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Adult Age-Structure Variability in an Amphibian in Relation to Population Decline

2015· article· en· W1871375864 on OpenAlexaff
Jessica Middleton, David M. Green

Bibliographic record

VenueHerpetologica · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsMcGill University
Fundersnot available
KeywordsPopulation declineSurvivorship curvePopulationBiologyThreatened speciesAmphibianDemographyEcologyHabitatZoology

Abstract

fetched live from OpenAlex

Animal populations that normally fluctuate in size in response to stochastic environmental events might be subject to unsustainably high mortality rates in the face of progressive habitat loss or degradation. This should perturb the population’s age-structure in predictable ways if the increase in mortality is age- or stage-specific (i.e., a decline in recruitment should result in a progressively older adult population, whereas a decline in adult survivorship should result in a progressively younger adult population). We used skeletochronology to ascertain the ages of adult individuals in a population of Fowler’s Toads, Anaxyrus ( = Bufo) fowleri, over 20 yr, spanning a period of regulated population fluctuation from 1992 to 2002 and a period of sustained population decline thereafter until 2011. Age structure was similar between sexes, but was highly variable during both periods. Although there was no temporal trend detected in average age among 420 toads during the predecline period, 1992–2000, there was an increase in average age among 469 toads during the decline period, 2002–2011, with no significant change in adult survivorship. This evidence of an aging population is consistent with a reduction in recruitment in the population, related to the progressive loss of breeding habitat caused by an invasive plant, demonstrating that amphibian populations may be threatened with decline even when mortal threats to adults have not increased.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.995

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)0.0010.000

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 head0.023
GPT teacher head0.268
Teacher spread0.246 · 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

Citations24
Published2015
Admission routes1
Has abstractyes

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