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Applications of Machine Learning in Marine Ecology Studies
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Applications of Machine Learning in Marine Ecology Studies

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (5 May 2021) | Viewed by 13589

Special Issue Editors

Dr. Marta Belchior Lopes

E-Mail Website
Guest Editor
Faculty of Science and Technology, NOVA University of Lisbon, Caparica, Portugal
Interests: machine learning; bioinformatics; marine ecology; precision oncology; bioprocess monitoring
Dr. Marta Belchior Lopes

E-Mail Website
Guest Editor
Portuguese Institute for the Sea and Atmosphere, IPMA, Division of Environmental Oceanography, Lisbon, Portugal
Interests: toxicology; seafood safety; environmental contamination by marine biotoxins

Special Issue Information

Dear Colleagues,

Recent technological advances in marine sciences enable the collection of large and complex datasets, with multiple interactions between variables, which hampers traditional methods in marine ecology seeking to translate the vast amount of information into a decision-making format. Machine learning methods have the ability to learn from large datasets to find patterns and predict outcomes via unsupervised and supervised learning tasks.

The purpose of this Special Issue is to foster discussions on state-of-the-art machine learning research directions across several areas in marine sciences, including, but not limited to, fisheries, oceanography, pollution, and biodiversity studies. Particular focus will be given to the challenges posed by today's ecological datasets, namely data quality and growing dimensionality. The Special Issue will strengthen the communication channels between marine and machine learning scientists for an efficient use of available data and proper extraction of meaningful information from marine ecology systems.

Dr. Marta Belchior Lopes
Dr. Pedro Reis Costa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Marine ecology
  • Machine learning
  • Time-series analysis
  • Model regularization
  • Bayesian inference
  • Deep learning
  • Remote sensing
  • High dimensionality

Published Papers (3 papers)

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Research

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16 pages, 753 KiB  
Article
On the Importance of Passive Acoustic Monitoring Filters
by Rafael Aguiar, Gianluca Maguolo, Loris Nanni, Yandre Costa and Carlos Silla, Jr.
J. Mar. Sci. Eng. 2021, 9(7), 685; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse9070685 - 22 Jun 2021
Cited by 2 | Viewed by 2154
Abstract
Passive acoustic monitoring (PAM) is a noninvasive technique to supervise wildlife. Acoustic surveillance is preferable in some situations such as in the case of marine mammals, when the animals spend most of their time underwater, making it hard to obtain their images. Machine [...] Read more.
Passive acoustic monitoring (PAM) is a noninvasive technique to supervise wildlife. Acoustic surveillance is preferable in some situations such as in the case of marine mammals, when the animals spend most of their time underwater, making it hard to obtain their images. Machine learning is very useful for PAM, for example to identify species based on audio recordings. However, some care should be taken to evaluate the capability of a system. We defined PAM filters as the creation of the experimental protocols according to the dates and locations of the recordings, aiming to avoid the use of the same individuals, noise patterns, and recording devices in both the training and test sets. It is important to remark that the filters proposed here were not intended to improve the accuracy rates. Indeed, these filters tended to make it harder to obtain better rates, but at the same time, they tended to provide more reliable results. In our experiments, a random division of a database presented accuracies much higher than accuracies obtained with protocols generated with PAM filters, which indicates that the classification system learned other components presented in the audio. Although we used the animal vocalizations, in our method, we converted the audio into spectrogram images, and after that, we described the images using the texture. These are well-known techniques for audio classification, and they have already been used for species classification. Furthermore, we performed statistical tests to demonstrate the significant difference between the accuracies generated with and without PAM filters with several well-known classifiers. The configuration of our experimental protocols and the database were made available online. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Marine Ecology Studies)
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16 pages, 2479 KiB  
Article
Comparative Study of Clustering Approaches Applied to Spatial or Temporal Pattern Discovery
by Kelly Grassi, Émilie Poisson-Caillault, André Bigand and Alain Lefebvre
J. Mar. Sci. Eng. 2020, 8(9), 713; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse8090713 - 15 Sep 2020
Cited by 2 | Viewed by 2277
Abstract
In the framework of ecological or environmental assessments and management, detection, characterization and forecasting of the dynamics of environmental states are of paramount importance. These states should reflect general patterns of change, recurrent or occasional events, long-lasting or short or extreme events which [...] Read more.
In the framework of ecological or environmental assessments and management, detection, characterization and forecasting of the dynamics of environmental states are of paramount importance. These states should reflect general patterns of change, recurrent or occasional events, long-lasting or short or extreme events which contribute to explain the structure and the function of the ecosystem. To identify such states, many scientific consortiums promote the implementation of Integrated Observing Systems which generate increasing amount of complex multivariate/multisource/multiscale datasets. Extracting the most relevant ecological information from such complex datasets requires the implementation of Machine Learning-based processing tools. In this context, we proposed a divisive spectral clustering architecture—the Multi-level Spectral Clustering (M-SC) which is, in this paper, extended with a no-cut criteria. This method is developed to perform detection events for data with a complex shape and high local connexity. While the M-SC method was firstly developed and implemented for a given specific case study, we proposed here to compare our new M-SC method with several existing direct and hierarchical clustering approaches. The clustering performance is assessed from different datasets with hard shapes to segment. Spectral methods are most efficient discovering all spatial patterns. For the segmentation of time series, hierarchical methods better isolated event patterns. The new M-SC algorithm, which combines hierarchical and spectral approaches, give promise results in the segmentation of both spatial UCI databases and marine time series compared to other approaches. The ability of our M-SC method to deal with many kinds of datasets allows a large comparability of results if applies within a broad Integrated Observing Systems. Beyond scientific knowledge improvements, this comparability is crucial for decision-making about environmental management. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Marine Ecology Studies)
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Review

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17 pages, 464 KiB  
Review
A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination
by Rafaela C. Cruz, Pedro Reis Costa, Susana Vinga, Ludwig Krippahl and Marta B. Lopes
J. Mar. Sci. Eng. 2021, 9(3), 283; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse9030283 - 05 Mar 2021
Cited by 55 | Viewed by 8317
Abstract
Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is [...] Read more.
Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Marine Ecology Studies)
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