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Record W2617837815 · doi:10.1098/rspb.2017.0580

Harvest and group effects on pup survival in a cooperative breeder

2017· article· en· W2617837815 on OpenAlexfundno aff
David E. Ausband, Michael S. Mitchell, Carisa R. Stansbury, Jennifer L. Stenglein, Lisette P. Waits

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
FundersAlberta Innovates Bio SolutionsAlberta Conservation AssociationIdaho Department of Fish and GameCoypu FoundationBernice Barbour Foundation
KeywordsBiologyBreeder (animal)Survival rateTurnoverAnimal scienceDemographyInternal medicineMedicine

Abstract

fetched live from OpenAlex

Recruitment in cooperative breeders can be negatively affected by changes in group size and composition. The majority of cooperative breeding studies have not evaluated human harvest; therefore, the effects of recurring annual harvest and group characteristics on survival of young are poorly understood. We evaluated how harvest and groups affect pup survival using genetic sampling and pedigrees for grey wolves in North America. We hypothesized that harvest reduces pup survival because of (i) reduced group size, (ii) increased breeder turnover and/or (iii) reduced number of female helpers. Alternatively, harvest may increase pup survival possibly due to increased per capita food availability or it could be compensatory with other forms of mortality. Harvest appeared to be additive because it reduced both pup survival and group size. In addition to harvest, turnover of breeding males and the presence of older, non-breeding males also reduced pup survival. Large groups and breeder stability increased pup survival when there was harvest, however. Inferences about the effect of harvest on recruitment require knowledge of harvest rate of young as well as the indirect effects associated with changes in group size and composition, as we show. The number of young harvested is a poor measure of the effect of harvest on recruitment in cooperative breeders.

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 score0.176
Threshold uncertainty score0.851

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.252
Teacher spread0.216 · 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

Citations52
Published2017
Admission routes1
Has abstractyes

Explore more

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