Black alder may enhance riparian buffer mitigation of pine-plantation effects on macroinvertebrate food webs in headwater streams
Bibliographic record
Abstract
Over the past century, drylands have undergone significant landscape transformations. Abandonment of traditional crops and pastures led to development of extensive afforestation programs with conifers, which often lacked an ecologically sound orientation, raising concerns on their potential consequences on recipient ecosystems. Forest streams heavily rely on inputs of terrestrial organic carbon and thus are particularly vulnerable to human-driven changes in catchment and riparian forests. One point of uncertainty is whether existing stands of deciduous trees in the riparian zone may buffer headwater stream food webs from the impacts of afforestation on the surrounding landscape. We used stable isotopes of carbon and nitrogen to investigate whether the presence of the nitrogen-fixing black alder in the riparian zone alters the impacts of pine plantations on macroinvertebrate food webs of headwater streams. We observed a consistent consumption of leaf litter by shredders, but a higher importance of autochthonous support to all macroinvertebrate functional feeding groups than initially expected, especially in absence of alder. In addition, we discerned a potential trend toward a food chain lengthening at streams holding riparian alder in winter. Overall, our results indicate that riparian alder can enhance the buffer effect exerted by other broadleaf species through a reduction of the usual wide nutritional imbalance existing between benthic consumers and resources, which may translate into longer food chains. These findings highlight the critical role of riparian vegetation, particularly deciduous species like black alder, in maintaining headwater stream ecosystem integrity within afforested landscapes. Incorporating riparian vegetation management into afforestation planning can enhance stream food-web stability and support more balanced aquatic ecosystems.
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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 | 0.000 | 0.000 |
| 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".