Biotic responses to multiple aquatic and terrestrial gradients in shallow subarctic lakes (Old Crow Flats, Yukon, Canada)
Bibliographic record
Abstract
Biotic communities in shallow northern lakes are frequently used to assess environmental change; however, complex interactions among multiple factors remain understudied. Here, we present analyses of a comprehensive data set that evaluates the influence input waters, catchment characteristics, limnology, and sediment properties on diatom and chironomid assemblages in surface sediments of ~49 shallow mainly thermokarst lakes in Old Crow Flats, Yukon. Multivariate analyses and ANOSIM tests identified that composition of diatom (119 taxa) and chironomid (68 taxa) assemblages differs significantly (p < 0.05) between lakes with snowmelt- versus rainfall-dominated input water. Redundancy analyses revealed strong correlation of limnological, sediment, and catchment variables with input waters. Variation partitioning analyses showed that unique effects of limnological variables account for the largest proportion of variation in diatom and chironomid assemblages (17.2% and 12.6%, respectively). Important independent roles of sediment properties (8.5% and 9.5%) and catchment characteristics (4.9% and 5.1%) were also identified. We suggest that the substantial variation shared among these classes (6.1% and 7.9%) is largely attributable to hydrological processes. Our study demonstrates the utility of multi-factor analysis in northern aquatic research and draws attention to the limitations of one-dimensional comparisons and their interpretations when modelling biotic responses to environmental change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".