APPLIED ISSUES

Functional biodiversity of macroinvertebrateassemblages along major ecological gradients of borealheadwater streams

JANI HEINO

Finnish Environment Institute, Research Programme for Biodiversity, University of Oulu, Oulu, Finland

SUMMARY

1. Biodiversity–environment relationships are increasingly well-understood in the context

of species richness and species composition, whereas other aspects of biodiversity,

including variability in functional diversity (FD), have received rather little rigorous

attention. For streams, most studies to date have examined either taxonomic assemblage

patterns or have experimentally addressed the importance of species richness for

ecosystem functioning.

2. I examined the relationships of the functional biodiversity of stream macroinvertebrates

to major environmental and spatial gradients across 111 boreal headwater streams in

Finland. Functional biodiversity encompassed functional richness (FR – the number of

functional groups derived from a combination of functional feeding groups and habit trait

groups), FD – the number of functional groups and division of individuals among these

groups, and functional evenness (FE – the division of individuals among functional

groups). Furthermore, functional structure (FS) comprised the composition and abundance

of functional groups at each site.

3. FR increased with increasing pH, with additional variation related to moss cover, total

nitrogen, water colour and substratum particle size. FD similarly increased with increasing

pH and decreased with increasing canopy cover. FE decreased with increasing canopy

cover and water colour. Significant variation in FS was attributable to pH, stream width,

moss cover, substratum particle size, nitrogen, water colour with the dominant pattern in

FS being related to the increase of shredder-sprawlers and the decrease of scraper-

swimmers in acidic conditions.

4. In regression analysis and redundancy analysis, variation in functional biodiversity was

not only related to local environmental factors, but a considerable proportion of variability

was also attributable to spatial patterning of environmental variables and pure spatial

gradients. For FR, 23.4% was related to pure environmental effects, 15.0% to shared

environmental and spatial effects and 8.0% to spatial trends. For FD, 13.8% was

attributable to environmental effects, 15.2% to shared environmental and spatial effects

and 5% to spatial trends. For FE, 9.0% was related to environmental variables, 12.7% to

shared effects of environmental and spatial variables and 4.5% to spatial variables. For FS,

13.5% was related to environmental effects, 16.9% to shared environmental and spatial

effects and 15.4% to spatial trends.

Correspondence: Jani Heino, Finnish Environment Institute, Research Programme for Biodiversity, PO Box 413,

FIN-90014 University of Oulu, Finland. E-mail: jani.heino@ymparisto.fi

Freshwater Biology (2005) 50, 1578–1587 doi:10.1111/j.1365-2427.2005.01418.x

1578 � 2005 Blackwell Publishing Ltd

5. Given that functional biodiversity should portray variability in ecosystem function-

ing, one might expect to find functionally rather differing ecosystems at the opposite

ends of major environmental gradients (e.g. acidity, stream size). However, the degree

to which variation in the functional biodiversity of stream macroinvertebrates truly

portrays variability in ecosystem functioning is difficult to judge because species traits,

such as feeding roles and habit traits, are themselves strongly affected by the habitat

template.

6. If functional characteristics show strong responses to natural environmental gradients,

they also are likely to do so to anthropogenic environmental changes, including changes in

habitat structure, organic inputs and acidifying elements. However, given the considerable

degree of spatial structure in functional biodiversity, one should not expect that only the

local environment and anthropogenic changes therein are responsible for this variability.

Rather, the spatial context, as well as natural variability along environmental gradients,

should also be explicitly considered in applied research.

Keywords: environmental gradients, functional diversity, functional feeding groups, macroinverte-brates, spatial structure, streams, traits, variance partitioning

Introduction

Biodiversity encompasses multiple dimensions of the

variability of nature, including genetic, taxonomic and

functional components. Present views also consider

ecological interactions, such as predation, herbivory

and detritivory, and their ecosystem effects as integ-

rated processes and components of biodiversity (Til-

man, 2001). Despite this multifaceted view on

biodiversity, most studies continue to address species

richness as its major component. Less well studied are

other components of biodiversity, and not until

recently has variability in functional diversity (FD)

been rigorously examined across major ecological

gradients (e.g. the latitudinal gradient, Stevens et al.,

2003). Broadly understood, FD relates to the number

and variability of ecological roles of species in an

ecosystem. FD can thus be measured using categori-

sation of species into functional groups, and exam-

ination of the division of species and individuals

among these categories.

Much of the biodiversity in running waters is

accounted for by benthic macroinvertebrates. Consist-

ing of aquatic insects, worms, molluscs and crusta-

ceans, macroinvertebrate diversity in streams clearly

exceeds that of fishes and macrophytes (Allan &

Flecker, 1993). Furthermore, benthic macroinverte-

brates serve as important contributors to ecosystem

functions, including detritus processing, animal–

microbial interactions, herbivory and energy transfer

to the consumers at higher trophic levels (Wallace &

Webster, 1996; Covich, Palmer & Crowl, 1999). Such

functional versatility and ecological importance result

from a multitude of adaptations that benthic inverte-

brates have evolved in response to the complex,

spatially and temporally dynamic nature of running

water habitats. Information on factors affecting the FS

and FD of macroinvertebrate assemblages is, there-

fore, not only vital for basic ecological understanding

and biodiversity conservation, but also as references

for monitoring, restoring and maintaining the quality

of stream ecosystems (Rosenberg & Resh, 1993;

Palmer, Ambrose & Poff, 1997).

Conceptual understanding of the functional roles of

invertebrates in stream ecosystems began to develop

rapidly in the 1970s (Cummins, 1974; Cummins &

Klug, 1979), and attained a major burst in the 1980s

(Vannote et al., 1980; Minshall et al., 1985; Minshall,

1988). One of the first rigorous attempts to relate

functional structure (FS) to ecosystem functioning in

running waters was the river continuum concept

(Vannote et al., 1980). This concept views running

waters as a continuum from headwaters to large

rivers downstream, with concomitant changes in the

allochthonous and autochthonous resource bases and

the FS of ecological communities. Although the river

continuum concept can be criticised on the basis of not

accounting for multiple environmental gradients, it

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remains as a ‘bold scheme in stream ecology’ (Allan,

1995), and provides a suitable background for further

evaluations of the relationships between stream envi-

ronments and FS. However, few studies have rigor-

ously examined FS and FD of macroinvertebrate

assemblages in relation to multiple environmental

gradients, although the need for such studies has been

clearly identified (Poff, 1997; Usseglio-Polatera et al.,

2000). Using FS, or more generally species traits, as

response variables instead of taxonomic composition

may increase our understanding of the relationship

between, and variability in, stream biodiversity and

ecosystem functioning. However, although most

recent studies have surely addressed multiple import-

ant traits (e.g. Poff & Allan, 1995; Lamouroux, Dole-

dec & Gayraud, 2004), there has typically been rather

little division between the traits that are truly func-

tional in terms of ecosystem functioning and those

that portray other life history characteristics of stream

invertebrates. Yet, directly emphasising the relation-

ships between particular traits and ecosystem func-

tioning might further increase our conceptual

understanding of stream ecosystems.

Species traits that are important to ecosystem

functioning include not only functional feeding roles,

but also where and how the resources are obtained.

Thus, I examined a combination of functional feeding

groups (Cummins, 1973) and habit traits (Merritt &

Cummins, 1996) as the basis for the present analyses

of the FS and FD of stream macroinvertebrate

assemblages. My aim was to identify the major

environmental factors accounting for variability in

these aspects of stream macroinvertebrate biodiver-

sity, and to examine whether any geographical

gradients exists. For this purpose, the present data

set is optimal, because it spans a relatively large

geographical extent (60�N to 70�N, 25�E to 32�E) and

incorporates wide variability in environmental condi-

tions of boreal headwater streams. This should make

it possible to examine the hypothesis that aggregate

measures of ecological assemblages, such as those

derived from a functional categorisation of species,

portray local environmental characteristics and con-

straints set by habitat templates (Southwood, 1977,

1988), as opposed to strong spatial gradients in

assemblage structure (e.g. Borcard, Legendre &

Drapeau, 1992) that are expected for species-level

taxonomic data (e.g. Heino et al., 2003a; Johnson,

Goedkoop & Sandin, 2004).

Methods

Stream surveys and field sampling

The 111 stream sites in this study spanned the five

ecoregions of Finland (e.g. Heino et al., 2002). Stream

sites were surveyed in 1998, and all material was

collected, processed and analysed by the same per-

sonnel. The field crew limited sampling to least

impacted streams with base flow <0.6 m3 s)1 and

catchment area <60 km2, with the aim to broadly

delineate the study to a single habitat type, i.e.

headwater streams. Therefore, spring-fed streams,

lake outlets, and streams disturbed by recent human

activities we not included in this study.

The field crew measured several riparian and in-

stream variables at each site. The tree species compo-

sition of the riparian zone was assessed in a 50-m

section along both banks directly upstream of the

sampling site. Canopy cover was measured at 20

locations in evenly spaced cross-channel transects.

Depth and current velocity (at 0.4 · depth) were

measured at 40 random locations in cross-channel

transects. Moss cover and substratum particle size

were assessed in 10, 50 · 50 cm quadrants placed

randomly in each riffle. The following classification of

particle sizes (modified Wentworth scale) was used:

(0) organic matter, (1) sand (diameter 0.25–2 mm), (2)

fine gravel (2–6 mm), (3) coarse gravel (6–16 mm), (4)

small pebble (16–32 mm), (5) large pebble (32–

64 mm), (6) small cobble (64–128 mm), (7) large

cobble (128–256 mm), (8) small boulder (256–

400 mm) and (9) large boulder and bedrock

(>400 mm). The proportion of each size class was

estimated for each quadrant, and these estimates were

subsequently averaged to give the mean substratum

particle size for a site. Mean stream width was also

measured at each sampling site based on five across-

stream lines. Water samples were collected simulta-

neously with physical measurements, and they were

subsequently analysed for pH, alkalinity, conductiv-

ity, total nitrogen, total phosphorus, colour and iron

following Finnish national standards. Physical and

chemical conditions of the study streams are summa-

rised in Table 1.

Macroinvertebrates were sampled between early

September and late October in 1998, depending on

the latitude of a stream. Northernmost sites were

sampled first, and the southernmost sites last. At

each site, the field crew took a 2-min kick-net (net

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mesh size 0.3 mm) sample covering most microhab-

itats present in a riffle of approximately 100 m2. This

sampling effort typically yields >70% of species

occurring at a site in a given season, mainly missing

species that occur only sporadically in streams (H.

Mykra, T. Ruokonen & T. Muotka, unpublished

data). Macroinvertebrates and associated material

were immediately preserved in 70% alcohol in

the field. Macroinvertebrates were identified to the

lowest feasible taxonomic level, usually species or

genus. Chironomid midges were omitted from the

analyses because they were not identified beyond

family level.

Functional groups, functional diversity and functional

structure

Macroinvertebrates were categorised into six func-

tional feeding groups and six habit trait groups

according to data in Merritt & Cummins (1996) for

aquatic insects and expert opinion for other minor

groups of macroinvertebrates. Functional feeding

groups included shredders, gatherers, filterers, scra-

pers, piercers and predators. Habit trait groups

included burrowers, climbers, clingers, divers, spraw-

lers and swimmers. Functional feeding groups thus

refer to the feeding mode and approximate food type

of macroinvertebrates, whereas habit trait groups

include information on the relative mobility and

where food is obtained, e.g. on stones versus within

sediments (Table 2). Both of these characteristics

should be important with regard to the functional

roles of macroinvertebrates in stream ecosystems.

Combinations of functional feeding groups and habit

trait groups were used, and the 20 observed combi-

nations were termed subsequently as functional

groups (Table 3). The macroinvertebrates could have

been allocated to several other trait groups (e.g.

Usseglio-Polatera et al., 2000; Lamouroux et al., 2004)

as well, but these two are perhaps most directly

related to ecosystem functioning in running waters.

Table 1 Mean (±SE) and range of environmental variables and

measures of functional diversity of macroinvertebrate assem-

blages

Variable Mean SE Min. Max.

Deciduous trees (%) 70.80 2.36 10 100

Canopy cover (%) 46.55 2.07 0 91

Current velocity (cm s)1) 37.37 1.34 7 97

Stream width (m) 3.16 0.19 0.6 10.0

Depth (cm) 21.49 0.73 5 39

Moss cover (%) 25.97 2.19 0 86

Substratum particle size 5.61 0.15 0.75 8.20

pH 6.71 0.07 4.7 8.4

Alkalinity (mmol L)1) 0.24 0.03 0 1.88

Conductivity (mS m)1) 4.40 0.29 1.20 19.90

Total nitrogen (lg L)1) 363 24 32 1200

Total phosphorus (lg L)1) 16.71 1.59 0 100

Colour (mg Pt L)1) 122 13 5 600

Iron (lg L)1) 999 128 5 6000

FR 8.84 0.21 3 14

FD 1.28 0.03 0.46 1.94

FE 0.59 0.01 0.24 0.88

FR, functional richness; FD, functional diversity; FE, functional

evenness.

Table 2 Characterisation of functional feeding groups and habit

traits groups of stream macroinvertebrates. Modified from

Merritt & Cummins (1996).

Functional category Ecological characteristics

Functional feeding

group

Feeding mode and food type

Gatherers Feed on fine particulate detritus on

stream bottom

Filterers Filter suspended particulate material

from water column

Piercers Feed on living vascular hydrophytes

and algae by piercing and sucking

cell and tissue fluids

Predators Attack other animals and engulf whole

prey or suck body fluids

Scrapers Feed on periphytic algae and associated

material on mineral and organic

substrates

Shredders Feed on living or decomposing vascular

plant tissue, coarse particulate organic

material, by chewing large pieces

Habit trait group Mode of existence

Burrowers Inhabit fine sediments and may

construct burrows with protruding

tubes or ingest their way through

sediments

Climbers Live on vascular hydrophytes or

detrital debris, moving vertically on

stem-type surfaces

Clingers Possess behavioural or morphological

adaptations for attachment on to

surfaces on current-swept riffles

Divers Swim by rowing with the specially

adapted hind legs, usually associated

with low-current habitats

Sprawlers Inhabit the surfaces of floating leaves of

vascular hydrophytes or fine sediments

Swimmers Adapted for short periods of swimming

between benthic objects

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Furthermore, while the feeding roles of stream

macroinvertebrates may vary during the larval devel-

opment, as well as seasonally, and while many species

may be rather opportunistic (e.g. Mihuc, 1997; Dan-

gles, 2002), I supposed that aggregate assemblage

variables would ameliorate the influences of such

species-level factors on finding general patterns in

functional biodiversity.

Three measures of FD were calculated for each site:

(i) functional richness (FR), i.e. the number of func-

tional groups; (ii) FD, i.e. Shannon–Wiener diversity

index, describing both the number of functional

groups and the division of individuals among the

functional groups and (iii) functional evenness (FE),

based on Shannon–Wiener index and describing the

division of individuals among the functional groups.

FS referred to the composition and abundance of

functional groups at each site.

Statistical methods

Variability in FR, FD and FE and FS was partitioned

between two explanatory variable groups: (i) local

environmental variables and (ii) spatial location.

Spatial variables included national north (x) and

east (y) coordinates that were centred on their

respective mean values and standardised. Sub-

sequently, a third order spatial polynomial of the

form

Z ¼ b1xþ b2yþ b3x2 þ b4y

2 þ b5xyþ b6x2y

þ b7xy2 þ b8x

3 þ b9y3

was constructed to describe the spatial location of

each stream site. Using these multiple spatial varia-

bles allows one to model more complex spatial

patterns than mere north and east coordinates (Bor-

card et al., 1992; Legendre, 1993). I used both linear

multiple regression and constrained ordination ana-

lyses to examine relationships between functional

characteristics and explanatory variables.

For partitioning variation in FS [ln (x + 1) trans-

formed abundance data] between local environment

and spatial location, each group of variables was first

screened using forward selection with Monte Carlo

randomisation test in redundancy analysis (RDA;

CANOCO version 4.5, ter Braak & Smilauer, 2002).

Only variables significantly (a ¼ 0.05) related to FS

were retained in the final models. A series of three

RDAs was then run for FS: (i) matrix constrained by

both environmental and spatial location variables

(a + b + c; fractions following Legendre & Legendre,

1998: a ¼ pure environmental; b ¼ shared environ-

mental and spatial; c ¼ pure spatial), (ii) constrained

by environmental variables (a + b) and (3) constrained

by spatial position variables (b + c). In RDA, the sum

of canonical eigenvalues relates to the amount of

explained variation. Variation in FS was subsequently

partitioned into shared environmental and spatial

Table 3 Occurrence of the observed func-

tional groups in the study streams. Func-

tional groups were based on a

combination of functional feeding groups

and habit trait groups. Also shown are

example genera for each functional group.

Functional group No. of sites Example genera

Filterers Clingers 109 Polycentropus, Ceratopsyche, Hydropsyche

Filterers Sprawlers 36 Pisidium, Sphaerium

Gatherers Burrowers 58 Ephemera, Eiseniella, Berdeniella

Gatherers Clingers 43 Ephemerella

Gatherers Sprawlers 9 Athripsodes, Ceraclea, Molannodes

Gatherers Swimmers 41 Leptophlebia, Cloeon

Piercers Climbers 12 Agraylea, Oxyethira

Predators Burrowers 60 Onychogomphus, Sialis, Bezzia

Predators Climbers 13 Calopteryx, Enallagma, Oligostomis

Predators Clingers 107 Diura, Isoperla, Rhyacophila

Predators Divers 26 Agabus, Platambus

Predators Sprawlers 90 Somatochlora, Atherix, Dicranota

Scrapers Climbers 28 Gyraulus, Lymnaea, Elodes

Scrapers Clingers 72 Heptagenia, Elmis, Oulimnius

Scrapers Swimmers 93 Baetis, Ameletus, Habrophlebia

Shredders Burrowers 12 Tipula, Prinocera

Shredders Climbers 19 Agrypnia, Semblis

Shredders Clingers 42 Micrasema, Potamophylax

Shredders Sprawlers 110 Nemoura, Protonemoura, Leuctra

Shredders Swimmers 2 Gammarus

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position [b ¼ (a + b) + (b + c) ) (a + b + c)], pure

environmental [a ¼ (a + b) ) b]; pure spatial position

[c ¼ (b + c) ) b] and unexplained fractions [d ¼1 ) (a + b + c)]. RDA was used here because of

relatively short functional gradients (gradient lengths

<2 SD units in preliminary detrended correspondence

analysis).

For partitioning variation in the measures of FD

between local environment and spatial position, linear

regression analysis was employed. Each group of

explanatory variables was first screened via forward

stepwise regression to obtain a reduced set of signi-

ficant (a ¼ 0.05) variables for the final regression

models. FR, FD and FE were subsequently regressed

(i) on both environmental and spatial position varia-

bles (a + b + c), (ii) regressed on environmental var-

iables only (a + b) and (iii) regressed on spatial

position variables only (b + c). The R2 values from

the analyses were subsequently used in partitioning

the variation in FD between shared environmental

and spatial position, pure environmental, pure spatial

position and unexplained fractions as above (see also

Legendre & Legendre, 1998).

Results

The final environmental regression models (after

forward selection) for FR, FD and FE were, res-

pectively: FR ¼ )8.859 + 1.453(pH) + 0.0443(moss) +

5.165(nitrogen) ) 2.404(colour) ) 0.267(substratum

particle size); FD ¼ 0.489 + 0.153(pH) ) 0.005(canopy

cover) and FE ¼ 0.789 ) 0.002(canopy) ) 0.053(col-

our). Thus, FR increased with increasing pH, with

additional variation significantly related to moss

cover, total nitrogen, water colour and substratum

particle size. FD similarly increased with increasing

pH and decreased with increasing canopy cover. FE

decreased with increasing canopy cover and water

colour. These models accounted for 38.4%, 29.0% and

21.7% of variability in FR, FD and FE, respectively.

RDA showed that six environmental variables were

significantly related to FS and accounted for 30.4% of

variability (Table 4).

The final spatial regression models incorporated

four, three and two terms of the spatial polynomial,

and accounted for 23.0%, 20.2% and 17.2% of

variability in FR, FD and FE, respectively. RDA

selected all terms of the spatial polynomial, account-

ing for 32.3% of variability in FS (Table 4). Models

incorporating both environmental and spatial varia-

bles accounted for 46.4%, 34.0%, 26.2% and 45.8% of

variability in FR, FD, FE and FS of macroinvertebrate

assemblages (Table 4).

Partitioning variability in FR, FD, FE and FS

between environmental and spatial variables yielded

additional insights on the variability of functional

characteristics (Fig. 1). For FR, 23.4% was related to

pure environmental effects, 15.0% to shared environ-

mental and spatial effects, 8.0% to spatial effects and

53.6% to unexplained fraction. For FD, 13.8% was

attributable to environmental effects, 15.2% to shared

environmental and spatial effects, 5% to spatial effects

and 66.0% remained unexplained. For FE, 9.0% was

related to environmental variables, 12.7% to shared

effects of environmental and spatial variables, 4.5% to

spatial variables and 73.8% remained unexplained.

For FS, 13.5% was related to environmental effects,

16.9% to shared environmental and spatial effects,

15.4% to spatial effects and 54.2% remained unex-

plained (Fig. 1).

Table 4 Summary of final models of

regression analyses for functional richness

(FR), functional diversity (FD) and func-

tional evenness (FE) of stream macroin-

vertebrates. Redundancy analysis was

used to model variability in functional

structure (FS). Analyses were conducted

separately for environmental variables,

spatial variables and for both groups to-

gether as independent variables. All

models were significant at P < 0.001.

F-value R2-value Variables in the model

FR-environment 13.085 0.384 pH, moss, nitrogen, colour, substratum

FR-space 7.918 0.230 y3, x2, xy, xy2

FR-total 9.703 0.464

FD-environment 22.109 0.290 pH, canopy

FD-space 9.010 0.202 xy2, x3, x

FD-total 10.796 0.340

FE-environment 14.965 0.217 Canopy, colour

FE-space 11.196 0.172 xy2, x3

FE-total 9.385 0.262

FS-environment 7.577 0.304 pH, width, moss, substratum, nitrogen, colour

FS-space 5.343 0.323 x, y, x2, y2, xy, x2y, xy2, x3, y3

FS-total 5.352 0.458

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Discussion

Variability along major environmental gradients in

many components of biodiversity remains poorly

understood. While species richness gradients are

becoming increasingly well known for various organ-

ismal groups (Huston, 1994; Mittelbach et al., 2001),

including stream macroinvertebrates (Vinson &

Hawkins, 1998; Malmqvist & Hoffsten, 2000; Heino,

Muotka & Paavola, 2003b), patterns of FD have rarely

been studied in general (e.g. Stevens et al., 2003) and

in stream ecosystems (cf. studies on FS). This is

surprising given the link between ecosystem proces-

ses and functional biodiversity. The present findings

that the measures of FD decreased with water acidity,

canopy cover and water colour, and increased with

moss cover, and that FS varied with pH, stream size

and moss cover should thus be of interest to both

research addressing the functioning of stream ecosys-

tems and the management of stream resources. More

importantly, however, these measures of functional

biodiversity showed surprisingly strong spatial struc-

ture.

Recent experimental and observational studies on

biodiversity and ecosystem functioning have concen-

trated on examining the effects of species diversity on

ecosystem processes (reviewed in Kinzig, Pacala &

Tilman, 2002). For example, Jonsson, Malmqvist &

Hoffsten (2001) found that increasing number of

shredder species increased the rate of leaf litter

breakdown along a stream size gradient. In a further

elaboration of the same theme, Dangles, Malmqvist &

Laudon (2004) found that naturally acidic and neutral

streams did not differ in their species richness and

rate of leaf litter breakdown. Thus, it appears that,

along natural environmental gradients, biodiversity

and ecosystem functioning may or may not remain at

a relatively unchanged level. However, these studies

concentrated on a single functional group, i.e. shred-

ding macroinvertebrates, and a single ecosystem

function, i.e. the breakdown of coarse particulate

organic material. Thus, it is unclear whether other

ecosystem processes, including microbial interactions,

grazing, predation, and transfer of energy to higher

trophic levels would change along environmental

gradients.

The present findings, however, refer to clear chan-

ges in FD and FS of macroinvertebrate assemblages

along major environmental gradients of headwater

streams, suggesting that ecosystem processes exhibit

corresponding changes. One might assume, for exam-

ple, that there is a dearth of grazing scrapers in acidic

streams, because major groups of grazing inverte-

brates, such as mayflies, are negatively affected by

water acidity (e.g. Heino et al., 2003c). A further

relationship between grazing and biodiversity might

relate to humic content of water because this factor

may inhibit algal growth, and thereby affect the

importance of grazing in an ecosystem (e.g. Vuori &

Muotka, 1999). By contrast, leaf litter breakdown may

not be negatively affected by water acidity because

major groups of leaf-shredding macroinvertebrates,

such as stoneflies, may not be as sensitive as mayflies

to water acidity (e.g. Hamalainen & Huttunen, 1990).

Indeed, a present finding supporting this observation

was that (mayfly) scrapers-swimmers showed a

positive relationship to pH (r ¼ 0.637, P < 0.001),

whereas (stonefly) shredders-sprawlers showed an

opposite relationship (r ¼ )0.441, P < 0.001). Simi-

larly strong relationships might be expected between

canopy cover and FS of stream macroinvertebrate

assemblages (e.g. Hawkins, Murphy & Anderson,

1982), suggesting that riparian environmental factors

strongly shape patterns in functional characteristics. It

is more notable, however, that such discernible

changes in FD and FS (Tables 1 and 4) occurred

within a single habitat type (headwater streams),

although drastic functional changes have typically

been associated with long stream size gradients

(Vannote et al., 1980; Minshall et al., 1985; Grubauch,

Wallace & Houston, 1996; Heino et al., 2005).

0

20

40

60

80

100

FR FD FE FS

% V

aria

tion Unexplained

Pure S

Shared E and S

Pure E

Fig. 1 Variation partitioning between environmental variables

(E) and spatial variables (S) for functional richness (FR), func-

tional diversity (FD), functional evenness (FE) and functional

structure (FS). Variation partitioning was based on the results of

either linear regression analysis or redundancy analysis (see

Table 4).

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As FD changes along environmental gradients, one

is tempted to ask whether FD portrays changes in

ecosystem functioning or whether FD merely follows

the variability of ‘ecological opportunities’. Disentan-

gling these two aspects may be difficult as they are

intimately intertwined. However, as taxonomic struc-

ture of macroinvertebrate assemblages has often been

observed to respond to the same gradients as I found

here for FS (see Malmqvist & Hoffsten, 2000; Heino

et al., 2003c), the latter one appears to be closer to the

truth. Thus, environmental variables may filter spe-

cies with traits suitable to prevailing conditions (Poff,

1997; Lamouroux et al., 2004), and this filtering pro-

cess is then mirrored in the FD in an ecosystem.

However, once present, species of different functional

types undoubtedly contribute to ecosystem function-

ing, and environmentally harsh conditions may thus

indirectly reduce the versatility and rate of ecosystem

processes, as suggested by the decreased FD in acid

streams. In the same vein, one might expect a wider

array of functional groups where habitat heterogen-

eity is higher, because habitat heterogeneity increases

ecological opportunities and, thereby, diversity both

within and between functional groups (Huston, 1994).

This relates not only to the diversity of food resources,

but also to where resources can be obtained, i.e.

habitat structural characteristics. The relationships

that I found between FD, moss cover and substratum

characteristics may indeed refer to the existence of

such relationships. It is important to note that such

relationships to habitat characteristics are likely to be

more pronounced if combinations of functional feed-

ing groups (Cummins, 1973) and habit trait groups

(Merritt & Cummins, 1996) are used. That is, habitat

characteristics do affect whether burrowing gatherers

or swimming gatherers, for example, are dominant in

an ecosystem, with probable repercussions for eco-

system processes (e.g. processing of fine organic

material within sediments versus on sediments).

Thus, this combination of functional groups also

generates more variability in functional data to por-

tray environmental conditions.

Ecological opportunities may also change along

geographical gradients. For example, regional differ-

ences in stream conditions may lead to regionally

varying patterns of FS (e.g. Mykra, Heino & Muotka,

2004). Such effects were also suggested by the shared

environmental and spatial component of variability in

FS and FD in this study. Further, the pure spatial

component in functional characteristics of macroin-

vertebrate assemblages may have resulted either (i)

from regional gradients in some unmeasured envir-

onmental factors or (ii) true spatial (latitudinal)

gradients in functional biodiversity (e.g. Legendre &

Legendre, 1998). The former reasoning is supported

by the supposition that species aggregates, such as

functional groups, should portray local ecosystem

characteristics more than vary geographically (cf.

Johnson et al., 2004). The latter reasoning is supported

by the fact that spatial gradients are proxies for

variation in climate that may control ecosystem

processes and functional groups, or the distributions

of individual species and consequent differences in

functional groups. Either way, scrapers seem to be

more abundant in northernmost clear-water streams,

whereas shredders dominate more southern humic

and acid streams in the present study area (Heino

et al., 2002).

The present findings bear a number of implications

for the management and conservation of stream

ecosystems. First, as FS and FD respond to environ-

mental gradients, conserving this component of bio-

diversity requires preservation of streams at different

positions of environmental gradients, e.g. streams

differing in natural acidity. This also suggests that

liming of naturally acidic streams, for example,

changes typical ecosystem characteristics, be they

functionally diverse or not, and should thus be

avoided (see Dangles et al., 2004). Thus, there is a

wide degree of natural variability from functionally

poor to functionally diverse stream ecosystems at the

regional level. Second, although this study did not

concentrate on other aspects of FD, i.e. the division of

species among functional groups and functional

redundancy within functional groups (e.g. Rosenfeld,

2002), such examinations undoubtedly are important

with regard to biodiversity conservation and under-

standing of ecosystem functioning in streams. Finally,

for the assessment of stream ecosystem condition,

functional characteristics of stream macroinvertebrate

assemblages provide an alternative, or perhaps a

complementary way to taxonomic approaches (e.g.

Poff, 1997; Usseglio-Polatera et al., 2000; Statzner et al.,

2001). If functional characteristics show strong re-

sponses to natural environmental gradients, they also

are likely to do so to anthropogenic environmental

changes, including changes in habitat structure,

organic inputs and acidifying elements. However,

Functional biodiversity of macroinvertebrate assemblages 1585

� 2005 Blackwell Publishing Ltd, Freshwater Biology, 50, 1578–1587

given the considerable degree of spatial structure in

functional biodiversity, one should not expect that

only the local environment and anthropogenic chan-

ges therein are responsible for this variability; rather,

the spatial context should also be explicitly considered

in applied research on stream biodiversity. This

general suggestion is not limited to stream macroin-

vertebrates (e.g. Murphy & Davy-Bowker, 2005; H.

Mykra, J. Heino & T. Muotka, unpublished data), but

is likely to apply to other organism groups (e.g.

Magalhaes, Batalha & Collares-Perreira, 2002; Soini-

nen, Paavola & Muotka, 2004) and the variability in

their functional importance in stream ecosystems.

Acknowledgments

I thank all the persons and institutes involved in the

data acquisition, and notably T. Muotka and R.

Paavola for their collaboration throughout the early

phases of the study. I also thank H. Mykra, J. Soininen

and an anonymous referee for comments on an early

draft of this paper. This study was financially

supported by the Academy of Finland (grants to

T. Muotka and J. Heino).

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(Manuscript accepted 14 June 2005)

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