Modeling the foraging habitat of humpback whales
Bibliographic record
Abstract
Knowing how species will respond to environmental variability and climate change requires understanding the factors that influence their distribution and movement patterns. I investigated the processes that drive individuals to concentrate in specific areas of their home range by modeling encounter rates of humpback whales (Megaptera novaeangliae) in relation to environmental variables using GIS tools, generalized additive models, and remote sensing and in situ data. I conducted this work at two foraging areas: the coastal waters of British Columbia, Canada, and the Bransfield and Gerlache Straits, Antarctica. Humpback whales in British Columbia were strongly associated with latitude and bathymetric features. The relationships with remotely sensed variables reflecting primary productivity were not consistent, but higher numbers of whales seemed to be associated with higher productivity. In fact, the highest concentrations of humpback whales appeared to reflect areas where concentration and retention processes lead to higher biological productivity, including south Dixon Entrance, middle and southwestern Hecate Strait and off Juan de Fuca Strait. Humpback whales in the Southern Ocean also preferred areas of enhanced biological productivity. In Gerlache Strait, humpback whales were associated with areas of higher chlorophyll-a concentration in the central and northern sections of the strait, which also corresponded to relatively higher temperatures and shallower mixed layer depths for the in situ data. In Bransfield Strait, humpback whales appeared to prefer the near-frontal zones and the deep basins, where surface waters are influenced by the Bransfield Current. Interannual variability in both humpback and minke whale encounter rates in Gerlache Strait was correlated with the Oceanic Niño Index, the oceanic component of ENSO. In addition to investigating species-habitat relationships with statistical models, I conducted the first study to describe the satellite-monitored movements of humpback whales on their feeding grounds along the Antarctic Peninsula. Results showed considerable individual variation in direction, speed and range of movements, and an overall pattern characterized by short- and long-distance movements between presumed foraging areas with relatively short residency times. All told, the results of my research show that humpback whale distribution within foraging habitat is influenced by physical and biological variables that enhance biological productivity.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".