Marine predators segregate interspecifically by space and time in a sheltered coastal bay
Bibliographic record
Abstract
Marine predators are vital to the healthy functioning of coastal ecosystems, but to understand their roles, it is necessary to elucidate their movement ecology, particularly in relation to one another. A decade's worth of acoustic telemetry data (2011-2020) from Algoa Bay, South Africa, was investigated to determine how two mesopredatory species (teleosts: dusky kob Argyrosomus japonicus, n = 11, and leervis Lichia amia, n = 16) and two top predatory species (sharks: ragged-tooth sharks Carcharias taurus, n = 45, and white sharks Carcharodon carcharias, n = 31) used and shared this bay ecosystem. Multi-annual seasonal fidelity to the bay was exhibited by all species, but differences in residency were observed among species. Similarly, species used space in the bay differently-the teleosts moved less and had movements restricted to the central and western inshore regions of the bay. Conversely, the sharks roamed more, but detections were concentrated in the western part of the bay for C. taurus and in the eastern part of the bay for C. carcharias. Social network analysis showed that species segregated in space and time on a fine scale. However, there was some interaction observed between C. taurus, L. amia, and A. japonicus, but to varying degrees. This is likely because of strong habitat preferences exhibited by each species and predator-prey relationships between these predatory guilds. Results highlight that the sheltered marine Algoa Bay is a resource-rich environment, supporting multiple predators with different hunting strategies albeit similar prey preferences. Finally, these species are likely afforded some protection by the current Greater Addo Elephant National Park Marine Protected Area in the bay but are vulnerable to fishing pressure when they leave this ecosystem.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".