Habitat Classification Based on Bottom Topography and its Predictability for Describing Polychaeta and Trait Distribution

Abstract

There is an increasing demand for simple and cost-efficient methods for describing habitats, communities and distribution of organisms on the seabed for management, conservation and prediction of ecological patterns. To meet the requirements of the European Union Water Framework Directive, will require a lot of effort to be met. Traditionally based methods for describing diversity and patterns may not be adequate. The aim of this study was to investigate how a habitat classification model based on bathymetric properties works for predicting patterns in species distribution and trait distribution of sediment polychaetes. Five replicate samples were taken from each of the 10 stations at Vestfjorden, inner Oslofjord. The stations covered a range of different habitat classes based on topographic properties. For each station 21 environmental variables were measured and/or estimated. Multivariate NMDS and cluster analysis were used to visualise and compare the habitat classes. Biological trait analysis was used to describe distribution of a number of functional traits (e.g. size, reproductive method, feeding method) which again in turn was split into a number of trait categories. Using a fuzzy coding procedure allow each species to have an affinity for each trait category. Patterns of species and trait composition were matched to environmental variables best describing these patterns. Four main habitat groups (crest, depression, slope and flats) which again were divided into more detailed classifications (e.g. narrow crest, open slope), were used in the analysis. Sediment characteristics and depth were the most important structuring factors in both species and trait composition. The habitat classification was not adequate for predicting the species composition, but is probably more correlated with sediment characteristics than the topographic properties of the classification. In relation to the biological trait analysis the habitat classifications seem adequate in predicting trait composition in two of the habitat groups (depression and flat), but heterogeneity in some habitat groups (crest and slope) was observed. When using topographic properties in predicting distribution patterns in benthic habitats, using trait composition seem like a better approach than using species composition. For future research combining the habitat classifications based on topography with sediment characteristics and using a larger dataset are recommended.