Human disturbance has contrasting effects on niche partitioning within carnivore communities
Bibliographic record
Abstract
Among species, coexistence is driven partly by the partitioning of available resources. The mechanisms of coexistence and competition among species have been a central topic within community ecology, with particular focus on mammalian carnivore community research. However, despite growing concern regarding the impact of humans on the behaviour of species, very little is known about the effect of humans on species interactions. The aim of this review is to establish a comprehensive framework for the impacts of human disturbance on three dimensions (spatial, temporal and trophic) of niche partitioning within carnivore communities and subsequent effects on both intraguild competition and community structure. We conducted a systematic review of the literature on carnivore niche partitioning (246 studies) and extracted 46 reported effects of human disturbance. We found evidence that human disturbance impacts resource partitioning, either positively or negatively, in all three niche dimensions. The repercussions of such variations are highly heterogeneous and differ according to both the type of human disturbance and how the landscape and/or availability of resources are affected. We propose a theoretical framework of the three main outcomes for the impacts of human disturbance on intraguild competition and carnivore community structure: (i) human disturbance impedes niche partitioning, increasing intraguild competition and reducing the richness and diversity of the community; (ii) human disturbance unbalances niche partitioning and intraguild competition, affecting community stability; and (iii) human disturbance facilitates niche partitioning, decreasing intraguild competition and enriching the community. We call for better integration of the impact of humans on carnivore communities in future research on interspecific competition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".