L
Journal of Experimental Marine Biology and Ecology, 240 (1999) 229–258
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)
Cerastoderma glaucum (realism). Finally the factors and processes restricting mesocosm
per-formance 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).
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 experimen-tal 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 3
(i.e. artificial ecosystems bigger than 1 m ) 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.
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 conse-quences 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
(608139N, 228069E). The mesocosms consisted of 11–15 (depending on year) circular
3
outdoor basins (volume 8 m ) equipped with a flow-through system of brackish water (salinity 6‰), which was pumped unfiltered to the mesocosms from a nearby bay (8 m
21
of depth). The mean flow rate into each mesocosm was 168 l h . 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
meso-cosms in June (1990: early July, 1992: late May) and the mesomeso-cosms 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 transplanta-tion 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
¨
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.
˚
oceanicus Segerstrale and G. zaddachi Sexton; Idotea spp., i.e. I. baltica (Pallas) and I.
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).
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).
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 A B C D E F G H I
Cerastoderma 0.46 3.89 0.11 0.45 – 16.19 4.37 150.38 2.93
Gammarus spp. 65.46 8.85 8.41 10.50 6.96 9.02 3.49 6.54 9.81
Gasterosteus – 0.83 0.24 0.26 – – – 0.52 –
Idotea spp. 51.86 8.30 4.55 2.35 0.90 37.77 2.20 6.10 0.69
Lymnaea spp. 6.18 0.38 1.43 – – 24.28 6.04 70.32 33.30
Mytilus 22.05 42.17 32.06 33.79 65.70 80.47 91.51 184.77 205.35
Palaemon 0.46 0.22 0.23 0.77 – – 0.20 – 0.46
Theodoxus 200.49 230.64 464.05 387.55 171.83 277.13 383.00 209.47 210.81
Total 346.96 295.28 511.08 435.67 245.39 444.86 490.81 628.10 463.35
(b) Biomass in mesocosms A B C D E F G H I
Cerastoderma 0.092 0.118 0.014 0.057 – 0.529 0.358 3.101 0.149
Gammarus spp. 3.846 0.379 0.511 0.804 0.435 0.460 0.258 0.260 0.748
Gasterosteus – 0.231 0.051 0.083 – – – 0.076 –
Idotea spp. 3.455 0.574 0.361 0.157 0.128 2.205 0.065 0.146 0.061
Lymnaea spp. 1.531 0.128 0.070 – – 1.795 1.020 4.078 5.902
Mytilus 6.126 10.732 9.077 12.568 15.509 14.155 18.850 5.336 21.610
Palaemon 0.203 0.137 0.126 0.625 – – 0.294 – 0.356
Theodoxus 6.452 6.349 9.062 7.770 4.123 5.380 7.659 3.393 5.142
Total 21.706 18.648 19.272 22.063 20.194 24.523 28.504 16.389 33.970
(c) Abundance in the field N89 S90 N90 S91 N91 S92 N92
Cerastoderma 0.65 – – – 2.07 1.28 1.50
Gammarus spp. 26.77 179.73 14.19 57.29 86.53 302.20 42.75
Idotea spp. 4.85 36.18 1.02 6.60 1.38 8.56 1.67
Mytilus 37.40 78.32 58.61 44.71 100.53 151.94 111.55
Palaemon – – – 3.70 – – –
Theodoxus 2.01 187.35 8.58 4.05 7.92 25.12 2.27
Total 71.68 481.59 82.40 116.35 198.42 489.10 159.75
(d) Biomass in the field N89 S90 N90 S91 N91 S92 N92
Cerastoderma 0.008 – – – 0.015 0.034 0.025
Gammarus spp. 0.346 1.029 0.432 0.182 2.039 1.963 0.374
Idotea spp. 0.240 1.115 0.015 0.240 0.199 0.337 0.233
Mytilus 4.058 10.436 10.136 7.728 7.189 4.684 4.036
Palaemon – – – 2.532 – – –
Theodoxus 0.010 5.300 0.141 0.088 0.177 – 0.025
Total 4.660 17.880 10.724 10.770 9.619 7.418 4.693
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, P50.038
for both abundance and biomass) and the test of ecological realism (Table 4, P50.018
for both). For the replicability test (Table 2) only one out of eight ‘significant’ P-values
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
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
(d 532.80 for abundance andd 531.70 for biomass data), F and G 1991 (d 528.28 for
i i i
abundance data) and H and I 1992 (d 530.60 for abundance data), whereas C and D
i
1990 were the most similar of all parallel controls (d 519.45 for abundance data), which
Table 2
One-way ANOSIM test of differences in community structure (square-root transformed abundance and a
fourth-root transformed biomass data) between parallel controls 1989–1992
Controls Statistical Possible Significant Significance Adjusted
compared value permutations statistics level sign. level
Abundance
A–B 1989 0.511 462 1 0.002 ** 0.024 *
C–D 1990 20.002 126 55 0.437 0.714
C–E 1990 0.612 126 2 0.016 * 0.198
D–E 1990 0.244 126 11 0.087 0.348
F–G 1991 0.740 126 1 0.008 ** 0.088
H–I 1992 0.664 126 1 0.008 ** 0.088
Biomass
A–B 1989 0.492 462 4 0.009 ** 0.088
C–D 1990 0.072 126 30 0.238 0.714
C–E 1990 0.432 126 4 0.032 * 0.198
D–E 1990 0.064 126 33 0.262 0.714
F–G 1991 0.304 126 4 0.032 * 0.198
H–I 1992 0.628 126 1 0.008 ** 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
a transformed biomass data
Variable Global R Permutations Number of Uncorrected Corrected
sign. statistics P-value P-value
Abundance 0.444 1260 38 0.030 * 0.038 *
Biomass 0.533 1260 24 0.019 * 0.038 *
a
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
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.
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
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 a
between mesocosms and the mother system at the end of experimental periods 1989–1992
Variable Global R Permutations Number of Uncorrected Corrected
sign. statistics P-value P-value
Abundance 1.000 108 1 0.009 ** 0.018 *
Biomass 0.772 108 1 0.009 ** 0.018 *
a
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 d 89AB d 90CD d 90CE d 90DE d 91FG d 92HI
i i i i i i
(a) Abundance
Cerastoderma 2.16 0.60 0.27 0.55 3.47 11.19
Gammarus 7.67 2.37 2.11 2.94 2.29 1.21
Gasterosteus 0.76 0.66 0.57 0.38 – 0.52
Idotea 6.83 2.12 2.70 2.03 6.76 2.12
Lymnaea 2.62 1.63 1.81 – 3.59 3.01
Mytilus 5.53 3.92 4.10 6.55 5.43 5.97
Palaemon 0.71 1.00 0.36 1.17 0.22 0.42
Theodoxus 6.53 7.15 15.20 12.74 6.53 6.16
Totald % 32.80 19.45 27.12 26.36 28.28 30.60
(b) Biomass
Cerastoderma 3.55 1.76 0.93 – 4.00 5.05
Gammarus 4.62 2.13 1.76 2.92 2.87 2.38
Gasterosteus 2.06 2.61 2.22 1.55 – 1.86
Idotea 5.05 3.74 5.32 4.06 7.77 2.94
Lymnaea 5.06 3.84 4.23 – 3.42 1.42
Mytilus 7.27 4.79 3.50 6.63 5.05 4.36
Palaemon 2.65 4.12 1.60 4.46 – 2.59
Theodoxus 1.44 0.66 3.46 3.11 1.96 2.16
Totald % 31.70 23.65 23.03 24.17 26.41 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
Ceras-toderma 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
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 d 89–90 d 89–91 d 89–92 d 90–91 d 90–92 d 91–92
i i i i i i
(a) Abundance
Cerastoderma 1.67 2.96 7.19 3.54 8.32 6.22
Gammarus 5.05 4.95 3.81 2.28 1.66 1.63
Gasterosteus 0.62 0.35 0.52 0.26 0.43 0.26
Idotea 6.07 4.99 4.73 4.37 1.73 3.53
Lymnaea 1.79 3.97 7.75 4.74 9.09 4.42
Mytilus 5.23 6.16 10.46 5.66 9.71 6.98
Palaemon 0.72 0.47 0.52 0.47 0.54 0.32
Theodoxus 9.36 7.31 6.40 7.44 8.89 7.01
Totald % 30.51 31.16 41.39 28.76 40.37 30.38
(b) Biomass
Cerastoderma 3.07 3.56 4.90 4.69 7.09 4.02
Gammarus 3.13 3.88 2.99 2.84 2.07 2.45
Gasterosteus 1.89 1.05 1.69 1.06 1.71 0.98
Idotea 5.76 5.07 5.24 4.92 3.10 4.27
Lymnaea 3.79 5.85 8.43 7.32 11.07 4.16
Mytilus 6.17 5.79 5.30 4.67 4.05 3.96
Palaemon 2.81 2.06 2.33 2.20 2.53 1.81
Theodoxus 1.87 1.59 2.16 1.83 2.62 2.27
Totald % 28.49 28.85 33.04 29.53 34.24 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 transplanta-tions (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
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 (totald %558.54) and (b) fourth-root transformed biomass data (totald %544.73)
(a) Abundance
Species Average Average Average Ratio Percent Cumulative
abundance abundance term percent
field mesocosm d
i
Cerastoderma 0.88 19.11 3.88 0.72 6.63 98.67
Gammarus 37.90 15.31 6.02 1.23 10.29 76.37
Idotea 2.41 13.48 4.58 1.05 7.82 92.04
Lymnaea 0.00 15.24 4.60 0.96 7.86 84.22
Mytilus 69.10 81.99 8.68 1.36 14.83 66.08
Palaemon 0.00 0.26 0.45 0.45 0.77 99.44
Theodoxus 5.37 278.85 30.00 2.54 51.25 51.25
(b) Biomass
Average Average
biomass biomass
field mesocosm
Cerastoderma 0.01 0.47 4.48 1.06 10.02 85.96
Gammarus 0.73 0.91 3.35 1.09 7.49 93.45
Idotea 0.16 0.85 5.89 1.28 13.16 75.94
Lymnaea 0.00 1.58 6.99 1.09 15.62 47.94
Mytilus 6.63 12.48 6.64 1.34 14.83 62.78
Palaemon 0.00 0.19 1.84 0.47 4.12 97.57
Theodoxus 0.09 6.16 14.46 2.66 32.33 32.33
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
Fig. 7. NMDS-ordination of Fucus macrofauna field samples from 1991 after square-root transformation of abundance data (stress 0.13).
Fig. 8. NMDS-ordination of Fucus macrofauna field samples from 1991 after fourth-root transformation of biomass data (stress 0.13).
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 Mean 6S.D. Mean 6S.D.
samples present abundance biomass g
Acarina 1 0.06 0.50 ,0.01 ,0.01
Balanus improvisus 43 226.08 371.15 11.78 20.17
Cerastoderma sp. 7 0.14 0.54 ,0.01 0.01
Chironomidae 64 1030.94 766.34 0.32 0.51
Cottus gobio 1 0.02 0.13 0.01 0.04
Gammarus spp. 64 237.66 207.39 0.70 0.57
Gasterosteus aculeatus 2 0.03 0.18 0.01 0.07
Hydrobia sp. 35 14.38 23.08 0.10 0.16
Idotea baltica 35 2.26 3.51 0.29 0.46
Idotea chelipes 48 4.54 5.12 0.10 0.11
Jaera albifrons 48 28.75 35.40 0.03 0.04
Lymnaea peregra 16 0.28 0.56 0.07 0.15
Macoma baltica 1 0.02 0.13 ,0.01 ,0.01
Mytilus edulis 38 68.10 143.14 2.68 4.69
Nereis diversicolor 1 0.02 0.13 ,0.01 ,0.01
Palaemon adspersus 12 0.19 0.47 0.18 0.53
Praunus inernis 13 0.23 0.59 ,0.01 ,0.01
Pungitius pungitius 1 0.02 0.13 ,0.01 0.03
Theodoxus fluviatilis 51 66.28 72.72 1.88 1.83
Tricoptera 2 0.03 0.18 ,0.01 ,0.01
Zygoptera 3 0.05 0.21 ,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 environmen-tal (or economical) damage (Crane, 1997). When in turn the replicability is poor only extreme differences can be detected (Kraufvelin, 1998).
meso-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 Mean CV 95% CI
Cerastoderma abundance 104.2 91.4–117.1 112.4 89.8–135.0
Cerastoderma biomass 96.0 77.2–114.8 80.9 14.4–147.4
Chironomidae abundance 18.0 14.8–21.1
Chironomidae biomass 39.2 30.2–48.1
Gammarus abundance 21.8 14.4–29.1 54.8 15.9–93.7
Gammarus biomass 17.1 10.6–23.6 64.4 27.6–101.2
Gasterosteus abundance 149.6 118.6–180.6 91.3 25.7–156.9
Gasterosteus biomass 150.0 119.2–180.8 89.6 29.4–149.8
Hydrobia abundance 34.8 25.8–43.9
Hydrobia biomass 39.2 29.5–48.9
a
Idotea baltica abundance 31.4 22.5–40.2 104.3 79.4–129.2
a
Idotea baltica biomass 33.2 25.7–40.7 76.1 26.3–125.9
a
Idotea chelipes abundance 26.8 15.6–38.0 104.3 79.4–129.2
a
Idotea chelipes biomass 30.1 19.0–41.2 76.1 26.3–125.9
Jaera abundance 26.9 18.7–35.1
Jaera biomass 28.3 20.7–35.9
Lymnaea abundance 64.1 40.2–88.0 108.3 56.7–159.9
Lymnaea biomass 73.1 54.1–92.0 96.0 16.9–175.1
Mytilus abundance 47.8 34.9–60.7 26.4 5.8–47.0
Mytilus biomass 41.7 34.0–49.4 42.9 14.0–71.8
Palaemon abundance 64.3 53.2–75.4 113.7 71.0–156.4
Palaemon biomass 69.2 52.4–86.0 110.1 57.3–162.9
Praunus abundance 57.8 39.0–76.5
Praunus biomass 64.0 34.0–94.0
Theodoxus abundance 22.1 16.8–27.4 19.4 0.8–38.0
Theodoxus biomass 21.9 16.3–27.6 22.7 7.4–38.0
Tricoptera abundance 132.8 107.5–158.1
Total abundance 9.4 6.5–12.3 18.5 6.6–30.4
b
Total biomass 32.4 23.7–41.1 18.6 23.2–40.9
Number of species 12.6 9.9–15.4
b ´
Margalefs species richness, abundance data 13.8 10.6–17.0 15.4 2.8–27.8 b
Shannon–Wiener diversity, abundance data 12.5 8.3–16.7 31.6 23.7–39.5 b
´
Pielous evenness, abundance data 12.5 9.3–15.7 39.7 24.9–54.5
a
Idotea baltica and Idotea chelipes were pooled 1989–1992.
b
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 methodolo-gy, 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.
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 bladder-wrack 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
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 BHB-mesocosms 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-¨
ally were gathered from another mother system (Utterholm instead of Havero), has the 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
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 experimen-tal 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
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 ¨
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.
References
Adey, W.H., Loveland, K., 1991. Dynamic Aquaria: Building Living Ecosystems, Academic Press, San Diego, pp. 1–643.
´
Alcaraz, M., Saiz, E., Marrase, C., Vaque, D., 1988. Effects of turbulence on the development of phytoplankton biomass and copepod populations in marine microcosms. Mar. Ecol. Prog. Ser. 49, 117–125.
¨
Anders, K., Moller, H., 1983. Seasonal fluctuations in macrobenthic fauna of the Fucus belt in Kiel Fjord (western Baltic Sea). Helgol. Meeresunters. 36, 277–283.
Bakke, T., 1990. Benthic mesocosms: II. Basic research in hard-bottom benthic mesocosms. In: Lalli, C.M. (Ed.), Enclosed Experimental Marine Ecosystems: A Review and Recommendations. Coastal and Estuarine Studies, Vol. 37, Springer-Verlag, Heidelberg, pp. 122–135.
Boyle, T.P., Fairchild, J.F., 1997. The role of mesocosm studies in ecological risk analysis. Ecol. Appl. 7, 1099–1102.
Brazner, J.C., Heinis, L.J., Jensen, D.A., 1989. A littoral enclosure for replicated field experiments. Environ. Toxicol. Chem. 8, 1209–1216.
Cairns, J., McCormick, P.V., 1991. The use of community- and ecosystem-level end points in environmental hazard assessment: a scientific and regulatory evaluation. Environ. Auditor 2, 239–248.
Carpenter, S.R., 1996. Microcosm experiments have limited relevance for community and ecosystem ecology. Ecology 77, 677–680.
Carr, M.R., 1996. In:PRIMERUser Manual, Ver 4.0, Plymouth Marine Laboratory, Plymouth, UK, pp. 1–42.
Chen, C.-C., Petersen, J.E., Kemp, W.M., 1997. Spatial and temporal scaling of periphyton growth on walls of estuarine mesocosms. Mar. Ecol. Prog. Ser. 155, 1–15.
Clark, J.R., Cripe, C.R., 1993. Marine and estuarine multi-species test systems. In: Calow, P. (Ed.), Handbook of Ecotoxicology, Vol. I, Blackwell Scientific, Oxford, pp. 227–247.
Clarke, K.R., 1993. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143.
Clarke, K.R., 1999. Nonmetric multivariate analysis in community-level ecotoxicology. Environ. Toxicol. Chem. 18, 118–127.
Clarke, K.R., Green, R.H., 1988. Statistical design and analysis for a ‘biological effects’ study. Mar. Ecol. Prog. Ser. 46, 213–226.
Clarke, K.R., Warwick, R.M., 1994. In: Changes in Marine Communities: an Approach To Statistical Analysis and Interpretation, Plymouth Marine Laboratory, Plymouth, UK, pp. 1–144.
Crane, M., 1997. Research needs for predictive multispecies tests in aquatic toxicology. Hydrobiologia 346, 149–155.
Crossland, N.O., La Point, T.W., 1992. The design of mesocosm experiments. Environ. Toxicol. Chem. 11, 1–4.
Davies, J.M., Gamble, J.C., 1979. Experiments with large enclosed ecosystems. Phil. R. Soc. Lond. B 286, 523–544.
Day, R.W., Quinn, G.P., 1989. Comparison of treatments after an analysis of variance in ecology. Ecol. Monogr. 59, 433–463.
˚
Fagerholm, H.P., 1978. The effects of ferry traffic (artificial wave action) on the rocky shore macrofauna in the
˚
southern Aland archipelago in the northern Baltic: 2. The Fucus zone (a quantitative study). Kieler Meeresforsch. Sonderheft 4, 130–137.
Fairweather, P.G., 1991. Statistical power and design requirements for environmental monitoring. Aust. J. Mar. Freshwater Res. 42, 555–567.
Gamble, J.C., 1990. Mesocosms: statistical and experimental design considerations. In: Lalli, C.M. (Ed.), Enclosed Experimental Marine Systems: A Review and Recommendations. Coastal and Estuarine Studies, Vol. 37, Springer-Verlag, Heidelberg, pp. 188–196.
Gearing, J.N., 1989. The role of aquatic microcosms in ecotoxicologic research as illustrated by large marine systems. In: Levin, S.A., Harwell, M.A., Kelly, J.R., Kimball, K.D. (Eds.), Ecotoxicology: Problems and Approaches, Springer-Verlag, Heidelberg, pp. 188–196.
Giesy, J.P., Odum, E.P., 1980. Microcosmology: introductory comments. In: Giesy, J.P. (Ed.), Microcosms in Ecological Research. Symposium Series 52 (Conf-781101), US Department of Energy, Washington DC, pp. 1–13.
Giesy, J.P., Allred, P.M., 1985. Replicability of aquatic multispecies test systems. In: Cairns, J. (Ed.), Multispecies Toxicity Testing, Pergamon Press, New York, pp. 18–24.
Haage, P., 1975. Quantitative investigations of the Baltic Fucus belt macrofauna. 2. Quantitative seasonal ¨
fluctuations. In: Contr. Asko Lab. No. 9, University of Stockholm, pp. 1–88.
Heimbach, F., Pflueger, W., Ratte, H.-T., 1992. Use of small artificial ponds for assessment of hazards to aquatic ecosystems. Environ. Toxicol. Chem. 11, 27–34.
Holm, S., 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70.
Howarth, R.W., Butler, T., Lunde, K., Swaney, D., Chu, C.R., 1993. Turbulence and planktonic nitrogen fixation — a mesocosm experiment. Limnol. Oceanogr. 38, 1696–1711.
Hurlbert, S.H., 1984. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211.
¨
Jansson, A.-M., 1967. The food-web of the Cladophora-belt fauna. Helgolander Wiss. Meeresunters. 15, 574–588.
Jansson, A.-M., 1970. Production studies in the Cladophora-belt. Thalassia Jugosl. 6, 143–155.
Jenkins, D.G., Buikema, A.L., 1990. Response of a winter plankton food web to simazine. Environ. Toxicol. Chem. 9, 693–705.
Jenkins, D.G., Buikema, A.L., 1998. Do similar communities develop in similar sites? A test with zooplankton structure and function. Ecol. Monogr. 68, 421–443.
˚
¨
Kangas, P., Autio, H., Hallfors, G., Luther, H., Niemi, A., Salemaa, H., 1982. A general model of the decline ¨
of Fucus vesiculosus at Tvarminne, south coast of Finland in 1977–81. Acta Bot. Fennica 118, 1–27. Kautsky, H., 1991. Influence of eutrophication on the distribution of phytobenthic plant and animal
communities. Int. Rev. Ges. Hydrobiol. 76, 423–432.
Kautsky, H., Van der Maarel, E., 1990. Multivariate approaches to the variation in phytobenthic communities and environmental vectors in the Baltic Sea. Mar. Ecol. Prog. Ser. 60, 169–184.
Kautsky, H., Kautsky, L., Kautsky, N., Kautsky, U., Lindblad, C., 1992. Studies on the Fucus vesiculosus community in the Baltic Sea. Acta Phytogeogr. Suec. 78, 33–48.
Kedwards, T.J., Maund, S.J., Chapman, P.F., 1999a. Community level analysis of ecotoxicological field studies: I. Biological monitoring. Environ. Toxicol. Chem. 18, 149–157.
Kedwards, T.J., Maund, S.J., Chapman, P.F., 1999b. Community level analysis of ecotoxicological field studies: II. Replicated-design studies. Environ. Toxicol. Chem. 18, 158–166.
Kennedy, J.H., Johnson, Z.B., Wise, P.D., Johnson, P.C., 1995. Model aquatic ecosystems in ecotoxicological research: considerations of design, implementation, and analysis. In: Hoffman, D.J., Rattner, B.A., Burton, G.A., Cairns, J. (Eds.), Handbook of Ecotoxicology, Lewis Publishers, Boca Raton, FL, pp. 117–161. Kolding, S., 1981. Habitat selection and life cycle characteristics of five species of the amphipod genus
Gammarus in the Baltic. Oikos 37, 173–178.
Kotak, B.G., Robinson, G.G.C., 1991. Artificially-induced water turbulence and the physical and biological features within small enclosures. Arch. Hydrobiol. 122, 335–349.
Kraufvelin, P., 1998. Model ecosystem replicability challenged by the ‘soft’ reality of a hard bottom mesocosm. J. Exp. Mar. Biol. Ecol. 222, 247–267.
Kuiper, J., Brockmann, U.H., van het Groenewoud, H., Hoorsman, G., Hammer, K.D., 1983. Influences of bag dimensions on the development of enclosed plankton communities during POSER. Mar. Ecol. Prog. Ser. 14, 9–17.
de Lafontaine, Y., Leggett, W.G., 1987. Evaluation of in situ enclosures for larval fish studies. Can. J. Fish. Aquat. Sci. 44, 54–65.
Lalli, C.M. (Ed.), 1990. Enclosed Experimental Marine Ecosystems: A Review and Recommendations. Coastal and Estuarine Studies, Vol. 37, Springer-Verlag, Heidelberg, pp. 1–218.
Lamont, B.B., 1995. Testing the effect of ecosystem composition / structure on its functioning. Oikos 74, 283–295.
Landis, W.G., Matthews, R.A., Matthews, G.B., 1997. Design and analysis of multispecies toxicity tests for pesticide registration. Ecol. Appl. 7, 1111–1116.
Landner, L., Blanck, H., Heyman, U., Lundgren, A., Notini, M., Rosemarin, A., Sundelin, B., 1989. Community testing, microcosm and mesocosm experiments: ecotoxicological tools with high ecological realism. In: Landner, L. (Ed.), Chemicals in the Aquatic Environment — Advanced Hazard Assessment, Springer-Verlag, Heidelberg, pp. 216–254.
Lawton, J.H., 1995. Ecological experiments with model systems. Science 269, 328–331.
Lehtinen, K.-J., Tana, J., 1992. Effects in mesocosms exposed to effluents from bleached hardwood kraft pulp mill. Publ. Fin. Natl. Board Water Environ. Adm. Series A 105, 1–146.
¨ ¨
Lehtinen, K.-J., Tana, J., Mattsson, K., Hardig, J., Karlsson, P., Grotell, C., Hemming, S., Engstrom, C., Hemming, J., 1993. Ecological impact of pulp mill effluents. Publ. Fin. Natl. Board Water Environ. Adm. Series A 133, 1–139.
¨ ¨ ¨
Lehtinen, K.-J., Tana, J., Mattsson, K., Grotell, C., Backstrom, S., Engstrom, C., Hemming, J., 1994. In: Effects in Model Ecosystems of Total Mill Effluents, Bleach Plant Effluents and Subprocess Streams from Production of ECF and TCF Pulp. MFG-report, Finnish Environmental Research Group, Esbo, pp. 1–82.
¨ ¨
Lehtinen, K.-J., Tana, J., Mattson, K., Engstrom, C., Grotell, C., Vartiainen, T., 1995. In: Kalanviljelysta
¨ ¨ ¨ ¨ ¨
aiheutuvat ymparistovaikutukset seka ekologinen perusta niiden vahentamiseksi (Environmental effects of fish farming and the ecological foundation for their decrease), Vol. 17, Kala-ja Riistahallinnon Julk, Helsinki, pp. 1–47, (in Finnish).
Lehtinen, K.-J., Mattsson, K., Tana, J., Grotell, C., 1996. Light energy as a cause of structural and functional variations in brackish water littoral mesocosms used in ecotoxicological research. Boreal Environ. Res. 33, 71–83.
¨
Lehtinen, K.-J., Mattsson, K., Tana, J., Grotell, C., Engstrom, C., 1998. Effects on ecosystem structure and function of fish farming as simulated in littoral brackish water mesocosms. Aquaculture 165, 179–202. Lundgren, A., 1985. Model ecosystems as a tool in freshwater and marine research. Arch. Hydrobiol. 70,
157–196.
Mapstone, B.D., 1996. Scalable decision criteria for environmental impact assessment: effect size, Type I and Type II errors. In: Schmitt, R.J., Osenberg, C.W. (Eds.), Detecting Ecological Impacts: Concepts and Applications in Coastal Habitats, Academic Press, San Diego, pp. 67–80.
Maund, S.J., Chapman, P.F., Kedwards, T.J., Tattersfield, L., Matthiessen, P., Warwick, R.M., Smith, E.P., 1999. Application of multivariate statistics to ecotoxicological field studies. Environ. Toxicol. Chem. 18, 111–112.
˚
¨
Notini, M., Nagell, B., Hagstrom, A, Grahn, O., 1977. An outdoor model simulating a Baltic Sea littoral ecosystem. Oikos 28, 2–9.
Osenberg, C.W., Schmitt, R.J., Holbrook, S.J., Abu-Saba, K.E., Flegal, A.R., 1996. Detection of environmental impacts: natural variability, effect size, and power analysis. In: Schmitt, R.J., Osenberg, C.W. (Eds.), Detecting Ecological Impacts: Concepts and Applications in Coastal Habitats, Academic Press, San Diego, pp. 83–108.
Perez, K.A., 1995. Role and significance of scale to ecotoxicology. In: Cairns, J., Niederlehner, B.R. (Eds.), Ecological Toxicity Testing — Scale, Complexity and Relevance, Lewis Publishers, CRC Press, Boca Raton, pp. 49–72.
Peterman, R.M., 1990. Statistical power analysis can improve fisheries research and management. Can. J. Fish. Aquat. Sci. 47, 2–15.
Pilson, M.E.Q., 1990. Application of mesocosms for solving problems in pollution research. In: Lalli, C.M. (Ed.), Enclosed Experimental Marine Systems: A Review and Recommendations. Coastal and Estuarine Studies, Vol. 37, Springer-Verlag, Heidelberg, pp. 155–168.
Pilson, M.E.Q., Oviatt, C.A., Nixon, S.W., 1980. Annual nutrient cycles in a marine microcosm. In: Giesy, J.P. (Ed.), Microcosms in Ecological Research, Technical Information Center, Springfield, VA, pp. 753–778. Rice, W.R., 1989. Analyzing tables of statistical tests. Evolution 43, 223–225.
Rosenzweig, M.S., Buikema, A.L., 1994. Phytoplankton colonization and seasonal succession in new experimental ponds. Environ. Toxicol. Chem. 13, 599–605.
Scheiner, S.M., 1993. MANOVA: multiple response variables and multispecies interactions. In: Scheiner, S.M., Gurevitch, J. (Eds.), Design and Analysis of Ecological Experiments, Chapman and Hall, New York, pp. 94–112.
Schindler, D.W., 1998. Replication versus realism: the need for ecosystem-scale experiments. Ecosystems 1, 323–334.
SETAC-Europe, 1991. Guidance document on testing procedures for pesticides in freshwater mesocosms. In: From the Workshop: A Meeting of Experts On Guidelines For Static Field Mesocosm Tests, 3–4 July 1991, Monks Wood Experimental Station, Abbots Ripton, Huntingdon, UK, pp. 1–46.
Skoog, G., 1971. Variations in the distribution of Theodoxus fluviatilis on the stony localities in the northern Baltic proper. Thalassia Jugosl. 7, 363–372.
Smith, E.P., 1995. Design and analysis of multispecies experiments. In: Cairns, J., Niederlehner, B.R. (Eds.), Ecological Toxicity Testing — Scale, Complexity and Relevance, Lewis Publishers, CRC Press, Boca Raton, pp. 73–95.
Sokal, R.R., Rohlf, F.J., 1995. Biometry, 3rd ed, W.H. Freeman, New York, pp. 1–887. Steele, J.H., 1979. The uses of experimental ecosystems. Phil. R. Soc. Lond. B 286, 583–595.
Steele, J.H., Farmer, D.M., Henderson, E.W., 1977. Circulation and temperature structure in large marine enclosures. J. Fish. Res. Board. Can. 34, 1095–1104.
Stephenson, G.L., Hamilton, P., Kaushik, N.K., Robinson, J.B., Solomon, K.R., 1984. Spatial distribution of plankton in enclosures of three sizes. Can J. Fish. Aquat. Sci. 41, 1048–1054.
Strickland, J.D.H., 1967. Between beakers and bays. New Sci. 33, 276–278.
Takahashi, M., Thomas, W.H., Seibert, D.L.R., Beers, J., Koeller, P., Parsons, T.R., 1975. The replication of biological events in enclosed water columns. Arch. Hydrobiol. 76, 5–23.
¨
Tana, J., Rosemarin, A., Lehtinen, K.-J., Hardig, J., Grahn, O., Landner, L., 1994. Assessing impacts on Baltic coastal ecosystems with mesocosm and fish biomarker tests: a comparison of new and old wood pulp bleaching technologies. Sci. Total Environ. 145, 213–234.
Taub, F.B., Kindig, A.C., Meador, J.P., Swartzman, G.L., 1991. Effects of ‘seasonal succession’ and grazing on copper toxicity in aquatic microcosms. Verh. Int. Ver. Limnol. 24, 2205–2214.
Underwood, A.J., 1996. On beyond BACI: sampling designs that might reliably detect environmental disturbances. In: Schmitt, R.J., Osenberg, C.W. (Eds.), Detecting Ecological Impacts: Concepts and Applications in Coastal Habitats, Academic Press, San Diego, pp. 151–175.
Underwood, A.J., 1997. Experiments in Ecology: Their Logical Design and Interpretation Using Analysis of Variance, Cambridge University Press, Cambridge, UK, pp. 1–504.
Underwood, A.J., Peterson, C.H., 1988. Towards an ecological framework for investigating pollution. Mar. Ecol. Prog. Ser. 46, 227–234.
Van den Brink, P.J., Ter Braak, C.J.F., 1998. Multivariate analysis of stress in experimental ecosystems by Principal Response Curves and similarity analysis. Aquat. Ecol. 32, 163–178.
Van den Brink, P.J., Ter Braak, C.J.F., 1999. Principal response curves: analysis of time-dependent multivariate responses of biological community to stress. Environ. Toxicol. Chem. 18, 138–148.
Wallentinus, I., 1991. The Baltic Sea Gradient. In: Niemhuist, D.H., Mathieson, A.C. (Eds.), Intertidal and Littoral Ecosystems. Ecosystems of the World, Vol. 24, Elsevier Science, Amsterdam, pp. 83–108. Winner, R.W., Owen, H.A., Moore, M.V., 1990. Seasonal variability in the sensitivity of freshwater lentic
