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Performance of the
Macroinvertebrate Community Index:
Effects of sampling method, sample
replication, water depth, current
velocity, and substratum on index
values
John D. Stark
a
a
Cawthron Institute , Private Bag 2, Nelson, New Zealand
Published online: 21 Sep 2010.
To cite this article: John D. Stark (1993) Performance of the Macroinvertebrate Community
Index: Effects of sampling method, sample replication, water depth, current velocity, and
substratum on index values, New Zealand Journal of Marine and Freshwater Research, 27:4,
463-478, DOI: 10.1080/00288330.1993.9516588
To link to this article: http://dx.doi.org/10.1080/00288330.1993.9516588
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New Zealand Journal of Marine and Freshwater Research, 1993: Vol. 27: 463-478
0028-8330/93/2703-0463 $2.50/0 © The Royal Society of New Zealand 1993
463
Performance of the Macroinvertebrate Community Index: effects of
sampling method, sample replication, water depth, current velocity,
and substratum on index values
Downloaded by [176.9.124.142] at 04:50 06 October 2014
JOHN D. STARK
Cawthron Institute
Private Bag 2
Nelson, New Zealand
Abstract The influences of sampling method, water
depth, current velocity, and substratum on two
macroinvertebrate-based biotic indices were
investigated based upon 523 samples from 55 stony
riffle sites on 23 New Zealand streams. A single
hand-net sample estimated the site Macroinvertebrate
Community Index (MCI) within ± 15% and four
replicates yielded ± 10%. Between 8 and 10 replicate
Surber samples produced ± 10% precision.
Quantitative MCI (QMCI) values were more variable,
with 10 or 11 replicate Surber samples required for
± 10% precision. Two procedures for detection of
statistically significant differences between paired
MCI or QMCI values are described. The detectable
difference method (equal sample sizes) is preferred
for statistical reasons but a f-test method can be used
for unequal sample sizes. MCI and QMCI were
relatively independent of depth, velocity, and
substratum within the sampled ranges of these
variables. This is a major advantage for the assessment
of water pollution or enrichment using these indices.
However, to avoid possible complications brought
about by extreme values, sampling within the
following ranges of these variables is suggested: depth
0.1-0.4 m, velocity 0.2-1.2 m s-1, and substrate 60-140 mm median diameter.
Keywords benthic invertebrates; aquatic insects;
sampling; substrate; current velocity; water depth;
biotic indices; biological monitoring
M93028
Received 11 May 1993; accepted 30 September 1993
INTRODUCTION
Macroinvertebrates are normally abundant and
important components of riverine ecosystems
(Hilsenhoff 1977; Quinn & Hickey 1990), and they
are easily the most commonly used group of
freshwater organisms for the assessment of water
quality (Rosenberg & Resh 1992). The individual
species have different habitat preferences and,
consequently, the community present in a given
situation reflects its environment. Since the effect of
stream pollution is to alter the aquatic environment,
and its biological community, it is logical to detect
pollution by monitoring the aquatic ecosystem
(Hilsenhoff 1977).
Usually, macroinvertebrate-based biomonitoring
or pollution assessment is intended to monitor water
quality through its influence on community
composition. However, macroinvertebrate community
structure is affected also by habitat characteristics
such as current velocity, water depth, and substratum
type. As Winterbourn (1981) noted, benthic
invertebrates probably respond to a complex of smallscale environmental factors, and some species may
respond to a hierarchical arrangement of such
parameters. Whatever the case, temporal or betweensite differences in factors such as these will also be
reflected in biotic index values and can complicate
the assessment of water pollution.
The Macroinvertebrate Community Index (MCI)
and its quantitative variant (QMCI), proposed by
Stark (1985) for use in stony riffles in New Zealand
streams and rivers, are biotic indices. A preliminary
version of the MCI was included in the Taranaki
ringplain biological report (Taranaki Catchment
Commission 1984). The MCI and QMCI were
developed from the British Biological Monitoring
Working Party Score System (BMWP1978; Armitage
et al. 1983) and are similar to indices developed by
Chutter (1972) in South Africa and Hilsenhoff (1977,
1987) in Wisconsin, USA.
The MCI relies on prior allocation of scores
(between 1 and 10) to taxa (usually genera) of
464
New Zealand Journal of Marine and Freshwater Research, 1993, Vol. 27
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Table 1
Taxon scores for use in calculation of MCI and QMCI index values.
INSECTA
Ephemeroptera
Ameletopsis
Arachnocolus
ktalophlebioid.es
Austrodima
Coloburiscus
Deleatidium
Ichthybotus
Isothraulus
Mauiulus
Neozephlebia
Nesameletus
Oniscigaster
Rallidens
Siphlaenigma
Zephlebia
Plecoptera
Acroperla
Austroperla
Cristaperla
Halticoperla
Megaleptoperla
Spaniocerca
Spaniocercoides
Stenoperla
Zelandobius
Zelandoperla
Megaloptera
Archichauliodes
Odonata
Aeshna
Antipodochlora
Austrolestes
Hemicordulia
Xanthocnemis
Procordulia
Hemiptera
Diaprepocoris
Microvelia
Sigara
Coleoptera
Antiporus
Berosus
Dytiscus
Elmidae
Hydraenidae
Hydrophilidae
Liodessus
Ptilodactylidae
Rhantus
Scirtidae
10
8
9
9
9
8
8
8
5
7
9
10
9
9
7
5
9
8
8
9
8
8
10
5
10
7
5
6
6
5
5
6
5
5
5
5
5
5
6
8
5
5
8
5
8
Coleoptera (cont.)
Staphylinidae
Diptera
Anthomyiidae
Aphrophila
Austrosimulium
Calopsectra
Ceratopogonidae
Chironomus
Cryptochironomus
Culex
Empididae
Ephydridae
Eriopterini
Harrisius
Hexatomini
Limonia
Lobodiamesa
Maoridiamesa
Microchorista
Mischoderus
Molophilus
Neocurupira
Orthocladiinae
Parochlus
Paradixa
Paralimnophila
Paucispinigera
Peritheates
Podonominae
Polypedilum
Psychodidae
Sciomyzidae
Stratiomyidae
Syrphidae
Tabanidae
Tanypodinae
Tanytarsini
Tanytarsus
Zelandoptipula
Trichoptera
Aoteapsyche
Beraeoptera
Confluens
Conuxia
Costachorema
Ecnominidae
Helicopsyche
Hudsonema
Hydrobiosella
Hydrobiosis
freshwater macroinvertebrates based upon their
pollution tolerances. Taxa that are characteristic of
pristine conditions score more highly than taxa that
may be found predominantly in polluted conditions.
Taxon scores are given in Table 1. MCI values are
5
3
5
3
4
3
1
3
3
3
4
9
6
5
6
5
3
7
4
5
7
2
8
4
6
6
7
8
3
1
3
5
1
3
5
3
3
6
4
8
5
8
7
8
10
6
9
5
Trichoptera (cont.)
Hydrochorema
Kokiria
Neurochorema
Oeconesidae
Olinga
Orthopsyche
Oxyethira
Paroxyethira
Philorheithrus
Plectrocnemia
Polyplectropus
Psilochorema
Pycnocentrella
Pycnocentria
Pycnocentrodes
Rakiura
Tiphobiosis
Triplectides
Zelolessica
Lepidoptera
Hygraula
Collembola
ACARINA
CRUSTACEA
Amphipoda
Copepoda
Cladocera
Isopoda
Ostracoda
Paranephrops
Paratya
Tanaidacea
MOLLUSCA
Ferrissia
Gyraulus
Latia
Lymnaea
Melanopsis
Physa
Physastra
Potamopyrgus
Sphaeriidae
OLIGOCHAETA
HIRUDINEA
PLATYHELMINTHES
NEMATODA
NEMATOMORPHA
NEMERTEA
COELENTERATA
Hydra
9
9
6
9
9
9
2
2
8
8
8
8
9
7
5
10
6
5
10
4
6
5
5
5
5
5
3
5
5
4
3
3
3
3
3
3
5
4
3
1
3
3
3
3
3
3
calculated from macroinvertebrate presence-absence
data using the following equation:
site score
MCI =•
•x20
no. of scoring taxa
Stark—Freshwater invertebrate biotic indices
where the site score is the sum of individual taxon
scores for all taxa present in a sample, and 20 is a
scaling factor.
In theory, MCI values can range between 200
(when all taxa present score 10 points each) and 0
(when no taxa are present), but in practice it is rare to
find MCI values greater than 150. Only extremely
polluted stony riffle sites score less than 50. At the
other extreme, stony riffle sites with MCI > 120 may
be regarded as pristine.
The QMCI is calculated using the following
equation:
i=S
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QMCI =
N
where S = the total number of taxa in the sample, «,• is
the number of individuals in the /-th scoring taxon, a,
is the score for the i-th taxon, and N is the totail
number of individuals collected in the sample. The
QMCI uses quantitative macroinvertebrate data.
In this paper, I draw upon an extensive unpublished
macroinvertebrate community database (with
associated current velocity, water depth, and
substratum data), which has been collected as part of
contract environmental consultant projects, supplemented by limited additional sampling (primarily to
compare Surber and hand-net techniques). All
sampling was concentrated in stony riffle habitats
where macroinvertebrate diversity and density
normally are greatest (Pridmore & Roper 1985).
The specific aims of these investigations were to:
1. compare the efficiency of Surber and "foot-kick"
hand-net sampling for MCI estimation;
2. assess the impact of sample size (or replication)
on MCI and QMCI estimation;
3. define a statistical procedure for determining
significant differences in MCI and QMCI values
between two sets of samples;
4. assess the impact of current velocity, water depth,
and substratum on MCI and QMCI values; and
465
5. provide practical guidance to improve the
application of these indices for pollution
surveillance and monitoring.
METHODS
Data collection
Twelve matched-pair replicate samples were collected
by Surber sampler (area 0.1 m2, 0.5 mm mesh) and
"foot-kick" hand-net (0.5 mm mesh) from stony riffle
habitat at four river sites near Nelson, New Zealand
(Table 2). Hand-net samples were standardised by
sampling within an area of about 0.5 m2 for about
10 s. Each sample was transferred to a white tray for
inspection and then into a labelled 250 ml or 500 ml
plastic jar where it was preserved with 70% alcohol /
4% formalin.
At each Surber sample collection point, the
following measurements were made: water depth,
mean column current velocity (at 0.6 of the depth),
and the median diameter of the six largest rocks
within the quadrat (as an index of substratum). The
median diameter is defined as the maximum width of
the rock (where length > width > height). Although
these measurements were not repeated, each handnet sample was collected near the equivalent numbered
Surber sample (where depths, velocities, and
substratum were similar).
Macroinvertebrates were separated from debris
in the laboratory by wet-sieving through a series of
Endecott sieves (4 mm, 1 mm, 0.5 mm mesh). All
animals in Surber samples were identified and
counted. For hand-net samples, although only
presence-absence data are required for MCI
calculation, relative abundances of taxa were recorded
on a Rare/Common/Abundant/Very Abundant scale.
This additional information normally is worth
collecting as it permits some discussion of community
composition and often aids the interpretation of
monitoring results.
Table 2 Macroinvertebrate sampling site locations in the Maitai and Wakapuaka Rivers near Nelson.
Sites on the Wakapuaka River are numbered from upstream (Wakl) to downstream (Wak3).
Code
Mai
Wakl
Wak2
Wak3
Maitai River
Wakapuaka River at third picnic area
Wakapuaka River below Teal River confluence
luence
Wakapuaka River opposite rifle range
Grid reference
NZMS 260 027
Date
369 923
440 976
433 977
438 000
8Nov91
20 Nov 91
22 Nov 91
20 Nov 91
466
New Zealand Journal of Marine and Freshwater Research, 1993, Vol. 27
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Existing data were collated from Surber sampling
in the Wanganui catchment (20 sites, 239 samples),
Motueka catchment (25 sites, 145 samples), Waimea
catchment (5 sites, 31 samples), and the Wairau River
(1 site, 57 samples). Details of site locations, sampling
dates and numbers of replicate samples collected are
given in Appendix 1. All samples were collected
from stony riffles. Water depths, mean water column
current velocities, and median diameters of rocks
present in sampler quadrats were measured for each
sample collected (although rock dimensions were not
measured for the Wairau data) (Stark 1988, 1989,
1990).
Data analyses
For all analyses, macroinvertebrate identifications
were at the level required for MCI and QMCI
calculation (see Table 1). The term "scoring taxa"
refers to taxonomic richness at this (mostly generic)
level.
Changes in scoring taxa, densities, MCI, and QMCI
with sample size
The impact of sample size on MCI and QMCI values
was investigated using four sets of 12-replicate handnet samples and 20 sets of 12-replicate Surber samples.
Since numbers of scoring taxa and densities are also
involved in index calculation, the influence of sample
size was assessed for them too.
For each taxa x 12-replicate data file the following
procedure was followed. This procedure was required
so that accurate minimum, mean, and maximum
estimates of numbers of scoring taxa, densities, MCI,
and QMCI could be calculated.
For each sampling site, additional data files
containing all possible combinations of 2, 3,...., 12
replicates were created from the original taxa by 12replicate data file. There are 66, 220, 495, 792, 924,
792,495,220,66,12, and 1 way(s) in which different
combinations of 2 to 12 samples can be drawn from a
series of 12 replicates.
Numbers of scoring taxa, densities (for Surber
samples), MCI, and QMCI (for Surber samples) were
calculated for the original taxa by 12-replicate data
file and for each of the 11 combination files.
Taxonomic richness, densities, and biotic index
values calculated for a site from all 12 replicates
combined were termed the "total number of taxa
caught", the "site mean density", the "site MCI" and
the "site QMCI".
Accretion curves for scoring taxa, densities, MCI,
and QMCI were determined for hand-net (4 sites)
and Surber (20 sites) samples, by expressing all values
as percentages of the "site" values calculated from all
12 replicates combined. The mean, minimum, and
maximum values for all combinations of sample size
were calculated.
All the above analyses were run using
QuickBASIC 4.50 programmes written by the author.
Influence of water depth, current velocity, and
substratum on MCI and QMCI values
Relationships between MCI and QMCI values and
water depths, current velocities, and mean median
rock diameters (as an index of substratum) were
explored by X-Y plotting and calculating least-squares
linear regressions for individual series of replicate
Surber samples. The Bartlett %2 test was used to test
whether any real correlations existed within the matrix
of MCI, QMCI, depths, velocities, and substratum
data. Bonferroni-adjusted probabilities were computed
to provide protection for multiple tests (Wilkinson et
al. 1992). The slope of each regression line was
tested to determine whether it differed significantly
from zero. These analyses were undertaken using
SYSTAT.
Similar plots with data from all Surber samples
combined (n - 523, except substratum data which
were available for only 454 samples), were unhelpful
because mean index values differed between sites
owing to factors other than water depth, current
velocity, or substratum (primarily water quality). To
overcome this difficulty, MCI and QMCI values were
standardised within sites to average 100 for each
series of replicates. Relationships between standardised index values (referred to hereafter as sMCI and
sQMCI) and depths, velocities, and substratum were
explored as explained above.
Detection of statistically significant differences in
MCI and QMCI between sites
Narf et al. (1984) defined a procedure for obtaining a
best estimate of Hilsenhoff s (1977) Biotic Index
(HBI) standard deviation, and based upon this,
described two statistical procedures to test for
significant differences between paired HBI valuesthe detectable difference (DD) method for equal
sample sizes (statistically preferred) and the Mest for
unequal sample sizes.
Narf et al. (1984) calculated mean HBI values
and variances for 42 sites with six replicate samples
per site. ANOVA was used to determine the withinsamples mean square {- variance), the square root of
which is the best estimate of HBI standard deviation.
Stark—Freshwater invertebrate biotic indices
467
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Variances were homogeneous over the range of HBI
values and mean HBI values and variances were not
correlated.
Similar procedures to those employed by Narf et
al. (1984) were applied to Surber sample data from
91 sampling sites (Table 1, Appendix 1). These data
comprised between 3 and 22 replicates per site.
Relatively limited hand-net data series were
available. Four sets of 12 replicates (from Maitai,
Wakl, Wak2 and Wak3: Table 2) were supplemented
by one series of 9 hand-net samples from the Patea
River, King Edward Park, Stratford (24 September
1984), to repeat the ANOVA as described above to
define detectable differences for MCI values based
upon hand-net samples.
RESULTS
Additional taxa scores for MCI calculation
Stark (1985) presented a table of taxon scores for use
in MCI (and QMCI) calculation and noted that any
use of the MCI or QMCI should cite such a published
list of taxon scores and note any additional or amended
scores. Since then, scores have been allocated to
additional taxa, primarily by professional judgement.
The expanded list of taxon scores is given in Table 1
so that calculation of the MCI/QMCI may continue
to be standardised New Zealand-wide.
Comparison of Surber and hand-net sampling
A series of 12 replicate Surber samples contained
between 35 (Maitai) and 52 (Wakapuaka 2) scoring
taxa per site, whereas hand-net samples yielded
between 41 (Maitai) and 54 (Wakapuaka 2) (Table
3). Hand-net samples generally yielded higher
minimum, maximum, and mean numbers of scoring
taxa per sample than Surber samples (although the
reverse was true for Wakapuaka 1); most differences
were not statistically significant (Mest, P > 0.05).
Only at Wakapuaka 3 was the mean number of scoring
taxa per sample greater in Surber samples (27.3 taxa)
than hand-net samples (23.0 taxa) (f=3.55; P < 0.01).
MCI
Mean MCI values from four series of 12 replicate
Surber samples ranged from 96.1 (Maitai) to 129.2
Table 3 Summary of numbers of scoring taxa and MCI values for 12 replicate Surber and hand-net
samples collected from the Maitai and Wakapuaka Rivers (November 1991). SE = standard error of the
mean, ^-values and probabilities are given for within-site comparisons between sampling methods for
numbers of scoring taxa and MCI values. The only statistically significant difference was at Wakapuaka
3 where the mean number of scoring taxa per sample was greater in Surber samples than hand-net
samples.
Maitai
Taxa
Surber
Hand-net
Total
Mean/sample
Min/sample
Max/sample
SE
Total
Mean/sample
Min/sample
Max/sample
SE
t
P
MCI
Surber
Hand-net
t
P
Mean
Min
Max
SE
Mean
Min
Max
SE
Waikapuaka 1
Wakapuaka 2
Wakapuaka 3
35
18.17
10
25
1.47
41
19.42
14
28
1.31
1.53
0.15
41
25.58
22
32
1.05
42
24.5
19
30
1.22
0.81
0.44
52
24.83
19
29
0.92
54
25.17
20
34
1.09
0.34
0.74
51
23.00
18
29
1.24
48
27.33
19
32
1.14
3.55
<0.01
96.10
85.33
110.67
2.16
97.03
88.89
109.29
1.76
0.34
0.74
129.21
122.61
133.65
0.93
127.15
117.27
137.69
1.53
1.34
0.21
123.10
113.79
129.47
1.38
125.58
118.82
133.91
1.29
1.31
0.22
115.42
106.32
128.89
1.81
116.35
110.34
123.75
1.32
0.34
0.74
New Zealand Journal of Marine and Freshwater Research, 1993, Vol. 27
468
CO
120
co
e
u
1
o
c
o
X
C0
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9
O.
E
co
«
i
CM
Replicate hand-net samples
Fig. 1 Macroinvertebrate taxonomic richness and MCI accretion curves. Means and ranges are derived from all
possible combinations of sample order based upon series of 12 replicate hand-net samples from four sites (i.e., 48
samples). All values are expressed as a percentage of the cumulative total of the 12-replicate samples.
(Wakapuaka 1), whereas MCI values from the handnet samples ranged from 97.0 (Maitai) to 127.2
(Wakapuaka 1) (Table 3). The rank order of sites
based on MCI values was the same for both sampling
methods (i.e., Wakapuaka 1 > Wakapuaka 2 >
Wakapuaka 3 > Maitai). This site order is effectively
from upstream to downstream.
Hand-net samples generally yielded higher mean
MCI values than Surber samples (although the reverse
was true for Wakapuaka 1), but differences were
small (usually much less than 2%) and statistically
insignificant (Mest, P > 0.05).
Since higher scoring taxa tend to be amongst the
mayflies, stoneflies, and caddisflies, consistent noncollection of taxa in these groups could result in
reduced MCI values. However, no consistent evidence
of bias between sampling methods in the numbers of
taxa collected was found within any of six major
groups of invertebrates (Table 4), suggesting that
MCI values calculated from either Surber or handnet samples would not be biased through noncollection of particular taxonomic groups.
Changes in scoring taxa, densities, MCI, and
QMCI with sample size
Numbers of scoring taxa collected increased with
sampling effort (Fig. 1 and 2). For both hand-net and
Surber samples, the scoring taxa accretion curves
had not reached asymptotes, indicating that 12
replicates were insufficient to collect all taxa present.
Table 4 Numbers of macroinvertebrate taxa in six major groups recorded in 12 Surber (S) and handnet (H) samples from the M^aitai and Wakapuaka Rivers (November 1991).
Major group
Ephemeroptera (mayflies)
Plecoptera (stoneflies)
Coleoptera (beetles)
Diptera (true flies)
Trichoptera (caddisflies)
Other taxa
Total taxa
Maitai
S
H
2
2
2
9
12
8
35
4
3
2
11
11
10
41
Wakapuaka 1
S
H
6
3
3
10
13
6
41
5
3
3
13
12
6
42
Wakapuaka 2
S
H
4
4
5
17
13
9
52
5
4
4
11
14
7
54
Wakapuaka 3
S
H
6
4
5
13
14
9
51
7
3
3
13
14
8
48
Stark—Freshwater invertebrate biotic indices
t?
469
150
150
o
c
o
E
o
o
100
I
o
110
a
90
a
a
0
p
E
50
X
•=
10
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•
100
CO
i
50
IN
70
60
10
CO
*
-
S? 120
(0
(0
u
10
10
400
400
CM
^? 300
|
O
IS
o
200
a.
E 100 -n>
0
•a
2
-
150
100
Q.
(0
I
E
CM
CO
10
15
50
50
15
Replicate Surber samples
Fig. 2 Macroinvertebrate taxonomic richness, density, MCI, and QMCI accretion curves. Means and ranges are
derived from all possible combinations of sample order based upon series of 12 replicate Surber samples from 20 sites
(i.e., 240 samples). All values are expressed as a percentage of the cumulative total of the 12-replicate samples.
Nevertheless a series of 12 replicate samples appears
to capture a sufficiently high proportion of the true
total number of taxa present in the stream reach for
acceptable estimates to be gained of the true total
taxonomic richness, mean densities, MCI, and QMCI
values for the site. This is based upon the assumption
that "true" site values are approached as sampling
effort increases, tempered by the fact that it is rarely
practical to collect as many as 12 replicates in monitoring situations. For example, only 4% of 46 studies
surveyed by Resh & McElravy (1993) took more
than 12 replicates, whereas 85% collected 5 or fewer.
A single hand-net sample captured between 34.2%
and 77.3% (mean 54.7%) of the total number of
scoring taxa collected in 12 replicates (Fig. 1), whereas
a single Surber sample captured between 15.2% and
78.1% (mean 45.8%) (Fig. 2). Once three samples
had been collected an average of 77.1 % (range 51.295.5%) and 69.0% (range 36.4-96.3%) of the total
number of scoring taxa collected in 12 replicates had
been recorded (Fig. 1 and 2).
These results indicate that numbers of taxa
collected are very dependent upon the number of
samples taken, and that considerable sample
replication (perhaps 15-20 samples) is likely to be
required if a primary objective is to compile a
reasonably accurate inventory of the species present
at a site. In this regard, approximately 20% fewer
New Zealand Journal of Marine and Freshwater Research, 1993, Vol. 27
470
scoring taxa, but also on accurate estimation of
macroinvertebrate densities. As noted above, many
Surber samples (7-11) are needed for estimation of
macroinvertebrate densities with acceptable precision
(say ± 20-50%). Ten or 11 Surber samples are required
for estimation of QMCI values to within ± 10% of the
combined 12-replicate value (Fig. 2).
200
150
-
100
-
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QMCI
Fig. 3 Relationship between MCI and QMCI based upon
data from 523 Surber samples. The linear regression line
and 95% confidence limits are shown.
hand-net samples than Surber samples would achieve
similar results.
Density
Macroinvertebrate densities were available only for
the quantitative Surber samples. Although the mean
of all possible combinations of 12 replicates was
constant, as expected, increasing sampling effort did
improve the precision of density estimates (Fig. 2).
Data from 20 sites indicated that a single Surber
sample may yield a density between 17.3 and 317.7%
of the 12-replicate value. Even triplicate samples
fared little better (23.3-240.0%).
MCI
The mean MCI from 12 single hand-net samples was
101.3% of the combined 12-replicate value (range
86.8-115.6%). Thus, any single hand-net sample may
yield an MCI value with an "error" of approximately
±15% (note that the upper and lower bounds are not
symmetrical about the mean). Only four hand-net
samples are required for + 10% precision in MCI
estimation (Fig. 1).
Between 8 and 10 replicate Surber samples were
required to achieve MCI estimates within ±10% of
the 12-replicate value (Fig. 2).
QMCI
QMCI values appear to be considerably more variable
than MCI values as they rely not only on collection of
Relationship between MCI and QMCI
There was strong evidence of a positive linear
relationship between MCI and QMCI values based
upon data from 523 Surber samples (Fig. 3).
MCI = 83.378 + 5.880 QMCI
& = 0.477 (P = 0.0025)
The strong rank correlation found between MCI and
QMCI (Spearman's 7?s = 0.70) exceeded the Rs of
0.60 between MCI and QMCI noted by Quinn &
Hickey (1990) for 88 New Zealand rivers.
Influence of physical habitat on biotic index
values
Macroinvertebrate samples were collected from stony
riffles encompassing a wide range of water depths
(0.06-0.56 m, n = 523), current velocities (0.00-1.83
m s"1, n = 523) and substrata (median rock sizes 38300 mm, n = 454) (Fig. 4).
No clear relationships occurred between index
values and water depths, current velocities, and mean
median rock diameters within the sampled ranges of
these variables. For most replicate series, linear
regression slopes did not differ significantly from
zero.
Within the sampled ranges of water depths, current
velocities, and median rock sizes, no clear linear
correlations were found with standardised biotic index
values (sMCI and sQMCI) (Fig. 5). Absolute values
of Pearson correlations ranged from 0.033 to 0.124,
and Bonferroni adjusted probabilities were nonsignificant (0.081-1.000).
The lack of any consistent relationships between
index values and these three habitat descriptors at
individual sites indicates that MCI and QMCI values
are relatively independent of current velocity, water
depth, and substratum in stony riffles within the
sampled ranges of these variables. This is a major
advantage for the application of these indices.
Detection of statistically significant differences in
MCI and QMCI between sites
The variability of MCI and QMCI values must be
known or estimated to permit detection of significant
Stark—Freshwater invertebrate biotic indices
471
250
For hand-net samples, ANOVA yielded best
estimates of MCI standard deviation Smh = 4.859
with 52 degrees of freedom. Single-sample hand-net
MCI values ranged from 88.89 to 137.69. The
following statistical techniques should be applied
with caution to hand-net data because of the relatively
limited database from which Smh was derived.
150
Detectable difference (DD) method
The DD method requires that the same number of
replicate samples be collected at each location. DD is
the smallest difference (or change) that can be detected
statistically for a given sample size. Based upon a =
0.1 (Type I error) and P = 0.2 (Type II error), Narf et
al. (1984) derived the following equation:
0.0 0.2 0.4 0.6 0.8 1.0
Water depth (m)
0.25
0.20
(5 ° - 1 5
a 0.10(5°-15
•- 0.05
fl)
a
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o
a
o
——
100
550
4=
0.0 0.4
0.8 1.2 1.6 2.0
o
O
Current velocity (m s"1)
DD =
u.o -
-
0.4
200
150
0.3
100
0.2
—i
50
0.1
r~
20
80 140 200 260 320
Median rock diameter (mm)
Fig. 4 Frequency histograms of water depth (n = 523),
current velocity (n = 523), and median rock diameter (n =
466) sampled at 55 stony riffle sites on 23 New Zealand
streams.
differences in water quality between streams, at
different locations in the same stream, or for the same
location sampled at different times.
MCI values for single Surber samples ranged
from 62.22 (Site WPipi) to 173.33 (Site Ml) with site
means between 79.99 (WPipi) and 159.67 (Ml).
QMCI values for single Surber samples ranged from
1.85 (Site Wml) to 9.18 (Site Ml) with site means
between 2.49 (Rl) and 8.36 (Ml).
For Surber samples, ANOVA yielded best
estimates of MCI standard deviation Sm = 8.661 and
QMCI standard deviation Sq = 0.625 with 431 degrees
of freedom. Most MCI values were between 70 and
150 and QMCI values between 2 and 8. It is within
these ranges that most reliance should be placed on
these best estimates of index standard deviation (i.e.,
Sm and Sq). This restriction is not a serious limitation
as, in practice, these ranges should cover all but the
most polluted stony riffle sites.
ZU4S
where N is the sample size (i.e. number of replicates),
S is the best estimate of biotic index standard deviation.
DDs for MCI and QMCI were calculated by
substituting N and Sm^, Sm or Sq in the equation above
(Table 5).
f-test method
Narf et al. (1984) proposed a method based on the ttest to determine significant differences between biotic
index values when there were unequal numbers of
Table 5 MCI and QMCI detectable differences for
between 1 and 12 replicate hand-net (MCI only) and
Surber samples from stony riffles. Hand-net samples were
standardised by sampling for 10 seconds and covering an
area of approximately 0.5 m2. The difference in mean
index values between two locations must be equal to or
greater than the tabulated value below for the difference to
be statistically significant.
Number of
replicates
1
2
3
4
5
6
7
8
9
10
11
12
Detectable difference
Surber
Hand-net
MCI
QMCI
MCI
10.32
7.31
5.96
5.16
4.62
4.21
3.90
3.65
3.44
3.26
3.11
2.98
18.40
13.01
10.62
9.20
8.23
7.51
6.95
6.50
6.13
5.82
5.55
5.31
1.33
0.94
0.77
0.66
0.59
0.54
0.50
0.47
0.44
0.42
0.40
0.38
New Zealand Journal of Marine and Freshwater Research, 1993, Vol. 27
472
200
o
o
O
•o
9
"i
•o
• ' • •••• i f i i • ' I ' ••• ••' ' •
CO
150
9
.2
'•$
•Q
CO
a
TJ
100
t»
CO
CO
.0 0.1 0.2 0.3 0.4 0.5 0.6
(0
0.0 0.1
0.3 0.4 0.5 0.6
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Water depth (m)
a
•a
a
5>
1.0
1.5
2.0
J(
O.O
0.5
1.0
1.5
2.0
200
300
400
1
Current velocity (m s' )
O
•o
9
m
ft
•o
(9
•*
CO
100
200
300
400
0
100
Mean median rock diameter (mm)
Fig. 5 Relationships between standardised MCI/QMCI values and water depth, current velocity, and an index of
substratum size for all Surber sample data (Appendix 1). There are data from 91 site/times (523 samples) for depth and
velocity and 88 site/times (466 samples) for substratum. Linear regression lines have been plotted within the range of xaxis values.
Stark—Freshwater invertebrate biotic indices
samples in each group. This equation (with the key to
symbols adapted for the MCI and QMCI) was:
test statistic = - x - x7
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where:
x, = sampled mean MCI or QMCI for location 1
x2 = sampled mean MCI or QMCI for location 2
«! = number of replicate samples for location 1
n2 - number of replicate samples for location 2
S = standard deviation Surber MCI (5m = 8.661)
Surber QMCI (5q = 0.625)
hand-net MCI ( S ^ = 4.859)
The test statistic is compared with the table value
of 1.645 (a = 0.1 and two-tailed test). If the test
statistic is < 1.645, then the MCI (or QMCI) values
do not differ significantly and do, therefore, denote
similar pollution status or habitat quality.
DISCUSSION
The MCI (in particular) and QMCI have found widespread use throughout New Zealand by researchers,
regional council, consultant, and university freshwater
ecologists (e.g., Quinn & Hickey 1990; Quinn et al.
1992; Bay of Plenty Regional Council 1991, 1992;
Stark 1993; F. M. Patrick, Royds Garden Ltd pers.
comm.; D. Scott, University of Otago pers. coiran.).
In some instances the indices probably have been
calculated from a few replicate samples (often only
one large hand-net sample), with accept-ance of the
values generated rather than any appre-ciation of the
statistical precision of index estimates.
Biological monitoring often involves the collection
of macroinvertebrate samples upstream and
downstream of effluent discharge points or temporal
comparisons of macroinvertebrate communities at
sampling sites. This so-called BACI (Before-After,
Control-Impact) design, first described by Green
(1979), has since been extended by including spatial
replication, to aid detection of many types of
environmental impact that BACI designs would
overlook (Underwood 1993). Sound sampling
programme design is essential if the results of
biomonitoring are to be interpretable. Interpretation
almost always involves detecting significant changes
in community composition and determining whether
or not these changes can be attributed to the effects of
effluent discharge (for example). This second, usually
more difficult question is not the key issue here.
473
Determination of significant differences requires
sample replication (so that precision can be calculated)
or a knowledge of the precision likely to be obtained
if prescribed methods are followed. Since one of the
primary aims of the MCI is to convey complex
biological information to water managers in a costeffective manner, the number of samples required to
be collected should, ideally, remain low.
The number of samples collected in benthic
macroinvertebrate surveys should be based upon the
number of replicates required in order to estimate the
value of a particular benthic measure (e.g., mean
density of individuals or MCI) with a desired degree
of precision and risk of error, or the number required
to determine if a given degree of change has occurred
(again with a certain risk of error). The size of the
sampling unit, the magnitude of the mean of the
particular benthic measure, the degree of aggregation,
and the desired precision are all factors that influence
the number of replicates that should be collected
(Resh & McElravy 1993). Many authors have
provided formulae for calculating precision (based
upon sample variances) and methods for calculating
sample size (i.e., the number of replicates) from pilot
survey data (Elliott 1977; Resh & McElravy 1993).
In practice, the number of replicates generally is
"determined by experience or intuition and then
modified by cost considerations" (Resh 1979). In
view of the relatively high cost of sample processing
(usually between 1 and 4 hours per Surber sample),
as few samples as possible are desirable.
Influence of sampling method on biotic index
estimation
My investigations have compared the efficiency of
hand-net collections and Surber sampling for biotic
index calculation. Both sampling methods are widely
used: 107 of 122 studies surveyed by Winterbourn
(1985) used these (or similar) sampling methods.
The present study has confirmed the finding of
Mackey et al. (1984) that qualitative hand-net samples
collect more taxa than the same number of Surber
samples. This is almost certainly because hand-net
samples tend to be of larger volume and collect
animals from a larger area of streambed than Surber
samples. Since the list of scoring taxa is fundamental
to the calculation of the MCI, it follows that hand-net
samples are more efficient for estimating MCI values
than Surber samples (e.g., Table 5). It appears that
about 20% fewer hand-net samples than Surber
samples achieve similar results.
Furse et al. (1981) found that 50%, 68%, and
80% of species in a series of six replicate hand-net
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474
New Zealand Journal of Marine and Freshwater Research, 1993, Vol. 27
samples were caught in the first, second, and third
sample and that this was in accord with other published
information. In my study, I found that the first, second,
and third samples collected 55%, 69%, and 77%,
respectively, of the taxa that were captured by all 12
replicates. When my data were recalculated (using
data from the first six replicates from each data series
only) the percentages of taxa collected (i.e., 61%,
77% and 86%) were even greater than those noted by
Furseet. al (1981).
MCI values, however, are less influenced by
sampling effort than species richness. Whereas a
single hand-net sample may collect only 55% (on
average) of the taxa collected by 12 replicates, the
MCI is likely to be within 15% of the 12-replicate
value. At least four replicate hand-net samples, which
yield MCI estimates within ± 10%, are recommended,
for studies where small differences in MCI between
sites or times must be detected. If precise MCI
estimates (i.e., within + 10%) are to be calculated
from Surber samples, then 8 to 10 replicates should
be collected.
QMCI values are dependent on macroinvertebrate
densities as well as numbers and kinds of taxa, and
can only be determined from quantitative samples.
Elliott (1977) suggested that density estimates within
± 20% of the mean may be acceptable in many
stream surveys (unless the aims of the investigation
dictate otherwise). My data indicate that 10 or 11
replicates would be required to achieve such precision
in density estimates; 7 or 8 samples if ± 50% was
acceptable. Similar numbers of replicates are required
for estimation of QMCI values with the same
precision.
Quinn & Hickey (1990), in a survey of
macroinvertebrate communities in 88 New Zealand
rivers, found "moderately strong" correlations
between both MCI and QMCI and indicators of
enrichment (such as TKN, A270F, CHLa, PAFDW).
They suggested that these indices were useful for
monitoring water quality, and that the MCI, which
requires less effort in sample processing, was slightly
more sensitive than the QMCI as an index of water
enrichment. However, although Quinn & Hickey
(1990) collected seven Surber samples from each
site, these were combined in the field so that the MCI
and QMCI values they calculated were effectively
based on single samples with no indication of how
precise their estimates were. The present study
indicates that MCI and QMCI values determined
from seven replicate Surber samples were within
approximately + 1 5 and ± 25 % of the 12-sample
combined values, respectively (Fig. 1 and 2).
The findings of this study call into question the
scientific defensibility of the interpretation of longterm monitoring programme results where a relatively
low level of effort is expended on each monitoring
occasion, if temporal variation in index values is
accepted at face value. For example, the biological
monitoring programme for Petrochem's AmmoniaUrea Plant relies upon the collection of single handnet samples from a number of monitoring sites on
four occasions per year. The programme has been
ongoing since September 1981 (Stark 1993).
Admittedly, the hand-net samples collected are
approximately three times the volume of those
collected as part of the present study so it is likely that
estimates are within ± 10-12% of site MCI values.
Such precision appears acceptable for monitoring of
this type, particularly where trends in MCI over time
are given greater weight in the interpretation than
individual index values (Stark 1993).
Influence of water depth, current velocity, and
substratum
It appears unnecessary to target particular depths or
velocities for MCI or QMCI determination when
sampling in stony riffles (at least within the ranges of
these variables that I sampled). However, the influence
of substratum on index values requires further
investigation. This study has concentrated on stony
riffles where the substratum comprised primarily
cobbles between 50 mm and 150 mm in median
diameter (range: 40-300 mm, mean 94 mm). Within
this range, MCI and QMCI values showed no obvious
dependence on substratum. Extrapolation to other
habitats (e.g., pools, backwaters, deep runs) and
beyond the surveyed ranges of water depths, current
velocities, and substratum would be unwise. For
example, since muddy habitats support quite different
macro-invertebrate communities than do stony riffles
(comprising, on average, lower-scoring taxa), lower
index values may be anticipated.
In summary, the MCI and QMCI are useful indices
of water quality and for the assessment of enrichment
in stony streams (Quinn & Hickey 1990; Stark 1993).
For cost-effectiveness, the MCI is recommended over
the QMCI as it is more reliably estimated from fewer
samples. Hand-net samples are preferred over Surber
samples, with a single hand-net sample yielding MCI
estimates with error of ± 10.32 MCI units (detectable
difference method) or within ± 15% of the value
obtained from 12 replicate samples combined.
Knowledge of the precision achieved enhances the
performance and scientific defensibility of biomonitoring programmes and synoptic biological surveys.
Stark—Freshwater invertebrate biotic indices
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Water depths, current velocities, and substratum
commonly encountered in stony riffle habitats appear
to have little impact on index values and my results
indicate that there is no need to sample specific
microhabitats within riffles. Nevertheless, it may still
remain prudent to minimise between-site (or time)
variation in these parameters, and to limit sampling
to water depths of 0.1-0.4 m, current velocities to
0.2-1.2 m s"1; and substratum median rock diameters
to 60-140 mm. These suggested limits, which are
within the ranges of these variables sampled during
my investigations, are unlikely to impose any practical
difficulties with sampling point selection.
Further research into the influence of substratum
type on biotic index values is desirable, with particular
emphasis on extending the application of these useful
tools to habitats other than stony riffles (e.g. pools,
runs, sandy or muddy areas, and macrophyte beds).
ACKNOWLEDGMENTS
I thank the following for assistance with field work: Susan
Bamfield and Jane MacGibbon (Bioresearches Ltd)—
Wanganui catchment; Mace Ward (Nelson Marlborough
Fish and Game Council) and Graeme Body (Water
Resources Survey)—Motueka River; Yvonne Stark and
Rod Asher (Cawthron Institute)—all other rivers; and the
Nelson-Marlborough Regional Council for the loan of a
Gurley Pygmy current meter. Some preliminary
macroinvertebrate sample processing was undertaken by
Susan Bamfield and Jane MacGibbon (Bioresearches Ltd),
but most macroinvertebrate samples were processed by
Yvonne Stark, Rod Asher, Krystyna Ponikla, Sue Cleaver,
and John Kapa (Cawthron Institute). This research, which
was supported by a grant from the Foundation for Science
Research and Technology, owes much to the extensive
macroinvertebrate database that I have been able to compile
while undertaking freshwater biological consultancies. In
particular, I am grateful to the following for permission to
use macroinvertebrate data sets collected under contract:
Dr Ian Johnstone (North Island Hydro Group Manager,
Electricorp Production)—Wanganui catchment; Mr Bill
Allen (Technical Services Manager, Marlborough
Electric)—Wairau River; and Mr Andrew Fenemor (Water
Resources Manager, Nelson Marlborough Regional
Council (now Tasman District Council))—Motueka,
Riwaka and Waimea catchments. I thank Dr Paul Gillespie
(Cawthron Institute), Dr Mike Winterbourn (Department
of Zoology, University of Canterbury) and one anonymous
referee for their helpful comments on the manuscript.
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Stark—Freshwater invertebrate biotic indices
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APPENDIX 1 Macroinvertebrate Surber sampling site locations, dates, and numbers of replicates collected in the
Wanganui, Wairau, Motueka, and Waimea River catchments.
Site
code
Grid Reference
NZMS 260
Wanganui River catchment
Whpti
Whakapapaiti Stream at SH47
Whpni
Whakapapanui Stream at SH47
Whlnus
Whakapapa River upstream of intake
S19 236 255
S19 269 256
S19 234 288
Whlnds
WhFb
Whakapapa River downstream of intake
Whakapapa River at Footbridge recorder
S19 233 288
S19 225 292
WhOioF
WhOio
WhOwh
Whakapapa River at Oio Farms
Whakapapa River at Oio
Whakapapa River at Owhango
S19 195 327
S19 172 370
S19 170 415
WhKak
Whakapapa River at Kakahi
S19 188 488
WSH47
WWanRd
WWhpki
WPiri
WChGr
WTeMa
WPipi
WJeru
Puke
Manus
Mang
Wanganui River at SH47
Wanganui River at Wanganui Road
Wanganui River at Whangapeki Bridge
Wanganui River at Piriaka
Wanganui River at Cherry Grove
Wanganui River at Te Maire
Wanganui River at Pipiriki
Wanganui River at Jerusalem
Pukehinau Stream at old mill site
Mangatepopo Stream upstream of intake
Manganuiateao River east of Raetahi
Wairau River catchment (Marlborough)
Wu
Wairau River downstream of Woolshed Strm
confluence
Date
Number of
replicates
T19 337 354
S19 297 408
S19 212 476
S18 130513
S18 058 545
S19 997 491
R21 859 897
R21 890 809
S19 263 364
T19 312 361
S20 966 044
21 Dec 88
21 Dec 88
20 Dec 88
22 Mar 89
20 Dec 88
21 Dec 88
22 Mar 89
22 Mar 89
20 Dec 88
19 Dec 88
23 Mar 89
19 Dec 88
21 Mar 89
20 Mar 89
15 Mar 89
21 Mar 89
21 Mar 89
25 May 89
23 May 89
24 May 89
24 May 89
20 Mar 89
20 Mar 89
24 May 89
12
12
12
12
8
12
12
12
12
12
12
12
12
9
12
12
9
6
6
6
6
9
6
6
N29 037 272
08 Jul 87
15
Motueka catchment (Nelson)
Br
Brooklyn at DSIR
BG1
Blue Glen Creek at road culvert
B1
Baton River at Baton Flats
B2
Baton River at Loveridge's Ford
Dl
Donald Creek in beech forest
D2
Donald Creek at Tui
E
Ellis River 800m above Baton River confluence
G
Graham River at main road bridge
Ml
Motueka River at Blue Glen
N26 079 122
N28 016 532
M 2 7 869874
N27 936 923
M28 881 592
M28 872 628
M 2 7 852 898
N27 961 994
N28 028 528
M2
Motueka River at Golden Downs
N28 992 647
M3
Motueka River above Tadmor River confluence
N27 933 827
28 Sep 87
24 Nov 87
22
20
27 Feb 89
20 Feb 89
21 Feb 89
28 Feb 89
27 Feb 89
27 Feb 89
28 Feb 89
28 Feb 89
31 May 88
23 Aug 88
17 Nov 88
17 Feb 89
05 Jul 92
31 May 88
23 Aug 88
17 Nov 88
17 Feb 89
31 May 88
25 Aug 88
17 Nov 88
10 Feb 89
05 Jul 92
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
(Continued overleaf)
New Zealand Journal of Marine and Freshwater Research, 1993, Vol. 27
478
Appendix 1 (Continued)
Grid Reference
NZMS 260
Downloaded by [176.9.124.142] at 04:50 06 October 2014
Site
code
M4
Motueka River at Woodstock
N27 951944
M5
Motueka River at Woodman's Corner
N27 067 095
Mp l
Mp2
P
Rl
Motupiko River above Korere
Motupiko River at Quinney's Bush
Pearse River at picnic area
Riwaka River below Moss Bush
N28 918 599
N28 947 719
N27 923 969
N26 041175
R2
Ra
St
Tl
T2
Wl
W2
W3
Riwaka River at Little's
Rainy River at ford
Stanley Brook at Barker's
Tadmor River at Tui Road bridge
Tadmor River at Rakau footbridge
Wangapeka River above Rolling River confluence
Wangapeka River below Nettleton's
Wangapeka River at Walter Peak
N26 028 183
N29 938 484
N27 949879
M28 869 632
N28 930 781
M28 749 738
M28 837 785
N27 901 850
Waimea River catchment (Nelson)
Wml
Waimea River 200m downstream of Appleby Bridge
N27 209 887
Wm2
Waimea River at Challies Island
N27 204 868
Wrl
Wairoa River off Clover Road West
N27 194 828
Wr2
Wairoa River at Gorge
N28 210 791
Wi
Wai-iti River off Livingston Road
N27 188 831
Date
01 Jun 88
17 Nov 88
16 Feb 89
05 Jul 92
01Jun 88
25 Aug 88
17 Nov 88
10 Feb 89
07 Jul 92
20 Feb 89
20 Feb 89
28 Feb 89
01Jun 88
24 Aug 88
17 Nov 88
10 Feb 89
07 Jul 92
27 Feb 89
20 Feb 89
28 Feb 89
27 Feb 89
21 Feb 89
21 Feb 89
21 Feb 89
21 Feb 89
05 Jul 92
26 Apr 88
30 Apr 92
12 May 92
26 Apr 88
30 Apr 92
26 Apr 88
30 Apr 92
26 Apr 88
30 Apr 92
26 Apr 88
30 Apr 92
Number of
replicates