Trait-based community dynamics of a North-Sea ecosystem

Introduction

Ecological experiments and theoretical considerations indicate that ecosystem functioning depends on biodiversity only in terms of the “functional characteristics” of organisms present in the ecosystem, which are expressed through their traits [1]. This leads to the question: are some traits more important than others for community dynamics and structure, and which traits are the important ones? We set up an ecological model for trait-specific biomass dynamics in North Sea macrofauna. We believe that the identification of dynamically-dominant traits (DDT) would provide some evidences to understand the benthic community structure. Additionally, prediction of benthic functioning and its performance in interaction with environmental variability are expected to be more accurate. This model will assist in identifying significant interactions among traits, and thus those traits that determine benthic community development.

Methods

We used a database from a long-term macro-zoobenthos time series at four sites in the North Sea (AWI time series data). Data include benthos samples collected each spring from 1969 to 2011. Additionally, an autecological database was generated from different traits covering life history, behavioral characteristics, morphological attributes, and environmental preferences of benthic species. ‘Multivariate Autoregressive State-space Model’ (MARSS) was used to implement different scenarios in our analysis. The core functionality of MARSS is based on likelihood maximization using the Kalman filter, combined with an EM algorithm.

Results

The model showed all possible interactions among traits, and between traits and environmental factors in each sampling sites. The largest interactions between traits and environmental variables were seen in the case of sea surface temperature and salinity.

Conclusion

Trait-level description provides more abstract and generic representation of benthic communities. At the same time, dynamically-dominant traits are preferable as they capture great fraction of variability and likely to be highly significant to exhibit greater developmental stability.

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Last edited: 02 February 2016