A test of the habitat amount hypothesis as an explanation for the species richness of forest bird assemblages
Bibliographic record
Abstract
Abstract Aim For the past 20 years, researchers have been challenged to demonstrate that the spatial arrangement of habitat patches actually influences the distribution of organisms and the persistence of their populations, beyond the effects of its sheer amount. More recently, it has been argued that habitat amount in the ‘local landscape’ surrounding a site is sufficient to predict species richness (SR) in that site, irrespective of habitat configuration. Here, we tested four predictions derived from the habitat amount hypothesis (HAH). Location Eastern Ontario, Canada (c. 44°55′–45°15′ N, 75°10′–75°45′ W). Methods Point counts (n = 157) were conducted in five subregions to estimate forest bird SR while accounting for detectability. Surveys were conducted in mature, deciduous‐dominated forest fragments, and landscape structure was quantified at three spatial scales (500, 1000 and 1500 m). Results Although we found a significant positive correlation between SR and either fragment area (FA) or habitat amount in the local landscape, predictions emphasizing the dominant influence of habitat amount and the lower influence of FA were either not supported or weakly so. Main conclusions Contrary to the HAH, we conclude that habitat amount in the local landscape is not a sufficient predictor of SR on its own. However, we agree with the contention that, in most landscape types, ‘local landscapes’ represent more natural spatial units than habitat fragments or ‘patches’.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".