Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Shifts in the timing of spawning in sole linked to warming sea temperatures

Phenotypic traits such as peak spawning time may vary within and differ between populations in relation to environmental factors, such as temperature. Sole (Solea solea) is a valuable, commercially exploited species that spawns in late winter or spring. The date of peak spawning was estimated for each year for seven stocks from monthly fish samples collected from commercial fisheries since 1970. Four out of seven stocks showed a significant long-term trend towards earlier spawning (Irish Sea, east-central North Sea, southern North Sea, eastern English Channel) at a rate of 1.5. weeks per decade. The other three stocks (Bristol Channel, western English Channel and western-central North Sea) failed to show a relationship, but the available time series were limited for these stocks (<10. years). Sea surface temperature during winter significantly affected the date of peak spawning, although the effect differed between stocks. The implications of the effect of winter temperature on the timing of spawning for the population dynamics are discussed

Machine learning in marine ecology: an overview of techniques and applications

International audienceMachine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets