Species co-occurrence, nestedness and guild–environment relationships in stream macroinvertebrates

Species co-occurrence, nestedness and guild–environment relationships in stream macroinvertebrates

JANI HEINO*, †

*Finnish Environment Institute, Research Programme for Biodiversity, University of Oulu, Oulu, Finland†Department of Biology, University of Oulu, Oulu, Finland

SUMMARY

1. Describing species distribution patterns and the underlying mechanisms is at the heart

of ecological research. A number of recent studies have used null model approaches to

explore mechanisms behind spatial variation in community structure.

2. However, unexplored questions are the degree to which single guilds of potentially

competing stream macroinvertebrate species show: (i) interspecific segregation among-

stream sites (i.e. occur together less often than expected by chance), suggesting competitive

interactions; (ii) interspecific aggregation (i.e. occur together more often than expected by

chance), suggesting similar responses to the environment; (iii) comply with nestedness,

suggesting the existence of selective extinctions or colonisations and (iv) show similar

environmental relationships.

3. The present analyses showed that guilds of stream macroinvertebrates exhibit non-

random co-occurrence patterns that were generally contingent on the weighting of sites by

stream size. Despite significant segregation of species, each guild also showed significantly

nested patterns. Species richness was correlated with different environmental factors

between the guilds, although these correlations were relatively low. By contrast,

correlations between the major ordination axes and key environmental variables were

slightly stronger in canonical correspondence analysis, and generally the same factors

were most strongly correlated with variation in the species composition of each guild.

4. The present findings are the first to show that species within each stream macroinver-

tebrate guild show significant negative co-occurrence at the among-stream riffle scale.

These findings present challenges for future studies that aim to disentangle whether

these patterns comply with the habitat checkerboard or the competitive checkerboard

explanations.

Keywords: aggregation, habitat checkerboards, interspecific competition, null models, segregation

Introduction

Describing species distribution patterns and the

underlying mechanisms is at the heart of ecological

research. One commonly followed approach uses the

match between organisms and habitat templets to

infer if environmental variation through effects on

species traits affects distribution patterns (Southwood,

1977; Townsend & Hildrew, 1994). This is basically a

descriptive autecological approach, and the underly-

ing mechanisms responsible for distribution patterns

may thus remain obscure. By contrast, many exper-

imental studies aiming to account for the mechanistic

basis of distribution patterns have shown that com-

munities are strongly structured by interspecific

interactions (Schoener, 1983; Morin, 1999). It is largely

unclear, however, if the patterns detected in small-

scale experiments apply to nature, and if experiments

Correspondence: Jani Heino, Finnish Environment Institute,

Research Programme for Biodiversity, PO Box 413, University of

Oulu, FI-90014 Oulu, Finland. E-mail: [email protected]

Freshwater Biology (2009) 54, 1947–1959 doi:10.1111/j.1365-2427.2009.02250.x

� 2009 Blackwell Publishing Ltd 1947

thus describe true differences in community structure

among localities (Peters, 1991; Levin, 1992; Kohler &

Wiley, 1997; Maurer, 1999). Furthermore, variation in

community structure at scales larger than the typical

small patches used in experiments cannot usually be

examined by experimental approaches. Alternative

approaches to understand mechanisms behind com-

munity structure have thus utilised null models to

examine patterns occurring by chance and patterns

produced by interspecific interactions (Gotelli &

Graves, 1996; Gotelli & McCabe, 2002).

The null model approach has been shown to be

powerful in estimating the potential frequency of

interspecific competition in ecological communities at

scales pertaining to true natural differences between

localities (Gotelli & Graves, 1996; Gotelli, 2000).

However, the null model approaches are plagued by

the fact that various processes may contribute to the

patterns found, and multiple competing explanations

may not be mutually exclusive. For example, although

a set of communities may show less co-occurrence

among species than expected by chance, which

suggests the existence of interspecific competition

(e.g. checkerboard pattern; Diamond, 1975; Stone &

Roberts, 1990), the same set may also comply with

other idealised patterns that do not require competi-

tion as the mechanism behind the observed patterns

(e.g. nestedness; Patterson & Atmar, 1986; Wright

et al., 1998). Furthermore, species distribution pat-

terns, such as checkerboards and nestedness, may also

be simply a result of extinction and speciation

dynamics (e.g. neutral theory; Hubbell, 2001; Ulrich,

2004) or differing responses of species to abiotic

environmental variation (e.g. niche theory; Pulliam,

2000; Chase & Leibold, 2003). These findings thus

suggest that ecologists should examine simulta-

neously different idealised patterns in the structure

of ecological communities (Leibold & Mikkelson,

2002; Heino, 2005a,b; Heino & Soininen, 2005; Meyer

& Kalko, 2008).

One pattern that is not typically associated with

competitive interactions is nestedness. Nestedness

occurs in a set of communities if species at low

diversity localities are proper subsets of those at high

diversity localities (Patterson & Atmar, 1986; Wright

et al., 1998). This pattern has been found in various

ecological systems, including landbridge islands (e.g.

Patterson & Atmar, 1986), oceanic islands (e.g.

Patterson, 1987), habitat fragments (e.g. Kerr, Sugar

& Packer L, 2000), lakes (e.g. Heino & Muotka, 2005)

and streams (e.g. Soininen, 2008). Despite a high

number of studies suggesting significant nestedness

(Wright et al., 1998), a recent meta-analysis showed

that nestedness is far less common than previously

thought (Ulrich & Gotelli, 2007). This deviance

between the two meta-analyses published in the last

decade stemmed from the use of different types of

null models assessed to estimate the significance of

nestedness (Wright et al., 1998 versus Ulrich & Gotelli,

2007). Most previous studies have used liberal null

models for testing significance, leading to a high

prevalence of nestedness (Wright et al., 1998), whereas

more stringent null models have been used more

recently, leading to a relatively low prevalence of

nestedness (Ulrich & Gotelli, 2007). Thus, there is a

need for studies in different ecological systems using

novel methods and more stringent null models to test

for the significance of nestedness (Rodrıguez-Girones

& Santamaria, 2006). Furthermore, also the mechanis-

tic basis of nestedness should be given more attention.

These mechanisms included selective extinctions and

colonisations, nested habitat structure and nested

environmental tolerances of species (Wright et al.,

1998; Heino, Mykra & Muotka, 2009b).

Many, if not most, co-occurrence and nestedness

studies have considered taxonomically defined

groups of species (Kerr et al., 2000; Gotelli & McCabe,

2002; Leibold & Mikkelson, 2002; Heino, 2005a; Heino

& Soininen, 2005), while ecologically defined groups

of species have received less attention (Feeley, 2003;

Meyer & Kalko, 2008). A typical ecologically defined

set of species is a guild. A guild comprises species that

use similar resources in a similar way (Hawkins &

MacMahon, 1989; Fauth et al., 1996). For example, in

the context of stream macroinvertebrates, species are

typically divided from four to six functional feeding

groups that are roughly equivalent to guilds

(Cummins, 1973; Cummins & Klug, 1979; Merritt &

Cummins, 1996; Allan & Castillo, 2007). Although

there has been some disagreement about the use of

these functional feeding groups because of the fact

that stream macroinvertebrates may be quite flexible

in food resource use (Mihuc,1997; Dangles, 2002),

these delineations of guilds have considerably

increased our understanding of stream ecosystem

functioning and community organisation (Cummins,

1974; Vannote et al., 1980; Heino, 2005b; Lepori &

Malmqvist, 2007). However, unexplored questions are

1948 J. Heino

� 2009 Blackwell Publishing Ltd, Freshwater Biology, 54, 1947–1959

the degree to which single guilds of potentially

competing stream macroinvertebrate species (i) show

interspecific segregation among-stream sites (i.e. spe-

cies occur together less often than expected by

chance), suggesting competitive interactions; (ii) show

interspecific aggregation (i.e. species occur together

more often than expected by chance), suggesting

similar responses to the environment; (iii) comply

with nestedness, suggesting the existence of selective

extinctions or colonisations and (iv) exhibit similar

environmental relationships among guilds, which has

not been rigorously examined before.

I explored these topics and examined alternative

patterns in macroinvertebrate community composi-

tion. First, I expected that if species in a guild show

more interspecific segregation among sites than

expected by chance, then competitive interactions

are a plausible underlying mechanism (Diamond,

1975; Gotelli & McCabe, 2002). Secondly, if species in

a guild are more aggregated than expected by chance,

then similar responses to the environment are the

most likely reason underlying the detected patterns

(Gotelli & Ellison, 2002; Sanders et al., 2007). Thirdly,

if species in a guild are not more segregated or

aggregated than expected by chance, then patterns

may comply with nestedness, suggesting selective

extinctions and colonisations as possible mechanisms

(Patterson & Atmar, 1986; Wright et al., 1998). Finally,

I expected that environmental relationships differ

between the guilds, although stream size and related

factors might be effective in determining the species

composition of all guilds (Malmqvist & Hoffsten,

2000; Mykra, Heino & Muotka, 2007). These alterna-

tive patterns were examined using data from a set of

headwater streams in a boreal drainage basin.

Methods

Study area

The study area is located in the Koutajoki drainage

basin just south from the Arctic Circle in north-eastern

Finland (66–67�N, 28–30�E). The bedrock of the study

area is highly variable, and there are extensive

occurrences of calcareous rocks. Accompanied by

considerable relative altitudinal differences, this geo-

logical variability is reflected in highly variable

vegetation, ranging from old-growth coniferous for-

ests to mixed-deciduous riparian woodlands and

from nutrient-poor bogs to fertile fens. These factors

also provide the basis for a high variability of stream

habitats within the drainage basin. Headwater

streams and small rivers in the drainage basin are

generally near-pristine, and they are characterised by

circumneutral to alkaline water, low to high levels of

humic substances and low to moderate nutrient

concentrations (Heino, 2005a). A detailed description

of the drainage basin can be found elsewhere

(Malmqvist et al., in press).

The field crew surveyed altogether 20 first- to third-

order streams that were selected from those available

in the Finnish part of the Koutajoki drainage basin.

There were two restrictions in the sampling scheme:

(i) the streams had to be located within a 6-km

distance from the nearest road and (ii) stream chan-

nels should not suffer from any considerable recent

anthropogenic disturbance. In general, the catchments

of most streams were subject to some forestry influ-

ences, whereas other major anthropogenic pressures

were not evident. This was due to the fact that half of

the stream sites were located in the Oulanka National

Park, and the other half of the sites were in the

immediate vicinity of the protected area. However,

there were no differences in macroinvertebrate com-

munity structure between the protected and managed

streams (Heino et al., 2009a).

Macroinvertebrate samples

Stream macroinvertebrates were sampled in the last

week of May 2008. This is the season when the

majority of macroinvertebrates in northern boreal

streams are still in the larval stage. At the time of

sampling, snow covered sheltered locations in stream

valleys, lakes had still ice cover, and few adult

stoneflies were observed flying next to the streams. I

therefore concluded that little insect emergence had

occurred, with exception of the winter stonefly

Taeniopteryx nebulosa (L). I thus believed that the

samples accurately represented spring-time macroin-

vertebrate communities. The timing of sampling also

facilitated the identification of aquatic insect larvae,

which are at, or close to, their last instar in this time of

the year.

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

(net mesh size 0.3 mm) sample covering most micro-

habitats (i.e. based on visual estimates of current

velocity, macrophyte cover and depth) present in a

Guild organisation in stream macroinvertebrates 1949


riffle of approximately 100 m2. This sampling effort

typically yields a majority of species occurring at a site

in a given season, mainly missing very rare species

that occur only sporadically in streams (Mykra,

Ruokonen & Muotka, 2006). Such a sample generally

yields hundreds to thousands of macroinvertebrate

individuals. Macroinvertebrates and associated mate-

rial were immediately preserved in 70% ethanol in the

field, and samples were taken to the laboratory for

further processing and identification. Macroinverte-

brates were identified to the lowest possible level of

identification, typically species, species group or

genus. This was deemed important, as many previous

studies on the community ecology of stream macro-

invertebrates have not considered the dipteran fam-

ilies Chironomidae and Simuliidae that account for

much of the diversity and abundance of stream

macroinvertebrate communities.

Functional feeding guilds

Macroinvertebrates were assigned into five functional

feeding groups according Moog (2002), with addi-

tional information taken from Merritt & Cummins

(1996). Functional feeding groups included shredders,

gatherers (=detritivores), filterers, scrapers (=grazers)

and predators. These guilds are delineated on the

basis of food resource use: (i) shredders feed on coarse

leaf material by shredding large fragments; (ii) gath-

erers feed on fine particulate organic matter (FPOM)

deposited on stream bottom; (iii) filterers obtain

FPOM from the water column; (iv) scrapers graze

algae and biofilm growing on mineral or organic

surfaces and (v) predators either consume parts of

other animals or engulf whole prey individuals

(Wallace & Webster, 1996; Allan & Castillo, 2007).

Such divisions of species into simple guilds may be

criticised. For example, there is evidence that the

feeding roles of freshwater macroinvertebrates may

vary with the larval stage, as well as temporally and

geographically, and many taxa may be highly flexible

in their feeding habits (Mihuc, 1997; Dangles, 2002).

There are some classification systems which take

flexibility in feeding habits into account by a point

scoring system (Schmedtje & Colling, 1996; Moog,

2002), whereby a score is assigned to each taxa with

regard to functional feeding groups it represents (e.g.

eight points for scraper and two points for gatherer of

the total of 10 points). This classification system was

utilised in a modified form, with a species having

greater than or equal to five points for a given

functional feeding group being assigned as such

according to the data for genera or species (Moog,

2002).

Environmental variables

The field crew measured 16 riparian, in-stream habitat

and water chemistry variables at each site. The

percentage of deciduous trees was assessed in a 50-

m section on both banks directly upstream of the

sampling site. Shading was estimated visually as per

cent canopy cover at the whole study section. Current

velocity (at 0.6· depth) and depth were measured at

30 random locations along cross-stream transects, the

number of which depended on stream width. Stream

width was measured at each site based on five cross-

stream transects. Moss cover (%) and substratum

particle class cover (%) were assessed at 10 randomly

spaced 50 · 50 cm quadrates. Visual estimates of the

percentage cover of five particle size classes were

made for each quadrate using a modified Went-

forth scale: (i) sand (diameter 0.25–2 mm); (ii) gravel

(2–16 mm); (iii) pebble (16–64 mm); (iv) cobble

(64–256 mm) and (v) boulder (256 mm). Pielou’s

evenness index based on the proportions of the

particle size classes was also calculated for each site.

Water samples were collected simultaneously with

the field sampling, and they were analysed for pH,

conductivity, water colour and total phosphorus

using Finnish national standards. Most of the mea-

sured environmental variables showed considerable

variation among the surveyed sites, and 10 variables

(Table 1) were selected for the guild–environment

analyses based on trial analyses and ecological

knowledge on the guilds in the study area.

Co-occurrence analysis

All analyses below were based on species presence–

absence data. A commonly utilised index was used to

describe patterns of species co-occurrence (the check-

erboard pattern) in each guild: the C-score (Stone &

Roberts, 1990). The C-score measures the average

pairwise species co-occurrence, and if communities

are structured by competition, then the C-score

should be larger than expected by chance. That is,

the larger the C-score, the less the average pairwise

1950 J. Heino


species co-occurrence. The number of checkerboard

units (CU) for each species pair is:

CU ¼ ðri � SÞðrj � SÞ

where S is the number of sites containing both species,

and ri and rj are the matrix row totals for species i and

j. The C-score is then averaged across all possible

checkerboard pairs, and it is calculated for species

that occur at least once in the matrix (Stone & Roberts,

1990). Of the available measures of species co-occur-

rence, the C-score has been shown to have the greatest

statistical power for detecting non-randomness

(Gotelli, 2000).

The significance of the C-score was tested using

three null models. In the first null model (fixed–

fixed), the row and column sums were fixed, i.e.

each random stream site contained the same num-

ber of species as the original stream site and each

species occurred in the same frequency as in the

original assemblage (Gotelli & Ellison, 2002). In the

second null model (fixed–weighted), column

weights were adjusted by stream width. Such a

weighting is important, as the probability of a

species to occur at a given site may depend on

stream size and not competition alone. Stream size

is probably the most important variable affecting

stream macroinvertebrate communities (Malmqvist

& Maki, 1994; Mykra et al., 2007), and thus species

in different guilds are presumed to show responses

to this variable. Furthermore, stream size is a

complex environmental gradient, with stream width,

current velocity and depth typically increasing,

shading decreasing and allochthonous and autoch-

thonous production bases changing with stream size

(Vannote et al., 1980; Allan & Castillo, 2007). In the

third null model (weighted–fixed), row weights

were adjusted by the regional abundance of each

species. Such a weighting should be done to account

for species’ abundance-related sampling effects on

the observed patterns. This weighting assumes a

positive occupancy–abundance relationship, which

has previously been found for stream macroinver-

tebrates in the study system (Heino, 2008). Both

above weightings have been used in co-occurrence

analyses previously, as they are important to sepa-

rate the importance of competition and other factors

(Peres-Neto, Olden & Jackson, 2001; Gotelli &

Ellison, 2002; Banado et al., 2005; Jenkins, 2006;

Meyer & Kalko, 2008; Tello, Stevens & Dick, 2008).

In contrast to unweighted analyses that treat sites

and species with equal probabilities, such weigh-

tings adjust the probability of species occurring at a

site during randomisations. For example, if stream

size is primarily responsible for unweighted co-

occurrence patterns, a random pattern might be-

come significant in analyses weighted by stream

size, and vice versa.

For all three null models, the random guild matrices

were produced by shuffling the original matrix

through repeated swapping of random submatrices

(Manly, 1995). This algorithm has been shown to have

good statistical properties, especially when used with

the C-score, with a low propensity for types I and II

errors (Gotelli, 2000). In all three analyses of co-

occurrence for each guild, 5000 random matrices were

constructed and mean and standard deviation for the

index values thus obtained were calculated. Statistical

significance was then assessed by comparing the

observed index value from the original matrix to the

distribution of values derived from the random

matrices (Manly, 1995). Finally, to facilitate compar-

ison with other studies, a standardised effect size

(Gurevitch et al., 1992) was calculated as (Gotelli &

McCabe, 2002):

ðobserved C-score�mean simulated C-scoreÞ=standard deviation of simulated C-scores

which indicates the number of standard deviations

that the observed C-score (‘treatment group’) is above

or below the mean C-score from simulated matrices

(‘control group’). Species co-occurrence analyses and

associated randomisation tests were conducted using

ECOSIMECOSIM7 (Gotelli & Entsminger, 2006).

Table 1 Variation in the selected environmental variables used

in correlation and constrained ordination analyses

Variable Min Max Mean SE

pH 6.8 7.9 7.33 0.07

Total phosphorus (lg)1) 3.4 12.0 7.63 0.53

Colour (mg Pt)1) 50 150 98.25 6.56

Depth (cm) 9.93 46.40 24.66 2.15

Shading (%) 5 85 44.25 5.72

Deciduous trees (%) 10 75 43.50 3.33

Substratum evenness 0.40 0.95 0.78 0.03

Conductivity (mS m)1) 2.75 17.50 6.99 0.84

Stream width (m) 0.78 12.00 2.99 0.59

Current velocity (m s)1) 0.21 1.02 0.51 0.04



Nestedness analysis

For nestedness analyses, presence–absence matrices for

each guild were first constructed where columns and

rows were species and streams, respectively. What is

measured here is the degree to which species in

depauperate assemblages are a non-random subset of

species in progressively more diverse assemblages

(Wright et al., 1998). The degree of nestedness was

assessed using the binary matrix nestedness tempera-

ture calculator (BINMATNEST) (Rodrıguez-Girones &

Santamaria, 2006). BINMATNEST applies an isocline of

perfectly nested order that is unambiguously defined

and is based on robust genetic algorithms to determine

the re-ordering of rows and columns that leads to

minimum matrix temperature. Matrix T = 0 for a set of

perfectly nested assemblages and T = 100 for a com-

pletely disordered one. BINMATNEST also provides a

set of three null models for testing the significance of

nestedness. As suggested by the authors, I used the null

model 3 (fixed–fixed) for testing the null hypothesis

that T is not lower than expected by chance and other

defaults of the BINMATNEST software (Rodrıguez-

Girones & Santamaria, 2006). Five thousand permuta-

tions were run to provide P-values.

Guild–environment relationships

Canonical correspondence analysis (CCA; ter Braak,

1986; ter Braak & Prentice, 1988), a constrained

ordination method, was used to examine the environ-

mental relationships of species composition of each

guild. For this purpose, I relied on the inter-set

correlations between 10 environmental variables and

the first two CCA axes. Inter-set correlations measure

the relationships between site scores based on species

composition and environmental variables. CCAs were

run using PC-ORDPC-ORD version 4.25 (McCune & Mefford,

1999). Prior to the final CCAs, correlation matrices

were scanned and trials were run to limit the set of

environmental variables to those not strongly corre-

lated and that were a priori considered ecologically

meaningful for assessing variation in guild structure

in the present study area. Given that the aim of the

CCAs was to compare patterns between the guilds,

the selected 10 environmental variables were retained

and no forward selection procedure was used. Finally,

variation in species richness of each guild was

correlated with environmental variables.

Results

There were in total 35 predator, 29 scraper, 17

shredder, 59 gatherer and 22 filterer taxa in the faunal

samples. The patterns in the C-scores denoting species

co-occurrence varied only slightly among the five

guilds (Table 2). Only a single analysis based on the

fixed–fixed algorithm was significant (P < 0.05), with

predators showing significant aggregation. Three

other guilds showed nearly significant segregation

based on the fixed–fixed algorithm (P < 0.10). Two

analyses based on fixed species richness and species

occurrence weighted by abundance were significant,

with shredders showing significant segregation and

gatherers significant aggregation. By contrast, species

in each guild were significantly segregated when

species occurrences were fixed and sites weighted by

stream size. These results thus suggest that, after

controlling for stream size, species in each guild occur

together less than expected by chance. Despite signif-

icant patterns of co-occurrence, each guild also

showed significant nestedness, ranging from moder-

ate for filterers (T = 17.118, P = 0.024) and shredders

(T = 18.027, P = 0.003) to relatively weak for gatherers

(T = 20.332, P < 0.001), scrapers (T = 23.695,

P < 0.001) and predators (T = 27.268, P = 0.039).

Species richness–environment relationships varied

among the guilds (Table 3). Predator richness in-

creased with increasing depth, but none of the

remaining nine environmental variables was signifi-

cantly correlated with their species richness. Scraper

richness showed positive correlations with depth and

stream width. Shredder richness increased with total

phosphorus concentration of stream water. Gatherer

richness was negatively related to current velocity,

suggesting high gatherer richness in low-velocity

depositional sites. Filterer species richness was not

significantly related to the measured environmental

variables.

Guild structure–environment relationships as re-

vealed by the inter-set correlations of the CCAs

showed that species in different guilds responded

more or less to the same or correlated environmental

variables (Table 4). The species composition of pre-

dators changed most along the gradients in current

velocity (first axis) and conductivity (second axis).

Scraper species composition was in turn most strongly

correlated to stream width (first axis) and current

velocity (second axis). Shredder species composition

1952 J. Heino


was strongly correlated to current velocity on the first

axis, while none of the environmental variables

showed particularly strong correlations with the

second axis. Gatherer species composition was most

strongly correlated with water colour and currently

velocity along the first axis, and with stream width

and current velocity along the second axis. Filterer

species composition varied mostly along gradients in

current velocity (first axis) and stream width (second

axis).

Discussion

A commonly followed avenue in ecological research

considers species distribution patterns in the context

of the habitat templet theory (Southwood, 1988;

Townsend & Hildrew, 1994). Given that the habitat

templet theory is basically an autecological approach,

it ignores interspecific interactions as determinants of

distribution patterns. By contrast, the null model

analysis of species co-occurrence aims to find patterns

Table 3 Results of Pearson’s correlation analysis for the relationships between species richness and environmental variables

Predators Scrapers Shredders Gatherers Filterers

r P-value r P-value r P-value r P-value r P-value

pH 0.143 0.546 0.195 0.410 0.264 0.260 )0.274 0.242 0.085 0.723

Total phosphorus 0.204 0.388 0.349 0.131 0.617 0.004 0.373 0.106 0.274 0.242

Colour 0.062 0.794 0.038 0.873 0.330 0.156 0.128 0.590 )0.159 0.504

Depth 0.469 0.026 0.688 0.001 0.210 0.375 0.095 0.689 0.430 0.059

Shading 0.021 0.930 )0.204 0.388 0.364 0.115 0.348 0.132 0.215 0.362

Deciduous trees )0.019 0.937 0.130 0.585 0.311 0.183 0.201 0.395 0.093 0.696

Particle evenness 0.119 0.617 )0.015 0.951 0.285 0.223 0.111 0.641 )0.001 0.995

Conductivity * 0.266 0.257 0.203 0.390 0.381 0.098 )0.025 0.917 0.152 0.524

Stream width* 0.084 0.726 0.548 0.012 )0.180 0.447 )0.201 0.369 0.121 0.613

Current velocity* )0.023 0.924 0.256 0.277 )0.233 0.322 )0.628 0.003 )0.149 0.531

Significant correlations are in bold font.

*Logarithmic transformation.

Table 2 Results of co-occurrence analyses for each guild

Guild Observed C

Simulated C

P (obs £ exp) P (obs ‡ exp) SESMean Variance

Fixed–fixed

Predators 7.258 7.379 0.006 0.035 0.966 )1.696

Scrapers 9.328 9.179 0.009 0.919 0.085 1.539

Shredders 9.323 9.087 0.022 0.946 0.059 1.594

Gatherers 5.521 5.406 0.005 0.935 0.065 1.648

Filterers 5.419 5.379 0.0145 0.665 0.348 0.338

Fixed–weighted

Predators 7.258 5.680 0.217 1.000 <0.001 3.388

Scrapers 9.328 7.872 0.450 0.989 0.011 2.168

Shredders 9.323 6.899 0.717 0.999 <0.001 2.863

Gatherers 5.521 4.591 0.087 1.000 <0.001 3.152

Filterers 5.419 4.120 0.229 0.999 0.001 2.716

Weighted–fixed

Predators 7.258 8.141 1.233 0.215 0.785 )0.794

Scrapers 9.328 8.790 1.427 0.687 0.313 0.450

Shredders 9.323 6.238 1.407 0.987 0.013 2.602

Gatherers 5.521 7.191 0.479 0.004 0.996 )2.411

Filterers 5.419 6.842 2.252 0.170 0.829 )0.947

There were three different types of null models for significance of the C-Score: (i) fixed–fixed; (ii) fixed species occupancy-weighted by

stream size and (iii) weighted by species abundance-fixed species richness. Also shown is standardised effect size (SES) for each

analysis.



that might suggest the presence of interspecific inter-

actions (Stone & Roberts, 1990; Gotelli & Graves,

1996). A number of recent studies have simulta-

neously analysed species co-occurrence and nested

subset patterns (Leibold & Mikkelson, 2002; Feeley,

2003; Meyer & Kalko, 2008), but corresponding stud-

ies on stream organisms are rare (Heino, 2005a; Heino

& Soininen, 2005). The present analyses showed that

the guilds of stream macroinvertebrates exhibit non-

random co-occurrence patterns that were generally

contingent on the weighting of sites by stream size.

Despite the significant segregation of species, each

guild also showed significantly nested patterns. Max-

imum degree of nestedness (i.e. species richness) was

correlated with different environmental factors be-

tween the guilds, but even these correlations were

relatively low. By contrast, correlations between the

major ordination axes and environmental variables

were slightly stronger, and generally the same factors

were most strongly correlated with variation in the

species composition of each guild.

There were some differences between the fixed–

fixed and fixed–weighted null model analyses. For

predators, the significant aggregation in the fixed–

fixed analysis was likely due to the fact that species’

responses reflect that environment acts as a similar

filter for species, as has been shown for terrestrial

predatory ants (Gotelli & Ellison, 2002; Sanders et al.,

2007). By contrast, predators showed significant seg-

regation when null models were adjusted by stream

size. This finding suggests that stream size acted as a

strong filter in affecting predator guild structure, and

once it was accounted for in the co-occurrence

analysis, species were more segregated than expected

by chance. Slightly different patterns were seen in

other guilds, where species showed significant segre-

gation in the analyses where randomisations were

adjusted by stream size, whereas patterns at best

bordered significance in the fixed–fixed analyses.

These findings are opposite to those of a previous

study on bats using site weighting by environmental

factors, where analyses weighted by habitat area

rendered previously significant patterns of interspe-

cific segregation to randomness (Meyer & Kalko,

2008). By contrast, Jenkins (2006) found that weighting

by habitat area increased the frequency and degree of

species segregation in zooplankton, which concurs

with the present findings for all guilds.

Fixed–weighted and weighted–fixed null model

analyses also showed some differences between the

guilds. While all analyses where randomisations were

weighted by stream size showed significant segrega-

tion, only shredders showed a similar pattern in

analyses weighted by species’ abundance. This sug-

gests that shedder species co-occur less than expected

by chance even when sampling effects related to

abundance are accounted for. However, this may be

due to the presence–absence data used, as shredders

have previously been shown to exhibit strong intra-

specific aggregation, weak interspecific aggregation or

Table 4 Results of canonical correspondence analysis for the relationships between guild structure and environmental variables

Predators Scrapers Shredders Gatherers Filterers

Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2

Eigenvalue 0.384 0.308 0.310 0.266 0.374 0.254 0.447 0.376 0.573 0.423

% variance 11.2 9.0 12.3 10.5 15.2 10.3 11.5 9.6 13.9 10.3

Correlations

pH )0.516 )0.518 0.109 0.550 0.500 )0.049 )0.271 )0.475 )0.217 0.226

Total phosphorus 0.271 0.075 )0.112 )0.251 )0.445 )0.148 0.389 0.389 0.498 )0.072

Colour 0.369 0.544 0.105 )0.046 0.022 0.084 0.559 0.114 0.321 )0.094

Depth 0.477 )0.462 0.341 )0.419 0.095 0.290 0.275 )0.435 0.018 )0.353

Shading )0.134 )0.071 )0.594 )0.072 )0.393 )0.440 )0.115 0.513 0.274 0.263

Deciduous trees 0.180 )0.317 0.057 )0.367 )0.162 0.419 0.229 0.108 0.366 0.070

Particle evenness )0.175 )0.064 0.022 0.284 )0.100 0.268 )0.062 )0.010 )0.034 0.172

Conductivity* )0.388 )0.583 )0.222 0.338 0.295 )0.235 )0.332 )0.179 )0.033 0.242

Stream width* 0.234 )0.147 0.754 )0.219 0.353 0.456 0.220 )0.712 )0.408 )0.525

Current velocity* )0.656 0.175 0.300 0.757 0.892 0.118 )0.502 )0.655 )0.721 0.273

Shown are eigenvalues, % variance explained and inter-set correlations between the first two axes and environmental variables. The

highest correlation on each axis is in bold font.

*Logarithmic transformation.

1954 J. Heino


be randomly distributed at smaller scales when

abundance data per se have been analysed (Schmera,

2004; Presa-Abos et al., 2006; Schmera, Eros & Green-

wood, 2007). By contrast, gatherers showed significant

aggregation when randomisations were weighted by

species abundance. This latter finding suggests that

gatherer species are aggregated perhaps to sites with

large amounts of FPOM on the stream bottom. FPOM

may be a rare resource in many stream riffle sites, and

thus species may be forced to aggregate to such high-

quality patches of food resources. This aggregation

may occur not only at the within-stream, but also at

the among-stream scale. However, that null models

weighted by abundance changed the co-occurrence

pattern from segregation to aggregation has not been

reported previously in other similar studies (Meyer &

Kalko, 2008; Tello et al., 2008). This is surprising given

that sampling effects as related to variation in species’

abundance might easily be envisaged to confound co-

occurrence patterns.

The finding of significant segregation in fixed–

weighted analyses suggests that competitive interac-

tions, historical factors or habitat checkerboards may

be underlying the observed patterns (Gotelli &

McCabe, 2002; Jenkins, 2006). First, given that the sites

were located within a small area in a single drainage

basin, historical effects as related to differential colo-

nisation are unlikely to be an important determinant of

significant segregation. This reasoning is also strength-

ened by other studies that have found that spatial

variation in stream insect assemblages in the same

drainage basin is negligible at best, suggesting that all

sites are potentially accessible to most, if not all,

species in the long term (Heino & Mykra, 2008). Thus,

‘historical checkerboards’ are unlikely to account for

the observed patterns. Secondly, although the fixed–

weighted null model analyses were weighted by

stream size, other environmental factors might also

contribute to species segregation, leading to ‘habitat

checkerboards’. Previous analyses on stream midges

in the same drainage basin suggested that habitat

checkerboards might apply well with significant seg-

regation as shown by the C-scores (Heino, 2005a). This

reasoning rests on the assumption that purely taxo-

nomically defined sets of species, such as non-biting

midges, are not likely to show strong competition for

resources, except perhaps for space. By contrast,

species belonging to the same guild are supposed to

show segregation due to competitive interactions, and

such ‘competitive checkerboards’ might also account

for the patterns shown in the present study. However,

to ascertain that competitive interactions are strong

enough to inflict variation in species co-occurrence at

the among-stream scale requires that food resources,

space or both are limiting, leading to exploitative or

interference competition (Morin, 1999). Small-scale

studies on stream macroinvertebrates suggest that

there may be strong competition for space and inter-

ference in at least filter-feeding macroinvertebrates

(Hemphill & Cooper, 1983; Hemphill, 1988), strong

density compensation among some species pairs

(McAuliffe, 1984; Dudley, D’Antonio & Cooper,

1990) and partitioning of time and habitat among

closely related species (Grant & Mackay, 1969; Malas &

Wallace, 1977). The question is, however, if small-scale

interactions lead to competitive exclusion at the

among-stream riffle scale (Kohler & Wiley, 1997).

Thus, until further research shows that this is the case,

a reasonable conclusion is that both competition and

especially habitat checkerboards determine the co-

occurrence of species in stream macroinvertebrate

guilds within a drainage basin.

If habitat checkerboards were highly influential,

then a guild’s species composition should show

strong relationships with environmental variation. In

fact, some correlations suggest that the main gradients

in species composition in each guild followed rela-

tively closely environmental variation across the sites.

In particular, current velocity and stream width

correlated relatively strongly with variation in each

guild’s species composition along the first two con-

strained ordination axes, whereas water chemistry

was clearly second to these physical variables. The

mentioned physical variables are typically closely

associated with stream size, and the results of

constrained ordinations thus also supported the use

of stream size as a weighting factor in co-occurrence

analyses. Previous studies on taxonomically defined

stream macroinvertebrate assemblages have sug-

gested that the same physical factors are influential

for variation in the species composition of stream

macroinvertebrates (Malmqvist & Maki, 1994; Malmq-

vist & Hoffsten, 2000; Wiberg-Larsen et al., 2000;

Mykra et al., 2007).

Despite that significant segregation was detected,

all guilds also showed significant nestedness. This

was somewhat unexpected, given that strong

segregation of species among sites should lead to



anti-nestedness. However, similar patterns of segre-

gation and nestedness in a same data set have been

previously reported from such disparate organisms as

bats (Meyer & Kalko, 2008), stream midges (Heino,

2005a) and stream diatoms (Heino & Soininen, 2005).

However, at least in the last of the mentioned studies

nestedness was very weak, suggesting that interspe-

cific segregation was also influential, as was similarly

found in the present study.

Species richness–environment correlations varied

among the guilds, suggesting that the maximum

degree of nestedness might be governed by different

factors for different stream macroinvertebrate guilds.

Many studies have found that nested subset patterns

are structured by either habitat area, suggesting the

prevalence of selective extinction (Boecklen, 1997;

Feeley, 2003), or isolation, suggesting the importance

of selective colonisation (Patterson & Atmar, 1986;

Meyer & Kalko, 2008). The degree to which these two

processes contribute to nestedness in stream macro-

invertebrates has been evaluated recently, and it was

suggested that recurring extinctions and colonisations

as related to stream size are behind significantly

nested, yet weak patterns in these organisms (Heino

et al., 2009b). Although stream size was not signifi-

cantly correlated with the species richness of all

guilds, similar recurring extinctions and colonisations,

perhaps also mediated by factors other than stream

size and isolation, might account for the weak nest-

edness found in the present study. However, a closer

examination of these mechanisms is difficult based on

purely correlative data.

To conclude, species in the macroinvertebrate guilds

showed significant segregation when null model ran-

domisations were weighted by stream size. This sug-

gest that competitive interactions may be contributing

to interspecific segregation, although to prove their

existence would necessitate experimental verification,

as well as examination of variation in the abundances of

species instead of mere presence–absence data (Kohler

& Wiley, 1997; Murphy, Giller & Horan, 1998; Presa-

Abos et al., 2006). However, to my knowledge, the

present findings are the first to show that species within

stream macroinvertebrate guilds show significant

segregation among-stream sites. These findings present

challenges for future studies that aim to disentangle

whether these patterns comply with the habitat check-

erboard or competitive checkerboard explanations. To

obtain these explanations would require determining

(i) variation in the levels of different food resources in

the field; (ii) estimating overlap in the resource use of

species and (iii) using experimental approaches to

estimate possible competitive exclusion of species at

various spatial and temporal scales. The absence of

these measures was indeed a weakness of this study,

and further studies with such measures are therefore

required to understand more fully patterns and mech-

anisms of species co-occurrence in stream organisms.

Acknowledgments

I would like to thank Heikki Mykra for help with the

field work, Lauri Paasivirta and Jari Ilmonen for

identifying the macroinvertebrates, Tibor Er}os, Timo

Muotka, Denes Schmera, Janne Soininen and two

anonymous referees for constructive comments on

earlier drafts of the manuscript. This study was

financially supported by grants from the Maj and

Tor Nessling Foundation and the Kone Foundation.

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(Manuscript accepted 15 April 2009)



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Species co-occurrence, nestedness and guild–environment relationships in stream macroinvertebrates