STARDUST II - Spatial and Temporal Assessment
of high Resolution Depth profiles Using novel Sampling Technologies The fate of pollutants in fluvial and marine sediments in cross-border zones
Multimodel inference to quantify the relative importance of abiotic factors in the population dynamics of marine zooplankton
Everaert, G.; Deschutter, Y.; De Troch, M.; Janssen, C.R.; De Schamphelaere, K. (2018). Multimodel inference to quantify the relative importance of abiotic factors in the population dynamics of marine zooplankton. J. Mar. Syst. 181: 91-98. https://dx.doi.org/10.1016/j.jmarsys.2018.02.009
In: Journal of Marine Systems. Elsevier: Tokyo; Oxford; New York; Amsterdam. ISSN 0924-7963; e-ISSN 1879-1573
The effect of multiple stressors on marine ecosystems remains poorly understood and most of the knowledge available is related to phytoplankton. To partly address this knowledge gap, we tested if combining multimodel inference with generalized additive modelling could quantify the relative contribution of environmental variables on the population dynamics of a zooplankton species in the Belgian part of the North Sea. Hence, we have quantified the relative contribution of oceanographic variables (e.g. water temperature, salinity, nutrient concentrations, and chlorophyll a concentrations) and anthropogenic chemicals (i.e. polychlorinated biphenyls) to the density of Acartia clausi. We found that models with water temperature and chlorophyll a concentration explained ca. 73% of the population density of the marine copepod. Multimodel inference in combination with regression-based models are a generic way to disentangle and quantify multiple stressor-induced changes in marine ecosystems. Future–oriented simulations of copepod densities suggested increased copepod densities under predicted environmental changes.
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STARDUST II is a project funded by the INTERREG III A programme (France/Walloon Region/Flanders
French-Flemish subprogramme) of the European Community's Regional Development Fund.
Hosted by the Flanders Marine Institute (VLIZ)
