Investigation of benthic community change over a century-wide scale in the western English Channel

Since the early part of the 20th Century the impact of a range of anthropogenic activities in our coastal seas has steadily increased. The effect of such activities is a major cause for concern but in the benthic environment few studies exist that date back more than a few decades. Hence understanding long term changes is a challenge. Within this study we utilized a historic benthic dataset and resurveyed an area west of Eddystone reef in the English Channel previously investigated 112 years ago. The aim of the present work was to describe the current benthic community structure and investigate potential differences between 1895 and 2007. For each of the four major phyla investigated (Polychaeta, Crustacea, Mollusca and Echinodermata), multivariate community analysis showed significant differences between the historic and contemporary surveys. Echinoderm diversity showed a clear reduction between 1895 and 2007. The sea urchins Echinus esculentus, Spatangus purpureus, and Psammechinus miliaris and large star-fish Marthasterias glacialis showed reductions in abundance, in some cases being entirely absent from the survey area in 2007. Polychaetes showed a shift from tubiculous species to small errant and predatory species such as Glycera, Nephtys, and Lumbrineris spp. Within the group Mollusca large species such as Pecten maximus and Laevicardium crassum decreased in abundance while small species increased. Crustaceans in 1895 were dominated by crab species which were present in similar abundances in 2007, but, the order Amphipoda appeared to show a significant increase. While some of the differences observed could stem from differences in methodologies between the surveys, in particular increases of small cryptic species, the loss of large conspicuous species was judged to be genuine. The study area is an important beam trawling and scallop dredging ground; the differences observed are concomitant with changes generally associated with disturbance from demersal fishing activities such as these.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Assessing marine plankton community structure from long-term monitoring data with multivariate autoregressive (MAR) models: a comparison of fixed station versus spatially distributed sampling data

We examined how marine plankton interaction networks, as inferred by multivariate autoregressive (MAR) analysis of time-series, differ based on data collected at a fixed sampling location (L4 station in the Western English Channel) and four similar time-series prepared by averaging Continuous Plankton Recorder (CPR) datapoints in the region surrounding the fixed station. None of the plankton community structures suggested by the MAR models generated from the CPR datasets were well correlated with the MAR model for L4, but of the four CPR models, the one most closely resembling the L4 model was that for the CPR region nearest to L4. We infer that observation error and spatial variation in plankton community dynamics influenced the model performance for the CPR datasets. A modified MAR framework in which observation error and spatial variation are explicitly incorporated could allow the analysis to better handle the diverse time-series data collected in marine environments.

Relationships between biodiversity and the stability of marine ecosystems: comparisons at a European scale using meta-analysis.

The relationship between biodiversity and stability of marine benthic assemblages was investigated using existing data sets (n = 28) covering various spatial (m-km) and temporal (1973-2006) scales in different benthic habitats (emergent rock, rock pools and sedimentary habitats) through meta-analyses. Assemblage stability was estimated by measuring temporal variances of species richness, total abundance (density or % cover) and community species composition and abundance structure (using multivariate analyses). Positive relationships between temporal variability in species number and richness were generally observed at both quadrat (<1 m2) and site (100 m2) scales, while no relationships were observed by multivariate analyses. Positive relationships were also observed at the scale of site between temporal variability in species number and variability in community structure with evenness estimates. This implies that the relationship between species richness or evenness and species richness variability is slightly positive and depends on the scale of observation, suggesting that biodiversity per se is important for the stability of ecosystems. Changes within community assemblages in terms of structure are, however, generally independent of biodiversity, suggesting no effect of diversity, but the potential impact of individual species, and/or environmental factors. Except for sedimentary and rock pool habitats, no relationship was observed between temporal variation of the aggregated variable of total abundances and diversity at either scale. Overall our results emphasise that relationships depend on scale of measurements, type of habitats and the marine systems (North Atlantic and Mediterranean) considered.