Tree diversity drives abundance and spatiotemporal β‐diversity of true bugs (Heteroptera)
Bibliographic record
Abstract
Abstract. 1. Spatiotemporal patterns of canopy true bug diversity in forests of different tree species diversity have not yet been disentangled, although plant diversity has been shown to strongly impact the diversity and distribution of many insect communities. 2. Here we compare species richness of canopy true bugs across a tree diversity gradient ranging from simple beech to mixed forest stands. We analyse changes in community composition by additive partitioning of species diversity, for communities on various tree species, as well as for communities dwelling on beech alone. 3. Total species richness (γ‐diversity) and α‐diversity, and abundance of true bugs increased across the tree diversity gradient, while diversity changes were mediated by increased true bug abundance in the highly diverse forest stands. The same pattern was found for γ‐diversity in most functional guilds (e.g. forest specialists, herbivores, predators). Temporal and even more, spatial turnover (β‐diversity) among trees was closely related to tree diversity and accounted for ∼90% of total γ‐diversity. 4. Results for beech alone were similar, but species turnover could not be related to the tree diversity gradient, and monthly turnover was higher compared to turnover among trees. 5. Our findings support the hypothesis that with increasing tree diversity and thereby increasing habitat heterogeneity, enhanced resource availability supports a greater number of individuals and species of true bugs. Tree species identity and the dissimilarity of true bug communities from tree to tree determine community patterns. 6. In conclusion, understanding diversity and distribution of insect communities in deciduous forests needs a perspective on patterns of spatiotemporal turnover. Heterogeneity among sites, tree species, as well as tree individuals contributed greatly to overall bug diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".