Examining the effects of heterospecific abundance on dispersal in forest small mammals
Bibliographic record
Abstract
Abstract The effects of conspecific densities on dispersal have been well documented. However, while positive and negative density-dependent dispersal based on conspecific densities often are shown to be the result of intraspecific competition or facilitation, respectively, the effects of heterospecific densities on dispersal have been examined far less frequently. This gap in knowledge warrants investigation given the potential for the analogous processes of interspecific competition and heterospecific attraction to influence dispersal patterns and behavior. Here we use a long-term live-trapping study of deer mouse (Peromyscus maniculatus), eastern chipmunk (Tamias striatus), red-backed vole (Myodes gapperi), and jumping mice (Napaeozapus insignis and Zapus hudsonius) to examine the effects of variation in conspecific and heterospecific abundances on dispersal frequency. In terms of conspecific abundance, jumping mice were more likely to disperse from areas with fewer conspecifics, while red-backed voles and chipmunks did not respond to variation in conspecific abundances in their dispersal frequencies. While there were no statistically significant effects of variation in heterospecific abundances on dispersal frequency, some effect sizes for heterospecific abundance effects on dispersal met or exceeded those of conspecific abundances. Conspecific abundances clearly can affect dispersal by some species in this system, but the effects of heterospecific abundances on dispersal frequency are less clear. Based on effect sizes, it appears that there may be potential for heterospecific effects on dispersal by some species in the community, although the strength and causes of these relationships remain unclear.
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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 | 0.001 | 0.003 |
| 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".