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Record W2163328838 · doi:10.1139/f09-170

Fluctuations in harvest of native and introduced crayfish are driven by temperature and population density in previous years

2009· article· en· W2163328838 on OpenAlexvenueno aff
Karin Olsson, Wilhelm Granéli, Jörgen Ripa, Per Nyström

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsnot available
Fundersnot available
KeywordsCrayfishPacifastacusAstacusPopulationBiologyEcologyPopulation densityAbundance (ecology)FisheryEnvironmental scienceDemography

Abstract

fetched live from OpenAlex

The effects of temperature and density on annual fluctuations in catches of large adult crayfish are evaluated using time series analysis. We tested if temperature during different stages of the crayfish life cycle influenced observed catches from 1946 to 2007 in Lake Bunn (Sweden). From 1946 to 1974, native noble crayfish ( Astacus astacus ) inhabited the lake, but then, crayfish plague wiped out the whole population. In 1985, the exotic signal crayfish ( Pacifastacus leniusculus ) was introduced and is still present. This made it possible to model the two species separately and compare how temperature and density influence the abundance of large adult crayfish. The best models indicate that both climatic- and density-dependent factors influence the observed fluctuations and there was a time lag for most factors included in the best models. Winter temperature had the strongest influence on fluctuations of both species. Also, density dependence was included in the best model for both species. Growth season for noble crayfish and temperature during the mating season for signal crayfish were also present in the most parsimonious model. Based on our findings, it is difficult to predict how further climate warming will affect crayfish population dynamics in freshwaters.

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 score0.200
Threshold uncertainty score0.810

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 head0.007
GPT teacher head0.206
Teacher spread0.198 · 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

Citations18
Published2009
Admission routes1
Has abstractyes

Explore more

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