Maximising the value of transmitted data from PSATs tracking marine fish: a case study on Atlantic bluefin tuna

Abstract

Background: The use of biologging tags to answer questions in animal movement ecology has increased in recent decades. Pop-up satellite archival tags (PSATs) are often used for migratory studies on large fish taxa. For PSATs, movements are normally reconstructed from variable amounts of transmitted data (unless tags are recovered, and full data archives accessed) by coupling geolocation methods with a state-space modelling (SSM) approach. Between 2018 and 2019, we deployed Wildlife Computers PSATs (MiniPATs) from which data recovery varied considerably. This led us to examine the effect of PSAT data volume on SSM performance (i.e., variation in reconstructed locations and their uncertainty). We did this by comparing movements reconstructed using partial (< 100%) and complete (100%) geolocation data sets from PSATs and investigated the variation in Global Position Estimator 3 (GPE3; Wildlife Computers’ proprietary light-based geolocation SSM) reconstructed locations and their certainty in relation to data volume and movement type (maximum dispersal distance). Results: In this analysis, PSATs (n = 29) deployed on Atlantic bluefin tuna (Thunnus thynnus) transmitted data after detaching from study animals for between 0.3 and 10.8 days (mean 4.2 ± 3 days), yielding between 2 and 82% (mean 27% ± 22%) of total geolocation data. The volume of geolocation data received was positively related to the amount of time a tag transmitted for and showed a weak negative relationship to the length of the tag deployment. For 12 recovered PSATs (i.e., 100% of geolocation data; mean ± 1 S.D. = 301 ± 90 days of data per fish), (i) if ABT travelled short-distances (< 1000 km), movements reconstructed from partial data sets were more similar to their complete data set counterpart than fish that travelled over longer distances (> 1000 km); (ii) for fish that travelled long distances, mean distance of locations from corresponding complete data set locations were inversely correlated with the volume of data received; (iii) if only 5% of data was used for geolocation, reconstructed locations for long-distance fish differed by 2213 ± 647 km from the locations derived from complete data sets; and, (iv) track reconstructions omitted migrations into the Mediterranean Sea if less than 30% of data was used for geolocation. Conclusions: For Wildlife Computers MiniPATs in our specific application, movements reconstructed with as little as 30% of the total geolocation data results in plausible outputs from the GPE3. Below this data volume, however, significant differences of more than 2000 km can occur. Whilst for a single species and manufacturer, this highlights the importance of careful study planning and the value of conducting study-specific sensitivity analysis prior to inclusion of modelled locations in research outputs. Based on our findings, we suggest general steps and refinements to maximise the value of light geolocation data from PSATs deployed on aquatic animals and highlight the importance of conducting data sensitivity analyses.