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Geomorphology 89 (2007) 127 – 146 www.elsevier.com/locate/geomorph

Hierarchical patterns of physical–biological associations in river ecosystems Melissa Parsons a,⁎, Martin C. Thoms b a

Centre for Water in the Environment, University of the Witwatersrand, Private Bag 3, WITS 2050, Johannesburg, South Africa b Riverine Landscapes Laboratory, University of Canberra, ACT 2601, Australia Received 1 July 2005; received in revised form 27 July 2006; accepted 27 July 2006 Available online 22 September 2006

Abstract The interplay of biological and physical patterns and processes within river ecosystems generates a complex matrix of interactions. A challenge in interdisciplinary river science is to dissect patterns and processes in multi-causal river ecosystems into hierarchical levels of organization. Hierarchy theory, and the associated concept of scale, provides a sound framework for achieving this. We present two interdisciplinary case studies that demonstrate how a multi-scale approach can dissect hierarchies of organization in river ecosystems. The first case study examined patterns of large wood character and distribution at three scales of a hierarchy of morphological river system organization in the large, lowland River Murray. The character and distribution of large wood was uniform at the largest reach scale (95 km length of river) because stream energy conditions are relatively uniform within the reach. However, there was an association between lower-level functional sets (straight or bend sections of river) and functional units (12 quadrats within each functional set) and the character and distribution of large wood, because stream energy differs between straight and bend morphologies, and the inner- and outer-channel functional units. Thus, functional sets and functional units are important levels of organization for large wood in the River Murray. The second case study examined the associations between macroinvertebrate assemblage distribution and environmental influences across a hierarchy of river system organization in the upland Murrumbidgee River catchment. We previously demonstrated that macroinvertebrate assemblages were arranged hierarchically at the region, cluster within region, reach within cluster and riffle within reach scales, with region and reach being the strongest signatures. In this study we related different scaled environmental factors, collected across a hierarchy of catchment, zone (valley confinement), reach (similar stream orders) and riffle scales to the region and cluster levels of macroinvertebrate distribution. The hierarchical pattern of large, region-level and local, reach-level macroinvertebrate distribution was matched by a large catchment-scale and local reach-scale of environmental influence. Intermediate zone-scale environmental factors and smaller riffle-scale factors were not important influences. Thus, large regions and catchments and local reaches are important levels of organization for macroinvertebrate-environment associations in rivers of the upper Murrumbidgee catchment. Both case studies support the applicability of hierarchy theory to describe the organization of physical–biological associations in river ecosystems. The multi-scaled approach allowed the detection of levels of hierarchical organization, and showed other hierarchical characteristics such as emergent properties and top–down constraint/bottom– up influence. Hierarchical understanding of river ecosystem organization will enhance river conservation and management because it facilitates a holistic, ecosystem perspective rather than a partial, single-scale, single-component or single-discipline perspective. © 2006 Elsevier B.V. All rights reserved. Keywords: Scale; Hierarchy theory; Large wood; Channel morphology; Macroinvertebrates

⁎ Corresponding author. Tel.: +27 11 717 6430; fax: +27 11 717 6499. E-mail addresses: [email protected] (M. Parsons), [email protected] (M.C. Thoms). 0169-555X/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.geomorph.2006.07.016

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M. Parsons, M.C. Thoms / Geomorphology 89 (2007) 127–146

1. Introduction Definitions of the term ecosystem emphasize the interaction between biotic and abiotic factors. Tansley (1935) defined an ecosystem as an interacting system of biological organisms and the abiotic physical environment. Although views of ecosystems have since expanded to include concepts of stability and equilibrium (Odum, 1971), panarchy (Holling, 2001), hierarchical organisation (O'Neill et al., 1986; Holling, 1992) and scale (Levin, 1992), the focus on biological– physical interactions remains a fundamental principle of contemporary ecosystem science (Chapin et al., 2002; Pickett and Cadenasso, 2002). River ecosystems can be viewed as interacting systems of biological and physical components. Physical habitat provides the templet upon which evolution acts to forge characteristic life history strategies (Southwood, 1977). Accordingly, the physical properties of any given habitat within a river ecosystem will influence the type, abundance and arrangement of biological assemblages found there. Interactions between the biological and physical components of a river ecosystem generate pattern, and it is the goal of ecology and geomorphology to decipher the causal mechanisms, or processes, underlying observed patterns (Levin, 1992; Fisher, 1994; Schumm, 2005). However, the interaction between pattern and process is not uni-dimensional but, rather, occurs hierarchically across multiple scales (Wiens, 1989; Levin, 1992; Peterson and Parker, 1998). A pattern at one scale may be generated by processes operating at different hierarchical levels. Similarly, a process may be influenced by patterns occurring at multiple scales. The interplay of pattern and process within an ecosystem generates a complex matrix of interactions. A challenge in river science is to dissect the patterns and processes in hierarchical, multi-causal ecosystems into spatial and temporal domains of influence. Hierarchy theory provides a sound conceptual framework for dissecting spatial and temporal domains of influence in river ecosystems (O'Neill, 1989). As has been described in Parsons et al. (2004), a hierarchical system can be viewed as a series of organisational levels, or holons, that are constrained within a nested vertical structure (O'Neill et al., 1986). Each holon is defined by a boundary that encloses its components, but any holon higher up in the hierarchy exerts some constraint on all lower holons with which it communicates (Allen and Starr, 1982; O'Neill, 1988). The boundary of a holon is generally identified on the basis of functional process rates (O'Neill and King, 1998) or

structural spatial criteria (O'Neill et al., 1986; Bergkamp, 1995). Three main properties govern the exchange of information between holons within a hierarchy (Bergkamp, 1995). First, specific levels of organisation are linked to specific spatial and temporal scales, where higher levels correspond to larger spatial scales and longer temporal scales, and lower levels correspond to smaller spatial scales and shorter temporal scales. Second, rate differences of at least one order of magnitude exist between different levels, so that higher levels have lower frequencies of behaviour than lower levels. Third, higher levels constrain lower levels because of these differences in frequencies of behaviour. Thus, an ecological hierarchy is considered to be nearly decomposable because each level of organisation responds at a characteristic spatial and temporal scale (Bergkamp, 1995). Holons within a nested hierarchy have a dual nature in that they are simultaneously a whole and a part of another whole (King, 1997). A holon at one level is composed of component lower level holons and is also a component of a higher level holon. This nested relationship between holons creates emergent properties. Emergent properties are the properties of higher level holons that can not be deduced from the functioning of their parts (Allen and Starr, 1982; Bergkamp, 1995) and arise because only the averaged, filtered or smoothed properties of a lower holon provide input to higher levels of the hierarchy (O'Neill et al., 1986; Meentemeyer and Box, 1987). Mechanistic explanation of the dynamics of a holon at one level within a nested structure is found by examining constituent lower level holons, while constraints on the dynamics of that holon are found by examining the holon at the next higher level (Ahl and Allen, 1996; King, 1997). However, an increase in the number of intervening levels separating 2 holons of interest is accompanied by a corresponding decrease in the recognisable influence of a lower level holon on a higher level holon and vice versa (O'Neill et al., 1986), because of the effects of emergent properties. Hence, distant holons within a hierarchy have little direct influence on each other but are linked via intervening levels. The scales at which we observe an ecosystem are the windows through which we look at the levels within a hierarchy of organization (Ahl and Allen, 1996). As such, scales and levels are not the same. Levels are the ‘levels of organization’ within a hierarchy of organization and are quantified by a rank ordering relative to other levels (King, 2005). Scales are the physical spatial and temporal dimensions of an object or event and involve quantifiable units of measure (King, 2005).


When we observe part of a river ecosystem at a certain scale we detect patterns or processes that sit at a certain level within a hierarchy of organization. However, the patterns and processes observed through our scale window are linked within a hierarchy of organization. At any one scale a particular observation will be a summary of lower level influences, but at the same time will be constrained by higher-level factors. If we view the ecosystem at only one scale, we may fail to detect the bottom–up influences and top–down constraints that link to the focal level of observation and hence, we may make spurious inferences about the ecosystem (Allen et al., 1987). A multi-scale approach, in which we look through several windows simultaneously, can help to capture and dissect the hierarchical levels of patterns and processes in river ecosystems. Viewing patterns and processes in a hierarchical manner becomes even more critical in an interdisciplinary setting because river scientists generally impose scales of observation commensurate with their disciplinary experience of the system in question (Allen et al., 1987; Thoms and Parsons, 2002; Dollar et al., 2007this issue). Each discipline views patterns and processes according to the paradigms and theories that constitute the accepted wisdom of that discipline. Sometimes the paradigms and theories may be hierarchical within an individual discipline. For example, fluvial geomorphologists often take a hierarchical view of phenomena such as river channel formation (Couper, 2004), and landscape ecologists have long acknowledged the hierarchical, multi-scaled nature of reciprocal pattern– process relationships (Turner et al., 2001). Rarely, though, is a hierarchical paradigm applied in an interdisciplinary context. Patterns and processes generated through physical–biological interactions will interact across hierarchical levels to produce imprints at multiple scales within a hierarchy of organisation. Mismatches of scales of observation between disciplines may fail to recognise important junctures of pattern and process in hierarchical systems (Dollar et al., 2007-this issue). Hierarchy theory provides a sound framework for integrating pattern and process across disciplines. This paper uses two case studies to demonstrate how a multi-scale approach can dissect hierarchies of organization in river ecosystems. The first case study examines hierarchical patterns of large wood distribution in relation to the morphology of a large, lowland river. The second case study examines the associations between macroinvertebrate assemblage distribution and environmental influences across a hierarchy of river system organisation. Both case studies examine a biological component (large wood, macroinvertebrate

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distribution) in relation to a physical component (river morphology, environmental influences respectively). In searching for levels of organization we need to choose the scales of observation that we think might best reveal hierarchical levels. Previously we advocated that the use of scales of measurement derived from a process-based hierarchy, rather than from size-based units, would increase the likelihood of detecting holons of organization (Parsons et al., 2004). We took this process-based approach to selecting our scales of observation in both case studies. The case studies are also interdisciplinary not only because they involve biological and physical phenomena but because the hierarchical study approach emerged as a useful means to collaborate across the disciplines of fluvial geomorphology and freshwater ecology. We do not purport that these case studies will provide definitive answers to the interdisciplinary problem of dissecting hierarchical levels in river ecosystems. Rather, we hope that they will add to an evolving body of knowledge about hierarchical organization of the physical and biological components of river ecosystems (e.g. Poff, 1997; Fisher et al., 1998; Parsons et al., 2003; Royer and Minshall, 2003; Thoms and Parsons, 2003; Parsons et al., 2004). 2. Case study I: hierarchical associations between channel morphology and large wood in a lowland river 2.1. Background Large wood is an important ecological and physical component of many riverine ecosystems (Gurnell, 1995; Gippel et al., 1996). Ecologically, large wood provides physical habitat for aquatic organisms by acting as substratum for plants (Biggs, 1996), invertebrates (Hax and Golloday, 1998) and fish eggs (Harris and Rowland, 1996). Large wood influences patterns of water movement, thereby providing hydraulic relief for organisms such as plants (Biggs and Stokseth, 1996) and fish (Aadland, 1993). Patterns of erosion and deposition are also influenced by large wood, thereby creating larger-scale physical habitats such as pools and scour holes (Keller and Macdonald, 1995). Indeed, the ecological importance of large wood has been explored by many (see Crook and Robertson (1999) for review), and this stems in part from an increasing focus on physical–biological associations in freshwater ecosystems over the past 10 years (c.f., Townsend and Hildrew, 1994). Large wood is also an important physical influence on the hydraulic and morphological character of rivers

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(Montgomery and Piegay, 2003). The presence of large wood in streams and rivers influences hydraulic flow resistance, although there appears to be no simple relationship between flow resistance and the abundance or complexity of large wood in a river reach (Shields and Gippel, 1995). Large wood influences channel morphology, but the association between large wood and channel morphology differs among higher-energy upland rivers and lower-energy lowland rivers (Piegay and Gurnell, 1997; Hughes and Thoms, 2002). In highenergy rivers, large wood can be highly mobile and may be transported considerable distances during flood events (Piegay and Gurnell, 1997; Piegay et al., 1999; Pettit et al., 2005), where it can subsequently act as the nuclei for the formation of channel features such as gravel bars (Gurnell et al., 2000). Debris jams have also been reported to have a profound influence on fluvial processes such as bed-load mobility (Assani and Petit, 1995) and, in some instances, large wood exerts significant control on channel morphology (Marston, 1982; Webb and Erskine, 2005). In lower-energy rivers, wood is not subject to active transport but essentially remains where it falls for periods exceeding 100 years (Hughes and Thoms, 2002). Large wood in lowland rivers has been demonstrated to increase local bank erosion rates (Shields and Gippel, 1995) and, in smaller lowland rivers, large wood can exert a significant modifying influence on channel cross-section and planform (Triska, 1984). Thus, large wood plays an important role in the formation of morphological channel features in many different river systems. Studies examining the associations between channel morphology and large wood have generally been conducted at a single scale, the most common being the local scale of tens of metres (e.g. Keller and Swanson, 1979; Abbe and Montgomery, 1996). However, in accordance with hierarchical principles, upland and lowland river systems have been described as a nested hierarchy of morphological units (Petts and Amoros, 1996; Thoms, 2003). Each morphological unit is constrained by processes operating at higher levels within the hierarchy but at the same time, is also influenced by bottom–up factors emerging from lower levels of the hierarchy. Thus, we may expect that the association between large wood and channel morphology will change at different levels within the river system hierarchy, because each level is set by different top–down processes and bottom–up influences. Some studies have demonstrated that the character and ecological role of large wood differs among morphological units in river systems. In a recent study of native fish habitat in the Murrumbidgee River, Australia,

Crook et al. (2001) demonstrated the role of large wood as a habitat for fish at the local and reach scales. Similarly, Pettit et al. (2005) described the distribution of piles of large wood following an extreme flood in the Sabie River, South Africa, and noted that the volume and character of large wood piles varied according to river channel setting and flood frequency zone. These studies suggest hierarchical influences on the pattern and ecological role of large wood in river systems. However, associations between channel morphology and large wood have not been studied in an explicitly hierarchical manner. In this case study we examine the association between channel morphology and large wood at three scales of a hierarchy of river system organization in a large, low-energy Australian river. Knowledge of the hierarchical levels at which channel morphology and large wood are related is crucial for understanding the physical and ecological roles of large wood in river ecosystems. 2.2. Study area and methods This study was conducted in a 95 km reach of the River Murray in SE Australia (Fig. 1). Unlike many lowland rivers in the Murray–Darling Basin, there has been little removal of large wood in this reach of the River Murray and the riparian zone is relatively intact (Hughes and Thoms, 2002). The study reach is located within the Riverine Plains Tract of the River Murray. There are four main tracts, or regions, of the River Murray: the Headwaters Tract or upland area between the river's source and Albury; the Riverine Plains Tract which extends westward to the junction of the River Murray and Murrumbidgee River; the Mallee Tract between there and the junction with the Darling River; and, the South Australian Tract between the Darling River and Lake Alexandrina (Walker, 1992). Generally, the lowland Riverine Plains, Mallee and South Australian tracts cut into predominantly sand-sized Tertiary alluvium that is up to 60 m thick (Twidale et al., 1978). These tracts have well-developed floodplains (up to 30 km wide) that are heavily dissected by meandering channels. A feature of the River Murray is its extended long profile (Thoms and Walker, 1993), where 89% of its length (2560 km) has a channel gradient less than 0.00017 m km− 1. Enhanced flow variability is a feature of the River Murray and is a function of erratic rainfall, low relief and high evaporation potential. The coefficient of variation for the long-term annual flows (1900–1998) at Tocumwal is 125%, compared to an average of 33% for similar sized rivers elsewhere in the world (Finlayson and McMahon, 1988). The average annual discharge of


the River Murray is 150 m3 s− 1 at Tocumwal (range 20– 1564; 1900–1998). The hierarchical river organization schema of Petts and Amoros (1996) and Thoms et al. (2004) was used to set scales of observation. Reaches are deemed to be repeatable lengths of river channel with similar morphology, nested within larger functional process zones. In this study, each tract of the River Murray can be defined as a functional process zone, and our reach is

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the 95 km study area within the Riverine Plains Tract. Functional sets are smaller in size and are identified as sections within a reach determined by ecological or physical properties. In this study, functional sets were determined on the basis of channel planform morphology using geomorphic variables such as bankfull width, radius of curvature, arc angle, amplitude, direction of bend, meander wavelength and meander belt width (see below). Functional units are nested within functional

Fig. 1. Location of the study reach (A) and division of the study reach into scales of measurement to examine the relationship between channel morphology and large wood (B).

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sets and can be arranged along a gradient such as water depth. Functional units were derived by dividing each functional set longitudinally into quarters and then laterally into thirds giving 12 functional units for each functional set (Fig. 1). Longitudinal division into functional units was achieved by dividing the inner and outer banks into four equal sections and connecting these points across the channel. Hence, the inner, mid and outer-channel functional units are different sizes (Fig. 1). The location and character of pieces of large wood were mapped from a series of 90 small-scale (1:5000) aerial photographs of the study reach, taken during a period of extreme low flow (equivalent to the 98th percentile on the annual flow duration curve at Tocumwal). Field verification indicated that the aerial photographic mapping procedure used in this study accounted for 62% of the large wood in the study reach. Each piece of large wood along the study reach was described according to its position in the channel, length, angle to flow, distance from bank and structural complexity, and whether the site was erosional, depositional or indeterminate. Linear measurements were made in 1 mm increments (5 m at scale) and angle to flow was recorded as one of 6 classes representing 30° increments between 0° and 180°. Structural complexity was measured on the aerial photographs as one of three branching categories, with single stemmed pieces considered less complex than those with multiple stems or branches. Pieces for which structural complexity could not be determined from the photographs were designated into an ‘unknown’ category, but still included in the analysis. Eroding sites were identified on the aerial photographs by the presence of deeper water with no obvious deposition and banks displaying a vertical form. Depositing sites were identified by the presence of shallow water with depositional features such as sandbars or a bank form showing a smooth shape. The aerial photographs of the study reach covered sufficient floodplain area to allow an analysis of riparian vegetation cover within a 100 m strip extending laterally from each bank. The percentage cover of vegetation was estimated at 0.5 km intervals along the study reach using the method of Jolly (1996). River Red Gum (Eucalyptus camaldulensis) is the dominant vegetation along this section of the River Murray, thus the analysis of riparian vegetation was restricted to trees. Stepwise regression was used to determine if there was a relationship between the abundance and density of riparian trees on the banks and the abundance and density of large wood in the adjacent channel.

Associations between channel morphology and large wood were examined at each scale using multivariate, univariate and descriptive statistics. It was initially intended to find groups of large wood pieces with similar character at the reach, functional-set and functional-unit scales. However, reach-scale ordination of large wood character (Semi-Strong Hybrid Multidimensional Scaling (SSH) on the characteristics of all wood pieces within the reach, using the Gower association measure; Belbin, 1993) showed no distinct groups (see Fig. 2), prompting us to use the morphological variables to dissect the river reach at the functional-set scale. We used cluster analysis (Flexible UPGMA using the Gower association measure; Belbin, 1993) to identify functional sets with similar morphological features. We then examined the amount and character of large wood among functional sets, and Chisquare analysis was used to compare large wood distribution in each of the functional sets against that for all sets combined. The amount and character of large wood among functional units nested within functional sets was also examined. 2.3. Results A total of 6322 pieces of wood were identified along the study reach. Of these, 88% were less than 20 m in length, 57% were classed as being structurally simple (i.e. having single trunks or branches), and 37% had a trunk with one level of branching. The majority of the large wood was close to the riverbank, where 90% of the

Fig. 2. Reach-scale ordination of large wood character. Each point on the ordination bi-plot is the group centroid for the inner, centre and outer channels within each 1 km section within the reach (otherwise all 6000 wood pieces would be displayed on the plot). No distinct patterns of large wood character emerged at this scale.


pieces were within 25 m of the bank and 87% were orientated at 90° or less to the angle of flow. Large wood was predominantly found at eroding sites (59% at eroding sites, 8% at depositional sites). Mean tree cover (and standard error) on the banks was 70.7 ± 1.25% (range = 68.3–73.2%, n = 370), indicating the riparian zone to be well vegetated in the immediate vicinity of the channel. There was no significant relationship between the density of large wood in the channel and tree cover on the adjacent bank (r2 = 0.01) or between tree cover and distance along the study reach (r2 = 0.07). Moreover, the density of riparian vegetation did not differ between the left or right bank (Students t test, p = 0.36). Reach-scale ordination of large wood revealed no distinct groupings of large wood in multi-dimensional space, suggesting there are no parts of the study reach with distinct large wood character at this scale (Fig. 2).

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Classification of the morphological variables at the functional-set scale revealed eight sub-reaches within the study reach, each with a distinct planform character (Fig. 3). One functional set was comprised of straight sections (‘straights’ — no curvature of the river channel) and the remaining seven functional sets were comprised of bends of various length and radius of curvature. Two types of bends dominated the study reach: there were 48 ‘very tight bends’ which had a mean length of 538 m across all sets, and 43 ‘open short bends’ with a mean length of 391 m (Fig. 3). The abundance, character and distribution of large wood varied among functional sets. There was an association between the curvature of the sub-reaches and the number of pieces of large wood on either side of the channel. In the straight functional set, large wood was distributed evenly between the left and right sides of

Fig. 3. Derivation and physical character of functional sets within the study reach. Sub-reaches were classified on the basis of geomorphic variables to derive the 8 functional sets shown in the dendrogram (top). Gower dissimilarity is shown at each major split of the dendrogram. Means and standard errors (in brackets) of geomorphic variables are summarized for each functional set (bottom), where MBW = meander belt width and MWL = meander wavelength.

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the channel (Fig. 4). In the bend functional sets, 33% of large wood was located along the inner channel and 67% in the outer channel (Fig. 4). The outer channel is larger in area by 21% on average than the inner channel but that is not sufficient to account for the outer channel having 100% more large wood than the inner channel. These differences in the distribution of large wood between different functional sets are statistically significant, shown by the results of a chi-square analysis

comparing large wood distribution in each of the functional sets with that for all bends combined (χ2 = 23.42; df = 7; ρ < 0.01). At the functional-unit scale three pertinent findings can be noted; the first two relate to the proportional distribution of large wood among functional units and the third relates to differences in the character of large wood among functional units. First, only 4.7% of large wood was located in the central channel functional units

Fig. 4. Distribution of large wood among the inner (I) and outer (O) functional units in each functional set. Numbers at the top of the bars are the proportional distribution (%) of wood, but figures do not add up to 100% because the centre channel was excluded from the graphs because it contained small amounts ( I2 > I3 = I4; Fig. 5). Thus, large wood becomes closer to the bank with increasing distance into the bend in the inner channel. The opposite occurs in the outer

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channel (O1 < O2 < O3 < O4; Fig. 5) where large wood becomes further from the bank with increasing distance into the bend. Angle to flow in the inner channel appeared to increase with increasing distance into the bend (I1 < I4), although the error range of the inner 3 and inner 4 functional units make this difficult to confirm (Fig. 5). In the outer channel, angle to flow declines with increasing distance into the bend (O1 = O2 > O3 = O4; Fig. 5). 2.4. Discussion The physical character of large wood, river channel morphology, interactions between large wood and river channel morphology, recruitment of wood to the river channel and hydrological and hydraulic processes that redistribute wood once it has entered the channel are factors that govern patterns of large wood accumulation. Because of these multiple factors and the various scales at which they operate, it has been concluded that the spatial distribution of large wood within rivers displays irregular patterns (Piegay, 1993; Abbe and Montgomery, 2003; Kraft and Warren, 2003). However, most studies of large wood accumulation in rivers are only reported at a single scale, commonly the reach scale extending from tens to hundreds of metres. There is a lack of studies that have considered top–down constraints and bottom–up influences on the distribution of large wood in rivers, despite its importance being noted by Montgomery and Piegay (2003). Ignoring hierarchical influences on the distribution of large wood will only reinforce the commonly held view that large wood occurs in specific but as yet unpredictable locations within river systems (Kraft and Warren, 2003). As expected, the association between channel morphology and the abundance and character of large wood differed at each hierarchical level of river system

Fig. 5. Large woody debris distance to bank (A) and mean angle class (B) by inner (I) and outer (O) channel functional units across all bend functional sets combined. Error bars = 1.96 standard errors. For mean angle class, the higher the value, the more perpendicular to flow.

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organization in the River Murray. Channel morphology and large wood were associated at the functional set and functional unit scales, but not at the reach scale. Hence, functional sets and functional units are the important holons of physical river system organization for large wood in the River Murray. Had we not applied a multiscale study approach we would have incorrectly concluded that the character of large wood was uniform within the study reach when, in fact, there is an association between channel morphology and large wood character at the lower functional set and functional unit levels of hierarchical organization. The associations between channel morphology and large wood can be related to dominant geomorphological and hydrological processes operating at each level of river system organization. At the reach scale, large wood was distributed uniformly, which can be related to the relatively uniform nature of the flow and sediment regimes (Thoms et al., 1998) and riparian vegetation (Bren, 1993) in this reach of the River Murray. Processes such as rates of bank erosion and stream power that affect the recruitment and subsequent fate of large wood are relatively similar for the entire reach (Thoms et al., 1998), resulting in a relatively uniform pattern of distribution of large wood at this scale. At the functional set level, the distribution of large wood in the River Murray was associated with different types of channel bends. The relatively even distribution of large wood on both banks of the study reach in the ‘straight’ functional set is consistent with uniform energy patterns in straight river reaches (Dietrich, 1987). By comparison, greater proportions of large wood were found along the outer channel of ‘bend’ functional sets, which is consistent with higher stream velocities and energies in that part of a river meander (Dietrich, 1987; Thorne, 1992). At the functional unit level, patterns of large wood were also associated with stream energies. Large wood was found a greater distance from the bank and was more aligned to the prevailing flow direction in functional units located on the outer bends, and which are associated with higher stream energies (Hughes and Thoms, 2002). In contrast, large wood in functional units located on the inner bend were closer to the bank and had a greater variety of angles to the bank. This probably reflects the character of large wood pieces when they originally entered the river channel through bank erosion processes. Thus, the pattern of large wood within the river channel is a nested hierarchy, where the character of large wood varied between functional units and within functional sets, according to different top– down processes and bottom up influences at each level of organization. Spatial patterns have important effects on a variety of physical and ecological processes. To understand

hierarchical interactions between pattern and process it is necessary to characterize spatial patterns over an array of scales (Wu et al., 1997). Single-scale studies ignore the fact that patterns are scale dependant and therefore fail to understand the complexities of hierarchical systems, where top–down constraints and bottom–up influences are important attributes. Hence, results attained at a single scale should only be applied to that particular scale. Studies of the associations between large wood and channel morphology have previously been undertaken at a habitat scale (tens of metres) only (Keller and Swanson, 1979; Abbe and Montgomery, 1996). The distribution of large wood in this reach of the River Murray was mainly associated with eroding sites and is relatively immobile. This contrasts with the associations described by Piegay (1993) and Piegay and Gurnell (1997) for relatively higher-energy European rivers, where large wood was predominantly located in depositional sites and hence thought to be highly mobile. A preliminary model of the dynamics of large wood in the River Murray suggests large wood is recruited through local bank erosion, most likely from meander development, and generally remains where it falls into the channel. In the relatively higher energy environments of the channel, such as those associated with bend functional sets and outer bend functional units, large wood undergoes some form of realignment rather than being actively transported. Development of this model would not have been as complete without a multi-scale approach demonstrating that the associations between geomorphology and large wood distribution and character differ among hierarchical levels. The re-introduction of large wood is a popular river management tool (Reich, 1999). Efforts to date have been relatively crude and are generally aimed at placing individual pieces of large wood in high-energy river channels to create features such as riffles and pools (Till, 2000). In low-energy rivers, management of large wood is generally undertaken at the habitat scale because the aim of restoration projects is often linked to singlespecies conservation (e.g. Murray Cod: Koehn et al., 2004). There is no consideration of other scales at which large wood is ecologically or physically important. The results of this study suggest that reintroduction of large wood should occur at the functional set scale (individual meander bends), with consideration of the amount and character of large wood along the nested inner and outer functional units of each functional set. The use of a hierarchical approach to large wood re-introduction in low energy rivers may help to restore ecosystem function, rather than just habitat for single species, because a hierarchical approach encompasses interrelated


physical–biological associations that occur across a range of hierarchical levels within a river ecosystem. 3. Case study II: hierarchical environmental influences on macroinvertebrate assemblage distribution in an upland catchment 3.1. Background A current focus of stream ecological research seeks to decipher the relative influence of different scaled environmental factors on the distribution and composition of macroinvertebrate assemblages (Carter et al., 1996; Richards et al., 1996; Allan et al., 1997; Richards et al., 1997; Lammert and Allan, 1999; Sponseller et al., 2001; Townsend et al., 2003; Weigel et al., 2003; Sandin and Johnson, 2004). The theme emerging from these studies is that macroinvertebrate assemblages are influenced by two scales of environmental factors: local reach-scale factors such as riparian vegetation, channel morphology, functional habitat composition, water chemistry and substratum type; and, large catchment or regional-scale factors such as landuse, geology, and indicators of geographical position such as latitude, longitude, altitude, distance from source and stream order. The association between environmental factors and macroinvertebrate assemblages is expected because there is an inherent relationship between the biological and physical components of ecosystems (Southwood, 1977). However, why are large- and local-scale factors consistently the dominant environmental influences on macroinvertebrate assemblages? Viewed hierarchically, environmental factors do not occur randomly within a river system. Rather, environmental factors sit within a hierarchy of organization where geomorphological processes operate at different levels to manifest differentlyscaled physical features (Schumm and Lichty, 1965; Knighton, 1984). It is feasible that factors across a whole continuum of scales might be important top–down constraints or bottom–up influences on macroinvertebrate assemblages. For example, factors that are intermediate within a hierarchy of river system organization, such as planform channel morphology, sediment transport, and floodplain character, may act directly or via other levels of the hierarchy to influence macroinvertebrate assemblages. However, the environmental factors used in studies of macroinvertebrate-environment associations are rarely considered in relation to a hierarchy of organization. In this case study we examine the influence of a hierarchical continuum of environmental factors on patterns of macroinvertebrate assemblage distribution in a high-energy, upland river system in Australia.

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In addition to the established theme of large and local-scale environmental influence on macroinvertebrate assemblages, patterns of macroinvertebrate assemblage distribution have also been observed at different scales (see review in Parsons et al., 2004). The separate themes of multi-scaled macroinvertebrate distribution and multi-scaled environmental influence on macroinvertebrate distribution indicate that macroinvertebrate assemblages and the environmental factors that influence macroinvertebrate assemblages both occur at multiple scales. Hence, macroinvertebrate assemblages and environmental factors may interact hierarchically. Indeed, the frequently observed pattern of large and local-scale environmental influence on macroinvertebrate distribution may constitute the levels where top–down constraint and bottom–up influences intersect within a hierarchy of interacting biological and physical patterns and processes. Although several studies have considered the environmental factors that influence multi-scale patterns of macroinvertebrate assemblage distribution (e.g. Downes et al., 1995; Boyero and Bailey, 2001; Li et al., 2001), we know of no studies that have attempted to simultaneously decipher multi-scale environmental influences on macroinvertebrate assemblage in light of multi-scale patterns of macroinvertebrate assemblage distribution. Therefore, we also examined the influence of a hierarchical continuum of environmental factors on hierarchical macroinvertebrate assemblage distribution, by using the hierarchical levels of macroinvertebrate assemblage organization reported in Parsons et al. (2003). In this way, we dissect interacting hierarchical domains of biological and physical patterns and processes. 3.2. Study area and methods The study was conducted in the Upper Murrumbidgee River Catchment, south-eastern Australia. Parsons et al. (2003) give a full account of the study area, nested hierarchical study design and scale selection. In summary, the study area was divided into nested catchment, zone, reach and riffle scales representing a hierarchy of geomorphological influences on river systems. Catchments were defined as topographic drainage divisions. Zones were defined on the basis of channel confinement and valley shape, and correspond to broad, confined, and unconfined lengths of river. Reaches were defined as the length of stream between 2 tributaries of the same stream order or higher. Riffles were defined as the individual riffles at the top and bottom ends of 2 riffle-pool sequences.

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Collection of macroinvertebrate samples was stratified by the catchment, zone, reach and riffle scales (Parsons et al., 2003). Multivariate analyses and Nested Analysis of Variance were used to dissect scales of macroinvertebrate assemblage distribution. It was found that macroinvertebrate assemblages were similar among riffles within a reach, but were dissimilar at the zone and catchment scales. A subsequent search for alternative levels of organization revealed a region scale of macroinvertebrate distribution that was larger than the geomorphologically derived catchment scale. Thus, there was a large, region-scale and local reach-scale pattern of macroinvertebrate assemblage distribution. Partitioning of data by regions subsequently revealed a nested cluster-scale of distribution that corresponded broadly to the original, geomorphologically-derived catchment and zone scales. It was concluded that the region, cluster, reach and riffle scales were the holons of biological organization within the Upper Murrumbidgee River Catchment. In the present study we assess the relative influence of a continuum of catchment, zone, reach and rifflescale environmental factors on the region and cluster levels of macroinvertebrate assemblage distribution. We did not examine the relative influence of environmental factors on reach-level macroinvertebrate distribution because environmental factors were invariant (i.e. all four macroinvertebrate samples from the two riffles within a reach had the same measurement for catchment, zone and reach-scale variables). For the same reason, we did not examine the influence of environmental factors on riffle-level assemblage distribution. We do not see this as an analytical limitation but, rather, as an unavoidable hierarchical principle of progressive constraint across successively smaller nested levels. Additionally, we assessed the influence of a continuum of environmental factors on all macroinvertebrate samples combined (henceforth called the non-hierarchical data set), to mimic the approach taken in other studies of large and local-scale environmental influences (e.g. Carter et al., 1996; Townsend et al., 2003). Comparison of the dominant scales of environmental influence on the hierarchical (region and cluster levels) and nonhierarchical macroinvertebrate data sets allows dissection of hierarchical domains of biological and physical processes. Environmental factors were measured at the catchment, zone, reach and riffle scales (Table 1). Different types of variables were collected at each scale to represent dominant physical, chemical and ecological influences within a hierarchy of organization. For example, geology, discharge and landuse were measured

at the catchment scale because these factors operate at large spatial scales and long-temporal scales to constrain the formation of lower level factors (Schumm and Lichty, 1965). Substrate and hydraulic character were measured at the riffle scale because these factors operate at small spatial and temporal scales (Schumm and Lichty, 1965). The variables that indicate geographic position are difficult to assign to a scale because they indicate the position of a reach in relation to the landscape. These variables were assigned to the catchment scale (Table 1) but, in effect, are interchangeable between the reach and catchment scales. Macroinvertebrate samples were partitioned into region-level, cluster-level and non-hierarchical data sets. The region-level data set was further partitioned into 3 constituent groups (Region 1, Region 2 and Region 3; Parsons et al., 2003). The cluster-level data set was partitioned into 7 constituent groups (Clusters A– G; Parsons et al., 2003), but clusters within Region 3 were not examined at this scale because of small numbers of samples in each cluster. The non-hierarchical data set includes all samples together. Each macroinvertebrate data set was ordinated in 3 dimensions (SSH; Belbin, 1993). The relationship between different scale environmental variables and the position of macroinvertebrate samples in ordination space was determined using Principal Axis Correlation (Belbin, 1993). Principal Axis Correlation (PCC) generates a correlation value (R2) for each attribute, with high values being indicative of a strong relationship between an environmental variable and the position of macroinvertebrate samples in ordination space. Only those variables with an R2 above the 75th percentile (when the R2 values of all 117 variables were considered) were included. This assumes that variables with an R2 above the 75th percentile were the strongest indicators of environmental influence on assemblage distribution. Of these variables, many were from the same category and, thus, may be correlated with each other. However, no attempt was made to identify and remove correlated variables because it was desirable to see if a dominant scale of environmental influence emerged. The variables associated with macroinvertebrate ordination space were tallied according to the catchment, zone, reach and riffle scales, to identify the dominant scale of environmental influence. 3.3. Results Catchment and reach-scale environmental factors were associated with regional-level macroinvertebrate assemblage distribution (Table 2). In regions where

M. Parsons, M.C. Thoms / Geomorphology 89 (2007) 127–146 Table 1 Environmental variables collected at the catchment, zone, reach and riffle scales Scale & variable Variable type

Units

Catchment Geology

% alluvium % mafic volcanics % felsic volcanics % mafic intrusives % felsic intrusives % shale, siltstone and slate % conglomerates % limestone Rainfall Median annual rainfall Catchment Total catchment characteristics area Catchment circularity index Stream discharge Flood index (Q10 / Q90) Median discharge (Q50) Colwell’s predictability Colwell’s constancy Colwell’s contingency CV median annual discharge Landuse % urban and other hard surfaces % water % good grass % forest % poor grass % bare ground % crops Geographical Latitude position Longitude Altitude Stream order Log catchment area upstream Distance from source Zone Zone characteristics

Reach Water chemistry

Data set association(s)

Table 1 (continued ) Scale & variable Variable type Reach Morphology

DEN 2DE 2DE 23DE 2DE 2DE

mm

23DE 23DE 12DFN

km2

2DE

Riparian vegetation

23DE 2AEF m/s

23EF 1AF 3AEF EF AEFG 123

km2

A 12ACDFN 12ACDFN 12ACDFGN 2N 3B 3G DFN 13CDGN 1BDEN 12ABCDEFN

km

12ABFN

m

Zone slope Zone length Sinuosity % reach along zone Valley width

m/km 1 3 G N km AG 3DFG C m

B

Alkalinity

mg/L

BFG

139

Instream habitat

Inorganic substratum

Organic substratum

No. of channel flow paths Riffle to riffle spacing Reach width (water) Reach width (bankfull) Pool width (water) Pool width (bankfull) Sub-reach length Width of riparian zone % trees > 10 m high % trees < 10 m high % shrubs and vines % grasses, ferns and sedges % blackberries (within grasses, ferns and sedges) % willows (within trees > 10 m) % willows (within trees < 10 m) % shading of stream channel % native vegetation % exotic vegetation % riffle in reach % run in reach % pool in reach % edge in reach % bank area unstable % reach bedrock % reach boulder % reach cobble % reach pebble % reach gravel % reach sand % reach silt % reach clay % reach detritus % reach muck/mud % reach periphyton % reach moss % reach filamentous algae % reach macrophytes

Units

Data set association(s) AG

m

EF

m m

2FN 12EFN

m m

2BCEFN 123BEFN

m m

3FG C 3GN 3BC BG CDG 13

BG 1B

1N 13N 13N

2ABD 1DFN 3ABFG EFG F 1A 2C 1AC BG 2CD 3FG G 3CN A 3CD 13CD AE G (continued on next page)

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Table 1 (continued ) Scale & variable Variable type Habitat assessment (United States EPA, Barbour et al., 1999)

Riffle Riffle characteristics

Water chemistry

Organic substratum

Inorganic substratum

Table 1 (continued ) Units

Bottom substrate score Embeddedness score Velocity/depth category score Channel alteration score Bottom scouring score Pool/riffle ratio score Bank stability score Bank vegetative stability score Streamside cover score Total habitat score

Riffle width (water) Riffle width (bankfull) Riffle length Riffle slope % riffle shading Conductivity pH Dissolved oxygen % saturation Turbidity % riffle detritus cover % riffle periphyton cover % riffle filamentous cover % riffle bedrock % riffle boulder % riffle cobble % riffle pebble % riffle gravel % riffle sand % riffle silt % riffle clay Median surface particle size Median subsurface particle size Median overall particle size

Data set association(s)

Scale & variable Variable type

ACE

Morphology, hydraulic character and sedimentology

BE 3A 1CEG BDG 3ABG ABG 13ACGN 1ABCGN 1ACG

m

BG

m

N

m m/m

2AN N μ S / ABFG cm 2BC D

FNU

A

1 B D F mm mm

B

mm

3

Armouring Relative bed stability Log unit stream power Bankfull wetted perimeter Bankfull hydraulic radius Hydraulic depth (water) Hydraulic depth (bankfull) Channel area (water) Channel area (bankfull) Water velocity Bankfull velocity

Units

Data set association(s) EN

ω per 1 B N kg m/ s− 3 m

m2

BC

m2 m/s m/s

12EN

Data set association(s) with different macroinvertebrate data sets are indicated by codes, where 1–3 are Regions, A–G are Clusters and N = non-hierarchical data set. For example, the variable ‘median annual rainfall’ was associated with Regions 1 and 2 of the Region-level data set, Clusters D and F of the Cluster-level data set, and the nonhierarchical data set.

reach-scale factors dominated, catchment-scale factors tallied second highest, and vice versa (Table 2). Some riffle and zone-scale factors were associated with regional-level macroinvertebrate distribution (Table 1), but variables from these scales occurred infrequently in all regions (Table 2). Thus, there is a strong large and local-scale pattern of environmental influence on regional-level macroinvertebrate assemblage distribution. The dominance of catchment and reach-scale factors was not as prominent at the cluster level as at the region level, and different patterns of environmental influence emerged in different clusters. In Region 1, Cluster A was dominated by catchment and reach-scale factors, Cluster B was dominated by reach and riffle-scale factors, Cluster C was dominated by reach-scale factors and Cluster D was dominated by catchment-scale factors (Table 2). In Region 2, Cluster E was dominated by catchment-scale factors, Cluster F was dominated by catchment and reach-scale factors and Cluster G was dominated by reach-scale factors (Table 2). Riffle-scale factors occurred infrequently in Clusters E–G, and zone-scale factors occurred infrequently in all clusters (Table 2). Thus, the large and local-scale pattern of environmental influence is maintained at the clusterlevel of macroinvertebrate assemblage distribution, but

M. Parsons, M.C. Thoms / Geomorphology 89 (2007) 127–146 Table 2 Tally of catchment, zone, reach and riffle-scale environmental variables associated with different macroinvertebrate data sets Macroinvertebrate data-set

n

Number of environmental variables from each scale

Region level

29 29 28 28 27 23 28 28 28 29 30

10 19 10 10 4 5 18 17 13 4 11

Catchment Zone Reach Riffle

Cluster level

Region 1 Region 2 Region 3 Cluster A Cluster B Cluster C Cluster D Cluster E Cluster F Cluster G

Non-hierarchical

1 0 2 1 1 1 1 0 1 3 1

15 7 15 14 15 15 7 9 12 20 12

3 3 1 3 7 2 2 2 2 2 6

Clusters A–D are from Region 1 and Clusters E–G are from Region 2. Region 3 was not analysed at the cluster-level because of low numbers of samples in each cluster. n = number of variables that had an R2 above the 75th percentile. The variables associated with each data set are detailed in Table 1.

each cluster shows a unique scale signature of large, local, large and local, or local and small-scale environmental influence. Catchment and reach-scale factors were associated with non-hierarchical macroinvertebrate assemblage distribution (Table 2). Six riffle-scale factors were also important, but again, zone-scale factors occurred infrequently (Table 2). Thus, the large catchment, local reach, and smaller riffle-scale environmental influence on non-hierarchical macroinvertebrate assemblage distribution is a summary of the patterns that emerged at the separate region and cluster levels of macroinvertebrate assemblage distribution. 3.4. Discussion Environmental aspects of a river system sit within a hierarchy of influence (Knighton, 1984), where factors at one level constrain the expression of factors at successively lower levels (Schumm and Lichty, 1965; O'Neill et al., 1986). The use of a continuum of catchment, zone, reach and riffle-scale environmental factors allowed the established theme of large- and localscale environmental influence to be considered within the context of hierarchical river system organization. Despite the inclusion of a continuum of environmental factors, large catchment and local reach-scale factors consistently emerged as dominant influences on the region-level, cluster-level and non-hierarchical distribution of macroinvertebrate assemblages. The theme of large catchment and local reach-scale environmental

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influence has hierarchical origins, because catchmentscale factors constrain the expression of reach-scale factors and reach-scale factors provide the mechanistic understanding of emergent riffle-scale factors. For example, catchment-scale landuse factors were an important influence on macroinvertebrate assemblage distribution, but this influence also manifested at a local reach scale by factors describing riparian vegetation. Hence, the type of catchment-wide landuse to which humans put the land constrains the expression of reachscale riparian vegetation character. Similarly, the largescale geographical-position factors catchment area upstream, distance from source, altitude and stream order indicate the relative size of a stream within a catchment context, and manifested at a reach scale by factors describing stream morphology. The dominance of catchment and reach-scale environmental influences indicates that these scales are levels, or holons, present within a physical hierarchy of river system organization within the Upper Murrumbidgee River Catchment. In comparison to catchment and reach-scale influences, zone-scale factors occurring at an intermediate position in the hierarchy did not have a dominant association with hierarchical macroinvertebrate assemblage distribution. Few of the studies that examine the association between differently scaled environmental variables and macroinvertebrate distribution have included geomorphological variables such as sinuosity, zone slope and valley width (but see Richards et al., 1996, 1997 for exceptions), even though these variables are commonly measured in geomorphological surveys. The relative unimportance of zone-scale factors suggests that the zone scale is not a holon within the hierarchy of physical river system organization in the Upper Murrumbidgee River Catchment. Riffle-scale environmental factors were also less influential when viewed alongside catchment and reachscale factors. Carter et al. (1996) demonstrated that in comparison to reach, segment or catchment-scale factors, smaller riffle-scale factors had a weak association with macroinvertebrate assemblage distribution. However, hydraulic character and substrate are strong influences on macroinvertebrates (Statzner and Higler, 1986; Hubert et al., 1996) and several riffle-scale factors were associated with macroinvertebrate assemblage distribution in our study. From a hierarchical perspective, riffle-scale factors are constrained by the conditions set by larger-scale factors, but may also be emergent properties from processes that operate at a smaller, micro-scale. The recurrent appearance, albeit in low numbers, of riffle-scale variables among the factors associated with macroinvertebrate distribution suggests

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that the riffle scale is a holon in the hierarchy of physical river system organization. If a smaller micro-scale had been included in this study, the riffle-scale may have increased in prominence because these scales would be closer together in the hierarchy. In our physical hierarchy of organization containing catchment, reach and riffle holons, the distance between the catchment and riffle holons was probably sufficient to reduce the relative prominence of the lower-level riffle holon. Parsons et al. (2004) demonstrated that the hierarchical patterns of macroinvertebrate assemblage distribution in the Upper Murrumbidgee River Catchment corresponded to region, cluster, reach and riffle holons of biological organization, with the region and reach levels being the strongest signatures. The present study showed that the previously described large and local-level holons of macroinvertebrate organization were matched by a large, catchment-scale and local, reach-scale pattern of environmental influence. The catchment-scale factors representing the higher-level holons of the physical hierarchy, and the reach-scale factors representing the lower-level holons of the physical hierarchy correspond with the region- and reach-scale holons of the ecological hierarchy. In other words, the distribution of macroinvertebrate assemblages generally matches the scales of dominant environmental influence, and vice versa. Duplication of the theme of large and local-level patterns in the biological and the physical domains suggests that biological and physical processes in this river ecosystem interact according to hierarchical principles. However, the prominence of large- and local-scale environmental influences was not uniform across the hierarchical and non-hierarchical macroinvertebrate data sets, suggesting that subtle, hierarchically driven patterns in macroinvertebrate-environment associations emerge when multiple levels of macroinvertebrate assemblage distribution are considered. At the region level, catchment- and reach-scale factors were associated with macroinvertebrate distribution but at the cluster level, only two clusters were dominated by a combination of catchment- and reach-scale factors and the other clusters were dominated by either catchmentor reach-scale factors. Riffle-scale factors also became prominent in one cluster. Thus, different levels of the physical hierarchy were associated with different levels of the biological hierarchy. Had we not applied a hierarchical, multi-scale approach to the macroinvertebrate data we would only have observed the nonhierarchical pattern of catchment and reach-scale environmental influence that, essentially, is a summary of hierarchical interactions operating across a range of scales within each biological and physical hierarchy.

Studies examining the relative influence of different scaled environmental factors on the distribution and composition of macroinvertebrate assemblages do not apply a hierarchical approach to the macroinvertebrate data. Rather, knowledge about differently scaled environmental influences is usually used to understand the types of factors that best predict the spatial arrangement and composition of macroinvertebrate assemblages for biological monitoring of river condition (e.g. Lammert and Allan, 1999; Townsend et al., 2003; Sandin and Johnson, 2004). By applying a hierarchical, multi-scale approach to both the environmental and macroinvertebrate data we have shown that environmental factors and macroinvertebrate assemblages interact hierarchically across multiple scales, and can not be uncoupled. Prediction of the distribution and composition of macroinvertebrate assemblages in biological monitoring programs may be improved by explicitly accounting for multiple hierarchical levels of macroinvertebrate-environment association. 4. Integrating the theory and practice of hierarchy in river ecosystems Rivers were initially described as one-dimensional, equilibrial ecosystems (e.g. River Continuum Concept, Vannote et al., 1980), but these paradigms are being replaced by conceptual frameworks that emphasize the importance of scale, hierarchy, complexity, variability, heterogeneity and stochasticity in river ecosystems (e.g. Benda et al., 2004; Dollar et al., 2007-this issue; Thorp et al., 2006). River scientists generally agree that these factors are important characteristics of riverine ecosystems, but, despite a well-developed theoretical and/or terrestrially-based literature on each factor, few river studies explicitly include scale, hierarchy, complexity, variability, heterogeneity and stochasticity. Instead, these factors are often eliminated so as not to confound statistical interpretations. We suggest that scale, complexity, variability, heterogeneity and stochasticity can not be understood in river ecosystems without an explicitly hierarchical approach that seeks to dissect holons, or levels, of organization. Complexity, variability, heterogeneity and stochasticity are products of pattern–process interactions between and within hierarchical levels of organization that, in turn, occur at characteristic spatial and temporal scales. But how might we better apply concepts of hierarchy theory in river ecosystems? Despite the general acceptance of hierarchy and scale as an important paradigm in river ecology and fluvial geomorphology (Schumm and Lichty, 1965; Giller et al., 1994), there are


only a handful of applied tests of hierarchy theory in aquatic ecosystems (Poff, 1997; Fisher et al., 1998; Parsons et al., 2003; Royer and Minshall, 2003; Parsons et al., 2004). Hierarchy seems to be a topic that is considered esoteric or difficult to access, understand or incorporate into established approaches and methods in river science. However, hierarchy has utility as a unifying theory for the issues of complexity, variability, heterogeneity and stochasticity that river scientists grapple with regularly. The scientific method is an adaptive learning cycle of problem identification and model conceptualization, testing, synthesis and model re-conceptualization. By using hierarchy as an overarching paradigm in our conceptual models, study designs and analyses we can build a comparative picture of the hierarchical organization of river ecosystems. Dissecting hierarchies is not necessarily straightforward and, indeed, applying hierarchy theory across a science that involves many sub-disciplines is a challenge (but see Dollar et al. (2007-this issue) for a conceptual hierarchical framework integrating ecology, geomorphology and hydrology). However, the increasing focus on spatial and temporal scaling in river science over the past decade suggests that scale should now be placed into its higher-order theoretical context to enhance collaborations between the sub-disciplines working in rivers, and to engage comparisons of scale-related studies of biological and physical river ecosystem components. The two case studies presented in this paper support the applicability of hierarchy theory to describe the organization of physical–biological associations in river ecosystems. By examining a biological component (wood, macroinvertebrates) in relation to a physical component (channel morphology, environmental factors) we detected holons of organization. In the study of large wood, holons of organization occurred at the functional set and functional unit levels, and in the study of macroinvertebrate-environment associations, holons of organization occurred at the larger region and catchment levels and at the smaller reach level. However, the dominant holons of organization differed among the two case studies. In the study of large wood distribution the larger reach scale was not a holon of organization, but rather, associations between morphology and large wood occurred at the smaller nested functional set and functional unit levels. In contrast, macroinvertebrate-environment interactions occurred at the large and local levels, but not at the intervening zone scale or at the smaller riffle scale. These differences in the placement of holons within the hierarchy not only reflect the principle of decreasing influence between

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distant holons, but suggest that hierarchical levels of physical–biological association vary among rivers and ecosystem components. In addition, the levels of hierarchical organization we detected did not always match with the scales of observation we chose, and the detection of holons from the scales we used was far clearer in the large wood study than in the macroinvertebrate-environment study. Holons of large wood distribution matched with two of the scales we chose on the basis of a hierarchy of river system organization. However, the holons of macroinvertebrate distribution did not match with the scales we chose on the basis of a geomorphological hierarchy (Parsons et al., 2003). Rather, we first had to allow the holons of macroinvertebrate assemblage distribution to self-emerge (Parsons et al., 2003), before we could detect holons of environmental influence in relation to these holons of macroinvertebrate distribution. This suggests that the interaction of biological and physical elements can create complex dual hierarchies of closely associated biological and physical holons in river ecosystems. While derivation of an individual physical or biological hierarchy may be relatively clear (e.g. Petts and Amoros, 1996), merging the physical and biological can create additional levels of organization that arise from ecological associations between biological and physical elements. The case studies presented in this paper also highlight the hierarchical principles of emergent properties and top–down constraint and bottom–up influence. The distribution of wood within functional sets and functional units was related to the interplay of stream energy and channel morphology. Thus, the functional-set pattern of large wood emerged from processes operating within functional units, but was constrained by higher levels of morphological organization that determine where each functional set will occur within the river system. Similarly, the dual large and local-level holons of macroinvertebrate distribution and environmental influence suggest that these are the hierarchical levels at which top down constraints and bottom–up influences intersect. Large-scale factors such as geology, land cover and climate set local-scale factors such as substrate and riparian vegetation character. Macroinvertebrates respond to the availability of suitable local environmental conditions, but higher-level factors set the arrangement of those local environmental conditions at different places within a river system. However, when considering biological phenomena, we can not lose sight of the other factors that may influence macroinvertebrate assemblage distribution such as biotic interactions, disturbance and evolutionary processes, each of which

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may also occur hierarchically. Nonetheless, rivers are an ideal setting to test hierarchical principles, and further applied tests of hierarchy theory by river scientists can make a valuable contribution to the theoretical hierarchy literature, within the iterative scientific process of model conceptualization, testing, synthesis and model reconceptualization. The application of hierarchy theory in river science is in its infancy, and further testing is needed to determine how aspects of hierarchy theory such as holons of organization, emergent properties, and top– down constraint and bottom–up influence manifest in different river settings. We must also determine how best to use hierarchy as a unifying concept for interdisciplinary river studies. River science has entered a challenging era where society is faced with increasing pressure on water resources. Hierarchical understanding of river system organization will allow issues and concerns to be examined at meaningful scales, with recognition of the hierarchical linkages to and from a focal level. In turn, a hierarchical approach may enhance river conservation and management because it allows pattern–process relationships to be considered across hierarchical levels, facilitating a holistic ecosystem perspective rather than a partial single-scale, single-component or single-discipline perspective. Acknowledgements The large wood study was funded by a grant to MCT from the Murray Darling Basin Commission, Australia. Vic Hughes assisted with the collection, analysis and interpretation of the large wood data as part of his BSc. Honours project at the University of Canberra, and we thank Vic for allowing us to use those data as one of our case studies. The multi-scale macroinvertebrate-environment study was funded by an Australian Postgraduate Award and CRC for Freshwater Ecology Postgraduate Scholarship to MP at the University of Canberra. Richard Norris and MCT supervised MP's PhD project. MCT thanks the Centre for Water in the Environment for support while in South Africa. References Aadland, L.P., 1993. Stream habitat types: their fish assemblages and relationship to flow. North American Journal of Fisheries Management 13, 790–806. Abbe, T.B., Montgomery, D.R., 1996. Large woody debris jams, channel hydraulics and habitat in large rivers. Regulated Rivers: Research and Management 12, 201–221.

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