Computer Science Meets Ecology (Dagstuhl Seminar 17091)

This report summarizes the program and main outcomes of the Dagstuhl Seminar 17091 entitled ``Computer Science Meets Ecolog\u27\u27. Ecology is a discipline that poses many challenging problems involving big data collection, provenance and integration, as well as difficulties in data analysis, prediction and understanding. All these issues are precisely the arena where computer science is concerned. The seminar motivation was rooted in the belief that ecology could largely benefit from modern computer science. The seminar attracted scientists from both fields who discussed important topics in ecology (e.g. botany, animal science, biogeochemistry) and how to approach them with machine learning, computer vision, pattern recognition and data mining. A set of specific problems and techniques were treated, and the main building blocks were set up. The important topics of education, outreach, data and models accessibility were also touched upon. The seminar proposed a distinctive perspective by promoting cross-fertilization in a unique environment and a unique set of individuals

APPLICATION OF AUTONOMOUS UNDERWATER VEHICLES TO THE STUDY OF DEEP-SEA BENTHIC ECOLOGY

Rising anthropogenic pressure in the deep sea prompts concerns for its short and long term conservation, however, it remains mostly unexplored. Effective conservation strategies need to be based on a sound understanding of the target ecosystem, or ecosystems, which is not the case in the deep sea, owing largely to the lack of sufficient data. Autonomous Underwater Vehicles (AUV) could help address several long-standing challenges in the study of deep-sea ecology, thanks to their capacity to efficiently sample this remote environment. This thesis aims to investigate how these vehicles can contribute to the study of deep-sea benthic ecology through applying AUV acquired data (presented in chapter 2) to address fundamental questions in deep-sea ecology (Chapters 3 and 4), as well as asking how the benefits of AUVs, their capacity to quickly gather data in the form of large numbers of seafloor images, can be fully realised (Chapter 5). The research conducted in this thesis suggests AUVs are able to quickly and efficiently obtain representative samples, allowing efficient and statistically robust quantification of the density and diversity of benthic epifauna. They can also successfully detect consistent structure in the fine scale distribution of a model benthic epifaunal species (Syringammina fragilissima). However, the AUV derived dataset, including high resolution data on environmental variables, failed to clearly establish the environmental parameters driving this distribution. This suggests that although AUVs are capable of gathering large high-resolution datasets, the number of data-points is not the only important criterion for a representative sample. Finally, the application of Computer Vision and Artificial Intelligence methods to the AUV data set demonstrated that useful results can be obtained for some taxa, and the fast development of this technology promises future progress