Thresholds in Forest Bird Response to Habitat Alteration as Quantitative Targets for Conservation
Bibliographic record
Abstract
Abstract: The notion that species might exhibit thresholds in their response to habitat alteration is appealing from a conservation perspective. Such thresholds could be used as targets for conservation in managed landscapes. In New Brunswick, Canada, forest management produces mosaics of varying stand age, species composition, and structure. We sampled this gradient in habitat suitability to examine the shape of species response functions and to look for evidence of statistically significant thresholds. We focused our attention on bird species breeding in late‐seral forest and surveyed them at 390 point‐count stations sampling broad‐leaved deciduous to pure coniferous stands and a variety of silvicultural treatments (patch cutting, single‐tree selection, spruce plantation [35–45 years old], and no recent treatment). A principal components analysis (PCA) on local vegetation separated stations along two axes reflecting gradients in stand composition and habitat alteration (increasingly open canopy and decreasing density of large trees/snags), respectively. We combined logistic regression and receiver‐operating characteristic (ROC) analysis to detect thresholds in species occurrence along these gradients. Of the 42 species frequent enough to be included in the analyses, 13 (31%) showed a significant ( p < 0.01) negative response to habitat alteration. Eight of the 13 species sensitive to habitat alteration exhibited thresholds in their occurrence after controlling for the suitability of local habitat. According to curves of the expected number of sensitive species (based on their ROC‐derived thresholds), canopy closure and the density of large trees (>30 cm dbh) should be at least 70% and 80 stems/ha, respectively, to expect to find the complete assemblage of bird species. However, these values should be viewed as liberal, given the nature of our response variable. More refined (e.g., fitness) parameters should be used to be conservative. Nonetheless, the approach allowed us to establish preliminary quantitative targets for conservation planning based on time‐efficient sampling methods, and to explicitly account for the continuous variability existing within and among silvicultural treatments rather than to assume homogeneity within treatments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".