dataesr/scientific-paragraphs-categorization huggingface dataset:
This (already large) nucleus of papers was further grown using the references cited within them, starting from the most recent and going backwards in time. This procedure was iterative (the references of the newly added papers being also examined) and the search was stopped after several rounds of such tentative additions did not yield any new reference. After assembling this large body of potentially relevant literature, the authors screened the suitability of each paper for its inclusion in the database according to the following criteria: (i) the paper is peer-reviewed, (ii) its "Methods" section describes the ML approach used, and (iii) it applies it to a marine dataset. Because some classical statistical techniques can be perceived as ML, we further reduced the scope to studies that follow the general process of Figure  2  (i.
e. include a validation and/or a test dataset). Then, papers were organized through tags, defining the type of data they analyse ("data: * " tag), ML tasks achieved ("task: * "), the algorithms used ("method: * "), and other useful characteristics (e. g. availability of code and/or data; "meta: * "). The content of the resulting database, organized according to data type, is summarized in Figure  3. This selection process still yielded over 1000 papers, which cannot all be described in this review. To decide which ones to cite, we considered the following additional criteria: the paper (i) has been widely adopted by the research community (e. g. is cited very often, defines a method widely applied), (ii) is easily reproducible because its methodology is welldescribed and/or code and data are publicly available, or (iii) is representative of a body of work not covered by criteria (i) or (ii).
The general process of (supervised) machine learning. After being collected (1), data need to be labelled  (2) , which means associating the inputs with a number or a name as output (l = 1, 2, or 3 in the example). The data are then split into training, validation, and test datasets (3) while taking its structure into account (e. g. ensure that all labels are represented in each dataset). Each input in the training set can be summarized into features (4). The (transformed) training set is used to train the model (5), by minimizing a loss function (L) that computes the value of one or several performance metrics (M). The validation set undergoes the same transformation as the training set, if any, and is then used to evaluate the predictive performance of the model, ideally with the same metric(s) (6).
Several versions of the model can be trained with different hyperparameters (i. e. settings, noted h * ) of the machine learning system, and the one with the best performance on the validation set is retained. At this point, the model is frozen and its final performance is assessed on the test set (7). If external information, different from the original data, is available, it should be used to ensure that model predictions are reasonable, in addition to achieving a given performance (8). Finally, the model is ready to be deployed and used with newly collected data (9). While this database is not exhaustive, the methodical approach described above should avoid overt biases and large omissions. We therefore consider it representative of the diversity of approaches and of the relative volume of research in various domains.
(2020). More recently, object-based classification has replaced pixel-based classification  (Zhang et al. , 2013) , especially using CNNs, which reached much higher performance  (Gómez-Ríos et al. , 2019; Piechaud et al. , 2019). Growth in this field has naturally been accompanied by an increase in the number of images of benthic fauna and habitats. Though ML offers promise towards unlocking the catalogue of unused benthic images, many challenges remain. There are growing concerns regarding the identification of available data for training, the pre-training of deep nets, and the handling of class imbalance in training datasets. For example, of the millions of images acquired each year on coral reef surveys, just 1-2% are labelled  (Beijbom et al. , 2012). Lumini et al. (2020)  compared several CNN architectures and found that combinations of several models (i.
e. ensembles) were the most successful for image classification of coral (and plankton) datasets. Fincham et al. (2020) , who classified images from across multiple benthic habitats, found an imbalance in their training data due to the frequency of habitat occurrence, which was countered by using data augmentation to artificially expand the training by flipping, scaling, and rotating images. The challenge in accessing high-quality training datasets is now being addressed through developments such as standardized reference catalogues  (Althaus et al. , 2015; Fisher et al. , 2016; Howell et al. , 2019) , wide adoption of specialized annotation software such as BIIGLE 2  (Langenkämper et al. , 2017) , SQUIDLE+  (Williams and Friedman, 2018) , and CoralNet  (Beijbom et al. , 2015) , and annotated image databases e.
g. FathomNet  (Boulais et al. , 2020). In addition, the development of user-friendly software such as VIAME and Superannotate is making ML more accessible to benthic ecologists. However, for researchers to best apply these tools, much remains to be learned regarding model performance under different conditions (e. g. depending on the number of classes used), on training dataset size, on the use of single models versus ensembles of models, etc. (Durden et al. , 2021).
, 2020). Region-based convolutional neural networks (R-CNNs), designed for object detection, have been increasingly applied to automatically detect and classify beached  (Watanabe et al. , 2019) , floating  (Lieshout et al. , 2020) , and seafloor  (Politikos et al. , 2021)  macrolitter items. Additionally, traditional CNN classifiers have been used to categorize litter types from segmented images  (Garcia-Garin et al. , 2021). Such studies have generally shown that the classification and detection performance of neural networks is high for floating litter (>80%) but often lower for underwater and seafloor litter, which can be attributed to the challenges of underwater imagery (various camera angles, zoom levels, light shadings, litter buried in the seabed). Several authors have used open and experimental datasets for their analysis, focusing mainly on the predictive performance of the algorithms.
Nekton Monitoring of nekton informs decision-making for biodiversity conservation and sustainable fisheries management. Imaging surveys constitute a non-invasive complement to conventional monitoring. However, it yields large datasets and ML has come into play to automate and speed up the data processing. Nekton monitoring from images is challenging due to the diversity of tasks that need to be solved (e. g. species classification but also morphometric estimations) and the very different conditions in which images are collected (e. g. both underwater and on ships). In early fish imaging studies, classic ML methods were used with data obtained in controlled, experimental setups. For example, in  Storbeck and Daan (2001) , the image acquisition system consisted of a camera and a laser, which allowed obtaining images but also information on fish volume.
They classified six species of fish with 95% accuracy using a shallow artificial neural network (ANN) based on fish contour features. In  Zion et al. (2007) , three edible fish species were sorted using a minimum Mahalanobis distance classifier that combined geometric features and object contours as inputs to yield an accuracy of >96%. In situ monitoring of nekton is largely focused on fish as well, but those studies present additional challenges due to the wide variations in observation conditions. Datasets are typically collected with underwater cameras  (Fisher et al. , 2016) , but larger organisms, such as marine mammals, are also monitored via satellite images. Here also, before the development of CNNs, global features were used together with background modelling to detect and track objects under water.
CNNs will likely be increasingly relied upon in the future. Their implementation within dedicated plankton imaging software such as EcoTaxa  (Picheral et al. , 2017)  or the IFCB dashboard will facilitate their routine use by a wide community of ecologists (https://ifcb-data. whoi. edu/dashboard). The separation of their feature-extraction part from their classification part seems like a promising avenue for transfer The plot displays the proportions rather than absolute numbers. The time bins on the x-axis are not regular. The plot highlights the quick adoption of support vector machines and their current decline, the rise and fall of random forests, the increase of convolutional neural networks (particularly since 2015), and their current dominance. learning (i. e. using a model initially trained on one, often general, dataset to quickly "fine-tune" it on a another dataset, here a plankton one;  Orenstein and Beijbom, 2017) , unsupervised classification  (Schroeder et al.
, 2020) , and active learning procedures, whereby only few images representative of the diversity of the dataset are shown to the user  (Bochinski et al. , 2019). An important problem in plankton image datasets, like in many other biological ones, is class imbalance (a few classes dominate the samples). Among several solutions, generating synthetic images in the rare classes using a generative adversarial network (GAN) was recently tested  (Li et al. , 2021). Alternatively, quantification approaches, which do not aim to perfectly classify each individual object but rather to directly derive concentration estimates for each class, deal intrinsically with the distribution among classes  (Gonzalez et al. , 2019).
Identifying microorganisms from single-cell spectra Flow cytometry has been used since the 1990s to study marine microbial communities. In flow cytometry, scatter and fluorescent properties of individual particles are measured at very high rates (i. e. hundreds to thousands of particles per second). Although most researchers manually analyse the resulting "cytograms", automated methods have become available for the analysis of such microbial flow cytometry data  (Rubbens and Props, 2021). Since the 1990s and early 2000s, artificial neural networks (ANNs) have been developed to identify up to 72 lab-grown phytoplankton species using flow cytometry  (Boddy et al. , 2001). Supervised single-cell classifiers were then successfully applied for the identification of heterotrophic bacteria as well, by combining flow cytometry with a nucleic acid stain in most cases.
Passive acoustics monitoring Passive acoustic recordings are a reliable and cost-effective method to monitor habitat use, distribution, density, and behaviour of species over space and time. They can be obtained from boats, autonomous devices (either fixed or moving ones), cabled stations, and animal tags, making them usable in a variety of situations  (Kowarski and Moors-Murphy, 2021). However, because of their relative ease of use, hydrophones quickly generate large datasets that require automation to extract information from them  (Gibb et al. , 2019). The most common approach to process acoustic data is to detect and classify specific acoustic events in a supervised manner. Sound source classification studies have primarily focused on shipping  (Zaugg et al. , 2010)  and mammals' vocalizations (66 out of the 101 references we recorded;  Bittle and Duncan, 2013).
Often these features or other rule-based signal processing techniques are also used to first segment the data and then ML is used to classify the detected segments. Advances in image and speech-recognition algorithms have been applied to underwater sound, reducing the amount of preprocessing and improving performance and generalizations  (Schröter et al. , 2019). In DL approaches, sound is often converted into a spectrogram, which is considered as an image and input into a convolutional neural network (CNN) for classification, regression, or feature extraction and clustering  (Bermant et al. , 2019; Thomas et al. , 2020). However, recently some models have been developed that are applied directly on the waveform  (Roch et al. , 2021). In the marine context, sounds of interest can be very sparsely occurring and datasets can comprise long periods of time.
This leads to highly imbalanced datasets. This imbalance is usually solved by first detecting and then classifying the detected sounds, where the detection step is a rule-based signal-processing algorithm and the classification step is a DL approach (Stowell, 2022). However, the biggest limitation for the application of ML to passive acoustic recordings is the lack of knowledge regarding which sounds are produced by which species, because visual surveys to associate sound with images of the species are often impossible. This leads to a lack of data annotation and limits the usage of supervised ML approaches. To compensate for the lack of ground-truth data, unsupervised clustering algorithms are being developed to acquire general information about the ecology of certain habitats  (Ozanich et al.
ML approaches have long been used for genomics data analysis. This includes both translating raw signals into nucleotides using base-calling algorithms  (Wick et al. , 2019)  and sequence data analysis. For instance, hidden Markov models have been extensively used for functional annotations, multiple sequence alignments  (Yoon, 2009) , and more recently for viral signatures detections in metagenomic datasets  (Ponsero and Hurwitz, 2019). However, few studies have applied ML to strictly marine meta-omics data. We therefore provide a general overview of the analysis of metabarcoding data and highlight some ML applications to marine data. Metabarcoding datasets are usually processed by wellestablished bioinformatics software, e. g. QIIME 2  (Bolyen et al. , 2019) , which translates raw sequences into statistically exploitable species-to-sites count matrices.
Sequences are often grouped into operational taxonomic units (OTUs) or amplicon sequences variants (ASVs) based on their similarity. These sequence units then serve as a proxy for species/strains to document biodiversity changes. Current algorithms to cluster sequences into OTUs or ASVs are VSEARCH  (Rognes et al. , 2016) , which relies on an arbitrary similarity cutoff to delineate OTUs (e. g. 97%), SWARM  (Mahé et al. , 2015) , which aggregates neighbouring sequences to abundant, supposedly genuine, seed sequences, or DADA2  (Callahan et al. , 2016) , which uses base calling values to separate spurious from genuine sequences. These two latter methods find more "natural" boundaries of OTUs and, as such, can be considered as unsupervised approaches. Some OTUs are then assigned a taxonomic name based on similarities with known sequences from curated databases (e.
g. PR2,  Guillou et al. , 2013; SILVA, Quast et al. , 2013). To this end, several ML-based methods have been developed, including naive Bayes (NB) classifiers (the RDP classifier,  Wang et al. , 2007)  and classification trees using k-mers distributions across sequences  (Murali et al. , 2018). More recent work successfully applied convolutional neural networks (CNNs) to process and taxonomically annotate raw metabarcoding data faster, without relying on operational OTUs or ASVs  (Flück et al. , 2022). Resulting OTU-to-site count matrices are then amenable to biodiversity analysis using compositionality-aware multivariate statistics  (Quinn et al. , 2019). For example, ML allows routine monitoring of the impact of industries on marine biodiversity. Based on metabarcoding datasets labelled with ecological states obtained by conventional methods, random forest (RF) models can be trained to assess the ecological status of new samples, based on their metabarcoding profiles alone.
, 2020). Microbial networks can be inferred from genomics data  (Faust and Raes, 2012; Lima-Mendez et al. , 2015)  as a means to predict putative biotic interactions, which opens new avenues for understanding the links between marine microbial communities and the large-scale functioning of marine ecosystems  (Guidi et al. , 2016; Chaffron et al. , 2021). Finally, ML is expected to contribute to improve our capacity to analyse massive meta-datasets composed of numerous collated cross-study genomics data, by controlling for covariates  (Wirbel et al. , 2021).
, 2018). The predictor variables can be measured with very high accuracy by autonomous floats and now ANN-based methods can spatially and temporally populate the fields of nutrients and carbon variables, which were previously loosely resolved. MLPs have also been used to predict the phytoplankton community composition from profiles of fluorescence of chlorophyll-a  (Sauzède et al. , 2015a) , making it possible to gather and homogenize tens of thousands of fluorescence profiles available from historical databases, which could not be integrated in global analyses before  (Sauzède et al. , 2015b).
Capturing dynamic ecological relationships As climate variability and long-term change drive nonstationarity in ecosystems, more research is needed to see how ML approaches can improve our ability to predict and forecast potentially changing species relationships with their environment and other species. Latent (hidden) variable modelling provides one way to detect an underlying systemic change, or to approximate an ecosystem component that is not represented in the dataset. Trifonova et al. (2015)  modelled the North Sea ecosystem using dynamic Bayesian networks with hidden variables (DBN-HVs), and concluded that a hidden variable in the model managed to learn the zooplankton biomass variations in all modelled areas. Trifonova et al. (2017)  used this model to predict ecosystem responses under different scenarios.
Discussion and perspectives General trends in machine learning applications: data, methods, and tasks The diversity in the sections above shows that ML is now used in many fields of marine ecology, albeit at different levels of advancement. Several factors can account for the success of the application of ML in a given scientific domain. Based on the examples above, a major one seems to be the type of data: Applications of ML were more successful when they could rely on techniques developed and tested in other fields, which could be repurposed to marine ecology because data were of the same type. This contributes to explaining the disproportionate number of applications of ML to images and videos from cameras, which constitute ∼45% of the references in the database to which ∼15% of references using satellite imagery can be added (Figure  3 ).
Many of those applications benefited from advances in ML motivated by the ubiquity of images in everyday life. For example, several CNN architectures were developed to classify general-purpose image datasets (often ImageNet;  Deng et al. , 2009) , and when they were successful at this task, they also proved relevant for marine applications; for example, the ResNet architecture alone is used in at least 60 papers in the database. Beyond architectures, the weights that result from training CNNs on such large generic datasets are freely distributed by companies (to promote their technology) and can be slightly modified by a short retraining on a marine dataset to yield domain-specific tools (e. g. detect fishes in recordings from underwater cameras). This is called fine-tuning and requires much fewer resources than training from scratch, while yielding very good results.
, 2012) ; this highlights an early adoption of ML innovations by the marine ecology community. In addition, after the initial adoption, the proportion of studies using them among all Year Proportion of references using the method (within the filed of marine ecology) Figure  7. Amount of references per time period using one of the four most common ML methods in the database. To avoid being misled by the global increase in the number of scientific publications, in any field, the amount is expressed as the proportion of the total number of references published in marine ecology in each time period (defined as the result of the query "WC = (Ecology) AND TS = (marine OR sea OR ocean)" on the Web of Science, i. e. Web of Science category is Ecology and title, abstract, or keywords contain "marine" , "sea" , or "ocean").
Finally, depending on the output shape and the loss function used, the same network architecture can be used for regression, classification, object detection, etc. (Goodwin et al. , 2022). Among the papers tagged in the database, ML algorithms are most often used to perform classification (∼60% of references) or regression (∼20%), and, finally, object extraction (detection or segmentation, ∼15%). Yet, the classification of signals, at least, first requires their extraction from the original data (e. g. the detection of an event in a continuous acoustic recording, the segmentation of an organism from an image), so the discrepancy in usage is puzzling. Actually, most automated signal extraction is performed using rules deterministically applied to the raw data. Those rules can be as simple as thresholding (e.
, 1998)  and their applications lag accordingly. Second, they are a bit more complex to set up than classifiers: Drawing bounding boxes or segmentation masks is more time-consuming than sorting files into folders, training classifiers can often start from just this set of sorted raw files, while object detectors/segmenters require text files in a specific format containing the labels linked to the raw data files, etc. However, as labelling tools (e. g. Labelbox), architectures, and reference datasets (e. g. Katija et al. , 2022)  continue to improve, such applications are likely to explode in the future. Finally, supervised ML approaches are much more common than unsupervised ones. This is partly linked with the dominance of classification tasks in the references reviewed. Supervised classification is the archetype of task where ML techniques outperform all others: mimic a simple human action, learn it only from examples generated by humans, and be evaluated almost solely on the quality of the prediction.
These developments are not unique to ecology and "explainable AI" is an active research domain  (Barredo Arrieta et al. , 2020). Another limitation, inherent to ecological data, is the longtailed distribution of almost everything in the natural world  (Preston, 1948). Ecosystems are dominated by some species and some processes, yet many others are present at low abundance/frequency and can be key in the response of the system to changes. Such distributions bias the usual loss functions or evaluation metrics (e. g. least-square error in regression, accuracy in classification) and wide data tables (number or variables larger than the number of observations) favour overfitting to the training dataset, which many ML techniques are already prone to. Dealing with imbalanced datasets is a current research topic in ML: In 2020 and 2021, dozens of papers targeted long-tailed distribution and/or imbalance at the Conference on Computer Vision and Pattern Recognition (CVPR), the major conference in the field (e.
g. https://openaccess. thecvf. com/CVPR2021, searching for "long-tail" or "imbalance"). Some of these solutions have been implemented for marine applications, such as rebalancing the training data using data augmentation  (Fincham et al. , 2020)  or generative adversarial networks (GANs;  Li et al. , 2021) , ensembles of several models  (Kerr et al. , 2020) , transfer learning from models trained on balanced data  (Lee et al. , 2016) , etc. , but the problem is not solved in the general case. A consequence of this imbalance is that some of the, numerous, rare classes can largely change in proportion from one data sample to the next, causing a mismatch between the class distribution in the training dataset (usually an average of several samples) and in the new data on which the model will ultimately be applied.