Long‐term automated visual monitoring of Antarctic benthic fauna

Abstract

The rapid changes in the climate of Antarctica are likely to pose challenges to living communities, which makes monitoring of Antarctic fauna an urgent necessity. Benthos is particularly difficult to monitor, and is sensitive to local environmental changes. At the same time, long‐term monitoring is complicated by logistical factors. It is therefore urgent to develop advanced instruments to set up autonomous and long‐term monitoring programmes to obtain the lacking biological knowledge needed to understand this complex and remote marine environment. We present a pilot study to set up a non‐invasive and sustainable autonomous monitoring activity in Antarctica, leveraging on a specifically designed automated camera recording, computer vision and machine learning image processing techniques. We also present and analyse the high‐resolution image dataset acquired for an extended period of time encompassing both the summer and the Antarctic night and the corresponding transition periods. The results of this study demonstrate both the effectiveness of such an autonomous imaging devices for acquiring relevant long‐term visual data and the effectiveness of the proposed image analysis algorithms for extracting relevant scientific knowledge from such data. The presented results show how the extracted knowledge discloses dynamics of the observed ecosystems that can be obtained only through continuous observations extended in time, not achievable with the state‐of‐the‐art monitoring approaches commonly implemented in Antarctica. The success of this pilot study is a step towards the collection of continuous data near shore in Antarctic areas and in general in all the remote and extreme underwater habitats. Moreover, the presented stand‐alone and autonomous imaging device can be used for increasing the number of the monitoring sites in remote environments and when complemented with the acquisition of physical and bio‐chemical variables it can be used for obtaining data collections of great scientific value difficult to acquire otherwise.

Advancing fishery-independent stock assessments for the Norway lobster (Nephrops norvegicus) with new monitoring technologies

“…Stationary cameras can improve estimates on the proportion of counted burrow systems in relation to resident animals, providing additional information to UWTV surveys. A similar application context can be found in (Marini et al, 2022) where the dynamics of the coastal Antarctic benthic fauna have been estimated for a whole year through a specifically designed autonomous and stand-alone camera (Corgnati et al, 2016;Marini et al, 2018a). The consistent monitoring of specific burrow systems (replicated over the seabed space by a network of cameras) can be used to fine-tune the demographic estimation of targeted populations with data on the temporal use of burrows by individuals (see Supplementary Table S1).…”

Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna

“…The monitored area is rich of sponges Mycale (Oxymycale) acerata Kirkpatrick, 1907, sea urchins Sterechinus neumayeri (Meissner, 1900) and sea stars, mainly Odontaster validus Koehler, 1906 and Diplasterias brucei (Koehler, 1907) present all over the year, while fishes and several benthic organisms show different behaviors depending on the Antarctic season 30 .…”

“…Within this context, this article describes the image dataset acquired within the ICE-LAPSE project 29 through a long-term autonomous video monitoring activity 30 continuously performed from January to November 2017 near the Italian Antarctic station Mario Zucchelli (Terra Nova Bay, Ross Sea).…”

“…The image dataset was acquired by using a stand-alone imaging device, specifically conceived for autonomous deployment extended in time 30 – 33 , positioned on the seabed of a coastal area at 20 m depth. The dataset consists of 755 images acquired every nine hours, encompassing both the Antarctic daylight and dark periods, including the corresponding transition phases.…”

Maka Niu: A low-cost, modular imaging and sensor platform to increase observation capabilities of the deep ocean

“…While the use of a Raspberry Pi camera for low-cost underwater imaging is not unique, few of these systems are designed for nor capable of reaching 1,500 m depth or more. (Almero et al, 2021;Bergshoeff et al, 2016;Marini et al, 2013;Marini et al, 2018;Marini et al, 2022). We also describe additional modules that are currently being engineered and suggest the construction of future modules to expand overall system capabilities.…”