Deciduous tundra shrubs shift toward more acquisitive light absorption strategy under climate change treatments
Bibliographic record
Abstract
The effects of climate change on plants are particularly pronounced in the Arctic region. Warming relaxes the temperature and nutrients boundaries that limit tundra plant growth. Increased resource availability under future climate conditions may induce a shift from a conservative economic strategy to an acquisitive one. Following the leaf economics spectrum that hypothesizes a strategy gradient between survival, plant size and costs for the photosynthetic leaf area, light absorption of tundra plants may increase. We investigated climate change effects on light absorptance and the relationship between light absorptance (fraction of absorbed photosynthetically active radiation, FAPAR) and structural and nutritional leaf traits, performing a soil warming and surface soil fertilization experiment on two deciduous tundra shrub species. Our results show that fertilization and warming both increase light absorptance in Arctic shrubs and that FAPAR is correlated with leaf nutrients but not with structural leaf traits. This indicates an economic strategy shift of shrubs from conservative to acquisitive induced by warming and fertilization. We found species-specific differences: FAPAR was influenced by warming alone in Betula nana but not in Salix pulchra, and FAPAR was correlated with leaf phosphorus in B. nana but not in S. pulchra. We attribute this to water limitation of B. nana that generally grows in drier areas within the study site compared to S. pulchra. We conclude that FAPAR is a measure that opens up more possibilities to estimate nutritional leaf traits and nutrient cycles, plant economic strategies, and ecological feedbacks of the tundra ecosystem on broader scales.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".