Quality versus quantity: response of riparian bird communities to aquatic insect emergence in agro-ecosystems
Bibliographic record
Abstract
In many agricultural landscapes where field drainage is required to enhance crop production, agricultural drainage ditches, and their associated banks and hedgerows can support riparian biodiversity, including bird communities. Against a global background of farmland bird and terrestrial insect decline due to agricultural intensification and extensification, emerging aquatic insects in these aquatic corridors can provide a pulse of energy-rich, nutritionally-important food for birds and other wildlife. In this paper, we quantify the value of drainage ditch habitats in terms of aquatic insect production as a potential food source for riparian foraging birds in a river basin in eastern Canada. Despite being highly managed, agricultural drainage ditches remained extremely productive in terms of emerging biomass of aquatic insects (high quantity), but large-bodied aquatic insects such as mayflies, stoneflies and caddisflies, which are rich in fatty acids, were more common in natural, forested streams and less common in agricultural streams and ditches. The proportion of riparian insectivorous birds was lowest along straight ditches running through agricultural fields and highest among meandering (sinuous) streams in more forested areas, suggesting that agricultural drainage systems may not be able to fully support resource use for foraging predators that rely on emerging aquatic insects. Agricultural producers can improve habitat provisioning for birds on their farms by supporting mosaicked farmscapes through careful conservation and management of ditches and ditch bank vegetation. Establishing larger forest blocks with natural or unmanaged streams between areas of more intense land use can ensure the provisioning of more high quality prey to riparian insectivorous birds, helping to find the balance between agricultural productivity and protection of declining bird populations.
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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 | 0.001 | 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".