catchment and reach-scale properties as indicators of macroinvertebrate species traits

frbiol0148

Freshwater Biology (1997) 37, 219–230

SPECIAL APPLIED ISSUES SECTION

Catchment and reach-scale properties as indicators ofmacroinvertebrate species traits

C A R L R I C H A R D S , * R O G E R J . H A R O , † L U C I N D A B . J O H N S O N A N D G E O R G E E . H O S TNatural Resources Research Institute, University of Minnesota, 5013 Miller Trunk Hwy, Duluth, MN 55811, U.S.A.*Author to whom correspondence should be sent†Present address: River Studies Center, University of Wisconsin-La Crosse, La Crosse, WI, U.S.A.

S U M M A R Y

1. We used catchment and reach-scale physical properties to predict the occurrence ofspecific species life history and behaviour traits of aquatic insects across fifty-eightcatchments in a mixed land use basin. Catchment-scale attributes were derived using ageographical information system (GIS). Logistic regression techniques were used tomodel the relationships.2. The reach-scale properties were highly predictive of species traits. Fourteen of thefifteen traits had significant models with concordance values greater than 68%. Cross-sectional area at bank full discharge, % shallow, slow-water habitats, and % fines werethe most important variables.3. Life history and behavioural attributes were best related to reach-scale physicalfeatures. This suggests that species traits exhibit strong relationships to localenvironmental conditions.4. Catchment-scale variables had fewer significant models with species traits (four offifteen), however these variables may have direct or indirect influence on reach-scaleproperties.5. Catchment features, in particular surficial geology, influence macroinvertebrateassemblages through their control over channel morphology and hydrologic patterns.6. The effects of land use were masked by geology (i.e. lacustrine clay geology androwcrop agriculture were correlated), lack of detail in land use data and the aggregationof the species data.7. These models reflect the coupling of local environmental conditions and the set ofadaptations among the local taxa. These observations underscore the idea that habitatplays a major role in organizing stream assemblages.8. Using these approaches, predictions can be made about the ability of varioustaxonomic groupings to track environmental change through time, or for projecting theimpact of alternative land management scenarios. Identifying fundamental life historyand other traits can improve the selection and evaluation of such indicators.

Introduction

The distribution and abundance of stream macroinver- variation in species distributions can be attributed topatterns of variation within the landscape, imposedtebrate and fish species are influenced by a variety of

physical and biological factors. When viewed across by factors such as geology or land use. Recent studieshave demonstrated that dominant surficial geologyrelatively large spatial scales such as ecoregions, much

© 1997 Blackwell Science Ltd 219

220 C. Richards et al.

within catchments can account for a large portion Townsend & Hildrew (1994) further developed thisidea with respect to lotic systems and expressed theof the variation in fish (Poff & Allan, 1995) and

macroinvertebrate (Richards, Johnson & Host, 1996) need to examine life history traits in the context ofstream habitat templates.distribution patterns. Geomorphic influences on biota

can be related both to direct substrate influences such Macroinvertebrate assemblages can be viewed innumerous ways. Species-specific taxonomic descrip-as particle size, as well as to hydrologic differences

which influence stream flow patterns (Velz & Gannon, tions of macroinvertebrate fauna provide the mostaccurate representation of zoogeographic distribution1960; Wiley, Kohler & Seelbach, 1997).

Variation in flow is one of the primary disturbance patterns. However, non-taxonomic aggregations oftaxa into behavioural, life history and functionalmechanisms in stream ecosystems and can influence

the functional organization of aquatic communities categories might be more effective for investigatingassemblage structure, understanding mechanisms(Poff & Allan, 1995). Geomorphically controlled

groundwater characteristics can influence flow vari- affecting species distributions, and conducting envir-onmental assessments. Functional feeding groups,ation. In Midwestern streams, for example, catch-

ments draining old lake bed deposits of clay or fine for example, have been used in stream ecologicalstudies by a variety of researchers (Cummins, 1973).silt (lacustrine geology) are characterized by poor

water infiltration and ‘flashy’ streams with flows Corkum & Ciborowski (1988) found that invertebratebehavioural and morphological attributes were moredominated by surface runoff. Under these drainage

conditions, stream temperature regimes tend to be effective than taxonomic categories for relatingmacroinvertebrates to large-scale environmental fea-variable and correlated with atmospheric temperature

patterns; such streams are characterized by ‘warm- tures. There is growing use of non-taxonomiccategories in developing environmental assessmentwater’ faunal assemblages. Catchments draining

coarse-textured morainal deposits are characterized indices (Rosenberg & Resh, 1993) and developmentof biological criteria (Kearns, Karr & Ahlstedt, 1992;by high infiltration capacity, resulting in more stable,

groundwater-driven streams. Stream temperature DeShon, 1995). Many of these approaches use acombination of taxonomic and functional categoriz-regimes in catchments dominated by alluvial out-

wash/moranic geology have lower annual thermal ations.The primary objective of this study was to quantifyamplitudes (Haro & Wiley, 1992) and support cool-

water faunal communities. In addition to geomorphic the relationship between a series of non-taxonomicdescriptors of macroinvertebrate assemblages andeffects, land use, including intensive agriculture and

urbanization, is expected to increase environmental reach/landscape-scale attributes of Midwestern agri-cultural catchments. Specifically, we aimed to developvariability for streams in either type of geology.

Agricultural development is frequently associated predictive models of the occurrence of specificspecies traits (e.g. large body size) given informationwith alterations in stream habitats that cause com-

positional changes to stream communities (Lenat, on geomorphology, land use and reach-scale charac-teristics. Identifying these relationships will improve1984; Corkum, 1990; Quinn & Hickey, 1990).

Geomorphic and hydrologic effects on stream our understanding of how landscapes influencespecies assemblages at multiple scales. This know-attributes are, to some degree, predictable (Frissell

et al., 1986; Poff & Ward, 1989). More importantly, ledge will have particular relevance for developingmacroinvertebrate indicators as monitoring andgeological attributes persist over relatively long time

scales, and provide a template for the development assessment tools for water resources management.and selection of life history and behavioural traitswithin the biotic community. Southwood (1977, 1988)

Materials and methodspostulates that habitat variation provides a backdropagainst which individual differences in fundamental Study regionlife history and other species traits result in differen-tial survival and reproduction. Consequently, over This study was conducted in the Saginaw Bay Basin

of Lake Huron in east-central Michigan (Fig. 1). Thisevolutionary time, there should be a correspondencebetween life history traits and habitat characteristics. basin was chosen because its component catchments

© 1997 Blackwell Science Ltd, Freshwater Biology, 37, 219–230

Landscape properties and species traits 221

span the spectrum from surfacewater- to groundwater- Organisms were removed from the selected grids until100 individuals were obtained or the entire sampledominated systems. Its Quaternary geology is domin-

ated by glacial remnants (Farrand & Bell, 1982; Fig. 1). had been processed. Over 80% of the samples hadfewer than 100 individuals. All macroinvertebratesLand use within the basin ranges from heavily

impacted agricultural to relatively undisturbed were counted and identified to genus whenever pos-sible. Data for six sites with two annual samplingssecond-growth forest areas. Detailed descriptions of

the study region are given by Richards et al. (1996) were pooled. A series of descriptive traits (describedbelow) was calculated for each site based on the insectand Johnson et al. (1997). The Saginaw River Basin,

encompassing 16 317 km2, contains four major drain- assemblage.ages: the Tittabawassee (6734 km2), Shiawassee(3626 km2), Flint (3108 km2) and Cass (2331 km2; Reach-scale physical measurements. Physical assessments

were also conducted during baseflow conditions. WeMichigan Department of Natural Resources, 1988).The study area encompasses four major catchments sampled a suite of habitat parameters at each site,

including cross-sectional and longitudinal channelwithin these drainages, including the Cass, Flint,Shiawassee and Chippewa/Pine. dimensions, substrate characteristics, bank conditions,

woody debris and hydraulic characteristics (Richards& Host, 1994; Richards et al., 1996). For the purposes

Study designof this study, five habitat parameters were used in thestatistical analyses (Table 2). These five parametersMacroinvertebrate collections and physical measure-

ments were made during the month of September were selected because they explained a large percent-age of between-site variation in macroinvertebratein 1991 (twenty sites), 1992 (twenty sites) and 1994

(twenty-four sites). For the purposes of this study, community structure during an earlier study in theregion (Richards et al., 1996). All physical assessmentsfifty-eight different sites were sampled in all. A unique

subcatchment was delineated for each site and a were conducted during baseflow conditions.variety of catchment characteristics were incorporatedinto a geographical information system (GIS) database Catchment-scale measurements. The percentage land

cover in lacustrine clays, outwash sands and agricul-(Richards et al., 1996). These subcatchments werechosen to reflect a gradient of land use and physio- tural rowcrops was assessed for each subcatchment

as described by Johnson et al. (1997). These factorsgraphic conditions in the Saginaw River Basin. Sub-catchments varied in size from 713 to 56 952 ha. were used as catchment-scale covariates to characterize

the potential hydrologic variation at each site.

Sampling methodsStatistical analyses

Macroinvertebrate collection. Our primary objective wasto estimate the presence of macroinvertebrate species Species traits. Five general categories of species traits

were analysed: life history, trophic relation, habitin a 100-m reach at each site. Specific collectionmethods depended on the combination of flow and (i.e. mode of locomotion, attachment or concealment),

habitat specificity and level of mobility. Various classi-habitat types encountered along a particular reach.Reaches were established at least 50 m upstream from fication schemes are possible, however, biological

information for many traits often is not completebridges or culverts. Qualitative sampling methods(Lenat, 1988) were used to collect macroinvertebrates enough to apply across a regional faunal assemblage.

We selected fifteen traits which could be generallyfrom each site (Table 1). All sampling took place underbaseflow conditions. Each sample was preserved and assigned across the assemblage with the least amount

of ambiguity (Table 3). Classification of the aquaticreturned to the laboratory for processing and identi-fication. insect species into trait groups largely followed eco-

logical information provided by Merrit & CumminsDuring sample processing, large inorganic andorganic materials were removed and the remaining (1996), a regional taxonomic key (Hilsenhoff, 1982),

and the authors’ knowledge.material was spread over a shallow white plastic traywith grid lines. Square grids were randomly selected. Recent studies have proposed qualitative trends for



Table 1 Methods used for qualitative macroinvertebrate collections

Flow conditions Habitat/substrate Sampler No. of samples Type/method

Fast water Individual rocks, logs Kick net 3–6 Composite/substrate washDebris piles, banks Kick net 3 1-min samples Composite/substrate

disturbanceSlow water Sand, macrophyte beds Ekman dredge 3–6 Composite/substrate wash

Debris piles, banks Kick net 3 1-min samples Composite/substratedisturbance

Table 2 Reach-scale habitat parameters measured at, or taxa), moderate representation (two up to the mediancalculated for, each study site (reach) number of taxa) or high representation (taxa numbers

equalling or exceeding the median; Table 3). If theParameter Methodsminimum representation among sites exceeded 1,

Cross-sectional area Average product of the width (m) and these class boundaries were shifted to provide for anat bankfull depth (m) of the stream channel at even distribution of sites among classes. These classes

bankfull discharge calculated from threebecame the dependent variable in the logistic regres-transects

% shallows Visual estimates of % wetted area less sion model. Using logit transformations, logisticthan 10 cm in depth regression was used to estimate the cumulative prob-

% fines Proportion of erosional substrates less abilities of each class from continuous explanatorythan 2 mm in diameter measured

variables. To simplify graphical interpretation, thevolumetrically from grab samples (Plattsestimated probabilities for moderate and high repres-et al., 1983)

% canopy Visual estimate of % of wetted area entation classes were summed and plotted againstshaded by shrub or tree canopy individual explanatory variables.

Woody debris Length of large woody debris (.5 cmTwo sets of logistic multiple regression modelsdiameter) per metre of stream channel

were developed, one using reach-scale habitat proper-ties and the other using catchment-scale properties(e.g. surficial geology and land use) as explanatory

specific species traits over gradients of streamflow variables. Our intention was to examine the explanat-regime (Poff & Ward, 1989) and habitat heterogeneity ory power at each of these scales independently,(Scarsbrook & Townsend, 1993; Townsend & Hildrew, and to identify ‘important’ variables. We recognize,1994). Several traits analysed in this study were however, that interactions between these two scalesincluded to further test these predictions. For example, exist; models for predicting reach-scale attributeswe propose that streams with high flow predictability from landscape variables are presented in Richards(e.g. groundwater streams) would be more likely to et al. (1996). In the present study, we were explicitlypossess invertebrate populations with large body size, interested in how catchment-scale indices of land-functional specialists and/or an increased proportion scape structure can be used as appropriate metricsof long-lived species (sensu Poff & Ward, 1989). for predicting aquatic resource quality.

Logistic regression. We used logistic regression to predictResultsthe frequency of particular species traits from catch-

ment- or reach-scale variables. Logistic regression is a Reach and catchment propertiesgeneralized linear model that predicts the probabilityof an outcome given multiple predictor variables Landscape properties for the above fifty-eight sites

incorporated two major gradients. Geological(Weisberg, 1985; Trexler & Travis, 1993). Specificspecies traits were assigned to each taxon, as described gradients reflected hydrologically distinct land sur-

faces (i.e. lacustrine clay and glacial outwash);above, and frequencies of each trait were tabulatedfor each site. Three frequency classes were established anthropogenic conditions reflected the gradient from

homogeneous (i.e. agriculture) to heterogeneous (i.e.for each trait: poor representation (less than two



Table 3 Range and median numbers of taxa representing selected aquatic insect traits at fifty-eight stream sites in the SaginawRiver Basin. Frequency class limits (# 5 the number of taxa representing a particular trait) for the response levels (moderate v highfrequencies) used in the logistic regression models

Frequency class limits

Trait Range Median Moderate High

Life historyMerovoltine 0–4 2 0 , # ø 2 # . 2Multivoltine 0–11 4 4 ø # ø 5 # . 5Large body size 0–8 2 0 , # ø 2 # . 2Small body size 0–13 3 1 , # ø 4 # . 4

Functional groupScrapers 0–3 2 0 , # ø 2 # . 2Shredders 0–6 2 1 , # ø 2 # . 2

Mode of existenceSwimmers 0–7 2 1 , # ø 3 # . 3Clinger 0–12 3 3 , # ø 5 # . 5Sprawlers 1–13 4 3 , # ø 5 # . 5Climbers 0–9 2 1 , # ø 3 # . 3Burrowers 0–9 4 1 ø # ø 4 # . 4

Habitat specificityObligate erosional 0–9 4 0 , # ø 4 # . 4Obligate depositional 1–20 7 4 , # ø 11 # . 11

MobilitySedentary 1–12 5 4 , #7 # . 7Free ranging 3–32 11 8 , # ø 15 # . 15

Table 4 Sample means 6 1 SE, medians, minimums, maximums and coefficients of variation (%) of the upstream catchmentproperties for fifty-eight Saginaw River Basin study sites

Catchment property Mean 6 1 SE Median Minimum Maximum CV (%)

Catchment area (ha) 15 620.0 6 1925.5 9984.0 712.5 56 952.0 64.0Shreave’s link number 24.0 6 3.2 13.0 1 85 54.0% of catchment in glacial outwash alluvium 10.9 6 1.9 0.3 0 49.2 17.0% of catchment in lacustrine clays 14.9 6 2.9 0 0 70.5 19.0% of catchment in agricultural rowcrops 59.2 6 3.2 58.6 13.8 98.0 5.0

multiple-use) land covers. Drainage area among given in Table 5. Average cross-sectional area atbankfull (CSAB) and percentage of reach in shallow,the subcatchments spanned nearly two orders of

magnitude (Table 4). The percentages of glacial slow-water habitats (SSWH) were both correlates(r 5 0.57, P , 0.0001 and r 5 0.40, P , 0.005, respect-outwash and lacustrine clay geology were negatively

correlated among sites (r 5 –0.43, P , 0.01). The ively) of subcatchment drainage area. The other threereach-scale habitat properties were not significantlymean percentage of cover in lacustrine clay among

the subcatchments was slightly higher than glacial related to drainage area. Shallow, slow-water habitatswere common, averaging 40% of the reach areaoutwash (Table 4). Agricultural land cover correlated

positively with lacustrine clay geology (r 5 0.41, (Table 5). Stream substrates generally containedmoderate to high percentages of fine materials. TheP , 0.01) and negatively with glacial outwash (r 5

–0.39, P , 0.05). mean quantity of large woody debris among siteswas 0.45 m m–1. Three debris-laden sites, however,The five reach-scale properties varied among the

sites. Descriptive statistics for these properties are disproportionately influenced the mean. Thus, the



Table 5 Sample means 6 1 SE, medians, minimums, maximums and coefficients of variation (%) of the reach-scale habitatproperties for fifty-eight Saginaw River Basin study sites. In-stream woody debris is expressed as the total linear amount of woodwith a minimum diameter of 5 cm divided by the total length of the sampling reach

Habitat property Mean 6 1 SE Median Minimum Maximum CV (%)

Average channel cross-sectional area (m2) 11.9 6 2.3 6.7 0.6 120.2 19.0% of reach in lateral slow-water habitat 39.6 6 5.1 20.0 0 100.0 13.0% of substrates as fines (, 2 mm) 59.0 6 3.8 56.0 0.1 100.0 7.0Average % canopy cover at mid-reach 34.1 6 4.0 30.0 0 100.0 12.0Woody debris per unit length of reach (m m–1) 0.45 6 0.1 0.2 0 3.1 16.0

Table 6 Statistics for logistic analyses modelling the relationship between the taxonomic response (i.e. poor, moderate and highrepresention) for selected aquatic insect traits and reach-scale stream habitat properties. Signs (1/–) indicate cases wherecovariates significantly (P ø 0.05) contributed to the model and direction of their effect on the response variable

Covariates: reach-scale habitat properties

Cross- % In-streamModel χ2 Adjusted % sectional shallow % canopy wood

Traits (P value) R2 concordance area areas % fines cover (m m–1)

Life historyMerovoltine 0.0003 0.38 77.4 – –Multivoltine 0.0001 0.54 82.7 – 1 –Large body size 0.0012 0.33 76.2 – – – 1

Small body size 0.0001 0.48 82.1 – 1

Functional groupScrapers 0.0001 0.41 81.6 – –Shredders 0.0409 0.21 68.4 – –

Mode of existenceSwimmers 0.0001 0.44 79.3 – 1 –Clinger 0.0002 0.38 78.0 – – 1

Sprawlers 0.0008 0.35 75.7 – 1 –Climbers 0.0009 0.34 76.6 1 –Burrowers 0.0088 0.27 75.7 – 1

Habitat specificityObligate erosional 0.0001 0.45 81.9 – –Obligate depositional 0.0027 0.30 73.9 1

MobilitySedentary NSFree ranging 0.0001 0.50 81.0 – 1 –

median value provides a better representation of concordance of the models, which indicates the abilityof the model to correctly predict distribution of theaverage debris conditions (Table 5).dependent variables in the original data, was alwaysgreater than 68% and was mostly greater than 75%.

Reach-scale modelsCSAB was a statistically significant habitat property

in ten of the fifteen models (Table 6). This variableThe reach-scale properties were highly predictive ofspecies traits. Of the fifteen species traits, only one was not important to functional groups, climbers

and obligate depositional species. When CSAB was(sedentary) did not exhibit a significant model(Table 6). Coefficients of determination (R2) for the significant, its effect was consistently unidirectional.

Free-ranging and merovoltine taxa illustrate this gen-models ranged between 0.21 and 0.54. The percentage



Fig. 2 Reach-scale habitat propertiesv estimated site probabilities (thecombined probability for moderateand high representation) for specificspecies traits. (a) Free-ranging taxa vcross-sectional area at bankfull(CSAB); (b) merovoltine taxa vCSAB; (c) obligate depositional taxav percentage shallow, slow-waterhabitat in reach; and (d) ‘clinger’taxa v percentage fines. Probabilitieswere estimated using logisticregression models.

eral trend (Fig. 2a,b). The likelihood of trait representa- declined sharply when fines approached 60% of thesubstrate composition (Fig. 2d).tion decreased with increasing area. This trend was

The remaining two habitat properties, % canopycompounded by an overall reduction in species rich-and in-stream wood, were important in fewer modelsness downstream. CSAB, an indicator of longitudinalthan the other habitat properties. However, six modelsposition, was significantly correlated (Pearson’s r 5

included negative relationships with % canopy cover–0.42, P , 0.001) with species richness.and two models included positive relationships withThe percentage of SSWH was a significant effect inin-stream wood. The effect of canopy cover is difficultnine of fifteen attributes (Table 6). SSWH had bothto interpret because it is a function of both localpositive and negative effects on trait probabilities.channel geometry and riparian quality.The likelihood of a site possessing representatives in

multivoltine, small body size, swimmers, sprawlers,climbers or free-ranging groups increased with the

Catchment-scale modelsamount of shallow habitat. Scrapers and shreddershad negative associations with increased shallow hab- Significant models were found with only four ofitat. The fact that SSWH accrue sediment may explain the fifteen species traits using the catchment-scaletheir influence in predicting the presence of obligate properties. Two of these traits were life history related,depositional taxa (Fig. 2c). one was a mode of existence, and one related to habitat

The percentage of fines in substrates was also specificity (Table 7). Each of the significant modelsimportant in several of the models (Table 6). Fines contained only one of the catchment-scale properties.appeared as a significant model effect in at least one Coefficients of determination (R2) for these modelstrait within all the generalized groups except mobility. were not as high as those obtained with the reach-All species traits except burrowers exhibited a negative scale properties. Concordance percentages were alsorelationship with increased fines (Table 6). The likeli- generally lower, although a 76% concordance rate was

noted for the obligate erosional taxa model.hood of site representation for several species traits



Table 7 Statistics for logistic analyses modeling the relationship between the taxonomic responses (i.e. poor, moderate, and highrepresentation) for selected aquatic insect traits and catchment-scale properties. Signs indicate cases where covariates significantly(P ø 0.05) contributed to the model and direction of their effect on the response variable

Covariates: catchment-scale properties

Model χ2 Adjusted % % Cover in % Cover in % Cover in agriculturalTraits (P-value) R2 Concordance glacial outwash Lacustrine clays row crops

Life historyMerovoltine 0.008 0.21 69.3 –Multivoltine NSLarge body size 0.022 0.17 69 –Small body size NS

Functional groupScrapers NSShredders NS

Mode of existenceSwimmers NSClinger 0.056 0.14 66.4 1

Sprawlers NSClimbers NSBurrowers NS

Habitat specificityObligate erosional 0.001 0.31 76.5 1

Obligate depositional NS

MobilitySedentary NSFree ranging NS

Only the two geological properties provided signi- fundamental to the development of macroinvertebrate-based monitoring and assessment tools.ficant model effects. Per cent glacial outwash, which

The percentage of SSWH is a composite of the lateralis associated with increasing large substrate material,and backwater classes of the ‘slow-water channel’ unitwas positively associated with clingers and obligatelisted by Hawkins et al. (1993). This habitat is subject toerosional taxa (Fig. 3a). An increasing percentage ofdesiccation during low flow conditions, and is tempor-lacustrine clay, indicative of increasing hydrologically unstable for certain macroinvertebrates. These hab-variation, was associated with decreasing numbers ofitats, however, often support rooted macrophytes (e.g.merovoltine (Fig. 3b) and large body size taxa.Sagittaria sp., Potamogeton spp. and Vallisneria sp.),which seasonally increase local habitat structure, per-iphytic resource availability and substrate stability. Riv-Discussionerine macrophyte beds also produce autochothonous

The occurrence of non-taxonomic macroinvertebrate organic materials and increase sedimentation of sus-traits in individual stream reaches can be predicted pended sediments conducive to the colonization of bur-using a limited number of reach- and catchment-scale rowing species (Fox, 1992). The amount of SSWH withinfactors. Life history and behavioural attributes were a reach was a good predictor of the number of taxa withbest related to reach-scale physical features. Three of the life history strategies adapted for ephemeral environ-reach-scale factors were most important in predicting mental conditions (e.g. multivoltine generation timesspecies traits. This suggests that species traits exhibit and small body size). Our results suggest this habitatstrong relationships to current local environmental con- provides two levels of potential refugia: one for macro-ditions when viewed at a relatively coarse level of invertebrates adapted for moving and foraging withinaggregation (i.e. presence or absence of species in and between small spaces in the macrophyte foliagestream reaches). Understanding the environmental fac- (i.e. swimmers, sprawlers and climbers), and another

for those specialized for depositional areas.tors responsible for the distribution of these traits is



effects of % fines on life history traits were less clear.Fine sediments are highly susceptible to disturbancebecause they typically move at much lower criticalstream velocities than do larger substrates (Newbury,1984). Experimental studies by Reice (1985) suggestthat patches dominated by fine materials displaylarger, more frequent fluctuations in macroinvertebratespecies richness, and higher rates of community recov-ery, than patches of unembedded cobble followingdisturbance. Given that the disturbance return inter-vals are likely to be higher in reaches with higherpercentages of fines, we would expect these areas tobe less conducive to long-lived (merovoltine) andlarge-bodied macroinvertebrates. The probability offinding higher frequencies of short-lived (multivoltine)species, however, also declined with increasing sedi-mentation. Where flow regimes are variable, evensmall organisms capable of rapid reproduction (e.g.chironomids) can be prevented from colonizing sand/silt habitats (Wiley, 1981; Rae, 1987).

The number of distinct taxa displaying a particularspecies trait declined with increasing CSAB. Threefactors influenced this phenomenon. First, CSAB ishighly correlated with drainage area in a given region(Dunne & Leopold, 1978) and provides an indexof longitudinal position within the stream network.Stream networks traverse a mosaic of geological sur-faces in the basin (Fig. 1). Transition from the peri-pheral moraine/outwash areas to the lacustrine plainsof the interior basin produces a distinct geomorphicand hydrologic ecotone in the drainage network.Headwater springbrooks arising in morainal areas aremore hydrologically stable than mainstem tributariesoriginating in areas dominated by lacustrine clays.Consequently, mainstem tributaries possess more vari-able flow characteristics and are more subject tohydrologic disturbance. Second, substrate types andFig. 3 Catchment-scale properties v estimated site probabilitiesstability also undergo sharp transitions. In morainal/(combined probabilities for moderate and high representation)

for specific species traits. (a) Obligate erosional taxa v outwash regions, coarse-particle bed materials arepercentage up-stream catchment in alluvial outwash geology; abundant. In the lacustrine zone, channel beds are(b) merovoltine taxa v percentage up-stream catchment in

dominated by fine, unstable materials. The contrasts inlacustrine clay geology. Probabilities were estimated usingthese geologies represent obvious hydraulic transitionslogistic regression models.

(sensu Statzner & Higler, 1986). Lastly, cumulativeimpacts of non-point source pollutants can alsoTaxa frequencies for behavioural traits (scrapper,

clinger and obligate erosional invertebrates) associated degrade habitats and species richness of downstreamsites (Karr & Schlosser, 1978). In the lowland streamswith large, more stable substrates for both habitat and

forage surfaces declined with increased sedimentation of the Saginaw drainage basin, all three factors maywork together to produce observed trends. Strong(% fines). Conversely, % fines was positively related

to the incidence of burrowing within a reach. The environmental gradients operate over a relatively short



distance to influence distribution patterns of broad local colonization sources (i.e. patch dynamics operat-ing within the reach; Reice, 1985; Pringle et al., 1988)categories of benthic insects.

The hierarchical influence of catchment features was than the persistence of populations. Species traits,when aggregated in the manner described in ourmost obvious with obligate erosional species requiring

the presence of larger substrate materials. These taxa work, are more likely to respond to reach-scale attrib-utes since both the temporal and spatial scales ofwere best represented in catchments dominated by

glacial outwash materials. Consequently, higher incid- disturbance and change in stream reaches are moreattuned to the scales of extinction and persistence ofence of obligate erosional taxa across glaciated land-

scapes may be delimited by the arrangement of glacial macroinvertebrate populations.The predictive power of these models reflects theoutwash pockets at spatial scales greater than the

stream reach. Catchment features, in particular surfi- coupling of local environmental conditions and the setof adaptations among the local taxa. These observationscial geology, influence macroinvertebrate assemblages

through their control over channel morphology and are in concert with the idea that habitat plays a majorrole in organizing stream assemblages. The habitat tem-hydrologic patterns. Reach-scale properties, such as

amounts of in-stream woody debris, reflect inter- plate of Southwood (1977, 1988) has been proposed as auseful framework for viewing ecological characteristicsactions between ‘fixed’ (e.g. geology, hydrology) and

‘management-influenced’ (e.g. land use patterns; in streams, including functional organization of streamfishes (Poff & Allan, 1995; Schlosser, 1987, 1990), andRichards et al., 1996) landscape factors.

The effects of land use on stream biota were masked invertebrates (Poff & Ward, 1990; Townsend & Hildrew,1994), as well as a variety of other stream processesby variation in geology among the catchments.

Rowcrop agriculture was correlated with lacustrine (Minshall, 1988). Our results suggest that individualresponses of macroinvertebrates to physical gradientsclay geology. Although large-scale land use practices

such as agriculture influence catchment hydrology shape life history and behavioural characteristics ofpopulations and that these responses result in patternsand other stream features, identification of effects on

species traits were confounded by the interactions which can be viewed when examining assemblage com-position across landscapes.of geology and land use. Separating land use and

geological effects on stream biota requires analyses There is much interest in the use of macroinverteb-rates as monitoring and assessment tools for manage-which account for the nested hierarchical interplay

between these two landscape features. Richards et al. ment of water resources (Rosenberg & Resh, 1993;Metcalfe-Smith, 1994). The most effective use of such(1996) found that the influence of land use and geology

on stream habitats varied distinctly depending on the tools occurs when there is a clear understanding of themechanisms which lead to the presence or absence ofhabitat feature of interest. In addition, other land use

features such as the presence of riparian buffer strips species in the environment. In a regional landscape,this means identifying species patterns that vary withoccur at relatively small spatial scales and may have

local influences on stream habitats. The land use normal geomorphic variations of the landscape, as wellas alterations caused by anthropogenic influences. Withassessments we used were not able to detect small-

scale variation in land use along stream courses and this knowledge, predictions can be made about the effi-cacy of various taxonomic groupings for tracking envir-consequently may have missed some of the importance

of these features to stream biota. However, the influ- onmental changes through time or for projecting theimpacts of alternative land management scenarios.ence of stream buffer regions on stream habitats and

biota can vary significantly and does not always follow Identifying fundamental life history and other traits canimprove the selection and evaluation of such indicators.distinct trends (see Johnson et al., 1997).

Patterns in species traits may not be observable For example, a candidate indicator of woody debris ina stream reach would be a species with large body sizeat fine-grained observations of stream habitats (e.g.

subhabitats, riffles, pools) frequently examined with and clinging behaviour (Table 6). Furthermore, EPTtaxa (Ephemeroptera, Plecoptera, Trichoptera; commonrespect to local variations in land use. At these small

scales, disturbance and environmental change can indicator groups used in bioassessment methods (Barb-our et al., 1992; Fore, Karr & Wisseman, 1996)) wouldoccur rapidly and the presence and absence of species

is regulated more by mobility and the availability of be a good indicator of sedimentation in riffles since



variability among reference stream sites. Journal ofthese taxa are predominantly obligate erosional speciesEnvironmental Toxicology and Chemistry, 11, 437–449.with many clinging and scrapping behaviours that are

Corkum L.D. (1990) Intrabiome distributional patterns ofmost suitable in larger substrate materials. Conversely,lotic macroinvertebrate assemblages. Canadian Journal ofEPT taxa would not be good indicators of the relativeFisheries and Aquatic Sciences, 47, 2147–2157.

quality or quantity of shallow water habitat since theseCorkum L.D. & Ciborowski J.J.H. (1988) Use of alternative

habitats are depositional. In general, strict taxonomic- classifications in studying broad-scale distributionalbased assemblage metrics do not provide insight into patterns of lotic invertebrates. Journal of the Northcausal mechanisms for stream impairment. Life history American Benthological Society, 7, 167–179.and behavioural attributes, because they reflect adapta- Cummins K.W. (1973) Trophic relations of aquatic insects.

Annual Review of Entomology, 18, 183–206.tions to dominant regional environmental stresses, giveDeShon J.E. (1995) Development and application of thea glimpse into the mechanisms responsible for assem-

Invertebrate Community Index (ICI). Biologicalblage structure and allow the selection of appropriateAssessment and Criteria—Tools for Water Resource Planningresponse metrics. Furthermore, life history and behavi-and Decision Making (eds W.S. Davis and T.P. Simon), pp.oural attributes are not constrained by biogeographic217–243. Lewis Publishers, Boca Raton.

distributions and thus can be applied across ecoregions.Dunne T. & Leopold L.B. (1978) Water in Environmental

The construction of indices utilizing macro- Planning. W.H. Freeman and Co., San Francisco.invertebrate species traits (e.g. B-IBI; Fore et al., 1996) Farrand W.R. & Bell D.L. (1982) Quaternary geology ofshould show wide applicability. However, our results Southern Michigan. Michigan Geological Surveydemonstrate the importance of regional calibrations of Division Map, 1 : 500 000.

Fore S., Karr J.R. & Wisseman R.W. (1996) Assessingsuch indices so that land use and other anthropogenicinvertebrate responses to human activities: evaluationginfluences can be differentiated from natural forms ofalternative approaches. Journal of the North Americanregional variability. Such knowledge will also aid inBenthological Society, 15, 212–231.delineating mechanisms leading to the assembly of

Fox A.M. (1992) Macrophytes. The Rivers Handbook:stream communities, as well as understanding con-Hydrological and Ecological Principles, Vol. I (eds P. Calow

sequences of environmental change.and G.E. Petts), pp. 216–233. Blackwell ScientificPublications, Oxford.

Frissell C.A., Liss W.J., Warren C.E. & Hurley M.D. (1986) AAcknowledgments hierarchical framework for stream habitat classification:

viewing streams in a watershed context. EnvironmentalThis research was funded through U.S. EPA grant nos Management, 10, 199–214.CR-818373–01–0 and CR-822043–01–0. This is a Centre Haro R.J. & Wiley M.J. (1992) Secondary consumers and thefor Water and the Environment publication no. 191 and thermal equilibrium hypothesis: insights from MichiganNatural Resources GIS lab no. 42. We thank Jack Arthur spring brooks. Proceedings of the First International

Conference on Ground Water Ecology, Tampa, Florida, 26–for crucial assistance in all phases of this project. Tom29 April, 1992 (eds J.A. Stanford and J.J. Simons), pp.Roush and Dan Breneman collected and identified the179–188. American Water Resources Association,macroinvertebrate samples. Frank Kutka, Paul Tucker,Bethesda, MD.Barb Piechel, Jackie Alexander, Aaron Undeland and

Hawkins C.P., Kershner J.L., Bisson P.A., Bryant M.D.,Rich Kleiman assisted in collection of field data and inDecker L.M., Gregory S.V., McCullough D.A., Overton

laboratory analysis. Connie Host, Shawn Boeser, Tim C.A., Reeves G.H., Steedman R.J. & Young M. K (1993)Aunan and Jim Westman assisted in the development A hierarchical approach to classifying stream habitatof GIS databases. Jeff Schuldt, J. David Allan and two features. Fisheries, 18, 3–12.anonymous reviewers provided helpful comments on Hilsenhoff W.L. (1982) Aquatic insects of Wisconsin. Keysearlier versions of this manuscript. to Wisconsin genera and notes on biology, distribution,

and species. Publication of the Natural History Council, 2,1–60.

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Fig. 1 Map of the Quaternary surficial geology for the Saginaw Bay Basin, Michigan. Red squares show study site locations(n 5 58). Heavy black lines are up-stream catchment boundaries for each site.

catchment and reach-scale properties as indicators of macroinvertebrate species traits

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