Littoral chironomids of a large Alpine lake: spatial variation and variables supporting diversity
Bibliographic record
Abstract
Chironomidae are a major group of littoral secondary producers whose spatial changes in assemblage structures are shaped by diverse variables. Using their subfossil remains, we aimed at disentangling the relative impact of environmental, topographic, littoral occupation and hydrodynamic variables on the littoral assemblages as well as identifying taxa and sites of primary importance for the β-diversity in a large lake (Lake Bourget). A redundancy analysis coupled with a variance partitioning indicated that 22 % of the assemblage variability was explained by slope, carbonate concentration and littoral occupation. A non-negligible fraction of non-separable variance among these variables likely highlights the topographic constraint on anthropogenic development and environmental conditions. Taxonomic turnover overwhelmingly (93.2 %) dominated the assemblage changes indicating site-specific taxonomic composition. The taxa contribution to β-diversity was positively correlated with their mean abundances. The local abundances were either positively or negatively correlated with local contribution of sites to β-diversity (LCBD) providing evidence for different contributions of taxa to β-diversity. A couple of taxa (i.e.Cricotopusspp.,Microtendipessp. andCladotanytarsussp.) and sites (i.e.LB57, LB31, LB2) clearly supported the major variations of β-diversity and are therefore identified as being of primary importance regarding conservation programs. Both LCBD variations and taxa spatial autocorrelations suggest that chironomid assemblages were the most variable at a spatial-scale ranging from 500 m to 1 km, characterizing the spatial successions of littoral contexts. These results illustrate the need for considering short spatial scales to reveal the extent of the benthic diversity in the littoral areas of large lakes.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".