Trait-based community dynamics of a North-Sea ecosystem

Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany

Mehdi Ghodrati Shojaei

Mehdi Ghodrati Shojaei

Species living in or on the seabed form an integral component of marine ecosystems, constituting a food source for fish and other predators. Distribution, abundance, and community composition of such benthic species are strongly influenced by a variety of physical, chemical, and biological factors: temperature, water depth, food supply, and sediment type have critical, though sometimes variable, effects on their distribution. Additionally, biological interactions (e.g., competition, predation) among species influence the diversity of marine assemblages. Predatory fish may directly reduce abundances of species living on the seabed, while their effects on species living in the seabed may be less pronounced.

Traditional analytical procedures, which derive information on biodiversity and community structure from species abundance/biomass data, do not take into account the functional features of species. However, functional diversity, i.e., the range and number of functional traits represented within an ecosystem, is a useful indicator of ecosystem functioning. In this project, I use biological trait analysis (BTA) to explore the ecological functioning of benthic assemblages and to compare functional diversity across different assemblages. BTA combines quantitative structural data (e.g., abundance) with information on biological characteristics to functionally characterize species assemblages. Based on this analysis, I will construct a trait-based model of the community dynamics. The ultimate objectives of this study are a) to detect functional responses of benthic assemblages to extreme events, and b) to examine the traits that contribute most to the observed temporal development of benthic functional structure over the region. The results will be discussed in the context of ecosystem functioning.

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Last edited: 24 March 2016

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