Carnivore Coexistence Mechanisms in Multi-Predator Systems Under Resource Competition
Bibliographic record
Abstract
The problem of coexistence among several carnivore species when resources are limited and vary is a key question in community ecology. Competitive exclusion can occur in multi-predator systems, but this is alleviated by ecological or behavioural means. The paper examines the coexistence mechanism of sympatric carnivores based on spatial, temporal, and dietary niche separation under resource competition. The camera traps (n = 140 stations), GPS telemetry (n = 35 individuals across four species), and scat analysis (n = 360 samples) were used to collect field data over 12 months in a semi-arid ecosystem. The findings indicate profound niche differentiation across all three axes. The values of the spatial overlap indices (Pianka index) ranged from 0.42 to 0.71, indicating moderate habitat segregation, and the values of the temporal activity overlap (Δ coefficient) ranged from 0.41 to 0.67, indicating partial temporal segregation. Dietary analysis revealed that the overlap of the prey among the species was small (Schoener D =0.36-0.52), and the carnivory was more dietary specialized (p=0.01). Generalized linear mixed models revealed that prey availability (β = 0.74, p = 0.001) and the presence of dominant predators (β = -0.62, p = 0.003) significantly affected habitat utilization. These results show that multidimensional niche partitioning helps coexistence and reduces direct competition despite resource scarcity. Behavioural plasticity and adaptive foraging also further increase species persistence. The research emphasizes the need for habitat and prey diversity to sustain stable carnivore groups. In general, this article presents quantitative data showing that coexistence in multi-predator systems is regulated by a balance between competition and ecological differentiation, with implications for biodiversity conservation and ecosystem management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".