Hindcasting the impacts of land‐use changes on bird communities with species distribution models of Bird Atlas data
Bibliographic record
Abstract
) over the last 30 yr (between 1984-1989 and 2010-2014) by using species distribution models (299,302 occurrences in 30,408 locations) from a hindcasting perspective. Results were grouped by functional guilds to infer potential impacts on ecosystem services, and to relate model transferability (i.e., ability of our models to be generalized to other times and scales) to specific functional and life-history traits. Overall, our models were able to accurately predict, both in space and time, habitat suitability for 69% of species, especially for granivorous, nonmigrant, tree-nesting species, and species that are tied to agricultural areas under intensive use. These findings indicate that model transferability depends upon specific functional and life-history traits, providing further evidence that species' ecologies affect the ability of models to accurately predict bird distributions. Declining bird species were mostly short-distance migrants that were associated with open habitats (agricultural and nonproductive forest) with aerial insectivorous or granivorous diets, which may be related to agricultural intensification and land abandonment. Land-use changes were positive for some forest bird species that were mainly associated with mixed and deciduous forests, generalist diets and tree-nesting strategies. Yet cavity-nesting birds have suffered substantial reductions in their distributions, suggesting that cumulative effects of intensive logging and wildfires on mature forests pose a threat for forest-specialist species. Habitat suitability changes predicted by our coarse-scale species distribution models partially agreed with the long-term trends reported by the North American Breeding Bird Survey. Our findings confirm land-use change as a key driving force for shaping bird communities in southern Quebec, together with the need to explicitly incorporate it into global change scenarios that better inform decision-makers on conservation and management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".