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Record W2204270660 · doi:10.1890/es15-00257.1

The role of seasonality and non‐lethal carry‐over effects on density‐dependent dispersal

2015· article· en· W2204270660 on OpenAlexafffund
Gustavo S. Betini, Aaron Pardy, Cortland K. Griswold, D. Ryan Norris

Bibliographic record

VenueEcosphere · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Guelph
KeywordsBiological dispersalEcologyBiologyDensity dependencePopulation densityIntraspecific competitionPopulationHabitatRange (aeronautics)SeasonalityDemography

Abstract

fetched live from OpenAlex

Understanding dispersal is critical for predicting a wide range of ecological dynamics. Variation in intraspecific density is widely regarded as a major factor influencing dispersal rates but it is not clear why dispersal is positively related to density in some systems and negatively related to density in other systems. Using seasonal populations of Drosophila melanogaster , we experimentally show that dispersal rates are both positively related to breeding density at the time of dispersal and negatively related to density at the beginning of the previous non‐breeding season. This suggests that flies use density at the time of dispersal as a cue for habitat quality but are also negatively influenced by the delayed, non‐lethal effects of density in the previous season. A parameterized model indicates that a carry‐over effect not only causes a decrease in the proportion of individuals that disperse, but also a decrease in population size caused by lower per capita breeding output. Our results demonstrate how density can have contrasting effects on dispersal and population size depending on when density is measured in the annual cycle and that non‐lethal effects on individuals can have important, but previously unrecognized, consequences for both the movement rates and long‐term dynamics of seasonal 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.337

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 head0.004
GPT teacher head0.208
Teacher spread0.204 · 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

Citations17
Published2015
Admission routes2
Has abstractyes

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