Project properties
Title Evaluating the impact of active restoration strategies on marine biodiversity using underwater imaging
Group Marine Animal Ecology
Project type thesis
Credits 24-39
Supervisor(s) Zhiyuan Zhao
Myron Peck
Examiner(s) Tinka Murk, Ronald Osinga, Reindert Nijland, Rosa van der Ven or Diede Maas
Contact info zhiyuan.zhao@nioz.nl, myron.peck@nioz.nl
Begin date 2025/02/01
End date 2025/09/01
Description Can also be an internship! Email mae.education@wur.nl for an information leaflet.

Marine biodiversity monitoring is essential for understanding ecosystem changes, and for
evaluating the effectiveness of conservation strategies such as marine protected areas and active
habitat restoration. Underwater imaging with AI has emerged as a highly promising avenue to
improve the efficiency of biodiversity data collection and analysis. AI can extract ecological data
from digital images, automatically identifying and classifying marine organisms (e.g., plankton,
benthic organisms, fish). This supports analyses of species distribution, population dynamics, and
behavioral patterns while significantly enhancing the speed and accuracy of data processing.

As part of the international BioBoost+ EU project, we will collect training subsets for AI
development while exploring the scientific question: “How does active habitat restoration impact
marine biodiversity?” The research will take place in large underwater labs simulating marine
environments (see the photo below), where different biodiversity restoration treatments will be
applied. These treatments include reintroducing habitat-forming species and manipulating habitat
complexity, with barren soft-bottom seabeds serving as the control. Underwater cameras will be
deployed to track the time course of changes in biodiversity within the different treatments. The
MSc student will work on a team to set up the underwater labs, maintain the cameras, and collect
/ manually annotate videos/images. Their MSc thesis will use digital imaging to evaluate early-stage changes in biodiversity among the treatments. Depending on their skills and interests, the student can choose to participate in AI development or focus exclusively on ecological research.

The location is NIOZ Yerseke (housing can be arranged if needed). The duration is at least six
months, starting between February and April 2025. The daily supervisors for this MSc project are Dr. Zhiyuan Zhao and Prof. Myron Peck. If you are interested, please contact them via email at zhiyuan.zhao@nioz.nl and myron.peck@nioz.nl (please include your c.v.).
Used skills
Requirements Interest in marine biodiversity monitoring; passion for AI methods; good R/Python skills