BUSCADOR RECOLECTA

[Methods] Data obtained from an acoustic telemetry experiment. The acoustic detection sequence was imported to R software to apply a Hidden Markov Model to separate the active and rest states and obtain the circadian-related traits., This dataset contains the necessary data to replicate the work entitled 'Circadian-related behavioural types in free-living marine fish revealed by high-throughput telemetry'. The data were obtained through a high-resolution acoustic telemetry experiment tracking a population of pearly razorfish between April and September 2019. The time series of detections were imported into the R computing environment. We discretized the detections generated by the individuals into bins of 5 minutes (time-steps). We fitted a Hidden Markov Model (HMM) to probabilistically assign two behavioural states to each temporal bin: rest (R) or active (A). We used a zero-inflated Poisson HMM implemented in the ziphsmm package. During these months, two different periods in the reproduction of this species were included: the pre-spawning period and the spawning period. For this purpose, the data were separated into two different datasets: the pre-spawning period dataset, which contains all individuals tracked for at least seven days between April 30 and May 31, and the spawning period dataset, which includes all individuals tracked for at least seven days between June 15 and July 31. The data between June 1 and June 15 were discarded due to maintenance tasks on the acoustic receivers. The data from August and September were discarded due to low data yields. Finally, a third dataset was created, which includes individuals tracked for at least seven days in each period. The three datasets are configured in the same manner, with ID as the identifier for each individual, Day as the tracking date, Dayn as the day of the trial, Awakening Time as the activity onset time in minutes relative to sunrise, Rest Onset as the rest onset time in minutes relative to sunset, RelActivityDuration as the active hours (calculated as the difference between the awakening time and rest onset) relative to daylight hours (calculated as the difference between sunrise and sunset), RelRestDuration as the resting hours (calculated as the difference between the rest onset time and awakening time of the next day) relative to night hours (calculated as the difference between sunset and sunrise of the next day), RelRestMidpoint as the midpoint of the rest relative to the middle of the night, Sex, Size (cm), Period, CHL as the concentration of chlorophyll (Relative Fluorescence Units, RFU), CurrentDirection as the direction in degrees of the surface current, CurrentSpeed as the speed in m/s of the surface current, Light as the daily mean light (lux), O2 as the concentration of dissolved oxygen in water (mV), Salinity (PSU), Temperature as the daily mean temperature (ºC), WavesHeight as the daily mean wave height (m), and WindSpeed as the speed in m/s of the wind., 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)., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2021-001198)., Pre-Spawning_Dataset, Spawning_Dataset, BothPeriods_Dataset., Peer reviewed

[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