Tree community composition stabilizes ecosystem functions in response to drought
Bibliographic record
Abstract
Abstract In summer 2018, Central Europe was hit by an extreme drought event that widely impacted ecosystems and markedly increased tree mortality in forest ecosystems across the continent. As climate models predict an increase in frequency and severity of such events, there is an urgent need to adapt forests in order to maintain the diverse benefits they provide. Soil processes play an essential role in this context and are key for a plethora of terrestrial ecosystem functions, but they are strongly dependent on water availability. Here, we investigated how tree species richness (TSR), composition, and identity in a 13‐year‐old temperate tree diversity experiment influenced selected ecosystem functions (as important representatives of different ecosystem processes) during the 2018 summer drought. We focused on the stability of soil microbial biomass and standard litter decomposition, as well as tree species‐specific mortality rates. Contrary to our expectations, TSR did not generally increase the resistance of soil functions or decrease tree mortality rates. However, the resistance of these functions was determined by tree species identity and community composition. For the resistance of both soil functions (microbial biomass and litter decomposition), we found that TSR effects depended on the presence of certain tree species. Moreover, we found that the performance of a specific tree species in monoculture, Norway spruce, was a poor predictor of its response to drought in tree species mixtures. Taken together, the results of our study demonstrate that the species composition of tree stands determines tree mortality and the resistance of soil functions under drought. This indicates that enhancing multiple ecosystem functions under environmental disturbance requires maintaining diverse forests.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 0.010 |
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; both teacher heads agree on what is shown here.
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".