Dispersal without drivers: Intrinsic and extrinsic variables have no impact on movement distances in a terrestrial amphibian
Bibliographic record
Abstract
Dispersive movements are often thought to be multicausal and driven by individual body size, sex, conspecific density, environmental variation, personality and/or other variables. Yet such variables often do not account for most of the variation among dispersive movements in nature, leaving open the possibility that dispersion may be indeterministic. We assessed the amount of variation in 24 h movement distances that could be accounted for by potential drivers of displacement with a large empirical dataset of movement distances performed by Fowler’s Toads (Anaxyrus fowleri) on the northern shore of Lake Erie at Long Point, Ontario (2002–2021, incl.). These toads are easy to sample repeatedly, can be identified individually and move parallel to the shoreline as they forage at night, potentially dispersing to new refuge sites. Using a linear mixed-effect model that incorporated random effect terms to account for sampling variance and inter-annual variation, we found that all potential intrinsic and extrinsic drivers of movement accounted for virtually none of the variation observed among 24 h distances moved by these animals, whether over short or large spatial scales. We examined the idea of movement personality by testing variance per individual toad and found no evidence of individuality in movement distances. We conclude that deterministic variables, whether intrinsic or extrinsic, neither can be shown to nor are necessary to drive movements in this population over all spatial scales. Stochastic, short-timescale movements, such as daily foraging movements, can instead accumulate over time to produce large spatial-scale movements that are dispersive in nature.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".