Association between clonal diversity and species diversity in subarctic zooplankton communities
Bibliographic record
Abstract
Genetic diversity in a single species and species diversity (SD) in a whole community are often interrelated because they are frequently influenced by a common factor or interact directly with each other. This study examined the effect of environmental and spatial factors on the distribution of clonal diversity in Daphnia pulex and of SD in zooplankton communities in subarctic ponds, and tested the association of these diversity levels when the effect of these factors was partitioned out. Forty-one ponds in the Kuujjuarapik area, Nunavik, Canada, were characterized according to their physico-chemical properties, Daphnia clonal composition and zooplankton assemblage. Variation in clonal distribution in Daphnia was mostly explained by water conductivity, pH and depth. Conductivity, altitude, temperature, phaeopigments, surface of the pond and spatial factors accounted for 12% of the variation in the zooplankton community. There was a positive association between diversity levels suggesting that clonal diversity and SD affected each other directly, probably via the effect of clonal diversity in Daphnia on the rest of the zooplankton community. Our results suggest that a better understanding of the interaction between genetic and SD in subarctic and arctic environments will be important to predict biodiversity changes resulting from climate change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".