Marjolaine Matabos

Astronomical and atmospheric impacts on deep-sea hydrothermal vent invertebrates

Ocean tides and winter surface storms are among the main factors driving the dynamics and spatial structure of marine coastal species, but the understanding of their impact on deep-sea and hydrothermal vent communities is still limited. Multidisciplinary deep-sea observatories offer an essential tool to study behavioural rhythms and interactions between hydrothermal community dynamics and environmental fluctuations. Here, we investigated whether species associated with a Ridgeia piscesae tubeworm vent assemblage respond to local ocean dynamics. By tracking variations in vent macrofaunal abundance at different temporal scales, we provide the first evidence that tides and winter surface storms influence the distribution patterns of mobile and non-symbiotic hydrothermal species (i.e. pycnogonids Sericosura sp. and Polynoidae polychaetes) at more than 2 km depth. Local ocean dynamics affected the mixing between hydrothermal fluid inputs and surrounding seawater, modifying the environmental conditions in vent habitats. We suggest that hydrothermal species respond to these habitat modifications by adjusting their behaviour to ensure optimal living conditions. This behaviour may reflect a specific adaptation of vent species to their highly variable habitat.

Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing?

1.Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, have acquired nearly 65 TB and over 90,000 hours of video data from seafloor cameras and Remotely Operated Vehicles (ROVs). Manual processing of these data is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. These videos are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea. 2.We compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) Anoplopoma fimbria, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited via a crowdsourcing platform and the second were experienced university students, who performed the task for their ichthyology class. Results were validated against counts obtained from a scientific expert. 3.All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest. 4.As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep-sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development.