Are diverse forests thirstier? A meta‐analysis reveals no evidence for a consistent effect of species or functional diversity on tree transpiration
Bibliographic record
Abstract
Abstract Tree species diversity can enhance forest productivity and resistance to climate extremes but can also increase transpiration rates, potentially exacerbating drought stress. A large variability in the effect of tree diversity on transpiration is found in the literature, and we conducted a meta‐analysis to help better understand drivers of this variability. We computed effect sizes by comparing tree transpiration of mixed plots to monocultures from 31 studies. We calculated effect sizes at the species (comparison of trees of a given species in monoculture vs. mixed plots) and community (comparison of transpiration by all trees in monoculture vs. mixed plots) levels and assessed the influence of species richness, functional richness, water limitation (drought or regional aridity) and functional identity at the species level. Our meta‐analysis revealed no overall effect of species or functional diversity on transpiration at the species or community level, instead emphasizing the large variability in the magnitude and direction of effects. Indeed, transpiration's response to diversity was not influenced by species richness nor functional richness, suggesting that these factors are not key drivers of variability on a large scale. At the species level, wood density and tree type (gymnosperm vs. angiosperm) mediated the effect of diversity on transpiration: under drought conditions, species with low wood density transpired less in mixtures than in monocultures while species with high wood density transpired more in mixtures than in monocultures. For gymnosperms, diversity had a diminishing influence on transpiration, mainly under drought condition. Synthesis : We found that neither species nor functional diversity had a systematic influence on the effect of diversity on transpiration. Instead, only functional identity was found to exert a driving influence. Overall, much of the variability in transpiration's response to diversity remained unexplained. These results highlight the challenge of predicting the response of transpiration to species mixing, suggesting the need to proactively investigate mixtures through experimentation to anticipate the implications of specific species assemblages on water resource use.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".