Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Reaktion benthischer mikrobieller Tiefsee-Gemeinschaften auf den Eintrag von partikulärem organischen Material: In situ Experimente in der Framstrasse (Arktischer Ozean)

This thesis aims at achieving deeper insights into the ecological functioning of heterotrophic microbial communities in high northern latitude deep-sea sediments, i.e. their structural and functional response to a sudden large input of particulate organic matter (POM).Three in situ studies, each divided into a short- (seven days) and long-term experiment (one year), were carried out by using a Sediment Tray Free Vehicle (STFV) which was deployed in the Arctic Ocean at the experimental site of the deep-sea long-term observatory HAUSGARTEN (Fram Strait, 2500 m water depth). Special emphasis was placed on the enrichment of deep-sea sediments with chitin as one of the most important biopolymer in aquatic ecosystems. Additionally, experiments were carried out in association with different sediment types (deep-sea sediments, glass beads, coarse sand) to assess how variations in sediment characteristics (e.g. particle size, particle shape, organic carbon content) affect the microbial response to POM supply.Different microbial parameters (cell number, biomass, hydrolytic enzyme potential) were measured and bacterial community composition was determined by using the fingerprint method of terminal-restriction fragment length polymorphism (T-RFLP).Results evidenced clear chitin-dependent response of benthic microbial communities in the deep Arctic Ocean and underlined their important role in recycling this highly insoluble organic substrate. Their functional in situ response following a large chitin input may be triggered by an initial change in community structure before efficient utilisation of chitin compounds can be made. Sediment type was found to be a significant factor influencing enzymatic activity and structure of deep-sea microbial communities. Overall, findings from these in situ studies demonstrated the important role of environmental conditions such as POM availability for driving microbial functioning and diversity at the Arctic deep seafloor