Assemblage stability in stream fishes: A review

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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.


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., 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


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


Species (N)

Test value

All All All All All

5 4 3 15 13

0.40 7.60a




.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


.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.


.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


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-


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)


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.

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Stream Fish Assemblage Stability

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