Local-Scale Association of Boreal Birds with Clearcuts Does Not Translate to the Landscape Scale
Bibliographic record
Abstract
Many boreal forest birds, including the Common Nighthawk and Eastern Whip-poor-will, have shown recent steep population declines.Both species are known to use clearcuts as habitat locally; however this relationship has not been tested at a landscape-scale.In this study I addressed the question: Are landscapes with more clearcut associated with higher occupancy of Common Nighthawk and Eastern Whip-poor-will?I selected 49 recent clearcuts (≤ 15 years old) of similar size and placed acoustic recorders on their edges.I measured the proportion of recent clearcut within multiple spatial extents around each bird sample site.I also measured the proportion of open wetland in the landscapes as a covariate.Occurrence of neither species was significantly affected by the proportion of clearcut in the surrounding landscape, at any of the tested spatial extents.However, Common Nighthawk occupancy was lower in landscapes with higher proportions of older (11-15years old) clearcuts, and for both species occupancy was higher in landscapes with higher proportions of open wetland, significantly so for Eastern Whippoor-will.I propose that these species nest on clearcut edges because they seem to be similar to other open habitat like wetland edges.However, clearcuts may not offer the same level of insect prey supply as offered in the open wetlands and therefore do not act as similar foraging habitat.Thus, clearcut edges may act as ecological traps, attracting birds to nest in areas of lower habitat quality.I demonstrated that local-scale habitat associations do not necessarily scale up to a landscape-scale.My results suggest that the conservation of open wetlands will be important for persistence of these boreal bird species.supervisors but have also been mentors and sources of inspiration to me throughout these years.I could not have asked for a more intelligent, invested, and optimistic group of scientists to work with.I would also like to thank Dr. Gabriel Blouin-Demers not only for taking the time be on my advisory committee, but also for his advice and helpful feedback throughout this process.I would also like to acknowledge the help of Dr. Steve Cooke and Dr. Stacey Robinson for their participation and input in the defence process.My field work could not have been completed without my field assistant Amanda Findlay and her dedication to the project.I thank Dr. Rob Mackereth with the Centre for Northern Forest Ecosystem Research, not only for his logistical help, but also for his belief in my abilities as a researcher.Thank you to Vince Caruana and Mona Caruana for their donation of field lodgings and Christine Eberl for her
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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 | 0.001 |
| 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 | 0.001 | 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".