Restoration‐oriented forest management affects community assembly patterns of deadwood‐dependent organisms
Bibliographic record
Abstract
Abstract Land‐use intensification leads to loss and degradation of habitats and is thus a major driver of biodiversity loss. Restoration strategies typically focus on promoting biodiversity but often neglect that land‐use intensification could have changed the underlying mechanisms of community assembly. Since assembly mechanisms determine the diversity and composition of communities, we propose that evaluation of restoration strategies should consider effects of restoration on biodiversity and community assembly. Using a multi‐taxon approach, we tested whether a strategy that promotes forest biodiversity by restoring deadwood habitats also affects assembly patterns. We assessed saproxylic (i.e. deadwood‐dependent) beetles and fungi, as well as non‐saproxylic plants and birds in 68 beech forest plots in southern Germany, 8 years after the commencement of a restoration project. To assess changes in community assembly, we analysed the patterns of functional–phylogenetic diversity, community‐weighted mean (CWM) traits and their diversity. We hypothesized that restoration increases habitat amount and heterogeneity of deadwood and reduces canopy cover and thereby decreases the strength of environmental filters imposed by past silvicultural intensification, such as a low amount in deadwood. With the restoration of deadwood habitats, saproxylic beetle communities became less functionally–phylogenetically similar, whereas the assembly patterns of saproxylic fungi and non‐saproxylic taxa remained unaffected by deadwood restoration. Among the traits analysed, deadwood diameter niche position of species was most strongly affected indicating that the enrichment of large deadwood objects led to lower functional–phylogenetical similarity of saproxylic beetles. Community assembly and traits of plants were mainly influenced by microclimate associated with changes in canopy cover. Synthesis and applications . Our results indicate that the positive effects of deadwood restoration on saproxylic beetle richness are associated with an increase in deadwood amount. This might be linked to an increase in deadwood heterogeneity, and therefore decreasing management‐induced environmental filters. Deadwood enrichment can thus be considered an effective restoration strategy which reduces the negative effects of intense forest management on saproxylic taxa by not only promoting biodiversity but also by decreasing the environmental filters shaping saproxylic beetle communities, thus allowing the possibly for more interactions between species and a higher functional diversity.
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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".