Increased reliance of stream macroinvertebrates on terrestrial food sources linked to forest management intensity
Bibliographic record
Abstract
Our understanding of how forest management practices affect the relative importance of autochthonous vs. allochthonous resource use by headwater stream food webs is relatively poor. To address this, we used stable isotope (C, N, and H) analyses of food sources and macroinvertebrates from 15 streams in New Brunswick (Canada) and assessed how different catchment conditions arising from the gradient in forest management intensity affect the contribution of autochthonous resources to these food webs. Aquatic primary production contributed substantially to the biomass of invertebrates in these headwater streams, especially for scrapers and collector-gatherers (25-75%). However, the contribution of algae to food webs decreased as forest management intensity (road density and associated sediments, water cations/carbon, and dissolved organic matter humification) increased, and as canopy openness decreased. This trend was probably due to an increase in the delivery of organic and inorganic terrestrial materials (dissolved and in suspension) in areas of greater harvesting intensity and road density, which resulted in more heterotrophic biofilms. Overall, results suggest that, despite the presence of riparian buffers, forest management can affect stream food web structure via changes in energy flows, and that increased protection should be directed at minimizing ground disturbance in areas with direct hydrological connection to streams and at reducing dissolved and particulate matter inputs from roads and stream crossings in catchments with high degrees of management activity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".