Scales of macroinvertebrate distribution in relation ...

J. N. Am. Benthol. Soc., 2003, 22(1):105–122 q 2003 by The North American Benthological Society

Scales of macroinvertebrate distribution in relation to the hierarchical organization of river systems MELISSA PARSONS1, MARTIN C. THOMS,

AND

RICHARD H. NORRIS2

Cooperative Research Centre for Freshwater Ecology, University of Canberra, Australian Capital Territory 2601, Australia Abstract. The multiscale distribution of macroinvertebrate assemblages may correspond to the hierarchical arrangement of river systems because geomorphological processes manifest characteristic environmental conditions at different scales. Macroinvertebrates were sampled according to a nested hierarchical design incorporating 4 geomorphologically derived scales: catchment, zone, reach, and riffle. Analysis of Similarity, mean similarity dendrograms, and nested analysis of variance were used to determine the scale(s) at which macroinvertebrate assemblages differed. Macroinvertebrate assemblages were similar among riffles within a reach, but were dissimilar at the zone and catchment scales. There also was a regional-scale pattern of macroinvertebrate distribution that was larger than the geomorphologically derived catchment scale. Subsequent partitioning of macroinvertebrate data into regions revealed a relationship between macroinvertebrate distribution and the catchment and zone scales of river system organization. Consideration of the hierarchical organization of river systems from a purely physical perspective may fail to encompass scales relevant to the biota, indicating that biological information should be included as a primary hierarchical component in multiscale stream studies. Key words:

spatial scaling, fluvial geomorphology, ANOSIM, cluster analysis, ecogeomorphology.

Despite the development of hierarchy theory (Allen and Starr 1982, O’Neill et al. 1986) and the adoption of scale as an important ecological paradigm (Wiens 1989, Levin 1992, Peterson and Parker 1998), there is only a handful of studies that explicitly examine the distribution and composition of stream macroinvertebrate assemblages at multiple scales. These studies generally encompass 2 perspectives, and use a range of spatial scales. Studies with a largescale perspective examine the distribution of macroinvertebrate assemblages among and within rivers across the landscape, using comparative scales such as ecoregions, rivers within ecoregions, rivers within catchments, and sites within rivers (Corkum 1991, 1992, Townsend et al. 1997, Sheldon and Walker 1998, Rabeni et al. 1999). In contrast, studies with a small-scale perspective examine the distribution of macroinvertebrate assemblages within a specified length of the same river, using scales such as sites within reaches, riffles within sites, and Present address: Centre for Water in the Environment, School of Animal, Plant and Environmental Sciences, University of the Witwatersrand, Private Bag 3, Wits 2050, South Africa. E-mail: [email protected] 2 To whom correspondence should be addressed. 1

patches within riffles (Downes et al. 1993, 1995, Murphy et al. 1998). A range of distribution patterns has emerged from both types of studies. Corkum (1991, 1992) found that macroinvertebrate assemblages differed significantly among biomes, among rivers within biomes, and among sites within rivers. Rabeni et al. (1999) found that macroinvertebrate assemblages were more similar among reaches from the same river than among reaches from different rivers. At a smaller scale, Downes et al. (1993) found no significant differences in macroinvertebrate species richness among sites within a 1.5-km stretch of river, riffles within a site, or groups of stones within a riffle, but found significant differences in density among riffles within a site and groups of stones within a riffle. These results suggest that characteristic patterns in macroinvertebrate distribution occur at both large and small scales, possibly in response to environmental influences operating at large and small scales (Carter et al. 1996, Allan et al. 1997, Richards et al. 1997, Lammert and Allan 1999). Habitat availability is one mechanism by which macroinvertebrate assemblages may respond to environmental influences at different scales. It has been proposed that habitat provides the templet on which evolution acts to

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forge characteristic life-history strategies (Southwood 1977, Townsend and Hildrew 1994). Accordingly, the environmental properties of any given habitat within a river system will determine the types of macroinvertebrate assemblages found. However, habitats do not occur randomly but, rather, the array of habitats found within a river system is created by predictable geomorphological processes (Brierley et al. 1996, Harper and Everard 1998). These geomorphological processes operate hierarchically, so that larger-scale processes constrain the expression of processes at successively smaller scales (Schumm and Lichty 1965, Schumm 1977, Knighton 1984, de Boer 1992). Consequently, river systems have been divided into discrete scales that capture the relationship between geomorphological processes and different environmental features (e.g., Frissell et al. 1986, Brierley et al. 1996, Thoms 1998). Given that macroinvertebrates may respond to habitat features, and habitat features are, in turn, controlled by geomorphological processes that operate hierarchically across different scales, patterns of macroinvertebrate distribution may correspond to the hierarchical organization of river systems. Our study examined whether there was any congruence between the distribution and composition of macroinvertebrate assemblages and the geomorphological organization of a river system at the catchment, zone, reach, and riffle scales. We used a sequential hypothesis approach (Schumm 1991), where the results of an initial question dictate subsequent questions. First, we asked: At which spatial scale do macroinvertebrate assemblages vary? Second, we asked: Is there another hierarchy that captures the distribution of macroinvertebrate assemblages more clearly than the geomorphologically derived scales? Methods Study area The Upper Murrumbidgee River Catchment covers an area of 13,000 km2 (Fig. 1A). Land use within the study area includes sheep and cattle grazing, forestry, urban settlement, and national parks or reserves managed for conservation or water supply. Most of the national parks are in the western part of the catchment, and grazing

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occurs predominantly in the eastern, northern, and southern parts of the catchment. The climate of the study area is temperate, characterized by cool winters (average daily maximum temperature 5 118C) and warm summers (average daily maximum temperature 5 278C). Rainfall is relatively uniform across the Upper Murrumbidgee River Catchment (average annual rainfall 5 600 mm) and snowfall is common on the western ranges above 1200 m. However, temperature and rainfall are variable over time and severe droughts and floods occur periodically. Selection of scales Spatial scales of measurement were selected to represent a hierarchy of physical and geomorphological influences on river systems. The scales of Frissell et al. (1986) were used as a base, but were supplemented with scales from other hierarchical river characterization schemes (Brierley et al. 1996, Thoms 1998). Catchment scale. Catchments were the largest scale used in the hierarchies of Frissell et al. (1986), Thoms (1998), and Brierley et al. (1996), and represent the constraining influences of geology and climate. Thus, catchments were chosen as the largest scale, being defined here as topographic subdivisions within the Upper Murrumbidgee River Catchment. Zone scale. Valley confinement was chosen as the second largest scale, representing the indirect influence of valleys on instream fluvial characteristics such as sediment transport (Thoms et al. 2000), gross habitat availability (Montgomery and Buffington 1997, Cohen et al. 1998), and hydrological regime (Whiting and Bradley 1993). This scale was called the zone scale, being defined here as the length of river between breaks in channel confinement, based on valley shape. Catchment characteristics such as geology, topography, and climate determine the juxtaposition of different types of valleys within catchments (Church 1992) and, thus, the zone scale is constrained by characteristics of the catchment scale. Reach scale. Reaches generally describe river planform (Thoms 1998, Brierley et al. 1996) which, in turn, is set by discharge (Brierley et al. 1996), sediment transport mode (Montgomery and Buffington 1997), and morphological channel dimensions (Brierley et al. 1996). Reach-

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es were defined here as the length of river between 2 tributaries of the same stream order or higher, and primarily represent the influence of discharge. We assumed that the influence of channel morphology and sediment transport mode also would be encompassed in this definition because discharge directly affects both factors. Zone characteristics such as sediment transport, gross habitat availability, and hydrological regime are similar to those of the reach scale; however, zone characteristics operate at a larger scale by constraining discharge character, sediment transport mode, and morphological channel dimensions of reaches. Riffle scale. Morphological habitat units were chosen as the smallest scale, to represent hydraulic character and substratum composition (Brussock et al. 1985). However, unique macroinvertebrate assemblages are often associated with morphological habitat units such as pools and riffles (Parsons and Norris 1996). Biological sampling was limited to one morphological habitat unit to eliminate the confounding influence of habitat type when comparing assemblages across scales. Riffles were chosen because they generally contain high densities of macroinvertebrates (Grubaugh et al. 1996). The riffle scale was defined as the individual riffles at the top and bottom ends of 2 riffle–pool sequences. Riffles are connected along riffle–pool sequences according to local-scale hydraulic and substrate characteristics (Knighton 1984). Hierarchically, these local-scale characteristics are constrained by reach or zone-scale characteristics such as discharge and sediment transport. Division of the study area into scales. Topographic maps (1:50,000) were used to divide the study area into catchment, zone, and reach scales. Eight catchments were excluded because they were affected by catchment-wide impacts such as agriculture, mine-waste pollution, or urbanization, or by reduced flows from drought (Fig. 1B). Zones of different valley confinement were delineated along the length of each major river within the remaining 6 catchments, using the topographic contour method of Bisson and Montgomery (1996). Three zone types were applied: broad, confined, and unconfined. Broad zones were river sections with a relatively wide floodplain and confined zones were river sections within steep-sided valleys with no floodplain development. Unconfined zones were stream sections that were intermediate between

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broad and confined zones, and had either an intermediate valley width or a variable degree of confinement on different sides of the channel. Reaches were delineated by determining stream order along the length of each river. A new reach was designated where there was a change in stream order. An exception was made for highorder river sections, where a new reach was designated at the confluence of a major tributary (e.g., at the confluence of a 6th and 7th order river). Riffles were designated in the field. Site selection A balanced, hierarchically nested design was used: catchments (6 catchments), zones within catchments (6 catchments 3 3 zone types), reaches within zones (6 catchments 3 3 zone types 3 2 reaches), and riffles within reaches (6 catchments 3 3 zone types 3 2 reaches 3 2 riffles). To provide replication at the riffle scale, 2 macroinvertebrate samples were collected from each riffle (see below). Potential sample reaches were identified by examining data collected in previous studies, or by considering the influence of landuse practices operating in the catchment. Minimally disturbed reaches were then fitted to the nested design. Where possible, sample reaches were selected to represent different rivers within the catchment, or different stream orders on the same river. Reaches were sampled once between March and May 1997. On completion of sample processing (see below) the biological condition of each reach was assessed using AUSRIVAS (Simpson and Norris 2000). All reaches included in the final data set were confirmed as having a biological composition equivalent to the reference condition. Macroinvertebrate collection and processing Macroinvertebrate samples were collected along a 10-m transect, using a hand-held 500mm-mesh triangular net. Replicate kicknet samples were taken side by side but where the riffle was narrow, replicates were taken from the upstream and downstream sections of the riffle. Samples were preserved in the field using 5% formalin. In the laboratory, each sample was evenly distributed in a subsampling box with 100 cells (Marchant 1989). The contents of individual

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FIG. 1. Major rivers, catchments, and locations of sampling reaches within the Upper Murrumbidgee River Catchment study area. A.—Major rivers for which zones and reaches were delineated. Shading indicates the extent of the Canberra and Queanbeyan urban areas. B.—The 14 catchments available within the study area. Symbols indicate the spatial layout of sample reaches within broad, confined, and unconfined zones of the 6 catchments that were ultimately used in the nested design.

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cells were extracted randomly using a vacuum pump. Cell contents were examined using a stereomicroscope until 200 individuals had been counted, and all specimens from the last cell were included in the count. The proportion of

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sample searched to attain 200 individuals ranged from 3 to 60%. A 15-min visual search of the residual sample portion was conducted immediately after each 200 animal subsample had been examined. The residual sample was

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spread over a large white tray and a magnifying lamp was used to aid searching. Eight minutes were spent removing large taxa and the remaining 7 min were spent searching for cryptic specimens and taxa not collected in the 200 animal subsample. Macroinvertebrates were identified to the lowest possible taxonomic level, usually genus or species, using the keys listed in Hawking and Smith (1997). Data analysis Macroinvertebrate data were converted to presence/absence because the focus was on the occurrence, rather than the relative abundance of taxa. Rare taxa (defined as taxa occurring in ,10% of all samples) were included in the analysis because we wanted to incorporate the influence of a comprehensive array of taxa. However, deletion of the rarest species has no effect on the detection of distribution patterns (Cao et al. 1998), so taxa that occurred in only one sample were deleted. Overall, 166 taxa were collected but 35 of these were deleted. The spread of the remaining 131 taxa across classes and orders was: Ephemeroptera (10), Plecoptera (13), Trichoptera (42), Coleoptera (24), Diptera (13), Odonata (9), Gastropoda (8), and others such as Oligochaeta, Decapoda, and Bivalvia (12). Macroinvertebrate distribution at the catchment, zone, reach, and riffle scales. Macroinvertebrate assemblages were examined at different scales using Analysis of Similarity (ANOSIM, Clarke and Green 1988). ANOSIM computes a test statistic (F) that is the ratio of average betweengroup similarity and average within-group similarity. The F-statistic was calculated using the Czekanowski similarity measure (equivalent to the Bray–Curtis similarity measure on abundance data) because it was not desirable to include joint absences. A Czekanowski similarity of 0 indicates that taxon composition is identical between 2 samples; thus, lower similarity values indicate greater assemblage similarity. ANOSIM was conducted separately for the catchment, zone, reach, and riffle scales, which consisted of 6, 18, 36, and 72 groups, respectively (see nested design explanation above). F-statistics increase as average within-group Czekanowski similarity values decrease. Thus, higher F-statistics indicate that samples from one group are, on average, more similar to each other than they are to samples from a different group. A permuta-

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tion test also was performed. Significant statistics indicate that 100 random re-allocations of samples to groups does not produce an F-statistic that is higher than that obtained for the a priori allocation. Significant F-statistics do not indicate significant differences among groups in the same manner as univariate tests. Rather, significant F-statistics indicate that the allocation of samples among scale groups is optimal. The relative contribution to overall similarity made by each component group is not evident from ANOSIM because the F-statistic is calculated from mean similarities. Thus, mean similarity dendrograms (Van Sickle 1997) were used to evaluate the relative similarity of component groups at each scale. Each dendrogram is partitioned into 3 sources of similarity: average similarity between an individual group and the other groups in the same scale, overall average similarity between all groups in the same scale, and average similarity within an individual group. Differences in log-transformed taxon richness among scales were examined using nested analysis of variance. The balanced hierarchical study design allowed unbiased estimation of the portion of overall variation in taxon richness that was contributed by each scale (Underwood 1997). Alternative scales of macroinvertebrate distribution. The patterns and processes observed in any ecological study are influenced by the selected scales of measurement (Sale 1998). Thus, it is possible to miss patterns that occur at scales that were not considered by the observer. The selection of scales of measurement from a geomorphological hierarchy assisted in counteracting the effects of observer bias; however, it is conceivable that other patterns in macroinvertebrate distribution occurred outside of these pre-imposed geomorphological boundaries. Introduction of observer bias into the selection of scales of measurement can be avoided by allowing levels of organization to self-emerge from ecological data (O’Neill et al. 1986, O’Neill and King 1998), so cluster analysis was used to determine whether hierarchical spatial patterns self-emerged from the macroinvertebrate data. Macroinvertebrate samples were classified using the flexible-Unweighted Pair-Groups using Arithmetic Averages (UPGMA) fusion strategy recommended by Belbin and McDonald (1993). Samples with similar assemblages were selected

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FIG. 2. Analysis of Similarity at the nested catchment, zone, reach, and riffle scales. The number listed next to each filled circle is the exact F-statistic for that scale. ** indicates significance # 0.01 in the permutation test.

by viewing a dendrogram representation of the classification. The new regional-scale distribution of macroinvertebrate assemblages was determined by overlaying all samples, based on the dendrogram groups, onto a map of the Upper Murrumbidgee River Catchment. Regions of similar assemblages identified from this map were considered to be separate data sets and were reclassified. The new cluster-scale distribution of macroinvertebrate assemblages was determined by identifying clusters from each regional dendrogram. Congruence between the original geomorphological scales and the biologically derived cluster scale then was examined by tallying the occurrence of samples from different catchments and zones within each cluster. Only the larger catchment and zone scales were considered in the tally because the lowest similarity of macroinvertebrate assemblages occurred at these scales.

Results At which spatial scale do macroinvertebrate assemblages vary? The catchment-scale F-statistic was close to 1, indicating that there was as much similarity among samples from different catchments as there was among samples from the same catchment (Fig. 2). The F-statistic was slightly larger at the zone scale (Fig. 2), but macroinvertebrate assemblages were generally not similar within zone groups. The main split in the continuum occurred at the reach scale, where there was more similarity among samples from the same reach than there was among samples from different reaches (Fig. 2). Sample similarity was even more pronounced at the riffle scale (Fig. 2). All F-statistics were significant, indicating that the allocation of samples to groups was optimal.

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FIG. 3. Catchment- (A), zone- (B), reach- (C), and riffle-scale (D) similarity dendrograms. Filled circles indicate the average Czekanowski similarity between an individual group and each other group in the same scale. The arms indicate individual within-group similarity. The dashed vertical line is the average similarity between all groups in the same scale. Lower Czekanowski values indicate greater assemblage similarity. For ease of presentation, the riffle-scale dendrogram is divided into 2 parts: the Murrumbidgee (M), Cotter (C), and Paddys (P) catchments in Part 1 and the Naas (N), Goodradigbee (G), and Queanbeyan (Q) catchments in Part 2. Group labels correspond to nested arrangements of catchments, zones within catchments, reaches within zones, and riffles within reaches. Catchments are described above, zones are broad (B), confined (C), and unconfined (U), reaches are X and Y, and riffles are the upstream (1) and the downstream (2) ends of 2 riffle– pool sequences. For example, in the riffle-scale dendrogram, MBX1 is Murrumbidgee Catchment, broad zone, reach X, riffle 1.

The similarity dendrograms were consistent with the results of ANOSIM, showing greater assemblage similarity at smaller scales (Fig. 3). For example, samples from within the Murrumbidgee Catchment were not similar (withingroup arms, Fig. 3A). Breaking the Murrumbidgee Catchment into zones increased withingroup similarity, particularly at the broad and confined zones (Fig. 3B). However, withingroup similarity increased markedly and became relatively even among component riffles and kicknet samples at the reach (Fig. 3C) and riffle (Fig. 3D) scales. Thus, across a continuum of scales, macroinvertebrate assemblages were

consistently similar within reaches and riffles and dissimilar within catchments and zones. The overall average between-group similarity did not change with scale (dashed vertical lines, Fig. 3) because it was calculated using the same samples in different combinations. Likewise, individual between-group similarities varied consistently with scale because each was an average of similarities between smaller-scale component groups (filled circles, Fig. 3). However, examination of individual between-group similarities can highlight specific instances of high or low assemblage similarity at each scale. At the catchment scale, the Murrumbidgee catchment was

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explained by differences in taxon richness among reaches within zones, and a further 40% was explained by differences in taxon richness among zones within catchments (19%) and in the error term (21%) (Table 1). Is there another hierarchy that captures the distribution of macroinvertebrate assemblages more clearly than the geomorphologically derived scales?

FIG. 3.

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the least similar to the other catchments (filled circles, Fig. 3A). This trend was carried through the other scales, where the Murrumbidgee zone (Fig. 3B), reach (Fig. 3C), and riffle (Fig. 3D) groups generally showed the lowest similarity to other groups in the same scale. Conversely, the Goodradigbee catchment generally showed the highest similarity to other groups in the same scale (Fig. 3). In the Naas catchment, between-group similarity was relatively even across all scales (Fig. 3) and between-group similarity for the Cotter, Paddys, and Queanbeyan catchments showed no clear trend by scale (Fig. 3). Thus, similarity of assemblages between component groups in each scale was variable. There was a highly significant difference in taxon richness among reaches within zones, and a significant difference in taxon richness among riffles within reaches (Table 1). No significant differences were found at the catchment or zone scales (Table 1). Half of the total variation was

Regional-scale patterns in macroinvertebrate distribution. Examination of the spread of samples across the Upper Murrumbidgee River Catchment revealed 3 distinct groups that represented a regional-scale pattern in macroinvertebrate distribution (Fig. 4). The dendrogram is not shown, but these regional groups corresponded to dendrogram groups 2 and 5, 1 and 3, and 4, and will henceforth be called Region 1, Region 2, and Region 3, respectively. Most samples from Region 1 were located in the Cotter, Paddys, and Goodradigbee catchments and all samples from Region 2 were located in the Naas, Queanbeyan, and Murrumbidgee catchments (Fig. 4). Samples from Region 3 were located in the headwater sections of the Murrumbidgee, Cotter, and Goodradigbee catchments. Cluster-scale patterns of macroinvertebrate distribution. Within each region, dendrogram clusters with similar macroinvertebrate assemblages corresponded with the original catchment and zone scales. In Region 1, Cluster A contained samples from unconfined zones within the Cotter and Goodradigbee catchments, and Cluster B was dominated by samples from unconfined zones within the Paddys and Naas catchments (Fig. 5). Cluster C was comprised entirely of samples from the Cotter catchment, and Cluster D was dominated by samples from confined zones within the Paddys and Goodradigbee catchments. Thus, clusters representing the macroinvertebrate distribution in Region 1 were related to zones. In Region 2, Cluster F was dominated by samples from the Naas catchment (Fig. 5). Clusters E and G consisted of equal numbers of samples from the Murrumbidgee and Queanbeyan catchments, and contained a mixture of all 3 zone types. Thus, clusters representing macroinvertebrate distribution in Region 2 were related to catchments. Patterns in Region 3 were difficult to determine because of the smaller num-

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FIG. 3.

ber of samples in this group. However, Clusters H, I, and J appeared to be more related to catchments than zones (Fig. 5). Discussion At which spatial scale do macroinvertebrate assemblages vary? Previous studies used a limited range of spatial scales to examine multiscale patterns of macroinvertebrate distribution. The present study demonstrated, using a continuum of nested scales ranging from catchment to riffle, greatest variation in macroinvertebrate assemblages at the reach scale. There are 2 explanations for this reach-scale variation: the homogeneity of geomorphologically derived scale units, and the spatial proximity of reaches. Homogeneity of geomorphological scale units.

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The selection of scales of measurement from a geomorphological hierarchy is based on the premise that a certain amount of physical homogeneity is contained within each scale (Schumm 1977, Frissell et al. 1986). Macroinvertebrate distribution patterns may be congruent with geomorphological scales because there is often a deterministic relationship between stream biota and the physical features of river systems (Giller and Malmqvist 1998). Similarity of macroinvertebrate assemblages within reaches suggests that the reach scale is the point in the geomorphological hierarchy where physical habitat becomes homogeneous, relative to larger scales. In contrast, dissimilarity of macroinvertebrate assemblages at the zone and catchment scales suggests that physical habitat is relatively heterogeneous at these scales, relative to smaller scales. Thus, congruence between the distribution and composition of macroinvertebrate as-

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TABLE 1. Nested analysis of variance on taxon richness. Taxon richness is defined as the number of taxa per combined 200 animal subsample and residual search portion (see Methods).

Source of variation

df

MS

F-ratio

p

Total Catchment Zone (catchment) Reach (zone) Riffle (reach) Error

143 5 12 18 36 72

39.77 187.26 157.75 95.51 14.42 8.60

1.187 1.652 6.625 1.677

.0.30 .0.10 ,0.001 ,0.05

semblages and the geomorphological organization of a river system occurs only at smaller scales, where geomorphology acts to form physical and habitat features that are relatively homogeneous. Macroinvertebrate assemblages collected from the 2 riffles within a reach were consistently similar. This similarity suggests that riffles located within the same reach provide a uniform environment for macroinvertebrates and that macroinvertebrate assemblages do not respond to differences in hydraulic and substratum character among riffles located in adjacent riffle–pool sequences. A similar result was reported by Rabeni et al. (1999), who found high metric reproducibility and assemblage similarity among duplicate samples collected within a morphologically homogeneous reach. However, other studies have reported differences in macroinvertebrate assemblages among adjacent riffles (Barmuta 1989, Downes et al. 1995, Mermillod-Blondin et al. 2000) or among patches within a riffle (Downes et al. 1993). In our study, some differences in assemblage similarity did occur at the riffle scale. For example, there was sometimes a large difference in between-group similarity among the 2 riffles within a reach (filled circles, Fig. 3D) and there was a significant difference in taxon richness among riffles within reaches (Table 1). Thus, macroinvertebrate assemblages can vary among riffles within a reach, but this variation has less influence when considered within a continuum of multiple scales. Macroinvertebrate assemblages were generally dissimilar among zones. Broad, confined, and unconfined zone types can be delineated easily off maps and are geomorphologically meaningful; however, macroinvertebrate assem-

Estimation of variance (%) 3 19 50 7 21

blages were not related to the physical character of each zone type. Few studies have investigated the influence of valley morphology on the distribution of aquatic biota. An exception is Maridet et al. (1998) who examined macroinvertebrate distribution in relation to valley morphology and riparian vegetation and found that valley shape explained only minor differences in benthic assemblages. Conversely, Brussock et al. (1985) asserted that channel form, which is influenced by valley shape, could influence biological assemblages. In our study, the lack of zonescale patterns of macroinvertebrate distribution suggests that assemblages were not influenced by differences in sediment transport character, mesohabitat expression, or hydrologic regime characteristic of each zone type. Rather, the consistent importance of the reach scale suggests that the geomorphological factors to which macroinvertebrates respond operate at scales smaller than the zone. Our study demonstrated that 6 moderately sized catchments within a larger area of 13,000 km2 had consistently low between- and withincatchment assemblage similarity. Recent examinations of the congruence between faunal distribution and landscape-based classifications such as ecoregions and catchments reported mixed results. For example, Hawkins and Vinson (2000), McCormick et al. (2000), Marchant et al. (2000), and Waite et al. (2000) found that faunal congruence was greater among catchments than among ecoregions, whereas Feminella (2000), Oswood et al. (2000), and Van Sickle and Hughes (2000) found that faunal congruence among ecoregions was greater than or equal to that obtained for catchments. Ecoregions and catchments are both delineated using large-scale factors that sit at the top of the geo-

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FIG. 4. Regional distribution of macroinvertebrate samples by dendrogram groups. The dashed lines represent the regional separation of samples by dendrogram groups. Circled samples (numbered as 6) indicate a small dendrogram group that contained ,5 samples; this group was not considered further. Site details are given in Fig. 1.

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morphological hierarchy. Thus, it is relatively easy to derive meaningful landscape-based units that represent the broad-scale similarities in climate, geology, vegetation, soils, and basin physiography to which biota may respond (Bailey 1983). However, the lack of congruence between catchments and faunal distribution reported in this study suggests that macroinvertebrates did not respond to the homogeneity of physical conditions provided within catchments. Rather, macroinvertebrates appeared to respond to the homogeneity at the reach scale. This result supports the assertion of Hawkins et al. (2000) that, relative to the landscape scale, most variation in macroinvertebrate assemblages can be explained by local-scale factors. However, care must be taken with the application of this assertion because the success of the abovementioned studies, and others (e.g., Whittier et al. 1988, Harding et al. 1997), in partitioning biotic variation by ecoregions demonstrates that biota can respond strongly to homogeneity of physical character at large scales. Spatial proximity of reaches. An alternative explanation for the observed reach-scale variation in macroinvertebrate assemblages is the spatial proximity of samples. According to the study design, sample sites are equivalent to sample reaches and, as such, the reach is the scale at which macroinvertebrate samples become spatially distinct across the landscape. Macroinvertebrate assemblages may be similar within a reach because riffle samples were located close to each other, whereas macroinvertebrate assemblages may be dissimilar between reaches, zones, and catchments because samples were located further apart. Spatial autocorrelation is a well-known ecological phenomenon (Bell et al. 1993, Cooper et al. 1997) and these types of proximity effects were commonly reported in the recent series of papers examining congruence between faunal distribution and landscapebased classifications (Hawkins and Norris 2000). For example, Van Sickle and Hughes (2000) showed that a classification based on the proximity of sites was stronger than any landscapebased classification, and McCormick et al. (2000) showed that similarity among sites decreased as a function of the distance between sites. Hawkins and Vinson (2000) concluded that spatial proximity was important because the spatially noncontiguous life-zone classification was weaker than the contiguous ecoregion or catch-

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ment classifications, and Oswood et al. (2000) also found the highest similarities of fish faunas among adjacent catchments. Our study showed that, across a continuum of large to small scales, the effect of spatial proximity on assemblage similarity was most pronounced at the reach scale, where samples became distinct across the landscape. Is there another hierarchy that captures the distribution of macroinvertebrate assemblages more clearly than the geomorphologically derived scales? Examination of the spread of samples across the Upper Murrumbidgee River Catchment identified 3 regional groups with similar macroinvertebrate assemblages, and clusters within these regional groups subsequently revealed a relationship with the catchments and zones derived from the geomorphological hierarchy. An alternative hierarchy consisting of a combination of scales that self-emerge from the biological data (region and cluster) and of scales derived from the geomorphological hierarchy (reach and riffle) was more suitable for capturing patterns in macroinvertebrate distribution. We conclude that multiscale investigations of macroinvertebrate distribution should consider biological patterns as a primary hierarchical component. Regional-scale patterns in macroinvertebrate distribution. The regional-scale pattern in macroinvertebrate distribution in the Upper Murrumbidgee River Catchment crossed topographical catchment boundaries and was bigger than the largest geomorphologically derived scale of measurement. The search for biologically based patterns was undertaken because of concern that the geomorphological scales had missed important patterns of macroinvertebrate distribution (see Methods). Thus, the regional-scale pattern of macroinvertebrate distribution would not have been discovered without exploration of biotic patterns that occur independently of any a priori imposition of geomorphological boundaries. There are 3 factors that may explain the regional-scale pattern in macroinvertebrate distribution. First, distributions may be influenced by the relationship between macroinvertebrates and characteristic environmental conditions occurring within each region. For example, Region 3 contained samples collected from the headwater sections of several catchments and had a

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physical environment characterized by high bankfull velocity and substratum instability (Parsons 2001). Second, land use may play a role in determining regional-scale macroinvertebrate distribution because there was a higher incidence of agricultural activity in Region 2, compared to Regions 1 and 3 (Parsons 2001). Third, the regional-scale pattern may be related to biogeographical factors such as evolutionary history and dispersal ability of individual taxa, and it is possible that the 3 observed regions represent the junction of 3 larger zoogeographical distributions that extend outside the study area. Cluster-scale patterns in macroinvertebrate distribution. Patterns in macroinvertebrate distribution at the geomorphologically derived catchment and zone scales were embedded within the region scale. The broad relationships between biological clusters and the geomorphologically derived catchments and zones suggest that macroinvertebrates displayed a deterministic response to the physical character of catchments and zones. For example, even though samples were spread over the Paddys and Goodradigbee catchments, Cluster D within Region 1 was dominated by samples from the confined zone type (Fig. 5). This finding indicates that environmental conditions associated with confined sections of the river, such as gross habitat availability and sediment transport, may be influencing this particular macroinvertebrate assemblage. The occurrence of catchment- and zone-related influences varied among each cluster, further suggesting that the environmental conditions determining macroinvertebrate assemblages may operate across both of these scales in tandem, in accordance with principles of constraint within a geomorphological hierarchy (Schumm and Lichty 1965). However, it is also possible that the relationship between biological clusters and catchments and zones may be an artefact of the chance occurrence of taxa at certain sites that, in turn, has driven the derivation of clusters independently of catchment or zone influence. Nonetheless, the discovery of these catchment- and zone-scale patterns nested

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within a larger regional-scale highlights the importance of the biota as a scaled entity. Usefulness of geomorphological scales in studies of macroinvertebrate distribution The use of scales of measurement derived from a geomorphological hierarchy is a new approach to the examination of multiscale patterns in macroinvertebrate distribution (Parsons 2001). The hierarchical relationships between physical processes that operate at different scales within a river system are well-studied, and have been a fundamental tenet of fluvial geomorphology for decades (Schumm 1977, Knighton 1984). A geomorphological hierarchy is relatively easy to conceptualize because river systems are constrained spatially and temporally, and each successive tier of the hierarchy is nested neatly within each larger tier (e.g., Frissell et al. 1986, Brierley and Fryirs 2000). The geomorphological properties of a river are interactive and dynamic (Montgomery 1999) but, importantly, processes that constrain the expression of other processes at sequential levels of the geomorphological hierarchy predominantly belong to the inorganic physical domain. Consideration of biological pattern within a framework of geomorphology requires the introduction of an additional domain: the organic biological domain. There is often a relationship between macroinvertebrates and environmental factors and, thus, it is valid to hypothesize that biological patterns may match physical patterns of river-system organization. However, the results of our study suggest that overlaying the biological domain onto the physical domain is not a straightforward task. The lack of congruence between macroinvertebrate distribution and the larger scales of the geomorphological hierarchy, and subsequent identification of the biologically based region and cluster scales indicate that hierarchical interactions between the physical and the biological domain are complex. Factors associated with the biological domain that may influence macroinvertebrate distribu-

← FIG. 5. Macroinvertebrate clusters (A–J) within Regions 1, 2, and 3. Czekanowski similarity is shown at each major cluster split because the dendrograms are not drawn to scale. The catchment and zone tally refers to the number of samples within each cluster that belonged to each original catchment and zone type.

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tion include differences in the dispersal capabilities of individual taxa, biotic interactions such as competition and predation, chance resettlements of drifting taxa, and the influence of highmagnitude, infrequent disturbance events such as large floods or prolonged droughts. In conclusion, the disciplines of stream ecology and fluvial geomorphology are intricately related because the physical dimension of a river provides the templet upon which biological processes occur. However, these disciplines have evolved separately with limited interaction of theory or practice. Although focused predominantly in the arena of stream ecology, our study has taken some basic principles of fluvial geomorphology and incorporated them into the design of a stream ecology study. The results indicate that although the marriage of ecology and geomorphology is not straightforward, concepts of fluvial geomorphology have the potential to provide fresh insight into many aspects of stream ecology. Likewise, concepts of stream ecology may enhance geomorphological understanding of river systems. Given the relatively advanced state of knowledge that exists individually in stream ecology and fluvial geomorphology, integration and application of this knowledge across the disciplines represents an exciting future opportunity in aquatic science. Acknowledgements This work was funded by a CRCFE Postgraduate Scholarship and an Australian Postgraduate Award to MP. MP thanks Melanie Stutsel, Cathy Francis, Kylie Peterson, Matt Hensalite, Chris Williams, Louisa Oswald, Lorne Doig, Ken Thomas, and Elizabeth Kimberly for assistance in the field. MP also thanks the many students and staff of the CRCFE and the University of Canberra for assistance with data collection, taxonomic, administrative, statistical, and information technology aspects of the project. MP is also grateful to Michele Light, Sue Moir, Jacqui and Craig Jones, Bronwyn Smith, Lisa Evans, Patrick Driver, Kylie Peterson, Cathy Francis, Pam Gray, Richard Norris, and Martin Thoms for providing unending support and encouragement throughout her PhD project. Literature Cited ALLAN, J. D., D. L. ERICKSON, AND J. FAY. 1997. The influence of catchment land use on stream integ-

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