Research Article |
Corresponding author: Michele Mistri ( msm@unife.it ) Academic editor: Lucilla Capotondi
© 2019 Valentina Pitacco, Michele Mistri, Vanessa Infantini, Adriano Sfriso, Andrea Augusto Sfriso, Cristina Munari.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Pitacco V, Mistri M, Infantini V, Sfriso A, Sfriso AA, Munari C (2019) Benthic studies in LTER sites: the use of taxonomy surrogates in the detection of long-term changes in lagoonal benthic assemblages. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 247-272. https://doi.org/10.3897/natureconservation.34.27610
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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 (TS) has been proposed as a short-cut method for quantifying changes in biological assemblages in environmental monitoring. In this paper, we set out to determine whether and how the taxonomic complexity of a benthic assemblage influences the results of TS at two different long-term ecological research (LTER) sites in the Po delta region (north-eastern Italy). Specifically, we investigated whether TS can be used to detect natural and human-driven patterns of variation in benthic assemblages from lagoonal soft bottoms. The first benthic dataset was collected from 1996 to 2015 in a “choked” lagoon, the Valli di Comacchio, a lagoon characterised by long water residence times and heavy eutrophication, while the second was collected from 2004 to 2010 in a “leaky” lagoon, the Sacca di Goro, a coastal area with human pressure limited to aquaculture. Univariate and multivariate statistical analyses were used to assess differences in the taxonomic structure of benthic assemblages and to test TS on the two different datasets. TS seemed to work from species to family level at both sites, despite a higher natural variability of environmental conditions combined with multiple anthropogenic stressors. Therefore, TS at the family level may represent effective taxonomic surrogates across a range of environmental contexts in lagoon environments. Since the structure of the community and the magnitude of changes could influence the efficiency of taxonomic surrogates and data transformations in long-term monitoring, we also suggest periodic analyses at finer taxonomic levels in order to check the efficiency of the application of taxonomic substitutes in routine monitoring programmes in lagoon systems.
LTER sites, Taxonomic sufficiency, Mediterranean lagoons, Benthic community
Taxonomic sufficiency (TS) is an analysis technique developed in light of the current need for rapid and reliable procedures in marine impact assessment and monitoring. The basic concept behind TS (
In particular, TS is mandatory when non-destructive sampling techniques have to be used and taxonomic resolution is low (
That being said, the applications of TS, particularly in conservation studies, have been criticised (
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 TS in long-term monitoring at each of the two LTER sites. Information loss was calculated for (i) different levels of taxonomic aggregation (from species to phylum) and (ii) different data transformations (row data, square root, logarithm, presence/absence), in order to understand whether and how the structure and taxonomic complexity of benthic assemblages influence TS results and whether TS can be used to detect natural and human-driven patterns of variation in long-term environmental monitoring.
The Valli di Comacchio (Figure
Sacca di Goro (Figure
Valli di Comacchio | Sacca di Goro | |
Extension (km2) | 100 | 26 |
Average depth (m) | 0.5–1.5 | 1.2–1.5 |
Residence time (d) | 115 | 5 |
Salinity (psu) | 10.9–40.1 | 6–30 |
Temperature (°C) | 2.0–30.5 | 2.0–33.0 |
OM ‡ | 37.5 ± 5.5 † | 44 ± 4.6 † |
Sand (%) | 44.3 ± 6 † | 24.7 ± 2.6 † |
Silt (%) | 37.9 ± 5.9 † | 59.5 ± 0.2 † |
Clay (%) | 17.8 ± 3 † | 17 ± 3 † |
References |
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In the Valli di Comacchio lagoons (COM), sampling was performed from 1996 to 2015 at four sampling stations, COM1 to COM4 (Figure
In the Sacca di Goro lagoon (GOR), sampling was performed from 2004 to 2010 at three sampling sites, GOR1, GOR3 and GOR4 (Figure
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.
At each of the LTER sites, the annual averages of the following structural indices were calculated: species richness (S), Shannon diversity index on loge basis (H’) and Pielou index (J’). A chi-square test, applied to Kruskal-Wallis (KW) ranks (
For each of the two LTER sites, abundance matrices were produced for each of the six taxonomic levels (species, genus, family, order, class and phylum) and for each of four different transformations (none, square-root, logarithm and presence/absence). Affinities between years were established using the Bray-Curtis similarity. For each dataset, a second-stage non-metric multi-dimensional scaling (MDS) ordination was plotted to visualise differences between similarity matrices at different levels of taxonomic aggregation and data transformation. Spearman’s rank correlation coefficient (rs) was calculated between matrices at the species and higher taxonomic levels. The stress of the two-dimensional plot was calculated using Kruskal’s stress Formula 1 (
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 (COM) dataset comprised a total of 122 taxa at the lowest taxonomic level; these belonged to 9 phyla: Annelida, Arthropoda, Mollusca, Nemertea, Cnidaria, Platyhelminthes, Sipuncula, Echinodermata and Chordata. The annual average species richness (S), Shannon diversity (H’) and Pielou equitability index (J’) varied significantly throughout the study period (KW, p < 0.05), but displayed a general decreasing trend (Figure
In contrast, the Sacca di Goro (GOR) dataset comprised a total of 88 taxa at the lowest taxonomic level, in this case belonging to 7 phyla: Annelida, Arthropoda, Mollusca, Cnidaria, Nemertea, Sipuncula and Platyhelminthes. The annual averages for S, H’ and J’ (Figure
At the COM site, the taxonomic complexity was highly variable throughout the study period, with total ‘loss of information α’, from species to phylum level, showing the highest values (45) in 2001 and 2002 and the lowest (8) in 2011 (Figure
At the GOR site too, the taxonomic complexity was more or less constant throughout the study period, with information loss (α) ranging from 24 to 40% from the lowest to the highest level (Figure
The ordination of similarity matrices in second-stage MDS plots (Figure
For the COM dataset, there were good correlations between ordination plots at the species and genus levels (always rs > 0.96; p < 0.05) and between the species and family levels (always rs > 0.88; p < 0.05), whatever the type of transformation considered (Figure
For the GOR dataset, the ordination plot showed a clear clustering pattern amongst untransformed similarity matrices at all different taxonomic levels (Figure
Second-stage MDS ordination of resemblance matrices derived from species, genus, family, order and phylum abundance data at the two LTER sites: Valli di Comacchio lagoon (A) and Sacca di Goro lagoon (B). unt: untransformed data, sqr: square-root transformed, log: log-transfomed, pa: presence/absence data.
Spearman correlations (rs) resulting from the third-stage correlation matrix, showing the effect of data transformation on differences between aggregation matrices.
COMACCHIO | |||
None | Square root | Logarithm | |
Square-root | 0.975 | ||
Logarithm | 0.932 | 0.907 | |
Presence/absence | 0.686 | 0.632 | 0.854 |
GORO | |||
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 (p < 0.05) in macrobenthic assemblages between groups identified by cluster analysis at the species level when each of the three transformations were used, but not when data remained untransformed. For each of the transformations (square-root, logarithm and presence/absence data), the significance of the differences decreased with increasing taxonomic level, with no significance being detected at the phylum level. Additionally, in this case too, pairwise comparisons revealed that not all possible pairwise combinations of clusters differed significantly and the number of significant pairwise differences decreased from the species to higher taxonomic levels (Table
Significance of cluster groups (PERMANOVA), percentage of significant pairwise combinations between those groups and the significance of differences in dispersion between cluster groups (PERMDISP).
LTER dataset | Data transformation | Cluster groups | Taxonomic resolution | ||||||
Species | Genus | Family | Order | Class | Phylum | ||||
COM | 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 | |||
GOR | 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 | 0.05 | 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 LTER sites were characterised by reduced richness and diversity (low S and H’) and were badly structured (low J’), as is typical in transitional environments, in particular those of the Po River delta (e.g.
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 COM site. Indeed, the suitability of TS for taxonomically complex communities, as well for simple, species-poor ones, has also been observed in different habitat types (
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 TS differed between the two datasets for higher levels of taxonomic aggregation. In fact, information loss, due to both location and dispersion components, at taxonomic levels higher than the family level was quite high with respect to the species level at both sites. Our results are consistent with investigations performed in a western Mediterranean lagoon, where the ordination models derived from species and family abundances were very similar both in terms of location and dispersion effects, while further aggregation to the class level altered the observed spatial patterns (
Although loss of information about the structure of the benthic assemblages increased with decreasing taxonomic accuracy at the COM site, this was not the case for the GOR dataset. In fact, at the GOR site, aggregation at the phylum level yielded better results than aggregation at the class level. Hence, TS, using family as surrogate, could be a good compromise between time/costs and efficiency, while genus remains the best surrogate for the identification of temporal variations in the macrobenthic community. That being said, the sufficiency level of taxonomic resolution could be strongly context-dependent (
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. Atl= Atlantic Ocean, NS = North Sea, Pac = Pacific Ocean, Med = Mediterranean Sea, CS= Caribbean Sea, Arc = Arctic ocean, Aus = Australia, SA= South Africa.
Habitat | Area | Taxonomic group | Sufficient taxonomic level | Effect of data transformation | Reference |
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Soft bottom – pollution gradients | |||||
Marine water, intertidal to subtidal | Atl | Macro and meiobenthos | Family for meio, phylum for macrobenthos | Not considered |
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Marine waters, 0–3000 m | NS | Macrobenthic invertebrates | Family or phylum | Not considered |
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Transitional waters, estuarine | Pac | Macrobenthic invertebrates | Family (1 mm mesh), species (0.5 mm mesh) | Not considered |
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Coastal waters, 14–380 m | NS | Macrobenthic invertebrates | Family | Increased from order to higher levels, no effect for family |
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Coastal waters | Med | Macrobenthic invertebrates | Family | Not strong till family level |
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Coastal waters, 65-380 m | NS | Macrobenthic invertebrates | Family | Evident complex effect |
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Soft bottom – natural gradients | |||||
Coastal water, fjord | Atl | Macrobenthic invertebrates | Family | Not strong |
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Coastal water | Atl | Macro and meiobenthos | Family (except nematodes) | Not considered |
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Coastal water, fine sand | CS | Polychaetes, crustaceans, molluscs | Phyla (from genera) | Effect of fourth root only at phylum level |
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Transitional waters, lagoons | Med | Macrobenthic invertebrates | Family | Effect only for levels above order, with P/A data |
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Transitional waters, estuarine | Med | Macrobenthic invertebrates | Family | Not considered |
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Transitional waters, estuarine | Atl | Macrobenthic invertebrates | Order | Not considered |
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Coastal waters, 38–380 m | Arc | Macrobenthic invertebrates | Order | Not considered |
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Coastal waters, 1–120 m depth | Med | Macrobenthic invertebrates | Family | Not considered |
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Transitional waters, coastal lagoons | Med | Macrobenthic invertebrates | Family | Not considered |
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Transitional waters, coastal lagoons | Med | Macrobenthic invertebrates | Family | Important |
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Coastal waters, silty-sandy, 31–37 m depth | Med | Polychaetes | Family | Not considered |
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Soft bottom – natural and pollution gradients | |||||
Coastal water, Intertidal to subtidal | Aus | Macrobenthic invertebrates | Family and order | Not considered |
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Hard bottom – pollution and natural gradients | |||||
Coastal area, rocky intertidal | SA | 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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Hard bottom – natural gradients | |||||
Intertidal area, mangrove forest | Aus | 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 | Med | Macrobenthic invertebrates | Family | Evident complex effect |
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Intertidal and subtidal, ≤ 5 m | Atl | Macrobenthic invertebrates | Phylum | Not considered |
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Coastal area, kelp forest | Pac | Macrobenthic invertebrates | Family | Mild effect |
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Hard and soft bottom – pollution gradients | |||||
Coastal waters, 5–90 m depth | Med | Molluscs and polychaetes | Order for molluscs, genus for polychaetes | Absent or mild, stronger on hard bottom |
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Hard and soft bottom – natural gradients | |||||
Transitional waters, estuarine | Pac | Macrobenthic invertebrates | Family | Not considered |
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Coastal water, till 200 m | Pac | Molluscs | Family for tropical–polar latitudinal diversity, not for regional climatic gradients | Not considered |
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Marine and estuarine | Pac | Fish, invertebrates and plants | Genus for plants, family for fishes, class for invertebrates | Not considered |
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Coastal waters, 11–380 m | Med, Atl, Pac, Arc | 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 GOR site, the correlation between second-stage matrices (representing different transformations) was, in general, very low, indicating a stronger effect of transformation on the efficiency of TS. Interestingly, for the GOR dataset, weak data transformation (i.e. raw data) was sufficient to allow good correspondence amongst similarity matrices at the species level and all taxonomic surrogates up to the phylum level. This suggests that abundance was fundamental in determining temporal changes in the macrobenthic community at the GOR site. Nevertheless, both PERMANOVA and PERMDISP analyses on untransformed data failed to discriminate between groups identified by cluster analysis. For the GOR dataset, therefore, the choice of data transformation was more important to the correct evaluation of the efficiency of TS. Indeed, the populations within transitional waters typically show dramatic seasonal, annual and interannual variations, ranging from disappearance to complete dominance during periods of dystrophic crisis (
Untransformed data is the most commonly used for TS in environmental monitoring (
Our results showed that TS could be an efficient tool for long-term monitoring programmes. Our results also showed how LTER observations are critical for detecting meaningful ecological shifts and assessing whether ecological changes are due to human or natural causes. LTER data are particularly important for the identification of temporal trends, as many ecological processes develop at temporal scales that are longer than have typically been considered in traditional short-term ecological research studies. The higher natural variability of environmental conditions, combined with multiple stressors of anthropogenic origins at the two LTER sites analysed, did not represent an impediment for TS in detecting temporal changes in the macrobenthic community. For the two LTER sites analysed, the solution providing the best compromise between time/cost and information loss was a square-root transformation using family data as the taxonomic surrogate. Given the rising importance of long-term data series for detecting trends and changes at community levels, in particular in greatly fluctuating environments such as transitional waters, TS could be a useful method of enabling an increase in sampling frequency, together with a higher spatial resolution, while still reducing costs. Increasing the available information on a temporal scale could also help reduce the bias of seasonal and local variations and, therefore, increase the efficiency of environmental management actions and biodiversity conservation measures.
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 TS is necessary, in particular at sites where the effect of disturbance on community structure is not marked, as was the case for the GOR dataset. The best choice could be a function of environmental conditions, habitat types and biogeographic area. Therefore, care is needed when generalising outcomes in the field of TS and pilot studies are required to distinguish the most suitable procedure on a case-by-case basis (
We are indebted to S. Gamito and A. Perez Ruzafa for their helpful comments.