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Record W2138027252 · doi:10.1139/z10-054

The trade-off of reproduction and survival in slow-breeding seabirds

2010· article· en· W2138027252 on OpenAlexvenueno aff
F. Stephen Dobson, Pierre Jouventin

Bibliographic record

VenueCanadian Journal of Zoology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersInstitut Polaire Français Paul Emile Victor
KeywordsBiologyReproductionTrade-offBreedEcologyLife history theorySeabirdPelagic zoneInterspecific competitionLife historyZoologyPredation

Abstract

fetched live from OpenAlex

A trade-off between reproduction and survival is one of the most consistent empirical aspects of life-history diversification. One explanation for this interspecific pattern is evolved differences in the balance of allocation to reproduction versus individual maintenance and survival. The same pattern is expected, however, simply as a result of differences among species in body size. We tested these alternatives using original data from 44 species of albatrosses and petrels, long-lived seabirds that breed very slowly. After application of regression techniques to remove the effects of body size and phylogeny, annual reproduction and survival exhibited a significant trade-off. Our measures of reproductive effort also exhibited significant trade-offs with age at maturity, the latter strongly associated with survival. Feeding rate of chicks, success at fledging chicks, and annual chick production were also significantly associated. In conclusion, after removing the effects of body size, we found a significant trade-off of reproduction and survival, in spite of the fact that these long-lived birds lay only one egg at a time. Our examination of the pattern among life-history traits of these slow breeders and their pelagic feeding ecology provide support for the evolutionary explanation of a trade-off of reproduction and survival.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.226
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2010
Admission routes1
Has abstractyes

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