Water source dynamics influence macroinvertebrate communities across groundwater-fed streams in a glacierized catchment
Bibliographic record
Abstract
Abstract Groundwater contributions to streamflow significantly influence the structure and function of riverine ecosystems, particularly in glacierized catchments where there are marked differences in water sources and subsurface flow paths. Here, we investigated spatial and temporal variation in relationships between water sources, flow paths, physical and chemical processes, organic matter, microbial biofilms, and macroinvertebrates across groundwater-fed streams in the glacierized Toklat River catchment of Denali National Park, Alaska. Streams fed predominantly by seepage from the valley sides were perennial, whereas streams sustained by glacial meltwater seepage were ephemeral. Differences in environmental conditions between flow regimes appeared to influence spatial and temporal patterns of organic matter, linking to macroinvertebrate community dynamics. Macroinvertebrates in perennial streams were supported by fine particulate organic matter from subsurface flow paths during summer, transitioning to a combination of fine particulate matter and leaf litter in autumn. In comparison, macroinvertebrates inhabiting ephemeral streams, which only flowed during autumn, were supported by leaf litter. Some macroinvertebrate taxa were unaffected by turnover in organic matter, indicating potential plasticity in organic matter resource use. Findings highlight the importance of considering spatial and temporal variation in groundwater-fed streams, considering that projected hydrological changes under a changing climate may have significant implications for these systems.
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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".