This Andrzejaczek2021_readme.txt file was generated on 2020-15-03 by Samantha Andrzejaczek GENERAL INFORMATION 1. Title of Dataset: Data from: Regional movements of satellite-tagged whale sharks Rhincodon typus in the Gulf of Aden 2. Author Information A. Principal Investigator Contact Information Name: Samantha Andrzejaczek Institution: Hopkins Marine Station; Stanford University Address: 120 Ocean View Bld, Pacific Grove, California, USA Email: sandrzejaczek@gmail.com B. Associate or Co-investigator Contact Information Name: Sabrina Fossette Institution: Department of Biodiversity, Conservation and Attractions Address: 17 Dick Perry Av., Kensington, WA, 6151 Email: sabrina.fossette-halot@dbca.wa.gov.au 3. Date of data collection (dates of tag deployment – pop-up) 104072: 2012-01-19 – 2012-04-27 104073: 2012-01-19 – 2012-04-28 157783: 2016-01-07 – 2016-01-13 157782: 2016-12-17 – 2017-04-17 42856: 2017-12-17 – 2018-01-02 165699: 2016-12-22 – 2017-04-01 165698: 2017-12-18 – 2018-04-02 42858: 2017-12-17 – 2018-02-21 4. Geographic location of data: Satellite tags were deployed on whale sharks Rhincodon typus in the Arta Bay region of the Gulf of Tadjoura, Djibouti. 5. Information about funding sources that supported the collection of the data: Exagone provided financial support. SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.fqz612js1 2. Links to publications that cite or use the data: https://doi.org/10.1002/2688-8319.12051 3. Recommended citation for this dataset: Andrzejaczek, S., Vely, M., Jouannet, D., Rowat, D, & Fossette, S. (2021). Data from: Regional movements of satellite-tagged whale sharks Rhincodon typus in the Gulf of Aden. Dryad Digital Repository, https://doi.org/10.5061/dryad.fqz612js1 DATA & FILE OVERVIEW 1. File List: Files are all outputs from pop-up satellite archival (PSAT) tags (6 x MK10, 2 x MiniPAT; Wildlife Computers, Inc., WA, USA) deployed on eight whale sharks Rhincodon typus. Start of file names identify the individual manta the tag was attached to. Further file descriptions can be found at https://static.wildlifecomputers.com/Spreadsheet-File-Descriptions-1.pdf METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: Satellite tags were deployed on whale sharks in the Arta Bay region of the Gulf of Tadjoura (11.57°N, 42.77°E; Fig. 1) in January and/or December in 2012 (n = 2), 2016 (n = 3) and 2017 (n = 3; Table 2). Sharks were visually located by boat-based searches from a 6 m long skiff with a single outboard engine and then approached slowly. Sharks larger than 3.5 m were targeted (1) to satisfy a minimum size for tagging and (2) due to the assumption that only sharks of a certain size migrated from the Gulf of Tadjoura study site. Free-divers entered the water from the vessel to tag and measure sharks, as well as take photo-ID images. Tags were deployed by a pole-spear with a welded plate and rubber buffer to prevent insertion greater than 8-10 cm. 2012 tags were leadered with a ~15 cm length of 45 kg nylon filament covered with several layers of heat shrink tubing and attached via a titanium flat anchor M dart (Wildlife Computers) and placed at the base of the first dorsal fin, on the left side. 2016 and 2017 tags were connected to a large titanium anchor (Wildlife Computers) via a 50 cm stainless steel tether. Tether lengths were selected to allow the anchor to be placed 8-10 cm below the skin and leave space to let the tag lie flat against the body surface in the case of the former, and to facilitate breaking the air-surface barrier for transmission during deployment for the latter. Individual sharks were measured (total length, TL) by one of two methods; 1) by using visual observation and comparing the shark to an object of known size, and/or 2) by an intense photogrammetric laser measurement campaign using the methods as described in Jeffreys et al. (2013). Photo-ID images were also taken of the left and right flanks of tagged individuals and matched with the existing Djibouti database using the public domain pattern-recognition software I3S (Interactive Individual Identification System; Van Tienhoven et al. 2007). All fieldwork was approved by, and conducted with the knowledge of, the Ministry of Environment, Djibouti and local authorities in Arta. All procedures followed standard international guidelines for tagging whale sharks and staff were trained by experts in the field (D. Rowat and M. Meekan; Wilson et al. 2006, Robinson et al. 2017). Pop-up satellite archival transmitting (PSAT) tags (6 x MK10, 2 x MiniPAT; Wildlife Computers, Inc., WA, USA) recorded light levels, depth and ambient temperature and were programmed to remain attached for 100 days (2012), 120 days (2016) or 153 days (2017), or programmed to detach if recording depths greater than 1800 m or a constant depth reading (± 1.0 m) for more than one week. The tags recorded depth and temperature data in predefined bins every six hours for transmission, with depth bin size varying slightly between 2016 and 2017 deployments (0-2, 2-5, 5-10, 10-25, 50-100, 100-200, 200-300, 300-400, 400-500, >500 and 0-10, 10-50, 50-100, 100-250, 250-500, >500 respectively). Histogram sampling was offset by three hours so that depth and temperature data were collected for local day (6:00-12:00 and 12:00-18:00) and night (18:00-00:00 and 00:00-6:00) periods. The 2012 tags recorded and archived depth data at five-minute intervals, which were subsequently summarized into the 2016 bins. A Wilcoxon rank sum test was used to compare median day- and night-time depths for time-series data from the two 2012 tags. Location data and/or processed archived data were transmitted and retrieved through the Argos satellite system when the tags detached. Detachment of the tag from the shark was identified by a combination of near-continuous high quality Argos transmissions for the first few hours of each day and depth summaries from histograms consistent with surface records (Hearn et al. 2013). The tags deployed in 2016 and 2017 also transmitted data when sharks swam at the surface, and, in addition, housed a Fastloc global positioning system (GPS) for acquiring location information. 2. Methods for processing the data: Data were extracted from the raw tag data using the Wildlife Computers Data Analysis Program 3.0 (available at https://wildlifecomputers.com/support/downloads/). 3. Instrument- or software-specific information needed to interpret the data: A combination of techniques was used to estimate the most probable track for a given individual based on the type and quality of the data transmitted (Table 2). 2.2.1. 2012 deployments: The two individuals tagged in 2012 were fitted with MiniPAT tags which did not have the capability to record locations during deployment. Track locations were thus estimated by light-levels based on data received via Argos transmission after the tags had detached from the host shark; consequently, these were not contiguous data streams. The transmitted data were first processed through the Data Analysis Program software suite (WC-DAP, Wildlife Computers, 2007) to extract the dawn/dusk light level as well as the temperature and depth data for each 24-hour period. These data were further processed through Global Position Estimator suite of programs (WC-GPE, Wildlife Computers, 2007) to derive geolocations based on the time of dawn and dusk each day by determining the relationship between the sun’s zenith angle and the tag’s received light-level, corrected for any depth attenuation. The date, time, latitude, longitude, estimation error and sea surface temperature (SST) data were then extracted and formatted into an input file for the Iknos Walker particle filter program (Tremblay et al., 2009) which refines the likely track of the animal by bootstrapping random walks biased by forward particles. The model uses the computed accuracy estimates of the location data and can assimilate other sources of data such as SST and animal speed to further refine the location estimations. The SST recorded by the tags was compared with matching daily remotely sensed geolocated SST data at a grid of 11km2, sourced from the NOAA CoastWatch Program, the NOAA NESDIS Office of Satellite Data Processing and Distribution, the NASA’s Goddard Space Flight Centre, and Ocean Color Web (http://coastwatch.pfel.noaa.gov/infog/BA_ssta_las.html). Processing was carried out in the MATLAB numerical computing environment (MATLAB 8.0, The MathWorks, Inc., Natick, Massachusetts, United States). The Iknos Walker routine can shorten tracks before the tag detachment location due to the last GPE location(s) being too far away compared to the speed limit set; this can be mitigated by increasing the acceptable animal speed. However, rather than use unlikely speeds the tracks were re-run in reverse, starting at the detachment location, and then the two tracks were combined and the most parsimonious daily locations were retained for track output. 2.2.2. 2016 and 2017 deployments The six satellite tags deployed on individuals in 2016 and 2017 were programmed to acquire a location estimate from ARGOS and GPS satellites while at the surface. Position estimates acquired from ARGOS satellites were provided with an associated error (Location Class 3: <250 m, 2: 250–500 m, 1: 500–1500 m, 0: >1500 m, A and B: not specified, www.argos-system.org), and Fastloc GPS positions were expected to have an error of <100 m (Bryant, 2007). For the deployment period, all locations reported from above sea level were removed, as well as a small number of locations with A and B error classes that were obviously erroneous (<1%), i.e. they were well beyond the bounds of possible distances the shark could have travelled based on both earlier and later location estimates of higher accuracy for the track. More advanced filtering methods, similar to the ones used for the 2012 tracks were attempted. However, none of the models converged when using the WC-GPE3 processor, and the Iknos Walker routine described above could not be applied to the data. These convergence issues were due to large gaps in light, SST and location data (location and light data available for 9-68% of tracking days for tracks more than six days in length), as well as the highly coastal nature of the tagged individuals, with locations being classified as ‘on land’ in several cases in the 0.25° grids of GPE3. For the longer tracks, all but one tag displayed transmission gaps >20 days (up to 86 days), preventing unbiased interpolation of tracks between consecutive locations (Queiroz et al., 2016). Attempts were also made to thin known locations from tracks in order to reduce clustering and facilitate model convergence as per Lipscombe et al. (in press), however, resulting track paths diverged significantly from known locations and were deemed unreliable. Depth and processed location data can be interpreted in several ways. Softwares used by the authors include Microsoft Excel, R Statistical Environment (R Core Team 2020) and Google Earth. R packages used include: ‘ncdf’, ‘raster’, ‘ggmap’, ‘ggplot2’, ‘marmap’ and ‘suncalc’. 4. Describe any quality-assurance procedures performed on the data: Detachment of the tag from the shark was identified by a combination of near-continuous high quality Argos transmissions for the first few hours of each day and depth summaries from histograms consistent with surface records (Hearn et al., 2013). DATA-SPECIFIC INFORMATION FOR: Please refer to the PDF at https://static.wildlifecomputers.com/Spreadsheet-File-Descriptions-1.pdf for more detail on each file.