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Record W1997914846 · doi:10.1139/f06-161

Spatial and environmental correlates of fish community structure in Canadian Shield lakes

2006· article· en· W1997914846 on OpenAlexfundvenueaboutno aff
Andrea Bertolo, Pierre Magnan

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
FundersGroupe de recherche interuniversitaire en limnologie
KeywordsEsoxCommunity structureEcologyGeographyPikeBiomass (ecology)BeaverFisheryBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We used data on fish species biomass from 38 lakes of the Canadian Shield (Québec) to determine the contribution of environmental (lake and watershed morphometry) and spatial (e.g., hydrographic connectivity and geographic coordinates) variables on fish community structure. By using a combination of multivariate analyses, we show that nearly half of the variation in the fish community structure is explained by the independent contributions of spatial and environmental factors. Walleye (Sander vitreus) and lake whitefish (Coregonus clupeaformis) were significantly associated with the absence of beaver (Castor canadensis) dams, whereas northern pike (Esox lucius) was positively correlated with beaver dam presence. Altitude and longitude, but not current patterns in lake connectivity, were the main explanatory spatial variables accounting for the observed pattern in fish community structure. Large piscivorous fish were associated with a reduced richness and biomass of small prey, suggesting that predation is a structuring factor in these lakes. By showing that geographic coordinates and altitude are better descriptors of fish community structure than hydrographic connections, our study suggests that past colonization routes are relatively more important than current ones in structuring fish communities at the landscape level. This interpretation is supported by recently published genetic data.

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 score0.138
Threshold uncertainty score0.669

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 studies0.0000.002
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 head0.009
GPT teacher head0.170
Teacher spread0.161 · 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

Citations25
Published2006
Admission routes3
Has abstractyes

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