Niche Breadth: Causes and Consequences for Ecology, Evolution, and Conservation
Bibliographic record
Abstract
Niche breadth is a unifying concept spanning diverse aspects of ecology, evolution, and conservation biology. Niche breadth usually refers to the diversity of resources used or environments tolerated by an individual, population, species, or clade. Here we review key research in ecology, evolution, and conservation biology in light of niche breadth. Namely, we explore the role of niche breadth in shaping geographic distributions and species richness from local to landscape scales, how niche breadth evolves and influences lineage diversification, and its use for understanding species invasions, responses to climate change, vulnerability to extinction, and ecosystem functioning. This diverse literature informs a research agenda that identifies focused needs for further progress: testing the hierarchical nature of niche breadth (e.g., of individuals, populations, and species); quantifying correlations in niche breadth among different niche axes and the role of environmental drivers and organismal constraints in generating these correlations; and evaluating the factors that decouple fundamental and realized niches. We describe how this research agenda could help unify disparate subdisciplines and shed light on key questions in ecology, evolution, and conservation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".