Congruence of water column, contemporary and pre‐industrial sediment cladoceran assemblages in 85 Canadian lakes of contrasting human impact levels
Bibliographic record
Abstract
Abstract Crustacean zooplankton occupy a key position as primary consumers within lake food webs, acting as early indicators of wider ecosystem shifts that can cascade to higher trophic levels. Their communities can be assessed via water column net hauls or via sediment samples containing preserved subfossil remnants from various time periods; the latter probably integrate multiple years of data, habitats within lakes, and seasonal patterns. The degree to which such samples do or do not correspond has never been assessed across a wide variety of lake types and gradients. As part of a larger sampling of over 600 lakes across lake‐rich Canada, 85 lakes were selected along a gradient of human impact, according to contemporary catchment land use, to analyse cladoceran zooplankton assemblage relatedness among snapshot water column, contemporary and pre‐industrial sediment samples. We used a combination of ordinations and matrix correlations (RV coefficients), along with partial redundancy analyses, to determine the degree to which the different sample types were comparable and whether they responded similarly to >30 environmental variables. We also compared conclusions obtained using the full cladoceran assemblages versus pelagic‐only taxa and whether our results varied according to degree of human impact. Overall, while all sample types were correlated, congruence was highest between contemporary and pre‐industrial sediment sample types. This result could arise from the under‐representation of littoral taxa in water column samples relative to those from the sediment. In high impacted catchments, however, we observed greater similarity between water column and contemporary sediment samples, as water quality stressors potentially structure assemblages in parallel directions over time. In these modern‐day sample types and among measured variables, we identified lake depth and pH as the most important structuring variables across Canada. In this first study comparing cladoceran assemblages in water column, contemporary and pre‐industrial sediments across many lakes, we conclude that water column and sediment samples respond similarly to environmental drivers but should not be considered entirely analogous or redundant.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".