Determinants of macroinvertebrate diversity in headwater streams: regional and local influences

Journal of Animal Ecology

2003

72

, 425–434

© 2003 British Ecological Society

Blackwell Publishing Ltd.

Determinants of macroinvertebrate diversity in headwater streams: regional and local influences

JANI HEINO*, TIMO MUOTKA† and RIKU PAAVOLA*

*

Department of Biological and Environmental Sciences, University of Jyväskylä, POB 35, 40351 Jyväskylä, Finland; and

†

Finnish Environment Institute, Research Section, POB 140, 00251 Helsinki, Finland

Summary

1.

Multiscale determinants of diversity and the relationship between regional (RSR)and local richness (LSR) have recently attracted increased attention, yet such studies onstream organisms remain scarce. We studied the relationships among RSR,

β

-diversity,LSR and local environmental variables in 120 headwater streams in Finland. Approx-imately similar-sized areas of eight drainage systems were defined as regions, and 15stream riffles (= locality) per region were sampled.

2.

RSR showed a strong positive relationship with mean LSR (

R

2

= 0·686), and therewas no sign of curvilinearity within the observed range of RSR. RSR was also posit-ively, although non-significantly, related to

β

-diversity (

r =

0·662).

3.

In stepwise regression, RSR was the first variable to enter the model, and a modelincorporating RSR and stream width explained 32·5% of variation in LSR. If RSR wasomitted from the model, then stream width emerged as the most important variable,followed by water pH, which together accounted for 20·6% of variation in LSR.

4.

At the within-region scale, different variables were important in accounting forvariation in LSR. Factors correlated with LSR reflected either stream size, spatialheterogeneity, adverse water chemistry conditions (pH), or a limiting resource base formacroinvertebrates (nutrient concentrations).

5.

The major role of RSR in setting the upper limit to LSR suggests that macroinver-tebrate assemblages of boreal headwater streams are unsaturated. This finding is sup-ported by evidence from experimental studies, where it has been shown that competitiveinteractions among stream macroinvertebrates are effective only at very small spatialscales, and competitive exclusion is prevented typically by frequent disturbances.However, although RSR was generally the most influential variable contributing toLSR, it is far from clear whether RSR consistently sets the limits to LSR, or vice versa.For instance, uniformly adverse water chemistry conditions across a region may lead tolow levels of local richness and low species turnover among sites, leading eventually toan impoverished regional species pool.

6.

These findings do not deny the importance of local factors, but emphasize thatunderstanding the organization of stream benthic communities requires simultaneousexamination of factors prevailing at multiple spatial scales.

Key-words

: local richness, regional richness, species diversity, stream macroinverte-brates, turnover diversity.


(2003)

72

, 425–434

Introduction

An emerging paradigm in community ecology emphasizesthe role of regional factors and history in regulating the

organization of biotic communities (Menge & Olson1990; Ricklefs & Schluter 1993). Empirical support forstrong regional effects has been obtained from a numberof studies reporting a linear relationship betweenregional species richness (RSR) and local species rich-ness (LSR) for various taxonomic groups (e.g. Cornell& Karlson 1996; Caley & Schluter 1997; Griffiths 1997;Shurin

et al

. 2000). Theoretically, if LSR increases

Correspondence: Jani Heino, Department of Biological andEnvironmental Sciences, University of Jyväskylä, POB 35,40351 Jyväskylä, Finland. E-mail: [email protected]

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J. Heino, T. Muotka & R. Paavola

© 2003 British Ecological Society,


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linearly with RSR, local communities are consideredunsaturated and mainly free from local control. Bycontrast, if LSR reaches an asymptote at high levels ofRSR, then communities are regarded as saturated andcontrolled by interspecific competition and other localprocesses (Cornell & Lawton 1992; Cornell 1993). As atentative generalization emerging from studies conductedthus far, local communities seem rarely saturated, bioticinteractions are relatively weak and regional processeshave a large impact on setting the upper limits to localspecies richness.

The degree of interdependence among RSR (

γ

-diversity) and LSR (

α

-diversity) is connected closelywith patterns of species turnover among sites, i.e.

β

-diversity (Ricklefs & Schluter 1993; Srivastava 1999).Regional

β

-diversity is determined by (i) variation inenvironmental characteristics among local habitats,and (ii) the degree of habitat specialization of the biota.If local habitat conditions determine local diversity,and there is a high degree of species turnover amongsites, then it is in fact RSR that mirrors variation inLSR across a region. Thus, regional differences indiversity may stem from smaller-scale processes, ratherthan from among-region differences in dispersal andtaxa diversification (Ricklefs & Schluter 1993). Clearly,this generates a chicken-and-egg problem (Cornell &Lawton 1992; Huston 1999): is it really RSR that deter-mines LSR, or vice versa? Understanding the determin-ants of diversity across multiple scales thus necessitatesa simultaneous examination of

β

-diversity and theRSR–LSR relationship.

Stream communities are structured by processesprevailing at multiple spatial scales (Minshall 1988;Poff 1997), yet surprisingly few studies have addressedregional–local species richness relationships amongrunning water biota. Studies on fish communities sug-gest either strong regional control of local diversity(Hugueny & Paugy 1995; Oberdorff

et al

. 1998) or thejoint influence of regional and local factors (Angermeier& Winston 1998) in determining the number of locallyco-occurring species. Similar studies on stream inver-tebrates are virtually lacking, with the exception of astudy reporting a linear relationship among RSRand LSR for river-dwelling mussels (Vaughn 1997). Asstream invertebrates experience frequent and unpre-dictable disturbances and show a high capability fordispersal, one may predict that their species richnessshould be primarily under regional control (Palmer,Allan & Butman 1996). Alternatively, because evenneighbouring streams may differ widely in environ-mental conditions, local factors may modify macro-invertebrate diversity considerably (Townsend, Hildrew& Francis 1983; Malmqvist & Mäki 1994; Paavola,Muotka & Tikkanen 2000).

In broad-scale surveys, the number of macroinverte-brate taxa has been observed to increase with streamsize, substratum heterogeneity and the amount ofmacrophytes, suggesting a trend of increasing richnesswith higher environmental heterogeneity (e.g. Vinson

& Hawkins 1998). By contrast, low water quality (e.g.low pH) has been associated with low-diversitymacroinvertebrate communities (Townsend

et al

. 1983;Hildrew & Giller 1994). However, the relative import-ance of local physicochemical factors and the regionalspecies pool as determinants of macroinvertebraterichness in streams remains largely unstudied (seeVinson & Hawkins 1998).

In boreal areas, stream macroinvertebrate assem-blage structure varies considerably at both regionaland local scales (Malmqvist & Hoffsten 2000; Sandin& Johnson 2000; Heino

et al

. 2002), making themamenable systems for assessing large-scale determinantsof benthic biodiversity. In this study, we analyseddata from 120 streams in eight regions (i.e. drainagesystems) in Finland. We specifically examined (1) therelationship between RSR and LSR, (2) the relation-ship between RSR and

β

-diversity and (3) the relativecontribution of RSR and local environmental factorsto LSR. Furthermore, at the within-region scale, weexamined (4) which environmental factors are cor-related most strongly with variation in LSR.

Materials and methods

The majority of stream surveys were conducted in 1998,but additional data from 1992, 1994, 1997 and 2000 wereincluded if field sampling and laboratory methods wereidentical to those used in 1998. All material was col-lected, processed and analysed by the same personnel.We limited our consideration to near-pristine streamswith base flow < 0·6 m

3

s

−

1

and catchment area < 60 km

2

,to delineate our analysis to a single habitat type, i.e.headwater streams. Therefore, we excluded spring-fedstreams, lake outlets and streams disturbed by recenthuman activities. Otherwise, streams for each region wereselected randomly, with the restriction that they had tobe within 2 kilometres from the nearest road. We sampled15 headwater streams in each of eight regions (Fig. 1).

We measured several riparian and in-stream vari-ables at each site. The integrity (% riparian zone withoutobvious human impact) and tree species compositionof the riparian zone were assessed in a 50-m sectionalong both banks directly upstream of the samplingsite. Shading by overhanging vegetation was measuredat 20 locations in evenly spaced cross-channel transects.Depth and current velocity (at 0·4

×

depth) were meas-ured at 40 random locations in cross-channel transects.Moss cover and substratum particle size were assessedin 10 50 cm

×

50 cm quadrats placed randomly in eachriffle. We used the following classification of particlesizes (modified Wentworth scale): (0) organic matter;(1) sand (diameter 0·25 mm

−

2 mm); (2) fine gravel(2 mm

−

6 mm); (3) coarse gravel (6 mm

−

16 mm);(4) small pebble (16 mm

−

32 mm); (5) large pebble(32 mm

−

64 mm); (6) small cobble (62 mm

−

128 mm);(7) large cobble (128 mm

−

256 mm); (8) small boulder

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Macroinvertebrate diversity in headwater streams



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(256 mm

−

400 mm); and (9) large boulder and bedrock(> 400 mm). The proportion of each size class wasestimated for each quadrat, and these estimates weresubsequently averaged to give the mean substratumparticle size for a site. Mean stream width was alsomeasured at each sampling site. Water samples werecollected simultaneously with benthic sampling, and

they were subsequently analysed for pH, alkalinity,conductivity, total nitrogen [TN], total phosphorus[TP], colour and iron [Fe] following Finnish nationalstandards. Physical and chemical conditions of thestudy streams by region are summarized in Table 1.

All invertebrate samples were collected in late aut-umn (early September–late October). At each site, we

Fig. 1. Geographical locations of the study regions. River systems were as follows: upper Kymijoki (A), Kyrönjoki (B),Kiiminkijoki (C), upper Oulujoki (D), Koutajoki (E), Muonionjoki (F), Kemijoki (G) and Tenojoki (H).

Table 1. Regional richness (RSR), β-diversity (calculated according to Harrison et al. 1992) and means and ranges of localrichness (LSR) and major environmental variables for each region. For the measurement of substratum particle size, see text

Region

Variable A B C D E F G H

RSR 92 53 77 43 96 73 81 62β2 9·71 10·88 9·52 6·82 12·45 9·68 9·87 8·13LSR 29·9 15·9 19·5 16·3 23·6 22·8 21·6 20·5

(22–39) (5–21) (9–33) (7–22) (11–35) (12–31) (12–34) (11–29)Deciduous trees (%) 66 42 53 32 69 68 77 98

(10–100) (20–75) (20–85) (5–75) (30–95) (30–95) (50–100) (90–100)Current velocity (cm/s) 36 35 39 29 48 41 28 38

(28–49) (20–67) (20–97) (20–45) (23–71) (21–63) (17–57) (25–53)Stream width (m) 3·8 2·0 2·7 2·8 3·2 2·6 3·4 3·6

(1·5–8·4) (0·8–4·5) (0·5–7·0) (0·7–5·2) (1·0–6·5) (0·6–6·0) (0·9–9·0) (0·8–8·5)Moss cover (%) 33 10 21 42 23 50 33 22

(0–37) (3–79) (0–57) (0–93) (0–67) (1–83) (1–86) (0–86)Particle size 5·9 4·6 5·8 7·4 6·2 4·8 5·5 6·8

(3·5–7·0) (0·8–8·8) (0–8·2) (5·0–9·0) (4·7–7·1) (1·5–6·9) (2·5–8·3) (4·3–7·5)pH 6·5 5·8 5·6 5·5 7·8 6·8 7·3 7·3

(5·7–7·0) (4·7–6·6) (4·6–6·6) (4·8–6·2) (7·2–8·4) (6·3–7·3) (6·3–7·9) (6·6–7·5)Total N (µg /L) 433 1097 593 257 343 165 281 114

(280–710) (298–3700) (400–1100) (180–340) (160–487) (63–320) (123–710) (43–270)Colour (mg Pt/L) 95 272 255 160 91 59 72 13

(40–200) (100–600) (140–400) (100–200) (20–286) (10–150) (13–150) (5–35)

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took a 2-min kick-net sample (net mesh size 0·3 mm),aiming to cover most benthic microhabitats in a rifflesection of approximately 100 m

2

. Invertebrates andassociated material were preserved immediately in70% alcohol, and they were subsequently sorted andidentified in the laboratory. Invertebrates were mainlyidentified to species or genus level. Chironomids andsimuliids were not identified beyond the family level,and they were thus omitted from all analyses.

Defining ‘region’ and ‘locality’ is critical for studies ofregional vs. local determinants of species richness. Anideal region should be environmentally homogeneous,it should have ecologically meaningful boundaries, andall localities within it should be easily accessible to allspecies in the regional pool (Cornell & Karlson 1996;Angermeier & Winston 1998). Furthermore, instead ofspanning multiple habitat types, the analysis shouldfocus on comparing a specific habitat type acrossregions (Srivastava 1999). Streams are organized asnatural spatio-temporal hierarchies, where drainagenetwork forms the highest level. Therefore, drainagesystems represent a natural, objectively defined regionfor examining the regional–local richness relationshipin stream biota (see also Vaughn 1997; Angermeier &Winston 1998). A locality, in turn, should be an environ-mentally homogeneous spatial unit within whichorganisms could encounter each other during ecolo-gical time, thereby forming a potentially interactivecommunity (Cornell & Karlson 1996; Srivastava 1999). Formost stream organisms, an individual riffle site clearlyqualifies for an appropriately scaled sampling unit.

In our study, the streams (

n

= 15) within each regionbelong to the same drainage system. The regionscomprised the following drainage systems (Fig. 1):Kymijoki (A), Kyrönjoki (B), Kiiminkijoki (C), upperOulujoki (D), Koutajoki (E), Muonionjoki (F), Kemijoki(G) and Tenojoki (H).

Variation in region size may modify the shape of theregional vs. local richness plot, by increasing the prob-ability of detecting a curvilinear relationship (Caley &Schluter 1997; Srivastava 1999). We estimated thespatial extent of our regions using a simple rectanglemethod (Shurin

et al

. 2000). Thus, we drew a rectangleconnecting the southern- and northernmost sites, andthe western- and easternmost sites for each region. Thesize of the rectangle was then measured with a ruler andconverted to square kilometres. The region size variedbetween 756 km

2

(region E) and 7742 km

2

(region G).However, because region size and RSR were non-significantly correlated (

P =

0·216) and because spatialvariation among our regions was low (all < 8000 km

2

),we did not attempt to correct for variation in regionalextent.

We defined local richness (LSR) as the number oftaxa found at a single stream riffle, and regional rich-

ness (RSR) as the cumulative number of taxa across allsampling locations within a drainage system. Thistechnique introduces an element of interdependencein our data, which could lead to spurious correlations(Cresswell, Vidal-Martinez & Crichton 1995; Zobel1997). A potential way to solve this problem is to esti-mate regional richness from distribution maps orregional checklists (Srivastava 1999). In our case, how-ever, this was inconceivable, because comprehensivespecies lists of stream macroinvertebrates are lackingin our study area. Nevertheless, this would constituteonly a partial solution to the problem: even if such listswere available, regional and local richness estimateswould still be spatially autocorrelated. Furthermore,the reliability of data compiled from various secondarysources is questionable (Gaston & Blackburn 1999).For example, incomplete knowledge of taxonomy,inadequate sampling and inconsistent sampling effortacross regions and localities may introduce unknownbias to richness estimates. However, because we usedstrictly standardized sampling methods and the samesampling effort (15 streams/region) throughout thestudy, our estimates of regional and local richnessshould not be biased. It is still possible that 15 streamsmight constitute an inadequate sample size for esti-mating regional richness. However, our preliminarydata (

n

= 55 streams) from the most taxon-rich region(E) suggest that 15 streams sample approximately80% of taxa present in headwater streams within aregion. Therefore, we consider our sampling effortsufficient for a reliable estimate of the size of theregional pool.

We used the decision tree approach of Griffiths (1999)to determine whether the relationship between LSRand RSR was linear (implying proportional samplingfrom the regional pool) or curvilinear (implying localsaturation). Thus, we first fitted a second-order poly-nomial regression to our data, and if the second orderterm was non-significant, we used linear regression totest for a linear relationship among RSR and LSR.Then, we tested for deviation of the intercept

a

fromzero in an unconstrained model; if this was insignific-ant, we used unconstrained regression to test for the fitof the linear model, i.e. significance of the regressioncoefficient

b

. Many previous workers have used con-strained regression (forced through the origin) basedon the premise that ‘when regional diversity is zero, sotoo is local diversity’ (Caley & Schluter 1997). Thismay be inadvisable, however, because it involvesextrapolation beyond the range of the data, therebyinflating

R

2

values (Griffiths 1999; Srivastava 1999).We ran separate regression analyses on four data

sets, testing for two slightly different questions: (i) theinfluence of

regional richness

on local richness (RSR vs.mean LSR; RSR vs. maximum LSR; RSR vs. mini-mum LSR,

n

= 8 in each analysis), and (ii) the influence

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Macroinvertebrate diversity in headwater streams



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of

region

on local richness (RSR vs. LSR,

n

= 120) (seeSrivastava 1999). The latter approach was dismissed bySrivastava (1999) because it constitutes pseudoreplica-tion and thus confounds site and regional effects onLSR. However, as pointed out by Karlson & Cornell(2002), use of multiple estimates of LSR within aregion is necessary if the relative contribution of RSRand local factors to LSR is being assessed. For thispurpose, we used stepwise linear regression, where thevariable that first enters the model explains most of thevariation in LSR, the second one most of the remainingvariation, and so on. Data on environmental variableswere first screened for intervariable correlations and aset of eight variables representing the physicochemicalhabitat templet, and which were not strongly inter-correlated, were used as proxies for local environmentalconditions in subsequent analyses. The variables in-cluded were: stream width, moss cover, particle size,current velocity, proportion of deciduous trees in theriparian zone, pH, total nitrogen and water colour. Ifnecessary, variables were transformed (log or arcsine-square root) to improve normality and stabilize vari-ances. We also ran another stepwise regression, thistime without RSR, to reveal whether this variablemasked the influence of any of the environmentalvariables. Finally, we used stepwise regressions at thewithin-region level to examine whether different localfactors were influential in explaining variation in taxonrichness in different regions.

We assessed the relationship between RSR andturnover diversity using Pearson correlations. Wecalculated a beta-diversity index for each region usingthe formula of Harrison, Ross & Lawton (1992):

β

2

= (S/

α

max

−

1)/(

N

−

1)

×

100,

where S is the total number of taxa in a region,

α

max

isthe maximum number of taxa recorded at a single site,and

N

is the number of sites. Thus,

β

2

ranges from 0 to100, and measures the degree by which regional rich-ness exceeds the maximum local richness. However,

β

2

is directly dependent on, and can thus generate spuri-ous correlations with, RSR. Therefore, we calculatedthe average among-site dissimilarity in taxon com-position for each region to obtain an index of turnoverthat was independent of RSR. We used Sørensen’scoefficient for calculating pairwise dissimilarities amongall sites, using the formula:

β

Sor

= 1 – 2

W

/(

A

+

B

),

where

W

is the sum of shared taxon occurrences and

A

and

B

are the sums of taxon occurrences in individualsample units. Each pairwise dissimilarity and theaverage among-site dissimilarity for each region thusrange from 0 to 1, high values indicating high turnoveramong sites. All analyses were performed using SPSS(SPSS Inc. 1999), except dissimilarities which werecalculated using PC-Ord (McCune & Mefford 1999).

Results

.

RSR ranged from 43 (region D) to 96 (region E), andmean LSR varied from 15·9 (region B) to 29·9 (regionA) (Table 1). RSR showed a positive linear relationshipwith mean LSR (Fig. 2a). The quadratic term in RSR–LSR regression was insignificant. Furthermore, theintercept

a

of the linear model did not differ signific-antly from zero and the slope coefficient

b

was signific-ant (Table 2). Thus, within the observed range ofRSR, there was no ceiling to LSR, suggesting lack oflocal saturation. RSR was strongly related to maximumLSR (Fig. 2b), whereas RSR showed insignificant,positive relation to minimum LSR (Fig. 2c).

: -

In stepwise regression analysis, RSR was the first vari-able to enter the model, accounting for 24·8% of vari-ation in LSR (Fig. 2d). The final model, explaining32·5% of variation in LSR, incorporated RSR and streamwidth (Table 3). In regression analysis omitting RSR,stream width emerged as the most important local vari-able, followed by water pH. This model, however,accounted for only 20·6% of variation in LSR (Table 4).

: -

Different factors appeared important in explainingvariation in LSR in different regions. In regions A, B

Table 2. Summary of the coefficients of the linear andsecond-order regression models for the relationship amongmean LSR and RSR

Table 3. Results of stepwise multiple regression analysis of therelationships between local richness, regional richness, andlocal environmental variables. Cumulative R2 and F includethe contribution of the variable named at each step and the onepreceding it. The overall model was significant at P < 0·001

Coefficient SE t P

Y = a + bxConstant 6·805 4·103 1·659 0·148RSR 0·200 0·055 3·623 0·011

Y = a + bx + bx2

Constant 11·267 18·054 0·624 0·560RSR 0·064 0·537 0·120 0·909RSR2 0·001 0·004 0·255 0·809

Source of variation d.f. SS MS F R2

Model 2 1872·550 936·275Regional richness 1 1430·275 38·931 0·248Stream width 1 442·275 28·139 0·325Residual 117 3892·917 33·273Total 119 5765·467

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and G, no variable was related significantly to LSR,due probably to the small number of sites. In-streamvariables were the best correlates of LSR in regions C(moss cover) and D (current velocity). Percent decidu-ous trees in the riparian zone accounted for 42% of vari-ation in LSR in region E, and stream width for 69% inregion F. In the northernmost region (H), LSR wasstrongly positively related to potential stream produc-tivity, i.e. [TN] (Table 5).

Beta diversity varied relatively little among the regions,but was overall positively, although non-significantly,correlated with RSR, for both

β

2

(

r =

0·662,

P

= 0·074)and

β

Sor

(

r =

0·494,

P

= 0·213) (Fig. 3a,b). These twomeasures of turnover diversity were not stronglycorrelated (

r =

0·411,

P

= 0·311), implying that theyemphasized slightly different aspects of taxon turn-over. Using both

β

2

and

β

Sor

, turnover diversity was high-est in region E, whereas the identity of the region withthe lowest turnover diversity varied with the indexused. Turnover diversity was lowest in region D whenmeasured by β2, whereas βSor recorded lowest turnoverfor regions A, B, F and H.

Fig. 2. Relationships between four measures of local (LSR) and regional richness (RSR) of stream macroinvertebrates. LSR isrepresented by the mean (a), maximum (b) and minimum (c) values for each region, as well as by including all estimates of localrichness for a region (d). Solid line represents the associated regression equation, whereas dashed line indicates the theoreticalupper limit for LSR.

Table 4. Results of stepwise multiple regression analysis ofthe relationships between local richness and local environ-mental variables. Regional richness was omitted from thisanalysis. Cumulative R2 and F include the contribution of thevariable named at each step and the one preceding it. Themodel was significant at P < 0·001

Table 5. Final models of stepwise multiple regression on therelationships between taxon richness and environmentalvariables at the within-region level. No variables weresignificantly related to taxon richness in regions A, B and G

Source of variation d.f. SS MS F R2

Model 2 1188·492 594·246Stream width 1 713·689 16·670 0·124pH 1 474·803 15·191 0·206Residual 117 4576·975 39·119Total 119 5765·467

Region Independents R2 F P

C Moss cover† 0·267 4·738 0·049D Current velocity† 0·407 8·928 0·010E Deciduous trees† 0·418 9·331 0·009F Stream width† 0·691 29·040 < 0·001H Total N† 0·634 22·508 < 0·001

431Macroinvertebrate diversity in headwater streams

© 2003 British Ecological Society, Journal of Animal Ecology, 72,425–434

Discussion

Circumstantial evidence suggesting that regionalprocesses exert strong control over the number of locallyco-occurring species has been provided by numerousstudies reporting a linear relationship among LSR andRSR for various groups of organisms (e.g. Cornell1999; Shurin et al. 2000; Stevens & Willig 2002). How-ever, some studies on highly interactive fish com-munities refer to the predominance of local processesin determining local richness (Jackson & Harvey1989; Tonn et al. 1990), although RSR–LSR relation-ships suggesting the primacy of regional controlof local diversity have also been reported for both lentic(Griffiths 1997) and lotic fish (Hugueny & Paugy 1995;Oberdorff et al. 1998). In our study, stream macro-invertebrate richness showed a linear relationship toRSR, suggesting that regional-scale processes set theupper limit to LSR. For stream invertebrates, such arelationship between RSR and LSR should not be sur-prising, because they live in a frequently and unpredict-ably disturbed environment and exhibit high rates ofdispersal, both of which should enhance the regionalcontrol of local communities (Palmer et al. 1996).Streams are considered flashy environments whereflow-related disturbances move substratum particlesand thereby regulate the structure of biotic commun-ities strongly (Resh et al. 1988; Lake 2000). Further-more, many stream-dwelling invertebrate taxa possessconsiderable dispersal capacity, by either flying ordrifting, facilitating rapid colonization of denudedstream areas after disturbance (e.g. Giller, Sangpradub& Twomey 1991; Malmqvist et al. 1991). Althoughstrong competitive interactions do occur amongstream macroinvertebrates, these are typically effectiveonly at very small spatial scales (up to a few centi-metres) and spatial exclusion, even in the presence of astrict competitive hierarchy, is prevented by flow-related disturbances (e.g. Hemphill & Cooper 1983;McAuliffe 1984; Kohler 1992). There is thus little indi-cation of competition leading to species exclusion at a

scale pertinent to local macroinvertebrate assemblages(for a possible exception, see Kohler & Wiley 1997).Furthermore, streams are extremely heterogeneousenvironments and organisms typically exhibit stronglyaggregated distributions across this spatially variablelandscape. Although not directly shown for any streamorganism, this heterogeneity may contribute to speciescoexistence by allowing inferior competitors to persistin the community through ‘probability refuges’, i.e.patches left unoccupied by superior competitors (theaggregation model of coexistence, Atkinson & Shorrocks1984; see also Murphy, Giller & Horan 1998). All thesefactors suggest that stream macroinvertebrate commun-ities rarely attain saturation, even in high-diversityregions. Lack of a richness ceiling in our data, however,may also reflect the fact that benthic communitiesin boreal streams are relatively impoverished, andcurvilinearity would be observed only by including morespecies-rich regions. Finally, it is also possible thatsome rarely disturbed and productive lotic habitats(e.g. lake outlets, Malmqvist & Eriksson 1995) mightsupport interactive, locally saturated invertebratecommunities.

Including LSR estimates drawn from differenthabitat types (e.g. headwater streams vs. large river sites)in the same plot could create false asymptote (‘pseudo-saturation’, e.g. Griffiths 1997), and it would also in-validate the logic of RSR–LSR comparisons (Srivastava1999). Because several macroinvertebrate taxa arelargely restricted to either headwater streams or largerivers (Malmqvist & Mäki 1994; Furse 2000), speciespools for different habitat types incorporate organismsthat are never expected to occur or interact at the samelocal arena. Nevertheless, even if sampling were strati-fied strictly by habitat type, curvilinear RSR–LSRrelationships may emerge if beta-diversity is relatedpositively to RSR, i.e. assemblage turnover is higher inhigh-diversity than in low-diversity regions (Griffiths1997). The few studies that have tested this assumptiondirectly have concluded that turnover diversity contrib-utes relatively little to regional richness (Harrison et al.

Fig. 3. Relationship between β-diversity, measured as Harrison et al.’s (1992) beta-2 (a), or average Sorensen’s dissimilarity beta-Sor (b), and regional richness (RSR) of stream macroinvertebrates.

432J. Heino, T. Muotka & R. Paavola


1992; Griffiths 1997; but see Stevens & Willig 2002).For lotic macroinvertebrates, within-region variationin assemblage composition often follows a nestedpattern, i.e. low-diversity sites support a subset of taxafrom more diverse sites (e.g. Malmqvist & Hoffsten2000; J. Heino et al. unpublished), suggesting thatturnover diversity in species-rich regions is probablynot high enough to render the RSR–LSR relationshipcurvilinear.

Despite strong regional influences, environmentalfilters at the within-region scale eventually determinehow large a portion of the regional species pool prevailsin each local community (Tonn 1990; Poff 1997). In ourstudy, LSR showed wide among-stream variationwithin each region, suggesting that even in diverseregions some localities provide conditions that limitthe kind (and, ultimately, number) of taxa able to per-sist under such circumstances. This finding concurswith former studies showing that streams in closeproximity to each other, but with contrasting waterchemistry, may harbour markedly differing macro-invertebrate assemblages (Townsend et al. 1983;Paavola et al. 2000). Nevertheless, our results suggestthat stream environmental factors are secondary toRSR in accounting for variation in LSR of stream macro-invertebrate assemblages.

Among-region differences in LSR and RSR mayalso arise through regional differences in stream environ-mental conditions. For instance, streams in regions Band D mostly drain peatland landscapes, being acidicand brown-coloured (Heino et al. 2002). Such uni-formly adverse environmental conditions across aregion may eventually affect the size of the regionalspecies pool. Thus, the control of macroinvertebraterichness is not necessarily unidirectional, i.e. fromregional to local, but stream environmental factorsmay also have a feedback effect on RSR (see alsoVinson & Hawkins 1998).

Environmental factors related to variation in macro-invertebrate richness varied among the regions.Factors correlated with richness reflected either spatialheterogeneity, adverse environmental conditions ora limiting resource base. Macroinvertebrate richnessshowed a generally positive relationship to streamwidth (area effect) and moss cover (heterogeneityeffect), both often cited as important correlates of loticmacroinvertebrate diversity (Brönmark et al. 1984;Malmqvist & Mäki 1994; Vinson & Hawkins 1998).Conversely, low pH and high humic content of streamwater are known to have strong negative effects onmacroinvertebrate diversity (Otto & Svensson 1983;Townsend et al. 1983; Hildrew & Giller 1994). Aninteresting finding was the positive relationship ofnitrogen content with macroinvertebrate richness inthe northernmost study region H (70°N). Nutrientconcentrations in these subarctic streams rangedfrom very low (P < 2 µg/l, N < 50 µg/L) to moderate(P = 5 µg/ l, N = 270 µg/L), probably limiting ecosys-tem productivity and biotic communities. Even slight

increases in nutrient concentrations could increasealgal and macroinvertebrate productivity (e.g. Petersonet al. 1993), with potential ‘bottom-up’ effects onmacroinvertebrate diversity. By contrast, no significantrelationships between stream water nutrients and taxarichness were detected in regions further south, imply-ing that nutrients did not generally limit stream bio-diversity across the study area.

Because RSR appeared as the primary factor relatedto variation in LSR, future studies should also addressthe determinants of RSR for stream macroinverte-brates. Latitudinal gradients in climate, environmentalproductivity and glaciation history have often beenproposed as potential explanations to large-scalevariation in species richness (Currie 1991; Ricklefs& Schluter 1993; Huston 1994). However, studies onstream organisms have produced somewhat equivocalconclusions regarding regional differences (e.g. latitu-dinal gradients) in species richness (Patrick 1975;Hildrew & Giller 1994; Jacobsen, Schultz & Encalada1997). Regional differences in stream macroinver-tebrate diversity do exist, however, but they do notnecessarily follow any consistent latitudinal trends. Forinstance, the regional diversity of stoneflies increaseswith increasing latitude, whereas that of caddisfliesexhibits an opposite pattern, and a parallel trend is alsoseen weakly at the local scale of stream riffles in ourstudy area (J. Heino et al. unpublished). The mixing oftaxa with differing biogeographical patterns may be areason why latitudinal diversity gradients for streammacroinvertebrates are somewhat obscured, althoughregional patterns at the among-river system scale areobvious. In addition to historical factors and climate,possible correlates for variation in RSR includeregional differences in landscape heterogeneity (e.g.altitude, geology and vegetation) which could in turnaffect stream riffle area and the variety of stream typesavailable within a region. These factors might affectregional-scale extinction probabilities, metacom-munity dynamics and the degree of species turnoveramong sites (Cornell & Lawton 1992). Within-regionhabitat variability is an important component of turn-over diversity (e.g. Harrison et al. 1992), and althoughwe did not find significant relationship among RSRand β-diversity, differences in within-region environ-mental heterogeneity still remain a potential determin-ant of variation in RSR.

To conclude, our results suggest that the upperlimit of local macroinvertebrate diversity is controlledlargely by regional factors. This finding, however, doesnot decline the importance of local and basin-scalefactors, which produce considerable variation in LSRwithin regions, and may even have feedback effects onRSR. A potentially fruitful avenue for future studies isto focus on the balance between α- and β-diversity (seeLoreau 2000). Because dispersal is clearly a key processdetermining shifts in the relationship between α and βcomponents of regional diversity (Loreau & Mounquet1999), a comparison between organisms with widely

433Macroinvertebrate diversity in headwater streams


differing dispersal abilities (e.g. mayflies vs. blackflies;stream insects vs. bryophytes) might prove especiallyrewarding. That regional and local diversities arelinked strongly in stream communities (Vaughn 1997;Oberdorff et al. 1998) clearly indicates that streamecologists should abandon their traditional adherenceto local, in-stream processes as the sole or even primaryregulators of local diversity. Studies on the determin-ants of stream biodiversity are likely to be ineffectiveunless regional aspects, especially the size and com-position of the regional species pool, are consideredexplicitly. Such studies are indeed needed urgently,because effective conservation of stream biodiversityrequires a proper understanding of the determinants ofdiversity across multiple scales and taxonomic groups.

Acknowledgements

We thank the environmental centres of Central Fin-land, Lapland, Northern Ostrobothnia and WestFinland, and Oulanka Biological Station for analysingthe water samples. R. Salvain provided guidance andinspiration during data collection. We also thank B.Malmqvist and an anonymous referee for constructivecomments on the manuscript. This paper is part of theFinnish Biodiversity Programme (FIBRE), funded bythe Academy of Finland. The study was also supportedby a grant from the Maj and Tor Nessling Foundation.

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Received 27 May 2002; accepted 20 January 2003

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Determinants of macroinvertebrate diversity in headwater streams: regional and local influences