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.

Machine learning techniques to characterize functional traits of plankton from image data.

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

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.

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.