An International Journal on Computational Ecology and Ecological Data Science

The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science, biogeography, and ecosystem analysis. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable ecosystem management in view of global environmental and climate change.

The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling of ecological data, and uncertainty analysis.

The journal invites papers on:

    novel concepts and tools for monitoring, acquisition, management, analysis, and synthesis of ecological data,
    innovative strategies and applications of eco-acoustics, eco-genomics, digital image processing, machine and deep learning,
    Bayesian inference and uncertainty analysis techniques,
    species distribution modelling,
    understanding and forecasting of ecosystem functioning and evolution, and
    use of quantitative tools to inform management decisions on environmental issues like ecosystem sustainability, climate change, and biodiversity.

Special Issue on Computational methods and machine learning for Oceans
提出日: 2025-02-15

Oceans play a crucial role in maintaining global ecological balance and climate regulation, making them a focal point in the UN's 2030 Agenda for Sustainable Development (Goal 14). However, understanding ocean dynamics is challenging due to the intricate nature of these expansive ecosystems and the complexity of managing, integrating and analysing diverse datasets. Consequently, the ever increasing need of advanced computational methods, ecosystem modeling approaches, and computer vision technologies, driven by machine learning, has become evident.

This Special Issue delves into extensive studies with the aim of deepening ecological knowledge about our oceans, in order to ensure the conservation and sustainable use of them and their resources. The focus is to address the vulnerability of the oceans to a spectrum of environmental risks, including anthropogenic pressures, such as overfishing, oil spills, microplastics, chemical and physical pollutants and climate change. Through a combination of traditional and next-generation computational methods in the acquisition and/or analysis of data pertinent to marine-related tasks, this special issue endeavors to explore profound insights into the intricate dynamics of marine environment.

Oceans play a crucial role in maintaining global ecological balance and climate regulation, making them a focal point in the UN's 2030 Agenda for Sustainable Development (Goal 14). However, understanding ocean dynamics is challenging due to the intricate nature of these expansive ecosystems and the complexity of managing, integrating and analysing diverse datasets. Consequently, the ever increasing need of advanced computational methods, ecosystem modeling approaches, and computer vision technologies, driven by machine learning, has become evident.

This Special Issue delves into extensive studies with the aim of deepening ecological knowledge about our oceans, in order to ensure the conservation and sustainable use of them and their resources. The focus is to address the vulnerability of the oceans to a spectrum of environmental risks, including anthropogenic pressures, such as overfishing, oil spills, microplastics, chemical and physical pollutants and climate change. Through a combination of traditional and next-generation computational methods in the acquisition and/or analysis of data pertinent to marine-related tasks, this special issue endeavors to explore profound insights into the intricate dynamics of marine environment.

Guest editors:

Dr. Rosalia Maglietta
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (CNR-STIIMA)
rosalia.maglietta@cnr.it 

Dr. Simone Franceschini
University of Hawaiʻi at Mānoa
simonefr@hawaii.edu 

Dr. Pasquale Ricci
University of Bari.
Baripasquale.ricci@uniba.it 

Manuscript submission information:

Submission Deadline: Feb 15, 2025

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Managing Guest Editor: Dr. Rosalia Maglietta.

The journal’s submission platform (Editorial Manager®) is now available for receiving submissions to this Special Issue. Please refer to the Guide for Authors to prepare your manuscript and select the article type of “VSI:Ocean Learning” when submitting your manuscript online.

Keywords:

machine learning, deep learning, computational methods, computer vision, remote sensing, ecological modeling, marine environment monitoring, automated species classification, habitat modeling, specie distribution modeling