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Fish age is an important biological variable required as part of routine stock assessment and analysis of fish population dynamics. Age estimates are traditionally obtained by human experts from the count of ring-like patterns along calcified structures such as otoliths. To automate the process and minimize human bias, modern methods have been designed utilizing the advances in the field of artificial intelligence (AI). While many AI-based methods have been shown to attain satisfactory accuracy, there are concerns regarding the lack of explainability of some early implementations. Consequently, new explainable AI-based approaches based on U-Net and Mask R-CNN have been recently published having direct compatibility with traditional ring counting procedures. Here we further extend this endeavor by creating an interactive website housing these explainable AI methods allowing age readers to be directly involved in the AI training and development. An important aspect of the platform presented in this article is that it allows the additional use of different advanced concepts of Machine Learning (ML) such as transfer learning, ensemble learning and continual learning, which are all shown to be effective in this study. [ABSTRACT FROM AUTHOR]

This study investigated to what extent the extrapallial fluid (EPF) of the marine bivalve Arctka islandica (Linneaus, 1767) is involved in shell formation. With in situ pH microscopy, pH gradients were identified between inner shell surface and outer mantle epithelium (OME). pH at the OME varied rapidly between neutral and values above 9, suggesting active [H.sup.+] pumping. Microsensor measurements showed also remarkable short-term dynamics in pH and [Ca.sup.2+] concentrations, again suggesting active ion pumping. Further focus was on pH, [Ca.sup.2+], and dissolved inorganic carbon dynamics within the EPF to determine whether calcium carbonate precipitation is possible within the EPF. The data show that the bulk of the inner EPF rarely reaches calcium carbonate saturation and, thus, cannot be the site of shell formation. At the OME surface, however, pH levels of up to 9.5 were observed, corresponding to a 30-fold carbonate supersaturation. Thus, ion pumping by the OME can drive calcification when the OME is just a few [micro]m distant from the inner shell surface, as it is the case in the outer EPF. KEY WORDS: Arctica islandica, calcification, bivalves, ion transportation, microsensors, calcium carbonate saturation, INTRODUCTION Biomineralization is a regulated process within living organisms where calcium carbonate precipitates on an organic matrix (Crenshaw 1972a, Lowenstam 1981, Falini et al. 1996, Nudelman et al. 2006). Shell [...]

Profound environmental changes, such as drastic sea-ice decline, leave large-scale ecological footprints on the distribution and composition of marine biotas in the Arctic. Currently, the impact of such stressors is not sufficiently understood due to the lack of pan-Arctic data that allow for estimating ecological baselines, as well as modelling current and forecast potential changes in benthic biodiversity and ecosystem functioning. Here, we introduce a PAN-Arctic data collection of benthic BIOtas (PANABIO), and discuss its timeliness, potential, and details of its further development. The data collection contains records of benthic fauna at genus-level or species-level identified in field samples obtained by means of grabs, towed gear, or seabed imaging. Currently, it includes records of 2,968 species or genera, ranging from presence to counts, densities or biomass, grouped per sample. The data represent the pan-Arctic realm, covering all major marine areas, i.e., the central Arctic Ocean, Chukchi Sea, East Siberian Sea, Laptev Sea, Kara Sea, Barents Sea (incl. White Sea), Svalbard waters, Greenland Sea, Norwegian Sea, Canadian Archipelago, Beaufort Sea, and Bering Sea. All data are point-referenced sampling locations. Currently, 124,040 records from 10,645 samples taken at 10,631 stations between 1800 and 2014 are included, but these numbers will increase with more data becoming available. The data collection is available in a PostgreSQL-based data warehouse that can be accessed and queried through an open-access frontend web service at https://critterbase.awi.de/panabio. A snap shot of the current data collection (of 26 June 2023) is also available from the data publisher PANGAEA. [ABSTRACT FROM AUTHOR]