Forest management affects the functional traits of birds and mammals differently
Bibliographic record
Abstract
Abstract Forest management is an important component of global change as more than half of the world's forests are managed for human use. Although the effect of forest management on taxonomic diversity is well‐studied, we do not fully understand its impact on functional diversity. Understanding this is important to better predict how ecosystem processes will respond to global change scenarios and to implement efficient conservation actions. We conducted two large‐scale (~81 800 km 2 ) research projects over 4 years in temperate forests of the northeastern USA to investigate how the functional structure of bird and mammal communities are affected by forest disturbance. We surveyed 85 bird species distributed in 115 sites using point counts, and 14 mammal species across 197 sites using camera traps. For each species, we selected functional traits that summarize key features of their biology, and for each site, we collected data on the level of forest disturbance based on forest loss events. We found that functional richness increased with forest disturbance for mammals but not for birds. Our results also showed that niche breadth (diet), morphological (body mass and wing length), and physiological (litter size) factors were the main determinants of the functional structure of both groups. These findings emphasize the complexity of making predictions about responses to forest management given the heavy dependence on the context and taxa studied. Overall we observed a limited response of functional diversity to forest management, which might indicate that the environmental changes generated by forest management in this region are less extreme than deforestation or conversion of natural forest to plantations of exotic species. Nonetheless, our results underscore the importance of investigating the effects of forestry on individual traits to develop strategies for managing for ecosystem functions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".