Effectiveness of the River Environment Classification in the Auckland Region

New Zealand Geographer

(2008)

64

, 181–193 doi: 10.1111/j.1745-7939.2008.00144.x

© 2008 The AuthorsJournal compilation © 2008 The New Zealand Geographical Society Inc. Published by Blackwell Publishers Ltd.

Blackwell Publishing Asia

Research Article

Effectiveness of the river environment classification in the Auckland Region

Liza Inglis,

1,2

Ian K. G. Boothroyd

1,3

and Gary Brierley

1

1

School of Geography, Geology and Environmental Science, The University of Auckland, Private Bag 92019, Auckland, New Zealand,

2

Tonkin and Taylor Ltd, PO Box 5271, Wellesley St, Auckland, New Zealand,

3

Golder Associates (New Zealand) Ltd, PO Box 33849, Takapuna, Auckland, New Zealand

Abstract:

The River Environment Classification (REC) provides a GIS-based tool to differentiatebetween biophysical attributes of river systems. This study tests the effectiveness of theREC as a tool to differentiate physical habitat and macroinvertebrate assemblages betweenfour classes of stream in the Auckland region. Results indicate significant within-classvariability in physical habitat and macroinvertebrate assemblages, with considerable overlapamong the four classes. Biophysically meaningful stream classes cannot be identified fromGIS-derived data applied in this research. Field analyses of physical habitat (i.e. geomorphicriver condition) are required as a supplementary tool to interpret ecological relationships fordiffering stream types.

Key words:

classification, geomorphology, habitat, macroinvertebrates, rivers.

The inherent diversity and variability of thenatural world present many challenges toenvironmental management. The practicalitiesof policy, planning and on-the-ground actionsrequire that boundaries are placed upon environ-mental gradients in the application of manage-ment programmes. The process of ‘drawing linesupon maps’ is essentially a task in classification,for which a myriad of procedures can be applied.This issue has been particularly fractious in riverscience and management (e.g. Goodwin 1999;Simon

et al.

2007). Rivers operate as highlyconnected systems, with a continuum of formsand processes that adjust over a range of times-cales (Cullum

et al.

2008). However, local-scaleinteractions may fashion distinctive physicaland biotic responses at any given site, inducingdiscontinuities along river courses (e.g. Poole2002). As a consequence of these considerations,

river scientists promote the use of catchment-specific understanding as the most appropriatebasis for river management strategies and actions(e.g. Hynes 1975; Brierley & Fryirs 2005; Wohl

et al.

2005). As each catchment has its own setof attributes and behavioural traits, with its ownhistory, many dangers are faced in managingfor ‘general’ relationships relative to ‘distinctiveor unique’ features that fashion the biodiversityand/or geodiversity of any given system.

Despite the difficulties and limitations indicatedabove, there is increasing agreement amongresearchers and managers in differing parts ofthe world on the most appropriate approachwith which to frame the classification of riversystems. As noted by Poff (1997), controls uponriver character and behaviour can be concep-tualized as a series of environmental filters thatoperate at differing scales. This understanding

Note about the authors: This research was completed by Liza Inglis as part of her MSc thesis in EnvironmentalScience at The University of Auckland, under the supervision of Ian Boothroyd (freshwater ecologist) and GaryBrierley (fluvial geomorphologist). Liza now works for Tonkin and Taylor in Auckland.

E-mail: [email protected]

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© 2008 The AuthorsJournal compilation

© 2008 The New Zealand Geographical Society

has prompted the emergence and uptake ofnested hierarchical approaches to the classifi-cation of river systems. Examples include NorthAmerica (Frissell

et al.

1986; Naiman

et al.

1992;Bohn & Kershner 2002); Europe (Petts & Amoros1996); South Africa (Rogers & O’Keefe 2003);Australia (Brierley & Fryirs 2005). An equiva-lent set of procedures has been developed inNew Zealand, termed the River EnvironmentClassification (Snelder

et al.

2004a,b).In general terms, these various frameworks

promote the use of

ecoregion

-scale thinkingto address issues such as biodiversity manage-ment at national and/or state levels. Planningof environmental strategies and prioritizationof actions is typically applied at the

catchment

scale. However, the implementation of on-the-ground actions is largely undertaken at the

reach

scale, incorporating concerns for

landform

and

hydraulic unit

scale interactions that fashionthe availability and viability of habitat (andassociated ecosystem values). Determinationof reference reaches is a particularly importantaspect of classification procedures, as thesedecisions influence what we seek to achieve inmanagement applications, especially relating torehabilitation initiatives. For example, assess-ment of river condition (or health) is typicallyframed relative to a reference condition orguiding image (‘leitbild’), providing a templateagainst which rehabilitation goals and applica-tions can be measured (Kern 1992; Jansson

et al.

2005; Palmer

et al.

2005). Ideally, guidingimages are derived for differing river types,reflecting variability in topography, climate,vegetation, land use and species distribution atvarious spatial scales (Maddock 1999; Fryirs2003; Palmer

et al.

2005). Also, classificationprocedures guide the development of represent-ative monitoring programmes, an integral com-ponent of adaptive management practices.

The New Zealand River Environment Class-ification (REC) aims to identify rivers with sim-ilar physical and biological attributes (Snelder

et al.

2004a). This scheme was developed as atool to assist planning decisions and the develop-ment of environmental policy. River classes or‘management units’ provide river managers witha tool to place resource issues into an environ-mental context, as outlined in the ResourceManagement Act (1991). The application andeffectiveness of the REC to stream management

relies on the ability of the methodology tocorrectly classify different biological com-munities using a hierarchical physical template.To date, limited research has been undertakento test the ability of the REC to classify streamsat the regional scale. Snelder

et al.

(2004b)examined the ability of the REC to explainvariation in macroinvertebrate assemblagesbetween the Waikato and Canterbury Regions.Their work considered the differences betweenthe first four classification levels produced bythe REC (climate, topography, geology andland cover) but did not include the fine scalefactor of valley landform. Findings indicatedthat the REC has a higher classification strengththan the ecoregion approach which is basedon labelling and mapping geographical regionswith homogenous and distinctive landscapescale attributes such as climate and topographycompared to other regions (Snelder

et al.

2004b).However, it was also found that the overallclassification strength of the REC, a measurehow well a classification explains variation inthe objects being classified, was low.

In this study, the effectiveness of the RECas a tool to classify physical and biologicalattributes of rivers is appraised for four classesof stream in the Auckland region. The validityof the REC was tested with the hypothesis thathabitat and macroinvertebrate communitieswithin the same REC class were more similarrelative to habitat and communities in otherREC classes. Physical (habitat assessment)and biological (macroinvertebrate assessment)attributes were sampled at 10 sites for each ofthe four REC classes identified in Fig. 1.

Methods

Site selection

Study sites were selected from four REC classesthat are representative of the predominantcharacteristics in the Auckland region, wherelow order streams flow atop soft sediments withpastoral land cover. The four REC classes chosenwere determined based on all six physicallevels of the classification (Fig. 1). Four physicalfactors remained constant at each site: climate(warm wet), topography (low elevation), landcover (pastoral) and network position (loworder). The characteristics used to define eachphysical factor are presented in Table 1. Two

Effectiveness of REC in Auckland

183



physical factors varied: geology (hard sedimen-tary and soft sedimentary) and valley landform(low gradient and high gradient). In summaryterms, the four classes studied wereHSLG – Warm-climate, Low elevation,

Hardsedimentary

, pasture, Low order,

Low gradient

HSHG – Warm-climate, Low elevation,

Hardsedimentary


High gradient

SSLG – Warm-climate, Low elevation,

Softsedimentary


Low gradient

SSHG – Warm-climate, Low elevation,

Softsedimentary


High gradient

Ten sample sites were randomly selectedaround the Auckland region from each RECclass (Fig. 2). Sites were then assessed basedon accessibility and the presence of water.Sites that were not accessible and/or hadlimited water were excluded due to difficultiesin sampling for macroinvertebrates. For con-sistency, sampling was completed at the lowerportion of each reach.

Habitat assessment

Habitat quality at each sample site was measuredusing procedures developed by the Auckland

Regional Council (unpublished). The habitatassessment method includes factors which directlyinfluence biotic communities such as riparianvegetation and aquatic substrate. The qualitativehabitat assessment method uses a ranking systemto assess overall river condition producinga Habitat Quality Index (HQI) score. Sevenhabitat parameters (aquatic habitat abundance,aquatic habitat diversity, hydrologic hetero-geneity, channel alteration, bank stability, channelshading and riparian vegetation) were visuallyassessed over a 100-m reach at each site. Theseare given a rating on the basis of comparison witha reference state which is defined by the ARCmethod as a stream with minimal human dis-turbance. The quantitative habitat assessmentconsisted of 11 stream profiles along a 100-mreach where three components were assessedat each; the riparian zone, the stream bank andinstream factors such as aquatic substrate, wettedwidth and flow type (run, riffle or pool).

Macroinvertebrate survey

The aim of the macroinvertebrate survey wasto observe variability in the macroinvertebrate

Figure 1 Sampling design used to differentiate among four REC classes of stream in the Auckland region.

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community structure between the sample sitesand determine whether samples taken from siteswithin the same REC class were more similarrelative to assemblages determined at sitesfrom other REC classes. All macroinvertebratesampling was undertaken during March 2005when streams were under baseflow conditions,keeping the sampling consistent between thesites.

Macroinvertebrate sampling was performedby applying ‘protocols for sampling macro-invertebrates in wadeable streams’ (Stark

et al.

2001). As any given sample reach may compriseboth hard bottomed and soft bottomed sections,a semi-quantitative sample was taken in accord-ance with the soft bottomed protocol (C2)developed by Stark

et al.

(2001), with the additionthat if riffle habitat was found within a site, itwas also sampled. Four habitats were sampled(wood, bank margins, macrophyte beds andriffles). The habitats were selected in proportionto their prevalence along a 100-m reach of thestream with a total of 10 sampling units (0.3 m

2

)being sampled over that reach. Sampling wasundertaken using a triangle framed net with a0.5-mm mesh size. A 0.5-mm sieve was used totransfer samples to a plastic sample container(usually 500 ml in volume).

Procedures for subsampling entailed both afixed count (Protocol C2–200(+)) and scan forrare taxa (Stark

et al.

2001), as recommendedby Duggan

et al.

(2002). Samples were placedin a tray divided into 6 cm

×

6 cm squares.Squares were chosen at random using a randomnumbers table and all individuals within eachsquare were removed until 200 individuals wereremoved. Individuals were then identified togenus level with the following exceptions: Platy-helminthes to phylum, Oligochaeta to order,Mucidae, Empididae, Elmidae, Ostracoda tofamily and Orthocladiinae and Tanypodinaeto subfamily. In order to use the data collectedfrom the 200(+) fixed count method, the areaabundance was determined for each site bydividing the number of individuals by the numberof squares counted and then multiplying thatby the total number of squares on the sampletray.

Data analysis

Non-parametric tests.

The Kruskal-Wallis test(nonparametric equivalent of a one-way analysisof variance) was applied to the habitat datato determine whether there was a statisticallysignificant difference between any two of the

Table 1 Definitions of each characteristics that make up each REC factor (Snelder et al. 2004a)

REC factor Definitions

Climate: Warm-Wet (WW) Annual temperature of equal to or greater than 12 °C and effective rainfall of 500 to 1500 mm per year

Source of Flow: Low-elevation (L)

Locations where 50% of cumulative rainfall fell at 400 m or lower

Geology: Soft sedimentary (SS) and Hard sedimentary (HS)

Soft Sedimentary (SS) – Soft sediment rock type in the North Island is made up of siltstone, mudstone and limestone. If 25% or more of the river section is made up of soft sedimentary rock types then the section is classified as soft sedimentaryHard Sedimentary (HS) – Hard sedimentary rock type is made up of greywacke and schist. If hard sedimentary rock types makeup the greatest proportion of a river section, that section is classified as hard sedimentary

Land Cover: Pastoral (P)

Environments where at least 25% of land cover as primarily pastoral or primarily horticulture

Network-Position: Low-order (LO)

Streams were all either first or second order

Valley Landform: Low Gradient (LG) and High Gradient (HG)

Low Gradient (LG) – determined as having a valley slope less than 0.02High Gradient (HG) – by having a valley slope greater than 0.04


185



four REC classes. When a significant differenceoccurred, a Mann-Whitney U-test was appliedto determine which REC classes contributedto the difference. All nonparametric tests wereundertaken using SPSS software (SPSS 2002).

Analysis of similarity.

Two-way nested analysisof similarity (ANOSIM) was used to determinewhether significant differences existed betweenmacroinvertebrate assemblages from the fourREC classes. ANOSIM is a nonparametric

Figure 2 Location of 10 sample sites for each REC class in the Auckland region.

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permutation method similar to the parametricequivalent Analysis of Variance (

ANOVA

).ANOSIM is based on a similarity/dissimilaritymatrix (Bray-Curtis similarity matrix) producedfrom the original data set on macroinvertebrateabundance data.

The nested factors in this case were thesamples from various rivers, which were thencompared with the main treatment groups (RECclasses). ANOSIM was run using 999 randompermutations. The test produces a samplestatistic (global R) and a significance level forthe sample statistic. If there is a significantdifference, a pairwise test is produced showingthe possible groups and which groupings showsignificant differences with an R statistic.

Multivariate analysis.

Non-metric multidimen-sional scaling (MDS) was undertaken on themacroinvertebrate abundance data with theremoval of species found in less than five percentof the sites and the rare taxa counts which mighthave skewed the results. The MDS was con-structed from a Bray-Curtis similarity matrix,fourth root data transformation and run at 30restarts. Hierarchical cluster analysis was usedon the macroinvertebrate data to check theconclusions drawn from the MDS analysis.Multivariate analyses were undertaken usingthe statistical computer software PRIMER(Plymouth Routines In Multivariate EcologicalResearch) (PRIMER-E Ltd 2001). Finally, theSpearman Rank Correlation coefficient was usedto identify which environmental variables andmacroinvertebrate indices correlated with theaxes produced from the multivariate analyses.

Results

Habitat assessment

Thirty physical parameters were measuredas part of the quantitative habitat assessment(see Table 2; Inglis 2005). Of the thirty physicalparameters measured, only twelve showedsignificant differences between at least two ofthe four REC classes. As expected, the factorsthat did show differences tended to be factorsrelated to catchment area and functions ofgradient. The lower gradient sites tended to bewider and deeper, having more soft sediment,higher erosion and more algae present whencompared with the high gradient sites. Large

variations within each REC class reduced thepossibility of determining differences betweenREC classes (Table 2).

The SSLG and HSHG classes showed thegreatest differences in the catchment and habitatdata, with a greater within-class similarity thanbetween classes (Table 2). For example, pastoralland use, and the presence of soft sediment andpools is greatest in the SSLG sites and lowestin the HSHG sites. Indigenous forest cover wasgreatest within HSHG (mean = 43%) and theSSLG class was dominated by pastoral land use(mean = 78%). The HSHG sites had narrowerstream channels (mean = 1.59 m), with a highpresence of cobbles, boulders and low algaeand macrophyte cover. In contrast, channels atthe SSLG sites were wider (mean = 2.39 m) andwere dominated by soft sediment and macrophytecover. As a result of the large variation withinclasses, 18 of the 30 parameters assessed showedno significant differences between any of thefour REC classes.

The presence of boulders differed betweenthe four REC classes (

P

< 0.05, Table 3). TheMann-Whitney

U

-test showed that the presenceof boulders exhibited a significant differencebetween the low gradient HSLG and SSLGclasses and the high gradient HSHG andSSHG classes. The HSHG class had a meancoverage of 16.4% of boulders and 37.9%cobbles compared to the SSLG class which hada mean of 1.6% boulders and 20.4% cobbles.The HSHG class also had the least amount ofsoft sediment (21.3%) compared to the SSLGclass (60%) (Table 2). There was no statisticallysignificant difference (

P

> 0.05) in the presenceof soft sediment between REC classes becauseof the high variation in results found withineach class.

The results of the HQI qualitative habitatassessment did not provide any conclusivedifferences between the four REC classes, withthe exception of the bank stability parameter(Table 4). The two hard sedimentary classes(HSLG and HSHG) had significantly morestable banks than the SSLG class. The rest ofthe physical parameters showed high overlapbased on the HQI’s physical criteria. The totalHQI scores also showed large variation withinclasses with some sites having high HQI scorescompared with other sites which had verylow HQI scores within the same class (Table 5).


187



The Kruskal-Wallis test confirmed that there wasno significant difference in HQI scores betweenthe four REC classes (

P

> 0.05) (Table 4).Considerable overlap is evident between

physical factors for all REC classes. In severalinstances, sites within a given class were moresimilar to other classes, relative to other siteswithin their own class. For example, within theHSHG class, site P3 had no riparian vegetation,high amounts of soft sediment and a low HQIscore. As a result, this site was more similar tothe SSHG and HSLG classes. Similarly, site

Table 2 Kruskal-Wallis test of habitat and water quality data amongst four REC classes in the Auckland region

Parameter

REC Class

P-value

HSLG HSHG SSLG SSHG

Mean SE Mean SE Mean SE Mean SE

Catchment area (km2) 4.05 0.69 1.61 0.26 3.49 0.86 1.18 0.37 0.000**% of Pastoral land use 69.26 6.99 37.85 5.50 78.29 5.66 73.32 8.22 0.003*% of Indigenous forest 21.82 6.06 42.84 8.88 8.60 3.09 11.42 4.99 0.031*% of Scrub 3.96 1.46 4.18 1.70 11.23 4.78 3.62 2.17 0.168% of Wetlands 0.00 0.00 0.00 0.00 0.05 0.03 0.00 0.00 0.104% of Exotic forest 4.97 4.92 15.14 10.01 0.16 0.16 11.65 8.05 0.622% of Urban land use 0.00 0.00 0.06 0.06 1.68 1.27 0.00 0.00 0.261Stream width (m) 2.31 0.18 1.59 0.19 2.39 0.27 1.39 0.14 0.002**Gradient (°) 0.77 0.17 1.64 0.27 0.49 0.08 1.26 0.22 0.001**% Unstable bank 34.09 7.40 21.92 5.42 5.91 1.92 17.27 5.15 0.026*DO concentrations (%) 77.17 6.87 97.55 4.22 61.07 11.31 89.96 7.70 0.009**pH 6.84 0.06 7.15 0.07 7.26 0.17 7.26 0.09 0.019*Temperature (°C) 17.24 0.44 15.89 0.37 17.55 0.68 17.19 0.49 0.093% of Bedrock substrate 0.91 0.91 3.78 3.78 5.64 3.95 11.82 7.30 0.307% of Boulder substrate 3.09 1.78 16.48 4.89 1.64 0.99 7.27 3.30 0.015*% of Cobble substrate 16.00 7.13 37.91 6.78 20.36 8.18 19.96 7.79 0.154% of Gravel substrate 37.45 10.84 17.75 4.79 9.27 2.77 19.16 5.08 0.261% of Soft sediment substrate 40.55 12.88 21.31 5.15 60.00 12.71 38.91 9.58 0.250% of Hard clay substrate 2.00 1.10 2.56 1.39 2.18 0.97 2.66 1.23 0.895% of Man made substrate 0.00 0.00 0.20 0.20 0.91 0.91 0.20 0.20 0.787% of Detritus substrate 11.45 2.97 15.81 4.82 17.89 5.95 19.29 4.60 0.650% of Bryophytes substrate 0.73 0.56 1.78 1.78 0.00 0.00 0.91 0.49 0.324% of Macrophyte cover 30.91 6.61 26.73 7.12 38.93 11.41 22.65 9.13 0.451% of Algae cover 23.82 4.10 14.44 3.78 12.91 3.05 7.54 2.04 0.034*% Woody debris present 5.45 2.15 3.33 1.40 4.05 1.89 2.73 0.73 0.941% of Roots present 7.09 1.57 6.34 0.89 7.12 2.66 10.06 2.84 0.621% No organic substrate 20.55 5.41 31.56 2.96 19.09 5.07 36.83 5.28 0.039*% of Pools 21.82 9.79 14.33 3.43 42.73 9.10 30.00 8.25 0.057% of Riffles 13.64 5.29 34.65 7.05 8.18 3.70 10.00 4.17 0.019*% of Runs 64.55 10.45 47.38 7.72 49.09 10.34 59.09 7.81 0.418

*Difference is significant at the 0.05 level (2-tailed).**Difference is significant at the 0.01 level (2-tailed).

Table 3

Mann-Whitney

U

-test for percent of boulders present

Pairwise comparisons U Z

P

HSLG – HSHG 17 –2.533 0.011*HSLG – SSLG 41 –0.77 0.441HSLG – SSHG 41.5 –0.687 0.492HSHG – SSHG 29.5 –1.573 0.116HSHG – SSLG 13.5 –2.853 0.004**SSLG – SSHG 34.5 –1.323 0.186

*

Correlation is significant at the 0.05 level (2-tailed).

**

Correlation is significant at the 0.01 level (2-tailed).

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SSLG B6, which had good native vegetation,(elongate) pools and riffles and a high HQIscore, was more similar to the SSHG andHSHG classes than the sites within its ownREC class.

Macroinvertebrate data

Ninety macroinvertebrate taxa were recordedfrom the forty streams studied. The dominanttaxa found were the pond snail

Potamopyrgusantipodum

, Diptera

Austrosimulium tillyardium

,Trichoptera

Oxyethira albiceps

and Amphipoda

Paracalliope.

Rare taxa included Ephemeroptera

Ichthybotus hudsoni

and

Austroclima sepia

, andTrichoptera

Costachorema spand

.Macroinvertebrate communities were similar

for all REC classes (Fig. 3). A Kruskal-WallisTest undertaken on the proportional datafor the seven major macroinvertebrate groupsrevealed that only Crustacea exhibited a signifi-cant difference in abundance between the fourREC classes (

P

< 0.5, Table 6). Macroinverte-brate communities differed between at least

two of the four REC classes (ANOSIM R = 0.084,

P

< 0.05). Pairwise comparisons showed that themacroinvertebrate communities in the HSHGclass were significantly different to the HSLG(

P

< 0.05) and the SSLG class (

P

< 0.01) (Table 7).There was no significant difference betweenthe remaining REC classes.

The MDS plot produced for the macro-invertebrate data, and associated clusters, isshown in Fig. 4. The stress value for the MDSis 0.19. According to Clarke and Warwick(2001) if the stress value is above 0.2 then thepoints are close to being arbitrarily placed and

Figure 3 Percent abundance of seven major macroinvertebrate groups from four REC classes. Note the similarityin abundance for each group.

Table 4 Kruskal-Wallis test for all HQI parameters between all four REC classifications

Aquatic habitat abundance

Aquatic habitat diversity

Hydrological heterogeneity

Channel alteration

Bank stability

Channel shade

Riparian vegetation integrity HQI score

0.058 0.956 0.868 0.298 0.002† 0.882 0.711 0.563

†Difference is significant at the 0.01 level (2-tailed)

Table 5 Total HQI score

REC class Mean St error Min Max Range

HSLG 73.50 5.86 44.00 99.00 55.00HSHG 83.00 6.13 43.00 105.00 62.00SSLG 82.90 7.82 55.00 118.00 63.00SSHG 83.40 5.69 58.00 122.00 64.00All sites 80.54 6.22 43.00 122.00 79.00


189



the results should be treated with scepticism.If the stress value is below 0.2, the plot gives areasonably useful representation of the data.Two main groups are identified, with one out-lier (B3). The only REC class which appearsto group out in ordination space is the HSHGclass, which is found within the cluster on theright of the ordination plot. However, whenthe cluster analysis is examined at the 45%

Bray-Curtis similarity level, the HSHG sitescan be divided into two clusters, one of whichhas higher EPT counts and higher MCI scoresthan the other (Fig. 4). Other than the HSHGclass, the remaining REC classes are spreadacross all clusters in the MDS plot.

The Spearman Rank correlations betweenthe habitat data and the two MDS axes are usedto define the axes for the MDS plot. Axis one

Figure 4 Macroinvertebrate community two-dimensional MDS plot, with superimposed clusters at similarity levelsof 37% (dashed lines) and 45% (dotted lines). Negative and positive gradients with physical parameters are identifiedbased on Spearman rank correlations along the axes.

Table 6 Mann-Whitney U-test for the percentage abundance of Crustacea

Pairwise comparisons U Z P

HSLG – HSHG 24 –2.008 0.045*HSLG – SSLG 48 –0.151 0.88HSLG – SSHG 41 –0.681 0.496HSHG – SSHG 13 –2.818 0.005**HSHG – SSLG 27 –1.752 0.08SSLG – SSHG 36 –1.058 0.29


Table 7 ANOSIM Test Pairwise comparisons for the REC classes

Pairwise comparisons R statistic

Significance level % P

HSLG – HSHG 0.182 1.2 0.012*HSLG – SSLG 0.017 35.5 0.355HSLG – SSHG –0.025 59.5 0.595HSHG – SSLG 0.286 0.2 0.002**HSHG – SSHG 0.091 9.3 0.093SSLG – SSHG –0.074 88.3 0.883


190 L. Inglis et al.

© 2008 The AuthorsJournal compilation © 2008 The New Zealand Geographical Society

has a gradient of soft sediment, macrophytes,filamentous algae, and a lack of canopy covertowards the left of the plot. High amounts ofshade, good canopy cover, hard bottomed habitat,high HQI scores and also higher DO and pHconcentrations occur towards the right. Axistwo has a lack of canopy and high coverage ofalgae at the top of the plot compared with highshade, detritus and native understorey towardsthe bottom of the plot.

The distribution of the sample sites based onmacroinvertebrate communities in ordinationspace can be related to the physical habitat gra-dients that have been identified. The HSHGsites, for example, are found towards the lowerright hand side of the plot. However, because theremaining classes showed such large variationin their inorganic substrates, they are distributedwithin the ordination plot based on inorganicsubstrate, rather than REC classes. Thus, thesites to the left of the plot were generally softbottomed and those to the right were generallyhard bottomed.

Discussion and conclusions

Results from this study indicate that biologicalattributes between sample sites within the sameclass varied considerably in response to vari-ations in the physical conditions of the samplesites. The major findings were the correlationsbetween the soft bottomed habitat and reducedriparian vegetation cover, reduced DO con-centrations, increased macrophyte and algaegrowth and the presence of Mollusca andCrustacea. In contrast, the hard bottomed siteshad increased riparian vegetation, increasedDO concentrations, increased wood and detritusand the presence of EPT taxa. Only the HSHGclass, which was predominantly hard bottomed,showed more similar characteristics to siteswithin the same REC class than to sites fromother REC classes (Fig. 4). The HSLG, SSLGand SSHG classes had a larger variation ofsubstrate types. For example, some sites in theSSHG class were dominated by hard bottomedsubstrates including riffle habitat, whereas other

Table 8 Physical and biological characteristics of sites from the four REC classes which showed good biological and habitat condition as defined by the HQI and MCI. Sites within the SSHG and HSHG class which had less pasture land cover within the catchment than specified by the REC

have been excluded from this synthesis

HSLG class HSHG class SSLG class SSHG class

Physical characteristics Physical characteristics: Physical characteristics: Physical characteristics:

Runs Runs/riffles/shallow pools Runs/riffles/elongate Runs/riffles/elongate Gravel and soft sediment Boulders, cobbles and pools poolsGood riparian vegetation gravel substrate Cobbles substrate Bedrock, boulders and

exceeding 50% shade Good riparian vegetation Good riparian vegetation soft sedimentexceeding 65% shade exceeding 65% shade Good riparian vegetation

exceeding 65% shade

Biological characteristics Biological characteristics: Biological characteristics: Biological characteristics:

Potamopyrgus Potamopyrgus Potamopyrgus Potamopyrgus Paracalliope sp Coloburiscus humeralis Neozephlebia scita Coloburiscus humeralisNeozephlebia scita Neozephlebia scita Aoteapsyche colonica Zephlebia

Aoteapsyche colonica Elmidae sp Aoteapsyche colonicaChironominae HydrobiosidaeTanytarsini Chironominae Tanytarsini

Example: G4 Example: P7 Example: B6 Example: R3

Effectiveness of REC in Auckland 191


sites were dominated by soft bottomed sub-strates and macrophytes. As a result, the bioticcommunities found at these sites were differentfrom each other, leading to large variabilitywithin the REC classes (Fig. 4).

Physical and biological differences betweenthe HSHG class and other classes reflectdifferences in physical habitat, as HSHG siteshave greater abundance of riffle habitat andwhite water – these attributes were seldomseen in the other classes. Physical habitat hasa strong influence on the type of species thatcan survive at any given locality. Coarser sub-strates are generally more stable and are ableto accumulate more periphyton and coarseorganic matter than finer-grained bed materials– attributes for which particularly species ofmayflies, stoneflies, cased caddisflies and dipterahave a preference (Quinn & Hickey 1990; Death2000). Although the HSHG class had a greateroverall presence of riffle habitat, some sitesfrom the other classes also showed similarphysical habitat. Macroinvertebrate communitiesat these sites were similar to those recorded atthe majority of the HSHG sites, indicating theprimacy of physical habitat as an influence oncommunity structure in instances where markeddifferences in geomorphic river structure can beidentified. Although clear distinctions amongphysical and biological attributes could notbe identified for REC classes in this study,findings indicate that the underlying ecologicalbasis upon which REC have been framedare correct in their own right. Unfortunately,however, clear differentiation of physicalhabitat among the four classes of rivers hasnot been achieved using the GIS-derived dataapplied in this research. Hence, caution isurged in the use of these overly generalizeddata, as significant variability may be evidentat finer scales. Prospectively, increasedavailability of fine scale remote sensing data willimprove the efficacy of GIS-based mappingapplications.

The key constraint in the application of riverclassification is not the tools themselves, but theway in which they are used. Differing tools mustbe applied for the purposes for which they weredeveloped. This often proves to be difficult, asmany regions lack information on the physicaland ecological status of rivers, and managershave to resort to use of available information.

Results generated using poor quality informa-tion should be treated with caution or as generalguidance only. For example, significant cautionmust be applied in framing monitoring pro-grammes or designing rehabilitation initiativesin relation to reference conditions based uponthe use of GIS-based analyses of 25 m digitalelevation model data. Potential refinementusing finer resolution LiDAR data and othertools may provide clearer insights into thephysical structure of rivers, and associatedhydraulic diversity and habitat relationships.

Findings from this study attest to the criticalimportance of field-based investigations in thedetermination and application of monitoringand rehabilitation programmes. Of particularimportance with this regard is meaningfuldifferentiation not only of river types, but alsoof differing condition variants of any given rivertype (e.g. Fryirs 2003). Analyses of geomorphicriver type and condition must be combined toprovide a meaningful physical platform withwhich to appraise ecological relationships alongriver courses (e.g. Chessman et al. 2006). Suchinvestigations must also incorporate water qualityattributes to provide a coherent biophysicaloverview of a given site. In this study, it is likelythat differences in the geomorphic condition andwater quality at selected sample sites exerteda significant influence upon ecological relation-ships, further complicating this simple test ofthe efficacy of the REC. Ideally, such a testwould compare good condition variants of eachriver type, as this presents the most appropriatebasis to characterize reference conditions orguiding images for rehabilitation efforts. Anindicative guide to the distinguishing factorsthat represent good ecological condition foreach REC class, as synthesized from the best-condition variant of the 10 samples sites in thisstudy, is presented in Table 8.

In summary, ecologically meaningful differ-entiation of river types has not been achievedin this test of the REC. In large part, this isconsidered to reflect the scale/resolution of dataused to classify streams in desk-top exercises.Hence, there are significant limitations in theuse of this classification scheme in the designof representative sampling or monitoring pro-grammes, or determination of guiding imagesfor rehabilitation efforts. Field analyses arerequired to confirm the physical basis of river

192 L. Inglis et al.


assessment procedures on the one hand, and toperform analyses of geomorphic river condi-tion on the other (Fryirs 2003). These insightsunderpin management applications that striveto ‘work with nature’, enhancing prospects toreinstigate the inherent diversity and variabilityof river systems (Brierley & Fryirs 2008).

Acknowledgements

We would like to thank the Auckland RegionalCouncil who supported this project, DamienInglis and various colleagues for field supportand Susan Owen, Nadine Trahan and the twoanonymous journal reviewers for their helpfulcomments on this manuscript.

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Effectiveness of the River Environment Classification in the Auckland Region