2000. Are macroinvertebrate communities influenced by seagrass structural complexity? -Ecography 23: 114 -121.A study was undertaken within a sub-tidal Zostera marina seagrass bed (Devon, U.K.), with the aim of elucidating the relationship between seagrass structural complexity and the size and composition of the associated macroinvertebrate community. Samples of macroinvertebrates were recovered from three designated areas of shoot density. Various physical characteristics were measured for individual plants, and an a priori complexity index was determined relevant to the associated target organisms. Resulting data were analysed using linear regression and multivariate techniques. Significant relationships were found between shoot density and number of leaves/shoot, leaf length, stem length and algal epiphyte biomass. Neither the number of species nor abundance of macroinvertebrates was significantly related with the derived complexity index. Multivariate analysis indicated that macroinvertebrate communities from the three areas of shoot density were significantly different, the pattern of macroinvertebrate community composition being best explained by seagrass biomass. Linear regression of seagrass biomass with macroinvertebrate number of species and abundance revealed significant positive relationships. Regression also indicated that there was no significant increase in complexity with increasing seagrass biomass. The results suggest that within a seagrass bed the size and composition of the associated macroinvertebrate community is not determined by the structural complexity of the plants, but by the amount of plant available. This finding indicates a simple species-area relationship, and arguably one brought about as a result of a sampling artefact. Thus, the current paradigm that structural complexity of seagrass is responsible for increased species diversity, can only be justifiably applied to comparisons between seagrass and other habitats, and not within a seagrass bed itself.

2000. Are macroinvertebrate communities influenced by seagrass structural complexity? -Ecography 23: 114 -121.A study was undertaken within a sub-tidal Zostera marina seagrass bed (Devon, U.K.), with the aim of elucidating the relationship between seagrass structural complexity and the size and composition of the associated macroinvertebrate community. Samples of macroinvertebrates were recovered from three designated areas of shoot density. Various physical characteristics were measured for individual plants, and an a priori complexity index was determined relevant to the associated target organisms. Resulting data were analysed using linear regression and multivariate techniques. Significant relationships were found between shoot density and number of leaves/shoot, leaf length, stem length and algal epiphyte biomass. Neither the number of species nor abundance of macroinvertebrates was significantly related with the derived complexity index. Multivariate analysis indicated that macroinvertebrate communities from the three areas of shoot density were significantly different, the pattern of macroinvertebrate community composition being best explained by seagrass biomass. Linear regression of seagrass biomass with macroinvertebrate number of species and abundance revealed significant positive relationships. Regression also indicated that there was no significant increase in complexity with increasing seagrass biomass. The results suggest that within a seagrass bed the size and composition of the associated macroinvertebrate community is not determined by the structural complexity of the plants, but by the amount of plant available. This finding indicates a simple species-area relationship, and arguably one brought about as a result of a sampling artefact. Thus, the current paradigm that structural complexity of seagrass is responsible for increased species diversity, can only be justifiably applied to comparisons between seagrass and other habitats, and not within a seagrass bed itself.

There is an increasing demand for environmental assessments of the marine environment to include ecosystem function. However, existing schemes are predominantly based on taxonomic (i.e. structural) measures of biodiversity. Biodiversity and Ecosystem Function (BEF) relationships are suggested to provide a mechanism for converting taxonomic information into surrogates of ecosystem function. This review assesses the evidence for marine BEF relationships and their potential to be used in practical monitoring applications (i.e. operationalized).Five key requirements were identified for the practical application of BEF relationships: 1) a complete understanding of strength, direction and prevalence of marine BEF relationships, 2) an understanding of which biological components are influential within specific BEF relationships, 3) the biodiversity of the selected biological components can be measured easily, 4) detail which ecological mechanisms are the most important for generating marine BEF relationships, e.g. identity effects or complementarity, and 5) establish what proportion of the overall functional variance is explained by biodiversity, and hence BEF relationships.Many positive and some negative BEF relationships were found within the literature, although many reproduced poorly the natural species richness, trophic structures or multiple functions of real ecosystems. Null relationships were also reported. The consistency of the positive and negative relationships was often low that compromised the ability to generalize BEF relationships and confident application of BEF within marine monitoring. Equally, some biological components and functions have received little or no investigation.Expert judgement was used to attribute biological components using spatial extent, presence and functional rate criteria. This approach highlighted the main contributing biological components to the ecosystem functions, and that many of the particularly influential components were found to have received the least amount of research attention.The need for biodiversity to be measureable (requirement 3) is possible for most biological components although difficult within the functionally important microbes. 3Identity effects underpinned most marine BEF relationships (requirement 4). As such, processes that translated structural biodiversity measures into functional diversity were found to generate better BEF relationships.The analysis of the contribution made by biodiversity, over abiotic influences, to the total expression of a particular ecosystem function was rarely measured or considered (requirement 5). Hence it is not possible to determine the overall importance of BEF relationships within the total ecosystem functioning observed. In the few studies where abiotic factors had been considered, it was clear that these modified BEF relationships and have their own direct influence on functional rate.Based on the five requirements, the information required for immediate 'operationalization' of BEF relationships within marin...

Marine habitat mapping provides information on seabed substrata and faunal community structure to users including research scientists, conservation organizations, and policy makers. Full-coverage acoustic data are frequently used for habitat mapping in combination with video ground-truth data in either a supervised or unsupervised classification. In this investigation, video ground-truth data with a camera footprint of 1 m2 were classified to level 4 of the European Nature Information System habitat classification scheme. Acoustic data with a horizontal resolution of 1 m2 were collected over an area of 130 km2 using a multibeam echosounder, and processed to provide bathymetry and backscatter data. Bathymetric derivatives including eastness, northness, slope, topographic roughness index, vector rugosity measure, and two measures of curvature were created. A feature selection process based on Kruskal–Wallis and post hoc pairwise testing was used to select environmental variables able to discriminate ground-truth classes. Subsequently, three datasets were formed: backscatter alone (BS), backscatter combined with bathymetry and derivatives (BSDER), and bathymetry and derivatives alone (DER). Two classifications were performed on each of the datasets to produce habitat maps: maximum likelihood supervised classification (MLC) and ISO Cluster unsupervised classification. Accuracy of the supervised habitat maps was assessed using total agreement, quantity disagreement, and allocation disagreement. Agreement in the unsupervised maps was assessed using the Cramer's V coefficient. Choice of input data produced large differences in the accuracy of the supervised maps, but did not have the same effect on the unsupervised maps. Accuracies were 46, 56, and 49% when calculated using the sample and 52, 65, and 51% when using an unbiased estimate of the population for the BS, BSDER, and DER maps, respectively. Cramer's V was 0.371, 0.417, and 0.366 for the BS, BSDER, and DER maps, respectively.

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