Context-dependent effects of woody layer complexity on arthropod biomass and abundance in deciduous forests
Bibliographic record
Abstract
Forest structural complexity influences arthropod communities by shaping habitat availability, microclimatic conditions, and resource distribution. However, the extent to which structural complexity and specific structural components drive arthropod abundance and biomass remains poorly understood in temperate forests. This study examined how local and landscape-scale forest characteristics influence arthropod communities across vertical strata (forest floor (FF), herb layer, and shrub layer (SL)) in 19 temperate deciduous forests in Belgium, dominated by pedunculate oak, European beech, or Canadian poplar. At the local scale, we assessed dominant tree species identity, overall forest structural complexity, and its components (vertical and horizontal structure, woody layer, herbal layer, and deadwood). At the landscape scale, we evaluated forest area, edge length, forest cover, and vegetation greenness (normalized difference vegetation index (NDVI)). Contrary to expectation, arthropod biomass and abundance did not consistently increase with higher structural complexity. Instead, woody layer complexity, dominant tree species, and NDVI emerged as key drivers, with effects varying by context and stratum. Arthropod abundance and biomass were the highest in oak- and poplar-dominated forests and the lowest in beech forests, likely due to differences in litter quality, microhabitat availability, and understory development. Woody layer complexity positively influenced forest floor arthropods in poplar forests but had a negative effect in oak forests. At the landscape scale, NDVI unexpectedly showed negative relationships with arthropod abundance across strata and with arthropod biomass in the herb layer, likely reflecting dense canopy suppression of understory productivity. Arthropod biomass on the forest floor increased with forest cover, while abundance in the shrub layer decreased with forest cover but increased with forest area. These findings highlight the complex interplay between forest structural attributes, dominant tree species, and landscape factors in shaping arthropod communities. By identifying the key drivers of arthropod abundance and biomass, this study contributes to a better understanding of biodiversity patterns in temperate forests and their ecological dynamics.
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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 | 0.000 | 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 | 0.001 | 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".