Downing, J.A.
1991.
The effect of habitat structure
on the spatial distribution of freshwater invertebrate
populations,
p. 87-106.
In S.S. Bell, E.D. McCoy and
H.R- Mushinsky (eds.)
Habitat Structure.
Chapman and
Hall,
5
London.
The effect of habitat
structure on the spatial
distribution of freshwater
invertebrate populations
J. A. Downing
For thousands of years, philosophers, writers, poets and artists have been
aware both of the physical diversity of the fabric of nature and of the
relationship of the biotic community to this framework. For example, a
religious writer wrote the following parable nearly 2000 years ago:
A sower went out to sow his seed; and as he sowed, some fell along the
path, and was trodden under foot, and the birds of the air devoured it.
And some fell on the rock; and as it grew up, it withered away, because it
had no moisture. And some fell among thorns; and the thorns grew with it
and choked it. And some fell into good soil and grew, and yielded a
hundredfold. (Luke 8: 5-8),
indicating that the ancients were aware not only- of the variability of the
physical mosaic, but of the geometric consequences of this patchiness to the
successful elaboration of living biomass.
Ecologists now call the construction of the natural environment 'habitat
structure* and very few habitats can now be said to be spatially homogeneous
in the elements that constitute the habitat of organisms. Paradoxically, the
earliest ecologists assumed that organisms are randomly or uniformly
distributed throughout their geographic ranges (Lussenhop, 1974). The
uniformity hypothesis was most plausible in planktonic systems. Victor
Hensen, one of the earliest quantitative population biologists, performed
experiments with floating glass balls that convinced him (Hensen, 1884) that
weakly motile particles such as fish larvae and plankton released into the sea
would quickly assume a random or uniform spatial distribution. The
etymological derivation of the term 'plankton' implies that planktonic
organisms should be randomly distributed (Ruttner, 1953. pp. 106, 258).
Early limnologists (e.g., Birge, 1897) found the aquatic habitat to be
highly heterogeneous in factors such as light, temperature, oxygen, and
SS
Effect of habitat structure on spatial distribution
14
15
16
17
18
19
20
21
22
23
24
Temperature (X)
Figure 5.1
Relationship between temperature and water depth on live dates in
Lake Mendota, Wisconsin (redrawn from Birge, 1897). The figure shows the
classical temperature stratification of a temperate lake.
limiting nutrients (e.g., Figure 5.1). Many lakes, for example, are highly
thermally structured, with distinct transition zones called thermoclines
between water masses. Both plants and animals have been found to be
distributed in close correlation with gradients (e.g., Figure 5.2). More
curious, however, were observations made in the early twentieth century
showing that planktonic organisms were highly heterogeneous in space,
even in non-clinical habitats (e.g., Moberg and Young, 1918). We now know
that many invertebrate populations in aquatic ecosystems are spatially
aggregated (e.g., Elliott, 1977; Downing, 1979; Morin, 1985; Downing et
a/., 1987; Vezina, 1988), including the zooplankton (Pinel-Alloul et a/.,
1988), yet experimental evidence of its causes is rare.
Habitat variation is usually only advanced as an explanation for the spatial
patchiness of aquatic populations where environmental heterogeneity is
strikingly visible. In the absence of obvious habitat heterogeneity, many
different causes for spatial aggregation of aquatic invertebrates have been
Effect of habitat structure on spatial distribution
0
10
20
30
40
50
60
70
80
90
89
100
% of plankton
Figure5.2 Relationship between percentage abundance of crustacean zooplankton
and depth on four dates in Lake Mendota, Wisconsin (redrawn from Birge, 1897).
proposed, including predation, sexual reproduction, asexual reproduction,
lack of dispersal of young, asynchronous local population fluctuation,
intraspecific attraction, improved feeding efficiency, epidemics, catas
trophes, competition, and physical processes. An alternative explanation
for spatial heterogeneity of populations in non-clinical aquatic environ
ments is that these environments are structured; i.e., are indeed significantly
heterogeneous in character states, and that organisms aggregate in response
to habitat variation that ecologists have failed to perceive (e.g., Cassie,
1959; Vodopich and Cowell, 1984; Wilson and Fitter, 1984).
The purpose of this chapter is to examine the relationship between habitat
structure and spatial variations in the abundance of aquatic invertebrate
populations. The structural components of aquatic ecosystems are water
and bottom substrate that are of different qualities. 'Patches' (sensu Pielou,
1977) are only rarely discernible to the naked eye, but both the fluid and
solid components of aquatic ecosystems are often highly heterogeneous in
characteristics that are of ecological importance to the invertebrate fauna. It
90
Effect of habitat structure on spatial distribution
seems unlikely that the visibility or the degree to which a structure seems to
be discontinuous to the human observer will have any bearing upon its status
as a structure to other animals. I therefore consider a habitat structured if
the environment exhibits significant spatial variation in any quality, and
further suggest that the analyses should be allowed to indicate whether this
structure is of any consequence to the organisms in question. The objective
of this chapter is to present some of the paths that have been followed to
examine the effect of non-clinical environmental heterogeneity on the
spatial distribution of populations of invertebrates in lentic aquatic habitats.
5.1
ZOOPLANKTON
The pelagic zone is structured by vertical gradients and horizontal patchi-
ness. Thermal stratification renders upper waters warmer, brighter, more
food rich, well oxygenated, higher in pH, and generally more favourable
habitats for planktonic organisms than deep hypolimnetic waters. Zoo
plankton usually tend, therefore, to be most abundant, active and produc
tive a few centimetres to a few metres under the lake's surface (Goldman
and Home, 1983; Wetzel, 1983). This clinal structure is routinely analysed
by visual inspection of graphs of zooplankton biology against depth (e.g.,
Figure S.2). Analysis of the ecological importance of such dines is a major
component of limnological analysis (e.g., Wetzel, 1983), and will not be
considered further here.
Superimposed on this vertical structure is a significant degree of horizon
tal variability in habitat composition. Currents and wave action render the
pelagic habitat spatially heterogeneous (e.g., George and Edwards, 1973),
especially in large lakes. In small lakes, wind may tend to homogenize the
pelagic habitat, due to turbulent mixing (Hakanson and Jansson, 1983).
Zooplankton usually live in a milieu with a high degree of spatial structure in
food availability, temperature, and concentration of major ions (Malone
and McQueen, 1983).
The effect of environmental heterogeneity on zooplankton abundance
has been examined most frequently in marine environments. Cassie (1959)
and others have correlated zooplankton abundance with habitat hetero
geneity. Cassie (1959) found, for example, that marine zooplankton
abundance was significantly correlated with temperature and salinity. At a
different site, Cassie (1960) found correlations between temperature,
salinity, planktonic algae abundance and zooplankton patchiness, but the
signs of correlations did not agree with his previous research. More-recent
spectral analyses (Mackas and Boyd, 1979) suggest a negative spatial
autocorrelation between phytoplankton and zooplankton patches. Cassie
(1960) remarked that correlations among species of zooplankton were all
positive, suggesting a common force behind the spatial distribution of all
species of zooplankton in his samples. The lack of consistency in correlations
between habitat structure and animal abundance (e.g., Cassie 1959, 1960)
Zooplankton
91
suggests that other factors, such as chance, physical factors and intra- and
interspecific behavioural reactions may be involved in the consistently
aggregated distribution of marine plankton.
Correlation analyses have also not been entirely conclusive in freshwater
ecosystems. In 1918, Moberg and Young sampled the zooplankton of Devils
Lake, North Dakota, and wrote: 4No correlation is shown between the
animals and the plant or chemical constituents. Without further investiga
tion it cannot be said, however, that plankton animals are not in any way
affected by the amount of phytoplankton or dissolved chemicals/ (p. 258.)
Malone and McQueen (1983) investigated the spatial distribution of
plankton in Lake St George, Ontario, and found that patterns of plankton
distribution were significantly heterogeneous, but out of a possible 64
bivariate correlation analyses between zooplankton abundance and phyto
plankton patchiness, only two correlations were significant at P < 0.05.
Malone and McQueen (1983) hypothesize that three types of zooplankton
patches are caused by physical mechanisms of redistribution produced
mainly by wind and water movement, and one behavioural category of
plankton aggregation gives rise to 'plankton swarms*. These swarms were
first noticed over a century ago (reviewed by Malone and McQueen, 1983),
and described by Moberg and Young (1918, p. 259) as having animals'. . .
so numerous that (the swarms) could be seen from a distance of several
meters*, 'while the surrounding water was almost free from these animals'.
These dense plankton aggregations are hypothesized to result from zoo
plankton behaviours that are uncorrelated with habitat structure.
It is difficult to evaluate the relative roles of habitat structure, physical
forces, and social behaviour on the spatial structure of zooplankton
populations, because data on the effects of specific variables on spatial
heterogeneity are difficult to collect in nature. If intraspecific competition is
important in structuring spatial pattern, however, then larger organisms
might be less aggregated because their rates of locomotion are greater
(Peters, 1983) and thus they can be more widely spaced, yet still find mates.
On the other hand, if environmental forces tend to randomize populations
(e.g., turbulent mixing, random resource distribution), then larger, more
mobile organisms might maintain larger social aggregations or stronger
correlations with favourable patches of habitat. Allometric calculations
(Peters, 1983; Calder, 1984) suggest that larger terrestrial animals form
larger aggregations. The effect of physical factors on the heterogeneity of
zooplankton is also controversial because surface populations may be most
readily aggregated by wind-driven water motion (e.g., Langford and
Jermolajev, 1966; Malone and McQueen, 1983) whereas populations in
less-turbulent waters at greater depths have been observed to be most
heterogeneous (Dumont, 1967), suggesting a behavioural component.
Seasonal variation in spatial distribution might be induced by variations in
structuring variables such as competition, predation, reproduction, or
habitat structure. Taxonomy might also play a role, as many suggest that
92
Effect of habitat structure on spatial distribution
quantitative degree of spatial heterogeneity exhibited by species represents
the evolved balance between the maximization of habitat use and reproduc
tion, and the minimization of intraspecific competition and predation risk
(e.g., Taylor and Taylor, 1977; Taylor, 1984; but cf. Winsor and Clarke,
1940 for marine zooplankton taxa).
Controversy about the cause of spatial heterogeneity in zooplankton
populations led Pinel-AUoul et al. (1988) to test the hypotheses that spatial
heterogeneity in zooplankton varies systematically with body-size, spatial
scale of observation, and depth, and varies both seasonally and among
taxonomic classifications. These hypotheses were tested using a three-factor
(scale, depth, date) nested design, with each cell containing four replicate
samples of the zooplankton community (i.e., 4 replicates x 3 scales x 3
depths x 3 dates).
The hypotheses were tested simultaneously by calculating the mean (m)
and variance (s2; n-\ weighting) of each taxon within each scale-depth-date
combination, then usingy2 as the dependent variable in a multiple regression
analysis. The multiple regression model:
logio*2 -a+b log,0m + cS + dD + eZ +/log10W+ (error)
(5.1)
where S is the coded spatial scale, D is the coded date, Z is the coded depth,
W is the body mass (tig dry wt organisms"1), and a-f are fitted constants,
was fitted to the data. Such an approach avoids the potentially biasing effect
of 'aggregation indices' (e.g., C.V., j2 m~\ k, etc.), because m enters the
analysis as a covariable. The /-values associated with the regression
coefficients (Table 5.1) demonstrate that in addition to the usual significant
effect of m on s2 (e.g., Taylor, 1984), populations of small animals were
more heterogeneous than large ones, and populations sampled on larger
scales or at greater depths showed greatest spatial variability. The negative
Table 5.1
Pinel-Alloul et al. *s (1988) multiple regression analysis of data on the spatial
heterogeneity of zooplankton taxa in Lake Cromwell (n=458, F=412, P«0.0001). The
dependent variable is log|0£ 2 calculated among four randomly distributed replicate
samples taken at each scale—depth-date combination. The coefficient is the regression
coefficient associated with each variable, the r-value and associated probability (P) indicate
the significance of each variable in the prediction of log1Q52. Coded scales are 100, 2500
and 10 000 m2, coded depths are epilimnion, metalimnion and hypolimnion, and coded
dates are June, July and August, respectively.
Variable
log,0Mean
Coefficient
t-value
P
1.528
35.8
<0.0001
loglQ Body mass (fig)
-0.097
-3.5
0.0006
Sampling scale (1,2,3)
0.091
3.4
0.0009
Depth (1,2,3)
0.082
3.0
0.0030
-0.012
-0.4
0.6621
Season (1,2,3)
Profundal benthos
93
effect of body-size and the positive effect of depth suggest that spatial
pattern is bchaviourally mediated in the zooplankton. The stronger
swimmers (large animals) tend to disaggregate, whereas populations at
greater depth that are less influenced by turbulent mixing tend to be more
aggregated than organisms in surface waters. ANOVA of the residuals of
this regression indicated that spatial variation did not vary significantly
among taxonomic groups (Pinel-Allou! etal., 1988). Because the strength of
dines and wind-driven habitat heterogeneity vary greatly over the season,
and responses were not significantly different for different dates (Table 5.1),
the data imply that zooplankton spatial distributions are little influenced by
seasonal changes in the spatial structure of the pelagic habitat.
5.2
PROFUNDAL BENTHOS
Obvious environmental patchiness in streams and marine littoral zones has
caused many studies to link environmental structure with the distribution of
benthos in lotic (e.g., Wene, 1940; Cummins, 1964; Minshall and Minshall,
1977; Marchant et a/., 1985) and marine (e.g., Bader, 1954; Gray, 1974;
Rhoads, 1974; Tyler and Banner, 1977) ecosystems. The profundal habitat
in lakes often consists of organic sediment with a smooth and homogeneous
texture. Because sediments often seem homogeneous while benthic popula
tions are aggregated, many have suggested that social attraction structures
benthic populations. Studies of relationships between habitat structure and
the notorious spatial aggregation in the profundal benthos (Elliott, 1977;
Downing, 1979) are rare (Alley and Anderson, 1968; Shiozawa and Barnes,
1977).
Benthic habitats are significantly structured environments. For example,
sediment accumulation increases with depth (e.g., Lehman, 1975; Sly, 1977;
Davis and Ford, 1982; Hilton, 1985; Hilton etal., 1986) because sediments
are resuspended by wave action and, by seasonal or continuous mixing, are
displaced by slumping and sliding on steep slopes, and are degraded less
rapidly at depth (Hilton, 1985). Sediments are therefore usually distributed
in gradients, being less abundant and coarser in high-energy, shallow
environments, and thick, fine, and flocculent in deeper waters. Sediment
distribution patterns can also be affected by the deposition of river
sediments in lakes (Hakanson, 1982; Fabre and Patau-Albertini, 1986;
Hilton et al., 1986), and slumping, sliding and turbidity currents can
generate gradients and patches in sediments (Ludlam, 1969, 1974; Hakan
son, 1982; Hilton et ai., 1986). The study of the relationship of the
abundance, production and composition of the benthic fauna to gradients in
sediment distribution has been a standard component of benthic ecology for
many years (reviewed by Brinkhurst, The Benthos of Lakes, 1974b).
Much of the benthic habitat in lakes consists of open, flat areas of
sediments of very low slope, where gradient-producing mechanisms do not
operate. Until recently, it was believed that the random rain of sedimentary
94
Effect of habitat structure on spatial distribution
Table 5.2
Examples of Kruskal-Wallis one-way tests (Conovcr, 1971) for heterogeneity
among core samples within each of Downing and Rain's (1988) 12 sampling sites. The
first four tests examined the hypothesis that variation among cores was greater than the
combination of within-core micro-scale and analytical variation, while the final eight tests
examined the hypothesis that among-corc variation was greater than analytical variation
within core samples.
CHL
PHAEO
TP
ns
*
*
**
**
ns
*
*
**
**
*
-
-
OM
Water
Lac Cromwell (1)
*
Lac de l'Achigan (1)
**
Sampling site
Micro-scale vs. among-core
Lac Triton (1)
ns
ns
Lac Triton (2)
**
**
**
Analytical vs. among-core
**
**
-
-
Lac Connelly
*♦
**
Lac Cromwell (2)
**
**
Lac de l'Achigan (2)
**
**
*
**
Lac Hertel
**
♦♦
ns
ns
Lac Pin Rouge
**
**
*
ns
ns
Lac Thibault
«*
**
ns
ns
ns
Lac Triton (3)
ns
*
♦♦
ns
-
Lake Memphremagog
**
**
ns
ns
ns
ns
-
**
♦ Significant at P<0.05; ** Significant at P<0.01
ns, No significant difference
Abbreviations: OAf, organic matter per fresh mass; CHL, chlorophyll; PHAEO,
phaeopigment; TP, total phosphorus concentration per unit fresh mass.
material in such areas ensured that these sediments would be uniform in
composition. Evidence has accumulated that much of the variation in
sedimentation rates (Kimmel, 1978; Dillon and Evans, 1982; Hilton et al.,
1986), sediment lead burden (Evans and Rigler, 1985), sediment particle
size and organic matter content (Keulder, 1982) cannot be explained by
variation in water depth. Sonar scans (Muller, 1977) and visual observations
(Fabre and Patau-Albertini, 1986; Sweerts et al., 1986) have suggested
significant spatial structure to profundal sediments.
Downing and Rath (1988) examined several important sediment charac
teristics (organic matter, water content, phosphorus and pigments) in small,
potentially uniform areas of eight lakes, to determine the frequency of
significant habitat structure in the sedimentary environment. They took a
series of randomly distributed replicate core samples within zones that were
homogeneous with respect to large-scale, erosional-depositional processes.
Each of the 12 sites in eight morphometrically and ecologically diverse lakes
was nearly uniform in depth, near the centre of the lake or bay, and far from
inputs and obvious sources of sediment heterogeneity. Either the spatial
variation within core samples (measured with tiny subcorers) or its
Profundal benthos
95
equivalent, analytical error, was determined within each sediment sample,
and inter-core/intra-eorc variation in several sediment characteristics was
compared.
All of Downing and Rath's (1988) sampling sites showed significant
sediment heterogeneity (Table 5.2). A total of 59% of comparisons showed
significantly (P < 0.05) more variation among core samples than can be
explained by analytical variation, and 67% of comparisons showed signifi
cantly (P < 0.05) more variation among core samples than can be explained
by within-core spatial and analytical variation. F-statistics for normal
ANOVA (parametric equivalent of Table 5.2) show that the variance in
sediment characteristics among core samples, within sites, are from 2 to
320 times (median = 5) greater than the variance found among replicate
samples of the same core. Significant sediment structure was found at all
sampling sites (Table 5.2) regardless of the sampling depth, size of lake or
trophic status. Thus, the profundal benthic environment is a habitat that is
highly structured in sediment characteristics of broad ecological import
ance.
Rath (1986) found that benthic invertebrate populations in several of
these same lakes were significantly correlated with variations in sediment
composition (i.e., organic matter, water content, phosphorus, pigments,
pH, sediment temperature, etc.). Instead of testing for individual bivariate
correlations between each of the many habitat characteristics measured and
the 13 taxa of invertebrates, as others have done (e.g., Cassie, 1959,1960;
Brinkhurst, 1967; Malone and McQueen, 1983), Rath (1986) tested the
global hypothesis that there tend to be significant bivariate correlations
between variations in substrate characteristics and animal abundance. This
analysis was performed by calculating the alpha-probability levels associ
ated with the correlation coefficients of each of the many possible bivariate
combinations of animal abundance and substrate quality. If animal numbers
are uncorrelated with habitat characteristics, then the frequency distribu
tion of probability levels should be flat with an average value of 0.5 (Figure
5.3a). The actual frequency distribution of probability levels associated with
bivariate habitat:organism relationships shows a tendency towards low
alpha-levels (Figure 5.3b), a tendency that is marked in some lakes (e.g..
Figure 5.3c), suggesting that organisms tend to be correlated with habitat
structure in the profundal zones of these lakes. Rath (1986) reports that
multivariate regressions were more strongly correlated, yielding R2 values
as great as 0.99 for the relationship between animal density and substrate
structure. Kruskal-Wallis analyses of the bivariate probability levels (Figure
5.3b) show that correlations between sediment and organisms are stronger
for some taxa than others, but are most variable among lakes investigated
(Table 5.3). The average strength of these bivariate correlations varies
systematically with the sediment temperature and water transparency
(Figure 5.4), suggesting that benthic invertebrates are most frequently
correlated with habitat structure where water temperatures are high and
there is a higher rate of primary production.
flO-
(a)
50-
40-
in
30-
20
10-
n.
60
(b)
SO-
40-
§ 30
S
"■
20
10
0.1
0.2
0.3 0.4 0.8 0.0 0.7
0.0 O.«
Figure 5.3 Frequency distribution of alpha-probability levels associated with
multiple bivariate correlation analyses, (a) The theoretical frequency distribution if
there is no tendency for bivariate correlations to occur, (b) The actual frequency
distribution for the 380 possible bivariate correlation analyses between substrate
characteristics and numbers of different taxa of benthic invertebrates (data of Rath,
1986), where analyses were only performed for habitat structure variables shown to
be significantly variable in space by Downing and Rath (1988). (c) Frequency
distribution as in (b) but data shown only for correlation analyses from Lac Hertel.
Both (b) and (c) show that there is a significant tendency for bivariate substrate:organism correlations to occur.
Profundal benthos
97
230
210
190
170-
2 150-
S 130
5
2
6
10
14
18
22
26
Average sediment temperature
.£ 230
210
180
170
150-
130
2.6
3.5
4T5
Water transparency (Secchi depth in m)
Figure 5.4
6.6
The mean rank calculated within the six different lakes investigated by
Rath (1986) of the probability levels associated with bivariate substrate:organism
correlation analyses shown in Figure 5.3b. Mean ranks are shown plotted against (a)
the average sediment temperature at the time of data collection, and (b) the average
unnual water transparency as measured by secchi disk readings.
98
Effect of habitat structure on spatial distribution
Table 5.3
Kruskal-Wallis lest of probability levels associated with all possible bivariate
relationships between sediment characteristics and the number of organisms found in
replicate core samples of profundal sediments (Rath, 1986; Downing and Rath, 1988).
Probability levels shown in Figure 5.3b were divided into different categories and the
hypothesis that all categories yield equal distributions of probability levels was tested using
a Kruskal-Wallis one-way analysis (Conover, 1971)
Category
N
FJ?
/»
Statistic
Sediment characteristics
384
10.07
0.436
Taxa of invertebrates
384
25.79
0.011
Lakes
384
19.59
0.001
5.3
LITTORAL BENTHIC INVERTEBRATES
The preceding analysis suggests that invertebrates living in the shallow
littoral zone, where water temperatures are often high and light penetrates
to the lake bottom allowing very high rates of primary production, should be
strongly correlated with spatial variations in their habitat. The littoral
landscape is often dominated by the growth of aquatic macrophytes,
wherever unconsolidated sediments accumulate. These macrophytes are
highly aggregated in space (Carpenter and Titus, 1984; Downing and
Anderson, 1985; Howard-Williams et a/., 1986). Because the most striking
structural component of the littoral zone is the rich and varied macrophyte
flora, several have suggested that both the abundance and composition of
these invertebrate communities should be linked to the spatial heterogenei
ty of the macrophyte beds (Soszka, 1975; Vincent etal., 1982; Rooke, 1986).
Downing (1986) and Cyr and Downing (1988) tested the hypotheses that
the abundance of invertebrates in different macrophyte beds was related to
variations in structure of macrophyte beds, including plant abundance,
species composition, and architecture, and sediment quality and depth.
Relationships between animal and habitat variation were determined by
sampling the habitat and organisms randomly in space, and applying
multiple regression analysis to discern significant organisms-habitat rela
tionships. Correlations between substrate variation and animal abundance
were very strong (R2 between 0.43 and 0.81), were often multivariate and
showed that littoral invertebrate abundance was correlated with mac
rophyte biomass and architecture, and sediment composition. Downing
(1986) showed that within one macrophyte bed, the local species composi
tion of the macrophyte substrate had a significant effect on the abundance
and composition of the macrophyte-dwelling invertebrate fauna (see also.
Chapter 14).
Unionid mussels in the sandy littoral zone
5.4
99
UNIONID MUSSELS IN THE SANDY LITTORAL
ZONE
Large filter-feeding unionid pelecypods are often found in sandy sediments
in the littoral zones of lakes. Their high specific gravity makes it difficult for
them to hold themselves above flocculent, organic sediments. Their
distribution is therefore limited to turbulent areas where organic deposits
are swept away but where turbulence is not great enough to bury them in
sand or dash them against rocks. Because of the physics of sediment
deposition, unionid mussels are usually negatively correlated with water
depth (Cokerera/., 1922; Negus, 1966; Cvancara, 1972;Strayere/a/., 1981)
and macrophyte abundance (Coker et a/., 1922; Cvancara, 1972). Where
samples are taken over a range of depths, the local variation in abundance of
these mussels is negatively correlated with the sediment organic matter
content (Figure 5.5).
25-
20-
115-
0.02
0.04
0.06
0.08
Organic matter content of sediment
Figure 5.5
The relationship between the density of the unionid mollusc Elliptio
complanata and substrate organic matter content (loss on ignition at 550°C) over a
3 m depth gradient in Lac de I'Achigan, Quebec.
100
Effect of habitat structure on spatial distribution
0.004
0.006
0.008
0.01
0.012
0.014 0.016
Organic matter content of sediment
Figure 5.6
The relationship between the density of the unionid mollusc Elliptio
complanata and substrate organic matter content (loss on ignitioii at 550°C) within a
6 x 6 m area of uniform depth (ca. 2 m) in Lac de l'Achigan, Quebec.
We have recently mapped a population of the unionid mussel Elliptio
complanata to examine the consequences of spatial aggregation for growth
and reproduction in this species (Downing et aL, 1989). This population,
which is at almost perfectly uniform depth over its entire area, shows
considerable spatial aggregation. Attempts to explain this variation in terms
of attraction to patches of favourable habitat show that instead of the
expected negative correlation of abundance with sediment organic matter
content, the contrary was observed (Figure 5.6).
EUiptio complanata is a filter feeder that employs the same current to
procure both oxygen and food particles from the surrounding water. If its
respiratory demands are great, then it collects more food particles than it
ingests. These particles are agglomerated and egested as pseudofaeces
which might accumulate in the sediments around the mussels (Sephton et at.,
1980).
Union id mussels in the sandy littoral zone
Table 5.4
101
Results of a manipulation experiment carried out in Lac de I'Achigan, Qudbcc
(sec Downing and Rath. 1988, for description of lake) to determine the effect of aggregations
of Elliptio complanata on the spatial variation of organic matter in sediments. KruskalWallis test of the hypothesis that sediments in treatments with mussels had the same organic
matter content as treatments without mussels after several months of incubation in situ
Treatment/quadrat
N
SOM*
(g g'1 dry mass)
Without mussels
Quadrat 1
IS
0.0109
Quadrat 7
15
0.0114
Quadrat 10
14
0.0115
Quadrat 11
15
0.0116
With mussels
Quadrat 5
15
0.0150
Quadrat 6
15
0.0139
Quadrat 8
10
0.0161
Quadrat 9
15
0.0235
Quadrat 12
12
0.0168
Kruskal-Wallis test statistic = 21.8, /><0.0001.
*S0M, Sediment organic matter content measured by loss on ignition (550°C).
During August 1986, we arranged nine 1 m2 frames randomly on the
sediment surface of Lac de VAchigan (Downing et al., 1989). We then
collected all the animals from within these frames, and designated five of
them to be treated by the addition of about double the natural density of the
indigenous population, and four of them to remain free of mussels. Analysis
of organic matter content in 'corrals' showed no initial difference (P > 0.05)
among treatments. The animals were left in place for about 10 months, and
sediments were resampled and analysed for organic matter content. The
results (Table 5.4) show that the presence of mussels is correlated with the
accumulation of significant quantities of organic matter. Habitat structure is
being created by the activities of the mussels. Sephton et al. (1980) found
that the richer sediments around the mussels are preferentially colonized by
smaller invertebrates, thus in some cases the habitat structure exploited by
some organisms may result from the activities of others (see Chapter 17 for a
discussion of structure-producing and structure-utilizing organisms in a
different system). Further, the activities of mussels seem to lead to the
accumulation of unfavourable habitat around them, suggesting that spatial
patterns in mussel distribution must be dynamic if mussels are to survive.
102
Effect of habitat structure on spatial distribution
5.5
CONCLUSIONS
Several distinct approaches have been used to discern the relationship
between habitat structure and animal abundance in aquatic systems. Most of
these analyses have been based on regression (either bivariate or multiple)
or other probabilistic approaches, with little reliance on analysis of actual
two- or three-dimensional patterns. The reason for this is twofold. First, it is
rather difficult to measure positions of organisms in space, because they are
often small and very mobile. Second, it is probable that all such patterns
would be highly dynamic, because the habitat pattern itself is probably
changing rapidly over short time intervals.
The intensity of habitat structure-organism relationships varies greatly
within lakes. Correlations are very strong in the warm, energy-rich littoral
zone, and quite weak in the deep benthic and pelagic zones. This, of course,
might be simply because the important components of benthic and pelagic
habitat structure have not yet been identified, but probably reflects real
differences in the intensity of habitat structure itself. The physics of fluid
movement and behaviour seem responsible for aggregation in the zooplank-
ton, at least in shallow waters, while other behavioural factors and perhaps
bottom relief and slow water movement are important to the aggregation of
the profundal benthos. The macrophyte zone is highly spatially structured
and animal abundance and community composition are inextricably linked
to this structure.
The examples given here most often use multiple regression analysis to
discern relationships between patches of organisms and characteristics of
patchy environments. These are the methods that I have found most fruitful.
Regression analysis, applied with care, allows probability statements to be
made about relationships among particular types of organisms and particu
lar structural characteristics. Some methods, such as principal component
analysis (e.g., Legendre and Legendre, 1983), could be applied to summa
rize environmental variation and these axes could be used to test global
hypotheses about correlations between environmental and organismal
heterogeneity. An important limitation to this approach is that principal
axes represent aggregate variables which can be difficult to interpret. Spatial
autocorrelation could lead to erroneous interpretations of regression
analyses and other techniques; therefore, if actual spatial patterns can be
known, more reliable analyses could be performed (e.g., Sokal and
Thomson, 1987). Unfortunately, actual spatial distribution patterns are
difficult to determine in fluid, dynamic habitats.
Finally, the correlation of unionid mussel abundance with substrate
organic matter content illustrates the need for experimentation in elucidat
ing mechanisms underlying organism-habitat correlations. This is especially
important where spatial positions of individuals cannot be measured
reliably. Experiments on the mud-dwelling benthos of shallow ponds
(Rasmussen and Downing. 1988). for example, show that the spatial pattern
References
|()3
of larval insects is strongly influenced by the presence or absence of
predators. Aggregation in the unionid mussels appears to be a behavioural
mechanism that improves fertilization success (Downing et al., unpublished
results), but substrate structure results from activities of the respiratory and
feeding activities of the mussels. Not only can regression analysis and other
multivariate techniques indicate which characteristics of structured habitats
are ecologically important, but I agree with Fraser and Sise (1980) and
Gilinsky (1984) that exploratory analyses of interrelations among popula
tion and habitat structure should be more frequently followed by experi
mentation in situ.
Acknowledgements
Publication No. 346 of the Groupe d'Ecologie des Eaux Douces of
l'Universite* de Montreal.
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