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Application of Machine Learning in Marine Ecology

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Dr. Grant R.W. Humphries

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Guest Editor

HiDef Aerial Surveying Ltd. The Observatory, Dobies Business Park, Lillyhall, Workington, Cumbria, UK
Interests: machine learning; marine ecology; spatial ecology

Dr. Marianna Chimienti

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Guest Editor

Centre d'Etudes Biologiques de Chizé, 405 Route de Prissé la Charrière, 79360 Villiers-en-Bois, France
Interests: movement ecology; accelerometers; statistical modelling; hidden Markov models; machine learning; conservation biology
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Special Issue Information

Dear Colleagues,

Machine learning is a field of computational science which first emerged in the 1950s. However, our ability to effectively harness the power of machine learning techniques was only truly realised in the 1990s. In ecology, the earliest adoption of machine learning came about in the early 2000s, when regression tree algorithms were applied to spatial data to predict species distributions. This was quickly adapted in the field of marine ecology to study the distribution of many pelagic species. Since that time, machine learning algorithms have been adapted and applied in various studies in the marine environment, from population models, image recognition, and experimental studies. Because they are non-parametric in nature and able to incorporate complex information, machine learning techniques are ideal for application to ecological problems. Furthermore, machine learning and its sub-field, deep learning, allow researchers to extend their ability to monitor populations of difficult-to-access marine species using automated techniques, which will greatly enhance conservation efforts globally.

The purpose of this Special Issue is to highlight the use of machine learning algorithms for studying marine ecosystems. Topics may range from applications of machine learning in species distribution modelling, to image or vocal recognition. Studies focusing on any marine species, including those in the coastal environment, are welcome. Articles may address, but are not limited to, the following topics:

Species distribution modelling;
Marine protected area planning;
Machine learning for experimental research;
Image recognition for marine conservation;
Audio recognition;
Machine learning tools and tag development;
Sustainability planning using machine learning.

Dr. Grant R.W. Humphries
Dr. Marianna Chimienti
Guest Editors

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15 pages, 5036 KiB

Open AccessArticle

Machine Learning for Detection of Macroalgal Blooms in the Mar Menor Coastal Lagoon Using Sentinel-2

by Encarni Medina-López, Gabriel Navarro, Juan Santos-Echeandía, Patricia Bernárdez and Isabel Caballero

Remote Sens. 2023, 15(5), 1208; https://doi.org/10.3390/rs15051208 - 22 Feb 2023

Cited by 2 | Viewed by 2964

Abstract

The Mar Menor coastal lagoon in southeastern Spain has experienced a decline in water quality due to increased nutrient input, leading to the eutrophication of the lagoon and the occurrence of microalgal and macroalgal blooms. This study analyzes the macroalgal bloom that occurred in the lagoon during the spring-summer of 2022. A set of machine learning techniques are applied to Sentinel-2 satellite imagery in order to obtain indicators of the presence of macroalgae in specific locations within the lagoon. This is supported by in situ observations of the blooming process in different areas of the Mar Menor. Our methodology successfully identifies the macroalgal bloom locations (accuracies above 98%, and Matthew’s Correlation Coefficients above 78% in all cases), and provides a probabilistic approach to understand the likelihood of occurrence of this event in given pixels. The analysis also identifies the key parameters contributing to the classification of pixels as algae, which could be used to develop future algorithms for detecting macroalgal blooms. This information can be used by environmental managers to implement early warning and mitigation strategies to prevent water quality deterioration in the lagoon. The usefulness of satellite observations for ecological and crisis management at local and regional scales is also highlighted. Full article

(This article belongs to the Special Issue Application of Machine Learning in Marine Ecology)

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23 pages, 5132 KiB

Open AccessArticle

Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales

by Ryan R. Reisinger, Ari S. Friedlaender, Alexandre N. Zerbini, Daniel M. Palacios, Virginia Andrews-Goff, Luciano Dalla Rosa, Mike Double, Ken Findlay, Claire Garrigue, Jason How, Curt Jenner, Micheline-Nicole Jenner, Bruce Mate, Howard C. Rosenbaum, S. Mduduzi Seakamela and Rochelle Constantine

Remote Sens. 2021, 13(11), 2074; https://doi.org/10.3390/rs13112074 - 25 May 2021

Cited by 24 | Viewed by 7327

Abstract

Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection. Full article

(This article belongs to the Special Issue Application of Machine Learning in Marine Ecology)

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