Resultados de investigación

[Description of methods used for collection/generation of data] The data derived from the laboratory were obtained through various standardised tests, and recorded to gather behavioural data. For exploration and activity, positional data were acquired via a deep-learning object detection algorithm /YOLOv5). In the case of boldness and aggression, data were obtained by subsequently reviewing the videos. Regarding chronotypes, data were obtained from an acoustic telemetry experiment, but here we present only the scores obtained in a previous study., This dataset encompasses all necessary data required to replicate the study, `Chronotypes-Personality behavioural syndromes in wild fish’. The data were obtained through standardised behavioural tests conducted under laboratory conditions on 63 Pearly Razorfish (Xyrichtys novacula) individuals between April and July of 2019. Over a week, the fish were maintained in isolated aquariums to test their behaviours, including exploration, activity, boldness, and aggression, conducted daily. A Raspberry Pi system, equipped with the YOLOv5 deep-learning automatic tracking algorithm, was used to record these tests and calculate the fish's minute-by-minute position, providing essential data for evaluating exploration and activity. This system also stored videos to retrospectively obtain boldness and aggression data. Each test included only those individuals with at least two measurements. After the laboratory period, the fish were tagged with acoustic tags and returned to the sea to measure their chronotypes; only individuals with at least seven consecutive days of data were considered. The chronotype data, obtained from a previous study, are represented here through the previously derived scores. These laboratory-based experimental data were analysed using R software. In the exploration context, positional data were translated into total active time (TimeOut), minimum distance to the toy (MinDistance), and time spent near the toy (TimeToy). For activity, the data were converted into total active time (TimeOut), total distance covered (Distance), areas (CoreArea and Area), and direction angles (MeanAngle and KappaAngle). A Principal Component Analysis (PCA) was conducted to obtain the scores for exploration, activity, and aggressiveness. Upon acquiring these scores, trait repeatability was computed using a Linear Mixed-Effects Model, fitting the experimental day (Day), the total length of the individual (Size), and the internal condition (Condition) as fixed factors, and the individual (ID) and the experimental week (Week) as random factors. The chronotype scores (Awakening Time and Rest Onset) were subsequently included in each dataset and refitted into the Linear Mixed-Effects Model, including chronotypes as fixed factors. Lastly, a Multivariate Generalised Linear Mixed Model was fit to each pair of laboratory-based traits to derive their correlations., The research was carried out within the framework of the activities of the Spanish Government through the "Maria de Maeztu Centre of Excellence" accreditation to IMEDEA (CSIC-UIB) (CEX2021-001198). The CLOCKS I+D+I project funded this work (grant no. PID2019-104940GA-I00) funded by MCIN/AEI/10.13039/501100011033 and the FSE invierte en tu futuro. The telemetry system was financed by the German Federal Ministry of Education and Research (Grant No. #033W024A). This work is a contribution of the Joint Researcher Unit IMEDEA-LIMIA., With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001198)., Peer reviewed