Movement behaviour and dynamics of an aquatic insect in a stream benthic landscape
Bibliographic record
Abstract
Understanding not just where organisms move but how they move is an important step towards integrating animal behaviour into landscape ecology. The three-dimensional landscape of a streambed provides an ideal setting for forging this integration because of the persuasive effects of flowing water. In this study, we experimentally examine the larval movement of the case-building caddisfly Agapetus boulderensis Milne, 1936 in response to two current velocities in each of five levels of contrasting habitat types (i.e., smooth patches that facilitate movement and thick algal patches that constrain movement). Detailed behavioural observations showed that larvae employed two distinctly different strategies of movement in different current velocities: faster crawling and slower pivoting. Our results suggest that individual decision-making between crawling and pivoting is related to the magnitude of current velocity across the streambed, and the frequency at which larvae employ these behaviours translates into differential movement rates and directions. Strong concordance between a conceptual model and our results supports the notion that the presence of structural "nonhabitat" patches at high current velocities may create areas of local flow interruption and refugia. This, in turn, plays an important role in eliciting either crawling or pivoting and in shaping patterns and directions of larval movement, and by extension resource acquisition.
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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 | 0.000 | 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".