Emergent Properties of Patch Shapes Affect Edge Permeability to Animals
Bibliographic record
Abstract
Animal travel between habitat patches affects populations, communities and ecosystems. There are three levels of organization of edge properties, and each of these can affect animals. At the lowest level are the different habitats on each side of an edge, then there is the edge itself, and finally, at the highest level of organization, is the geometry or structure of the edge. This study used computer simulations to (1) find out whether effects of edge shapes on animal behavior can arise as emergent properties solely due to reactions to edges in general, without the animals reacting to the shapes of the edges, and to (2) generate predictions to allow field and experimental studies to test mechanisms of edge shape response. Individual animals were modeled traveling inside a habitat patch that had different kinds of edge shapes (convex, concave and straight). When animals responded edges of patches, this created an emergent property of responding to the shape of the edge. The response was mostly to absolute width of the shapes, and not the narrowness of them. When animals were attracted to edges, then they tended to collect in convexities and disperse from concavities, and the opposite happened when animals avoided edges. Most of the responses occurred within a distance of 40% of the perceptual range from the tip of the shapes. Predictions were produced for directionality at various locations and combinations of treatments, to be used for testing edge behavior mechanisms. These results suggest that edge shapes tend to either concentrate or disperse animals, simply because the animals are either attracted to or avoid edges, with an effect as great as 3 times the normal density. Thus edge shape could affect processes like pollination, seed predation and dispersal and predator abundance.
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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 | 0.002 |
| 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".