Biological hard parts and skeletons of aquatic organisms often archive information of past environmental conditions. Deciphering such information forms an essential contribution to our understanding of past climate conditions and thus our ability to mitigate the climatic, ecological, and social impacts of a rapidly changing environment. Several established techniques enable the visualization and reliable use of the information stored in anatomical features of such biogenic archives, i.e., its growth patterns. Here, we test whether confocal Raman microscopy (CRM) is a suitable method to reliably identify growth patterns in the commonly used archive Arctica islandica and the extinct species Pygocardia rustica (both Bivalvia). A modern A. islandica specimen from Norway has been investigated to verify the general feasibility of CRM, resulting in highly correlated standardized growth indices (r>0.96; p<0.0001) between CRM-derived measurements and measurements derived from the established methods of fluorescence microscopy and Mutvei’s solution staining. This demonstrates the general suitability of CRM as a method for growth pattern evaluation and cross-dating applications. Moreover, CRM may be of particular interest for paleoenvironmental reconstructions, as it yielded superior results in the analysis of fossil shell specimens (A. islandica and P. rustica) compared to both Mutvei staining and fluorescence microscopy. CRM is a reliable and valuable tool to visualize internal growth patterns in both modern and fossil calcium carbonate shells that notably also facilitates the assessment of possible diagenetic alteration prior to geochemical analysis without geochemically compromising the sample. We strongly recommend the CRM approach for the visualization of growth patterns in fossil biogenic archives, where conventional methods fail to produce useful results.
AWI Organizations > Biosciences > Functional Ecology
AWI Organizations > Graduate Research Schools > ESSReS
Helmholtz Research Programs > PACES II (2014-2018) > TOPIC 3: The earth system from a polar perspective > WP 3.3: From process understanding to enabling climate prediction