Implications of contrasted above‐ and below‐ground biomass responses in a diversity experiment with trees
Bibliographic record
Abstract
Abstract While tree diversity has been proven to enhance above‐ground forest biomass in many instances, the effect on below‐ground biomass has often been neutral, or even negative. This raises the questions of whether above‐ground results are the product of a reduced allocation below‐ground in more diverse forests, which could imply a more efficient acquisition of below‐ground resources per unit root, and whether the effects of diversity have been correctly quantified and interpreted in the past. We addressed these issues using data from a tree‐based functional diversity experiment where 24 mixtures and their respective monocultures were analysed in a fully replicated design. We used species‐level measurements of above‐ and below‐ground biomass to assess how adding below‐ground biomass changes our interpretations regarding the roles of complementarity effects (niche partitioning or facilitation between species) and selection effects (dominance of high‐yielding species within mixtures). Mixtures showed overyielding of above‐ground biomass but not below‐ground. Although remaining significantly positive, this translated into diversity effects of a lesser magnitude when adding below‐ground biomass. However, species did not reduce their allocation to below‐ground biomass within mixtures compared to monocultures. Rather, this combination of above‐ground overyielding and below‐ground neutral effect was caused by the dominance in mixtures of species with a low below‐ground allocation strategy. Another consequence of these species dominance is that their actual contribution to total productivity within mixtures was overestimated when using only above‐ground biomass, due to their relatively small below‐ground biomass. This caused an overestimation of the selection or dominance effects, thereby causing an underestimation of the relative importance of complementarity effects. Synthesis . This study emphasizes the need to consider both above‐ and below‐ground biomass components when determining the origin and strength of diversity effects, or when attempting to link tree diversity to ecosystem services such as carbon sequestration. The statistical partitioning of net effects is a widely used method, but one which can lead to an overestimation of the role of individual dominant species when below‐ground biomass is not taken into account, hence diminishing the role of positive interactions between species.
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 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 | 0.000 | 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 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".