Balancing conservation priorities for grassland and forest specialist bird communities in agriculturally dominated landscapes
Bibliographic record
Abstract
Effective conservation planning often requires difficult decisions when at-risk species inhabit economically valuable landscapes or if the needs of multiple threatened species do not align. In the agriculture-dominated landscape of eastern Ontario and southwestern Quebec, Canada, conflicting habitat requirements exist between threatened grassland birds benefiting from certain agriculture practices, and those of a diverse woodland bird community dependent on forest recovery. Using multi-scale species distribution models with data from the North American Breeding Bird Survey (BBS), we assessed habitat suitability for 8 threatened grassland and forest specialists within this region. We also identified landscapes that jointly maximize occurrence of the 8 focal species and diversity of the full avian community. Influential habitat associations differed among species at the territory (200 m radius) and landscape level (1 km), highlighting the importance of considering multiple spatial scales. Species diversity was maximized when forest or grassland/pasture cover approached 40–50%, indicating a positive response to land cover heterogeneity. We identified species diversity hotspots near Lake Huron, as well as along the shore and southeast of the St. Lawrence River. These areas represent mosaic landscapes, balancing forest patches, wetland, grassland/pasture, and row crops such as corn, soybean, and cereals. Despite drastic landscape changes associated with agroecosystems, we demonstrate that targeted habitat protection and enhancement that prioritizes land cover diversity can maximize protection of bird communities with directly contrasting needs. We highlight multiple pathways to achieve this balance, including forest retention or separating row crops with hedgerows and wooded fence-lines, improving flexibility in conservation approaches.
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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".