Estimating Trans-Seasonal Variability in Water Column Biomass for a Highly Migratory, Deep Diving Predator
Bibliographic record
Abstract
The deployment of animal-borne electronic tags is revolutionizing our understanding of how pelagic species respond to their environment by providing in situ oceanographic information such as temperature, salinity, and light measurements. These tags, deployed on pelagic animals, provide data that can be used to study the ecological context of their foraging behaviour and surrounding environment. Satellite-derived measures of ocean colour reveal temporal and spatial variability of surface chlorophyll-a (a useful proxy for phytoplankton distribution). However, this information can be patchy in space and time resulting in poor correspondence with marine animal behaviour. Alternatively, light data collected by animal-borne tag sensors can be used to estimate chlorophyll-a distribution. Here, we use light level and depth data to generate a phytoplankton index that matches daily seal movements. Time-depth-light recorders (TDLRs) were deployed on 89 southern elephant seals (Mirounga leonina) over a period of 6 years (1999-2005). TDLR data were used to calculate integrated light attenuation of the top 250 m of the water column (LA(250)), which provided an index of phytoplankton density at the daily scale that was concurrent with the movement and behaviour of seals throughout their entire foraging trip. These index values were consistent with typical seasonal chl-a patterns as measured from 8-daySea-viewing Wide Field-of-view Sensor (SeaWiFs) images. The availability of data recorded by the TDLRs was far greater than concurrent remotely sensed chl-a at higher latitudes and during winter months. Improving the spatial and temporal availability of phytoplankton information concurrent with animal behaviour has ecological implications for understanding the movement of deep diving predators in relation to lower trophic levels in the Southern Ocean. Light attenuation profiles recorded by animal-borne electronic tags can be used more broadly and routinely to estimate lower trophic distribution at sea in relation to deep diving predator foraging behaviour.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".