Impersonating predators and prey to study trophic interactions through real‐life simulations
Bibliographic record
Abstract
Abstract Predator–prey interactions are a fundamental aspect of ecology that has generated sustained research interests. Progress in the field stems from a diverse range of approaches, from highly controlled yet simplified mathematical and agent‐based models, to grounded but data‐limited field studies. As a compromise between mathematical and observation‐oriented methods, we introduce an original approach based on an outdoor game. In this game, biologged human players follow simple rules to impersonate predators and prey in a natural landscape augmented with synthetic resource patches and refuges. We investigated the behaviour, movement, functional response and spatial organization of over 25 players simultaneously monitored during nine simulations to determine whether the game could replicate realistic predator–prey dynamics. Results derived from our real‐life simulations were consistent with ecological patterns expected in natural systems. We found that (a) predator and prey movements were driven by risk and reward trade‐offs, (b) predators took advantage of linear features to travel at higher speed, making these areas risky for prey, (c) prey had nonlinear and risk‐sensitive functional responses and (d) consumer–resource interactions were spatially modular and defined by players' movement rates and landscape features. Moreover, the comprehensive dataset generated through the game allowed for the exploration of phenomena that are challenging to study in natural settings, such as spatial memory and the influence of satiety on resource acquisition rates. The approach offers a simple, computationally accessible and genuinely amusing way to explore the complex ramifications of predator–prey interactions and test otherwise data‐deficient hypotheses. The strength and originality of the method lies in the use of living agents—players—making decisions in a real‐world setting. This aspect alleviates the computational and empirical burden of defining and estimating decision‐related parameters needed to build simulators, while generating extensive datasets in a flexible experimental framework that is generally out of reach for empirical studies. It also offers immersive insights into predator–prey interactions, making it an engaging pedagogical tool that encourages creative thinking. The numerous possible scenarios that can be explored are only constrained by the investigator's creativity in adapting game rules and the players' desire to win.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".