Higher bat and bird γ-diversity in structurally complex forests is driven by distinct α- and β-diversity responses
Bibliographic record
Abstract
Summary Effective conservation management and habitat restoration rely on understanding how biodiversity responds to environmental change. Centuries of silviculture have homogenized forests and their species communities globally, reducing biodiversity. To test whether restoring forest structural complexity can promote biodiversity, we conducted a large-scale, spatially explicit landscape experiment. At 11 sites across Germany, we compared bat and bird diversity in forests with experimentally enhanced heterogeneity by increasing deadwood and canopy complexity to homogeneous production forests. Both taxa were investigated by autonomous acoustic recorders and automatic species identification. We quantified within-patch (α-), between-patch (β-), and landscape-level (γ-) diversity, emphasizing infrequent to highly frequent species for taxonomic, functional, and phylogenetic diversity. The pairwise comparisons of the sites were synthesized using a newly developed meta-analysis of rarefaction-extrapolation curves. γ-diversity increased significantly in structurally heterogeneous forests for both taxa, albeit through distinct taxon-specific mechanisms. Bat γ-diversity gains were primarily driven by higher β-diversity, indicating greater dissimilarity in species assemblages among patches, while bird γ-diversity increased via higher α-diversity within patches. Bat diversity increases were mainly taxonomic, suggesting functional similarity in the communities, whereas birds showed the highest gains in functional diversity, indicating that experimental treatments resulted in greater trait dissimilarity. Our results provide experimental evidence under real-world conditions that γ-diversity can be shaped by different diversity mechanisms. These patterns likely originate from differences in activity ranges, such as the large-scale movements of foraging bats in contrast to the more spatially restricted, territorial behavior of birds. This highlights the need for taxon-specific restoration strategies in homogenized landscapes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".