Assessing the conservation status of marine habitats: thoughts from a sandflat on the Isles of Scilly.

Statutory monitoring of the fauna of the ‘mudflats and sandflats not covered by seawater at low tide’ biotope complex on St Martin’s Flats, a part of the Isles of Scilly Complex Special Area of Conservation, was undertaken in 2000, 2004 and 2009. The targets set by Natural England for “characteristic biotopes” were that “composite species, abundance and diversity should not deviate significantly from an established baseline, subject to natural change”. The three specified biotopes could not be distinguished, and instead three assemblages were subjectively defined based on sediment surface features. There were statistically significant natural changes in diversity and species composition between years, especially in the association initially characterized by the razor-clam Ensis, and possible reasons for this are discussed. It is suggested that setting fixed local limits on natural variability is almost always impractical. Two possible approaches to distinguishing between natural and anthropogenic changes are suggested; a change in ecological condition as indicated by AMBI scores, and a significant change in average taxonomic distinctness (Δ+) compared with expectation. The determination of species biomasses as well as abundances might also open more possibilities for assessment. The practice of setting objectives for a marine SAC feature that include the range and number of biotopes cannot be supported, in view the difficulty in ascribing assemblages to recognised biotopes. A more realistic definition of species assemblages might best be gained from examination of the species that consistently make a substantial contribution to the Bray Curtis similarity among samples collected from specific sites

The influence of ocean acidification on nitrogen regeneration and nitrous oxide production in the North-West European shelf sea

The assimilation and regeneration of dissolved inorganic nitrogen, and the concentration of N2O, was investigated at stations located in the NW European shelf sea during June/July 2011. These observational measurements within the photic zone demonstrated the simultaneous regeneration and assimilation of NH4+, NO2− and NO3−. NH4+ was assimilated at 1.82–49.12 nmol N L−1 h−1 and regenerated at 3.46–14.60 nmol N L−1 h−1; NO2− was assimilated at 0–2.08 nmol N L−1 h−1 and regenerated at 0.01–1.85 nmol N L−1 h−1; NO3− was assimilated at 0.67–18.75 nmol N L−1 h−1 and regenerated at 0.05–28.97 nmol N L−1 h−1. Observations implied that these processes were closely coupled at the regional scale and nitrogen recycling played an important role in sustaining phytoplankton growth during the summer. The [N2O], measured in water column profiles, was 10.13 ± 1.11 nmol L−1 and did not strongly diverge from atmospheric equilibrium indicating that sampled marine regions where neither a strong source nor sink of N2O to the atmosphere. Multivariate analysis of data describing water column biogeochemistry and its links to N-cycling activity failed to explain the observed variance in rates of N-regeneration and N-assimilation, possibly due to the limited number of process rate observations. In the surface waters of 5 further stations, Ocean Acidification (OA) bioassay experiments were conducted to investigate the response of NH4+ oxidising and regenerating organisms to simulated OA conditions, including the implications for [N2O]. Multivariate analysis was undertaken which considered the complete bioassay dataset of measured variables describing changes in N-regeneration rate, [N2O] and the biogeochemical composition of seawater. While anticipating biogeochemical differences between locations, we aimed to test the hypothesis that the underlying mechanism through which pelagic N-regeneration responded to simulated OA conditions was independent of location and that a mechanistic understanding of how NH4+ oxidation, NH4+ regeneration and N2O production responded to OA could be developed. Results indicated that N-regeneration process responses to OA treatments were location specific; no mechanistic understanding of how N-regeneration processes respond to OA in the surface ocean of the NW European shelf sea could be developed

Developing conceptual models that link multiple ecosystem services to ecological research to aid management and policy, the UK marine example

Our understanding of ecological processes that lead to ecosystem services is still evolving but ecological research aims to understand the linkages between the ecosystem and services. These linkages can affect trade-offs between different ecosystem services. Understanding these linkages, by considering multiple ecosystem services simultaneously supports management of the environment and sustainable use of resources. The UK marine environment is relatively data rich, yet the links between ecosystem and several ecosystem services and linkages between services are poorly described. A workshop with 35 marine scientists was used to create a conceptual model that links ecosystem components and key processes to four services they provide and to highlight trade-offs between them. The model was subsequently further developed to include pressures and mitigating management measures. The models are discussed in terms of their application to marine data to facilitate evidence-based marine management and their usefulness to communicate management measures with managers and stakeholders

Introduction: In appreciation of K. Robert Clarke

BIOGRAPHY IN BRIEF Early years Professor Kenneth Robert Clarke (‘Bob’) was born on the 19th of June 1948. He was brought up largely in rural North Dorset in southern England, though his indefatigable love of travel can perhaps be traced to three years of childhood in Malta in the late 1950s, during which he was educated often as the sole English boy in the local schools, his father having taken the family there to head the English department of a newly opened secondary school for the island. Back in England in the 1960s, wise words from his older brother and an inspirational maths teacher at Blandford Grammar School determined Bob’s subject choice for life – and the specialised focus of English state education at that time ensured he was taught nothing except mathematics from the age of 16. This led to a first class degree in Mathematics at the University of Leicester in 1969 (which contained no statistics at all, as was the case at the time for both school and university mathematics)and, more importantly that year, marriage (a long and happy one) to Cathy, a Leicester classics graduate. An M.Sc. at the University of Newcastle upon Tyne, under a revered statistician, Robin Plackett, brought Bob into the world of statistical theory (and writing computer code, in the days when ‘cut and paste’ literally meant taking a pair of scissors and tape to hole-punched paper!). This was followed by a Newcastle Ph.D. in Stereology, a branch of geometric probability and integral equations which infers 3-d properties from 2-d sections and projections, with application in life sciences, metallurgy and other fields. Bob became known on the university seminar circuit for provisioning the audience at the tea break by slicing up a cherry cake to derive the cherry density and diameter distribution from the resulting plane sections. A 6-year stint (1973–1979) as a Lecturer in the Department of Statistics at the University of Glasgow, Scotland – under the tutelage and encouragement of two further giants of statistics, David Silvey and John Aitchison – turned Bob into a lecturer and taught him the trick of keeping just one step ahead of his students. It also showed him how rewarding it could be to work with academics from other departments to bring statistical theory to bear on their problems. He also, arguably, missed his vocation in life when in the mid-1970s a computerised golf game he programmed in machine code for a stand-alone pen plotter – with the correct differential equations for a ball in flight in the wind and on a sloping green with friction – stole the show of the Stats Department’s University Open Day offerin

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

Analysis of similarities (ANOSIM) for 2‐way layouts using a generalised ANOSIM statistic, with comparative notes on Permutational Multivariate Analysis of Variance (PERMANOVA)

In the study of multivariate data, for example of change in ecological communities, ANOSIM is a robust non-parametric hypothesis-testing framework for differences in resemblances among groups of samples. RELATE is a non-parametric Mantel test of the hypothesis of no relationship between two resemblance matrices. Details are given of the explicit link between the RELATE statistic, a Spearman rank correlation (ρ) between corresponding elements in the two resemblance matrices, and the ANOSIM statistic R, a scaled contrast between the among- and within-group ranks. It is seen that R can equivalently be defined as the slope of the linear regression of ranked resemblances from observations against ranked distances among samples, the latter from a simple model matrix assigning the values 1 and 0 to between- and within-group distances, respectively. Re-defining this model matrix to represent ordered distances among groups leads naturally to a generalised ANOSIM statistic, RO, suitable for testing, for example, ordered factor levels in space or time, or an environmental or pollution gradient. Two variants of the generalised ANOSIM statistic are described, namely ROc where there are replicates within groups, and ROs where there are only single samples (no replicates) within groups, for which an ANOSIM test was not previously available. Three marine ecological examples using ANOSIM to analyse an ordered factor in one-way designs are provided. These are: (1) changes in macrofaunal composition with increasing distance from an oil rig; (2) differences in phytal meiofaunal community composition with increasing macroalgal complexity; and (3) changes in average community composition of free-living nematodes along a long-term heavy metal gradient. Incorporating knowledge of an ordering structure is seen to provide more focussed, and thus stronger, ANOSIM tests, but inevitably risks losing power if that prior knowledge is incorrect or inappropriate

Analyses of sublittoral macrobenthic community change in a marine nature reserve using similarity profiles (SIMPROF).

Sublittoral macrobenthic communities in the Skomer Marine Nature Reserve (SMNR), Pembrokeshire, Wales, were sampled at 10 stations in 1993, 1996, 1998, 2003, 2007 and 2009 using a Day grab and a 0.5 mm mesh. The time series is analysed using Similarities Profiles (SIMPROF) tests and associated methods. Q-mode analysis using clustering with Type 1 SIMPROF addresses multivariate structure among samples, showing that there is clear structure associated with differences among years. Inverse (r-mode) analysis using Type 2 SIMPROF decisively rejects a hypothesis that species are not associated with each other. Clustering of the variables (species) with Type 3 SIMPROF identifies groups of species which covary coherently through the time-series. The time-series is characterised by a dramatic decline in abundances and diversity between the 1993 and 1996 surveys. By 1998 there had been a shift in community composition from the 1993 situation, with different species dominating. Communities had recovered in terms of abundance and species richness, but different species dominated the community. No single factor could be identified which unequivocally explained the dramatic changes observed in the SMNR. Possible causes were the effects of dispersed oil and dispersants from the Sea Empress oil spill in February 1996 and the cessation of dredge-spoil disposal off St Anne’s Head in 1995, but the most likely cause was severe weather. With many species, and a demonstrable recovery from an impact, communities within the SMNR appear to be diverse and resilient. If attributable to natural storms, the changes observed here indicate that natural variability may be much more important than is generally taken into account in the design of monitoring programmes

A generalised analysis of similarities (ANOSIM) statistic for designs with ordered factors

In the study of multivariate data, for example of change in ecological communities, ANOSIM is a robust non-parametric hypothesis-testing framework for differences in resemblances among groups of samples. RELATE is a non-parametric Mantel test of the hypothesis of no relationship between two resemblance matrices. Details are given of the explicit link between the RELATE statistic, a Spearman rank correlation (ρ) between corresponding elements in the two resemblance matrices, and the ANOSIM statistic R, a scaled contrast between the among- and within-group ranks. It is seen that R can equivalently be defined as the slope of the linear regression of ranked resem�blances from observations against ranked distances among samples, the latter from a simple model matrix assigning the values 1 and 0 to between- and within-group distances, respectively. Re-defining this model matrix to represent ordered distances among groups leads naturally to a generalised ANOSIM statistic, RO, suitable for testing, for example, ordered factor levels in space or time, or an environmental or pollution gradient. Two variants of the generalised ANOSIM statistic are described, namely ROc where there are replicates within groups, and ROs where there are only single samples (no replicates) within groups, for which an ANOSIM test was not previously available. Three marine ecological examples using ANOSIM to analyse an ordered factor in one-way designs are provided. These are: (1) changes in macrofaunal composition with increasing distance from an oil rig; (2) differences in phytal meiofaunal community composition with increasing macroalgal complexity; and (3) changes in average community composition of free-living nematodes along a long-term heavy metal gradient. Incorporating knowledge of an ordering structure is seen to provide more focussed, and thus stronger, ANOSIM tests, but inevitably risks losing power if that prior knowledge is incorrect or inappropriat

Analysis of similarities (ANOSIM) for 3‐way designs

Analysis of similarities (ANOSIM) is a robust non-parametric hypothesis-testing framework for differences in resemblances among groups of samples. To date, the generalisation and use of ANOSIM to analyse various 2-way nested and crossed designs with unordered or ordered factors has been described. This paper describes how the 2-way tests may be extended and modified for the analysis of 3-way designs, including the introduction of a different type of constrained permutation procedure for a design in which one factor is nested in another and crossed with a third. The construction of 3-way tests using the generalised statistic in various nested and crossed designs, with or without ordered factors, and with or without replication, is described. Applications of the new tests to ecological data are demonstrated using three marine examples. They are as follows: a study of changes in fish diet for fish of increasing size sampled in different locations at different times (a 3-way fully crossed design with ordered factors); a hierarchical spatial study of the fauna inhabiting kelp holdfasts (a 3- way fully nested design with unordered factors); and a study of infaunal macrobenthos in which sites within areas were resampled over a long time series (a design in which sites are nested in areas but crossed with years, both latter factors potentially being ordered). The magnitudes of the ANOSIM statistics provide information about relative effect sizes (accounting for other factors), which is often a focus for multifactorial designs. Though the described ANOSIM tests do not provide parallels for all the range of 3-way mixed-factor designs possible in ANOVA (and its multivariate semi-parametric counterpart PERMANOVA), it is seen that for nested factors these ANOSIM tests parallel the matching PERMANOVA random-effects models, and not their fixed-effects counterparts, thus allowing the same broader inference about the space from which these random factor levels are drawn

The relative impact of co-occurring stressors on the abundance of benthic species examined with three-way correspondence analysis

This paper presents a novel application of three-way correspondence analysis as a technique to analyse three-way contingency tables with abundance scores of several species. The example data analysis presented was taken from a previous mesocosm experiment and consists of a two-factor experimental design with physical disturbance and organic enrichment as factors, applied to sediment collected from the Oslofjørd, Norway. The focus of the original research was to evaluate the influence of the two factors and their interactions on the abundance of the species present in the sediment. In the current paper we demonstrate that by using a three-way correspon�dence approach it is possible to undertake simultaneous analysis of the species, identifying and evaluating their relative sensitivity to the environmental factors thus adding additional insight than was possible in the original analysis. In particular, this new approach allowed even relatively scarce species to be included in the analysis and evaluated together with abundant species. This paper demonstrates how three-way correspondence analysis can be a useful analytical tool in teasing out effects and interactions from multi-factorial studies