Functionally diverse tree stands reduce herbaceous diversity and productivity via canopy packing
Bibliographic record
Abstract
Abstract There is extensive experimental evidence for the importance of biodiversity in sustaining ecosystem functioning. However, such experiments typically prevent immigration by continuously removing non‐target species, thereby questioning the generality of these findings to real‐world ecosystems. This is particularly true in forest ecosystems where understorey herbaceous species are key biodiversity components but are usually weeded in tree diversity experiments. Consequently, little is known about how tree diversity influences the natural dynamics of understorey herbaceous layers. We conducted a 3‐year non‐weeded tree diversity experiment composed of eight woody species differing widely in plant economic strategies. We examined how the functional diversity and identity of tree species mixtures drive the temporal dynamics of understorey productivity, functional diversity and composition through canopy packing (CP). Tree mixtures with high functional diversity experienced increased CP over time, thereby decreasing understorey productivity and diversity. Furthermore, herbaceous communities were dominated by species with functional traits typical of low‐light conditions [lower community‐weighted mean (CWM) of maximum plant height, but larger CWM of specific leaf area] in response to increased CP. Our results provide mechanistic insights into the role of tree functional diversity in shaping the dynamics of biomass, functional diversity and composition of the understorey herbaceous layer during the early successional period. It is expected that the effects of tree functional diversity would also be relevant over time due to the increasing usage of canopy space. This study highlights the significance of natural community processes in determining the effects of tree diversity on the temporal dynamics of previously neglected ecosystem structures and functioning. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".