Contrasting influence of flow regime onfreshwater fishes displaying diadromous andnondiadromous life historiesShannan K. Crow, Doug J. Booker, Ton H. SnelderNational Institute of Water and Atmospheric Research, Riccarton, Christchurch, New Zealand

Accepted for publication August 7, 2012

Abstract – Flow variability structures freshwater fish community traits and life-history patterns such as migrationpatterns between fresh and saltwater (diadromy). Few studies, however, have explored relationships betweendiadromy and flow regime while accounting for other abiotic covariables. The present paper used canonicalordinations to remove the shared variation between groups of explanatory variables that explain variation in fishcommunities and examine two objectives with New Zealand fishes: (i) to compare the unique contributions ofHydrological Regime, Climate, Habitat and Spatial-Seasonal data sets to the variation of diadromous andnondiadromous fishes and (ii) to compare the relative contributions of a Hydrological Variability and Low-Flowdata set to community structure of both life-history patterns. All explanatory variables explained a total of 20.15%and 29.58% of the variation in diadromous and nondiadromous fishes, respectively. Objective 1 analyses showedthat the largest unique component of variation was explained by Hydrological Regime for nondiadromous fishes(12.17%), while Climate uniquely explained the most variation in diadromous fishes (4.3%), followed closely byHydrological Regime (3.08%). Objective 2 analyses showed that Hydrological Variability uniquely explained fiveand 11 times more variation than the Low-Flow data set in diadromous and nondiadromous fishes, respectively.Findings illustrate the importance of hydrological regime to New Zealand freshwater fishes. Specifically, aspects ofhydrological variability uniquely account for more variation than aspects of low flow. Differing relative influencesof hydrology between life-history patterns suggest that diadromy may mediate the influence of flow regime. Resultsoutline difficulties for environmental flow settings when biota display differing life histories.

Key words: flow regime; ecology; variation partitioning; community

Introduction

Freshwater fish communities are structured by avariety of interacting abiotic and biotic processes(Jackson et al. 2001). Historically, research hasfocussed on the biotic processes such as competition,suggesting that these are primary processes structur-ing ecosystems (McIntosh 1985; Ross 1986), butdeveloping work has identified the importance ofenvironmental variation to community structure(Levin & Paine 1974; Sousa 1984; Poff & Allan1995; Grossman & Ratajczak 1998; Grossman et al.1998). Specifically, the influence of hydrologicalvariability on freshwater communities has received

growing interest, and the important role this plays instructuring communities is well established (Poff &Zimmerman 2010). Understanding this form of envi-ronmental variation not only improves our knowledgeof community organisation (Grossman & Sabo2010), but also is likely to improve the reliability ofriver management decisions (Poff et al. 2010).Indeed, ignoring aspects of flow variability mayresult in misguided decisions or a failure to sustainvitality in rivers (Naiman et al. 2008).There is a pressing need to understand the relation-

ships between flow regimes and freshwater biota formanagement purposes, but identifying consistent rela-tionships is difficult given that ecological responses

Correspondence: S. K. Crow, National Institute of Water and Atmospheric Research, PO Box 8602, Riccarton, Christchurch, New Zealand. E-mail shannan.crow@niwa.co.nz

82 doi: 10.1111/eff.12004

Ecology of Freshwater Fish 2013: 22: 82–94Printed in Malaysia � All rights reserved

� 2012 John Wiley & Sons A/S

ECOLOGY OFFRESHWATER FISH

differ between life-history patterns. Hydrologicalalterations have been suggested to be the most press-ing threat to freshwater ecosystems (Naiman et al.2002), and the need to effectively manage freshwatersystems is further emphasised by the increasing bio-diversity threats facing this ecosystem (Sala et al.2000; Dudgeon et al. 2006). Historical managementmethodologies for stream-biota commonly focus onlow-flow indices (Tharme 2003), with restorationefforts rarely focussing on flow regimes (Bernhardtet al. 2005). However, with the improved understand-ing of the importance of flow regimes to freshwatercommunities (Arthington et al. 2006; Bunn &Arthington 2002; Poff et al. 2009; Poff & Zimmerman2010), managers are attempting to identify the rela-tionships between flow regime and biota. Identifyingconsistent responses of biota to flow regimes hasproven difficult, which could be associated with thedifferent community traits and life-history patternspresent (Poff & Ward 1989; Poff & Allan 1995). Forexample, life-history patterns vary across gradients ofhydrological variability (Poff & Allan 1995; Lytle2001; Lamouroux et al. 2002; Lytle & Poff 2004;Olden & Kennard 2010), with the hydrologicalregime dictating the relative success of the variouslife-history strategies present (Olden et al. 2006).Limited work, however, has focussed on the relation-ships between diadromous life histories and environ-mental variation (but see Leathwick et al. 2008), animportant life-history trait associated with fish distri-butions (McDowall 2010a).New Zealand freshwater fishes show two distinct

diadromous life-history patterns, which may mediatethe influence of hydrological regime on communitystructure. Diadromous freshwater fishes spend part oftheir lifecycle in the ocean, while nondiadromousfishes remain exclusively in freshwater (McDowall1988, 1990). As a result, nondiadromous fishesspend their entire lifecycle exposed to flow regimes,including the susceptible juvenile stages (Jowett &Richardson 1989). Diadromous fishes may alsominimise the impacts of flow regime on their distri-butional patterns given their ability to constantlyreinvade perturbed systems (McDowall 2010a). Incontrast, the ability of nondiadromous fishes to rein-vade perturbed systems is likely to be limited giventhe restricted movement implied by small spatial-scale genetic structuring (Burridge et al. 2006, 2007).Indeed, differences in dispersal ability has previouslybeen suggested as a mechanism that generates thewide geographical ranges of diadromous fishes, whilenondiadromous fishes appear to be limited to systemsof low disturbance (Leathwick et al. 2008). Hydro-logical regime, however, is only one abiotic factorthat may influence diadromous and nondiadromousfish communities, and isolating its influence relative

to the other abiotic factors remains a key focus inunderstanding fish ecology.Identifying the importance of hydrological regime

to the structure of fish communities can be difficultbecause of intercorrelations among hydrologicalindices (Olden & Poff 2003) and between hydrologi-cal indices and other abiotic variables (Snelder &Lamouroux 2010). More than 200 hydrological indi-ces have been proposed that quantify aspects of flowregimes (Monk et al. 2007), but many of these indi-ces are intercorrelated and redundant (Olden & Poff2003). Interpreting the importance of hydrologybecomes more complicated when the shared explan-atory power is also considered in relation to othernonhydrological data sets (e.g., space), but this hasbeen suggested as a necessary step towards accu-rately quantifying the effect of a single data set(Borcard et al. 1992; Legendre & Legendre 1998).This allows competing hypotheses to be addressed(see Peres-Neto 2004), generating more robust con-clusions because the unique explanatory power of adata set is identified (i.e., explanatory power notshared with other predictors). Moreover, failure toconsider the shared variation with other nonhydro-logical data sets will result in an overestimation ofthe importance of hydrological regime (Snelder &Lamouroux 2010). Identifying the relative influenceof hydrological regime compared with other metricsof low flow and other abiotic factors has receivedlittle research focus (but see Snelder & Lamouroux2010; Stewart-Koster et al. 2007). Quantifying therelative influence of flow variability to metrics oflow flow and other environmental variables is animportant step to advancing our understanding ofenvironmental flows (see Poff & Zimmerman 2010for discussion).The objectives of the present study were to (i)

examine the variation in New Zealand diadromousand nondiadromous fish communities that could beexplained by the hydrological regime (indicesdescribing aspects of low flow and hydrological vari-ability) relative to other abiotic explanatory variables(climate, habitat, spatial and seasonal variables); and(ii) examine the spatial variation in fish communitiesthat can be explained by indices of hydrological vari-ability relative to indices of low flow, and other abi-otic explanatory variables. To meet these aims, weapplied the methodology of Borcard et al. (1992) toidentify the variation in assemblage data that can beuniquely explained by individual data sets and isnot shared with any other abiotic explanatory vari-ables. These aims were examined separately for diad-romous and nondiadromous fishes, which allowedresults to be contrasted between the two life histories.We hypothesised that hydrological regime woulduniquely explain a significant amount of variation in

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diadromous and nondiadromous communities oncethe interacting effects of other abiotic variables havebeen taken into account. We also hypothesised thathydrological regime would have a larger influence onnondiadromous fish communities than on diadromousfish communities, given that the former communitiesare exposed to hydrological pressures throughouttheir entire lifecycles and have limited dispersal.Finally, we predicted that indices of hydrological var-iability (e.g., frequency and magnitude of floods andfreshes) would account for larger amounts of varia-tion than indices of low flow, given the importanceof environmental variation to the structure of fishcommunities (Grossman et al. 1998).

Methods

Fish data

The New Zealand Freshwater Fish Database(NZFFD) (McDowall & Richardson 1983) was usedto calculate the presence and absence of diadromousand nondiadromous fishes from throughout thecountry (Fig. 1). The NZFFD (http://www.niwa.co.nz/our-services/online-services/freshwater-fish-data-base) is an open resource where freshwater fish dataare entered into a predefined data sheet by any indi-vidual and then added to the database. The NZFFDcurrently contains 31,000 records with informationon the species present, sampling technique and col-lection site. Specifically, information on the site loca-tion is available that identifies the river segment of anational digital network as defined in the River Envi-ronment Classification (REC) of Snelder & Biggs(2002). This river network was derived from a 30-mdigital elevation model (DEM) and is comprised of560,000 uniquely numbered reaches (referred to asNZReach hereafter) with a mean length of ~700 m.To enable linkages between fish data from theNZFFD to various environmental data associatedwith the river network, all presence/absence data offishes were summarised by NZReach. All diadromousand nondiadromous species currently recognised withspecies descriptions and four taxa awaiting descrip-tion were included in the data set. The four taxa wereincluded because they are genetically distinct andhave been considered as evolutionary significant units(Waters & Wallis 2001a,b). Furthermore, morpholog-ical differences have recently been found betweensome of these taxa (Crow & McDowall 2011). Dataon fishes belonging to the Mugilidae family were notincluded as these taxa are marine. Fishes belongingto the families Tripterygiidae and Pleuronectidaewere removed from the data set as these fishes areestuarine based, with the exception of Rhombosolearetiaria. Rhombosolea retiaria which was included

as this fish predominantly occupies freshwater(McDowall 1990).To minimise sampling bias between NZReaches,

NZFFD records were only used if fish collectionswere completed by electric fishing after 1980 (Leath-wick et al. 2008). The number of NZFFD recordsavailable for each NZReach ranged from 1 to 27,with an average of 1.5 records. The number of cardsentered for each NZReach formed a positive relation-ship with the number of taxa found (R2 = 0.126,P < 0.001), generating a bias in community databetween NZReaches with differing numbers ofNZFFD records. To eliminate this bias, communitydata for each NZReach were taken from one ran-domly selected NZFFD record. Diadromous fish datawere also excluded from NZReaches that had barriers(e.g., Hydro schemes, see Leathwick et al. 2008)located downstream because these structures aresuggested to reduce migratory fish presence andrecruitment (Leathwick et al. 2008). Records ofnondiadromous species above barriers were allretained as they were likely to have negligible influ-ence on the recruitment and distribution. To reducethe effect of sampling frequency on the collection ofrare taxa, fish that were present at less than 5% of theNZReaches were also removed from the diadromousand nondiadromous data sets (Snelder & Lamouroux2010). These criteria resulted in a data set that con-tained presence/absence data from 6698 and 6150NZReaches for migratory and nonmigratory fish,respectively. This included 10 migratory fish speciesand six nonmigratory fish species (Table 1). Life-his-tory classification of the 13 native fishes was basedon table 1.1 in McDowall (2010a), while life-historyclassification of the two exotic fishes was based onMcDowall (1990).

Data sets of explanatory variables

We used findings from the previous literature to gen-erate four data sets of explanatory variables relatingto hydrological, climatic, habitat and spatial-seasonalinformation that were likely to explain the variationin fish communities. Using the previous literatureallowed us to reduce the intercorrelation with thehydrological explanatory variables, but also provideduseful insights into the relative influence of the otherdata sets. Justification for the explanatory variablesincluded in each data set and the methodology usedto obtain this information for each NZReach areoutlined below.

Climate and Habitat data sets

The REC (Snelder & Biggs 2002; Snelder et al.2005) and the Freshwater Environments of New

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Zealand (FWENZ) databases (see Leathwick et al.2011 for details) were used to generate two data setsthat related to climate (Climate data set hereafter) andhabitat (Habitat data set hereafter) explanatory vari-ables for each NZReach (Table 2). The eight climatevariables and two of the habitat variables (ReachSuband ReachHab) were selected based on table 4 inthe study by Leathwick et al. (2008), which liststhese responses as the most important predictors ofdiadromous and nondiadromous fish presence in New

Zealand based on Boosted Regression Tree models.The two further habitat variables, Upstream area ofnative vegetation (USNative) and Segment shade(SegShade), were included in the Habitat data set asthese variables were likely to reflect the amount ofplant coverage that may provide cover to fishes, animportant variable selected by New Zealand freshwa-ter fish (Bonnett & Sykes 2002; Crow et al. 2010).Details on the data used to populate the REC and

FWENZ databases are available in Snelder & Biggs(2002); Snelder et al. (2005); Leathwick et al. (2011),which we briefly summarise here: the majority ofvariables used in the three data sets were derived fromspatial layers describing environmental variables thathave been converted into catchment characteristics(weighted mean of the parent layer) for eachNZReach. The variable slope (SegSlope) was derivedfrom a DEM (Snelder & Biggs 2002). Observedclimatic data were interpolated onto a grid (resolution1 km) describing the spatial variation in summer andwinter air temperature (SegTCold and SegTWarm).Finally, the amount of upstream catchment coveredby native forest (USNative) was derived from theNew Zealand Land Cover Database (MFE 2007).

Spatial-Seasonal data set

The REC and FWENZ databases were used to obtainEasting and Northing coordinates with respect to theNew Zealand grid-coordinate system for the centre ofeach NZReach. These coordinates were then used tocalculate a two-dimensional matrix of x (Easting) and

Fig. 1. Locations of the New Zealand Freshwater Fish Database records that contained presence/absence data of non-diadromous and diad-romous fishes.

Table 1. Life-history classification and the number of NZReaches thatcontained each diadromous (N = 6698) and nondiadromous (N = 6150)taxon.

Scientific name Common name DiadromousNo. ofNZReaches

Anguilla dieffenbachii Longfin eel Y 4889Anguilla australis Shortfin eel Y 2072Gobiomorphus huttoni Redfin bully Y 1656Gobiomorphus cotidianus Common bully Y 1473Cheimarrichthys fosteri Torrentfish Y 1033Galaxias maculatus Inanga Y 929Galaxias brevipinnis Koaro Y 901Galaxias fasciatus Banded kokopu Y 813Gobiomorphus hubbsi Bluegill bully Y 493Retropinna retropinna Common smelt Y 406Salmo trutta Brown trout N 3833Gobiomorphus breviceps Upland bully N 1919Oncorhynchus mykiss Rainbow trout N 920Galaxias vulgaris Canterbury

galaxiasN 659

Gobiomorphus basalis Crans bully N 475Galaxias divergens Dwarf galaxias N 331

Taxa are ordered by their abundance in NZReaches.

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y (Northing) geographical coordinates with the cubictrend surface regression formula suggested in thestudy by Legendre (1990) (also see Legendre &Legendre 1998). This matrix was calculated becauseit ensures that linear and complicated patterns such aspatches or gaps can be explained by the spatial dataset.

y ¼ b1xþ b2yþ b3x2 þ b4xyþ b5y

2 þ b6x3

þ b7x2yþ b8xy

2 þ b9y3:

This spatial matrix was selected for analysis asseveral freshwater fishes in New Zealand have specificdistributions and biographical patterns (McDowall1990, 2010a; Leathwick et al. 2008), an importantaspect to quantify for understanding the ecology andbiogeography of organisms (Legendre & Fortin 1989).Diadromous fishes were also likely to be more

abundant during early spring–early summer (Septem-ber–October) when the juveniles migrate into fresh-water (McDowall 1990, 2010a). To account for thisseasonal change in the fish community, we includedthe month the NZFFD sample was collected (month)as a response variable in this data set (Spatial-Sea-sonal data set hereafter).

Hydrological data set

Hydrological regime for each NZReach was repre-sented by a suite of hydrological indices (Olden &Poff 2003) (Hydrology data set hereafter). Theseindices (Table 3) were estimated for each NZReach

using a regression modelling approach. In brief,relationships between hydrological indices andcatchment characteristics were derived for a nationalset of flow gauging sites. The values of these indi-ces were then estimated for each NZReach usingregression models outlined below (e.g., Snelder &Lamouroux 2010).Hydrological data were acquired from the New

Zealand national hydrometric database (Pearson1998) and comprised daily mean flows measured atselected gauging stations distributed throughout thecountry. Gauging stations were only used for regres-sion models if they were unaffected by significantflow modification (i.e., no upstream dams and diver-sions) and had data records covering more than30 years (1976–2006). Stations with gaps in thetime-series longer than 10 days were removed. Wethen selected 474 stations for which at least 4 yearsof continuous data were available (mean = 24 years).These stations were located throughout New Zealandand represented a range of catchments and hydrologi-cal characteristics (Booker and Snelder 2012).The daily flows were standardised by their long-

term means to characterise flow regimes by variability,rather than magnitude (Poff et al. 2006). Followingsimilar methods to Olden & Poff (2003) and Richteret al. (1996), we computed 47 hydrological indicesfor each gauging station that characterised fiveaspects of the hydrological regime: variation of flows,magnitude and duration of annual extreme flows, tim-ing or predictability of flows, frequency and durationof high- and low-flow pulses, rate and frequency of

Table 2. Abbreviations and descriptions of the Climate, Habitat and Spatial-Seasonal explanatory variables used in the present study.

Data set Abbreviation Index description

Climate SegTSeas Winter air temperature (°C), normalised with respect to summer temperature (see Leathwick et al. 2008)SegTWarm Summer air temperature (°C)SegSlope Segment slope (°), square-root-transformedDSDist Downstream distance to the coast (km)DSAveSlope Downstream average slope (°)DSMaxSlope Maximum downstream slope (°)SegTCold Winter air temperature (°C)USPhosphorus Average phosphorus concentration of underlying rocks (1 = low, 5 = high)

Habitat ReachSubstrate Weighted average of proportional cover of bed sediment using categories of 1 – mud; 2 – sand;3 – fine gravel; 4 – coarse gravel; 5 – cobble; 6 – boulder; 7 – bedrock

ReachHabitat Weighted average of proportional cover of local habitat using categories of 1 – still; 2 – backwater;3 – pool; 4 – run; 5 – riffle; 6 – rapid; 7 – cascade

USNative Upstream/catchment area with indigenous vegetation (proportion)SegShade NZReach area with riparian shade (proportion)

Spatial-Seasonal segX Easting coordinates of the NZReach centresegY Northing coordinates of the NZReach centresegXY Multiple of XY from the cubic trend surface regression formulasegY2 Square of Y from the cubic trend surface regression formulasegX3 Cube of X from the cubic trend surface regression formulasegX2Y Multiple of X2Y from the cubic trend surface regression formulasegXY2 Multiple of XY2 from the cubic trend surface regression formulasegY3 Cube of Y from the cubic trend surface regression formulamonth Month that the sampling occurred

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changes of flow (Table 3). We then used the databaseassociated with the river network to select catchmentcharacteristics that were then used as predictors forthe 47 hydrological indices in Random Forest models(see Booker & Snelder 2012 for details). TheseRandom Forest models were then used to estimateeach hydrological index for each NZReach.Limited information was available that quantified

the importance of specific hydrological indices toNew Zealand fishes, but based on findings in NewZealand invertebrate communities (Clausen & Biggs

1997), the frequency and duration between floodevents that exceed 1–10 times the median flow (FRE1–FRE10) were included in the data set. Durationbetween and sizes of high flows were included basedon their impacts on fishes in New Zealand streams(Jowett & Richardson 1989; Jowett 1990; Jowett &Duncan 1990). Duration and frequency of low-flowpulses were also included in the data set based on theimpacts of reduced flows and low-flow duration onfish community composition (Jowett et al. 2005; Da-vey & Kelly 2007).

Table 3. Hydrological indices predicted for each NZReach.

Abbreviation Description Low-Flow metric

BFI Base flow index (mean annual 7-day low flow/mean flow) YConstancy Constancy of mean-monthly flows (see Colwell 1974)Contingency Contingency of mean-monthly flows among years (see Colwell 1974)Mean1DayMin Mean annual minimum 1-day flow/mean flow YMean3DayMin Mean annual minimum 3-day flow/mean flow YMean7DayMin Mean annual minimum 7-day flow/mean flow YMean30DayMin Mean annual minimum 30-day flow/mean flow YMean90DayMin Mean annual minimum 90-day flow/mean flow YMean1DayMax Mean annual maximum 1-day flow/mean flowMean3DayMax Mean annual maximum 3-day flow/mean flowMean7DayMax Mean annual maximum 7-day flow/mean flowMean30DayMax Mean annual maximum 30-day flow/mean flowMean90DayMax Mean annual maximum 90-day flow/mean flowFRE1.Count Number of flows greater than the medianFRE2.Count Number of flows greater than two times the medianFRE3.Count Number of flows greater than three times the medianFRE4.Count Number of flows greater than four times the medianFRE5.Count Number of flows greater than five times the medianFRE6.Count Number of flows greater than six times the medianFRE7.Count Number of flows greater than seven times the medianFRE8.Count Number of flows greater than eight times the medianFRE9.Count Number of flows greater than nine times the medianFRE10.Count Number of flows greater than ten times the medianFRE1.MeanDurBetween Mean duration between flow events that exceed the medianFRE2.MeanDurBetween Mean duration between flow events that exceed two times the medianFRE3.MeanDurBetween Mean duration between flow events that exceed three times the medianFRE4.MeanDurBetween Mean duration between flow events that exceed four times the medianFRE5.MeanDurBetween Mean duration between flow events that exceed five times the medianFRE6.MeanDurBetween Mean duration between flow events that exceed six times the medianFRE7.MeanDurBetween Mean duration between flow events that exceed seven times the medianFRE8.MeanDurBetween Mean duration between flow events that exceed eight times the medianFRE9.MeanDurBetween Mean duration between flow events that exceed nine times the medianFRE10.MeanDurBetween Mean duration between flow events that exceed ten times the medianl1 First linear momentl2 Second linear momentlca Ratio of the first to second linear moment of daily flowslkur Third linear moment of daily flowsMeanPulseLengthHigh Mean duration of high pulsesMeanPulseLengthLow Mean duration of low pulses YMedianPulseLengthHigh Median duration of high pulsesMedianPulseLengthLow Median duration of low pulses YnNeg Number of all negative differences between daysnPos Number of all positive differences between daysnPulsesHigh Number of high pulses within each water yearnPulsesLow Number of low pulses within each water year YPredictability Predictability of mean-monthly flows (Colwell 1974)Rev Number of hydrological reversals

Mean and SD are shown for each index, and the indices included in the Low-Flow data set are shown. All remaining metrics were considered to represent vari-ous aspects of flow variability.

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Analysis

We carried out two separate series of canonical ordi-nation analyses to examine the two aims of the presentstudy. The first analysis aimed to examine the relativeinfluence of the Climate, Habitat, Spatial-Seasonaland Hydrology data sets outlined above, while thesecond analysis aimed to examine the relative influ-ence of low-flow indices, indices of hydrologicalvariability and all remaining explanatory variables.The second analysis required the four data setsoutlined above to be regrouped into three new datasets (referred to as the Low-Flow data set, the Hydro-logical Variability data set and the Climate, Habitat,Spatial-Seasonal data set: see below for details). Datatreatment and the details of the two analyses areoutlined below.

Data transformations and variable selection

A Hellinger transformation was used to reduce theinfluence of abundant taxa (Legendre & Gallagher2001), before a forward variable selection (Legendre& Legendre 1998) was used to select parsimoniousvariables for each data set prior to analysis. To avoidinflating type 1 error and overestimating theexplained variation in variable selection, we used theprocedure of Blanchet et al. (2008) for all forwardselection analyses. To ensure that data were approxi-mately normally distributed, all explanatory variableswere log10 (x + 1)-transformed.

Relative influence of Hydrology, Climate, Habitatand Spatial-Seasonal data setsThe procedure outlined in the study by Borcardet al. (1992) was used to partition the total variationexplained (TVE) in fish communities into the indi-vidual and unique contributions of the four datasets. This approach utilised a series of canonicalanalyses and partial canonical analyses to estimatethe variation in fish communities explained by eachdata set (individual fractions), and the variationexplained by each data set after controlling for theeffect of the other data sets (unique fractions).These unique fractions therefore represent the varia-tion in fish communities explained by each data setthat is not shared with other explanatory variables.While shared components can be calculated withthis approach (see Borcard et al. 1992), these frac-tions were not included in the present study as ourhypotheses related directly to unique componentsand were therefore omitted for the sake of clarity.We used the method of Peres-Neto et al. (2006) toenable valid comparisons between the variationsexplained by each data set. This approach elimi-nates any bias in the estimates of explained varia-

tion that may be caused by differing numbers ofindependent variables and data in models (Zar1999). A permutation test was then used to examinethe significance of the unique component of varia-tion explained by each data set (Legendre & Legen-dre 1998). The above methodology was carried outseparately using diadromous and nondiadromousfishes, and the results were contrasted.

Relative influence of Low-Flow and High-Flowdata setsVariables from the four data sets were re-groupedinto three new data sets. The first data set containedinformation on the frequency or duration of lowflows, called the Low-Flow data set hereafter, whileall remaining hydrological indices were assigned to asingle data set called the Hydrological Variabilitydata set. Assignment of hydrological variables to oneof the two groups was made subjectively and isshown in Table 3. Explanatory variables from theremaining Climate, Habitat and Spatial-Seasonal datasets were pooled (Habitat, Climate, Spatial-Seasonaldata set hereafter).The variation partitioning procedure outlined above

was then used to partition the TVE of nondiadromousand diadromous communities into the individual andunique contributions of the three data sets. AdjustedR2 values and permutation tests were also used in thesame manner as the above analysis.

Results

Relative influence of Climate, Habitat, Spatial-Seasonaland Hydrological Regime data sets

With all data sets, the TVE was 20.2% and 29.6%for diadromous and nondiadromous communities,respectively (Table 4). For diadromous fishes, thelargest individual components were explained by the

Table 4. Total, individual and unique fractions of fish community variationexplained by the Climate, Hydrological Regime, Habitat and Spatial-Seasonal data sets.

Variation fraction Data setDiadromousfishes

Nondiadromousfishes

Total All 20.2 29.6Individual Climate 11.4 11.6

Hydrological Regime 12.9 22.5Habitat 8.9 3.6Spatial-Seasonal 2.4 10.4

Unique Climate 4.5 (22.1%) 1.2 (4.1%)Hydrological Regime 3.1 (15.3%) 12.2 (41.1%)Habitat 1.2 (6.0%) 0.5 (1.8%)Spatial-Seasonal 0.3 (1.7%) 2.2 (7.3%)

Values in parentheses show the percentage of the total variation explainedby the unique components.

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Hydrological Regime and Climate data sets (12.9%and 11.4%, respectively), followed by Habitat dataset (8.9%), while the Spatial-Seasonal data setexplained 2.4%. For nondiadromous fishes, theHydrological Regime data set generated the largestindividual component of 22.5%, followed by Climate(11.6%), Spatial-Seasonal (10.4%) and Habitat(3.6%) data sets.The components of variation that were uniquely

explained by each data set followed a similar hier-archy to the individual components. For diadromousfishes, the largest unique component of variationwas attributed to Climate (4.5%), which accountedfor 22.1% of the TVE. Hydrological Regimeuniquely explained 3.1% (15.3% of the TVE) ofthe fish community data with the Habitat data setonly uniquely explaining approximately 1.2% (6.0%of the TVE). The Spatial-Seasonal data set uniquelyexplained 0.3% (1.7% of the TVE). All uniquecomponents were highly significant with permuta-tion tests generating P-values of <0.001 for all datasets.For nondiadromous fishes, the largest unique com-

ponent of variation was attributed to the HydrologicalRegime data set which explained 12.2%, accountingfor 41.1% of the TVE (Table 4). The Spatial-Sea-sonal data set uniquely explained 2.2% (7.3% of theTVE), while Climate and Habitat data sets uniquelyexplained 1.2% (4.1% of the TVE) and 0.6% (1.8%of the TVE), respectively. All unique components ofvariation explained significant amounts of variation,with permutation tests generating P-values of <0.001for all data sets.

Relative influence of the Low-Flow and HydrologicalVariability data sets

TVE by this analysis was 18.7% and 27.6% for diad-romous and nondiadromous fishes, respectively,which was slightly lower than the variation explainedby the previous model (Table 5). These small differ-ences in TVE were generated by differing variableselection from the stepwise analysis carried out

within each data set. For diadromous fishes, theHydrological Variability data set individuallyaccounted for 10.8% of the variation, while the Low-Flow data set accounted for 6.2%. The remainingvariables accounted for the largest individual compo-nent at 15.8%. For nondiadromous fish communities,the Hydrological Variability data set individuallyaccounted for the largest amount of variation at18.8%, which was followed by the Habitat, Climate,Spatial-Seasonal data set (15.9%). Low-flow indicesindividually explained 11.5% of the variation.The Hydrological Variability data set uniquely

accounted for five and 11 times more variation thanthe Low-Flow data set for diadromous and nondiadr-omous fishes, respectively. Remaining explanatoryvariables uniquely accounted for 6.6 (35.6% of theTVE) and 6.3% (22.6% of the TVE) of the variationin diadromous and nondiadromous fishes, respec-tively. Congruent with patterns seen in the previousseries of ordinations, both Hydrological Variabilityand Low-Flow data sets explained larger amounts ofvariation (both unique and individual components) innondiadromous than in diadromous communities.

Discussion

Hydrological regime uniquely explained significantproportions of variation in diadromous and nondiadr-omous fish communities. This supports our firsthypothesis that hydrology would explain a significantunique component, with results illustrating thathydrological variables explained the largest and sec-ond largest unique components in nondiadromousand diadromous fish communities, respectively. Simi-larly, hydrological variables explained the individualcomponent for both life-history strategies. Compari-sons between data sets for nondiadromous fisheswere particularly distinct, with the hydrology data setuniquely explaining more than twice the proportionof the total variation (41%) that was uniquelyexplained by all remaining explanatory variables usedin this analysis (13%). These fractions of variationalso had the influence of covariables removed

Table 5. Total, individual and unique fractions of fish community variation explained by the Low-Flow, Hydrological Variability datasets and the combinedHabitat, Climate, Spatial-Seasonal dataset.

Variation fraction Dataset Diadromous fishes Non-diadromous fishes

Total All 18.7 27.6Individual Habitat, Climate, Spatial-Seasonal 15.8 15.9

Hydrological Variability 10.8 18.8Low-flow 6.2 11.5

Unique Habitat, Climate, Spatial-Seasonal 6.6 (35.6%) 6.3 (22.6%)Hydrological Variability 2.1 (10.8%) 6.3 (22.8%)Low Flow 0.4 (2.2%) 0.6 (2.1%)

Values in parentheses show the percentage of the total variation explained by the unique components.

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(Borcard et al. 1992), which prevented overestimat-ing the importance of the hydrology data set (Snelder& Lamouroux 2010). The present study supports per-vious research showing that hydrological regime isone of the key drivers of freshwater fish distributionand abundance in New Zealand (Jowett 1990; Jowett& Duncan 1990), and is consistent with otherresearchers who outline the importance of the flowregime to freshwater biota (Poff & Allan 1995; Nai-man et al. 2002; Arthington et al. 2006).Contrasting the relative influence of hydrology

with other data sets emphasises the importance ofhydrological regime to freshwater fishes. Whileresults from the Variance Partitioning analysis outlinethe significant proportion of fish community variationexplained by each of the data sets, the analysis doesnot quantify the nature or direction of any relation-ships between fishes and hydrological variables. Thisrequires a different approach that is beyond the scopeof the present manuscript, but it will form the focusof an upcoming study. The present analysis allowedus to examine competing hypotheses that couldexplain the patterns in community structure withresults showing that the Hydrology data set uniquelyexplained the most variation in nondiadromousfishes, but the Climate data set explained a similaramount of variation in diadromous fishes. The impor-tance of variables included in the Climate data set todiadromous fishes is well established (Leathwicket al. 2008), particularly with respect to distanceinland (Hayes et al. 1989; McDowall 2010a). It wassurprising, however, to see such a small unique con-tribution of the Habitat data set to both life-historypatterns, given relationships seen between freshwaterfishes and habitat variables such as substrate size(van Snik Gray & Stauffer 1999; Jowett & Davey2007), cover (Crow et al. 2010) and pool–riffle–runsequences (Jowett & Richardson 1995), which wereall included in this analysis. Similarly, the minimalunique contribution of Spatial-Seasonal variables toboth communities was also surprising because distinctbiogeographical and spatial patterns are consis-tently seen throughout New Zealand’s freshwater fishcommunities (McDowall 1998, 2010a; Burridgeet al. 2007, 2008). Despite these proven relationshipsbetween the various abiotic explanatory variablesused in this study, hydrological regime still accountedfor by far the largest amount of variation in nondiadr-omous fishes. Diadromous fishes, however, appear tobe equally influenced by climate factors such asdistance inland, with hydrology having a similarinfluence on variation.Aspects of hydrological variability explained more

variation in fish communities than aspects of lowflow after accounting for covarying explanatory vari-ables. Following our predictions, the Hydrological

Variability data set uniquely explained five and 11times more variation than the Low-Flow data set fordiadromous and nondiadromous fishes, respectively.These findings do not suggest that indices of lowflows have negligible impact on fish communities;rather, aspects of hydrological variability uniquelyexplain more variation. Indeed, the importance oflow-flow indices to fish communities was reflectedby the highly significant fraction of variation for bothlife-history patterns, supporting the relationshipsbetween low flows and fishes in other New Zealandstreams (Jowett et al. 2005; Davey & Kelly 2007).Results of the present study, however, contrast thefindings of Jowett et al. (2005) who showed that themagnitude and duration of low flow was negativelycorrelated with adult fish abundance, but the magni-tude or frequency of high flows was not negativelycorrelated with adult abundance. The contrastingresults on the present study may be associated withthe removal of the covariation in the present analysiswhich was not carried out in the study by Jowettet al. (2005), or the much larger spatial scale of thedata set (national vs. within stream) which can influ-ence the observed strength of the relationshipsbetween abiotic factors and biota (Jackson et al.2001). The higher relative impact of flow variabilitycompared with low flow is also evident in a NewZealand stream where juvenile brown trout abun-dance and biomass was reduced by a high-flow event,but no adverse effects were observed in relation tolow flows, even when the 7-day low fell to 56% ofthe 7-day mean annual low flow (Hayes et al. 2010).The larger amount of variation explained by the

Hydrological Variability data set in the present studysupports the need to further our understanding of therelationships between ecology and flow regime (Poff& Zimmerman 2010). For example, relative to thevariables included in the present study, results sug-gest that failure to incorporate the associationbetween fishes and aspects of hydrological variabilityinto management decisions would ignore relation-ships uniquely accounting for approximately 22% ofthe explained variation. Furthermore, we found thathabitat variables explained only a small proportionof TVE and that hydrological variability explainedmore than low flows, yet many environmental flowsare set using information of physical habitat availabil-ity at low flows. Our findings support the premisethat although physical habitat may be one aspectinfluencing fish communities, other aspects such asflow variability should also be considered. Leathwicket al. (2008) also reported two measures of flow vari-ability (annual low flow/annual mean flow and thenumber of days per month with rainfall >25 mm)having a slightly higher contributions to regressionmodels of freshwater fish distributions than a measure

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of low flow (annual 7-day low flow, fourth roottransformed). Flow variability has previously beensuggested to be one of the major factors influenc-ing distributions of nondiadromous salmonids inNew Zealand (Jowett 1990; Jowett & Duncan 1990),a finding that our results suggest extends to othernondiadromous and diadromous fishes. The presentstudy supports researchers outlining the importanceof flow variability to the structure of freshwaterecosystems (Naiman et al. 2008) and cautions againstignoring flow variability (Arthington et al. 2006;Naiman et al. 2008). Flow variability has also beenshown to be associated with life-history strategiesacross two continents (Olden & Kennard 2010),outlining the relationships between environmentalvariation and freshwater fish communities (Grossmanet al. 1998; Grossman & Sabo 2010).Life-history patterns may mediate the influence of

flow regime. The Hydrological Regime data setuniquely explained 41% and 15% of the TVE fornondiadromous and diadromous fish communities,respectively, supporting our second hypothesis thathydrology would have a larger influence of nondiadr-omous than diadromous fish communities. Thissuggests that the diadromous life-history strategymay alleviate some of the pressures that nondiadrom-ous fishes experience with regard to flow regime. Inparticular, hydrological variability appears to be themost likely set of variables that are mediated by lifehistory, given that variability indices uniquelyaccounted for 22% of the total variation in nondiadr-omous fishes and only 10% in diadromous fishes. Incontrast, aspects of low flow uniquely explained 2%of the total variation for both life-history strategies.Leathwick et al. (2008) also found that two measuresof flow variability contributed a larger percentage toregression analyses of nondiadromous compared withdiadromous fishes (average contributions of 6.9%and 5.1%, respectively), while the contribution oflow flow was slightly higher for diadromous fishescompared with nondiadromous fishes (3.6% and2.1%), respectively. The limited influence of flowvariability in diadromous fishes may be associatedwith the ability of diadromous fish to consistentlyre-invade highly perturbed systems (McDowall2010a), while nondiadromous fishes illustrate dis-persal only within river sections (Woodford &McIntosh 2010), often displaying small-scale geneticstructuring indicative of limited movement andre-colonisation (King & Wallis 1998; Burridge et al.2007; Crow et al. 2009). Indeed, re-colonisation intoperturbed Islands and Archipelagos like New Zealandhas previously been suggested as one of the benefitsof a diadromous life history (McDowall 2010b), gen-erating ubiquitous distributions across New Zealandstreams (McDowall 2010a). Differing dispersal and

colonisation abilities have been recognised asprocesses structuring stream fish communities alonggradients of flow variability (Taylor & Warren 2001).Alternatively, juvenile diadromous fishes are poten-tially impacted by hydrological variability to a greaterextent by remaining in freshwater throughout theirlifecycle, exposing the juvenile stages to the effectsof hydrological regime. These juvenile stages aremore susceptible to impacts of large floods whichcan generate significant losses in juvenile abundance(Jowett & Richardson 1989). Life-history strategieshave also been shown to determine invasion successunder changing flow conditions and taxa abundanceunder differing conditions of hydrological variability(Olden et al. 2006; Olden & Kennard 2010),supporting the findings of the present study. Differingproportions of diadromous and nondiadromouscommunity variation explained by hydrologicalvariability supports other researchers showing thepatterns between hydrological regime and freshwatercommunity traits and evolution (Poff & Allan 1995;Lytle 2001; Lamouroux et al. 2002; Lytle & Poff2004).The different response of diadromous life-history

patterns to flow regime highlights the importance ofconsidering several aspects of the flow regime whensetting environmental flow standards (Poff et al.2010), and supports other researchers who have cau-tioned against using generic/simplistic flow rulesacross different systems (Arthington et al. 2006), or avariety of biota. Jowett & Biggs (2008) suggestedthat decisions on ecological flows should be made byidentifying the valued biological components (e.g.,specific taxa) and the components of flow regimesthat are redundant with respect to these (i.e., that arenot important for supporting the taxa). They sug-gested that these aspects of the flow regime can thenbe taken for out-of-channel use. Clearly identifyingthe specific biological values to be supported helps toestablish ‘designer flow regimes’ and is a pragmaticapproach (Jowett & Biggs 2006). However, theresults of the present study suggest that there arefewer components of the flow regime that are redun-dant for nondiadromous than for diadromous fishes.Subsequently, environmental flows that support onlydiadromous fishes will potentially adversely impactthe nondiadromous fish community. Similarly,responses of fishes to various components of the flowregime are also likely to differ between species andage classes (Stewart-Koster et al. 2011). Therefore, akey shortcoming with the designer flow regimeapproach is that it does not quantify the reduction insupport for nontarget taxa, nor does it evaluatechanges to the community as a whole. Progress withdefining environmental flow standards may beachieved by identifying the extent to which flow

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regime components are important for a broad rangeof species/life stages and then using this informationto evaluate the potential effects of a proposed envi-ronmental flow regime on high value/susceptible taxaand the community as a whole.

Acknowledgements

This work was jointly funded by the New Zealand Departmentof Conservation (DOC) and the New Zealand Foundation forResearch, Science and Technology, Environmental Flows Pro-gramme (C01X0308).

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