Revised Age Estimates for Northern Resident Killer Whales (<i>Orcinus orca</i>) Based on Observed Life‐History Events and Demographic Discounting
Bibliographic record
Abstract
ABSTRACT Long‐term field studies have been invaluable in the study of ecology and evolution; however, for particularly long‐lived species, even long‐term studies often rely on estimated ages, for example when investigating demographic processes. One approach is to estimate unknown birth dates from the known timing of other life‐history events. Building on previous methods, we update estimation techniques for Northern Resident killer whales (NRKW; Orcinus orca ) as part of an ongoing long‐term study that began in 1973. Despite almost 50 years of observation, many individuals were born before records began, and detailed understanding of NRKW life history relies on estimated ages. Our age estimation approach incorporates new data from photo‐identification surveys into a framework that relies on accrued knowledge of demographic rates from known‐age individuals. We use Bayes' law to determine conditional probability distributions from age‐at‐event data, incorporating mathematical descriptions of demographic patterns parameterised from the data. Key to our approach is the discounting of higher age estimates due to the increasing likelihood of mortality with age, a pattern not previously taken into account for NRKWs. We estimate ages for multiple age and sex classes of individuals, using related but tailored approaches, and we incorporate uncertainty into our estimates. Our revised age estimates suggest that individuals are often younger than previously thought (3.5 years on average across 73 individuals; range: 0–15 years). Moreover, the largest discrepancies appear for mothers with offspring at the onset of the study, a class of individuals instrumental for investigating menopause in killer whales—one of the few species other than humans known to exhibit this life‐history feature. Our results will ultimately enable a refined understanding of the evolutionary forces that produce such patterns. We discuss the implications of our findings for the study of resident killer whales and for age estimation in other long‐lived animals.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".