EDGE-MEDIATED DISPERSAL BEHAVIOR IN A PRAIRIE BUTTERFLY
Bibliographic record
Abstract
Animal responses to habitat boundaries will influence the effects of habitat fragmentation on population dynamics. Although this is an intuitive and often observed animal behavior, the influences of habitat boundaries have rarely been quantified in the field or considered in theoretical models of large scale processes. We quantified movement behavior of the Fender's blue butterfly (Icaricia icarioides fenderi) as a function of distance from host-plant patches. We measured the butterfly's tendency to move toward habitat patches (bias) and their tendency to continue to move in the direction they were already going (correlation). We found that butterflies significantly modify their behavior within 10–22 m from the habitat boundary. We used these data to predict large scale patterns of residence time as a function of patch size, using three dispersal models: homogeneous response to habitat, heterogeneous response to habitat, and heterogeneous response to habitat with edge-mediated behavior. We simulated movement for males and females in eight patch sizes (0.1–8 ha) and asked how residence time varies among the models. We found that adding edge-mediated behavior significantly increases the residence of Fender's blue butterflies in their natal patch. Only the model with edge-mediated behavior for females was consistent with independent mark–release–recapture (MRR) estimates of residence time; other models dramatically underestimated residence times, relative to MRR data.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".