Causes and consequences of invertebrate drift in running waters: from individuals to populations and trophic fluxes
Bibliographic record
Abstract
Invertebrate drift, the downstream transport of aquatic invertebrates, is a fundamental ecological process in streams with important management implications for drift-feeding fishes. Despite long-standing interest, many aspects of drift remain poorly understood mechanistically, thereby limiting broader food web applications (e.g., bioenergetics-based habitat models for fish). Here, we review and synthesize drift-related processes, focusing on their underlying causes, consequences for invertebrate populations and broader trophic dynamics, and recent advances in predictive modelling of drift. Improving predictive models requires further resolving the environmental contexts where drift is driven by hydraulics (passive drift) versus behaviour (active drift). We posit this can be qualitatively inferred by hydraulic conditions, diurnal periodicity, and taxa-specific traits. For invertebrate populations, while the paradox of population persistence in the context of downstream loss has been generally resolved with theory, there are still many unanswered questions surrounding the consequences of drift for population dynamics. In a food web context, there is a need to better understand drift-foraging consumer–resource dynamics and to improve modelling of drift fluxes to more realistically assess habitat capacity for drift-feeding fishes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".