Corresponding author: Michele Mistri (
Academic editor: L. Capotondi
In benthic studies, the identification of organisms at the species level is known to be the best source for ecological and biological information even if time-consuming and expensive. However, taxonomic sufficiency (
Pitacco V, Mistri M, Infantini V, Sfriso A, Sfriso AA, Munari C (2019) Benthic studies in
Taxonomic sufficiency (
In particular,
That being said, the applications of
This is mainly due to the lack of long-term monitoring programmes based on the description of variables at the species level (
At the same time, both sites are heavily affected by human pressures, mainly related to agricultural and aquacultural activities. The Valli di Comacchio is mostly affected by eel aquaculture and Sacca di Goro is subjected to intense bivalve fishing. Moreover, both are sites of eutrophication caused by excessive nutrient loads (
The aim of the present study was, therefore, to test the efficiency of
The Valli di Comacchio (Figure
Sacca di Goro (Figure
Physicochemical composition of the two
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100 | 26 |
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0.5–1.5 | 1.2–1.5 |
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115 | 5 |
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10.9–40.1 | 6–30 |
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2.0–30.5 | 2.0–33.0 |
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37.5 ± 5.5 † | 44 ± 4.6 † |
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44.3 ± 6 † | 24.7 ± 2.6 † |
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37.9 ± 5.9 † | 59.5 ± 0.2 † |
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17.8 ± 3 † | 17 ± 3 † |
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† Yearly averaged values, ‡ estimated by loss on ignition.
In the Valli di Comacchio lagoons (
In the Sacca di Goro lagoon (
Three replicates were collected seasonally for macrofaunal community analysis using a 4l Van Veen grab. For the scope of the present work, averages of the replicates were considered and sampling stations were used as replicates in order to obtain a global picture of the general status of each lagoon. These samples were sieved at 0.5 mm and preserved in 8% formalin. Animals were carefully sorted, identification was performed up to the species level in most cases (exceptions were due to the poor condition of the animals) and all specimens were counted.
Map of the studied sites.
At each of the
For each of the two
To test the effect of different data transformations on the effectiveness of taxonomic sufficiency, a third-stage resemblance matrix was built. This third-stage resemblance matrix, defined as a second second-stage resemblance matrix, constructed using rank correlations between corresponding elements in the set of second-stage matrices following
Identifying temporal changes in macrobenthic communities is fundamental for the efficiency of monitoring programmes. Therefore in order to identify breakpoints in each multivariate dataset, “Constrained Clustering Analysis” was performed on each of the following six matrices: species, genus, family, order, class and phylum. This technique, originally developed for stratigraphic analysis, is more suitable for time-series analysis than for ordinary unconstrained cluster analysis, since only adjacent clusters, according to sample order, are considered for merging. The Bray-Curtis similarity was calculated on the square-root transformed data and the CONISS algorithm, which relies on the incremental sum of squares (
In order to test the significance of variations in taxa-abundance matrices between identified clusters, permutational multivariate analysis of variance, PERMANOVA (
The Valli di Comacchio (
In contrast, the Sacca di Goro (
Variation in diversity indices across the study period at the two
At the
At the
‘Loss of information α’ from lower to higher taxonomic levels (NT1-NT6) at the two
The ordination of similarity matrices in second-stage MDS plots (Figure
For the
For the
Second-stage MDS ordination of resemblance matrices derived from species, genus, family, order and phylum abundance data at the two
Spearman correlations (rs) resulting from the third-stage correlation matrix, showing the effect of data transformation on differences between aggregation matrices.
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None | Square root | Logarithm | |
Square-root | 0.975 | ||
Logarithm | 0.932 | 0.907 | |
Presence/absence | 0.686 | 0.632 | 0.854 |
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None | Square root | Logarithm | |
Square-root | 0.568 | ||
Logarithm | 0.254 | 0.436 | |
Presence/absence | 0.333 | 0.361 | 0.861 |
In the Valli di Comacchio, constrained cluster analysis, based on square-root transformation (Figure
Constrained cluster analysis of macrobenthic data on Valli di Comacchio lagoon, aggregated to different taxonomic levels.
Constrained cluster analysis of macrobenthic data on Sacca di Goro lagoon, aggregated to different taxonomic levels.
The results of the PERMANOVAs and PERMDISPs applied at different levels of taxonomic resolution and data transformation on all datasets are summarised in Table
Regarding the Sacca di Goro dataset, PERMANOVA highlighted significant differences (
Significance of cluster groups (PERMANOVA), percentage of significant pairwise combinations between those groups and the significance of differences in dispersion between cluster groups (PERMDISP).
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None | 9 | PERMANOVA p-value | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0003 |
Significant pairwise combinations | 81% | 81% | 81% | 78% | 56% | 36% | |||
PERMDISP p-value | 0.001 | 0.001 | 0.001 | 0.001 | 0.016 | 0.004 | |||
Square root | 7 | PERMANOVA p-value | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0005 | |
Significant pairwise combinations | 81% | 86% | 86% | 71% | 57% | 38% | |||
PERMDISP p-value | 0.001 | 0.002 | 0.006 | 0.015 | 0.064 | 0.005 | |||
Logarithm | 8 | PERMANOVA p-value | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0004 | |
Significant pairwise combinations | 96% | 96% | 93% | 82% | 86% | 54% | |||
PERMDISP p-value | 0.005 | 0.019 | 0.017 | 0.113 | 0.189 | 0.011 | |||
Presence-absence | 6 | PERMANOVA p-value | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0697 | |
Significant pairwise combinations | 93% | 93% | 80% | 67% | 80% | 27% | |||
PERMDISP p-value | 0.220 | 0.253 | 0.191 | 0.233 | 0.022 | 0.002 | |||
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None | 4 | PERMANOVA p-value | 0.08 | 0.08 | 0.11 | 0.14 | 0.08 | 0.13 |
Significant pairwise combinations | 50% | 50% | 50% | 33% | 33% | 33% | |||
PERMDISP p-value | 0.299 | 0.291 | 0.372 | 0.153 | 0.401 | 0.463 | |||
Square root | 4 | PERMANOVA p-value | 0.01 | 0.01 | 0.02 | 0.02 |
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0.10 | |
Significant pairwise combinations | 50% | 50% | 50% | 33% | 50% | 33% | |||
PERMDISP p-value | 0.004 | 0.001 | 0.008 | 0.002 | 0.044 | 0.061 | |||
Logarithm | 5 | PERMANOVA p-value | 0.001 | 0.003 | 0.004 | 0.03 | 0.04 | 0.11 | |
Significant pairwise combinations | 50% | 50% | 50% | 20% | 0% | 0% | |||
PERMDISP p-value | 0.001 | 0.001 | 0.002 | 0.011 | 0.011 | 0.001 | |||
Presence-absence | 5 | PERMANOVA p-value | 0.003 | 0.01 | 0.01 | 0.02 | 0.04 | 0.28 | |
Significant pairwise combinations | 30% | 20% | 20% | 10% | 10% | 0% | |||
PERMDISP p-value | 0.001 | 0.002 | 0.001 | 0.308 | 0.090 | 0.025 |
The macrobenthic communities at both
Information loss, in terms of the percentage of ‘α’, was reasonably low for both datasets from both species-to-genus (< 20%) and genus-to-family levels (< 40%), despite the higher variability of the taxonomic complexity at the
Variations in the general structure of the macrobenthic community (multivariate analyses) were maintained with reasonably low information loss, considering both location and dispersion components, from species to genus and from species to family levels, almost irrespective of the data transformation, in both datasets (as shown by MDS, Spearman’s correlation, hierarchical clustering, PERMANOVA and PERMDISP analyses). However, the response to
Although loss of information about the structure of the benthic assemblages increased with decreasing taxonomic accuracy at the
A review of the current literature on taxonomic sufficiency (Table
The choice of data transformation required particular attention. It is well known that data transformation can influence the results of consequent analyses to a similar extent to the choice of taxonomic resolution (
Taxonomic sufficiency documented for natural and anthropogenically induced benthic patterns.
Habitat | Area | Taxonomic group | Sufficient taxonomic level | Effect of data transformation | Reference |
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Marine water, intertidal to subtidal |
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Macro and meiobenthos | Family for meio, phylum for macrobenthos | Not considered |
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Marine waters, 0–3000 m |
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Macrobenthic invertebrates | Family or phylum | Not considered |
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Transitional waters, estuarine |
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Macrobenthic invertebrates | Family (1 mm mesh), species (0.5 mm mesh) | Not considered |
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Coastal waters, 14–380 m |
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Macrobenthic invertebrates | Family | Increased from order to higher levels, no effect for family |
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Coastal waters |
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Macrobenthic invertebrates | Family | Not strong till family level |
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Coastal waters, 65-380 m |
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Macrobenthic invertebrates | Family | Evident complex effect |
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Coastal water, fjord |
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Macrobenthic invertebrates | Family | Not strong |
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Coastal water |
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Macro and meiobenthos | Family (except nematodes) | Not considered |
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Coastal water, fine sand |
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Polychaetes, crustaceans, molluscs | Phyla (from genera) | Effect of fourth root only at phylum level |
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Transitional waters, lagoons |
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Macrobenthic invertebrates | Family | Effect only for levels above order, with P/A data |
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Transitional waters, estuarine |
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Macrobenthic invertebrates | Family | Not considered |
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Transitional waters, estuarine |
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Macrobenthic invertebrates | Order | Not considered |
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Coastal waters, 38–380 m |
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Macrobenthic invertebrates | Order | Not considered |
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Coastal waters, 1–120 m depth |
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Macrobenthic invertebrates | Family | Not considered |
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Transitional waters, coastal lagoons |
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Macrobenthic invertebrates | Family | Not considered |
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Transitional waters, coastal lagoons |
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Macrobenthic invertebrates | Family | Important |
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Coastal waters, silty-sandy, 31–37 m depth |
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Polychaetes | Family | Not considered |
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Coastal water, Intertidal to subtidal |
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Macrobenthic invertebrates | Family and order | Not considered |
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Coastal area, rocky intertidal |
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Macrobenthic invertebrates | Phylum for regional differences, family for pollution differences | P/A data regional differences significant only at the species level |
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Intertidal area, mangrove forest |
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Macrobenthic invertebrates | 9 major groups (mix of orders, classes and phyla) | Fourth root retained differences between sites, but altered patterns within sites |
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Coastal waters |
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Macrobenthic invertebrates | Family | Evident complex effect |
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Intertidal and subtidal, ≤ 5 m |
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Macrobenthic invertebrates | Phylum | Not considered |
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Coastal area, kelp forest |
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Macrobenthic invertebrates | Family | Mild effect |
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Coastal waters, 5–90 m depth |
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Molluscs and polychaetes | Order for molluscs, genus for polychaetes | Absent or mild, stronger on hard bottom |
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Transitional waters, estuarine |
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Macrobenthic invertebrates | Family | Not considered |
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Coastal water, till 200 m |
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Molluscs | Family for tropical–polar latitudinal diversity, not for regional climatic gradients | Not considered |
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Marine and estuarine |
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Fish, invertebrates and plants | Genus for plants, family for fishes, class for invertebrates | Not considered |
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Coastal waters, 11–380 m | Molluscs | Family | Not considered |
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In our case, irrespective of data transformation, differences in dispersion amongst clusters remained significant, at least to the family level, in accordance with
Conversely, at the
Untransformed data is the most commonly used for
Our results showed that
At the same time, our results showed that the structure of the community and the magnitude of changes influence the efficiency of taxonomic surrogates and data transformations. Therefore, great care in the choice of those aspects of
We are indebted to S. Gamito and A. Perez Ruzafa for their helpful comments.