Demographic consequences and conservation implications of intermittent breeding in the common eider and black-browed albatross
Bibliographic record
Abstract
Modelling of populations and their components is central to theoretical and applied ecology, but the required demographic information is often unavailable or incomplete across the life cycle. Inferences drawn from models missing important life stages and/or population segments may be limited or flawed, with implications for conservation applications. The detailed data required for comprehensive models may be easiest to collect or access for abundant species, which are themselves often declining. This thesis contributes to population management for one such species (the common eider, Somateria mollissima) through data collation and population modelling. Chapter 2 presents a curated demographic database for this species, which should help to increase data re-use and act as a reference for the less-studied sea ducks. In Chapter 3, quantitative synthesis of this dataset presents global mean values for use in modelling, and uncovers a mismatch in study effort relative to influence on population dynamics. Breeding propensity (the probability of an adult individual attempting to breed in a given year) is proportionally understudied, and Chapter 4 identifies that the return to breeding after a period of non-breeding is a key life-stage transition which should be considered in future data collection and conservation interventions. In Chapter 5, a detailed individual-based dataset available for another long-lived marine bird (the black-browed albatross, Thalassarche melanophris) facilitates disaggregation of demographic parameter means, variances and covariances across different previous breeding states, revealing distinct ‘demographic profiles’ as an important source of heterogeneity within the population. Thus for both species, novel model formulations incorporate breeding propensity, a known knowledge gap in the demography and conservation of marine birds, to draw out its broader significance for our understanding of population dynamics and reproductive ecology.
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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 | 0.002 |
| 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) | 0.002 | 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".