Subcatchment deltas and upland features influence multiscale aquatic ecosystem recovery in damaged landscapes
Bibliographic record
Abstract
Assessing biological recovery in damaged aquatic environments requires the consideration of multiple spatial and temporal scales. Past research has focused on assessing lake recovery from atmospheric or catchment disturbance at regional or catchment levels. Studies have also rarely considered the influences of adjacent terrestrial characteristics on within-lake habitats, such as subcatchment delta confluences. We used Hyalella azteca, a ubiquitous freshwater amphipod, as a sensitive indicator to assess the importance of local subcatchment scale factors in the context of multiscale lake recovery within the metal mining region of Sudbury, Canada following a period of major reductions in atmospheric pollution. At the regional scale, data from repeated surveys of 40 lakes showed higher probabilities of H. azteca occurrence with higher lake water conductivity, alkalinity, and pH and lower metal concentrations. The importance of metals decreased through time and the importance of higher conductivity, alkalinity, and pH increased. At the subcatchment scale, a subset of six lakes sampled across a colonization gradient revealed higher H. azteca abundances at subcatchment delta sites than non-delta sites in early colonization stages, and that abundance at delta sites was correlated with both within-lake habitat and terrestrial subcatchment characteristics. For example, wetland cover reduced the strength of positive associations between H. azteca abundance and macrophyte density. A single lake from this subset also revealed higher abundances at delta sites associated with higher concentrations of terrestrial organic matter and larger subcatchments. Our results demonstrate that factors affecting recovery can change with the scale of study, and that managing terrestrial-aquatic linkages is important for facilitating recovery processes within damaged lake ecosystems.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".