Grazing rate of zebra mussel in a shallow eutrophicated bay of the Baltic Sea
In: Marine Environmental Research. Applied Science Publishers: Barking. ISSN 0141-1136; e-ISSN 1879-0291, more
Also appears in:Kennedy, R.; Allcock, L.; Firth, L.; Power, A.M. (Ed.) (2014). Managing biodiversity in a changing ocean: Proceedings of the 48th European Marine Biology Symposium (EMBS), Galway, Ireland, 19-23 August 2013. European Marine Biology Symposia, 48. Marine Environmental Research, 102(Suppl.). 130 pp., more
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| Keywords |
Dreissena polymorpha (Pallas, 1771) [WoRMS]
Marine/Coastal |
| Author keywords |
Dreissena polymorpha; Feeding; Chlorophyll; Salinity; Seston; BRT modelling |
| Authors | | Top |
- Oganjan, K.
- Lauringson, V.
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| Abstract |
Benthic suspension feeding is an important process in coastal ecosystems. Among all the World's oceans, coastal ecosystems are the most modified by human impact and changing at accelerating pace. It is complicated to understand, how various environmental factors affect feeding rates of suspension feeders in their natural habitats. Thus, shapes of such relationships are poorly described for several intersections of environmental gradients. In this study, relationships between grazing rates of an invasive bivalve Dreissena polymorpha and ambient environmental factors were investigated in a turbid eutrophic bay of the central Baltic Sea using a novel modelling method of Boosted Regression Trees (BRT), a statistical tool able to handle non-normal distributions, complex relationships, and interactive effects. Feeding rates of mussels were derived from field populations by measuring the content of algal pigments in specimens collected from their natural habitat. The content of pigments was converted to feeding rate separately each time using field experiments measuring simultaneously the content of pigments and biodeposition of mussels.The results suggest that feeding rates of D. polymorpha are related to several environmental factors which gradients outreach the optimal range for the local mussel population. All the observed effects were non-linear with complex shapes. Variability along the resource gradient was the most important predictor of mussel feeding, followed by salinity and disturbance caused by wind. The most important interaction occurred between disturbance and resource gradient, while feeding function showed more plasticity along the latter. Mapping of environmental tipping points with the aid of machine learning methods may enable to concentrate the most relevant information about ecological functions worldwide. |
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