How Many Seals Were There? The Global Shelf Loss during the Last Glacial Maximum and Its Effect on the Size and Distribution of Grey Seal Populations
Bibliographic record
Abstract
Predicting how marine mammal populations respond to habitat changes will be essential for developing conservation management strategies in the 21st century. Responses to previous environmental change may be informative in the development of predictive models. Here we describe the likely effects of the last ice age on grey seal population size and distribution. We use satellite telemetry data to define grey seal foraging habitat in terms of the temperature and depth ranges exploited by the contemporary populations. We estimate the available extent of such habitat in the North Atlantic at present (between 1.42 · 10(6) km(2) and 2.07 · 10(6) km(2)) and at the last glacial maximum (between 4.74 · 10(4) km(2) and 2.11 · 10(5) km(2)); taking account of glacial and seasonal sea-ice coverage, estimated reductions of sea-level (123 m) and sea surface temperature hind-casts. Most of the extensive continental shelf waters (North Sea, Baltic Sea and Scotian Shelf), currently supporting >95% of grey seals, were unavailable during the last glacial maximum. A combination of lower sea-level and extensive ice-sheets, massively increased seasonal sea-ice coverage and southerly extent of cold water would have pushed grey seals into areas with no significant shelf waters. The habitat during the last glacial maximum might have been as small as 3% of today's extent and grey seal populations may have fallen to similarly low numbers. An alternative scenario involving a major change to a pelagic or bathy-pelagic foraging niche cannot be discounted. However, hooded seals currently dominate that niche and may have excluded grey seals from such habitat. If as seems likely, the grey seal population fell to very low levels it would have remained low for several thousand years before expanding into current habitats over the past 12,000 years or so.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".