Biological invasions forming intraguild predation communities in homogeneous and heterogeneous landscapes
Bibliographic record
Abstract
Intraguild predation (IGP) allows for coexistence between two consumers of a single resource, as long as the intraguild prey (IG prey) is competitively superior to the Intraguild Predator (IG predator) and resource population productivity is neither abundant or limiting. Here we explore biological invasions forming IGP community modules by either introducing IG prey or IG predator species to established Consumer-Resource populations in homogeneous and heterogeneous landscapes, using reaction-diffusion equations as our modeling framework. Our main methods of analysis are comparing numerical solutions to linearization techniques and homogenization approximations. We find that in homogeneous landscapes, speeds are linearly determinate, i.e., depend on low invader population densities at the leading edge. We also find traveling wave solutions and dynamical stabilization regimes. On heterogeneous landscapes, our results show that depending on habitat preferences of the three species involved, coexistence regimes can occur regardless of IG-Prey being least effective consumer, or be hindered even when IG-Prey remais as the dominant competitor. Our work asses how fast can organisms invade novel landscapes in presence of a established IG prey or IG predator, and also demonstrates how habitat fragmentation and species habitat preference can disrupt or facilitate coexistence in IGP communities.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".