Five decades of ecological and meteorological data enhance the mechanistic understanding of global change impacts on the treeline ecotone in the European Alps
Bibliographic record
Abstract
Understanding the dynamics of treeline ecotones under global change requires long-term ecological and environmental data. The Stillberg ecological treeline research site in the Swiss Alps was established in 1975 by planting 92,000 seedlings of Larix decidua, Pinus cembra and Pinus mugo ssp. uncinata, and has been continuously monitored since then. Here, we present curated long-term data acquired over almost 50 years at the Stillberg site, and we synthesise the major research findings. The long-term datasets comprise 6.5 million ecological and environmental records from the 40-year afforestation experiment, as well as from a 9-year free-air CO2 enrichment experiment crossed with a 6-year soil warming experiment, a 12-year nutrient addition experiment and an 8-year multifactorial tree seedling recruitment experiment. Our datasets further include 38 million records of 25 meteorological parameters measured at an hourly resolution from 1975 to 1996, and at a 10 min resolution since 1997. We provide all datasets and the corresponding metadata as open research data. Almost five decades of research in this treeline ecotone showed high mortality after tree establishment that was closely related to microclimatic variability. The two Pinus species survived at a much lower rate than L. decidua, due to indirect pathogen interactions. Furthermore, CO2 enrichment only increased growth of L. decidua, while warming increased growth of P. mugo ssp. uncinata and two Vaccinium shrub species. Enhanced nutrient availability stimulated growth in tree and understorey shrub species. In addition, soil warming and CO2 enrichment stimulated microbial activity and decreased soil carbon stocks. These findings improve our understanding of ecological processes in the treeline ecotone under global change and confirm the importance of tree growth and establishment limitations. The enhanced availability and quality of these long-term data are expected to foster whole-system approaches and transdisciplinary research syntheses, supporting the development of effective global change adaptation strategies.
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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 | 0.000 |
| 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) | 0.000 | 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".