can be elucidated.
In addition, b1 was calculated for each pairwise comparison (i.e., reach I versus
reach II, I versus III, I versus IV, etc…; n = 15) to examine the relationship between
b-diversity and distance. This relationship was assessed using a Mantel test permuted
10,000 times. The test is used to estimate the association between two independent
similarity matrices describing the same set of entities and to test whether the association is stronger than one would expect from chance (Sokal & Rohlf 1995).
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Dave B. Arscott, Klement Tockner and J. V. Ward
Multivariate relationships
The first goal in investigating the benthic invertebrate community was to determine
temporal relationships along the longitudinal continuum based on faunistic abundance
data. A between groups principal components analysis (PCA) was used to separate
seasonal shifts in community structure from the spatial habitat typology (Foucart
1978, Dolédec & Chessel 1989). Prior to analysis, all abundance values were log10
(x +1) transformed to reduce the influence of abundant taxa (e.g., Chironomidae). The
between group PCA seeks axes representing the center of gravity among all groups or
subspaces and focuses on the between-group differences, in this case the temporal variation.
The second goal in determining spatiotemporal structure of benthic invertebrate
communities was to relate faunistic distributions to measured environmental variables.
Co-inertia analysis (CIA) was used to simultaneously study the structure in the environmental and faunistic data, and to determine if concordance (i.e., co-structure) between these two independent structures existed (Dolédec & Chessel 1994). Co-inertia analysis is a two-table ordination method similar to canonical correspondence analysis (CCA). However, unlike CCA, Co-inertia analysis can examine the co-structure
between tables having similar as well as different numbers of environmental variables,
species, and/or samples (Dolédec & Chessel 1994). Faunistic abundance data (individuals m –2) were log10 (x +1) transformed prior to analysis.
Substrate particle size classes, water depth, and velocity from replicate samples (n
= 3 per site/date) were averaged to produce one estimate per site-date. Substrate data,
originally reported as percentages, were arcsine square root transformed prior to data
analysis. All other environmental data were standardized, by subtracting the mean and
dividing by the standard deviation, prior to data analysis. Originally, thirty-one environmental variables were included in the analysis. However, inspection of the initial
environmental PCA indicated redundancy of some variables, particularly dissolved
and particulate variables. Therefore, total dissolved nitrogen, soluble reactive phosphorus, particulate phosphorus, particulate nitrogen, and sestonic ash-free dry-mass were
removed from the analysis. Twenty-six environmental variables were included in the
final Co-inertia analysis.
Concordance between the faunistic and environmental data was determined using a
Monte Carlo permutation test (10,000 permutations) and by inspecting the average distance between sites based on fauna distribution and on environmental variables in the
two-dimensional CIA space. The standard error in distance between faunistic and environmental ordinations for each reach and for each date was used as an indicator of
temporal (reach-scale) or spatial (catchment-scale) variation in concordance, respectively. Finally, the maximum distance between main channel scores (based on taxa)
within a reach (i.e., distance between dates) and within a date (i.e., distance between
reaches) was calculated to assess temporal and spatial influences on habitat typology.
Spatio-temporal patterns of benthic invertebrates
439
Results
Macroinvert ebrate abund ance and diversity
Fig. 2. Total abundance (ind.m – 2), taxa richness (ad), Fisher’s a, and Simpson’s index (SI) computed for each of
the four sampling dates (May, August, November 1998, and March 1999) in each of the six reaches (I –VI). Error
bars for total abundance are ± 1 S.D., while error bars for a and 1/D are boot-strapped 95 % confidence intervals
(sometimes too small to see on the graphic).
Total abundance of benthic invertebrates was highly variable in space and time
with a minimum density of 433 ± 158 ind. m – 2 ( ± 1 S.D.; n = 3) occurring in
reach IV after the autumnal floods (Nov. 1998). Maximum density, in reach VI
during late winter/early spring (Mar. 1999), was 111,226 ± 45,395 ind. m –2
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Dave B. Arscott, Klement Tockner and J. V. Ward
Fig. 3. Average abundance (ind. m – 2) in August 1998 in each of the six reaches (I –VI)
of (a) dominant insect taxa (excluding chironomids), (b) Chironomidae (Diptera), (c)
Crustacea, and (d) other common non-insect taxa.
(Fig. 2). At all sites, except reach VI, maximum densities were observed
during late summer (Aug. 1998). In August, insects comprised greater than
87 % of all taxa in each reach. Chironomidae (Diptera) and Baetidae (Ephemeroptera) were the most abundant insect families at all sites (Fig. 3). Chironomids, the most abundant taxon in each reach, composed from 35 % (reach IV)
to 60 % (reach VI) of all individuals. Crustaceans, primarily copepods and
amphipods, increased in abundance in lower reaches (Fig. 3). Percent of the total taxa represented by crustaceans was highest in March 1999 and increased
along the continuum from 1.5 % and 3 % in reaches I and II to 19, 14, 24, and
21 % in reaches III to VI, respectively. Oligochaeta and Nematoda also in-
441
Spatio-temporal patterns of benthic invertebrates
Table 2. Species richness, diversity, and rank abundance model fits for average main
channel invertebrate assemblages in each reach calculated from all sample dates. Fit of
models was tested using a c2 goodness of fit test where if p < 0.05 then the observed
distribution was different from the expected distribution and “No” is reported. Abbreviations are: gt = temporal gamma diversity (total taxa richness over time), N = average
abundance (ind. m – 2), Fisher’s a = parameter of log series equation, SI = Simpson index, aave = average alpha diversity, ns = number of sites in comparison, amax = maximum alpha diversity.
(A) Diversity
Species No. (gt)
Individuals (m –2; ± 1 SE)
Fisher’s a ( ± 95 % CI)
Simpson’s index
( – ln SI; ± 95 % CI)
b1 (gt/a ave – 1)/(ns – 1) * 100
b2 (gt/a max – 1)/(ns – 1) * 100
(B) Fit of models
Geometric series
Log series
Truncated log normal
Broken stick
I
II
III
IV
V
VI
51
6399 ± 3503
7.67 ± 0.525
1.39 ± 0.037
35
12601 ± 10507
4.72 ± 0.225
1.25 ± 0.024
40
11224 ± 13791
5.21 ± 0.218
1.34 ± 0.016
38
15418 ± 20272
4.85 ± 0.352
1.49 ± 0.015
38
14238 ± 19955
5.05 ± 0.368
1.05 ± 0.018
45
56657 ± 62901
3.59 ± 1.326
0.96 ± 0.012
34
19.8
26.5
6.9
26.6
2.7
24.9
11.9
27
5.1
22.9
3.3
No
Yes
Yes
No
No
Yes
Yes
No
No
Yes
Yes
No
No
Yes
Yes
No
No
Yes
Yes
No
No
Yes
Yes
No
creased in abundance along the continuum. In March 1999, oligochaetes and
nematodes accounted for 42 and 22, 57 and 7, and 23 and 1% of total individuals in reaches III, IV, and V, respectively. Otherwise they never accounted for
greater than 5 % of the total abundance.
Total faunal diversity (gt; total number of taxa at a site over all sampling
dates) was highest at both ends of the continuum (I and VI) and was similar
for reaches III, IV, and V (Table 2). Turnover of taxa from date-to-date (b1;
turnover at a site among dates) was highest in reach I and lowest in reach VI
(Table 2). b2 (extent to which temporal gamma diversity at a site exceeded diversity observed on a single date [alpha diversity]) turnover was highest in
reach I, indicating that on each sampling occasion gamma diversity (gt) considerably exceeded alpha diversity (ad), while in reaches III, V, and VI a low b2
indicated that ad was close to gt. Difference between b1 and b2 was considerably lower in reaches I and IV (~13) compared to all other reaches (~20), indicating that ad was more stable over time for reaches I and IV (see Fig. 2).
Turnover (b1) between any two reaches significantly increased with distance
between the two reaches under comparison (Mantel test for 15 pairwise comparisons; p = 0.004; r = 0.89).
Since rank abundance plots were not significantly different from log series
or truncated log normal distributions (Table 2), the use of Fisher’s a and SI to
compare taxa richness and equitability, respectively, was considered appropriate. Fisher’s a, among reaches, correlated with gt for all reaches except VI,
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Dave B. Arscott, Klement Tockner and J. V. Ward
Table 3. Pearson product-moment correlation’s (r2) between diversity indices and several environmental variables measured in each reach. Values in bold represent significant correlations (p < 0.05). gt is temporal gamma diversity (i. e., total taxa richness of a
reach), Fisher’s a is the parameter of the log series equation, –ln SI is Simpson’s index, and b1 and b2 are measures of beta diversity (temporal turnover of taxa; see Methods).
gt
Ave. summer temp.
Ave. winter temp.
Summer diel temp. pulse
Channel width
Channel discharge
Ave. velocity
Ave. depth
Elevation
Slope
% Boulder
% Cobble
% Lg. Gravel
% Sm. Gravel
% Sand
–0.16
–0.37
0.35
–0.36
–0.21
–0.59
–0.34
0.40
0.57
0.62
–0.49
–0.06
–0.03
–0.19
Fisher’s a
–0.51
–0.82
0.27
–0.56
–0.44
–0.97
–0.88
0.76
0.89
0.86
0.11
–0.35
–0.74
–0.53
–ln SI
b1
b2
–0.50
–0.71
–0.09
–0.22
0.37
–0.34
–0.57
0.41
0.45
0.34
0.25
–0.09
–0.72
–0.40
–0.55
–0.80
0.23
–0.64
–0.59
–0.99
–0.90
0.81
0.92
0.90
0.14
–0.40
–0.74
–0.58
–0.52
–0.70
0.38
–0.62
–0.27
–0.70
–0.75
0.74
0.85
0.79
–0.28
0.13
–0.77
–0.73
where taxa richness was not in accordance with the increased abundance.
Equitability (SI) was low in reach VI and peaked in reach IV and I (Table 2).
Temporal gamma diversity (gt) and SI did not correlate with environmental
variables representing average temperature, channel morphology (width,
depth, or slope), substrate, or flow velocity (Table 3). Fisher’s a, b1, and b2
correlated significantly with average winter temperature, average velocity,
average depth, slope, and percent substrate as boulder.
Patterns of ad and Fisher’s a followed abundance patterns, peaking in August 1998, falling to their lowest levels in November 1998, and rebounding in
March 1999 to values close to August 1998 (Fig. 2). In May 1998, equitability
(SI) was high, whereas in November communities were dominated by one or a
few taxa (low SI and high d).
Changes in abundance patterns along the continuum were evident for all of
the ten most abundant taxa except Chironomidae and Baetis spp. (Fig. 4). Caenis spp. and Seratella ignita (Ephemeroptera) were collected primarily from
lower reaches. Heptegeniid mayflies (Ecdyonurus spp. and Rhithrogena spp.)
were found along the entire continuum. Ecdyonurus spp. exhibited a unimodal
distribution pattern while Rhithrogena spp. were more patchy along the continuum. Most Plecoptera taxa were restricted to upstream locations, but
Leuctra spp. were ubiquitous and Chloroperla tripunctata extended down to
reach V. Non-insect taxa increased in abundance along the continuum, with
greatest abundance in reaches III, IV, and VI.
Spatio-temporal patterns of benthic invertebrates
443
Fig. 4. Abundance (ind. m – 2) of the ten most common taxa collected in each reach (I –
VI).
Multivariate analyses
Between PCA
The covariance PCA performed on log transformed abundance values for 75
taxa explained 49 % of the variance in the site-by-date matrix (F1 = 31%; F2 =
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Fig. 5. Between groups principal components analysis (PCA) on the site/date-by-taxa
matrix. Taxon specific relationships (a) with F1 and F2 dimensions of the between
PCA and site scores (b), grouped by date, illustrating seasonal influence of taxa composition on site/date relationships. See Appendix for taxon codes. The between groups
PCA accounted for 27 % of the variance in the covariance PCA that explained 49 %
(F1 = 31 % and F2 = 18 %) of the variance in the site/date-by-taxa matrix. Thirty-two
taxa clustered near the center of the ordination because they contributed little explanatory power to either F1 or F2 axes (represented by an irregular circle around the origin
with the number 32 in the center).
Spatio-temporal patterns of benthic invertebrates
445
18 %). Variance was then partitioned into either spatial or temporal groups
(i.e., within or between dates) and the between date PCA accounted for 27 %
of the variance explained in the covariance PCA. Factor F1 for the between
dates PCA was best explained by a negative relationship with Simulium spp.,
Ecdyonurus spp., Baetis spp., Leuctra spp., Hydrachnidia, Empididae, Hexatoma spp., Esolus spp., Chironomidae, Hydropsyche spp., and Caenis Gr.
macrura (Fig. 5 a). The F2 axis was best explained by a positive relationship
with Nematoda, Protonemura spp., Tricladida, Synurella ambulans, Brachyptera spp., Amphinemura spp., Asellus aquaticus, Gammarus spp., and Prosimulium spp. Thirty-two taxa clustered near the center of the ordination because they contributed little explanatory power to either F1 or F2 axes.
The site map (Fig. 5 b) shows the position of each date (centroid of reaches)
based on the position of reaches. Sites sampled in August 1998, characterized
by maximum abundance and taxa richness, were located towards the negative
F1 direction. Sites sampled in March 1999 were clustered towards the positive
F2 direction. Thus, summer and winter periods are characterized by different
faunal assemblages. Sites clustering towards the positive F1 and negative F2
directions (most sites in May 1998 and all sites in November 1998) are characterized by very low abundance of most taxa. These sites represent communities recently disturbed by flood/flow events. Summer communities in all
reaches were characterized by presence of insect taxa, particularly Baetis spp.,
Hydropsyche spp., Rhyacophila spp., Rhabdomastix spp., Laccobius spp., Empididae, Hexatoma spp., and Caenis macrura gr. Winter communities were
characterized by high densities of Oligochaeta and Nematoda in lower reaches
and Protonemoura spp. and Rhabdiopteryx spp. in headwater reaches (I and
II).
Co-Inertia analysis
Co-structure between environmental and faunistic data sets determined by CoInertia Analysis (CIA) was highly significant (Monte-Carlo permutation test;
p < 0.001). The first two CIA axes explained 58 % and 22 % of the total inertia.
Nutrient concentration (NO3, NH4, and TDP), temperature, presence of large
and small gravel, and the FBOM : CBOM ratio were all positively related to
the F1 axis (Fig. 6 a). Sulphate (SO4), % organic matter in seston (% OMses),
leafBOM, presence of boulders (Bldr) and cobbles, and specific conductance
(SpCnd) were related to the negative direction of the F1 axis. Maximum discharge from the previous 30 days, presence of sand, and current velocity (Vel)
were related to the positive F2 axis and CBOM, and sestonic and benthic
chlorophyll-a were related to the negative F2 axis. Axis F1 of the faunistic
data set indicated a positive relationship with Echinogammarus spp., Seratella
ignita, Oligochaeta, Elmis spp., Gammarus spp., Gastropoda, Asellus aquat-
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Fig. 6. Co-inertia analysis of 23 environmental variables and 75 taxa from six sampling
sites (reaches I–VI) on four dates. (a) Ordination diagram for environmental variables
(codes given below). (b) Taxa relationship based on log10 (x +1) transformed abundance data (ind. m – 2). Seven taxa were clustered too close together to display. (c) Standardized co-inertia scores of environmental and faunistic data projected on to a factorial map. Closed circles identify central positions of dates (from 1 to 4) for a site’s
environment and open circles identify the center of dates for a site’s faunal community. Numbers that represent each date (i.e., 1– 4) are based on scores for either environment or taxa (determined by connection to centroid). (d) Interpretation of the coinertia analysis demonstrating overlap between temporal and spatial typologies.
Qmax30 d = max. discharge in last 30 days; Sand, SmGrvl, LgGrvl, Bldr, or Cobble, = %
sand, small and large gravel, boulder, or cobble in benthic sample; Vel = average velocity; TIC = total inorganic carbon; FBOM or CBOM = fine or coarse benthic organic
matter; F : CBOM = FBOM-to-CBOM ratio; POC = particulate organic carbon; DON
= dissolved organic nitrogen; DP = total dissolved phosphorus; Temp. = temperature;
MABOM, NeedleBOM, or LeafBOM = macro algal, needle, or leaf benthic organic matter;
CHLben or CHLses = benthic or sestonic chlorophyll-a; % OMses = % organic matter in
seston.
Spatio-temporal patterns of benthic invertebrates
447
icus, Hydroptilia spp., Niphargus elegans, Synurella ambulans, Tricladida,
and Dryops spp. (Fig. 6 b). These taxa were typically abundant in lower
reaches. Chloroperla tripunctata, Brachyptera spp., Liponeura spp., Dolichopodidae, Rhabdiopteryx spp., Dicranota spp., Prosimulium reptans, and Drusus discolor were negatively related to the F1 axis and were more common in
headwater reaches. The positive direction of the F2 axis of the faunistic data
was poorly associated with most taxa, but Hydra spp., Rhabdomastix spp.,
Siphlonurus lacustris, and Cheilotrichia spp. did score close to this axis. Negatively related to the F2 axis were Simulium spp., Leuctra spp., Baetis spp.,
Hydrachnidia, Empididae, Rhyacophila spp., Hydropsyche spp., Chironomidae, Hexatoma spp., Rhithrogena spp., Ceratopogonidae, Esolus spp., Caenis
macrura gr., and Hydraena spp. These taxa were common at most sites and
were abundant in late summer and winter (August 1998 and March 1999).
Co-structure of two data-matrices can be illustrated by plotting both the
sites based on the environment and on faunal assemblage. Figure 6 c shows the
site scores for both environmental and faunal components of the CIA separated by reach. Within each reach a centroid (closed circle for environment or
open circle for taxa) is used to connect sampling dates (labeled 1– 4). High
taxa-to-environment concordance in each reach was evident based on the close
proximity of the environmental centroid to the fauna centroid. Environmental
and faunistic centroids moved along the F1 axis from left to right corresponding to the longitudinal or elevational gradient (Fig. 6 d). Variation along the F2
axis within each reach represents temporal changes in environmental and
faunal characteristics. November 1998 sample dates were always located towards the positive F2 direction of the CIA, while March 1999 samples were
variable, occurring towards both the positive and negative F2 directions depending on the reach. August 1998 samples were always located toward the
negative F2 direction. Scores aligned along the F2 axis represent a disturbance-stability gradient that is characterized by environmental variables such as
maximum discharge in the last 30 days, benthic and sestonic chlorophyll-a
concentration, and CBOM standing stock.
Average CIA distance between faunistic scores and environmental scores
(i.e., taxa-to-environment concordance) for a given reach (average of four
dates) was low (average 0.6 CIA units) and similar for all reaches (Fig. 7 a),
i.e., high and similar temporal concordance. Likewise, concordance for a
given date (average of six reaches) was also low and similar among dates
(Fig. 7 b), i.e., high and similar catchment-scale spatial concordance. The
standard error of the reach average concordance (i.e., variance among dates
within a reach) increased in a downstream direction, however this pattern was
disrupted in reach V where low variation in concordance was observed
(Fig. 7 c). Variation in concordance among dates (i.e., variance among reaches
within a date) was highest in November 1998 and low for all other dates
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Dave B. Arscott, Klement Tockner and J. V. Ward
Fig. 7. Average paired (by date; n = 4) distance between taxa and environment for each
reach (a) indicating taxa-to-environment concordance for a reach. Average paired (by
reach; n = 6) distance between taxa and environment for each date (b) indicating concordance for a date. Standard deviation by reach (c) from (a) or by date (d) from (b)
indicating variation in taxa-to-environment concordance. Maximum taxa-based distance in either the F1 or F2 dimension among dates for each reach (e) or among
reaches for each date (f). All data are computed from F1´ F2 scores for sites based on
either taxa or environment illustrated in Fig. 7c.
(Fig. 7 d). Finally, the maximum spread among dates for a given reach (i.e.,
measure of temporal similarity) based on CIA faunistic scores increased in a
downstream manner for both F1 and F2 scores, with sites generally grouped
closer on the F1 axis and further apart on the F2 axis (Fig. 7 e). Maximum distance among reaches (i.e., measure of spatial similarity along the entire river),
based on CIA faunistic scores, on the F1 axis was lowest in November 1998
Spatio-temporal patterns of benthic invertebrates
449
(low number of individuals and taxa due to flooding) and on the F2 axis was
lowest in August 1998 (Fig. 7f).
Discussion
Abundance and diversity
Abundance of Insecta, Crustacea, Oligochaeta, Nematoda, and Hydrachnidia
increased with distance downstream, as expected. Insect abundance was dominated by chironomids (Diptera) and baetid mayflies (Ephemeroptera). Stoneflies (Plecoptera) were mainly restricted to headwater sites and beetle abundance was greatest in lower reaches. Trichoptera never accounted for more
than 5 % of total insect abundance. In general, dominant insect taxa were represented by highly mobile taxa with potential for multivoltinism (Chironomidae, Baetidae, Simuliidae). Non-insect taxa were also well represented by high
mobility (e.g., Echinogammarus spp.) and ability to rapidly reproduce (e.g.,
Oligochaeta, Nematoda, Copepoda). Non-insect taxa were also characterized
by small body size (e.g., Oligochaeta, Nematoda, Copepoda), a characteristic
that allows these organisms to move through and reside in the interstitial environment, which may be important habitat during flood or high scour events.
The presence of several hypogean taxa, including Niphargus elegans and Synurella ambulans, in benthic samples from lowland reaches (particularly
reaches IV, V, and VI) suggests high connectivity with interstitial environments (hyporheic and phreatic zones). Characteristics of the invertebrate fauna
were in accordance with predictions put forth by Junk et al. (1989) who suggested that in highly flood disturbed rivers diversity of mobile taxa with ability to rapidly reproduce be higher compared to sessile taxa with uni- or semivoltine life histories.
All diversity measures (ah, Fisher’s a, and SI) of main channel zoobenthos
varied considerably in time (Fig. 2), in agreement with our hypothesis regarding spatio-temporal variability of taxa richness. Temporal variance in diversity
measures were related to two primary factors. First, flood events in spring and
autumn (May and Nov. 1998) removed individuals and lowered taxa richness
and Fisher’s a sampled during this time compared to communities collected in
summer and late winter (Aug. 1998 and Mar. 1999). Equitability (SI), however, was typically highest in May 1998 following the milder spring freshet,
while following the more harsh autumnal flood season invertebrate communities exhibited low equitability. Temporal shifts in these richness variables
were less apparent in the headwater reach (I), but beta diversity indices indicated that seasonal turnover of taxa was greatest in reach I, relating to the second factor primarily influencing temporal variance, life history. Crustaceans
and other non-insect taxa present in lower reaches tended to stabilize variation
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Dave B. Arscott, Klement Tockner and J. V. Ward
in diversity measures from summer to winter, whereas communities lacking
crustaceans in headwater reaches (particularly reach I) had greater temporal
variation as adults emerged to the terrestrial environment leaving the in-stream
community without large individuals that could be captured by our sampling
gear (egg bank was probably present). Highly seasonal taxa, e.g., stoneflies
(Taeniopterygidae and Nemouridae) and dipterans (e.g., Simuliidae, Blephariceridae, Limoniidae), contributed greatly to the observed temporal turnover in
reach I.
Overall, taxa richness (all dates combined for a reach) was highest in reach
I and reach VI (Table 2). Mid reaches (III–V) had similar number of taxa.
Fisher’s a, a measure of richness that accounts for difference in abundance,
was greatest for reach I where abundance was low but taxa richness was high.
Reach VI exhibited the lowest value of Fisher’s a, despite high taxa richness,
illustrating the importance of accounting for abundance effects. These results
contradict our first hypothesis that taxa richness (gt) would peak mid-way
along the Tagliamento continuum. Furthermore, gt and SI did not correlate
with any variables that represented structural and thermal components.
Fisher’s a and b1 both correlated significantly with average velocity, depth,
slope, and % of the substrate as boulder, all of which were inter-correlated
variables. These patterns do not support most predictions regarding aquatic invertebrate diversity along the river continuum (Vannote et al. 1980, Statzner & Higler 1986, Ward & Stanford 1995 a, Stanford et al. 1996). By
examining beta diversity it was clear that the high level of gt observed in reach
I was heavily influence by seasonal changes in faunal assemblages, whereas in
reach VI beta diversity contributed little to the relatively high level of gt. A relatively high and consistent level of ah produced high gt in reach VI. Hynes
(1970) discussed the multitude of studies reporting dramatic seasonal changes
in fauna assemblage of small streams and added that ‘because of the preponderance of molluscs and crustaceans in the fauna of large rivers, there is little
change in invertebrate biomass’. In the Tagliamento lower reaches contained a
greater number of non-insect taxa and multivoltine insect taxa whose yearround presence have a stabilizing effect on temporal patterns of richness,
abundance, and biomass.
Despite these emerging trends, we must recognize the limitations of largescale studies in regard to the number of samples processed. Only 3 samples
were counted per site and occasion. Streams are very heterogeneous and invertebrates patchily distributed. However, the effort of using a greater number of
samples would have been extremely demanding. Nevertheless, the low number of samples must lead to caution in interpretation of these results.
Spatio-temporal patterns of benthic invertebrates
451
Taxa-to-environment relationships
The between groups PCA was strongly driven by changes in abundance and illustrated the homogenizing effect of the autumnal flood season on invertebrate
composition and abundance. Community composition in November 1998 may
have been more longitudinally distinct if the members of the dominant taxon,
Chironomidae, were identified to a finer resolution. Trajectories of community
development (or recovery from flooding) differed between summer (August
1998) and winter (March 1999).
Many environmental factors were examined in the co-inertia analysis. Our
aim here was to characterize environmental gradients occurring along the continuum and not necessarily to test the influence of any specific factor on invertebrate distribution. Physical structure (substrate and flow), chemical conditions (dissolved constituents), temperature, and food resources (particulates
and algal resources) were all represented in the analysis. Cation concentration
and pH are water chemistry variables frequently cited as increasing with taxa
richness (Vinson & Hawkins 1998). Water chemistry variables changed markedly along the longitudinal continuum of the Tagliamento (decreasing pH,
specific conductance, and SO4; increasing N, P, particulates, and SiO2; see
Arscott et al. 2000). Concentrations of dissolved nutrients did not attain
nuisance levels (i.e., encouraging excessive eutrophication), although gypsum
deposits near the source contributed to a high concentration of SO4. We interpret the explanatory power of water chemistry variables on invertebrate distributions in the CIA as reflecting their concomitant change with substrate size,
temperature, flow, channel width, BOM, and Chlben variables. This interpretation is supported by the fact that most of the dissolved and particulate variables were grouped on the F1 axis (Fig. 6 a) between the substrate (sand and
small and large gravel), flow, and FBOM : CBOM ratio group and the temperature, Chlben, Chlses, MABOM , and FBOM group.
Longitudinal patterns revealed by the CIA corroborated diversity and
abundance patterns by illustrating the uniqueness of headwater reaches (I and
II) and reach VI. Invertebrate communities in reaches III–V were similar. In
general the longitudinal continuum was typified by the presence of stoneflies
(particularly, Rhabdiopteryx spp. and Brachyptera spp.) and several dipterans
(particularly Prosimulium rufipes, Liponeura spp., and Dicranota spp.) in
headwater reaches (I and II) and a preponderance of non-insect taxa (particularly Echinogammarus spp., Oligochaeta, and Nematoda) in lower reaches
(III–VI). In late summer (August 1998), the continuum was most fully developed with regard to consistent downstream turnover and pattern of invertebrates (Fig. 6 c). Late winter (March 1999) also exhibited a well developed invertebrate structure along the continuum but this structure was interrupted in
reaches III, IV, and V (Fig. 6 c), indicating reaches most sensitive to minor
fluctuations in water level.
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Dave B. Arscott, Klement Tockner and J. V. Ward
The degree to which taxa related to the environment among reaches and
dates was remarkably high (CoInertia analysis matched the taxa PCA with the
environment PCA to a very high degree; 80 % of variance between the two
was accounted for by the analysis) but also illustrated spatio-temporal dynamics. Interestingly, taxa from upper reaches more consistently matched environmental conditions than in lower reaches (Fig. 7 c). But in reach V taxa were
also consistently in concordance with measured environmental conditions.
Plausible mechanisms accounting for this pattern concern processes occurring
locally within reach V. This reach is situated downstream from a major upwelling zone. At approximately river-km 100, surface flow infiltrates into a
deep aquifer system and during low flow periods the riverbed is dry (from
river-km 100 –110). The influence of this subsurface flow path is to maintain
more constant environmental conditions downstream (river-km 110 –115), particularly with regard to dissolved nutrients and temperature. It seems likely
that the presence of such a major hydrogeomorphorphic feature would favor
high taxa-to-environment temporal concordance.
The greatest variation in temporal concordance was in reach VI. This high
dis-concordance (taxa did not relate well to environment) was primarily driven
by large differences in the taxa-to-environment relationship in November 1998
and may be explained, once again, by local conditions. The sampling site in
reach VI was just downstream of a tributary confluence. The tributary, Fiume
Varmo, is a large lowland spring-fed stream. Chemistry of this tributary is substantially different than Tagliamento chemistry (e.g., higher dissolved nutrient
concentrations, lower particulates and temperature; Arscott, unpublished
data). The combination of very low invertebrate density (550 ± 64 ind. m – 2), as
a result of recent flooding and physico-chemical conditions reflecting high stability (reflecting tributary conditions), resulted in low taxa-to-environment
concordance.
Overall, taxa-to-environment concordance among reaches was most variable in November 1998 and was least variable during all other dates (Fig. 7 d).
However, after removing the November 1998 reach VI sample from the analysis, variations in concordance within each reach were low and similar to other
dates. Therefore, despite major temporal changes in faunistic assemblages due
to both seasonal and hydrologic factors, most communities exhibited a consistent relationship to environmental conditions. Finally, it should be noted that
patterns observed using CIA were driven primarily by abundance-related patterns and secondarily by community composition. In this way, it is important
to recognize the complementary nature of comparing and contrasting measures
of diversity (driven primarily by composition and secondarily by abundance)
with multivariate analyses.
The sampling regime herein focused on the distribution along the mainstem of the river of the aquatic stage of macroinvertebrates. Dispersal is one of
Spatio-temporal patterns of benthic invertebrates
453
the ultimate processes through which diversity and abundance patterns manifest (see Palmer et al. 1996). It would appear, due to the nature of the faunal
assemblages, that seasonal terrestrial dispersal would be dominant in headwater reaches (uni-semi voltine insect fauna dominant) whereas in lower
reaches seasonality may be reduced and dispersal via the aquatic environment
(multivoltine non-insect fauna more abundant). Future studies of invertebrate
distribution in the Tagliamento should consider dispersal as a variable of interest.
Conclusions
In Vinson & Hawkins’ (1998) review of stream insect biodiversity they concluded that published data largely supported the hypothesis that physical complexity should promote biological richness. Second, based on the studies they
reviewed, richness should be highest in the most physically stable streams.
Our results generally support these statements. Maximum reach richness (gr)
was found in reach I where presence of large substrate (boulder and cobbles)
creates a physically complex benthic environment and, as reported by Minshall et al. (1992), increases retention of organic matter. Standing stock of
leafBOM, needle BOM , and CBOM was highest in the headwaters (reach I) and
contributed considerably to physical complexity of the benthic environment.
Arscott et al. (2000) quantified environmental heterogeneity along the Tagliamento at multiple scales using multiple factors and showed that point complexity of the physical environment was greatest in reach I. In reach VI, although physical complexity in the main channel was low, physical complexity
was high among floodplain water bodies. The sample location in reach VI was
just downstream of a confluence with a spring-fed tributary (Fiume Varmo, see
above discussion), accounting in part for the high level of gt and the highest ad
found along the continuum. The stable nature of this tributary undoubtedly
provides a consistent source of invertebrate colonizers below the confluence
and likely provides flow refugia for mobile taxa. Rice et al. (2001) illustrated
the importance of accounting for the influence of tributaries with regard to the
longitudinal organization of macroinvertebrate fauna along river systems. The
contribution of temporal shifts in invertebrate assemblages in reach I (i.e.,
high b1 and b2) and the importance of temporal stability provided by the spring
tributary just above reach VI (low b1 and b2) illustrated how gt diversity was
influenced by the dynamics of alpha and beta diversity.
Acknow ledgements
Many people assisted with field and laboratory work and without them this work could
not be accomplished. Many thanks to Y. Arscott, M. Monaghan, F. Mösslacher, P.
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Burgherr, C. Claret , C. Dambone-Boesch, M. Hieber, R. Illi, B. Klein, B. Keller, B. Ribi, C. Rust, C. and D. di Scandiuzzi. Our gratitude is extended to P. Marmonier and R. Glatthaar who provided identification of ostracods and simuliids,
respectively. Thanks to two anonymous reviewers that have helped refine this manuscript. This work was supported in part by a grant from the ETH-Forschungskommission (0-20572-98).
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Submitted: 4 June 2002; accepted: 15 September 2003.
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Dave B. Arscott, Klement Tockner and J. V. Ward
Appendix 1. Taxa list, taxon code, and presence/absence in main channel of reaches
I – VI.
Taxa nomenclature
Arthropoda
Insecta
Ephemeroptera
Heptageniidae
Rhithrogena spp.
Ecdyonurus spp.
Epeorus spp.
Baetidae
Baetis spp.
Ephemerellidae
Seratella ignita
Caenidae
Caenis macrura gr.
Leptophlebiidae
Habroleptoides modesta gr.
Siphlonuridae
Siphlonurus lacustris
Plecoptera
Leuctridae
Leuctra spp.
Chloroperlidae
Chloroperla tripunctata
Taeniopterygidae
Rhabdiopteryx spp.
Brachyptera spp.
Nemouridae
Protonemura spp.
Nemoura spp.
Amphinemura spp.
Perlodidae
Isoperla spp.
Dictyogenus spp.
Perlidae
Perla spp.
Trichoptera
Limnephilidae
Drusus discolor
Limnephilus spp.
Lepidostomatidae
Lepidostoma spp.
Hydropsychidae
Hydropsyche spp.
Rhyacophilidae
Rhyacophila spp.
Psychomyiidae
Psychomyia spp.
Code
I
II
III
IV
V
VI
RHI
ECD
EPE
X
X
X
X
X
X
X
X
X
X
X
X
X
BAE
X
X
X
X
X
X
X
X
X
X
X
X
X
X
SER
CAE
HAB
X
SIP
X
LEU
X
X
X
X
X
CHL
X
X
X
X
X
RHB
BRA
X
X
X
PRO
NEU
AMP
X
X
X
X
X
X
X
ISO
DIC
X
X
X
PLA
X
LIM
DRU
LNP
LEP
X
X
HYD
X
RHY
X
PSY
X
X
X
X
X
X
X
X
X
X
X
X
X
X
459
Spatio-temporal patterns of benthic invertebrates
Appendix 1. Continued.
Taxa nomenclature
Hydroptilidae
Hydroptila spp.
Glossosomatidae
Glossosoma spp.
Coleoptera
Elmidae
Elmis spp.
Esolus spp.
Limnius spp.
Dryopidae
Dryops spp.
Dytiscidae
Scarodytes spp.
Hydrophilidae
Laccobius spp.
Hydraenidae
Hydraena spp.
Ochthebius spp.
Carabidae
Gyrinidae
Orectochilus spp.
Diptera
Chironomidae
Limoniidae
Rhabdomastix spp.
Antocha spp.
Eloeophila spp.
Hexatoma spp.
Dicranota spp.
Molophilus spp.
Phylidorea spp.
Pilaria spp.
Cheilotrichia spp.
Tipulidae
Tipula spp.
Prionocera
Ceratopogonidae
Simuliidae
Prosimulium spp.
Simulium spp.
Empididae
Blephariceridae
Blephecera fasciata
Hapalothrix lugubris
Liponeura spp.
Athericidae
Dolichopodidae
Code
I
II
III
IV
HDT
GLO
ELM
ESO
LMN
VI
X
X
X
X
X
X
X
X
X
DRY
SCA
X
LCB
X
HDA
OCH
CAR
V
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
ORE
X
CHI
X
X
X
X
X
X
RHA
ANT
ELO
HEX
DCR
MOL
PHA
PIL
CHE
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
TIP
PRI
CER
X
X
PSI
SIM
EMP
X
X
X
BLE
HAP
LIP
ATH
DOL
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
460
Dave B. Arscott, Klement Tockner and J. V. Ward
Appendix 1. Continued.
Taxa nomenclature
Code
I
Ephydridae
Stratiomyidae
Tabanidae
Collembola
Crustacea
Cladocera
Copepoda
Ostracoda
Candona spp.
Pseudocandona albicans
Candoninae unid.
Cyclocypris ovum
Herpetocypris sp.
Prionocypris zenkeri
Cavernocypris sp.
Potamocypris spp.
Isopoda
Asellus aquaticus
Amphipoda
Gammaridae
Gammarus spp.
Echinogammarus spp.
Niphargidae
Niphargus elegans
Crangonyctidae
Synurella ambulans
Acari
Hydrachnidia
Oribatei
Annelida
Hirudinea
Oligochaeta
Nematoda
Platyhelminthes
Turbellaria
Tricladida
Coelenterata
Hydrozoa
Hydridae
Hydra spp.
Mollusca
Gastropoda
EPH
STR
TAB
CLL
X
X
X
CLA
COP
OST
CAN
PSE
CNU
CYC
HER
PRI
CAV
PTM
X
X
X
II
III
IV
V
VI
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
ASE
GAM
ECH
X
NIP
X
X
X
X
X
X
X
X
X
SYN
X
MIT
ORI
X
X
X
X
X
X
X
X
HIR
OLI
NEM
X
X
X
X
X
X
X
X
X
X
X
X
X
TRC
X
X
X
HRA
GAS
X
X
X
X
X
X
X
X