Size-specific apparent survival rate estimates of white sharks using mark–recapture models
Bibliographic record
Abstract
For species that exist at low abundance or are otherwise difficult to study, it is challenging to estimate vital rates such as survival and fecundity and common to assume that survival rates are constant across ages and sexes. Population assessments based on overly simplistic vital rates can lead to erroneous conclusions. We estimated sex- and length-based annual apparent survival rates for white sharks (Carcharodon carcharias). We found evidence that annual apparent survival differed over ontogeny in a system with competitive foraging aggregations, from 0.63 (standard error (SE) = 0.08) for newly recruiting subadults to 0.95 (SE = 0.02) for the largest sharks. Our results reveal a potential challenge to ontogenetic recruitment in a long-lived, highly mobile top marine predator, as survival rates for subadult white sharks may be lower than previously assumed. Alternatively, younger and competitively inferior individuals may be forced to permanently emigrate from primary foraging sites. This study provides new methodology for estimating apparent survival as a function of diverse covariates by capture–recapture study, including when sex assignment is uncertain.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".