Predicting the effects of land cover change on biodiversity in Prairie Canada using species distribution models
Bibliographic record
Abstract
Land cover change is the largest direct driver of global biodiversity loss but often the relationships between habitats and species occurrence are unknown. The conservation community requires tools to assess variation in biodiversity related to land cover for maximizing return on investment. Our objectives were to 1) develop a biodiversity mapping and assessment tool at a fine spatial scale for terrestrial vertebrates, and 2) test how much biodiversity is conserved by retaining natural habitats within agricultural landscapes. We built species distribution models for amphibians, birds, mammals, and reptiles (329 species, > 1.2 million observations) within Prairie Canada. Predicted biodiversity within 805 m × 805 m sites ranged from 0 to 238 species (66 ± 0.1). The proportion of annual cropland at a site had the largest negative effect on biodiversity among predictors. Using simulations of land cover change, we predicted that conserving 20 % of natural habitats would conserve an average of 26.5 % of maximum species richness in fields with annual cropland and 74.3 % of maximum species richness in fields with tame grass (perennial cropland). Our tool predicted that fields with conservation easements (n = 312) had more species (114 ± 2) and natural habitat (48 ± 1 %) compared to nearby unprotected sites (82 ± 3 species; 32 ± 2 % natural habitat). Our results highlight the importance of retaining natural habitats, including wetlands, grasslands, and forests within farms to support biodiversity. In addition, our predictions can be used to target areas for conserving and restoring habitats.
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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 | 0.000 |
| 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".