Assemblage Stability in Stream Fishes

Assemblage Stability in Stream Fishes: A Review
GARY D. GROSSMAN
JOHN F. DOWD
MAURICE CRAWFORD
School of Forest Resources
University of Georgia
Athens, Georgia 30602, USA
ABSTRACT/We quantified the stability of nine stream fish
assemblages by calculating coefficients of variation of population size for assemblage members. Coefficients of variation
were high and averaged over 96%; indicating that most assemblages were quite variable. Coefficient of variation (CV)
estimates were not significantly affected by: (1) years of
study, (2) mean abundance, (3) familial classification, or (4)
mean interval between collections. We also detected minor
regional differences in CVs. The high variability exhibited by
many stream fish assemblages suggests that it may be diffi-
Annual and season variations in flow regimes (i.e.,
droughts and floods) can produce substantial fluctuations in the physical environment of many lotic ecosystems. Because droughts and floods occur with a relatively high frequency, especially when compared to
many other natural disturbances (e.g., El Nino, hurricanes), lotic environments are excellent systems for
tests of equilibrium and nonequilibrium ecological
theories. Implicit tests of such theories occurred as
early as 1951, when William Starrett (1951) observed
large variations in species abundances in an Iowa riverine fish assemblage and attributed these variations to
unpredictable hydrologic events. Similar results were
obtained by later researchers (Larimore 1954, Metcalf
1959, Paloumupis 1958, Larimore and others 1959,
John 1964, Lowe and others 1967, Rinne 1975, Harrell and others 1967, Harrell 1978, Mills and Mann
1985, Moyle and Li 1979).
Prompted by the general ecological debate regarding the importance of equilibrium and nonequilibrium processes to assemblage dynamics, Grossman
and others (1982) reviewed the literature on stream
systems. This review, coupled with a reanalysis of assemblage structure data from an Indiana stream, led
them to reiterate Starrett's hypothesis and suggest that
hydrologic variability may facilitate coexistence in assemblages of many stream organisms. The proposed
KEY WORDS: Community; Structure; Assemblage structure; Assemblage stability; Community stability; Population variability; Stream fishes
Environmental Management Vol. 14, No. 5, pp. 661-671
cult to detect the effects of anthropogenic disturbances using
population data alone. Consequently, we urge managers to
exercise caution in the evaluation of the effects of these disturbances. More long-term studies of the ecological characteristics of undisturbed stream fish assemblages are needed
to provide a benchmark against which disturbed systems
can be compared.
We suggest that CVs are a better estimator of population/assemblage stability, than either Kendall's W or the standard
deviation of the logarithms of numerical censuses. This conclusion is based on the following reasons. First, CVs scale
population variation by the mean and, hence, more accurately measure population variability. Second, this scaling
permits the comparison of populations with different mean
abundances. Finally, the interpretation of CV values is less
ambiguous than either of the aforementioned metrics.
mechanism for coexistence is that mortality associated
with the occurrence of floods and droughts acts to
prevent resource limitation or competitive exclusion
within lotic systems. Species residing in these environments can utilize similar resources and coexist: a contradiction of several theoretical predictions (MacArthur 1972). If this mechanism is a general one for lotic
assemblages, ecological theory, especially equilibriumbased ecosystem stability and recovery models, may
have little relevance to streams and rivers (but see
DeAngelis and Waterhouse 1987). Grossman and
other's conclusions did not go unchallenged (Herbold
1984, Rahel and others 1984, Yant and others 1984).
Nonetheless, many investigators now agree that floods
and droughts can have a pervasive influence on both
structural and functional characteristics of lotic ecosystems (Resh and others 1988).
The variability of lotic assemblages, coupled with
the potentially restricted applicability of many theoretical models, poses a special problem for agencies
charged with the detection and mitigation of anthropogenic disturbances in streams and rivers (e.g., toxicant spills, dams, channelization, etc.). Although some
stream taxa apparently recover quickly from disturbance (Yount and Niemi 1990), recovery rates are
strongly affected by factors such as: (1) persistence of
the effects of disturbance, (2) species' differential abilities to survive disturbance and recovery (Kelly and
Harwell 1990, Yount and Niemi 1990), (3) presence of
refugia (Sedell and others 1990), and (4) hydrologic
conditions (Cairns 1990, Yount and Niemi 1990). In
11990 Springer-Verlag New York Inc.
662
G. D. Grossman and others
addition, considerable disagreement exists over the
definitions of disturbance and recovery (see Resh and
others 1988). Is recovery merely the reappearance of
species comprising the original assemblage, or the reestablishment of these species in their prior relative
abundances (i.e., previous assemblage structure)? Regardless of these problems, assessment of both disturbance and recovery in lotic ecosystems is dependent
upon characterization of the variability present in undisturbed streams and rivers. With this goal in mind,
we have quantified the variability of North American
stream fish assemblages through an analysis of data
from papers published since Grossman and others
(1982). Our purpose here is threefold: first, to ascertain the progress made since 1982 with respect to the
debate over assemblage organization in lotic fishes,
second, to suggest improvements in the methodologies
currently used to assess assemblage stability in stream
organisms, and third, to relate this general topic to the
detection of disturbance and facilitation of recovery in
lotic ecosystems.
Design of Stream Fish Assemblage
Organization Studies
At present, there are 10 published studies (Table 1)
that test for mechanisms determining the organization
of lotic fish assemblages. The basic design of these
studies is to delineate permanent station(s) along a
stream and then repeatedly sample the stations over
many years. Specific spatial and temporal requirements are necessary to assure the validity of these results (Grossman 1982, Grossman and others 1982,
Connell and Sousa 1983). First, sampling should include the minimum home-range sizes of the dominant
species. This increases the probability that population
variability is dominated by mortality and recruitment,
rather than by movement in and out of the station.
Secondly, sampling should comprise at least one mean
generation time of assemblage dominants to ensure
that stability is not an artifactual consequence of low
adult mortality coupled with great longevity and low
recruitment (Frank 1968, Davis and van Blaricom
1978). Although Connell and Sousa (1983) state that
the minimum temporal requirement for an assemblage stability study is one complete turnover of the
assemblage, this is unnecessary for species with a
quantifiable age structure (e.g., many fishes, trees,
etc.). Because an investigator can age individuals of
such species, population stability caused by relatively
equal levels of recruitment and mortality (i.e., true stability), can be differentiated from that due to great
longevity coupled with low adult mortality and recruitment (Grossman 1982, Warner and Chesson 1985).
After satisfying the spatial and temporal requirements for assemblage organization studies, most investigators tested for concordance of ranked abundances
of assemblage members (i.e., assemblage stability)
using Kendall's W. Unfortunately, this test possesses
methodological limitations that affect its interpretation
(Grossman and others 1985, Rahel and others 1984).
Because W presently is being used for analyses of assemblage stability, it seems worthwhile to describe the
consequences of these problems. First, confusion exists
over W's null and alternative hypotheses. The null hypothesis for W is that concordance of ranks is not significantly different from 0 (i.e., random). Conversely,
the alternative hypothesis merely states that concordance of ranks is significantly different from 0 (i.e.,
nonrandom). Hence, when one rejects the null hypothesis, it does not necessarily mean that ranked
abundances are stable, but that the relationship is significantly different from 0 or random. To determine
the level of stability present in an assemblage, it is necessary to examine the magnitude of W, which ranges
from 0.0 to 1.0. If W is high (e.g., >0.75) and significant, one could conclude that ranked abundances are
stable because the relationship is strong. Depending
on the number of rows and columns in the calculation,
however, there are cases where the null hypothesis for
W is rejected, yet the value of W is low (e.g., <0.50, see
Ross and others 1985, Matthews and others 1988). In
these cases, investigators have concluded that ranked
abundances were highly stable, a conclusion that may
not be warranted given W's low value.
Kendall's W possesses an additional problem; it is
strongly affected by the continued low abundances of
rare species (Grossman and others 1982, Rahel and
others 1984). Consequently, a statistically significant
result can be obtained if rare species remain rare while
common species fluctuate substantially. To control for
this possibility, it is necessary to sequentially delete the
rarest species from the analysis and recalculate W.
Table 2 presents an example where an artifactual conclusion of stability was reached as a result of this
problem.
Given the interpretational difficulties of W, perhaps
a different analytical tool is needed for the quantification of assemblage stability. We are currently using the
coefficient of variation (CV) of population size to assess assemblage stability patterns in Coweeta Creek,
North Carolina (Freeman and others 1988). This
metric has a variety of advantages over W: (1) it directly measures the parameter of interest (i.e., population variability); (2) it standardizes variability (i.e., vari-
Stream Fish Assemblage Stability
Table 1.
663
Review of stream fish assemblage organization studies published since 1982a
Study
Site
Station
Seasons
(N)
Species
(N)
Study
time span
(yr)
Annual
samples
(N)
1
2
5
9
3
4
6
1
5
7
1
1
1
1
1
1
1
1
1
1
1
3
15
15
15
15
9
13
14
13
13
12
12
15
15
15
15
18
18
18
6
6
6
10
6
6
6
6
5
5
5
3
3
3
5-7b
1
1
1
1
1
1
1
3
3
3
3
3
4
4
4
4
3
4
6
5
3
9-22b
10
11
4
6
5
5
5
5
5
4
9-13b
10
11
4
6
5
5
5
5
5
4
4-12b
3
ll-19 b
5-9b
3-5b
Matthews and others
(1988)
Piney Creek
Matthews and others
(1988)
Brier Creek
Matthews and Others
(1988)
Kiamichi River
Meffe and Berra
(1988)
Cedar Fork
Card and Flittner
(1974)
Sagehen Creekc
Moyle and Vondracek
(1985)
Martis Creek0
Freeman and others
(1988)
Coweeta Creek
Grossman and others
(1982)
Ross and others
(1987)
Erman (1986)
Otter Creek
8
9
10
lOa
2
3
4
1
2
3
1
Black Creek
1
Meffee and Minckley
(1987)
Sagehen Creek
d
Aravaipa Creek
e
Authors'
conclusions
Ranks concordant,
deterministic
organization
Ranks concordant,
deterministic
organization
Ranks concordant,
deterministic
organization
Ranks concordant,
some equilibrium
characteristics present
Not relevant
Ranks concordant,
deterministic
organization
Ranks concordant,
populations moderately
fluctuating
Ranks not concordant,
stochastic
Ranks concordant,
populations stable
Ranks concordant,
some population
fluctuation
Ranks concordant,
population data
not given
a
We omitted some studies (e.g., Ross and others 1985, Matthews 1986) because later papers (e.g., Matthews and others 1988) included these data.
Varied by season.
'Species subjected to substantial angling mortality (e.g., Salmo gairdneri) were excluded from calculations.
d
Due to changes in the stream fauna produced by an impoundment (Erman 1973), the data used in our reanalysis are the preimpoundment data
of Card and Flittner (1974).
"Because population data were not presented we could not include data from this study in our analysis.
b
ance is expressed as a percentage of the mean), which
facilitates comparison of species with different mean
abundances; and (3) because population variation is
expressed as a percentage of the mean, its interpretation is relatively unambiguous. Of course, we are left
with the problem of classifying CV values, and
Freeman and others (1988) have proposed the following classification scheme: (1) CV =£ 25% = highly
stable, (2) 25% < CV =£ 50% = moderately stable, (3)
50% <CV ^ 75% = moderately fluctuating, (4) CV
^ 76% = highly fluctuating. A determination of assemblage stability then is made by examining CV
values for all assemblage members. We use this classification in our paper, but recognize its arbitrary nature.
It is our hope that the classification system will be refined through dialectical exchanges and further comparative study. Finally, for illustrative purposes, we
present assemblage stability analyses based on W and
CV values in Table 3.
Methods
To determine the variability present in stream fish
assemblages, we calculated the CV of population size
664
G. D. Grossman and others
Table 2. Example of effects of rare species on
calculation of Wa
Table 3. Comparison of Kendall's W and CV values
for three midwestern lotic fish assemblages3
Year
Species
A
B
C
D
E
F
1
2
3
4
1604
481
1263
240
1
1
240
882
80
161
1
1
2791
6459
3987
1771
557
239
702
531
3126
439
1
1
0.83
Sig
<0.01
Total
abundance
(%)
100
0.71
<0.01
99
0.43
>0.20
97
W
All species
Delete rarest
species (F)
Delete two rarest
species (E and F)
a
Data are from a North American stream and have been multiplied
by a constant.
for assemblage members from nine drainages (Table
1). These data sets comprise the assemblage organization studies previously discussed and should be consulted for habitat descriptions and collecting methodologies. (For purposes herein, a site represents a
stream or river, whereas a station is the subsection of a
site within which collections were made.) Rather than
reanalyze data from all stations in all sites, we selected
data representing a longitudinal gradient along a
stream (e.g., Piney Creek stations 1, 2, 5, and 9), which
also generally contained a majority of species present.
An assemblage member was included in analyses if it
occurred in at least 50% of the annual censuses in a
site and was not potentially subjected to substantial angling mortality (i.e., salmonids and Micropterus spp.).
Although five sites were sampled only during
summer months, four additional sites were sampled in
at least two seasons (Table 1). Because of possible seasonal differences in population variability, it was necessary to use two seasonal data sets, one based on
summer data only and a second including data from
all seasons. To calculate population CVs, we selected
one census from each set of collections made within a
given year. Because some data sets consisted of multiple collections from multiple stations within a given
year, it was necessary to further partition the data set.
Consequently, we calculated CVs based on censuses
from the station with the highest mean abundance for
a species, as well as from the station with the lowest
mean abundance. We used this partitioning to test for
the effects of habitat suitability (i.e., we assumed that
Site
Brier Creek
Station 3
Station 4
Station 6
Piney Creek
Station 1
Station 2
Station 5
Station 9
Kiamichi River
Station 1
Station 5
Station 7
X abundance
(SD)
XCV
(SD)
W
14(17)
29 (50)
30 (23)
135 (38)
112(33)
133 (55)
0.347b NS
0.478C
0.434C
58 (112)
59 (70)
42 (40)
28 (17)
91 (31)
89 (36)
99 (33)
86 (57)
10(7)
16(17)
60 (105)
103 (44)
70 (33)
99 (47)
0.591d
0.502e
0.439=
0.463e
0.317 NS
0.542 NS
0.671'
Presented are mean abundance values ± 1 SD, mean values for the
coefficient of variaion of population size (± 1 SD) for assemblage
members, and W for each assemblage. We used the data and criteria
of Matthews and others (1988) for calculations of W. Conclusions
based on CV values indicate that these assemblages are composed of
highly fluctuating species whereas conclusions based on W values
yield a different result (Matthews and others 1988).
b
Some of our values for W differ slightly from those of Matthews and
others (1988) due to minor computational errors in the aforementioned paper. These small errors do not affect the conclusions of
Matthews and others (1988), however.
C
P < 0.05.
d
P < 0.001.
°P < 0.01.
estimates from the station with the species' highest
mean abundance represent its most favorable habitat,
whereas the station with the lowest mean abundance
represented its least favorable habitat). Thus, we conducted our analyses on four data sets: (1) CV estimates
based on censuses from the station with the highest
mean abundance during all seasons (set All-High), (2)
CV estimates based on censuses from the station with
the lowest mean abundance during all seasons (set AllLow), (3) CV estimates based on censuses from the
station with the highest mean abundance during
summer (set Summer-High), and (4) CV estimates
based on censuses from the station with the lowest
mean abundance during summer (set Summer-Low).
These data sets are not independent, and as a consequence, we used the 0.01 level of significance to control for experiment-wise error. Because all four data
sets approximated a normal distribution (Figure 1),
parametric statistics were used in most analyses. We
employed distribution-free statistics, however, when
deviations from normality were large because of small
sample sizes.
Besides our interest in quantifying population vari-
Stream Fish Assemblage Stability
o
m
O
UJ
EC
20
191817161514131211 •
109-
.
l
l
I...
COEFFICIENT OF VARIATION
Figure 1. The distribution of coefficient of variation of population size estimates for the All-High data set. Distributions
for the three remaining data sets were very similar.
ation within stream fish assemblages, we also examined the effects of several methodological techniques
on CV estimates. First, it has been suggested that
stream fish populations fluctuate greatly because the
dynamics of abundant, but variable, young-of-the-year
(yoy) overwhelm stable adult populations (Yant and
others 1984). We tested this hypothesis in two ways.
First, we calculated seasonal CVs for adults only and
compared these estimates to those based on all individuals in the population, at three stations in Coweeta
Creek. A t test for paired samples was used for hypothesis testing. Second, we compared CV estimates
based on spring and autumn samples for four sites. If
inclusion of yoy does significandy affect CV values,
then there should be a significant difference between
CV estimates made in seasons when yoy have the
smallest (i.e., spring) and the greatest (i.e., autumn) influence on population size. We tested this hypothesis
using the signed ranks test. We also tested for the
overall effect of mean abundance on CV estimates by
dividing species into 11 abundance classes (mean
abundance classes: (1) 1-2 individuals, (2) 3-4, (3)
5_6, (4) 7-8, (5) 9-10,..., (11) >20 individuals) and
performing an ANOVA on this data set. Abundance
classes containing fewer than five estimates were deleted from analyses.
To assess the effects of sampling regime on CVs, we
classified studies according to the number of annual
censuses in an estimate (classes = 3—5 years, 6—8
years, and >8 years). In addition, we examined
whether the mean number of years between censuses
665
significantly affected CV values (not all stations were
sampled annually). For this analysis, treatment classes
were: 0-1 years, 1+-2 years, 2 + — 3 years, and >3
years. We evaluated both effects statistically using
ANOVA and Tukey-Kramer a posteriori tests.
We also determined whether within-site, regional,
and taxonomic factors affected CV values. We tested
for seasonal differences in CV values within a station
using either Friedman's test (more than two seasons)
or the signed ranks test (two seasons), with species as
blocks and season as the treatment (Sokal and Rohlf
1981). A similar analysis was conducted to examine
among-station differences in CVs within a site. We examined regional effects on CV estimates by assigning
sites to geographical regions and using ANOVA and
Tukey-Kramer tests for significance testing. Regional
classifications were as follows: (1) midwestern—Brier
Creek, Kiamichi River, and Piney Creek; (2) northern
—Cedar Fork Creek and Otter Creek; (3) southeastern—Coweeta Drainage; (4) southern—Black
Creek; and (5) western—Martis Creek and Sagehen
Creek. Finally, we investigated the effect of taxonomic
classification on CVs by testing for significant differences among six families (Cyprinidae, Cottidae, Percidae, Catostomidae, Centrarchidae, and Cyprinodontidae). In a secondary analysis, we tested for differences between Notropis and non-Notropis cyprinids
using a Mann-Whitney U test. Both regional and taxonomic hypotheses were evaluated using ANOVA and
Tukey-Kramer tests.
Hydrologic variation is a major cause of variation in
stream fish assemblages. To illustrate how hydrologic
variation may be compared within and among
drainages, we constructed flow duration curves for
several rivers in Georgia (Inman 1971). The total period of record for US Geological Survey gauging stations was used to determine the cumulative frequency
distribution of the daily mean discharge (Searcy 1959).
We scaled the discharge associated with each frequency class by the mean discharge for the sampling
period to remove the effects of basin size and climatic
variation. Discharge then was plotted on a four-cycle
log scale (y axis); the frequency was plotted as the percent time the discharge was equaled or exceeded on a
normal probability scale (x axis).
Results
Mean CV values were high for all data sets (AllHigh X ± SD = 99 ± 42, All-Low = 105 ± 40,
Summer-High = 97 ± 41, Summer-Low = 102 ±
42). In addition, neither mean CV values nor variances differed significantly among data sets (X F =
666
G. D. Grossman and others
1.04, P > 0.05, Var F = 1.12, P > 0.05). Not surprisingly, mean abundances were significantly higher in
All-High and Summer-High data sets than in the remaining two data sets (All-High, X ± SD = 38 ± 68,
All-Low = 10 ± 19, Summer-High = 35 ± 67,
Summer-Low = 16 ± 29, X F = 10.92, P < 0.001).
Variances in mean abundance also differed significantly among data sets (Fmax = 12.29, P < 0.001). Redundancy among data sets ranged from a low of 21 %
(All-High - All-Low) to a high of 69% (All-Low Summer-Low) and averaged 54%. Redundancy occurred when an estimate fell into more than one classification (e.g., when there was only one CV estimate for
a species in a site, it occurred in both All-High and
All-Low data sets).
Inclusion of yoy did not significantly affect CVs at
station 1 in Coweeta Creek (Table 4). Because of insufficient sample sizes, tests were not possible for Coweeta stations 2 and 3, but we observed an identical
pattern for these stations. Spring—autumn comparisons for five stations at four sites also indicated a lack
of significant differences between spring and autumn
CV estimates (Table 5). These results suggest that inclusion of yoy in CV estimates does not significantly
affect CV values, at least in the sites examined.
Neither the number of samples in an estimate nor
the mean interval between samples significantly affected CV Values for any data set (Table 6). Coefficient of variation estimates also did not differ significantly among mean abundance classes (Table 6).
Within a station, season only had a significant effect
on CV values in one of six stations from four sites
(Table 7). Within a site, among-station comparisons of
CVs did not yield significant results, regardless of the
site (Table 8). Regional analyses indicated that
southern and southeastern CV estimates were significantly lower then northern CVs in the All-High data
set (Table 6). Taxonomic effects were not apparent for
any data set (Table 6). This was true even when the
Cyprinidae were separated into Notropis and non-TVotropis species:
All-Low
Summer-High
Summer-Low
All-High
T
T
T
T
= -1.26
= 0.35
= -0.37
= 0.19
P
P
P
P
=
=
=
=
0.21
0.72
0.70
0.85
Flow duration curves represent the flow characteristics of a stream throughout the entire range of discharge (Searcy 1959). If coexistence in stream fish assemblages is attributable to hydrologic variation
(Grossman and others 1982), then flow duration
curves may enable us to identify the systems in which
this mechanism is important. Flow duration curves are
illustrated for several Georgia streams in Figures 2—4.
Plotting the flow duration curve on a probability axis
expands both ends of the curve. This enhances the
differences between sites for both high flows and low
flows. Curves with steep slopes have flows that are
more highly variable, whereas a flat slope represents a
stream with regulated flows. Systems exhibiting steep
flow duration curves may be more likely to contain assemblages that are affected by disturbance. Stabilization of flows in such streams may have a greater effect
on coexistence than it would have in less variable
systems.
Flow duration curves varied both within and among
drainage systems. The curves for three locations on
the Oconee River have similar slopes for the range of
flows between 50% and 90% exceedence. However,
extreme flows for the basins are different. Allen
Creek, the smallest basin with a drainage area of 44.8
km2, was most variable for high flows but less variable
for low flows. The middle-sized basin, the Oconee
River near Athens, Georgia, had a drainage area of
1030.8 km2. Its extreme flows fell between those of
Allen Creek and the 2823.1-km2 drainage area of the
Oconee River near Greensboro, Georgia. These flow
differences are caused by differences in storage capacity of the systems. Headwater streams tend to be
more variable, especially for high flow events, because
they possess less channel storage. Downstream gauges
also usually display less variability because they integrate different headwater subwatersheds.
Curves for four different rivers in Georgia illustrate
the differences one can expect among systems. All
four streams have drainage areas around 1036 km2.
The Etowah River is in the Mountain province, the
Yellow River and Oconee River are in the Piedmont
province, and the Ogeechee River is substantially in
the Coastal Plain province. Flow duration curves for
these streams do not have the same slope between the
50% and 90% exceedence values. The Etowah River
and Oconee River are flatter than the other two.
Overall, there are more differences among rivers in
low flows than in high flows.
Discussion
Our findings suggest that many stream fish assemblages are composed of species that vary substantially
in population size (i.e., CV values for all four data sets
averaged over 96%). In addition, rare species did not
fluctuate more than abundant species because: (1)
mean CV values were virtually identical for low and
high data sets, and (2) mean abundance did not significandy affect CV values. Finally, sampling error probably did not produce the high CV values observed, be-
Stream Fish Assemblage Stability
667
Table 4. Coefficient of variation of population size estimates for all population segments and for adults only, for
station 1, Coweeta Creek, North Carolina3
Spring
Summer
Autumn
Species
All
Adults
only
All
Adults
only
All
Adults
only
Co. bairdi
Rh. cataractae
Cl. funduloides
On. mykiss
Ca. oligolepis
23
62
47
100
117
16
67
47
148
117
34
81
60
65
b
33
84
60
105
b
17
37
125
77
68
17
49
125
b
68
"There were no significant differences in CV estimates for all versus adults only comparisons ((test for paired samples, on each set of seasonal
data).
b
Absem.
Table 5. Estimates of coefficient of variation of population size for species present in a site during
multiple seasons3
Seasons
Species
(N)
T
Spring vs autumn
Spring vs autumn
Spring vs autumn
6
15
13
8.0
65.0
44.0
Spring vs autumn
Spring vs autumn
13
10
38.0
21.5
Site
Cowetea Creek
Station 1
Cedar Fork Creek
Otter Creek
Black Creek
Station 1
Station 2
a
H0: CV estimates do not differ by season. None of the test values are significant at the 0.05 level (signed ranks test).
Table 6. Significance tests for the effects of numbers of samples, mean interval (years) between samples, mean
abundance, region, and family, on CV estimates3
Data set
All-High
All-Low
Summer-High
Summer-Low
Number
of samples
(F)
Region
Mean
interval
(F)
Mean
abundance
(F)
F
3.15
3.05
1.21
4.24C
-0.16
-0.25
-0.83
2.77
2.94
2.39
1.66
2.40b
1.27
1.93
1.22
3.04d
Pairwise
differences
south, southeast,
< north
north, southwest, west
> southeast
Family
(F)
1.19
1.36
1.15
0.91
"Results are for F tests and Tukey-Kramer a posteriori tests. None of the hypothesis tests for differences in mean CV values between Notropis and
Non-Notropis cyprinids were significant.
b
/> = 0.0244. Abundance classes: 11 < 7.
C
P < 0.005.
d
P = 0.0194.
cause studies in which different collecting methods
were used (i.e., seining, draining, and electrofishing)
generally did not exhibit significant differences in CV
values (i.e., midwestern, northern, and southern
versus western and southwestern). These results support the findings of Grossman and others (1982). Although we would not postulate that these systems are
organized solely by stochastic mechanisms (Grossman
and others 1982), the application of equilibrium-based
models to these assemblages may be inappropriate.
The high variability present in stream fish assemblages poses a significant problem for resource managers. First, this variability may make the detection of
many anthropogenic disturbance difficult. We are not
suggesting that managers take a passive role with respect to this problem; quite the contrary. What we do
668
G. D. Grossman and others
Table 7. Friedman's test results for seasonal
differences in CV values within a station
Site
Coweeta Creek
Station 1
Station 2
Station 3
Cedar Fork Creek
Otter Creek
Black Creek
Station 1
— Allen Creek
- - near Athens
Season
Species
(N)
Test
value
All
All
All
All
All
5
4
3
15
13
0.40
7.60a
All
13
.154
.133
3.57
"P < 0.05.
.01 .1 1
10
50
90 99 99.999.99
Percent Time Discharge Equaled or Exceeded
Figure 3. Flow duration curves for three portions of the
Oconee River (Georgia) drainage.
Table 8. Significance tests for among-station
differences in CV values within a site3
Site
Black Creek
Autumn
Spring
Kiamichi River
Brier Creek
Piney Creek
Martis Creek
Sagehen Creek
Coweeta Drainage
Spring
Summer
Stations
(N)
Species
(N)
Test
value
2
9
3
3
4
3
4
9
13
5
7
10
2
3
19.00
45.00
0.40
2.00
3.00
3.00
1.80
3
3
3
3
0.67
4.67
>0.05).
.01 .1 1
10
50
90
99 99.999.99
Percent Time Discharge Equaled or Exceeded
Figure 2. A flow duration curve for the Oconee River near
Greensboro, Georgia.
recommend is that managers recognize that the effects
of even moderate levels of anthropogenic disturbance
may be masked by the natural variability of lotic fish
assemblages (for a similar conclusion, see Vaughan
and Van Winkle 1982). This necessitates very cautious
judgement regarding whether or not an impact has
Oconee R.
— Yellow R.
-- Oqeechee R.
--- Etowoh R.
1.00-;
.01 .1 1
10
5O
90
99 99.9 99.99
Percent Time Discharge Equaled or Exceeded
Figure 4. Flow duration curves for four Georgia rivers.
occurred! It also calls for the development of more sophisticated numerical techniques to enable researchers
to better identify disturbance-induced population
trends. The use of indices of environmental condition
such as the index of biotic integrity also show promise
for the identification of disturbance in stream fish assemblages (Fausch and others 1988, Hughes 1990), although recognition of the variability present in these
assemblages should be incorporated in such indices.
One prediction regarding disturbance can be made
from our results. If variability in stream fish populations is produced by hydrologic variation, and this
variation prevents competitive exclusion through its
negative effect on population size, then any action that
reduces hydrologic variability (e.g., hydroelectric and
flow-control structures) may cause a loss of species.
Unfortunately, because of the substantial habitat modifications caused by these structures, it will be difficult
to know whether a loss of species is due to: (1) flow
stabilization, (2) habitat modification, or (3) a combination of both factors. We suggest, however, that structures that reduce hydrologic variability may cause a
loss of species independent of habitat modifications.
Stream Fish Assemblage Stability
Although flow duration curves may aid us in identifying systems in which flow stabilization potentially
may have strong physical or biological effects, the precise relationship between these curves and population
variability or species richness of fish assemblages is not
well known.
Connell and Sousa (1983) proposed that the standard deviation of the logarithms (base 10) of sequential censuses be used as an estimator of population stability. They recognized that the CV of population size
could be used for this purpose, but stated that it was
sensitive to high values (Connell and Sousa 1983, p.
800). Connell and Sousa (1983) noted that their index
was sensitive to low values, but because they "were
more interested in population variation at low
numbers," they did not use the CV. Nevertheless, we
believe that CV values are a more relevant estimator of
population stability for the following reasons. First,
CVs express the standard deviation as a percentage of
the mean: the type of variability most relative to a population stability study. Connell and Sousa (1983) attempt to remove the effects of differential mean abundances by using logarithms as a scaling factor. Although this technique collapses the variability
observed, it does not scale variability by the original
mean abundance. This has important consequences
for the measurement of population variability (Table
9). Species of low abundance that also have standard
deviations greater than the mean may obtain low
values for Connell and Sousa's index (e.g., Table 9,
Oncorhynchus mykiss). This cannot happen with CV estimates. Although a more complete analysis of the behavior of these two estimators is in progress
(Grossman, unpublished data), we suggest that examination of assemblage members' CV values better represent the parameter of interest in a population and/or
assemblage stability study.
Second, because CV estimates are calculated by dividing the standard deviation of population estimates
by mean abundance, their interpretation is simple and
unambiguous. A CV value of 50% means that the
standard deviation is one-half the mean abundance. In
contrast, the interpretation of the standard deviation
of the logarithms of sequential censuses is less clear. In
fact, because of the use of logarithms, population variability estimates are compressed, even though they are
the parameter of interest.
Although we prefer CV values over W for tests of
population and/or assemblage stability, this technique
is not without error. First, because CV values are
ratios, they may possess unusual distributional properties, especially if the variance is correlated with the
mean. Neither of these problems influenced our data
set (e.g., Figure 1, Table 8), but they could affect
669
others. Second, by decomposing an assemblage into its
component populations, we are no longer examining
"assemblage level" behavior. Third, the classification
system proposed for CV values does not have a strong
a priori foundation. Fourth, CV values cannot distinguish between sampling variability and actual population variability. Fifth, CV values cannot detect time-dependent trends in population variation (i.e., long-term
increases or decreases or cyclical fluctuations) or correlated trends among assemblage members. Despite
these problems, most of which are shared by W (i.e., 3,
4, and 5), we still believe that CV values are a valuable
tool for quantifying assemblage stability. In addition,
some of these limitations (i.e., 1 and 5) can be addressed by examination of the abundance data upon
which CV estimates are based. Finally, the use of similarity indices also shows promise for tests of assemblage stability (see Matthews and others 1988); however, their behavior must be investigated better before
we can evaluate their efficacy.
We did not restrict our analyses to censuses taken at
frequencies representing at least one turnover of the
assemblage, as suggested by Connell and Sousa (1983).
We also did not heed our own criteria and directly examine population age structures, because, with one
exception (Coweeta drainage), these data were not
available. We did find, however, that increasing the
time interval between censuses did not significantly affect CV estimates.
Our analyses indicate that familial classification did
not have a strong effect on CVs. Centrarchid species
were just as variable as cyprinids and percids. Although more data are needed to examine the generality of this finding, our data base does include estimates from a variety of regions. Geographical analyses
showed that CV estimates varied significantly among
regions for All-High and Summer-Low data sets.
These findings must be viewed as tentative, because
some regions were represented by only one (i.e., south
and southeast) or two (western and northern) streams.
In fact, Coweeta Creek, the sole southeastern stream,
is a southern Appalachian trout stream. It is certainly
not representative of the majority of lotic systems in
the region (i.e., Piedmont and Coastal Plain systems).
Although our data indicate that stream fish populations are quite variable, we will not propose an organizational mechanism for these systems. Like Meffe and
Berra (1988), we believe that more detailed environmental data are necessary for causal inferences regarding the factors determining population levels and
variability. It is clear, however, that the assemblages
examined here are probably not in equilibrium. In addition, species within a given system may respond to
biological and physicochemical variation in a species-
670
G. D. Grossman and others
Table 9. Comparison of population variability estimtes made using CV of population size and standard deviation
of the logarithms (base 10) of sequential censuses (Connell and Sousa 1983)a
Species
Abundance
(X ± 1 SD)
CV
Standard deviation
of log (n + 1)
Calostomus tahoensis
Coitus bairdi
Salmo trutta
Rhinichthys osculus
Rhinichthys cataractae
Oncorhynchus rnykiss
Clinostomus funduloides
245a
54
24
22
17
8
7
160
20
123
191
24
111
49
0.66
0.08
0.76
0.75
0.10
0.43
0.17
±
±
±
±
±
±
±
393
11
29
42
4
9
4
"Data rounded to the nearest individual, fractional values to ±0.1 were included in calculations.
specific manner (Mills and Mann 1985, Schlosser
1985, Freeman and others 1988). The elucidation of
these mechanistic responses should yield insights into
the maintenance of assemblage structure in stream
fishes.
In conclusion, in contrast to other authors (Herbold
1984, Yant and others 1984, Matthews 1986, Ross and
others 1987, Matthew and others 1988), we suggest
that populations comprising stream fish assemblages
vary substantially. Our purpose here is not to criticize
the conclusions of earlier investigators but to identify
how different analytical techniques may yield differing
conclusions. These conclusions also may strongly affect how resource professionals manage lotic systems.
For example, we would urge resource managers to be
cautious with respect to the evaluation of anthropogenic impacts on stream systems, because even substantial impacts may be difficult to detect. A similar
caveat applies to the detection of recovery in damaged
streams. Mitigation effects should not be halted until
mean abundances and variability approximate predisturbance levels. Of course this requires data on predisturbance population dynamics, data which are lacking
not only for individual streams (but see Erman 1973,
1986) but also for entire geographical regions. Consequendy, we would urge management agencies to undertake more long-term studies of stream fish assemblages in undisturbed watersheds to provide a benchmark against which disturbed systems can be
compared.
Acknowledgments
We gratefully acknowledge T. Berra, D. Erman, W.
Matthews, G. Meffe, P. Moyle, S. Ross, and J. Whitaker, Jr., for providing access to their data sets on
stream fishes, and R. Ratajczak for data analysis. This
manuscript benefited from the comments of several of
the aforementioned investigators as well as those of L.
Barnthouse, J. Barrett, V. Boule, M. Freeman, P.
Harper, J. Hill, H. Li, G. Niemi, D. Stouder, and D.
Yount. We also appreciate the patience and support of
B. Dowd and B. Mullen. Finally, we thank the Environmental Protection Agency and the Natural Resources Research Institute, University of Minnesota,
for inviting us to participate in the Lode Ecosystems
Recovery Workshop. Portions of this work were supported by Mclntire-Stennis grant GEO-0035-MS to
the senior author.
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