The retention of non-commercial hardwoods in mixed stands maintains higher avian biodiversity than clear-cutting
Bibliographic record
Abstract
Abstract A diverse landscape can support a more diverse range of species and allow for more complex community structures. In forested habitats, openings and changes in tree composition allow for a higher species richness due to the greater chance of niche occupancy. Knowledge about these relationships may be useful for adapting forest harvesting strategies to, for example, support bird diversity conservation and studies are required to understand how different harvesting strategies influence forest structure and bird diversity. Here, we used Autonomous Recording Units (ARU) to record dawn signalling of forest birds between two forest-harvesting treatment types (complete clear-cuts and hardwood-retention patches) vs control forest patches in the John Prince Research Forest, British Columbia, Canada. We compared Species Richness and Shannon diversity as detected through identifying species in audio recordings, across treatments. The observed Species Richness and Shannon diversity did differ between the Retention treatment and Forest controls when controlling for number of individuals sampled, but both had higher Species Richness and Shannon diversity of passerine species than the Clear-cut treatments. When comparing species composition, we found that forest-associated species were disproportionately detected in Forest controls compared to Clear-cut treatments but detected at intermediate levels in Retention treatments. Species associated with early-seral habitats, though, had disproportionate detection in Clear-cut treatments compared to Forest controls, but also showed expected detections in Retention treatments. These results suggest that partial harvesting and retention of non-commercial hardwoods, can help retain forest-associated species while also helping attract early-seral avian species; this can help increase the overall diversity of the landscape while still making logging profitable. Further research should be conducted to determine the value of this retained habitat at different spatial scales to understand the impacts that it may have for larger-scale deployment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".