Predicting declines in avian species richness under nonrandom patterns of habitat loss in a Neotropical landscape
Bibliographic record
Abstract
One of the key concerns in conservation is to document and predict the effects of habitat loss on species richness. To do this, the species-area relationship (SAR) is frequently used. That relationship assumes random patterns of habitat loss and species distributions. In nature, however, species distribution patterns are usually nonrandom, influenced by biotic and abiotic factors. Likewise, socioeconomic and environmental factors influence habitat loss and are not randomly distributed across landscapes. We used a recently developed SAR model that accounts for nonrandomness to predict rates of bird species loss in fragmented forests of the Panama Canal region, an area that was historically covered in forest but now has 53% forest cover. Predicted species loss was higher than that predicted by the standard SAR. Furthermore, a species loss threshold was evident when remaining forest cover declined by 25%. This level of forest cover corresponds to 40% of the historical forest cover, and our model predicts rapid species loss past that threshold. This study illustrates the importance of considering patterns of species distributions and realistic habitat loss scenarios to develop better estimates of losses in species richness. Forecasts of tropical biodiversity loss generated from simple species-area relationships may underestimate actual losses because nonrandom patterns of species distributions and habitat loss are probably not unique to the Panama Canal region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".