Global comparison of habitat intactness models for predicting extinction risk in terrestrial mammals
Bibliographic record
Abstract
The effects of habitat condition on biodiversity have primarily been investigated using discrete (patch‐matrix) habitat models, which consider habitat fragments as islands embedded in an inhospitable matrix. Recently, continuum habitat models, which focus on ecological gradients without defining habitat or matrix, have emerged. However, no formal comparison between patch‐matrix, continuum, and hybrid habitat models (which combine characteristics of both) has been undertaken globally. Here, we compared the ability of patch‐matrix, continuum, and hybrid models of habitat intactness to explain the risk of extinction for terrestrial mammals on a global scale. We found that hybrid models consistently outperform both patch‐matrix and continuum models of habitat intactness in predicting extinction risk, regardless of a species' habitat specialization. Moreover, the magnitude of the relationship between habitat intactness and extinction risk was strongest when using hybrid habitat models. Our results suggest that combining discrete habitat patches with gradients of habitat condition, influenced by the surrounding matrix, can improve extinction risk analyses and provide valuable insights for conservation efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".