Old forest structural development drives complexity of nest webs in a naturally disturbed boreal mixedwood forest landscape
Bibliographic record
Abstract
Structural complexity generated by forest development processes and tree species compositional changes provide key habitat features for vertebrate communities that rely upon tree size and decay processes for foraging, denning or nesting. Complexity of forest structure in old stands could not only be key for harboring increased taxonomic species diversity but also greater functional diversity through more complexity in networks of tree cavity dependent species. Using a nest web approach that hierarchically links cavity-bearing trees with cavity formation agents (natural decay processes and avian excavators) and cavity users (non-excavator species), we compared network characteristics of nest webs along a time since fire gradient in a naturally disturbed boreal mixedwood forest landscape in eastern North America. Since 2003, twelve 24 to 40 ha plots ranging from 61 to more than 245 years after fire were surveyed at the Lake Duparquet Research and Teaching Forest in Abitibi, Quebec, Canada to detect active nesting, and denning cavities. We found that network complexity both in terms of number of vertebrate species and number of interactions among species, increased along the age gradient and was significantly higher in the older stands than predicted by chance. Whereas cavity-nesting communities in old forests used a higher diversity of tree species over a wide range of decay stages, trembling aspen remained a key cavity-bearing tree throughout the age gradient. Woodpeckers were the main cavity formation agents whereas less than 1% of cavities originated from natural decay. The structural development of older forests is thus a driver for functional diversity in cavity-using vertebrate communities through higher interaction richness in nest webs, among cavity-bearing trees, excavators and non-excavating users. The pivotal contribution of the entire gradient of old forest cover types to the overall complexity of nest webs in the boreal mixedwood zone is also a key for the resilience of the cavity-using vertebrate community to natural disturbances. We discuss how such resilience may be compromised by even-aged industrial timber harvesting with short rotations that shifts the age structure of boreal landscapes toward regenerating and young pole forests whereas old forest cover types become below their historical range of variability.
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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 | 0.001 | 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) | 0.001 | 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".