Examining Interspecific density-dependent dispersal in forest small mammals
Bibliographic record
Abstract
The effects of conspecific densities on dispersal have been well documented. However, while positive and negative density-dependent dispersal based on conspecific densities are often shown to be the result of intraspecific competition or facilitation respectively, the effects of heterospecific densities on dispersal are examined far less frequently. This despite the potential for the analogous processes of interspecific competition and heterospecific attraction to influence dispersal. Here we use 51 years of live-trapping data on deer mouse ( Peromsycus maniculatus ), eastern chipmunk ( Tamias striatus ), red-backed vole ( Myodes gapperi ), and jumping mice ( Napaeozapus insignis and Zapus hudsonius ) to examine the effects of both conspecific and heterospecific densities on dispersal frequency. In terms of conspecific densities, jumping mice were more likely to disperse from areas of low conspecific densities, while red-backed voles and chipmunks did not respond to conspecific densities in their dispersal frequencies. When considering interspecific density effects, while there were no statistically clear effects of density on dispersal frequency, the effects of chipmunk and jumping mice densities on dispersal by red-backed vole were greater than the effects of conspecific densities, with voles more likely to disperse from areas of high chipmunk densities and low jumping mice densities. Likewise, the effect of chipmunk densities on dispersal by jumping mice was greater than the conspecific density effect. Conspecific densities clearly can affect dispersal by two of these four species, but the effects of heterospecific densities on dispersal frequency are less clear. Based on effect sizes it appears that there is potential for heterospecific effects on dispersal by some species in the community, but future experimental work could elucidate the strength and causes of these relationships.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".