Do we create ecological traps when trying to emulate natural disturbances? A test on songbirds of the northern hardwood forest<sup>1</sup>This article is one of a selection of papers from the 7th International Conference on Disturbance Dynamics in Boreal Forests.
Bibliographic record
Abstract
Forest management inspired from natural disturbances is often claimed to have more benign effects on biodiversity than more traditional approaches but this premise has rarely been tested. In the northern hardwood forest, selection harvesting could be seen as a surrogate for the combined effects of windthrow, moderate ice storms, and senescence. Here, we quantified the response of two focal species of forest birds (Brown Creeper ( Certhia americana (Bonaparte, 1838)) and Ovenbird ( Seiurus aurocapilla (Linnaeus, 1766))) to this treatment (30%–40% basal area removal) in the first 5 years post-harvest using a replicated field experiment. We tested the possibility that selection harvesting creates ecological traps whereby individuals show a preference for a habitat type where their fitness is lower. We found that both focal species actually seemed to prefer control plots, where they reached a higher density than in treated plots. There was no evidence for a treatment effect on per capita productivity in either species. Hence, there was no evidence for an ecological trap. However, large-scale application of selection harvesting may have ecologically significant effects on productivity of the focal species per unit area of habitat. Future studies should test whether selection harvesting creates ecological traps for species naturally associated with canopy gaps.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".