Climate oscillations drive millennial‐scale changes in seabird colony size
Bibliographic record
Abstract
Seabird population size is intimately linked to the physical, chemical, and biological processes of the oceans. Yet, the overall effects of long-term changes in ocean dynamics on seabird colonies are difficult to quantify. Here, we used dated lake sediments to reconstruct ~10,000-years of seabird dynamics in the Northwest Atlantic to determine the influences of Holocene-scale climatic oscillations on colony size. On Baccalieu Island (Newfoundland and Labrador, Canada)-where the world's largest colony of Leach's storm-petrel (Hydrobates leucorhous Vieillot 1818) currently breeds-our data track seabird colony growth in response to warming during the Holocene Thermal Maximum (ca. 9000 to 6000 BP). From ca. 5200 BP to the onset of the Little Ice Age (ca. 550 BP), changes in colony size were correlated to variations in the North Atlantic Oscillation (NAO). By contrasting the seabird trends from Baccalieu Island to millennial-scale changes of storm-petrel populations from Grand Colombier Island (an island in the Northwest Atlantic that is subjected a to different ocean climate), we infer that changes in NAO influenced the ocean circulation, which translated into, among many things, changes in pycnocline depth across the Northwest Atlantic basin where the storm-petrels feed. We hypothesize that the depth of the pycnocline is likely a strong bottom-up control on surface-feeding storm-petrels through its influence on prey accessibility. Since the Little Ice Age (LIA), the effects of ocean dynamics on seabird colony size have been altered by anthropogenic impacts. Subsequently, the colony on Baccalieu Island grew at an unprecedented rate to become the world's largest resulting from favorable conditions linked to climate warming, increased vegetation (thereby nesting habitat), and attraction of recruits from other colonies that are now in decline. We show that although ocean dynamics were an important driver of seabird colony dynamics, its recent influence has been modified by human interference.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".