Innate behavioural mechanisms and defensive traits in ecological models of predator-prey types
Bibliographic record
Abstract
There are various examples of phenotypic plasticity in ecosystems that serve as the basis for a wide range of inducible defences against predation. These strategies include camouflage, burrowing, mimicry, evasive actions, and even counterattacks that enhance survival under fluctuating predatory threats. Additionally, the ability to exhibit plastic responses often influences ecological balances, shaping predator-prey coexistence over time. This study introduces a predator-prey model where prey species show inducible defences, providing new insights into the role of adaptive strategies in these complex interactions. The stabilizing impact of the defensive mechanism is one of several intriguing outcomes produced by the dynamics. Moreover, the predator population rises when the interference rate increases to a moderate value even in the presence of lower prey defence but decreases monotonically for stronger defence levels. Furthermore, we identify a bistable domain when the handling rate is used as a control parameter, emphasizing the critical role of initial population sizes in determining system outcomes. By considering the species diffusion in a bounded region, the study is expanded into a spatio-temporal model. The numerical simulation reveals that the Turing domain decreases as the level of protection increases. The study is subsequently extended to incorporate taxis, known as the directed movement of species toward or away from another species. Our investigation identifies the conditions under which pattern formation emerges, driven by the interplay of inducible defences, taxis as well as species diffusion. Numerical simulations demonstrate that including taxis within the spatio-temporal model exerts a stabilizing influence, thereby diminishing the potential for pattern formation in the system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".