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Record W1984785185 · doi:10.1086/598496

Chance Establishment for Sexual, Semelparous Species: Overcoming the Allee Effect

2009· article· en· W1984785185 on OpenAlexaff
Christopher L. Jerde, Caroline Bampfylde, Mark A. Lewis

Bibliographic record

VenueThe American Naturalist · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAllee effectPomacea canaliculataSemelparity and iteroparityFecundityBiologyPopulationExtinction (optical mineralogy)Extinction probabilityEcologyPopulation growthPopulation sizeDemographyReproductionSnail

Abstract

fetched live from OpenAlex

We formalize the establishment process for a sexual, semelparous organism through the use of hierarchical probability modeling from parameters of survival, probability of being female, probability of being fertilized, and expected fecundity. We show how to calculate the expected per capita growth rate and probability of extinction. An Allee effect is observed if the expected population growth rate decreases as the initial population size decreases. The model can be further extended as a stochastic process to evaluate the probability of extinction in subsequent generations. One of the novel results is the formulation of an analytical probability distribution for the next generation population size. As case studies, we use the Chinese mitten crab (Eriocheir sinensis) and the apple snail (Pomacea canaliculata), both of which appear on the World Conservation Union's list of 100 worst invaders. We evaluate the strength of the Allee effect and conclude that apple snails experience a weak Allee effect and Chinese mitten crabs experience a strong Allee effect. We emphasize one scenario where the stochastic process reveals that invasion risk can be estimated by the probability of the survival of one fertilized female, because the expected fecundity for one surviving female overwhelms the system such that population persistence is almost certain.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.941

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)0.0010.000

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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
Published2009
Admission routes1
Has abstractyes

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