Toward a decade of ocean science for sustainable development through acoustic animal tracking.

The ocean is a key component of the Earth's dynamics, providing a great variety of ecosystem services to humans. Yet, human activities are globally changing its structure and major components, including marine biodiversity. In this context, the United Nations has proclaimed a Decade of Ocean Science for Sustainable Development to tackle the scientific challenges necessary for a sustainable use of the ocean by means of the Sustainable Development Goal 14 (SDG14). Here, we review how Acoustic animal Tracking, a widely distributed methodology of tracking marine biodiversity with electronic devices, can provide a roadmap for implementing the major Actions to achieve the SDG14. We show that acoustic tracking can be used to reduce and monitor the effects of marine pollution including noise, light, and plastic pollution. Acoustic tracking can be effectively used to monitor the responses of marine biodiversity to human-made infrastructures and habitat restoration, as well as to determine the effects of hypoxia, ocean warming, and acidification. Acoustic tracking has been historically used to inform fisheries management, the design of marine protected areas, and the detection of essential habitats, rendering this technique particularly attractive to achieve the sustainable fishing and spatial protection target goals of the SDG14. Finally, acoustic tracking can contribute to end illegal, unreported, and unregulated fishing by providing tools to monitor marine biodiversity against poachers and promote the development of Small Islands Developing States and developing countries. To fully benefit from acoustic tracking supporting the SDG14 Targets, trans-boundary collaborative efforts through tracking networks are required to promote ocean information sharing and ocean literacy. We therefore propose acoustic tracking and tracking networks as relevant contributors to tackle the scientific challenges that are necessary for a sustainable use of the ocean promoted by the United Nations.

A mechanistic theory of personality-dependent movement behaviour based on dynamic energy budgets.

Consistent between-individual differences in movement are widely recognised across taxa. In addition, foraging plasticity at the within-individual level suggests a behavioural dependency on the internal energy demand. Because behaviour co-varies with fast-slow life history (LH) strategies in an adaptive context, as theoretically predicted by the pace-of-life syndrome hypothesis, mass/energy fluxes should link behaviour and its plasticity with physiology at both between- and within-individual levels. However, a mechanistic framework driving these links in a fluctuating ecological context is lacking. Focusing on home range behaviour, we propose a novel behavioural-bioenergetics theoretical model to address such complexities at the individual level based on energy balance. We propose explicit mechanistic links between behaviour, physiology/metabolism and LH by merging two well-founded theories, the movement ecology paradigm and the dynamic energetic budget theory. Overall, our behavioural-bioenergetics model integrates the mechanisms explaining how (1) behavioural between- and within-individual variabilities connect with internal state variable dynamics, (2) physiology and behaviour are explicitly interconnected by mass/energy fluxes, and (3) different LHs may arise from both behavioural and physiological variabilities in a given ecological context. Our novel theoretical model reveals encouraging opportunities for empiricists and theoreticians to delve into the eco-evolutionary processes that favour or hinder the development of between-individual differences in behaviour and the evolution of personality-dependent movement syndromes.

Response to Abolaffio et al. (2019): Avoiding misleading messages.

That this camera-based method is sparking debate regarding its applicability to specific-species is a symptom of the complexity of wildlife assessment in light of the increasing, unprecedented amount of ecological-data, this task requires strengthening links between theorists and empiricists to profit the new opportunities offered by the method.

How ecology shapes exploitation: a framework to predict the behavioural response of human and animal foragers along exploration-exploitation trade-offs.

Understanding how humans and other animals behave in response to changes in their environments is vital for predicting population dynamics and the trajectory of coupled social-ecological systems. Here, we present a novel framework for identifying emergent social behaviours in foragers (including humans engaged in fishing or hunting) in predator-prey contexts based on the exploration difficulty and exploitation potential of a renewable natural resource. A qualitative framework is introduced that predicts when foragers should behave territorially, search collectively, act independently or switch among these states. To validate it, we derived quantitative predictions from two models of different structure: a generic mathematical model, and a lattice-based evolutionary model emphasising exploitation and exclusion costs. These models independently identified that the exploration difficulty and exploitation potential of the natural resource controls the social behaviour of resource exploiters. Our theoretical predictions were finally compared to a diverse set of empirical cases focusing on fisheries and aquatic organisms across a range of taxa, substantiating the framework's predictions. Understanding social behaviour for given social-ecological characteristics has important implications, particularly for the design of governance structures and regulations to move exploited systems, such as fisheries, towards sustainability. Our framework provides concrete steps in this direction.

A camera-based method for estimating absolute density in animals displaying home range behaviour.

The measurement of animal density may take advantage of recent technological achievements in wildlife video recording. Fostering the theoretical links between the patterns depicted by cameras and absolute density is required to exploit this potential. We explore the applicability of the Hutchinson-Waser's postulate (i.e. when animal density is stationary at a given temporal and spatial scale, the absolute density is given by the average number of animals counted per frame), which is a counter-intuitive statement for most ecologists and managers who are concerned with counting the same individual more than once. We aimed to reconcile such scepticism for animals displaying home range behaviour. The specific objectives of this paper are to generalize the Hutchinson-Waser's postulate for animals displaying home range behaviour and to propose a Bayesian implementation to estimate density from counts per frame using video cameras. Accuracy and precision of the method was evaluated by means of computer simulation experiments. Specifically, six animal archetypes displaying well-contrasted movement features were considered. The simulation results demonstrate that density could be accurately estimated after an affordable sampling effort (i.e. number of cameras and deployment time) for a great number of animals across taxa. The proposed method may complement other conventional methods for estimating animal density. The major advantages are that identifying an animal at the individual level and precise knowledge on how animals move are not needed, and that density can be estimated in a single survey. The method can accommodate conventional camera trapping data. The major limitations are related to some implicit assumptions of the underlying model: the home range centres should be homogeneously distributed, the detection probability within the area surveyed by the camera should be known, and animals should move independently to one another. Further improvements for circumventing these limitations are discussed.

Altered trait variability in response to size-selective mortality.

Changes in trait variability owing to size-selective harvesting have received little attention in comparison with changes in mean trait values, perhaps because of the expectation that phenotypic variability should generally be eroded by directional selection typical for fishing and hunting. We show, however, that directional selection, in particular for large body size, leads to increased body-size variation in experimentally harvested zebrafish (Danio rerio) populations exposed to two alternative feeding environments: ad libitum and temporarily restricted food availability. Trait variation may influence population adaptivity, stability and resilience. Therefore, rather than exerting selection pressures that favour small individuals, our results stress the importance of protecting large ones, as they can harbour a great amount of variation within a population, to manage fish stocks sustainably.

ePyDGGA: automatic configuration for fitting epidemic curves.

Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html .

Measuring activity-rest rhythms under different acclimation periods in a marine fish using automatic deep learning-based video tracking.

Most organisms synchronize to an approximately 24-hour (circadian) rhythm. This study introduces a novel deep learning-powered video tracking method to assess the stability, fragmentation, robustness and synchronization of activity rhythms in Xyrichtys novacula. Experimental X. novacula were distributed into three groups and monitored for synchronization to a 14/10 hours of light/dark to assess acclimation to laboratory conditions. Group GP7 acclimated for 1 week and was tested from days 7 to 14, GP14 acclimated for 14 days and was tested from days 14 to 21 and GP21 acclimated for 21 days and was tested from days 21 to 28. Telemetry data from individuals in the wild depicted their natural behavior. Wild fish displayed a robust and minimally fragmented rhythm, entrained to the natural photoperiod. Under laboratory conditions, differences in activity levels were observed between light and dark phases. However, no differences were observed in activity rhythm metrics among laboratory groups related to acclimation period. Notably, longer acclimation (GP14 and GP21) led to a larger proportion of individuals displaying rhythm synchronization with the imposed photoperiod. Our work introduces a novel approach for monitoring biological rhythms in laboratory conditions, employing a specifically engineered video tracking system based on deep learning, adaptable for other species.

Chromosome-level assembly and annotation of the Xyrichtys novacula (Linnaeus, 1758) genome.

The pearly razorfish (Xyrichtys novacula), commonly known as raor in the Balearic Islands, is a wrasse within the family Labridae. This fish species has particular biological and socio-cultural characteristics making it an ideal model organism in the fields of behavioural ecology, molecular ecology and conservation biology. In this study, we present the first annotated chromosome-level assembly for this species. Sequencing involved a combination of long reads with Oxford Nanopore Technologies, Illumina paired-end short reads (2 × 151 bp), Hi-C and RNA-seq from different tissues. The nuclear genome assembly has a scaffold N50 of 34.33 Mb, a total assembly span of 775.53 Mb and 99.63% of the sequence assembled into 24 superscaffolds, consistent with its known karyotype. Quality metrics revealed a consensus accuracy (QV) of 42.92 and gene completeness > 98%. The genome annotation resulted in 26,690 protein-coding genes and 12,737 non-coding transcripts. The coding regions encoded 39,613 unique protein products, 93% of them with assigned function. Overall, the publication of the X. novacula's reference genome will broaden the scope and impact of genomic research conducted on this iconic and colourful species.

Chronotypes-personality behavioural syndromes in wild marine fish.

Chronotypes, the individual differences in daily activity timing, have profound associations with numerous physiological processes. Despite this, the covariance between chronotypes and other aspects of an individual's behaviour has been infrequently explored in non-human animals. This study delves into individual's variation across four axes of personality in a controlled environment, utilising the pearly razorfish, a model species for fish chronotype studies. We identified behavioural types across the aggressiveness continuum and established behavioural syndromes amongst exploration, activity, and boldness, irrespective of body size and condition. Subsequent to this, the experimental subjects were reintroduced to their natural habitat and individually tracked using high-resolution technology to ascertain their chronotypes. Our results revealed that whilst the exploration-activity-boldness syndrome bore no correlation with chronotypes, a significant association was observed between aggressiveness and chronotype. Hence, individuals with later awakening times and rest onsets were more aggressive than their counterparts with earlier awakening times and rest onsets. This study provides pioneering evidence linking fish chronotypes with other behavioural traits, such as aggressiveness, suggesting that behavioural variation could be potentially linked to the individuals' variation in internal clocks and the environmental variables influencing their expression.

Disparate behavioral types in wild and reared juveniles of gilthead seabream.

Fish differ consistently in behavior within the same species and population, reflecting distinct behavioral types (BTs). Comparing the behavior of wild and reared individuals provides an excellent opportunity to delve into the ecological and evolutionary consequences of BTs. In this work, we evaluated the behavioral variation of wild and reared juvenile gilthead seabreams, Sparus aurata, a highly relevant species for aquaculture and fisheries. We quantified behavioral variation along the five major axes of fish behavioral traits (exploration-avoidance, aggressiveness, sociability, shyness-boldness, and activity) using standardized behavioral tests and a deep learning tracking algorithm for behavioral annotation. Results revealed significant repeatability in all five behavior traits, suggesting high consistency of individual behavioral variation across the different axes in this species. We found reared fish to be more aggressive, social and active compared to their wild conspecifics. Reared individuals also presented less variance in their aggressiveness, lacking very aggressive and very tame individuals. Phenotypic correlation decomposition between behavioral types revealed two different behavioral syndromes: exploration-sociability and exploration-activity. Our work establishes the first baseline of repeatability scores in wild and reared gilthead seabreams, providing novel insight into the behavior of this important commercial species with implications for fisheries and aquaculture.

Activity-Rest Circadian Rhythm of the Pearly Razorfish in Its Natural Habitat, before and during Its Mating.

Recent technological advances in marine biotelemetry have demonstrated that marine fish species perform activity-rest rhythms that have relevant ecological and evolutionary consequences. The main objective of the present report is to study the circadian rhythm of activity-rest of the pearly razorfish, Xyrichtys novacula in its own habitat, before and during the reproduction season using a novel biotelemetry system. This fish species is a small-bodied marine species that inhabits most shallow soft habitats of temperate areas and has a high interest for commercial and recreational fisheries. The activity of free-living fish was monitored by means of high-resolution acoustic tracking of the motor activity of the fish in one-minute intervals. The obtained data allowed the definition of the circadian rhythm of activity-rest in terms of classical non-parametric values: interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), average activity during the most-active period of consecutive 10 h (M10), and average activity during the least-active period of consecutive 5 h (L5). We observed a well-marked rhythm, with little fragmentation and good synchrony with the environmental cycle of light-darkness, regardless of sex and the period studied. However, the rhythm was found to be slightly more desynchronized and fragmented during reproduction because of variations in the photoperiod. In addition, we found that the activity of the males was much higher than that of the females (p < 0.001), probably due to the peculiar behavior of the males in defending the harems they lead. Finally, the time at which activity began in males was slightly earlier than it was in females (p < 0.001), presumably due to the same fact, as differences in activity or for the individual heterogeneity of this species in the time of awakening are considered to be an independent axis of the fish's personality. Our work is novel, as it is one of the first studies of activity-rest rhythm using classical circadian-related descriptors in free-living marine fish using locomotory data facilitated by novel technological approaches.

Food determines ephemerous and non-stable gut microbiome communities in juvenile wild and farmed Mediterranean fish.

Novel insights were provided by contrasting the composition of wild and farmed fish gut microbiomes because the latter had essentially different environmental conditions from those in the wild. This was reflected in the gut microbiome of the wild Sparus aurata and Xyrichtys novacula studied here, which showed highly diverse microbial community structures, dominated by Proteobacteria, mostly related to an aerobic or microaerophilic metabolism, but with some common shared major species, such as Ralstonia sp. On the other hand, farmed non-fasted S. aurata individuals had a microbial structure that mirrored the microbial composition of their food source, which was most likely anaerobic, since several members of the genus Lactobacillus, probably revived from the feed and enriched in the gut, dominated the communities. The most striking observation was that after a short fasting period (86 h), farmed gilthead seabream almost lost their whole gut microbiome, and the resident community associated with the mucosa had a very much reduced diversity that was highly dominated by a single potentially aerobic species Micrococcus sp., closely related to M. flavus. The results pointed to the fact that, at least for the juvenile S. aurata studied, most of the microbes in the gut were transient and highly dependent on the feed source, and that only after fasting for at least 2 days could the resident microbiome in the intestinal mucosa be determined. Since an important role of this transient microbiome in relation to fish metabolism could not be discarded, the methodological approach needs to be well designed in order not to bias the results. The results have important implications for fish gut studies that could explain the diversity and occasional contradictory results published in relation to the stability of marine fish gut microbiomes, and might provide important information for feed formulation in the aquaculture industry.

Global responses to the COVID-19 pandemic by recreational anglers: considerations for developing more resilient and sustainable fisheries.

The global COVID-19 pandemic resulted in many jurisdictions implementing orders restricting the movements of people to inhibit virus transmission, with recreational angling often either not permitted or access to fisheries and/or related infrastructure being prevented. Following the lifting of restrictions, initial angler surveys and licence sales suggested increased participation and effort, and altered angler demographics, but with evidence remaining limited. Here, we overcome this evidence gap by identifying temporal changes in angling interest, licence sales, and angling effort in world regions by comparing data in the 'pre-pandemic' (up to and including 2019); 'acute pandemic' (2020) and 'COVID-acclimated' (2021) periods. We then identified how changes can inform the development of more resilient and sustainable recreational fisheries. Interest in angling (measured here as angling-related internet search term volumes) increased substantially in all regions during 2020. Patterns in licence sales revealed marked increases in some countries during 2020 but not in others. Where licence sales increased, this was rarely sustained in 2021; where there were declines, these related to fewer tourist anglers due to movement restrictions. Data from most countries indicated a younger demographic of people who participated in angling in 2020, including in urban areas, but this was not sustained in 2021. These short-lived changes in recreational angling indicate efforts to retain younger anglers could increase overall participation levels, where efforts can target education in appropriate angling practices and create more urban angling opportunities. These efforts would then provide recreational fisheries with greater resilience to cope with future global crises, including facilitating the ability of people to access angling opportunities during periods of high societal stress. Supplementary Information: The online version contains supplementary material available at 10.1007/s11160-023-09784-5.

Interpretable decision trees through MaxSAT.

We present an approach to improve the accuracy-interpretability trade-off of Machine Learning (ML) Decision Trees (DTs). In particular, we apply Maximum Satisfiability technology to compute Minimum Pure DTs (MPDTs). We improve the runtime of previous approaches and, show that these MPDTs can outperform the accuracy of DTs generated with the ML framework sklearn.

Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning.

Deep learning allows us to automatize the acquisition of large amounts of behavioural animal data with applications for fisheries and aquaculture. In this work, we have trained an image-based deep learning algorithm, the Faster R-CNN (Faster region-based convolutional neural network), to automatically detect and track the gilthead seabream, Sparus aurata, to search for individual differences in behaviour. We collected videos using a novel Raspberry Pi high throughput recording system attached to individual experimental behavioural arenas. From the continuous recording during behavioural assays, we acquired and labelled a total of 14,000 images and used them, along with data augmentation techniques, to train the network. Then, we evaluated the performance of our network at different training levels, increasing the number of images and applying data augmentation. For every validation step, we processed more than 52,000 images, with and without the presence of the gilthead seabream, in normal and altered (i.e., after the introduction of a non-familiar object to test for explorative behaviour) behavioural arenas. The final and best version of the neural network, trained with all the images and with data augmentation, reached an accuracy of 92,79% ± 6.78% [89.24-96.34] of correct classification and 10.25 ± 61.59 pixels [6.59-13.91] of fish positioning error. Our recording system based on a Raspberry Pi and a trained convolutional neural network provides a valuable non-invasive tool to automatically track fish movements in experimental arenas and, using the trajectories obtained during behavioural tests, to assay behavioural types.

Big-data approaches lead to an increased understanding of the ecology of animal movement.

Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.

A state-space model to derive motorboat noise effects on fish movement from acoustic tracking data.

Motorboat noise is recognized as a major source of marine pollution, however little is known about its ecological consequences on coastal systems. We developed a State Space Model (SSM) that incorporates an explicit dependency on motorboat noise to derive its effects on the movement of resident fish that transition between two behavioural states (swimming vs. hidden). To explore the performance of our model, we carried out an experiment where free-living Serranus scriba were tracked with acoustic tags, while motorboat noise was simultaneously recorded. We fitted the generated tracking and noise data into our SSM and explored if the noise generated by motorboats passing at close range affected the movement pattern and the probability of transition between the two states using a Bayesian approach. Our results suggest high among individual variability in movement patterns and transition between states, as well as in fish response to the presence of passing motorboats. These findings suggest that the effects of motorboat noise on fish movement are complex and require the precise monitoring of large numbers of individuals. Our SSM provides a methodology to address such complexity and can be used for future investigations to study the effects of noise pollution on marine fish.

Aggressiveness-related behavioural types in the pearly razorfish.

Behavioural types (i.e., personalities or temperament) are defined as among individual differences in behavioural traits that are consistent over time and ecological contexts. Behavioural types are widespread in nature and play a relevant role in many ecological and evolutionary processes. In this work, we studied for the first time the consistency of individual aggressiveness in the pearly razorfish, Xyrichtys novacula, using a mirror test: a classic method to define aggressive behavioural types. The experiments were carried out in semi-natural behavioural arenas and monitored through a novel Raspberry Pi-based recording system. The experimental set up allowed us to obtain repeated measures of individual aggressivity scores during four consecutive days. The decomposition of the phenotypic variance revealed a significant repeatability score (R) of 0.57 [0.44-0.60], suggesting high predictability of individual behavioural variation and the existence of different behavioural types. Aggressive behavioural types emerged irrespective of body size, sex and the internal condition of the individual. Razorfishes are a ubiquitous group of fish species that occupy sedimentary habitats in most shallow waters of temperate and tropical seas. These species are known for forming strong social structures and playing a relevant role in ecosystem functioning. Therefore, our work provides novel insight into an individual behavioural component that may play a role in poorly known ecological and evolutionary processes occurring in this species.