Voigt, Janett (2010) Determination of the critical aragonite saturation state under which skeleton structures of the cold-water coral Lophelia pertusa begin to dissolve. (Diploma thesis), Universität Rostock, Rostock, Germany, 91 pp.

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Abstract

The scleractinian Lophelia pertusa is a cosmopolitan reef-forming cold-water coral and creates a habitat for a diverse associated community. This ecosystem, however, is threatened by ocean acidification due to declining pH and shoaling of the carbonate saturation horizon resulting in dissolution of coral skeletons. The aragonite saturation state (ΩAr) given in literature refers to the pure mineral but organic compounds within the skeleton might have an impact on dissolution behaviour and thus could influence the solubility product of aragonite. In this study the critical .QAr, under which aragonite starts to dissolve, was determined for dead skeleton framework of L. pertusa through manipulating natural seawater by acidification (by use of HCl and Na2C03) and bubbling with C02-enriched air. Moreover, the organic portion of the skeleton was quantified by determining the ash-free dry weight. Additionally, scanning electron microscopy images were taken after the dissolution experiment to examine visible dissolution effects on the skeleton surface. Measurements revealed an ΩAr range of 0.795-0.971. Combustion of skeleton fragments of the white and red colour variety of L. pertusa presented an organic content of 3.37 and 3.11 % of total weight, respectively. This relatively high organic :fraction within the skeleton could explain the low saturation state obtained which is below the generally stated threshold of ΩAr = 1. Since the dead framework constitutes the major part of a coldwater reef the obtained saturation state might be regarded as the critical ΩAr for an entire L. pertusa reef. Consequently, this low critical saturation state could indicate that Lophelia pertusa reefs are probably more resistant to ocean acidification than thought before resulting in significant implications for future predicting models.

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