Elevated Allochthony in Stream Food Webs as a Result of Longitudinal Cumulative Effects of Forest Management
Bibliographic record
Abstract
The river continuum concept (RCC) predicts a downstream shift in the reliance of aquatic consumers from terrestrial to aquatic carbon sources, but this concept has rarely been assessed with longitudinal studies. Similarly, there are no studies addressing how forestry related disturbances to the structure of headwater food webs manifest (accumulate/dissipate) downstream and/or whether forest management alters natural longitudinal trends predicted by the RCC. Using stable isotopes of carbon, nitrogen and hydrogen, we investigated how: 1) autochthony in macroinvertebrates and fish change from small streams to larger downstream sites within a basin with minimal forest management (New Brunswick, Canada); 2) longitudinal trends in autochthony and food web length compare among three basins with different forest management intensity [intensive (harvest and replanting), extensive (harvest only), minimal] to detect potential cumulative/dissipative effects; and 3) forest management intensity and other catchment variables are influencing food web dynamics. We showed that, as predicted, the reliance of some macroinvertebrate taxa (especially collector feeders) on algae increased from small streams to downstream waters in the minimally managed basin, but that autochthony in the smallest shaded stream was higher than expected based on the RCC (as high as 90% for some taxa). However, this longitudinal increase in autochthony was not observed within the extensively managed basin and was weaker within the intensively managed one, suggesting that forest management can alter food web dynamics along the river continuum. The dampening of downstream autochthony indicates that the increased allochthony observed in small streams in response to forest harvesting cumulates downstream through the river continuum. Supplementary Information: The online version contains supplementary material available at 10.1007/s10021-021-00717-6.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".