A landscape approach to examining spatial patterns of limnological variables and long‐term environmental change in a southern Canadian lake district
Bibliographic record
Abstract
Summary 1. We explored patterns of limnological variables (physical, chemical and biological) with relation to landscape position (expressed as lake order) in 86 study lakes located on shield bedrock in south‐central Ontario, Canada. 2. Using anovas with lake order as the categorical variable, landscape position explained significant amounts of variation in major ion chemistry, physical and catchment characteristics, hypolimnetic oxygen, and community composition in algal (diatom, chrysophyte) and invertebrate (chironomid) assemblages preserved in surficial sediments. Several nutrient variables (TP, total phosphorus and TN, total nitrogen) and dissolved organic carbon did not have significant relationships with lake order. 3. The strongest relationships with lake order (as a fraction of variation explained in anovas) included silica concentrations (r2 = 0.40) and SO4 (r2 = 0.29) concentrations, surface area (r2 = 0.50) and hypolimnetic oxygen (r2 = 0.29). 4. Bedrock geology (carbonate metasedimentary versus non‐carbonate bedrock) had strong influences on spatial gradients of pH and major ion chemistry. It was difficult to separate geological influences from spatial influences on limnological variables in this study, as drainage patterns in the region are highly influenced by surface features of underlying geological formations because of the very thin glacial till or exposed bedrock that exists in most catchments. 5. Patterns of limnological variables indicated that low‐order, headwater lakes had the lowest concentrations of major ions, and, from algal inferences of pH change, had been most susceptible to acidic deposition. High‐order, downstream lakes were larger and deeper, and had higher concentrations of hypolimnetic oxygen, indicating that optimal lake trout habitat was primarily located in high‐order lakes. 6. Variance partitioning analyses indicated that lake order as a metric of landscape position explained comparable portions of community variation in algal and invertebrate assemblages compared with geographic position (latitude, longitude) and Cartesian coordinate position (e.g. x, y, x2, y2, etc.) metrics. Lake order explained more community variation in chironomid assemblages compared with other landscape metrics, possibly because of the strong relationships between lake order and lake morphometry variables.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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".