Reciprocating dispersal by habitat‐selecting white‐footed mice
Bibliographic record
Abstract
Theories of dispersal driven by density‐dependent habitat selection demonstrate that putative examples of source–sink dynamics and balanced dispersal may also be explained by a more general pattern of bi‐directional, reciprocating dispersal. Analyses of 19 years of data on dispersal by white‐footed mice confirm the theory. Fitness of territorial white‐footed mice living in an agricultural mosaic is higher in forest habitat than it is in either edge or fencerows. Density‐dependent habitat selection theory predicts that if net emigration by mice flows from the forest to forest‐edge during periods of population growth, animals should subsequently move from the edge to forest during population decline. The pattern of mouse dispersal varies between seasons as populations wax and wane in abundance. Mice tend, as predicted, to move from high‐density forest habitat into low‐density edge during periods of population increase, and from the low‐density edge into high‐density forest during periods of population decline. Over all years combined, dispersal by white‐footed mice was balanced. Each habitat tended to gain as many dispersing individuals as it lost. The results support a conditional dispersal strategy linked to density‐dependent habitat selection, but also suggest the possibility of multiple coexisting strategies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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 teacher head, 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".