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Record W2779874541 · doi:10.1086/695688

Temperature-Dependent Species Interactions Shape Priority Effects and the Persistence of Unequal Competitors

2017· article· en· W2779874541 on OpenAlexaff
Tess Nahanni Grainger, Adam I. Rego, Benjamin Gilbert

Bibliographic record

VenueThe American Naturalist · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyBiological dispersalAphidEcologyInterspecific competitionTrophic levelPopulationCompetition (biology)Global warmingAbundance (ecology)Climate changeBotany

Abstract

fetched live from OpenAlex

The order of species arrival at a site can determine the outcome of competitive interactions when early arrivers alter the environment or deplete shared resources. These priority effects are predicted to be stronger at high temperatures, as higher vital rates caused by warming allow early arrivers to more rapidly impact a shared environment. We tested this prediction using a pair of congeneric aphid species that specialize on milkweed plants. We manipulated temperature and arrival order of the two aphid species and measured aphid population dynamics and milkweed survival and defensive traits. We found that warming increased the impact of aphids on the quantity and quality of milkweed, which amplified the importance of priority effects by increasing the competitive exclusion of the inferior competitor when it arrived late. Warming also enhanced interspecific differences in dispersal, which could alter relative arrival times at a regional scale. Our experiment provides a first link between temperature-dependent trophic interactions, priority effects, and dispersal. This study suggests that the indirect and cascading effects of temperature observed here may be important determinants of diversity in the temporally and spatially complex landscapes that characterize ecological communities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.241
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations43
Published2017
Admission routes1
Has abstractyes

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