Fish use of deep-sea sponge habitats evidenced by long-term high-resolution monitoring
Bibliographic record
Abstract
It is critical that fish's habitat uses of benthic habitats are understood, to inform effective fisheries management and to predict the impacts of human activities and climate change. In this study, benthic landers were used to collect long-term high-temporal resolution data to gain insights into the habitat use of sponge grounds by fish at the Sambro Bank Conservation Area. An integrated ecosystem-based monitoring approach was used, involving data collected on the biology, food supply, and oceanography. Fish abundance, behaviour and complex benthopelagic interactions were analysed over spatial and extended temporal scales (i.e., 30-min intervals from 2021 to 2023). A total of 21 different planktivorous and benthivorous fish taxa were found to utilise the seafloor. We show that sponge grounds can act as a nursery, feeding and shelter habitats for commercially important fish. In-depth analyses of Redfish, urophycid hakes, and Silver Hake revealed distinct diel and seasonal patterns and showed how food, sponge density and current speed are important drivers of their abundance and behaviour. Supported by fishery trawl survey reports, high-temporal resolution benthic ecosystem monitoring revealed the importance of sponge grounds and environmental drivers to commercially important fish. Such information is crucial for developing and implementing robust, evidence-based policy and management.
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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 | 0.000 | 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".