BRUVs recordings of blacktip reef and lemon sharks in Tetiaroa
Data files
Aug 09, 2024 version files 27.15 KB
Abstract
There is growing evidence of the important role learning plays in shark foraging, but few studies have examined the relationship between learning and foraging behaviour in free-living settings. We addressed this knowledge gap by experimentally contrasting responses of blacktip reef (Carcharhinus melanopterus) and sicklefin lemon (Negaprion acutidens) sharks to an olfactory-only feeding stimulus – baited remote underwater video stations (BRUVS) – that was either offered repeatedly at the same location or spatially randomized throughout the lagoon of Tetiaroa, French Polynesia. Blacktip reef sharks exhibited behaviour consistent with the hypothesis that cue predictability fosters spatial associative learning, exhibiting increasing relative abundance upon introduction of the cue (MaxN at deployment) and decreasing arrival times as the experiment progressed, whereas sicklefin lemon sharks showed no evidence of varying responses to cue treatment type over time. Accordingly, our findings advance our understanding of shark cognition by highlighting that spatial associations can develop in response to stable feeding cues even when the olfactory attractant is not accompanied by a reward, while also indicating that shark responses to anthropogenic feeding cues can be species-specific. They also suggest that, for at least some shark species, olfactory cues alone could lead to habituation that confounds non-invasive efforts to monitor shark populations and communities (e.g., with BRUVS) and drive spatial associations with the potential to promote both ecotourism and negative human-shark interactions.
README: BRUVs recordings of blacktip reef and lemon sharks in Tetiaroa
https://doi.org/10.5061/dryad.6hdr7sr6j
The dataset includes detection data for 2 shark species (blacktip reef, sicklefin lemon) recorded on baited remote underwater video systems (BRUVs) deployed in Tetiaroa, French Polynesia, over the course of 23 days in 2016. For each BRUVs drop (lasting an hour), data are presented in separate rows for lemon and then blacktip sharks.
Description of the data and file structure
The data file has the following columns.
File: The specific drop ID (note again that there are two file IDs per drop; one for lemons and one for blacktips).
Treatment: Whether the specific BRUVs occurred at a randomly generated location within Tetiaroa's lagoon ('random'), or a pre-selected (single) location where BRUVs were deployed repeatedly ('repeated').
Camera: type of camera used in the BRUVs (for this study, all were mono GoPros).
Date: Date of the drop.
Time: Timing of the drop.
deployed.dt: Date and time combined.
Camera.bottom: Time it took for the camera to get settled on the bottom of the lagoon, with silt settled (data collection began at this stage of the deployment).
Species: data for either lemon or blacktip reef sharks.
MaxN: Highest number of any shark species seen in one frame (extracted via a frame-by-frame analysis).
MaxN.time: Time elapsing between the beginning of data collection (camera.bottom) and when MaxN for a particular shark species was achieved.
toa: time to first arrival for a particular shark species during a drop (minutes).
toa.maxN: initial MaxN (first 2 minutes of data collection)
depth: water depth (in m) in which a BRUVs was dropped.
Set: Whether the BRUVs was dropped during the first of second wave of the day (morning or afternoon).
Sample day: day of the study (over the course of 23 days) on which the drop occurred.
t.sample.day: 7 days added to random deployments to harmonize timing of repeated and random BRUVs (to compare results over the same time interval in a companion analysis - see appendices).
N/A: Note, for drops during which no sharks were observed in the video, a "NA" has been entered for "time to" variables (e.g., to a, or time of arrival, as no sharks arrived).