Conventional niche overlap measurements are not effective for assessing interspecific competition
Bibliographic record
Abstract
Interspecific competition is notoriously difficult to detect and quantify, especially in species that are wide-ranging or otherwise difficult to track in the wild. Research investigating interspecific competition usually relies on niche overlap measurements despite that this approach alone does not yield rigorous inference. As an illustration, we review published research assessing interspecific competition in mid-sized carnivores in North America (bobcat – Lynx rufus; Canada lynx – Lynx canadensis; coyote – Canis latrans), and report on shortcomings associated with commonly used study designs and types of inference. Niche overlap measurements typically focus on one or two resources (e.g., food, space, habitat), often using non-independent sampling units and inadequate replication. Few studies measure overlap variation through space, time, or resource variability, which is crucial for robust assessment. Niche overlap (or lack thereof) is used as evidence both for and against interspecific competition, reflecting a weak link between competition theory, predicted responses, and observations. Overall, challenges associated with conducting competition research in the field promote over-reliance on simple measurements, flawed study designs and weak inference. Minimally, niche overlap studies should include assessment across multiple niche dimensions and spatial or temporal variation in competitor density or resource availability. Dynamic investigative approaches should include new technologies for tracking inter-individual interactions, study designs that leverage quasi-experiments (e.g., decline in shared resources, biological control of one competitor), and synthetic analyses (e.g., meta-regression). Ultimately, better understanding of competition theory vis-à-vis study design and data needs will promote improved understanding of the role of interspecific competition in nature.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".