Environmental unpredictability and stochasticity underlie dispersive movements of a terrestrial amphibian
Bibliographic record
Abstract
Dispersive movements are often thought to be multiclausal and driven by individual body size, sex, conspecific density, environmental variation and/or other factors. Yet such factors rarely account for most of the variation present among dispersive movements in nature, leaving open the possibility that dispersion might be indeterministic and vary in response to environmental stochasticity. We assessed the amount of variation in movement distances that could be accounted for by potential predictors of dispersal 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 undertake dispersive movements parallel to the shoreline on a daily basis as they forage at night. Using a linear mixed-effect model that incorporated random effect terms to account for sampling variance and inter-year environmental variation, we found that all potential predictors of dispersive movements of these animals were, at best, weak predictors that accounted for virtually none of the variation observed among movement distances. We also used linear regression models to test for the impact of environmental stochasticity on dispersive movements and identified a strong positive correlation between the distribution of toad movement distances and variability in lake water level. We conclude that deterministic proximal factors, whether intrinsic or extrinsic, neither can be shown nor are necessary to drive dispersive movements in this population. Variation in dispersive movements can be ascribed, instead, to environmental unpredictability, consistent with nomadism.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".