HAUSGARTEN: Multidisciplinary investigations at a deep-sea, long-term observatory in the Arctic Ocean

The marine Arctic has played an essential role in the history of our planet over the past 130 million years and contributes considerably to the present functioning of Earth and its life. The global cycles of a variety of materials fundamental to atmospheric conditions and thus to life depend to a signifi cant extent on Arctic marine processes (Aargaard et al., 1999). The past decades have seen remarkable changes in key Arctic variables. The decrease of sea-ice extent and sea-ice thickness in the past decade is statistically signifi - cant (Cavalieri et al., 1997; Parkinson et al., 1999; Walsh and Chapman, 2001; Partington et al., 2003; Johannessen et al., 2004). There have also been large changes in the upper and intermediate layers of the ocean, which have environmental implications. For instance, the deep Greenland Sea has continued its decadal trend towards warmer and saltier conditions, with a corresponding decrease in oxygen content, refl ecting the lack of effective local convection and ventilation (Dickson et al., 1996; Boenisch et al., 1997). Changes in temperature and salinity and associated shifts in nutrient distributions will directly affect the marine biota on multiple scales from communities and populations to individuals, consequently altering food-web structures and ecosystem functioning (Benson and Trites, 2002; Moore, 2003; Schumacher et al., 2003; Wiltshire and Manly, 2004; Perry et al., 2005). Today, we do not know whether the severe alterations in abiotic parameters represent perturbations due to human impacts, natural long-term trends, or new equilibriums (Bengtson et al., 2004). Because Arctic organisms are highly adapted to extreme environmental conditions with strong seasonal forcing, the accelerating rate of recent climate change challenges the resilience of Arctic life (Hassol, 2004). The entire system is likely to be severely affected by changing ice and water conditions, varying primary production and food availability to faunal communities, an increase in contaminants, and possibly increased UV irradiance. The stability of a number of Arctic populations and ecosystems is probably not strong enough to withstand the sum of these factors, which might lead to a collapse of subsystems. To detect and track the impact of large-scale environmental changes in the transition zone between the northern North Atlantic and the central Arctic Ocean, and to determine experimentally the factors controlling deep-sea biodiversity, the German Alfred Wegener Institute for Polar and Marine Research (AWI) established the deepsea, long-term observatory HAUSGARTEN, representing the fi rst, and by now only, open-ocean, long-term station in a polar region

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