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Record W2103581279 · doi:10.1111/eea.12003

How protandry and protogyny affect female mating failure: a spatial population model

2012· article· en· W2103581279 on OpenAlexaff
Elise A. Larsen, Justin M. Calabrese, Marc Rhainds, William F. Fagan

Bibliographic record

VenueEntomologia Experimentalis et Applicata · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsBiologyMatingAsynchrony (computer programming)PhenologyPopulationEcologyEvolutionary biologyZoologyDemography

Abstract

fetched live from OpenAlex

Abstract Population growth and persistence depend on the collective ability of individuals to find mates in both space and time. When individuals are reproductively mature for only a portion of a population's breeding season, reproductive asynchrony can cause mating failure and a temporal A llee effect, which is exacerbated by spatial constraints in isolated populations. However, the effect of phenological variation by sex (protandry, protogyny) in spatiotemporal mate finding is not well understood. Here, we examine the interacting roles of sex‐specific and population‐wide individual asynchrony on female matelessness in spatially isolated populations. By incorporating sex‐specific phenology into a two‐sex reaction‐diffusion system, we explore female matelessness as a function of phenology, movement behavior, and patch size. Although individual asynchrony may lead to female mating failure in small and isolated populations, we find that moderate protandry reduces female mating failure across a variety of scenarios. We go on to examine model behavior for a case study based on the bagworm, T hyridopteryx ephemeraeformis H aworth ( L epidoptera: P sychidae), where many populations exhibit pronounced protogyny. Overall, we find a consistent benefit of moderate protandry, which may mitigate female matelessness for many populations.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.062
GPT teacher head0.275
Teacher spread0.213 · 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 designBench or experimental
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

Citations30
Published2012
Admission routes1
Has abstractyes

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