Assessing the potential of autonomous submarine gliders for ecosystem monitoring across multiple trophic levels (plankton to cetaceans) and pollutants in shallow shelf seas

Wildlife Population Assessment: Changing Priorities Driven by Technological Advances

Advances in technology are having a large effect on the priorities for innovation in statistical ecology. Collaborations between statisticians and ecologists have always been important in driving methodological development, but increasingly, expertise from computer scientists and engineers is also needed. We discuss changes that are occurring and that may occur in the future in surveys for estimating animal abundance. As technology advances, we expect classical distance sampling and capture-recapture to decrease in importance, as camera (still and video) survey, acoustic survey, spatial capture-recapture and genetic methods continue to develop and find new applications. We explore how these changes are impacting the work of the statistical ecologist.

Compact representation of temporal processes in echosounder time series via matrix decomposition

The recent explosion in the availability of echosounder data from diverse ocean platforms has created unprecedented opportunities to observe the marine ecosystems at broad scales. However, the critical lack of methods capable of automatically discovering and summarizing prominent spatio-temporal echogram structures has limited the effective and wider use of these rich datasets. To address this challenge, a data-driven methodology is developed based on matrix decomposition that builds compact representation of long-term echosounder time series using intrinsic features in the data. In a two-stage approach, noisy outliers are first removed from the data by principal component pursuit, then a temporally smooth nonnegative matrix factorization is employed to automatically discover a small number of distinct daily echogram patterns, whose time-varying linear combination (activation) reconstructs the dominant echogram structures. This low-rank representation provides biological information that is more tractable and interpretable than the original data, and is suitable for visualization and systematic analysis with other ocean variables. Unlike existing methods that rely on fixed, handcrafted rules, this unsupervised machine learning approach is well-suited for extracting information from data collected from unfamiliar or rapidly changing ecosystems. This work forms the basis for constructing robust time series analytics for large-scale, acoustics-based biological observation in the ocean.

Autonomous marine environmental monitoring: Application in decommissioned oil fields

Hundreds of Oil & Gas Industry structures in the marine environment are approaching decommissioning. In most areas decommissioning operations will need to be supported by environmental assessment and monitoring, potentially over the life of any structures left in place. This requirement will have a considerable cost for industry and the public. Here we review approaches for the assessment of the primary operating environments associated with decommissioning - namely structures, pipelines, cuttings piles, the general seabed environment and the water column - and show that already available marine autonomous systems (MAS) offer a wide range of solutions for this major monitoring challenge. Data of direct relevance to decommissioning can be collected using acoustic, visual, and oceanographic sensors deployed on MAS. We suggest that there is considerable potential for both cost savings and a substantial improvement in the temporal and spatial resolution of environmental monitoring. We summarise the trade-offs between MAS and current conventional approaches to marine environmental monitoring. MAS have the potential to successfully carry out much of the monitoring associated with decommissioning and to offer viable alternatives where a direct match for the conventional approach is not possible.

A review of unmanned vehicles for the detection and monitoring of marine fauna

Recent technology developments have turned present-day unmanned systems into realistic alternatives to traditional marine animal survey methods. Benefits include longer survey durations, improved mission safety, mission repeatability, and reduced operational costs. We review the present status of unmanned vehicles suitable for marine animal monitoring conducted in relation to industrial offshore activities, highlighting which systems are suitable for three main monitoring types: population, mitigation, and focal animal monitoring. We describe the technical requirements for each of these monitoring types and discuss the operational aspects. The selection of a specific sensor/platform combination depends critically on the target species and its behaviour. The technical specifications of unmanned platforms and sensors also need to be selected based on the surrounding conditions of a particular offshore project, such as the area of interest, the survey requirements and operational constraints.