Improving bird abundance estimates in harvested forests with retention by limiting detection radius through sound truncation
Bibliographic record
Abstract
ABSTRACT An inherent challenge with acoustically surveying birds is that the distance at which they can be detected depends on how far their song can be heard. We developed a distance-based sound detection space truncation method to correct for variable sampling radii due to surveying in forested or open conditions. The method was pivotal in evaluating bird responses to retention patches; without this methodological advancement, the impact of retention patches on songbird abundance was vastly underestimated. In the boreal forest, these patches of live trees are retained in regenerating harvested forests to provide ecological services for species adapted to natural disturbances. Although we did not verify our a priori assumption with ground observations, our findings suggest that limited-distance sampling better captures the effects of retention patches on bird use of harvested forests. When evaluated using unlimited distance surveys, retained trees had a negligible effect on bird abundance, whereas applying detection distance truncation highlighted the importance of retention on forest birds. We found that early to mid-seral forest songbirds benefited from retention patches, with notable increases in abundance after 10 years of regeneration. The size of retention patches, ranging from 0.1 to 1.2 ha, did not have a linear relationship with bird abundance. Instead, edge effects stemming from the configuration of these patches emerged as key determinants of abundance for the majority of the species studied. Retention patches that were nearest to unharvested forests were used the most, compared to further into harvest areas. Our research not only highlights the underestimated impact of small-scale live tree retention on forest songbirds but also introduces a significant methodological innovation in the field of acoustic monitoring.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".