Probing the troublesome peaked relationship between avian species richness and natural land cover
Bibliographic record
Abstract
Abstract Context : Biodiversity models postulate that the relationship between richness and the proportion of natural land cover (pNLC i.e., not dominated by human uses) is: 1) monotonic positive, 2) reasonably strong , 3) consistent in different geographic areas . Earlier work examining 100-km 2 human-dominated landcover in Ontario, Canada, observed that surveyed avian species richness is a peaked function of pNLC. Objective : We tested whether the same relationship between avian species richness and pNLC is also observed in an independent geographic area that has similar biomes. We also tested whether the peaked relations might be caused by temperature gradients, gradients in the size of species pools, grain size in the analyses, and landscape heterogeneity. Methods : Using breeding bird atlases of Ontario (Canada) and New York State (USA), we estimated species richness in landscapes varying from 25 to 900 km 2 . We related richness to the pNLC in each landscape and examined the same relationships after controlling for temperature, habitat heterogeneity, and species pool size. Results : Landscape-level species richness is a peaked, and not very strong function of pNLC. The relationship is not an artefact of temperature gradients, species pool size, or land cover heterogeneity. Conclusions : The proposition that increased amounts of natural land cover promotes species richness is clearly true at the limit, in landscapes with relatively little (<30%) natural cover. In landscapes with higher amounts of natural cover, a certain amount of human-modified land covers can provide habitat for species that prefer open habitats.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".