A framework to estimate the likelihood of species interactions and behavioural responses using animal‐borne acoustic telemetry transceivers and accelerometers
Bibliographic record
Abstract
Abstract Interactions between animals structure food webs and regulate ecosystem function and productivity. Quantifying subsurface behavioural interactions among marine organisms is challenging, but technological advances are promoting novel opportunities. Here, we present a framework to estimate when there is a high likelihood that aquatic animal subsurface interactions occur and test for a movement‐related behavioural response to those interactions over short temporal scales (days) using a novel multi‐sensor biologging package on a large marine predator, the Greenland shark (Somniosus microcephalus). We deployed a recoverable biologging package combining a VEMCO Mobile Transceiver (VMT), accelerometer and a temperature–depth tag to quantitatively assess fine‐scale behaviour during detection events, that is when sharks carrying the novel VMT package (animalR, n = 3) detected sharks independently tagged with transmitters in the system (animalT, n = 29). Concurrently, we developed simulations to estimate the distances between animalR and animalT by accounting for their swim speed, the estimated detection efficiency of the VMT and the number of consecutive transmissions recorded. Accelerometer‐derived activity indices were then used as a means to test for response to potential interactions when animals are expected to be in close proximity. Based on this approach, the three VMT‐equipped Greenland sharks exhibited higher body acceleration and greater depth changes during detections, suggesting a potential behavioural response to the presence of other sharks. A generalized additive model indicated a moderate increasing relationship in activity associated with a greater number of animalT detections. Through the proposed framework, detection events with varying probabilities of interaction likelihoods can be derived and those data isolated and explicitly tested using acceleration data to quantify behavioural interactions. Through inputting known parameters for a species of interest, the framework presented is applicable for all aquatic taxa and can guide future study design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".