Enhancing structural heterogeneity in managed forest landscapes promotes gamma but not beta diversity in understorey plant communities
Bibliographic record
Abstract
Abstract Although habitat heterogeneity is known to enhance local species diversity, the effects of management-driven structural heterogeneity on understorey plant communities across spatial scales remain poorly understood, despite their crucial role for forest biodiversity and ecosystem functioning. To analyse how forest understorey plant communities respond to an enhancement of structural heterogeneity in managed forests, we established 11 experimental sites consisting of two paired forest landscapes, an untreated homogenous control and a treatment district (ESBC). In treatment districts, structural heterogeneity was enhanced through different combinations of local patch-scale manipulations of light and deadwood features, leading to greater between-patch heterogeneity at the landscape scale. We performed a meta-analysis across these 11 sites using a Hill-Chao number and sample coverage standardisation framework. Gamma diversity increased across taxonomic, functional and phylogenetic facets in structurally heterogeneous forests (ESBC districts) via higher alpha diversity. This effect was positively associated with heterogeneity in light availability between forest patches, but not with their mean light availability. In contrast, we found no support that species turnover among patches (i.e., beta diversity) significantly contributes on average to the observed increase in gamma diversity. However, both the direction and magnitude of beta diversity responses varied substantially among landscapes. On average, structurally heterogeneous forests supported higher species richness for both open and closed forest habitat species. Synthesis and applications: Our findings highlight the benefits of enhancing structural heterogeneity for understorey plant diversity in managed forest landscapes. Specifically, management strategies that create a spatial mosaic of interventions, such as combining single-tree removal with group felling, can increase the variety of light niches among forest patches, thereby supporting the conservation of a wide range of understorey plant species, including forest specialists.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".