At a snail’s pace: the influence of habitat disturbance on terrestrial snail movement using experimentally manipulated mesocosms
Bibliographic record
Abstract
Abstract The Anthropocene marks great changes to environments and the animals that inhabit them. Changes, such as disturbance, can affect the manner in which animals interact with their environments, such as moving and selecting habitats. To test how animals might respond to changing disturbance regimes, we employ an experimental approach to movement ecology. We used integrated step selection analysis (iSSA) to test the behavioural responses of individually-marked grove snails ( Cepaea nemoralis ) exposed to a gradient of physical disturbance in their habitat. We used a before-after control-impact (BACI) experimental design within semi-controlled mesocosms to manipulate edge and disturbance variables by altering the area of the mesocosm covered by bricks. We showed that grove snails perceive edges of enclosures and edges of bricks as risks, and responded to such risks by altering their movement. Grove snails displayed a bimodal response to risk by taking shelter in place or moving faster to be farther from the disturbance. Furthermore, individuals tended to modulate their behavioural response to the degree of risk. Our study highlights the usefulness of experimental mesocosms in movement ecology and in determining the effects of habitat alteration and human-imposed risk on movement behaviour.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".