Regional variability in extinction thresholds for forest birds in the north‐eastern United States: an examination of potential drivers using long‐term breeding bird atlas datasets
Bibliographic record
Abstract
Abstract Aim Demand for quantitative conservation targets has yielded a search for generalities in habitat thresholds, particular amounts of habitat at which extinction probabilities change strongly. These thresholds are thought to vary across regions, but investigation of this variability has been limited. We tested whether thresholds (of forest separating extinction from persistence) increased as either average forest cover in landscapes decreased or the degree of fragmentation increased. Location Massachusetts, Michigan, New York, Ohio, Pennsylvania and Vermont. Methods We used segmented logistic regressions to estimate thresholds in the relationship between extinction probability and forest cover for 25 forest‐breeding birds, comparing estimated thresholds across states. We also selected landscapes from our entire study area in which landscape‐level forest cover and degree of fragmentation varied independently and compared thresholds. Results We found that thresholds in extinction probability varied widely among species (7–90% forest cover) and within species across states [e.g. 12–90% for white‐throated sparrow ( Zonotrichia albicollis )]. Additional analyses showed no indications that thresholds correlated with the degree of fragmentation or forest cover across the landscape; we found considerable variability in thresholds across landscapes, species and even landscapes in which (average) fragmentation and forest cover were similar. Main conclusions Extinction threshold estimates varied tremendously across species and landscapes. Thus, habitat thresholds are difficult to generalize as they depend on many factors beyond landscape fragmentation and habitat availability (e.g. landscape characteristics such as matrix quality). Our findings highlight the need to avoid oversimplification and generalization of habitat thresholds, especially as they might prove counterproductive to conservation efforts. Instead, we propose that we evaluate thresholds for individual species – preferably using species‐centred habitat definitions in threshold modelling – to derive generalities for ecological and conservation applications.
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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 | 0.001 | 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) | 0.000 | 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 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".