Ó Springer 2005
Hydrobiologia (2005) 544:201–214
DOI 10.1007/s10750-005-0545-2
Primary Research Paper
Temporally variable macroinvertebrate–stone relationships in streams
Dean Jacobsen
Freshwater Biological Laboratory, Institute of Biology, University of Copenhagen, Helsingørsgade 51, DK-3400 Hillerød,
Denmark
E-mail: [email protected]
Received 27 September 2004; in revised form 30 December 2004; accepted 12 January 2005
Key words: benthic fauna, density, richness, assemblage composition, stone characteristics, temporal variability,
Ecuador
Abstract
Stones were used to sample macroinvertebrates and characterise microhabitats at monthly or bimonthly
intervals in six Ecuadorian streams covering a gradient in four different stability measures and other stream
characteristics. The physical variables current velocity, water depth, horizontal position, embeddedness and
size were measured to characterise stone microhabitats and presumed to be affected by or related to
physical impact during hydrological disturbances. My first objective was to analyse how density, the
number of families and a richness measure (residuals from a power regression of families vs. individuals)
were related to the physical characteristics of individual stone habitats. My second objective was to
quantify temporal variability in fauna–stone relationships and to analyse if such variability was related to
overall stability of stream reaches. Partial Least Squares (PLS) multiple regression analyses showed high
temporal variability between sampling dates in factor loadings of specific stone micro habitat variables. In
spite of this, there was a clear negative effect of depth and a positive effect of current on density and number
of families. Stone size was consistently negatively related to density and positively related to number of
families. Patterns were less clear for richness residuals. Simple linear regressions of fauna vs. stone parameters generally confirmed the results reached by the PLS analysis, although few of the regressions were
significant. For all fauna–stone regressions the variability in slopes was much higher among sampling dates
within streams (temporal variability) than among streams (spatial variability), and significant slopes were
even inverted on different sampling dates. Although the coefficients of variation (CV) of slopes of a given
combination of fauna parameter and stone variable from different sampling dates (n ¼ 9–11) were rarely
correlated to any of the measures of stream stability, this study has demonstrated high temporal variability
in fauna–stone relationships (CV’s of regression slopes). Consequently, temporally un-replicated studies of
such relationships do not necessarily reveal general patterns.
Introduction
The patchy distribution of macroinvertebrates on
stream beds is notorious, with species and assemblages responding to small-scale heterogeneity in
abiotic and biotic characteristics of microhabitats
(Minshall, 1984; Cooper et al., 1997). Several
studies have shown significant relationships
between density and richness of invertebrates and
site characteristics such as amount of detritus,
algal biomass, physical stability, physical complexity, current velocity, water depth etc. (e.g.
Egglishaw, 1964; Barmuta, 1989). These relationships may apply to different spatial levels, from
individual stones (Downes et al., 1995; Matthaei
et al., 2000), or artificial substrata (e.g. Khalaf &
Tachet, 1980; Malmqvist & Otto, 1987) to small
sections of the stream bottom.
However, studies of relationships between
microdistribution of the fauna and environmental
202
factors in streams are often spatially unreplicated
or pseudoreplicated (Downes et al., 1993). What is
perhaps less obvious is that such studies are
usually also unreplicated in time, or relationships
are analysed on mean values of fauna parameters
(the dependent variable) and environmental parameters (the independent variable), while the
information contained by the temporal variability
in these parameters is generally discarded. Studies
ignoring the temporal variability in such relationships may not reveal general relationships.
The microdistribution of benthic invertebrates
in streams is in fact dynamic and is strongly
influenced by the hydrological disturbance regime.
Even moderate high-flow events may cause redistribution of the fauna (Townsend & Hildrew,
1976; Statzner et al., 1988; Davis & Barmuta,
1989). For example, it has been demonstrated that
invertebrates seek low-stress, spatial refugia during
spates (Lancaster & Hildrew, 1993; Winterbottom
et al., 1997). Such refugia may be large stones that
do not move easily (Townsend, 1989; Biggs et al.,
1997). Matthaei et al. (2000) found that following
spates, stable stones had higher densities and
richness of invertebrates than loosely embedded
stones, whereas there was no such difference before
spates. Hence, fauna–microhabitat relationships
(e.g. slopes of regressions) may be subject to
considerable temporal variability, driven by the
physical disturbance regime at the scale of stream
reaches. Palmer et al. (1997) argues that the
variability in biological relationships need not be
viewed as ‘statistical detail’ or nuisance, and if
explored may instead yield new ecological insight.
Temporal variability in relationships between the
distribution of fauna and properties of microhabitats used as the dependant variable has rarely
been studied in streams, and to my knowledge
never analysed with respect to differences in environmental characteristics of stream reaches.
The first objective of this study was to analyse
how three macroinvertebrate parameters, namely
density, number of families and a richness measure
were related to the physical characteristics of
individual microhabitats in streams. My second
and main objective was to quantify temporal
variability in relationships between fauna and
microhabitat variables and to analyse if such
variability was related to overall stability or other
characteristics of stream reaches. While habitat
preferences and biotic interactions may be important for regulating distribution of species at
stable or benign sites, assemblages are thought to
be more randomly distributed and loosely organised at disturbed sites (Peckarsky, 1983; Minshall
& Petersen, 1985; Death, 2004). Consequently,
fauna–microhabitat relationships are expected to
be weak and/or temporally variable at high disturbance levels. For comparative studies of richness, especially temporally unreplicated ones, it is
of great value to know if stones with certain physical characteristics have a particularly dense, rich
and stable fauna composition.
To achieve these objectives I collected fauna and
measured physical stone characteristics at monthly
or bimonthly intervals in six aseasonal, equatorial
streams in Ecuador, covering a gradient in disturbance level. The characteristics current, depth,
horizontal position, embeddedness and size (surface area) of stone microhabitats were assumed to
be affected by or related to physical impact during
hydrological disturbances (Matthaei et al., 1999).
Stones are well defined, discrete habitats that are
easily and accurately sampled (Douglas & Lake,
1994) and often have been used in studies of
microdistribution of invertebrates in relation to
environmental characteristics (Hart, 1978; Downes
et al., 1998). Most studies of fauna–stone relationships (either performed on natural stones or artificial tiles etc.) have concerned colonisation through
time (Lake & Doeg, 1985; Lake & Schreiber, 1991),
in particular in relation to disturbance through
‘rock-tumbling’ experiments (Boulton et al., 1988;
Rosser & Pearson, 1995) and to food resources by
manipulating periphyton biomasses (Downes et al.,
1998). Few studies have dealt with distribution of
fauna in relation to natural stone characteristics
(Downes et al., 1995; Matthaei et al., 2000), and to
my knowledge, none have focused on temporal
variability in fauna–stone relationships.
Materials and methods
Localities
The study was performed in six first to third order
streams (drainage areas approximately 0.5–6 km2)
previously included in studies of altitudinal changes in diversity (Jacobsen, 2003) and drift patterns
203
(Jacobsen & Bojsen, 2002) of macroinvertebrates
in streams on the eastern side of the Ecuadorian
Andes. All streams studied are headwaters of the
Napo river which eventually drains into the
Amazon river. The two lowland streams are
characterised as lowland streams in the transition
zone between piedmont streams and streams typical of the Amazon plains and were located on
the south side of the Napo, surrounded by primary
and secondary lowland forest at the foothills of the
Andes at 380 and 400 m a.s.l., with a total rainfall
of ca 6000 mm in 1999 (D. Jacobsen, unpubl.
data). The two midland streams were located in
cloud forest on the eastern slopes of the Andes at
2050 and 2210 m a.s.l. receiving approx 3000 mm
of rain annually (Inamhi, Instituto Nacional de
Meteorologı́a e Hidrologı́a). The two highland
streams were located on the grass and bushlands
(páramo) of the eastern cordillera at 3820 and
3850 m a.s.l. with a mean annual precipitation of
about 1500 mm (Inamhi). Monthly mean stream
temperatures vary about 1 °C during the year in
all regions (D. Jacobsen, unpubl. data).
Physico-chemical characteristics
To characterise the general physico-chemical
environment of the stream sites, a number of
parameters were measured at each sampling event.
Conductivity and pH were measured in the field
with an YSI model 30 and a Radiometer model
203, respectively. Water-samples were brought to
the laboratory and alkalinity measured by gran
titration with 0.1 N HCl (Mackereth et al., 1978)
within a few days of collection.
Current velocity and discharge were measured
by means of dilution gauging (White, 1978). A
bucket of a known amount of dissolved salt (volume
and conductivity) was added at the upstream end of
the 20 m stream reach and conductivity measured
every 5 or 10 s at the downstream end of the reach.
Maximum current velocity is the time elapsed before the conductivity begins to rise at the downstream end of the reach, divided by the length of the
reach, while the mean velocity is calculated as the
time elapsed for half of the salt to pass the stream
reach divided by the length of the reach.
The percentage of cobbles (6–26 cm particle
diameter) covering the stream bottom, and thus
the type of substratum sampled in the study, was
calculated by recording the substratum type at 10–
20 points along 10 transects across the stream at
2 m intervals, providing a total of at least 100
observations.
Stability measures
Four different measures were taken in an attempt
to quantify the available refugia and physical disturbance experienced by invertebrates in the six
streams: (1) The maximum : mean current velocity
ratio obtained from the dilution gauging is a
measure of hydrological dead space and was used
here as a measure of the availability of low impact
refugia in the 20 m stream reach. (2) The third part
of the Pfankuch score system concerning the
channel bottom (Pfankuch, 1975) was used as a
measure of overall stream stability (1–10, with 10
as the most stable). (3) To obtain a comparable
measure of physical stress on the stream bottom,
clay bricks were placed in the streams, recording
whether or not the bricks had moved (not measuring actual distance moved) at the next visit 2–
5 weeks later. Three sizes of bricks were used
(554 ± SD:
46 g,
1163 ± 101 g
and
2553 ± 105 g) and they were placed in three rows,
each row with one brick of each size (nine bricks in
total) in relatively homogenous runs. After having
concluded all measurements, each brick size was
given a score based on the total frequency of
movements in all streams. A brick-movement
score was calculated for each visit as the number of
bricks moved or disappeared multiplied by the
relevant score for each brick-size. Then the mean
of the 4–5 trials obtained in each stream was calculated. (4) The mean annual precipitation in the
drainage basins was used as the fourth measure of
stream stability.
Sampling of stone fauna
Samples of benthic macroinvertebrates were collected 9–11 times in each stream from December
1998 to December 1999. At each sampling occasion,
20 cobbles were collected from riffle/run habitats
within a 20 m reach. Stones were collected by rapidly lifting them into a 200 lm mesh hand net to
avoid the loss of active swimmers. Each stone was
brushed carefully in a bucket of water. All invertebrates were preserved in 70% ethanol.
204
After collection of each stone, five parameters
specific to each stone microhabitat were measured and recorded: depth, current velocity,
embeddedness, distance from stream middle and
size. Depth was measured as the depth to the
bottom of the stone-grove where the stone had
been laying. Current velocity was measured with
a Höntzsch wheel anemometer 1 cm above the
sediment at the upstream edge of the stonegrove. A score of embeddedness between 1 and 4
was attributed arbitrarily according to the force
that was needed to remove the stone. The distance of the stone to each bank was measured,
and the distance from the middle of the stream
calculated as the difference between the two
measurements. Stone length (L), width (W) and
height (H) were measured and stone surface area
(denoted size) estimated following the method
given by Dall (1979): 1.2 (LW + LH + WH).
In the laboratory, macroinvertebrates were
identified to family, except for Nematoda, Annelida, Hydracarina and Collembola, using
Thorp & Covich (1991), Roldán (1992), and
Merritt & Cummins (1996). The fauna in Ecuadorian streams is little known and only some
groups can be identified with certainty to a
taxonomical level lower than family. In comparative studies of richness I feel more confident
working consistently at the family level instead
of a mixture of different taxonomical levels.
Family richness of insects at individual stream
sites is highly correlated with species richness
(Bournaud et al., 1996, Wright et al., 1998). The
family level seems also to be sufficient in comparative analyses of community structure (Bowman & Bailey, 1997; Bailey et al., 2001) and to
characterise functional richness (Dolédec et al.,
2000).
Data treatment
I focused on three faunal parameters: density
(ind m)2), number of families and a corrected
richness measure. In addition to the number of
families actually found, I wanted to include a
measure of richness corrected for the effect of
sample size, i.e. number of individuals. Many
stones had so few individuals that rarefaction
and other randomisation techniques were
inappropriate. Instead, as the relationship between the number of families as a function of
sample size was satisfactorily described by the
power function S ¼ a I z (where S is number of
families and I is number of individuals), I used
the residuals from fittings of this function as a
corrected measure of richness. This was done
separately on each series of 20 stones. The
residuals were then used as the dependent variable in analyses of relationships with stone
variables. In that way I discarded the effect of
passive sampling in the analyses of regulation of
richness by habitat characteristics (such as stone
surface area). For other methods for identifying
and distinguishing between effects of passive
sampling and fragmentation on species-area
relationships, see Douglas & Lake (1994) and
references therein.
Partial least squares multiple regression (PLS)
(ter Braak & Juggins, 1993; Eriksson et al., 1995)
is increasingly applied in ecology to e.g. separate
the effect of several environmental factors on
macroinvertebrate communities in streams (Malmqvist et al., 1993; Englund et al., 1997; Zhang
et al., 1998). Here PLS regression was used to
analyse for the effect of the five independent
stone microhabitat variables on the three dependent fauna parameters. PLS reduces the
number of variables to one or several latent
components, where the values of the loadings
express the influence of each independent variable on the component (model). The loading of
each independent factor (stone variables) in the
PLS multiple regression analyses indicate its effect upon the dependent factor (fauna parameters), the direction of the relationship and,
consequently, the relative proportion of the total
variability in the dependent factor that can be
attributed to each independent factor. In contrast to multiple linear regression analysis, PLS
has no problem with correlated predictor variables and permits illustrative graphical presentation of loadings. The PLS regressions were
performed using the software STATISTICA
edition 99 (selecting auto scaling and no intercept). First components were calculated separately for each sample series of 20 stones. Mean
values of loadings of each independent stone
variable (n ¼ 9–11) for each fauna parameter in
each stream were then calculated. As it was not
205
the aim to construct predictive models, no cross
validation was performed. Instead, significant
departures of mean loadings from zero were
tested by t-test.
Linear regression models were constructed
separately for all 62 series of 20 stones, using all
combinations of the three fauna and five stone
microhabitat variables. The slopes from these
regressions expressed the relationships between
fauna and stone variables. To compare variance
in these fauna–stone relationships explained by
spatial variability (between different streams) and
temporal variability (between sampling dates
within streams), I performed one-level nested
ANOVA’s on these slopes using the software
STATGRAPHICS plus for windows 2.1. The
coefficient of variation (CV ¼ (SD/mean) *
100%) for slopes of specific combinations of
fauna and stone variables (n ¼ 9 –11) was used
as a measure of temporal variability in fauna–
stone relationships. The relationships between
CV of slopes and measures of stream stability
were analysed with Spearman Correlation.
I also wished to analyse if temporal variability in fauna parameters differed at different
levels of stone variables. To do this I used the
same regression models to predict the density
and number of families at fixed high and low
values of stone variables, for each stream and
sampling date. These fixed high and low values
of stone variables were chosen so that they did
not exceed the mean low and the mean high in
any stream. The values were: depth: 10 and
35 cm; current: 0 and 30 cm s)1; embeddedness:
1 and 3; distance: 0 and half stream width; size:
200 and 1000 cm2. The CV of each fauna parameter at low and high values of stone variables
(n ¼ 9–11) was used as a measure of temporal
variability in fauna parameters, and differences
between CV’s at high and low values of stone
variables tested with pair-wise t-tests (n ¼ 6).
This analysis was not done for richness residuals
because both means and SD’s were close to zero,
and CV’s therefore highly variable.
Where multiple t-tests (as for mean loadings)
or regression analyses (as for slopes in
fauna–stone relationships) were performed,
Bonferroni corrections were applied and significance levels adjusted accordingly from
p < 0.05 to p < 0.01.
Results
Stream characteristics and overall faunal
composition
The six streams covered a wide range in physical
characteristics such as mean current, mean depth,
depth CV, and the four stability measures
(Table 1). However, apart from temperature,
width and annual precipitation none of the physical characteristics varied systematically with
altitude.
A total of 76 taxa (mostly families) were collected. Insects contributed between 95.2 and
99.7% to the total number of individuals at a site.
The dominant families varied among streams and
included Baetidae, Leptophlebidae, Elmidae,
Glossosomatidae, Hydropsychidae, Chironominae
(subfamily), Orthocladiinae (subfamily) and
Simuliidae. The number of taxa collected in each
stream clearly declined with higher altitude.
Fauna parameters vs. stone microhabitat
characteristics
Stone microhabitat characteristics were mostly
uncorrelated with each other. Only depth and
current velocity were negatively (although weakly)
correlated in the streams Apayacu and Chalpi
Medio (r2 ¼ 0.126 and 0.135, respectively,
p < 0.05, regression analysis).
The variation in the dependent factor (rY2 )
explained by the PLS models using the five stone
variables ranged from 0.25 in Chalpi Sur to 0.44 in
Apayacu for density, from 0.22 in Shinguipino to
0.44 in Chalpi Medio for number of families and
from 0.20 in San Isidro to 0.32 in Chalpi Medio
for richness residuals (n ¼ 9–11 models, one for
each sampling date).
Loadings of specific stone microhabitat variables showed high temporal variability within
streams, indicated by the wide min–max ranges, as
well as considerable variation among streams
(Fig. 1). Nonetheless, for several combinations of
stone variables and fauna parameters, mean
loadings were consistently either positive or negative in all six streams, and in several cases significantly different from zero (t-test, p < 0.01).
The pattern was particularly clear regarding the
negative effect of depth and the positive effect of
380
400
2050
2210
3820
3850
San Isidro
Pumayaco
Chalpi Sur
Chalpi Medio
6.2
7.7
12.8
14.4
24.1
22.9
(25 °C)
(°C)
46
60
72
54
51
25
(lS/cm)
mean
mean
Conduc.
Temp,
Altitude
(m)
Apayacu
Shinguipino
Stream
6.43
7.05
7.17
6.61
7.21
6.79
mean
pH
0.24
0.56
0.51
0.33
0.44
0.12
(meq/l)
mean
Alkali.
0.10
0.14
0.13
0.07
0.10
0.23
(m)
mean
Depth,
1.08
0.56
0.53
0.70
0.77
0.73
CV
Depth
1.25
1.05
2.80
2.30
5.58
4.00
(m)
mean
Width,
0.06
0.17
0.30
0.10
0.09
0.03
(m/s)
mean
Current,
40
17
38
24
33
23
(%)
Cobble
Table 1. Values of physical and chemical site characteristics, measured at near base-flow conditions in six Ecuadorian streams
2.33
1.94
1.67
2.00
1.89
3.00
index
3
7
1
9
5
4
Index
space
Annual
12
6
16
5
18
22
index
1500
1500
3000
3000
6000
6000
tion (mm)
movement precipita-
Phankuch Stone
Dead
Stability measures
206
207
Figure 1. Factor loadings from the PLS multiple regression analyses for the physical stone variables depth (Dep.), current (Cur.),
embeddedness (Emb.) horizontal distance from middle (Dis.) and size (Siz.) in the models predicting each of the three fauna parameters
density (top row), number of families (middle row) and richness residuals (bottom row) in each stream. Columns denote mean
loadings, error bars denote SE and asterix denote max–min values from 9 to 11 analyses. Bonferroni corrections were applied due to
five multiple comparisons, and significance levels of mean loadings thus corrected from p < 0.05 to p < 0.01. Hatched columns
denote mean loadings significantly different from nil (p < 0.01; t-test).
current on density and number of families. Stone
size was consistently negatively related to density
and positively related to number of families.
Although the density of invertebrates (ind m)2
stone surface) was generally negatively related to
stone size, number of individuals was generally
positively related to size (results not shown). Patterns were less clear for richness residuals, but
embeddedness and distance seemed to have positive effects. Thus, overall density was highest on
small, loosely embedded stones at shallow depths,
in the middle of the stream and at high current
velocities. Number of families peaked on stones
that were large, firmly embedded located at low
depth, close to the bank and subjected to high
current velocity.
The overall patterns reached by the PLS multiple regression analyses were largely confirmed by
simple linear regressions (Table 2). However, few
of the regressions of fauna parameters vs. stone
variables were significant. Of the 90 possible
combinations (3 fauna parameters * 5 stone variables * 6 streams), 57 had no significant regressions on any date, 25 had significant regressions on
a single date, six had two significant regressions,
while density vs. current in Apayacu and
Pumayacu had four and five significantly positive
regressions, respectively (p < 0.01).
Temporal variability in fauna–stone relationships
The temporal variability between sampling dates
in the slopes of fauna–stone regressions was high
(Fig. 2). For all fauna–stone regressions the
variability in slopes was much higher between
sampling dates within streams (temporal variability) than between streams (spatial variability)
(Table 3).
In one case (Stream Apayacu: Density vs.
depth) significant slopes were even inverted on
1/1
–
–
–
Depth
Distance
Embed.
Size
0/1
–
–
1/0
Depth
Distance
Embed.
Size
–
–
–
Distance
Embed.
Size
98
415
226
107
223
1300
216
204
101
71
472
213
161
245
338
180
461
148
–
–
1/0
–
–
–
–
–
–
–
–
–
–
–
1/0
385
(22510)
894
247
253
145
234
140
133
691
112
95
328
155
134
135
733
484
–
–
–
1/0
–
–
2/0
–
–
1/0
0/1
–
–
1/0
2/0
+/)
535
501
173
761
280
962
320
323
119
786
271
101
168
144
134
190
166
205
CV
(n = 10)
San Isidro
–
–
1/0
–
–
0/1
1/0
1/0
–
1/0
0/1
–
–
–
5/0
+/)
347
144
359
290
490
450
525
596
48
1641
290
50
315
932
160
122
243
118
CV
(n = 11)
Pumayacu
–
2/0
–
1/0
–
1/0
1/0
–
–
1/0
–
–
–
–
–
+/)
193
159
135
225
255
189
193
87
45
294
228
312
584
730
1615
175
201
200
CV
(n = 11)
Chalpi Sur
–
2/0
–
–
0/1
2/0
1/0
–
0/1
–
–
1/0
1/0
0/1
–
+/)
103
225
56
96
80
57
85
66
55
180
59
66
345
205
246
691
168
416
CV
(n = 10)
Chalpi Medio
–
4/0
2/0
2/0
0/1
3/1
6/0
1/0
0/2
4/0
0/2
1/0
1/0
2/2
12/0
+/)
330
251
287
307
443
337
260
219
79
677
196
131
331
417
412
296
277
254
Mean CV
(n = 61)
Total
Mean CV’s are also given for each combination and for each fauna parameter in each stream. Bonferroni corrections were applied due to five multiple comparisons, and
significance levels for regressions thus corrected from p < 0.05 to p < 0.01.
Mean
–
–
Current
Depth
Richness resid.
Mean
1/0
Current
# of Families
Mean
4/0
Density
Current
+/)
+/)
CV
(n = 10)
(n = 9)
CV
Shinguipino
Apayacu
Table 2. Number of positive (+) and negative ()) significant linear regressions and the coefficient of variation (CV) of slopes of each combination of three fauna parameters
(density, # of families, richness residuals) and five stone characteristics
208
209
Figure 2. Regression lines illustrating contrasting relationships between five stone characteristics and invertebrate density (top row),
number of families (middle row) and richness residuals (bottom row) on different sampling dates in stream Apayacu (380 m). Thick
lines denote significant regressions (p < 0.05).
different sampling dates (Fig. 2, Table 2). Overall,
the CV of slopes of specific fauna–stone regressions (n ¼ 9–11) varied between 45% and 1641%
(except for one extremely high CV in Shinguipino,
which for that reason was omitted from the calculation of mean CV’s), with mean and median
values of 553 and 200%, respectively. The CV
exceeded 100% in 75 of 90 cases (Table 2).
Only one significant correlation was found
between the individual CV’s of slopes shown in
Table 2 and the four stability measures in Table 1,
namely that of density vs. current in relation to the
max–min current index (r ¼ 0.94; p ¼ 0.005,
n ¼ 6). None of the mean CV’s of slopes (Table 2)
correlated significantly with any of the four stability measures (p > 0.05).
Using the same regressions, the number of
families was clearly less temporally variable than
densities (Fig. 3). The only significant difference
(p < 0.05) between temporal variability of density
and number of families of macroinvertebrates at
fixed low and high values of stone microhabitat
characteristics (Fig. 3) was for stone size, with a
mean CV of density significantly greater on large
stones than on small stones (p ¼ 0.0003; Pair-wise
t-test). Temporal variability (CV) of density and
number of families for either high or low values of
stone variables were not related to any of the
stability measures in Table 1 (p < 0.05, n ¼ 6;
Spearman Correlation).
Discussion
This study has shown that physical characteristics
of stone microhabitats such as water depth, current
velocity, horizontal position, embeddedness and
size (surface area) all to a varying degree influenced
spatial distribution of invertebrate density and
richness in the studied equatorial streams. The
negative effect of depth and the positive effect of
current velocity on density and number of families
were most prominent. Quinn & Hickey (1994) also
found negative effect of depth and positive effect of
velocity on taxon richness and density of invertebrates in Surber samples. In another study of
stone faunas Downes et al. (1995) found positive
effect of velocity on density (although this significant effect of velocity disappeared when corrected for spatial autocorrelation), but no effect of
velocity on species richness. In contrast, Boulton
et al. (1988) found no correlation between neither
velocity nor depth and the number of taxa and
density of invertebrates, neither on the upper
1.13
2.78
38
113
82960
65690
513
729
822
832
0.41
2.96
0.79
1.42
1.01
0.84
0.02
0.56
0.23
0.42
0.00
2.78
38
7.33
82960
0.00
513
22
822
0.98
comp.
Var.
0
100
84
16
100
0
96
4
100
0
%
5
5
55
55
5
55
5
53
5
55
0.83
6.00
5.23
1.30
F
0.00004 2.51
0.00002
0.0002
0.0001
0.6076
3.6444
0.0036
0.0187
0.0075
0.0097
MS
0.04
0.53
0.0002
0.0006
0.28
P
0.000002
0.00002
0.0002
0.0000
0.6076
0.2990
0.0036
0.0015
0.0075
0.0002
13
87
100
0
67
33
70
30
97
3
Var. comp. %
5
55
55
5
55
5
53
5
55
5
DF
3.05
1.20
3.19
F
0.0000080.87
0.000009
0.00006
0.00006 0.92
0.2144
0.6535
0.0040
0.0048
0.0028
0.0089
MS
Richness residuals
0.50
0.48
0.02
0.32
0.01
P
0.00
0.000009
0.00006
0.00
0.2144
0.0432
0.0040
0.00008
0.0028
0.0006
0
100
100
0
83
17
98
2
82
18
Var. comp. %
The values express the percentage of the total variance in the data explained by differences between the six streams (Stream) and by temporal differences within streams between
sampling dates (Date).
Stream
Date
5
55
Date
Size
5
55
Stream
Distance
5
55
Date
53
Stream
Date
Embed.
Stream
5
Date
Current
5
55
Stream
Depth
P
DF
F
DF
MS
Families
Density
Table 3. Results from one-level nested ANOVA’s on the slopes from linear regressions between the five stone variables and the three fauna parameters showing the variance
components
210
211
Figure 3. Coefficients of variation between sampling dates of
faunal density and number of families among stones at low (L)
and high (H) values of depth, current, embeddedness, distance
from stream middle and size. Different symbols represent different streams and each data point denotes the CV across all
sampling dates for each stream. Squares: Apayacu; circles:
Shinguipino; upward triangles: San Isidro; downward triangles:
Pumayacu; diamonds: Chalpi Sur; hexagons: Chalpi Medio.
The horizontal bars denote the mean CV in each category.
surface nor on the underside of stones. Thus, the
effect of these environmental parameters on the
microdistribution of the macroinvertebrate fauna
is not clear, and reasons for diverging results may
be numerous.
Residuals in number of families from the power
regression to number of individuals were not
clearly related to any of the measured stone microhabitat variables, and the relatively clear effect
of depth, current and size on number of families
was not observed for richness residuals. Thus,
although local processes seemed to have some
degree of regulatory effect on richness per se, i.e.
patterns in richness being not just a passive result
of patterns in density (Douglas & Lake, 1994;
Downes et al., 1995), such regulatory factors could
not be identified in the present study.
The general patterns observed in the PLS
multiple regression analyses for the effect of stone
microhabitat variables on fauna parameters were
confirmed by the slopes and number of significant
linear regressions. However, these fauna–stone
regressions for specific dates were generally weak,
and few were significant. Despite the apparent
differences in stability between the studied streams,
all six streams may be relatively unstable. As assemblages in disturbed habitats are expected to be
randomly organised with small differences among
stones (e.g. Death, 2004), and in a perpetual state
of disequilibrium (Reice, 1985) the lack of strong
fauna–stone relationships is perhaps not surprising. However, there are other possible reasons for
these weak regressions. Firstly, measuring current
velocity in front of a stone is not a very accurate
descriptor of the flow environment experienced by
its fauna (Hart et al., 1996), and probably gives
only a crude estimate. The distribution of fauna on
individual rocks responds to highly heterogeneous
microflow patterns, and a much richer fauna has
been found in the wakes than at the front of
boulders (Bouckaert & Davis, 1998). Likewise,
higher density and richness has been demonstrated
on the underside than on the upper surface of
stones (Boulton et al., 1988). Secondly, it was not
the same 20 stones that were collected on every
sampling occasion. This means that stones experiencing high current velocity were not the same
from sampling to sampling, meaning that other
variables were not standardised. This inevitably
adds a lot of undesired ‘statistical noise’ to
regressions. Thirdly, the distribution of the fauna
is subject to multi-factorial regulation, and several
physical stone characteristics not recorded in this
study have been shown to affect the distribution of
the fauna, such as roughness (Erman & Erman,
1984; Downes et al., 1998), colour (Clifford et al.,
1989), algal biomass (Downes et al., 1995) and
turbulence (Froudes number) (Orth & Maughan,
1983; Quinn & Hickey, 1994). Finally, gradients in
stone-characteristics may not have been wide
enough to produce strong relationships.
Although the influence of the five stone
microhabitat variables varied among stream
212
reaches, the spatial variability was greatly
surpassed by the temporal variability between
sampling dates in all combinations of fauna and
stone microhabitat parameters. This means
that the differences in taxonomic composition
among streams were of minor importance to the
fauna–stone relationships compared to the intrinsic temporal variability. In temperate streams,
such temporal changes in distribution may be
due to changing habitat and food preferences
during the life cycles of aquatic insects (Minshall,
1984). However, most aquatic invertebrates in
tropical streams (Hynes, 1975; Jackson & Sweeney, 1995; Yule & Pearson, 1996) including invertebrates in Ecuadorian highland streams
(Turcotte & Harper, 1982) have asynchronised life
cycles, so this high temporal variability in relationships (e.g. regression slopes) is probably related to disturbance events and a continuous
redistribution of the fauna. The actual fauna–
stone regression probably depends on the length of
the period elapsed since the last disturbance event
and the magnitude of this disturbance. Further, I
expect that immediately after a spate, relationships
should be weak or non-existent due to a random
redistribution of the fauna, while relationships
should gradually develop with time following a
spate.
I expected temporal variability of fauna–stone
regressions (slope CV’s) to be negatively correlated
with the Phankuch index, positively correlated
with the stone movement index and annual precipitation, while I had no a priori expectation
regarding a possible correlation with hydrological
dead space. However, I did not find much relationship between slope CV’s and the four stability measures of stream reaches. That measures of
stream stability had no apparent effect on the
temporal variability in fauna–stone microhabitat
relationships was somewhat surprising. Obviously,
the four stability measures applied may not have
been the most adequate in describing the physical
environment that the fauna experiences. After
decades of research, there is still no consensus on
how the disturbance regime experienced by benthic
macroinvertebrates in streams should be adequately quantified and described (Resh et al.,
1988; Reice et al. 1990; Death & Winterbourn,
1994). For example, Townsend et al. (1997) found
that the relationship between richness of stream
invertebrates and disturbance of stream sites
depends on the actual disturbance measure
applied. Nevertheless, I suggest that the absence of
a relationship between slope CV’s and stability
measures across streams are due to the varying
taxonomic composition of the fauna between
streams, because different taxa respond differently
to disturbances (Rosser & Pearson, 1995; Matthaei
et al., 1997; Winterbottom et al., 1997). The
composition of the fauna of a given stream is, at
least partially, determined by the streams environmental characteristics such as temperature
(altitude) and physical stability. The macroinvertebrate community found in unstable streams
are thus expected to be more resistant and/or
resilient than in more stable streams. Consequently, the dependent response variable (the
fauna) was not standardised, but varied among
streams. This renders comparative studies among
streams of temporal variability in general fauna
parameters such as richness and total density in
relation to disturbance regime problematic,
because different streams have different faunas
adapted to the prevailing environmental conditions, including disturbance regime.
Acknowledgements
I thank Anne Jacobsen for assistance with sample
processing at the Freshwater Biological Laboratory, University of Copenhagen. Many students
from the biology department at the Universidad
Católica in Quito assisted in the laboratory and
field. This work was financed by grant 90880 from
RUF, Danida, Danish Ministry of Foreign Affairs.
References
Bailey, R. C., R. H. Norris & T. B. Reynoldson, 2001. Taxonomic resolution of benthic macroinvertebrate communities
in bioassessments. Journal of the North American Benthological Society 20: 280–286.
Barmuta, L. A. 1989. Habitat patchiness and macrobenthic
community structure in an upland stream in temperate
Victoria, Australia. Freshwater Biology 21: 223–236.
Biggs, B. J. F., M. J. Duncan, S. N. Francoeur & W. D.
Meyer, 1997. Physical characterisation of microform bed
cluster refugia in 12 headwater streams, New Zealand. New
Zealand Journal of Marine and Freshwater Research 31:
413–422.
213
Bouckaert, F. W. & J. Davis, 1998. Microflow regimes and the
distribution of macroinvertebrates around stream boulders.
Freshwater Biology 40: 77–86.
Boulton, A. J., G. M. Spangaro & P. S. Lake, 1988. Macroinvertebrate distribution and recolonization on stones subjected to varying degrees of disturbance: an experimental
approach. Archive für Hydrobiologie 113: 551–576.
Bournaud, M., B. Cellot, P. Richoux, & A. Berrahou, 1996.
Macroinvertebrate community structure and environmental
characteristics along a large river: congruity of patterns for
identification to species or family. Journal of the North
American Benthological Society 15: 232–253.
Bowman, M. F. & R. C. Bailey, 1997. Does taxonomic resolution
affect the multivariate description of the structure of
freshwater
benthic
macroinvertebrate
communities.
Canadian Journal of Fisheries and Aquatic Sciences 54: 1802–
1808.
ter Braak, C. J. F. & S. Juggins, 1993. Weighted averaging
partial least squares regression (WA-PLS): an improved
method for reconstructing environmental variables from
species assemblages. Hydrobiologia 269/270: 485–502.
Clifford, H. F., V. Gotceitas & R. J. Casey, 1989. Roughness
and color of artificial substratum particles as possible factors
in colonization of stream invertebrates. Hydrobiologia 175:
89–95.
Cooper, S. D., L. Barmuta, O. Sarnelle, K. Kratz & S. Diehl,
1997. Quantifying spatial heterogeneity in streams. Journal
of the North American Benthological Society 16: 174–188.
Dall, P. C., 1979. A sampling technique for littoral stone
dwelling organisms. Oikos 33: 106–112.
Davis, J. A. & L. A. Barmuta, 1989. An ecologically useful
classification of mean and near-bed flows in streams and
rivers. Freshwater Biology 21: 271–282.
Death, R. G., 2004. Patterns of spatial resource use in lotic
invertebrate assemblages. Hydrobiologia 513: 171–182.
Death, R. G. & M. J. Winterbourn, 1994. Environmental stability
and community persistence: a multivariate perspective. Journal of the North American Benthological Society 13: 125–139.
Dolédec, S., J. M. Olivier & B. Statzner, 2000. Accurate description of the abundance of taxa and their biological traits
in stream invertebrate communities: effects of taxonomic and
spatial resolution. Archiv für Hydrobiologie 148: 25–43.
Douglas, M. & P. S. Lake, 1994. Species richness of stream
stones: an investigation of the mechanisms generating the
species–area relationship. Oikos 69: 387–396.
Downes, B. J., P. S. Lake & E. S. G. Schreiber, 1993. Spatial
variation in the distribution of stream invertebrates: implications of patchiness for models of community organization. Freshwater Biology 30: 119–132.
Downes, B. J., P. S. Lake & E. S. G. Schreiber, 1995. Habitat
structure and invertebrate assemblages on stream stones: a
multivariate view from the riffle. Australian Journal of
Ecology 20: 502–514.
Downes, B. J., P. S. Lake, E. S.G. Schreiber & A. Glaister, 1998.
Habitat structure and regulation of local species diversity in a
stony, upland stream. Ecological Monographs 68: 237–257.
Egglishaw, H. J., 1964. The distributional relationship between
the bottom fauna and plant detritus in streams. Journal of
Animal Ecology 33: 463–476.
Englund, G., B. Malmqvist & Y. Zhang, 1997. Use of predictions to estimate effects of flow regulation on net-spinning
caddis larvae in North Swedish rivers. Freshwater Biology
37: 687–697.
Eriksson, L., J. L.M. Hermens, E. Johansen, H. J.M. Verhaar
& S. Word, 1995. Multivariate analysis of aquatic toxicity
data with PLS. Aquatic Sciences 57: 217–241.
Erman, D. C. & N. A. Erman, 1984. The response of stream
macroinvertebrates to substrate heterogeneity. Hydrobiologia 108: 75–82.
Hart, D. D., 1978. Diversity in stream insects: regulation by
rock size and microspatial complexity. Verh. Internat. Verein. Limnol. 20: 1376–1381.
Hart, D. D., B. D. Clark & A. Jasentuliyana, 1996. Fine-scale
field measurement of benthic flow environments inhabited by
stream invertebrates. Limnology & Oceanography 41: 297–
308.
Hynes, H. B. N., 1975. Annual cycles of macroinvertebrates of
a river in southern Ghana. Freshwater Biology 5: 71–83.
Jackson, J. K. & B. W. Sweeney, 1995. Egg and larval development times for 35 species of tropical stream insects from
Costa Rica. Journal of the North American Benthological
Society 14: 115–130.
Jacobsen, D., 2003. Altitudinal changes in diversity of macroinvertebrates from small streams in the Ecuadorian Andes.
Archiv für Hydrobiologie 158: 145–167.
Jacobsen, D. & B. Bojsen, 2002. Macroinvertebrate drift in
Amazon streams in relation to riparian forest cover and fish
fauna. Archiv für Hydrobiologie 155: 177–197.
Khalaf, G. & H. Tachet, 1980. Colonization of artificial substrata by macro-invertebrates in a stream and variations
according to stone size. Freshwater Biology 10: 475–482.
Lake, P. S. & T. J. Doeg, 1985. Macroinvertebrate colonization
of stones in two upland southern Australian streams. Hydrobiologia 126: 199–211.
Lake, P. S. & E. S. G. Schreiber, 1991. Colonization of stones
and recovery from disturbance: an experimental study along
a river. Verh. Internat. Verein. Limnol. 24: 2061–2064.
Lancaster, J. & A. G. Hildrew, 1993. Flow refugia and the
microdistribution of lotic macroinvertebrates. Journal of the
North American Benthological Society 12: 385–393.
Mackereth, F. J.H., J. Heron & J. F. Talling, 1978. Water
Analysis: Some Revised Methods for Limnologists. Freshwater Biological Association, Scientific Publication No. 36.
Windermere, Great Britain.
Malmqvist, B., A. N. Nilsson, M. Baez, P. D. Armitage & J.
Blackburn, 1993. Stream macroinvertebrate communities in
the island of Tenerife. Archiv für Hydrobiologie 128: 209–235.
Malmqvist, B. & C. Otto, 1987. The influence of substrate
stability on the composition of stream benthos: an experimental study. Oikos 48: 33–38.
Matthaei, C. D., C. J. Arbuckle & C. R. Townsend, 2000.
Stable surface as refugia for invertebrates during disturbance
in a New Zealand stream. Journal of the North American
Benthological Society 19: 82–93.
Matthaei, C. D., K. A. Peacock & C. R. Townsend, 1999. Patchy
surface stone movement during disturbance in a New Zealand
stream and its potential significance for the fauna. Limnology
& Oceanography 44: 1091–1102.
214
Matthaei, C. D., U. Uehlinger & A. Frutiger, 1997. Response of
benthic invertebrates to natural versus experimental
disturbance in a Swiss prealpine river. Freshwater Biology 37: 61–
77.
Merritt, R. W. & K. W. Cummins, 1996. An Introduction to the
Aquatic Insects of North America. Third edition. Kendall/
Hunt Publishing company.
Minshall, G. W., 1984. Aquatic insect-substratum relationships.
In Resh, V. H. & D. M. Rosenberg (eds), The Ecology of
Aquatic Insects. Praeger. New York, USA: 358–400.
Minshall, G. W. & R. C. Petersen, 1985. Towards a theory of
macroinvertebrate community structure in stream ecosystems. Archiv für Hydrobiologie 104: 49–76.
Orth, D. J. & O. E. Maughan, 1983. Microhabitat preferences
of benthic fauna in a woodland stream. Hydrobiologia 106:
157–168.
Palmer, M. A., C. C. Hakenkamp & K. Nielson-Baker, 1997.
Ecological heterogeneity in streams: why variance matters.
Journal of the North American Benthological Society 16:
189–202.
Peckarsky, B. L., 1983. Biotic interactions or abiotic limitations? A model of lotic community structure. In Fontaine T.
D. III & S. M. Bartell (eds), Dynamics of Lotic Ecosystems.
Ann Arbor. Scientific Publications, Michigan, USA: 303–
323.
Pfankuch, D. J., 1975. Stream reach inventory and channel
stability evaluation. US Department of Agriculture Forest
service, Region 1, Missoula, Montana.
Quinn, J. M. & C. W. Hickey, 1994. Hydraulic parameters and
benthic invertebrate distributions in two gravel-bed New
Zealand rivers. Freshwater Biology 32: 489–500.
Reice, S. R., 1985. Experimental disturbance and the maintainance of species diversity in a stream community. Oecologia 67: 90–97.
Reice, S. R., R. C. Wissmar & R. J. Naiman, 1990. Disturbance
regimes, resilience, and recovery of animal communities and
habitats in lotic ecosystems. Environmental Management 14:
647–659.
Resh, V. H., A. V. Brown, A. P. Covich, M. E. Gurtz, H. W. Li,
G. W. Minshall, S. R. Reice, A. L. Sheldon, J. B. Wallace &
R. C. Wissmar, 1988. The role of disturbance in stream
ecology. Journal of the North American Benthological Society 7: 433–455.
Roldán, G., 1992. Guı́a para el Estudio de los Macroinvertebrados Acuáticos del Departamento de Antioquia.
Universidad de Antioquia, Colombia.
Rosser, Z. C. & R. G. Pearson, 1995. Responses of rock fauna
to physical disturbance in two Australian tropical rainforest
streams. Journal of the North American Benthological Society 14: 183–196.
Statzner, B., J. A. Gore & V. H. Resh, 1988. Hydraulic stream
ecology: observed patterns and potential applications. Journal
of the North American Benthological Society 7: 307–360.
Thorp, J. H. & A. P. Covich, (eds) 1991. Ecology and classification of North American freshwater invertebrates. Academic Press, San Diego.
Townsend, C. R., 1989. The patch dynamic concept of stream
community ecology. Journal of the North American Benthological Society 8: 36–50.
Townsend, C. R. & A. G. Hildrew, 1976. Field experiments on
the drifting, colonization and continuous redistribution of
stream benthos. Journal of Animal Ecology 45: 759–772.
Townsend, C. R., M. R. Scarsbrook & S. Dolédec, 1997.
Quantifying disturbance in streams: alternative measures of
disturbance in relation to macroinvertebrate species traits
and species richness. Journal of the North American Benthological Society 16: 531–544.
Turcotte, P. & P. P. Harper, 1982. The macroinvertebrate fauna
of a small Andean stream. Freshwater Biology 12: 411–419.
White, K. E., 1978. Dilution methods. In Herschy, R. W. (ed.),
Hydrometry. John Wiley and Sons, Chichester: 47–55.
Winterbottom, J. H., S. E. Orton & A. G. Hildrew, 1997. Field
experiments on the mobility of benthic invertebrates in a
southern English stream. Freshwater Biology 38: 37–47.
Wright, J. F., D. Moss & M. T. Furse, 1998. Macroinvertebrate
richness at running-water sites in Great Britain: a comparison of species and family richness. Verh. Internat. Verein.
Limnol. 26: 1174–1178.
Yule, C. M. & R. G. Pearson, 1996. A seasonality of benthic
invertebrates in two tropical streams on Bougainville Island, Papua New Guinea. Archiv für Hydrobiologie 137:
95–117.
Zhang, Y., B. Malmqvist & G. Englund, 1998. Ecological
processes affecting community structure of blackfly larvae in
regulated and unregulated rivers: a regional study. Journal
of Applied Ecology 35: 673–686.