BUSCADOR RECOLECTA

Digital.CSIC. Repositorio Institucional del CSIC

oai:digital.csic.es:10261/303880

[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