Predator‐Prey Dynamics and Movement in Fractal Environments
Bibliographic record
Abstract
Previous research suggests that local interactions and limited animal mobility can affect population dynamics. However, the spatial structure of the environment can further limit the mobility of animals. For example, an animal confined to a river valley or to a particular plant cannot move with equal ease in all directions. We show that spatial architecture could influence the population dynamics of predator-prey systems using individual-based computer simulations parameterized with allometric relationships from the literature. Spatial forms (representing geographical features or plant architecture) of differing fractal dimension were generated, and simulated predators and prey were introduced into these computer environments. We claim that the alteration in interaction rates and population dynamics found in these simulations can be explained as a consequence of the anomalously slow rates of movement associated with fractal spaces and the diffusion-limited nature of predator-prey interactions. As a result, functional responses and numerical responses are substantially reduced in fractal environments, and the overall stability of the system is determined by the interaction between individual mobility and spatial architecture.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".