Journal of Experimental Marine Biology and Ecology,
240 (1999) 229–258
L
Baltic hard bottom mesocosms unplugged: replicability,
repeatability and ecological realism examined by
non-parametric multivariate techniques
Patrik Kraufvelin*
˚
Department of Biology, Abo
Akademi University, Artillerigatan 6, FIN-20520 Turku, Finland
Received 19 March 1998; received in revised form 16 April 1999; accepted 1 May 1999
Abstract
The general utility of large-scale artificial ecosystems for ecological and ecotoxicological
research is evaluated by a case study of the replicability, repeatability and ecological realism of a
Baltic Sea hard bottom littoral mesocosm (called BHB-mesocosm). The structure (species
abundance and biomass listings) of the macrofauna community associated with bladder-wrack,
Fucus vesiculosus L., in nine control mesocosms run during 4 separate years, is investigated by
descriptive and analytical multivariate statistical techniques.
Non-metric multidimensional scaling (NMDS) is used to visualise differences in community
structure between mesocosm controls run during the same year (replicability), differences among
mesocosms run during different years (repeatability) and differences between mesocosms and the
field mother system (ecological realism). Analytically the community structure of parallel
mesocosms is shown to differ significantly by use of one-way analysis of similarities (ANOSIM)
tests, which demonstrates a poor replicability. Despite this high degree of variability between
parallel mesocosms, the null hypothesis of no differences among years can in turn be rejected by a
two-way nested ANOSIM for both abundance and biomass data, which is evidence of a poor
repeatability. If it is assumed that the field samples taken in connection with the transplantation are
representative of the initial situation in the mesocosms, the divergence of the mesocosms from the
mother systems was considerable in all years. When all 4 years are used as replicates, i.e.
independent observations on dissimilarities between the mother system and the mesocosms after 5
months of enclosure, in a two-way crossed ANOSIM, significant differences for both macrofauna
abundance and biomass data are revealed, which is evidence of a poor ecological realism.
The similarity percentage breakdown procedure (SIMPER) establishes the species principally
responsible for these differences. Considering abundance data Theodoxus fluviatilis is always an
important discriminator between groups, mostly accompanied by Mytilus edulis and either Idotea
spp. (replicability) or Gammarus spp. (realism). For biomass data Mytilus and Idotea are the most
important overall discriminators, followed by Lymnaea spp. (repeatability and realism) and
*Tel.: 1358-2-215-4052; fax: 1358-2-215-4748.
E-mail address: [email protected] (P. Kraufvelin)
0022-0981 / 99 / $ – see front matter  1999 Elsevier Science B.V. All rights reserved.
PII: S0022-0981( 99 )00061-1
230
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
Cerastoderma glaucum (realism). Finally the factors and processes restricting mesocosm performance are outlined and their consequences are briefly discussed. It is concluded that the degrees
of replicability, repeatability and ecological realism are too low for straightforward use of these
and probably most other mesocosms in predictive risk assessment or in extrapolation of results to
natural ecosystems.  1999 Elsevier Science B.V. All rights reserved.
Keywords: Baltic Sea; Community structure; Ecological realism; Mesocosm; Multivariate
statistics; Repeatability; Replicability; Rocky shore macrofauna
1. Introduction
It is a disquieting fact that so much of the research in marine mesocosms just seems to
have been driven by the need to investigate the fates and effects of pollutants with the
consequence that basic underlying information on the principles that govern the
functioning of these experimental systems, and of the natural systems of which they are
living models, have been given inadequate attention (Pilson, 1990). Replicability,
repeatability and realism are all important aspects of experimental ecosystems, but
nevertheless almost unknown when it comes to larger, more complex systems.
Replicability is defined as the degree of similarity of spatially replicated experimental
units that are meant to represent the same conditions by definition and design, i.e. in this
paper mesocosm controls run during the same year. The term repeatability is used to
describe the similarity of responses in independent systems that are observed at different
points of time within the same research facility and by use of the same scientific
methods, i.e. in this study mesocosm controls run at the same place but during different
years. The ecological realism or accuracy of the mesocosm is the degree of similarity
between the artificial system and the natural ecosystem mimicked. All definitions
originate from Giesy and Allred (1985).
The economically and logistically optimal implementation of replicability and
repeatability is more or less diametrically opposed to the essential concept of test system
realism and a central problem of ecological experimentation (Kuiper et al., 1983;
Hurlbert, 1984; de Lafontaine and Leggett, 1987). This is mainly due to the fact that all
these aspects of mesocosm similarity are related to scale (Gamble, 1990; Cairns and
McCormick, 1991; SETAC-Europe, 1991; Landis et al., 1997). Ecological realism tends
to increase gradually with increasing spatial and temporal scale of a study, while our
abilities to replicate and repeat an experiment logically decrease more rapidly, because
of the restrictions all artificial designs impose on the included ecosystem parts
(Carpenter, 1996). Therefore a high degree of replicability and repeatability is generally
sacrificed on behalf of ecological realism, which mostly is stated to be the major reason
for carrying out a mesocosm study in the first place. Although it has been stressed that
the value of a model ecosystem resides in its ability to mimic some real system, not
itself (Perez, 1995), all these three aspects of internal and external mesocosm similarity
are closely interlinked when it comes to interpretation and validation of results. Since all
three cannot be met with the same intensity simultaneously and by use of the same
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
231
methods, the scientist is often forced to emphasise one or the other. The typical trade-off
from increased ecological realism gained with a larger mesocosm size is less experimental control, often expressed as less information on individual processes, less isolation of
cause and effect as well as less ability to define accurately the densities of contained
biota, which ultimately makes it harder to interpret results (Steele, 1979; Stephenson et
al., 1984; Crossland and La Point, 1992).
Only a few case studies are available, where the replicability of aquatic mesocosms
(i.e. artificial ecosystems bigger than 1 m 3 ) have been thoroughly described (Takahashi
et al., 1975; Pilson et al., 1980; Brazner et al. 1989; Heimbach et al. 1992; Rosenzweig
and Buikema, 1994; Jenkins and Buikema, 1998; Kraufvelin, 1998). The ecological
realism of aquatic mesocosms has on the other hand been discussed more frequently
(e.g. Gearing, 1989; Lalli, 1990; Adey and Loveland, 1991; Clark and Cripe, 1993;
Kennedy et al., 1995), although most comparisons between mesocosms and the field
have not been very ambitious or innovative (Pilson, 1990; Perez, 1995). Precise data on
mesocosm repeatability, finally, have never been presented at all. This last finding is
least to say surprising, especially taking into account the large number of aquatic
mesocosm test systems currently in operation world-wide and the fact that also some
insight in repeatability is needed if mesocosms are to be used for prediction of real
effects in natural systems (Crane, 1997). In the recent paper by Kraufvelin (1998) some
problems with the replicability of BHB-mesocosm were pointed out. Coefficients of
variation (CVs) were presented for a large number of variables, many of which
previously have been used as test endpoints in the mesocosm in question (e.g. Landner et
al., 1989; Lehtinen and Tana, 1992; Lehtinen et al., 1993, 1994, 1995, 1996, 1998; Tana
et al. 1994). These CVs were generally so high that they would probably have prevented
any detection of significant differences between controls and treatments, if only real
replicates not ‘simple pseudoreplicates’, as defined by Hurlbert (1984), had been used.
Partly in order to accentuate the problem with poor replicability and partly since many
large-scale community studies, that more effectively could be analysed by other means,
still are stuck with univariate methods, the paper by Kraufvelin (1998) was only
concerned with the univariate one-way ANOVA. A more proper (eco)logical approach to
analyse this data would have been to carry out some kind of multivariate statistical
analysis parallel to the univariate ones. This would increase the understanding of the
behaviour of the bladder-wrack macrofauna communities in several replicated and
repeated control mesocosms. Comparisons with simultaneous measurements in the field
would further strengthen the overall picture of the performance of BHB-mesocosms and
other large-scale experimental ecosystems.
In this paper I therefore first present the replicability, repeatability and ecological
realism of BHB-mesocosms visually by ordination. Then I discriminate the parallel
mesocosms (replicability), different years (repeatability) and the mesocosms and mother
system (ecological realism) analytically by various ANOSIM tests. This is first done by
using subsampled bladder-wrack plants (replicability) and then the mesocosms themselves as replicates (repeatability and realism). Note that the former tests do not imply
pseudoreplication as long as they are just considered along the line ‘is mesocosm A
different from mesocosm B’ and no issues of causality (e.g. confounding with
treatments) are incorporated (Hurlbert, 1984). Further objectives include to search for
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P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
the causes behind the observed patterns in macrofauna community structure and to
demonstrate some often overlooked problems when working with living communities.
This will be accomplished by examination of bladder-wrack macrofauna communities
both in the mesocosms and in the field mother system and by pin-pointing the species
and processes basically responsible for observed differences between studied groups.
This is possible thanks to the low number of species present in the mesocosms and in the
Baltic bladder-wrack zone in general, at least compared to fully marine environments
(Haage, 1975; Wallentinus, 1991; Kautsky et al., 1992). Finally I discuss the consequences of these findings for an effective use of large-scale mesocosms in ecological and
ecotoxicological research.
2. Materials and methods
Since a thorough description of the mesocosms 1989–1992, the field sampling,
transplantation to mesocosms, mesocosm sampling, and analytical methods already has
been given in Kraufvelin (1998), only information of immediate relevance for this paper
will be provided below. The BHB-mesocosms were situated at a field station belonging
to the Finnish Environmental Research Group in Nagu, Archipelago Sea, SW Finland
(608 139 N, 228 069 E). The mesocosms consisted of 11–15 (depending on year) circular
outdoor basins (volume 8 m 3 ) equipped with a flow-through system of brackish water
(salinity 6‰), which was pumped unfiltered to the mesocosms from a nearby bay (8 m
of depth). The mean flow rate into each mesocosm was 168 l h 21 . Most mesocosms
received different kinds of or dilution of pulp mill effluents, just one mesocosm for each
treatment, but two to three replicated mesocosms each year served as controls. These
latter unpolluted systems are the ones to be examined closer in this paper and they will
be labelled A and B (1989); C, D and E (1990); F and G (1991) and H and I (1992).
Bladder-wrack communities, consisting of brown alga (8 l per mesocosm) with
associated macroinvertebrates and periphyton, were usually transplanted to the mesocosms in June (1990: early July, 1992: late May) and the mesocosms were run for | 5
months until November. At the end of the experiments five (1990–1992) or six (1989)
subsamples, each consisting of one bladder-wrack specimen with associated algae and
macrofauna communities, were taken from each mesocosm, in order to estimate the
amount of organisms associated with the alga. Three to six samples were also taken from
the field each year. These field samples were taken both in connection with transplantation to mesocosms (to get a rough measure of the initial community structure in the
mesocosms) and in connection with termination of experiments. Two semi-sheltered
field locals, Havero¨ (1989, 1991–1992) and Utterholm (1991), were used. In 1991,
monthly samples were taken from Utterholm for examination of seasonal differences and
in 1996 a large number of field samples (64) were taken from Havero¨ to be used for
simulated transplantations to the mesocosms. The field samples from November are
labelled with the letter N and the year when they were gathered 89, 90, 91 and 92,
whereas field samples from early summer are labelled with the letter S followed by the
year (90, 91 and 92). Note that no start samples are available from 1989. The monthly
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
233
field samples from 1991 are labelled in the following way: JN (samples from June), JL
(July), A (August), S (September), O (October) and N (samples from November).
At the sampling for transplantation a plastic bag was carefully folded over each Fucus
specimen including the stone to which the alga was attached (at the sampling for
laboratory analyses the stone was removed). By using this method most of the mobile
and agile animals associated with the bladder-wrack could not escape or loosen. At the
laboratory the volumes of algae were determined by the water displacement method.
After termination of experiments, animal abundances and biomasses (wet weight) were
related to the dry weight of bladder-wrack (after 24 h at 608C) for comparisons among
samples. Just eight species or taxa of macrofauna associated with the bladder-wrack are
included in the analyses, i.e. groups for which both abundance and biomass data were
registered all 4 experimental years. These species / taxa are: Gammarus spp. i.e. G.
˚ and G. zaddachi Sexton; Idotea spp., i.e. I. baltica (Pallas) and I.
oceanicus Segerstrale
chelipes (Pallas); Palaemon adspersus (Rathke); Theodoxus fluviatilis L.; Lymnaea spp.,
¨
i.e. L. peregra (Moller)
and L. stagnalis (L.); Mytilus edulis L.; Cerastoderma glaucum
(Poiret) and Gasterosteus aculeatus L. The real species lists were, however, much
longer. The following species or taxonomic groups were also found at least once in the
mesocosm bladder-wrack 1989–1992: Turbellaria, Acarina, Chironomidae, Tricoptera,
Zygoptera, Gerris sp., Corixa sp., Notonecta sp., Gyrinus sp., Dytiscus sp., Balanus
improvisus Darwin, Jaera albifrons Leach, Praunus inernis (Rathke), Electra crus¨
tulenta (Pallas), Hydrobia spp., Macoma balthica (L.), Nereis diversicolor (Muller),
Oligochaeta, Cottus gobio L., Pungitius pungitius (L.), Alburnus alburnus (L.), Phoxinus
phoxinus (L.), Myoxocephalus quadricornis (L.) and Zoarches viviparus (L.).
Multivariate statistical analyses of community structure were done using the PRIMER
program (Plymouth Routines In Multivariate Ecological Research). The methods of this
program have been described in detail by Clarke and Warwick (1994) and Carr (1996).
Non-parametric multivariate techniques were used as recommended for biological data
by Clarke (1993, 1999). Abundance data were square-root transformed and biomass data
fourth-root transformed in order to maintain roughly the same importance of less
dominant species for both types of data. The transformed data were put into triangular
matrices based on Bray–Curtis similarities. Ordination of samples was performed by
NMDS (Kruskal and Wish, 1978; Clarke and Green, 1988), a robust method that deals
with non-linearities by using ranks. Significance tests for differences among parallel
mesocosms (replicability) were carried out using one-way ANOSIM permutation tests,
whereas differences among years (repeatability) were examined with a two-way nested
ANOSIM and differences between mesocosms and the mother system (realism) by a
two-way crossed ANOSIM. The species contributing to Bray–Curtis dissimilarities
between parallel mesocosms, among years as well as between mesocosms and the field
sites were investigated using the similarity percentage breakdown procedure, SIMPER
(Clarke, 1993).
The simulated transplantation of field bladder-wrack from 1996 to the mesocosms was
repeated 10 times. At each simulation the 64 bladder-wrack samples were distributed to
four mesocosms in such a way that each mesocosm received 16 samples (standardised to
100 g DWT). Separate coefficients of variation (CVs) were calculated for all 10
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P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
occasions and averaged to get a measure of the overall initial variability. The results
from the simulated transplantations were analysed using SPSS.
3. Results
3.1. Visualisation and analytical testing of mesocosm performance
Macrofauna raw data are initially presented to get a rough picture about the absolute
differences between the studied groups (Table 1): mesocosm abundance data (Table 1a),
mesocosm biomass data (Table 1b), field abundance data (Table 1c) and field biomass
data (Table 1d). NMDS-ordinations of Fucus macrofauna abundance (Fig. 1) and
biomass data (Fig. 2) using the five to six subsamples available from all nine control
mesocosms (labelled with the letters A–I) all 4 years, demonstrate that parallel
mesocosms are fairly different (the degree of replicability is poor). One-way ANOSIM
tests based on the same similarity matrices and with subsamples used as replicates
confirm that these differences are statistically significant (before correcting for multiple
testing) all 4 years for both abundance and biomass data (Table 2).
From the figures (Figs. 1 and 2) it is also obvious that there are differences among
different years, since 1989 mesocosm samples are almost exclusively found in the lower
middle or at the bottom, 1990 samples to the upper left, 1991 samples more or less in
the middle, whereas 1992 samples may be found most to the right of the two ordination
maps. The suspected differences among years could be confirmed for both abundance
and biomass data with a two-way nested ANOSIM using the mesocosms as replicates
with subsamples nested in the analysis. The uncorrected probability values (P-values) for
global tests of equal community structure among years are 0.030 for abundance and
0.019 for biomass data (Table 3).
Presentations of the initial start communities and the mesocosm and field mother
system communities in November in the same NMDS-ordinations demonstrate that the
mesocosms have diverged considerably not only from each other (poor replicability and
poor repeatability) but also from the field during the experimental periods both
considering abundance (Fig. 3) and biomass data (Fig. 4). In these figures a poor
replicability is shown by the angles and different lengths of the arrows connecting the
mesocosms with the respective original start community (transplantation data from field
samples in early summer). A poor repeatability is visualised by the different characteristics of the three ‘arrow complexes’ with 1990 to the left, 1991 in the middle and 1992 to
the right. A poor ecological realism is finally indicated by the ‘mesocosm’ and ‘field’
arrows spreading out in almost all directions, although they are originating from a
theoretically similar community structure each year at the start of experiments. When
just mesocosm samples from November and pooled November field samples are
presented in the same NMDS-ordination, the actual dissimilarities between mesocosms
and the field become even more evident for both abundance (Fig. 5) and biomass data
(Fig. 6). These differences in community structure are established as statistically
significant, with uncorrected P-values at 0.009 for both abundance and biomass data,
using two-way crossed ANOSIM (Table 4).
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
235
Table 1
Distribution of macrofauna in samples from control mesocosms and the field 1989–1992 (values per 100 g
Fucus DWT): (a) abundance in mesocosms, (b) biomass (g WWT) in mesocosms, (c) abundance in the field,
(d) biomass (g WWT) in the field
(a) Abundance in mesocosms
Cerastoderma
Gammarus spp.
Gasterosteus
Idotea spp.
Lymnaea spp.
Mytilus
Palaemon
Theodoxus
A
B
C
51.86
6.18
22.05
0.46
200.49
3.89
8.85
0.83
8.30
0.38
42.17
0.22
230.64
0.11
8.41
0.24
4.55
1.43
32.06
0.23
464.05
Total
346.96
295.28
(b) Biomass in mesocosms
Cerastoderma
Gammarus spp.
Gasterosteus
Idotea spp.
Lymnaea spp.
Mytilus
Palaemon
Theodoxus
A
B
Total
(c) Abundance in the field
Cerastoderma
Gammarus spp.
Idotea spp.
Mytilus
Palaemon
Theodoxus
Total
(d) Biomass in the field
Cerastoderma
Gammarus spp.
Idotea spp.
Mytilus
Palaemon
Theodoxus
Total
0.46
65.46
–
0.092
3.846
D
–
–
33.79
0.77
387.55
65.70
–
171.83
F
16.19
9.02
–
37.77
24.28
80.47
–
277.13
511.08
435.67
245.39
444.86
490.81
628.10
463.35
C
D
E
–
F
G
H
I
3.455
1.531
6.126
0.203
6.452
0.118
0.379
0.231
0.574
0.128
10.732
0.137
6.349
0.014
0.511
0.051
0.361
0.070
9.077
0.126
9.062
21.706
18.648
19.272
–
E
0.45
10.50
0.26
2.35
0.057
0.804
0.083
0.157
–
–
6.96
–
0.90
–
0.128
22.063
0.69
33.30
205.35
0.46
210.81
–
0.358
0.258
4.123
5.380
0.065
1.020
18.850
0.294
7.659
20.194
24.523
28.504
15.509
–
3.101
0.260
0.076
0.146
4.078
5.336
–
2.205
1.795
14.155
–
12.568
0.625
7.770
2.20
6.04
91.51
0.20
383.00
H
150.38
6.54
0.52
6.10
70.32
184.77
–
209.47
4.37
3.49
0.529
0.460
0.435
–
G
–
N89
0.65
26.77
4.85
37.40
–
2.01
S90
–
179.73
36.18
78.32
–
187.35
N90
–
14.19
1.02
58.61
–
8.58
S91
–
57.29
6.60
44.71
3.70
4.05
N91
2.07
86.53
1.38
100.53
–
7.92
S92
1.28
302.20
8.56
151.94
–
25.12
N92
1.50
42.75
1.67
111.55
–
2.27
71.68
481.59
82.40
116.35
198.42
489.10
159.75
N89
0.008
0.346
0.240
4.058
–
0.010
S90
–
1.029
1.115
10.436
–
5.300
N90
–
0.432
0.015
10.136
–
0.141
S91
–
0.182
0.240
7.728
2.532
0.088
N91
0.015
2.039
0.199
7.189
–
0.177
S92
0.034
1.963
0.337
4.684
–
–
N92
0.025
0.374
0.233
4.036
–
0.025
4.660
17.880
10.724
10.770
9.619
7.418
4.693
I
2.93
9.81
–
0.149
0.748
–
3.393
0.061
5.902
21.610
0.356
5.142
16.389
33.970
–
The P-values that have been corrected for multiple comparisons, using Holm’s (1979)
sequential Bonferroni, are still significant for the test of repeatability (Table 3, P 5 0.038
for both abundance and biomass) and the test of ecological realism (Table 4, P 5 0.018
for both). For the replicability test (Table 2) only one out of eight ‘significant’ P-values
(P 5 0.002 for abundance comparison between A and B 1989) is still significant after
236
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
Fig. 1. NMDS-ordination of square-root transformed Fucus macrofauna abundance data in control mesocosms
1989–1992 (stress 0.18).
correction, if a is set at 0.05. Since a P-value of 0.008 (for abundance comparisons
between F and G as well as H and I and biomass comparisons between H and I) is the
minimum attainable significance level with 126 distinct permutations, while a sequential
Bonferroni would demand a P-value , 0.004 for statistical significance, such corrections may be too conservative and have a limited usefulness (Clarke, 1999). Due to the
increased risks of Type II errors (to conclude no difference when one exists) comprised
by these adjustments and the fact that Type II errors are at least equally serious as Type I
errors (to conclude a difference when none exists) in environmental sciences (Peterman,
1990; Fairweather, 1991; Mapstone, 1996), also the uncorrected P-values will be
retained and considered in the discussion together with the corrected P-values.
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
237
Fig. 2. NMDS-ordination of fourth-root transformed Fucus macrofauna biomass data in control mesocosms
1989–1992 (stress 0.21).
3.2. Species responsible for the observed differences
3.2.1. Mesocosm replicability
The contributions of individual species to Bray–Curtis dissimilarities between parallel
mesocosms (replicability) were determined by similarity percentage analyses (SIMPER)
on square-root transformed abundance (Table 5a) and fourth-root transformed biomass
data (Table 5b). The highest average dissimilarities are found for A and B 1989
(di 532.80 for abundance and di 531.70 for biomass data), F and G 1991 (di 528.28 for
abundance data) and H and I 1992 (di 530.60 for abundance data), whereas C and D
1990 were the most similar of all parallel controls (di 519.45 for abundance data), which
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P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
Table 2
One-way ANOSIM test of differences in community structure (square-root transformed abundance and
fourth-root transformed biomass data) between parallel controls 1989–1992 a
Controls
compared
Statistical
value
Possible
permutations
Significant
statistics
Significance
level
Adjusted
sign. level
Abundance
A–B 1989
C–D 1990
C–E 1990
D–E 1990
F–G 1991
H–I 1992
0.511
20.002
0.612
0.244
0.740
0.664
462
126
126
126
126
126
1
55
2
11
1
1
0.002
0.437
0.016
0.087
0.008
0.008
**
0.024 *
0.714
0.198
0.348
0.088
0.088
Biomass
A–B 1989
C–D 1990
C–E 1990
D–E 1990
F–G 1991
H–I 1992
0.492
0.072
0.432
0.064
0.304
0.628
462
126
126
126
126
126
4
30
4
33
4
1
0.009
0.238
0.032
0.262
0.032
0.008
**
*
**
**
*
*
**
0.088
0.714
0.198
0.714
0.198
0.088
a
The right column shows P-values corrected by a sequential Bonferroni. The symbol * means that
0.01,P#0.05 and ** means that P#0.01.
also can be anticipated from the P-values in ANOSIM tests in Table 2. Gammarus is an
important discriminator between mesocosms 1989 (both considering abundance and
biomass) due to a mass occurrence in A, but otherwise this species does not contribute to
much of the dissimilarities. Idotea is a central discriminator between A and B 1989, C
and E 1990 as well as F and G 1991 and consistently more dominant in former
mesocosms each year. Palaemon adspersus is of some importance regarding biomass
comparisons between D (highest biomass) and the two other controls 1990 (C and E).
Theodoxus fluviatilis is important concerning abundance comparisons between all
parallel mesocosms, although the differences are remarkable only in 1990 and 1991.
Lymnaea is mainly important for biomass data, especially in discriminating A and B
1989, as well as C and the other two control mesocosms 1990 (D and E), with
consistently higher biomass in former mesocosms each year. Cerastoderma is most
important during later years, i.e. in discriminating F and G 1991, and especially H and I
Table 3
Results of a global two-way nested ANOSIM on differences in macrofauna community structure (abundance
and biomass) among years 1989–1992 (repeatability) on square-root transformed abundance and fourth-root
transformed biomass data a
Variable
Global R
Permutations
Number of
sign. statistics
Uncorrected
P-value
Corrected
P-value
Abundance
Biomass
0.444
0.533
1260
1260
38
24
0.030 *
0.019 *
0.038 *
0.038 *
a
All possible permutations (1260) were used. The right column shows P-values corrected by a sequential
Bonferroni. The symbol * means that 0.01,P#0.05.
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
239
Fig. 3. NMDS-ordination of mesocosm and field samples at the start and at the end of experiments for Fucus
macrofauna square-root transformed abundance data (stress 0.10). Arrows connect samples from the same year
and visualise the divergence of mesocosm and field communities. Note that no field samples from the start
were available in 1989.
1992 due to a mass occurrence in H. Mytilus edulis, finally, is a central discriminator
between all parallel mesocosms considering both types of data and has consistently
higher abundances and biomasses in latter pools every year, i.e. in B, E, G and I. See
also Table 1a and b for the exact numerical values.
3.2.2. Mesocosm repeatability
The two-way nested ANOSIM could only demonstrate global differences, i.e. that
there are overall differences in community structure 1989–1992, regarding the repeatability of BHB-mesocosms. A closer look at the years, two at a time with SIMPER
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P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
Fig. 4. NMDS-ordination of mesocosm and field samples at the start and at the end of experiments for Fucus
macrofauna fourth-root transformed biomass data (stress 0.09). Arrows connect samples from the same year
and visualise the divergence of mesocosm and field communities. Note that no field samples from the start
were available in 1989.
analyses (Table 6a and b), reveals that the most different pair of years are 1989 and 1992
(41.39% Bray–Curtis dissimilarity for abundance and 33.04% dissimilarity for biomass
data) and 1990 and 1992 (40.37% dissimilarity for abundance and 34.24% dissimilarity
for biomass data), which also can be anticipated from the NMDS ordinations. The
molluscs Lymnaea, Cerastoderma and Mytilus edulis, which all are found at considerably higher abundances in 1992, are the most important abundance discriminators
between 1992 and 1989–1990. Also Theodoxus fluviatilis is an important discriminator
for abundance data. Considering biomass data Lymnaea is especially important
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
241
Fig. 5. NMDS-ordination of mesocosm and field samples at the end of the experiment on Fucus macrofauna
square-root transformed abundance data (stress 0.07).
comparing 1992 with 1989 and 1990, and Mytilus edulis when just the earlier years are
compared with one another. The exact numerical values are given in Table 1a and b.
3.2.3. Ecological realism of the mesocosm
The highest overall dissimilarities in this paper are not surprisingly found for the
comparisons of macrofauna community structure between the mesocosms and the field
mother system, i.e. 58.54% for abundance data (Table 7a) and 44.73% for biomass data
(Table 7b). These differences also proved to be statistically significant using a two-way
crossed ANOSIM. SIMPER analyses demonstrate that Theodoxus fluviatilis L., whose
normal autumn migrations to deeper waters are prevented in the mesocosms (thus the
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P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
Fig. 6. NMDS-ordination of mesocosm and field samples at the end of the experiment on Fucus macrofauna
fourth-root transformed biomass data (stress 0.08).
Table 4
Results of global tests from a two-way crossed ANOSIM on differences in macrofauna community structure
between mesocosms and the mother system at the end of experimental periods 1989–1992 a
Variable
Global R
Permutations
Number of
sign. statistics
Uncorrected
P-value
Corrected
P-value
Abundance
Biomass
1.000
0.772
108
108
1
1
0.009 **
0.009 **
0.018 *
0.018 *
a
All possible permutations (108) were used. The right column shows P-values corrected by a sequential
Bonferroni. The symbol * means that 0.01,P#0.05 and ** means that P#0.01.
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
243
Table 5
Each species’ contribution (di ) to the average dissimilarities between parallel mesocosms: (a) square-root
transformed abundance data and (b) fourth-root transformed biomass data
Species
di 89AB
di 90CD
di 90CE
(a) Abundance
Cerastoderma
Gammarus
Gasterosteus
Idotea
Lymnaea
Mytilus
Palaemon
Theodoxus
Total d %
2.16
7.67
0.76
6.83
2.62
5.53
0.71
6.53
32.80
0.60
2.37
0.66
2.12
1.63
3.92
1.00
7.15
19.45
0.27
2.11
0.57
2.70
1.81
4.10
0.36
15.20
27.12
(b) Biomass
Cerastoderma
Gammarus
Gasterosteus
Idotea
Lymnaea
Mytilus
Palaemon
Theodoxus
Total d %
3.55
4.62
2.06
5.05
5.06
7.27
2.65
1.44
31.70
1.76
2.13
2.61
3.74
3.84
4.79
4.12
0.66
23.65
0.93
1.76
2.22
5.32
4.23
3.50
1.60
3.46
23.03
di 90DE
0.55
2.94
0.38
2.03
–
6.55
1.17
12.74
26.36
–
2.92
1.55
4.06
–
6.63
4.46
3.11
24.17
di 91FG
3.47
2.29
di 92HI
6.76
3.59
5.43
0.22
6.53
28.28
11.19
1.21
0.52
2.12
3.01
5.97
0.42
6.16
30.60
4.00
2.87
–
7.77
3.42
5.05
–
1.96
26.41
5.05
2.38
1.86
2.94
1.42
4.36
2.59
2.16
22.76
–
high abundance values), by far is the most important discriminator. Gammarus and
Mytilus edulis, which contribute to the abundance dissimilarities with more than 10%
each, are other important discriminators. Lymnaea, Mytilus edulis, Idotea and Cerastoderma are in addition to Theodoxus central contributors to biomass dissimilarities. See
also Table 1a–d for the exact numerical values.
3.3. Spatial and temporal variability in the field mother system
The field mother system was investigated in order to get basic information about the
natural spatial and temporal variability of bladder-wrack macrofauna and an idea of
possible consequences for mesocosm experiments. The spatial variability between
neighbouring bladder-wrack plants, differences between sampling locations (Havero¨
1990 and 1992, Utterholm 1991) and differences between years have partly been
reported previously (Kraufvelin, 1998, Table 5 on p. 259). The year-to-year differences
and differences between mother systems are recapitulated in this paper together with
some rough information about seasonal differences, i.e. early summer compared to late
autumn (Table 1). The seasonal variability is, however, better visualised by monthly
samples from Utterholm 1991 plotted out in an NMDS ordination scheme (Figs. 7 and
8), where a continuous temporal change during the year is evident. Although there is a
high degree of spatial variability between replicates, it may be noted that the samples
most similar to the June samples are the ones from November, i.e. the samples most
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P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
Table 6
Each species’ contribution (di ) to: (a) the average abundance dissimilarity (data square-root transformed) and
(b) the average biomass dissimilarity (data fourth-root transformed) between every pair of years 1989–1992
Species
di 89–90
di 89–91
di 89–92
di 90–91
di 90–92
di 91–92
(a) Abundance
Cerastoderma
Gammarus
Gasterosteus
Idotea
Lymnaea
Mytilus
Palaemon
Theodoxus
Total d %
1.67
5.05
0.62
6.07
1.79
5.23
0.72
9.36
30.51
2.96
4.95
0.35
4.99
3.97
6.16
0.47
7.31
31.16
7.19
3.81
0.52
4.73
7.75
10.46
0.52
6.40
41.39
3.54
2.28
0.26
4.37
4.74
5.66
0.47
7.44
28.76
8.32
1.66
0.43
1.73
9.09
9.71
0.54
8.89
40.37
6.22
1.63
0.26
3.53
4.42
6.98
0.32
7.01
30.38
(b) Biomass
Cerastoderma
Gammarus
Gasterosteus
Idotea
Lymnaea
Mytilus
Palaemon
Theodoxus
Total d %
3.07
3.13
1.89
5.76
3.79
6.17
2.81
1.87
28.49
3.56
3.88
1.05
5.07
5.85
5.79
2.06
1.59
28.85
4.90
2.99
1.69
5.24
8.43
5.30
2.33
2.16
33.04
4.69
2.84
1.06
4.92
7.32
4.67
2.20
1.83
29.53
7.09
2.07
1.71
3.10
11.07
4.05
2.53
2.62
34.24
4.02
2.45
0.98
4.27
4.16
3.96
1.81
2.27
23.92
distant in time. The samples from July are on the other hand the ones that differ most
from the June samples. This last fact has immediate consequence for transplantation to
mesocosms. The time lag in transplantation present both in 1989 and 1992 is thus an
important explanation for differences between control A and B as well as H and I, i.e.
the parallel mesocosms that differed most from each other.
The results from simulated transplantations to BHB-mesocosms are presented in
Tables 8 and 9. An estimate of the theoretical amount of organisms in the mesocosm
bladder-wrack zone at the start of experiments is received, if the values per 100 g DWT
(|500 ml of volume) in Table 8 are multiplied by 16 (corresponding to the total
bladder-wrack volume of 8 l). Judging from the field data from 1996 some numerically
important taxa (at least initially) like Chironomidae (about 16 500 individuals per
mesocosm), Balanus improvisus (3500), Jaera albifrons (450) and Hydrobia sp. (200)
have most likely been overlooked at the analyses of mesocosm fauna. The initial
variability is estimated for 48 variables by CVs averaged from 10 simulated transplantations (Table 9). These CVs are also compared with a number of real end CVs from the
mesocosms 1989–1992 for evaluation of how much the initial situation determines the
final mesocosm replicability. A closer look at these comparisons indicates that most of
the final variability for Cerastoderma, Mytilus, Palaemon and Theodoxus is determined
already by differences in the transplanted bladder-wrack. The same seems to be true for
Lymnaea and Gasterosteus, but as we will see later, this is not the case. Regarding
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
245
Table 7
Each species’ contribution (di ) to the average dissimilarities between mesocosms and the mother system at the
end of experimental periods 1989–1992: (a) square-root transformed abundance data (total d %558.54) and
(b) fourth-root transformed biomass data (total d %544.73)
(a) Abundance
Species
Cerastoderma
Gammarus
Idotea
Lymnaea
Mytilus
Palaemon
Theodoxus
Average
abundance
field
Average
abundance
mesocosm
Average
term
di
Ratio
Percent
Cumulative
percent
0.88
37.90
2.41
0.00
69.10
0.00
5.37
19.11
15.31
13.48
15.24
81.99
0.26
278.85
3.88
6.02
4.58
4.60
8.68
0.45
30.00
0.72
1.23
1.05
0.96
1.36
0.45
2.54
6.63
10.29
7.82
7.86
14.83
0.77
51.25
98.67
76.37
92.04
84.22
66.08
99.44
51.25
Average
biomass
field
0.01
0.73
0.16
0.00
6.63
0.00
0.09
Average
biomass
mesocosm
0.47
0.91
0.85
1.58
12.48
0.19
6.16
4.48
3.35
5.89
6.99
6.64
1.84
14.46
1.06
1.09
1.28
1.09
1.34
0.47
2.66
10.02
7.49
13.16
15.62
14.83
4.12
32.33
85.96
93.45
75.94
47.94
62.78
97.57
32.33
(b) Biomass
Cerastoderma
Gammarus
Idotea
Lymnaea
Mytilus
Palaemon
Theodoxus
Gammarus and Idotea the initial differences seem to be less important as long as no
time lags in transplantation are present.
4. Discussion
4.1. Examining mesocosm performance
4.1.1. Replicability, repeatability and realism
The degree of replicability and repeatability of a mesocosm depends on the
complexity of the system, the endpoints that are measured and the laboratory and field
conditions (seasonality and long-term changes), which determine the development of
systems and the variability over time (Smith, 1995; Crane, 1997; Kraufvelin, 1998).
Replicability and repeatability are both important aspects of mesocosm similarity,
although impossible to control for large-scale artificial ecosystems due to logistical and
economical limitations. Replicability is especially important in order to test whether
deviations from controls observed in treatments are statistically significant by using a
specific number of replicates at specific levels of acceptable statistical error postulated
(Kraufvelin, 1998). The importance of repeatability is in turn most evident when it
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P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
Fig. 7. NMDS-ordination of Fucus macrofauna field samples from 1991 after square-root transformation of
abundance data (stress 0.13).
comes to interpretation of results and in dealing with the ecological relevance of results,
i.e. in making predictions for the real-world or in controlling the reliability of results
(Kennedy et al. 1995; Crane, 1997). If the repeatability of a mesocosm is poor, we will
lack important generality of the results (Lamont, 1995) and experimenters cannot tell if
observed deviations are real or due to error introduced by time, space, experimental
method, chance (Crane, 1997) or just a natural quality of the dynamics in the (mother)
ecosystem selected for the test. Thus the possibilities to make real-world predictions of
the results will be restricted (Underwood and Peterson, 1988; Rosenzweig and Buikema,
1994). This is rather ironic, since the search for high ecological realism has led many
scientists to a point where the arguments for replicability and repeatability are sacrificed
(cf. Landner et al., 1989).
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
247
Fig. 8. NMDS-ordination of Fucus macrofauna field samples from 1991 after fourth-root transformation of
biomass data (stress 0.13).
The third aspect of mesocosm similarity, the similarity with the mimicked mother
system (ecological or environmental realism), is important for practically the same
reasons as repeatability. If you cannot even repeat a partial ecosystem in containers, how
can you then expect the results to be universally applicable? It may be argued to infinity
about which one of these three aspects that is most important, but even with profound
knowledge of all these aspects of similarity for the experimental ecosystem in question it
may be impossible to carry out reliable predictive risk assessments or extrapolate results
to real ecosystems. According to Perez (1995) accuracy or fidelity with the field should
be first priority, precision, while important, is a secondary consideration. A high degree
of ecological realism is indeed crucial for a study using experimental ecosystems and
aiming at realistic predictive assessments for the real world (not just a study focused on
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P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
Table 8
Mean abundance and biomass6S.D. of all animal species present in the 64 bladder-wrack samples from
Havero¨ 1996. The values are calculated per 100 g Fucus DWT. Multiply by 16 to get a theoretical start value
for mesocosms if these samples had been used for transplantation
Species
Number of
samples present
Mean
abundance
6S.D.
Mean
biomass g
6S.D.
Acarina
Balanus improvisus
Cerastoderma sp.
Chironomidae
Cottus gobio
Gammarus spp.
Gasterosteus aculeatus
Hydrobia sp.
Idotea baltica
Idotea chelipes
Jaera albifrons
Lymnaea peregra
Macoma baltica
Mytilus edulis
Nereis diversicolor
Palaemon adspersus
Praunus inernis
Pungitius pungitius
Theodoxus fluviatilis
Tricoptera
Zygoptera
1
43
7
64
1
64
2
35
35
48
48
16
1
38
1
12
13
1
51
2
3
0.06
226.08
0.14
1030.94
0.02
237.66
0.03
14.38
2.26
4.54
28.75
0.28
0.02
68.10
0.02
0.19
0.23
0.02
66.28
0.03
0.05
0.50
371.15
0.54
766.34
0.13
207.39
0.18
23.08
3.51
5.12
35.40
0.56
0.13
143.14
0.13
0.47
0.59
0.13
72.72
0.18
0.21
,0.01
11.78
,0.01
0.32
0.01
0.70
0.01
0.10
0.29
0.10
0.03
0.07
,0.01
2.68
,0.01
0.18
,0.01
,0.01
1.88
,0.01
,0.01
,0.01
20.17
0.01
0.51
0.04
0.57
0.07
0.16
0.46
0.11
0.04
0.15
,0.01
4.69
,0.01
0.53
,0.01
0.03
1.83
,0.01
0.01
what might happen in other mesocosms receiving the same treatment). Without any
information about the repeatability, however, experimenters lack an objective base for
judging the relevance of the results and may come to decisions that lead to environmental (or economical) damage (Crane, 1997). When in turn the replicability is poor only
extreme differences can be detected (Kraufvelin, 1998).
During all these 4 experimental years parallel mesocosms had a very different
macrofauna community structure, i.e. a low degree of replicability, which in this paper
has been shown by both descriptive and analytical multivariate statistical methods and
previously by the generally high mean CVs for a number of variables (Kraufvelin,
1998). This fact has immediate relevance for the ‘pseudoreplicated’ investigations
carried out in these mesocosms using the same control dataset as in this paper (Lehtinen
and Tana, 1992; Lehtinen et al., 1993, 1994; Tana et al. 1994), since if already the
controls can be shown to differ significantly, then how could treatment effects ever be
distinguished from basin effects (‘tankiness’)? In the light of the restricted replicability it
is not surprising that problems with the repeatability will occur as well. In this material
repeatability was poorer than the replicability, i.e. mesocosms run during different years
differed more in community structure than parallel mesocosms. This is by the way a
fundamental demand for the test of repeatability to be significant, since the ANOSIM
procedure here makes use of the observed differences among years (repeated mesocosms) contrasted with differences between replicates within years (individual meso-
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
249
Table 9
Estimated mean start CVs and 95% confidence intervals for a number of variables from 10 randomised
transplantations of 64 bladder-wrack specimens to four mesocosms (field data 1996) and real end CVs from
1989–92
Variable
Simulated start
Real results at the end
Mean CV
95% CI
Population variables
Acarina, Cottus, Macoma, Nereis, Pungitius,
abundance and biomass
Balanus abundance
Balanus biomass
Cerastoderma abundance
Cerastoderma biomass
Chironomidae abundance
Chironomidae biomass
Gammarus abundance
Gammarus biomass
Gasterosteus abundance
Gasterosteus biomass
Hydrobia abundance
Hydrobia biomass
Idotea baltica abundance
Idotea baltica biomass
Idotea chelipes abundance
Idotea chelipes biomass
Jaera abundance
Jaera biomass
Lymnaea abundance
Lymnaea biomass
Mytilus abundance
Mytilus biomass
Palaemon abundance
Palaemon biomass
Praunus abundance
Praunus biomass
Theodoxus abundance
Theodoxus biomass
Tricoptera abundance
Tricoptera biomass
Zygoptera abundance
Zygoptera biomass
200.0
44.6
45.0
104.2
96.0
18.0
39.2
21.8
17.1
149.6
150.0
34.8
39.2
31.4
33.2
26.8
30.1
26.9
28.3
64.1
73.1
47.8
41.7
64.3
69.2
57.8
64.0
22.1
21.9
132.8
194.0
97.4
97.3
32.2–57.1
33.0–57.0
91.4–117.1
77.2–114.8
14.8–21.1
30.2–48.1
14.4–29.1
10.6–23.6
118.6–180.6
119.2–180.8
25.8–43.9
29.5–48.9
22.5–40.2
25.7–40.7
15.6–38.0
19.0–41.2
18.7–35.1
20.7–35.9
40.2–88.0
54.1–92.0
34.9–60.7
34.0–49.4
53.2–75.4
52.4–86.0
39.0–76.5
34.0–94.0
16.8–27.4
16.3–27.6
107.5–158.1
191.7–196.3
74.2–120.5
74.3–120.3
Community variables all species
Total abundance
Total biomass
Number of species
Margalefs´ species richness, abundance data
Shannon–Wiener diversity, abundance data
Pielous´ evenness, abundance data
9.4
32.4
12.6
13.8
12.5
12.5
6.5–12.3
23.7–41.1
9.9–15.4
10.6–17.0
8.3–16.7
9.3–15.7
a
b
Idotea baltica and Idotea chelipes were pooled 1989–1992.
Mesocosm values are calculated just for the eight mesocosm species / taxa.
Mean CV
95% CI
112.4
80.9
89.8–135.0
14.4–147.4
54.8
64.4
91.3
89.6
15.9–93.7
27.6–101.2
25.7–156.9
29.4–149.8
104.3 a
76.1 a
104.3 a
76.1 a
79.4–129.2
26.3–125.9
79.4–129.2
26.3–125.9
108.3
96.0
26.4
42.9
113.7
110.1
56.7–159.9
16.9–175.1
5.8–47.0
14.0–71.8
71.0–156.4
57.3–162.9
19.4
22.7
0.8–38.0
7.4–38.0
18.5 b
18.6 b
6.6–30.4
23.2–40.9
b
2.8–27.8
23.7–39.5
24.9–54.5
15.4
31.6 b
39.7 b
250
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
cosms) in computing the test statistic, global R. The main problem with poor mesocosm
repeatability lies in the interpretation and application of results. If it, as with the
BHB-mesocosms, seems to be impossible to repeat even a set of controls during
subsequent years using the same or a neighbouring mother system, the same methodology, the same research facility, the same staff and the same equipment, then how can
possible important experimental results ever be controlled by repetition or reproduction
of tests, if necessary? If in addition the mesocosms can be demonstrated to diverge this
strongly from the field, then how could a reliable extrapolation to the real-world ever be
accomplished? The factors restricting replicability and repeatability of BHB-mesocosms
have already been discussed in depth by Kraufvelin (1998), whereas the factors
restricting ecological realism as well as the species principally responsible for the
observed differences will be outlined in this paper.
4.1.2. Statistical considerations
There are a number of important reasons for choosing multivariate statistical analyses
instead of multiple univariate ones (Scheiner, 1993; Clarke and Warwick, 1994; Clarke,
1999). Firstly, ecological questions are often multivariate, involving interactions among
response variables such as complexities of dependencies among species. Differences
among groups are seldom a feature of just one response variable alone, but more often of
an entire suite of variables. An examination of ‘all’ of the data at once may reflect the
nature of ecological structures more accurately (Landis et al., 1997; Kedwards et al.,
1999a,b). Moreover a multivariate approach provides a single global test of significance,
whereas multiple univariate tests inflate the a -value, i.e. increases the probability of
making a Type I error in hypothesis testing, which has to be corrected for. Additional
benefits of multivariate techniques are their ability to summarise large datasets (Van den
Brink and Ter Braak, 1998, 1999; Maund et al., 1999) and for some methods their
conceptual simplicity, which facilitates their use and understanding by environmental
managers and regulators (Clarke, 1999). Non-parametric techniques were chosen in this
paper instead of parametric, since the samples could not be transformed to approximate
(multivariate) normality due to the large number of zeros (Clarke and Warwick, 1994).
The relative importance for the scientific objectives of Type I and Type II errors,
respectively, should always be considered when a study is planned. Then the appropriate
methods for tests of the hypothesis can be selected with emphasis on the type of error to
be careful with. If the risk of Type I errors is emphasised, a correction for multiple
testing should be carried out whenever several tests are performed simultaneously (Day
and Quinn, 1989; Rice, 1989; Sokal and Rohlf, 1995; Underwood, 1997). Such
corrections will affect the minimum detectable differences (MDDs) and minimum
number of replicates (MNRs) dramatically, calling for even larger effect sizes or more
replicates for successful detection of effects.
An investigation of mesocosm performance by non-parametric multivariate methods is
entirely different from the univariate one-way ANOVA approach in Kraufvelin (1998).
ANOSIM is a multivariate distribution free method, comparable to the univariate
Mann–Whitney–Wilcoxon or Kruskall–Wallis tests. This means that the rank similarities in community structure determine if there will be significant differences between
controls and treatments in the permutation tests. The exact degree of variability between
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
251
replicates is not of primary importance and it is much more difficult to calculate for
example the minimum number of replicates (MNRs) needed for successful use of these
methods based on variability measures and effect size levels. Detection of differences
based on abundances and biomasses of several animal populations, as in this paper, may
of course be extra difficult to handle due to the large temporal variances of many
populations and the marked lack of concordance from one place to another (Osenberg et
al., 1996; Underwood, 1996).
4.2. What decreased mesocosm performance?
4.2.1. Mesocosm replicability
The main factors decreasing the replicability of BHB-mesocosms have already been
outlined by Kraufvelin (1998). Three major categories were distinguished: (1) initial
variability between transplanted bladder-wrack communities; (2) errors in experimental
design and execution, and (3) chance events or normal divergence of mesocosms.
Although an attempt to look at the initial variability in the mesocosms has been made in
this paper (Tables 8 and 9) much more research would be needed with regard to this
topic. We still have very vague ideas of the initial and inherent variability among
experimental ecosystems in general and how representative the initial conditions are to
those occurring in nature and how they shape the final results of an experiment (Boyle
and Fairchild, 1997). All eight species analysed were subjected to initial differences in
the transplanted communities (Table 9), but these differences may just have been
marginal for Lymnaea and Gasterosteus, which both were very rare in the field samples
taken in connection with transplantation (Kraufvelin, 1998). Most of the pond snails and
stickle-backs present at the end originated from separately transplanted individuals (in
known, equal numbers). Therefore the final differences for these groups are probably
caused by chance events affecting for example the reproductive success or the juvenile
survival.
The mass occurrences of Gammarus and Idotea in A compared to B 1989, as well as
Cerastoderma in H compared to I 1992, are in turn the most evident examples of errors
in experimental design and execution. Due to a time lag in transplantation of bladderwrack communities of several weeks, much more individuals of these three species
entered the former pools to which transplantation was accomplished later. The
seasonality of the bladder-wrack macrofauna community is known to be strong in the
¨
Baltic Sea (Haage, 1975; Fagerholm, 1978; Kolding, 1981; Anders and Moller,
1983;
Figs. 7 and 8). The close similarity of samples from June and November in Figs. 7 and 8
is probably due to the high degree of similarity in many important fauna structuring
variables (e.g. low water temperatures and a scarce epiphytic vegetation). Mobile snails
and juvenile crustaceans were almost absent in June and November, whereas these
taxonomic groups were rather dominant during the other months, including a rapid
change from June to July. These results imply that mesocosms meant to be parallel
should always be started simultaneously in order for them to contain a sufficiently
similar biological material for further meaningful analyses.
Idotea was in 1991 probably also affected by chance events like differences in
survival rate of the green alga, Cladophora sp., which provides essential food and
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P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
shelter for juvenile crustaceans. In F, where Cladophora was present for a longer period,
Idotea was markedly more abundant than in G, where the green alga died after just a
few weeks. Kraufvelin (1998) listed a number of other factors with the possibility to
cause divergence of mesocosms. Out of these factors the random occurrence of
Spirogyra sp. in three of the controls, B 1989 and D and E 1990, and the possible
presence of additional fish predators may have been of most importance for the
bladder-wrack macrofauna.
4.2.2. Mesocosm repeatability
Repeatability of mesocosms (year-to-year similarity) was poor partly due to the poor
replicability (1–3 above) and partly due to other reasons. The repetition of BHBmesocosms was also affected by: (4) variations between years and seasonality, which
prevents a transplantation of similar start communities during subsequent years; (5)
differences in weather conditions during the treatment periods during different years; (6)
the fact that bladder-wrack specimens were gathered from two different mother systems
(Kraufvelin, 1998). The last factor does not seem to contribute to much of the poor
repeatability, however, since 1991, the year when the bladder-wrack samples occasion¨ has the
ally were gathered from another mother system (Utterholm instead of Havero),
lowest mean total dissimilarities (i.e. lowest mean values of all pair-wise abundance or
biomass comparisons) in Table 3 of all individual years. For abundance data the mean
dissimilarities (n53) are: 1989534.35, 1990533.21, 1991530.10 and 1992537.38;
whereas for biomass data the mean dissimilarities are: 1989530.13, 1990530.75,
1991527.43 and 1992530.40. These differences are not crucial, but at least they
demonstrate that the mesocosms in 1991 were not at the extreme end in the between
year comparisons. In the ordination maps the samples from 1991 also cluster out in the
middle (Figs. 1 and 2). The temporal variations, both in the mother system before
transplantation and in the mesocosms after transplantation thus seem to be of much
greater importance in this material (together with the factors decreasing replicability).
A closer look at the individual species does not reveal very much about the causes of
poor repeatability, especially the possible impact of chance events is hard to evaluate in
this perspective. The initial variability between transplanted bladder-wrack communities,
natural or due to year to year differences, seems anyway to have been the major cause.
This is also indicated by the field samples taken in connection with transplantation to
mesocosms 1990–1992 (Kraufvelin, 1998), although according to Kautsky (1991)
fluctuations between years do not seem to be that pronounced in the Baltic Sea as for
example in the North Sea. Differences in the weather conditions during the experimental
period during different years may also have been important for certain species such as
Lymnaea and Theodoxus, whereas the differences for Gammarus (1989 compared to
other years) and Cerastoderma (1992 compared to other years) mainly were due to the
fact that one of the mesocosms each year had shown extreme values because of
transplantation errors.
4.2.3. Ecological realism of the mesocosm
The ecological realism was affected by a variety of abiotic and biotic factors (known
or unknown). It is impossible to simulate the abiotic environment exactly in an artificial
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
253
ecosystem. Containerisation always means loss of natural refuges, isolation of an
environment can result in artefacts caused by the physical containment devices and the
reduced size cause other restrictions that limit our ability to relate mesocosm results to
natural systems (Perez, 1995; Carpenter, 1996; Chen et al., 1997; Schindler, 1998).
Abiotic factors reducing the ecological realism of mesocosms include: wall effects
(Strickland, 1967; Giesy and Odum, 1980; Lundgren, 1985), differences in water
exchange rate and more or less accompanying differences in water temperature
fluctuations (Notini et al., 1977), smaller turbulence than in nature (Steele et al., 1977;
Davies and Gamble, 1979; Alcaraz et al., 1988; Kotak and Robinson, 1991; Howarth et
al., 1993), as well as lack of large predators, differences in the habitat structure and
differences in spatial scaling (Schindler, 1998). The spatial scale affects the physical
models in two major ways: species inclusion or exclusion, and potential artefacts
associated with containerisation (Perez, 1995). An understanding of both fundamental
scaling effects and direct consequences of the enclosure is crucial to the comparative
analysis of processes among ecosystems, and to extrapolation of results from experimental to natural ecosystems (Petersen et al., 1997).
The importance of biotic factors, relative to the abiotic, for structuring the mesocosm
fauna is not easily established. According to Kautsky and Van der Maarel (1990) abiotic
factors such as light, bottom type and wave exposure determine the zonation pattern in
the Baltic Sea, while biotic interactions are said to be of less importance. The biotic part
of the artificial ecosystem will on the other hand always deviate from the mother system
because of the possible taxonomic and structural simplicity and the differences in the
physical and chemical environment causing differences in fundamental ecological
processes (Schindler, 1998). Differences between species and individuals in their
capability to adapt to an artificial ecosystem, i.e. whether organisms that thrive in
mesocosms are representative of those that do not (Lawton, 1995), further complicate
the picture.
In the field Theodoxus fluviatilis has normally moved down to deeper waters in
November (Skoog, 1971; Wallentinus, 1991), while such migrations never can be any
alternative in the mesocosms. Thus unrealistically high abundances and biomasses of
this species could be demonstrated in the mesocosms in autumn. The pond snail,
Lymnaea, is subjected to similar prevented migration as Theodoxus, but the similarity
with the field for this species suffers in addition from the fact that Lymnaea never
reaches such high abundances and biomasses in the mother system as the snail did in the
stagnant ‘pond-like’ mesocosms. Another evident difference between the field and the
mesocosms is that the reproduction of many species is hampered or prevented in the
mesocosms (e.g. Idotea, Mytilus and Gammarus). There are just a small amount of
juveniles of these species present in the mesocosms, probably because of the lack of the
green algal belt, which serves as a nursery ground by offering food and protection
(Jansson, 1967, 1970; Fagerholm, 1975; Haage, 1975; Kangas et al. 1982) and severe
nutrient limitation caused by the low water flow-rate. Mytilus-larvae, both those present
in the incoming water and those descended from the mesocosm adults, may also fail to
settle because of the low degree of water movements, which normally serves as a
stimulus to settling (Bakke, 1990) and the larvae may thus just be flushed out from the
systems.
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P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
4.3. Concluding remarks
A thorough investigation of mesocosm performance is a challenging task, but due to
the large amount of resources necessary for its implementation, it is very hard to verify
mesocosm replicability, repeatability and realism in a satisfying way. Therefore it is easy
to understand that most data for such studies are more or less achieved as ‘by-products’
from other experiments with different scopes. According to Crane (1997) priorities for
further research into mesocosms and microcosms should be the extent to which results
can be repeated and reproduced, and the level of precision and accuracy of predictions
from model ecosystems to natural ecosystems. Without empirical evidence of the
repeatability and predictive ability of results from a model ecosystem study, a risk
assessor has no objective base for evaluation of the data and may come to decisions that
are potentially disastrous for the environment. Ten years ago Pilson (1990) stressed the
need for investigations of the ecological realism of mesocosms and pointed out that he
had not found a completely satisfactory comparison of the behaviour of any mesocosm
with that of the ecosystem from which it was derived. Still today his concern can only be
repeated.
A restricted degree of replicability is much easier dealt with than a poor repeatability
or a questionable realism. Of course replicates will never be and do not have to be
identical. Replicability just reflects the variability between whatever is being sampled
and the best action to prepare for future successful statistical analyses is simply to try to
increase the number of replicates. If the effects of the studied disturbances (also low
level) in turn are large and beyond any doubt, we do not have much of a problem. This
is definitely not the case for BHB-mesocosms, as will be proved by a follow-up paper on
pollutants tested 1989–1992 (Kraufvelin, unpublished data). Regarding repeatability and
realism experimenters have already lost so much in control compared to just dealing
with replicability, mostly due to the addition of inter-annual variability and inclusion of
natural field processes to the already present basin-to-basin variability and basin effects,
that the problems easily fall out of hand. Therefore more research into mesocosm
repeatability and realism is definitely needed, although such investigations ultimately
might show that currently popular model ecosystem approaches, experimental designs or
measurement endpoints are incapable of producing sufficiently accurate and precise
results for ecological risk assessors (Crane, 1997). Many measurement endpoints may
for example be so heavily dependent upon initial conditions, such as time of the start of
the treatment (Jenkins and Buikema, 1990; Winner et al., 1990), successional stage of
the system (Taub et al., 1991) or chance events (Kraufvelin, 1998) that experimental
results are unpredictable. At such occasions it would make little sense to proceed to the
stage of predictive validation, and these test systems, methods or endpoints should be
abandoned (Crane, 1997).
Acknowledgements
I am indebted to Anders Isaksson for his always critical comments on my manuscripts
¨
and for excellent statistical advice. I also thank Ismo Holopainen, Erkki Leppakoski,
and
P. Kraufvelin / J. Exp. Mar. Biol. Ecol. 240 (1999) 229 – 258
255
Paul Somerfield for valuable comments on the text and two anonymous referees, who
repeatedly reviewed the paper and helped me to improve it. Tove Holm assisted in the
field and carried out analyses of macrofauna samples in 1996, which is gratefully
acknowledged. The paper is a (late?) contribution to the late Graduate School
‘Integrated Aquatic Hazard Assessment’ established and abandoned by the Academy of
Finland and the Finnish Ministry of Education. Societas Pro Fauna et Flora Fennica
provided financial support for the field work in 1996.
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